{"ts": "2026-07-15T04:43:53.109906+00:00", "type": "system_prompt", "text": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}
{"ts": "2026-07-15T04:43:53.109906+00:00", "type": "user", "text": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}
{"ts": "2026-07-15T04:43:58.222468+00:00", "type": "llm_call", "call_index": 1, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:43:53.111427+00:00", "finished_at": "2026-07-15T04:43:58.221963+00:00", "duration_s": 5.110000000015134, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:43:53.111427+00:00", "finished_at": "2026-07-15T04:43:58.221963+00:00", "duration_s": 5.110000000015134, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "19633", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":[{"type":"text","text":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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{"ts": "2026-07-15T04:43:58.223471+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "name": "get_scene_info", "arguments": {"user_prompt": "Get initial scene info"}, "raw_arguments": "{\"user_prompt\":\"Get initial scene info\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:43:58.223471+00:00", "type": "tool_call", "id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "name": "get_scene_info", "arguments": {"user_prompt": "Get initial scene info"}, "raw_arguments": "{\"user_prompt\":\"Get initial scene info\"}"}
{"ts": "2026-07-15T04:43:58.247381+00:00", "type": "tool_result", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "name": "get_scene_info", "text": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:44:04.538406+00:00", "type": "llm_call", "call_index": 2, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:43:58.247884+00:00", "finished_at": "2026-07-15T04:44:04.538406+00:00", "duration_s": 6.2810000000172295, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:43:58.248390+00:00", "finished_at": "2026-07-15T04:44:04.538406+00:00", "duration_s": 6.2810000000172295, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "20033", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":[{\"type\":\"text\",\"text\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":[{"type":"text","text":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; 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{"ts": "2026-07-15T04:44:04.540662+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "name": "get_sketchfab_status", "arguments": {"user_prompt": "Check if Sketchfab is active"}, "raw_arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:44:04.540662+00:00", "type": "tool_call", "id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "name": "get_sketchfab_status", "arguments": {"user_prompt": "Check if Sketchfab is active"}, "raw_arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}
{"ts": "2026-07-15T04:44:04.566741+00:00", "type": "tool_result", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "name": "get_sketchfab_status", "text": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:44:07.886327+00:00", "type": "llm_call", "call_index": 3, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:44:04.566741+00:00", "finished_at": "2026-07-15T04:44:07.885327+00:00", "duration_s": 3.312999999994645, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:44:04.566741+00:00", "finished_at": "2026-07-15T04:44:07.885327+00:00", "duration_s": 3.312999999994645, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "20735", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":[{\"type\":\"text\",\"text\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":[{"type":"text","text":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. 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Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; 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{"ts": "2026-07-15T04:44:07.886327+00:00", "type": "tool_call", "id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "name": "get_polyhaven_status", "arguments": {"user_prompt": "Check Polyhaven status"}, "raw_arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}
{"ts": "2026-07-15T04:44:07.914430+00:00", "type": "tool_result", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "name": "get_polyhaven_status", "text": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:44:35.193596+00:00", "type": "llm_call", "call_index": 4, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:44:07.914430+00:00", "finished_at": "2026-07-15T04:44:35.193596+00:00", "duration_s": 27.280999999988126, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:44:07.914430+00:00", "finished_at": "2026-07-15T04:44:35.192592+00:00", "duration_s": 27.280999999988126, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "21371", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":[{\"type\":\"text\",\"text\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":[{"type":"text","text":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}],"reasoning":{"effort":"medium"}}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 04:44:09 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784090646-YOugCP0rgeY9fVCQ0hdY", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1b61c2cfb1bb725-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784090646-YOugCP0rgeY9fVCQ0hdY\",\"object\":\"chat.completion\",\"created\":1784090646,\"model\":\"google/gemini-3.5-flash\",\"provider\":\"Google\",\"system_fingerprint\":null,\"service_tier\":\"provisioned\",\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"native_finish_reason\":\"STOP\",\"message\":{\"role\":\"assistant\",\"content\":null,\"refusal\":null,\"reasoning\":\"**Building Classic Lantern**\\n\\nI'm focusing on developing the classic camping lantern from scratch. With Sketchfab and Polyhaven integrations currently offline, I'm leveraging Python scripting for this standard modeling task.\\n\\n**Modeling Lantern Components**\\n\\nI'm currently detailing the individual parts of the classic lantern. My focus is on replicating the specific elements like the fuel tank's bevels and finish, the globe's shape, and the intricate wire framing, aiming for a realistic and iconic representation.\\n\\n**Scripting Lantern Geometry**\\n\\nI'm now defining the core geometry for the lantern using Python. My plan involves scripting the creation of the base, burner support, and the distinctive glass globe, considering precise dimensions and modeling techniques like revolving or scaling loops for the glass.\\n\\n**Generating Lantern Details**\\n\\nI'm now scripting the generation of specific lantern components: the chimney's stacked cylinders, the wire cage's vertical tubes and rings, and the carry handle's arch. I'm also preparing to script a ground plane with a textured material for reflections.\\n\\n**Adding Material Properties**\\n\\nI'm now focusing on defining the materials for the lantern. I'll use a glass BSDF for the globe, an emission node for the warm light source, and principled BSDF with high metallic and low roughness for the polished steel components. This should render beautifully in Cycles.\\n\\n**Defining Material Properties**\\n\\nI'm now setting up the materials for the lantern. My approach is to predefine these using Python, focusing on a glass BSDF for the globe and a Principled BSDF with high metallicity and low roughness for the metallic parts. This ensures consistency as I proceed with the modeling.\\n\\n**Refining Material Textures**\\n\\nI'm now implementing detailed textures for the lantern's materials. Specifically, I'm using noise textures to drive the roughness and create subtle wear effects on the painted metal components, enhancing realism.\\n\\n**Adjusting Material Properties**\\n\\nI'm now fine-tuning the material properties, particularly the roughness and metallic values. I'm using a noise texture linked through a color ramp to control roughness variations, and I've set a metallic value of 0.4 for a subtle painted metal appearance.\\n\\n**Coloring Lantern Materials**\\n\\nI'm now applying specific colors and material properties. I've settled on a weathered brass look for the main body, using hex code #C5A059 with high metallicity and a roughness of 0.18. I'm also scripting a dark, rich brown for the ground plane, incorporating noise for subtle bump mapping.\\n\\n**Adapting Shader Logic**\\n\\nI'm now considering cross-version Blender compatibility for shader nodes, specifically adapting how \\\"Transmission\\\" is accessed to ensure the script functions correctly across different Blender releases, preventing potential errors.\\n\\n\",\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.text\",\"text\":\"**Building Classic Lantern**\\n\\nI'm focusing on developing the classic camping lantern from scratch. With Sketchfab and Polyhaven integrations currently offline, I'm leveraging Python scripting for this standard modeling task.\\n\\n**Modeling Lantern Components**\\n\\nI'm currently detailing the individual parts of the classic lantern. My focus is on replicating the specific elements like the fuel tank's bevels and finish, the globe's shape, and the intricate wire framing, aiming for a realistic and iconic representation.\\n\\n**Scripting Lantern Geometry**\\n\\nI'm now defining the core geometry for the lantern using Python. My plan involves scripting the creation of the base, burner support, and the distinctive glass globe, considering precise dimensions and modeling techniques like revolving or scaling loops for the glass.\\n\\n**Generating Lantern Details**\\n\\nI'm now scripting the generation of specific lantern components: the chimney's stacked cylinders, the wire cage's vertical tubes and rings, and the carry handle's arch. I'm also preparing to script a ground plane with a textured material for reflections.\\n\\n**Adding Material Properties**\\n\\nI'm now focusing on defining the materials for the lantern. I'll use a glass BSDF for the globe, an emission node for the warm light source, and principled BSDF with high metallic and low roughness for the polished steel components. This should render beautifully in Cycles.\\n\\n**Defining Material Properties**\\n\\nI'm now setting up the materials for the lantern. My approach is to predefine these using Python, focusing on a glass BSDF for the globe and a Principled BSDF with high metallicity and low roughness for the metallic parts. This ensures consistency as I proceed with the modeling.\\n\\n**Refining Material Textures**\\n\\nI'm now implementing detailed textures for the lantern's materials. Specifically, I'm using noise textures to drive the roughness and create subtle wear effects on the painted metal components, enhancing realism.\\n\\n**Adjusting Material Properties**\\n\\nI'm now fine-tuning the material properties, particularly the roughness and metallic values. I'm using a noise texture linked through a color ramp to control roughness variations, and I've set a metallic value of 0.4 for a subtle painted metal appearance.\\n\\n**Coloring Lantern Materials**\\n\\nI'm now applying specific colors and material properties. I've settled on a weathered brass look for the main body, using hex code #C5A059 with high metallicity and a roughness of 0.18. I'm also scripting a dark, rich brown for the ground plane, incorporating noise for subtle bump mapping.\\n\\n**Adapting Shader Logic**\\n\\nI'm now considering cross-version Blender compatibility for shader nodes, specifically adapting how \\\"Transmission\\\" is accessed to ensure the script functions correctly across different Blender releases, preventing potential errors.\\n\\n\",\"format\":\"google-gemini-v1\",\"index\":0},{\"type\":\"reasoning.encrypted\",\"data\":\"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{"id":"gen-1784090646-YOugCP0rgeY9fVCQ0hdY","object":"chat.completion","created":1784090646,"model":"google/gemini-3.5-flash","provider":"Google","system_fingerprint":null,"service_tier":"provisioned","choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"STOP","message":{"role":"assistant","content":null,"refusal":null,"reasoning":"**Building Classic Lantern**\n\nI'm focusing on developing the classic camping lantern from scratch. With Sketchfab and Polyhaven integrations currently offline, I'm leveraging Python scripting for this standard modeling task.\n\n**Modeling Lantern Components**\n\nI'm currently detailing the individual parts of the classic lantern. My focus is on replicating the specific elements like the fuel tank's bevels and finish, the globe's shape, and the intricate wire framing, aiming for a realistic and iconic representation.\n\n**Scripting Lantern Geometry**\n\nI'm now defining the core geometry for the lantern using Python. My plan involves scripting the creation of the base, burner support, and the distinctive glass globe, considering precise dimensions and modeling techniques like revolving or scaling loops for the glass.\n\n**Generating Lantern Details**\n\nI'm now scripting the generation of specific lantern components: the chimney's stacked cylinders, the wire cage's vertical tubes and rings, and the carry handle's arch. I'm also preparing to script a ground plane with a textured material for reflections.\n\n**Adding Material Properties**\n\nI'm now focusing on defining the materials for the lantern. I'll use a glass BSDF for the globe, an emission node for the warm light source, and principled BSDF with high metallic and low roughness for the polished steel components. This should render beautifully in Cycles.\n\n**Defining Material Properties**\n\nI'm now setting up the materials for the lantern. My approach is to predefine these using Python, focusing on a glass BSDF for the globe and a Principled BSDF with high metallicity and low roughness for the metallic parts. This ensures consistency as I proceed with the modeling.\n\n**Refining Material Textures**\n\nI'm now implementing detailed textures for the lantern's materials. Specifically, I'm using noise textures to drive the roughness and create subtle wear effects on the painted metal components, enhancing realism.\n\n**Adjusting Material Properties**\n\nI'm now fine-tuning the material properties, particularly the roughness and metallic values. I'm using a noise texture linked through a color ramp to control roughness variations, and I've set a metallic value of 0.4 for a subtle painted metal appearance.\n\n**Coloring Lantern Materials**\n\nI'm now applying specific colors and material properties. I've settled on a weathered brass look for the main body, using hex code #C5A059 with high metallicity and a roughness of 0.18. I'm also scripting a dark, rich brown for the ground plane, incorporating noise for subtle bump mapping.\n\n**Adapting Shader Logic**\n\nI'm now considering cross-version Blender compatibility for shader nodes, specifically adapting how \"Transmission\" is accessed to ensure the script functions correctly across different Blender releases, preventing potential errors.\n\n","tool_calls":[{"type":"function","index":0,"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}],"reasoning_details":[{"type":"reasoning.text","text":"**Building Classic Lantern**\n\nI'm focusing on developing the classic camping lantern from scratch. With Sketchfab and Polyhaven integrations currently offline, I'm leveraging Python scripting for this standard modeling task.\n\n**Modeling Lantern Components**\n\nI'm currently detailing the individual parts of the classic lantern. My focus is on replicating the specific elements like the fuel tank's bevels and finish, the globe's shape, and the intricate wire framing, aiming for a realistic and iconic representation.\n\n**Scripting Lantern Geometry**\n\nI'm now defining the core geometry for the lantern using Python. My plan involves scripting the creation of the base, burner support, and the distinctive glass globe, considering precise dimensions and modeling techniques like revolving or scaling loops for the glass.\n\n**Generating Lantern Details**\n\nI'm now scripting the generation of specific lantern components: the chimney's stacked cylinders, the wire cage's vertical tubes and rings, and the carry handle's arch. I'm also preparing to script a ground plane with a textured material for reflections.\n\n**Adding Material Properties**\n\nI'm now focusing on defining the materials for the lantern. I'll use a glass BSDF for the globe, an emission node for the warm light source, and principled BSDF with high metallic and low roughness for the polished steel components. This should render beautifully in Cycles.\n\n**Defining Material Properties**\n\nI'm now setting up the materials for the lantern. My approach is to predefine these using Python, focusing on a glass BSDF for the globe and a Principled BSDF with high metallicity and low roughness for the metallic parts. This ensures consistency as I proceed with the modeling.\n\n**Refining Material Textures**\n\nI'm now implementing detailed textures for the lantern's materials. Specifically, I'm using noise textures to drive the roughness and create subtle wear effects on the painted metal components, enhancing realism.\n\n**Adjusting Material Properties**\n\nI'm now fine-tuning the material properties, particularly the roughness and metallic values. I'm using a noise texture linked through a color ramp to control roughness variations, and I've set a metallic value of 0.4 for a subtle painted metal appearance.\n\n**Coloring Lantern Materials**\n\nI'm now applying specific colors and material properties. I've settled on a weathered brass look for the main body, using hex code #C5A059 with high metallicity and a roughness of 0.18. I'm also scripting a dark, rich brown for the ground plane, incorporating noise for subtle bump mapping.\n\n**Adapting Shader Logic**\n\nI'm now considering cross-version Blender compatibility for shader nodes, specifically adapting how \"Transmission\" is accessed to ensure the script functions correctly across different Blender releases, preventing potential errors.\n\n","format":"google-gemini-v1","index":0},{"type":"reasoning.encrypted","data":"AY89a1+ltubitZEA4zKKyU9G/1Wx3XEpU2MlgD4yow/+sVJ1spctV6T9dHp0QHxd5ei+AS/fggturG9r4iPUWou3CSlFVmFY9Ho3KZrujIa0Sewcwo3OWLdG3zDgCNI3isG9F1HiyHethclwA7QVL7KveGfd5H2X2/XUjSYsUDkmnNgtUPxtumXStI9XA8c9CQzF0DzjBUsykNqtXKOxjY6/NqTskRcoAEbF8bNQ6JdgTH4bKnj9EGc+lEokPUPKvxd0nWN2Kf1MN3ELtl7F7bLrPBX1uIhiLhffH/YnkKN9H0fV9JkrEOAStfHgG51OcCFVxkY0lh2smUZAlhnsQ/ecA3ajPIqjYoYGS4JRVppiGC8UFjYP32118ECYXtp9ewOr8EFMIAQRg+CGCGVgdUNfLq1cmjMrsWQdtNrhzP7JpqEAIYHqWz6H4ytDDrtjWJdrE+uN6aiuw9vLWsy6DZwGNd6FOLybO9MOOC+BPRDo+fQTxMb+2xl9rvoNXrCeHtAfzN+mdYXroGwm8ayY9u0oSNE8VIzu9W7rBb5LzxIqq8haEcwru3oNcTpPJioKMmXpI7ozEXKLPdgpFGVXlJx1HXKT7iYDJ5hXM5Wp4n46MBH67PtN6GvYfSvo1J00ruL6ay7+0Nuy+0nIzvzLbRAgNhZQLvhrfWCR5ZixMStEaQu6nBGFFLZyZpWtF10i5lNjiLcVcwmoTAtQXlqyHro0Oq+Dzth0LFjeU81/HXVYzAIyRXnYCX3v6L3cx23lLRf+o7zggku7dk96Fc1Z/K9nj8OP1sclmv+x5DyfFknVRO7ASASgbTu3i/ux2wurQr+qMTJinhyuhPrF4ZMT5gZGjcH66IpcN5rMzleIdeTaGWKDZwa2ERDOSARagBmmkkYp+QJ8coOJADQg8j5Cys0zgs/dL3Gy8xRloh93f5ZcvQ2JlAHnR90P3/o94aHgYJWyQOlXwElStdsy2Mm9gZgYAoR3VPUxubgFA40jZ7neMBdbQ4TO8aj6TjxLrJgwfRqksxx303199+lBt/cygi7cF7fkxGeklwVXIVwttM4pOV4B4Z+Dy/RO5apBDHqdyTPyei5r+RVenfbhfVAPAQ7f3KrdQDSe9QruhPzu0F40vh2PqcNASYNIlI2PDrKDcohFiKmnd84NyW33ZNuBc7SOPRpkYkKtu7yvA+oGO7YvtV3xSQOhBrgxw14GeQRiMWdQrI+zkEPQpXwXBUIHiOFBI4/pha3on9JbzlCd57mP/lJlR2+tCpUklKxH9Su5h87fPW7nGWzK9c8hjmuFYDbe0nUc43Esi3TUFrj6kN4aEQ9vhkgxKLsQAh8swpfn1lKZDCvfSQOFTMU5fknt1qm6OrfqTQgnUNWZyt1ys7T92OktVo+S0Zpzq78y79YxuvQ2309q3qCpwRIZneu+Ob5bwjYj+wOpzIk12hGwFVh77FvhEIaNnH3tsJ6oKX+7uC4pgfp2utaZPbRx79o4JncxtMKZ5ehn40BtuwY6IYoKAB9+4KdSsdYAjiG2CgfDngpSA2pSFKotWS2PX3DUPxjFFNUf59cRyIp5u/2C//x/sKeAUmIleS0X/AR9oVmiDW7XwXXftymKuwCAh+UIGer+fD3J7zKnAnBDROP+PvQZcdsNcKf9QE6pcCPeTvTDWjs5IZesy5wCssW601UlYPuF7r67tYxVctgva4VGMho3Lz3sczTFP1xPHX9qBYziTOUEo9GVzPhHW5pU32Jn6YKkPWi04uE+ScAYimmJ1XY8u9AJbBEdQD/sLJWMM3uazvaCNwovcWH5/SXmTJ95Gn8TO90HbsgtkGyzOW1UCjh/JY/I/e0Y7zkY22k/E9iTqh8CKgpjv6/+/BfKZwuBKoGLZCeaVzMhVvAI1w0JImAKDSfejGEiwnPo2R5T5yiR0Cqt3O7AZDGctL1tTm2UjjQ193NERNquvxUzf2EvVtvLwwJaUZ+zgF0GGWrsG3Unr1lGCp/ngXPBZzHD4pudq1BA2Hf/rZzKkFX144/M7HHW04oOMHlqKr2g1ufJWecvp/MdqawHIHxthHqbbVA14Y7MFRBSVwLpmAYH1oL8SgXbpytDvU17thLDDXvirlexahOBnmdrkO4YCe+dBarPCe+Xu6QlEPXExTwpj4TGuowN4M+EWpBIltDOuKafLudEn0EHey+ICVOFayLPh+aCEKNVtDOl1+ge3JVaeRnEY12KCIpwOkXbYUHVmXApy+xLPtInbi64/BrqKaOumHVRmcpfk6D3+v9cBqJmswWqhbcRj/L7lcFJo9ICph9JtmsTr8VTLR/wTIGm8IV7SxSap0EjMMshJIg+LWS+SW0AsaWf/xi8XhG/vYXdM4F2wTDkSh0+S7POGKIWUaoESvgbnnTyE1XshceVdZLop1EBFy4uCS3TqC9qLX170n3hGVpojufqxEZh4km7MrlDfM+2H0y46Om3QlEbqON6a2XXQR+LMh1sn0VDRs1nkSeKY4TzI0I635VyR+65e/EamdjH7qgpdETQ8oU+l9roi/CRsQWz1lES+pboiT/sWGNUrsZQ/9eSkhaYwiVO7Dv3sJenYMgEK5b78ID2zTXOurXK7Hfo3NLfwAo/Ci5cXKtKjgDJVGTvDkTeFNxi1OLtogGCxyQIqC2B6CZVg8I7U008D0QWtCJjyI+iJTy4WqVnX6LhIReduDf/s29lYXKYHwznVz5y0fDaQ+qjObIlKKaZgcN58cda/Y3JPgfd/IQAdlFbKHPUkZZHTwfk5rWBBlFYBKvvJDp3QmP7LwOxxMlOM+rMxwzQiarRvLllg7RtFANYLY77PzO8t3KlNtNPx4L2KXTP0wyl7Rqd4v4w72RUMr7tOG4McDMXLzVzgXy3bYN70xa2Y6twglQnrl0/kQcT1bBS1KUzp80mUjCAW0J43G/f+UcyQJNFWWFuxofUzo4zqtXEIaLVQvEhh/jTSmMED89dV47rf3GYXplYq6UcSeoytzlFgkl9Cega5o0vSPYuIhPmOKkGU6yPm0HZiZoepmEk1UBPxrv9jvOWAZk1y3vUwVthANJ5GtWHTe8rHqZcsLEZMBoW8XYJgrirzLhw/Vhc17r2fGiMeqKaEwc/vjJQKtxKYlWJ9a6YmUkLmYmVCkwRizLm+hRaY6hnHSYHD3EBbvcgRy15xfCENpHivD3dzpzqCA5wllRacvAjhDhOc0yUHXkugkBq4rs8lTa/qCV73j6vQ3D9mZAqnik0TF3Nv/PJt84pYBgI9ADRRc6PwndDMKxHBqkQSb9kbUqELNMMbXY/3+gP8bFh3JkqgnKvrktIudgsWHnaHhIRbuwermadRzfQ9t0Bcyz+k27fmRQWqxNGbhWqmwaYgKwK4SH0vP/xw7NF6mlcJhBXGpjbR6WV9I6FHBQQpk/1aX6OMFeuJ+HOWIJIGg6mfWDLur9DlV2iHBpaYT+umB6cyiFxa+C6d73GNr2ZwIKnND3H+NJo6tVSvzI9YBYbAJ8liC/UT2gK86Lq0z/dNz7LFehnGc74W33TY0uK61zH8Af6qMeX4uhwo69TWWHr0jYd1s9nGBk3RrcvQUWvFxbljfHIJ7Qppa/7N+mXHkIwtY8NFlaGT6H1E3DMGDLHWrpwxs8hKLWb1iXPFHWxQ7HXgmvDwoN3VpboRjqpWoMLUOp/n/vcIgpIF2PLBsR4vL06hGsJFA98gNoqQKbVkdAm3vi+ucuXyWb36XyRKjuoRRsnfresVQO3f90+JsPwSoh14gAl0NJYqvfJOjpZm26O1DhA+hICcSHoWJCdiiieyFLBE/MSA0bSkGGbIRogu3mNIBjBMxxVhUy0YpRrHOM5Uys/cK+3ytiGncSrq70EZqkaE0GyWNPCMoHQMKkusLJiUQNlPb69tDIw4G0uy/1g+/6Ly8Hou2EGWzedBfrsbWmcmfqlVsvsk1Ohxf/4wKZNUV/o3bIJCx4wiB6JHc///o7YXIOBHsb/dQ1AP6xesNgEdFuqcWkzbEtP0jluboFZLpK7PF1RuVtkby45SwhuSTNVM5XShmzGh7hGi0SXyN5/+LvCCG8cD0VjdZ+l5Bx//cYUfJ6IsbXfIrzK9cFRpm+3x3M8A0C/+9PAai+ew3MSXnmTJ3kNufgcdJQHuZmgdwmxnz+dDVyd4b0aLAbcD9Lt2ZWi84IdplY8RVx4oNgarMoVcxlSr9Qa1fNZ2VIHcWhu8DO6+ACWq6ZW0rdsR+d06yvz8Ts8ytKA8gJPWtgHLoGykbbGPdEnMKmlp0qo/m3RmWfmPFL9YxXaao2eQ67V8gAqy+M8jVa/L/1L3fuE6UJ6sw0vH3KdY8AVCV2Ln5mHaVno9nh89e6TiD2c6COUmZdv7RIcX9O2St/RqccSR52XuZxBbHgvGID3HyiP+h5ipRAGox52sOXDI/nV8qnzqWTSQoVYjsrEr8jZRo/IWIljyMV+xsj+M2+fBSP5py5T0Ox0fLk/XgmXPgTG+sjq/vL1xQKOPLdflHx4AvUkAZrvi1OSRtk7BOlM4WVqdUQlW3sRKvtU6KYi15lNBsbYmYNwyyDITouRaSWmlszLveWKh4AMMTImANsH36YzZsUOLVvAyr0P9UCdZqUS8YNx8iYWdUliBSpTtyHhQ+HdI3KVxj6yhkM8n4p8aBrArao/cezJr11ksMJARJg0FhGAX8evL9nsPGrrdxcFapT5rqmcWyjvlny4xcKVL4OecBLW8BwLFlRqdFVq0q9GIbdp7W3YkEEFrLS7hysMqL8tOWDY5AEtOwOXCOHhA9yc3IWKWMypEziBwi9QoSbd80RCFCwdoJ049d0QuazS1cyyAFLuxgKWR7DPR7qz2juX7eGTKyoBTQ6OowIjwuNQINt2njaSlh39OIdL29uj3s5JqPLXh9CNdHX2IN5OZEJRkeTEL0fDbBIxe+iifsL5jFsOtfh19Vj3QfhOwPpaLkgxeDeVB/of7tD0vSoZ7hW9x6vP3/o5qs9PXkuyN45Z8lZPEgINbhY5wA/fErUb3u+f4GsVhoR2fB4QCwlmMisp2LiuuuRlT3qYYsS5wZeZLxH+c87L21kUKaHHr8ig52wGjvSfFFJNW1knOWTxwYusuL44Ozwi7kVrH3ufdOy/F3j3C/Z3VfMTRy46891suHNwut/DQSduKLKZcROlLAQO8tJLAbIT+Uv6SnfkPSDf1raX0BwhH34yEsqFGxJz1D0LYZ8vvDTnhs6ZLo25ccl5mYO/Dt6MEPEV+QmpAePIaABrfj//HUvGkx2abrEg6R/SOyevHdZvj/vqlNx2gToeUeYbdNVxI1k//84IhUyWV6RyXvffDsr6Qm1qkScY3hf8iq6ErNraMkik04QU+tw12wDGKP6GyoOHXO5IrIA/F4OSraZML2h5M0tCaHLHVhY8gXxTLt1oUR7GR9FksZIUPjAKR29VxJNmGQihu7p6mIQlvirfLKivf6eNseHFTPnmyrWyB9QapGOHPm8y+ZLjyCgleXCH5SlDs1EQ7qpivmCY/tv1u8koLKc+5b4JVKfqSyRGn/1nrttj7kKlaKaGpGyYSZsQAzbuJ4He7tipmXZ2El6Bq5+C4xQ/uANryJD7Y2SL1JtCPob2MNEecy9AXNsTl5Bl1hrb0A8wt7RffOlXtvaS/fdO5V63hrPK/VQy9TBnTllN4AiPVUSFXEmdOLXVUhF3zimfKhigEPV7kNPP/rxNX1hCNdWdo+ZHdNZ+0EGsZnF7Qyp7tq5Pm1zUUcC4as+gSWRKANtQW8BxRplqrowSONx7AcqnK8CcbcAdl42gGV+GLv7Mtqa7uovRVcOLHxfnKk/AD5oqrbqPlv+j2w/gw0P6hFeXT3B8EbyYJgD6V6VuilpmSuzQQBU99WJ6c8uvdM792I0M0PfOJ+2cCswqMmePH7NvdA6vNuamNW0994hXPp+777/JRpn+/9JmlgvlIH48PT0QJdhCeis4lBQn1Ki3+qJe/VlAVqx+0oPi9ShfrQgukbNXrdSrLUMC3IzQvkqXgGIQbeHEPFKqUdzp6bWhmZQSq9vkp7Y+ARN4qLc0HZKGMD+Eumuc5kl7kC7soE4frivI/outiJsnH8rnKDhiQDnyyCj9ApA3GqXFNbBllJJQ3Ykdnk5gwMxR9q4iupq+UYSTfN3oFvWDZa3u+wgxWvYt0IuQtQ3DwrJUSLbVyyl3CgYflTL8W8i86tDs2v1V957mYCMe/mrODrLm2FaOd9EenbYhUiXOA50ddsD0Fr74WeR43mNHIerBM6qHL0Vg4YymTHRk2MowSHUYIdpdJS9Me04K7JTEjWmpQT+43vL6zX7P1HQHXIheB45Xl6cf6WaK4fUT7foR1UL3TPjUoTY9ynaK3mP1iBIvAN9zuQkfAvrdpYEc926UsaZpZiZc6gmH5LKpDK1s0hnCISN5zygi3Vh1TVlGx35il15V9pNBqWJFNj5dkJPHZYpESS3f5Y09D1pHy0mdXTS7tUj+hVeb8x89n8G6x7+cru6XhNKDMXNkMxWSPo8w8IdQ2Wak5XmgQ/tu5cPPFVFuhdXGfMMN2RifAXYPK4IWB1w2nRIf8TnLGpmUozg8bzaFNIzh0C5ZGci7ZkxINgklpD3b2xHDU320vmltnpD+ycXYULDuRqX6u6XMEPaAkGdv3fqtIUuOOlHEauKPGvl6FH60m7Q7+tdMXM4LBhUXCTqe2ggt76vWwDzUK0mvcDVCYp5imheiBUiF7xUDNQYgu/iwpNOrdpZmO4vfpifX+3rKy80I/QkF6rDWctCJOUkw4Qbn5Jcaher1n1HxtqmQLew8f5D7pg8NM0xriBWk6VtjqAvJxCzFVwrWnov47HbxE8BS644kJuiy2yHdTPSbJcKkKGGNAioLfqchLKoLsVwY/5CG3+a2eFBYfMWeYv4Q1IMzOqk146fb6quyZRoNBb2L6sdOV4q8oyM3knGpdHZ0ZZ5pEnZ0s9OiRDdjkq8NbHUkx3u7EsVfztyWR70i9wOAHsrIKe1EycLoXRQtuwRm82Bx1jDAnve2LKLi2S/ykiOXEfgtMZXzx5Zm88fj06fX/61qRG8xR2jr6plH8RekpPQaQ6wfj7bt/EGX5AOxEf7LESWpzskO9E65ZHRca3DxO/W80VCZTsELfN0HfF5McT4SiU91KPEy0c42Ank7pmAt4GPqSRqwLfmNF/BMyCkL0jJvcHlk7FF/cgA2V+tBmtdmJqV9GsGrIBWMTsTNgqOckF3KBX8uVg+EmsiwtSjIL47HcF13I1ay/S0DXbZoGJX0ye8+OV9onkDRHrVe95FXKwsXKkSxGvYcXXW9LCG4LQpPVgA5gObU86HWw2r4JB/Q9RMEyyliQ2UyguXNDb9O4ayiFDCIG0X7bjY6zrCA7HTThhWa1PZZP+iG0mUXy2sio8v6VhmtWhWtpLr+CRl2ajlgG0BOEs4G9RpEfu/P/IE8Xx3UT02IHkv77uT10SBn4F7SHkXPcaGexMWFRx5Zc0H4LJdZ+oR+QK6IDCZVytaafCA8S4559JpExFa6dNYnxV30fyVo0YBW0lgRB989ywuh6lAYQ0RO75kWwWPdo0izuWKFjFrWPikHjYTLbwbrHKOPhMElTIMDH4J5382a/iL51zl6rO4Ivooz5G6fMAw9kaZN67K5wYWe3emCU0c4gsHDVVtHXli1edAaEWQE+gXRJGjC3IC0M03lwS1tWjI3lgT0Mo0IeK3h59eglpfQr4k0RpuYzPJRSu55GNR3012Lb2xSyGUraDhKZzyBsqHw2EVMygTKvpWhVbt69eQw3zF5CTWlD1f5VItNOiNTWtj5TGuZUPsFr68LoLRI5jDKPvDHGHjIHCkVmhpw0+28ltIfvamuKdmZ6FDCAsCrbxRTyiVi3IlSpoK0HBsH2xiRw0Rrv7+i+d5mrAfCSWvRFEU4dPOJF/Dk2GXje3Iqou+j1Vt8dwDZwGYbX7OwRDEDGfb00fc+WcHGoHJaYfGBrMK2ABCl0mKqu2il8mNKW++53U/lr1hVceeYN5Q/EEEpB4PNFBgtHZMHXvq8QsIN87vnQgIRw5UE8la1jc4ScHT+oTCHG1JTV8Re3FDuz9hnDifzWf3ueiWRIwqAhHHaKNzoQMXTMaYXRQnjZnwEYW7Lvpo8lUC6hsNS+7PMlmaW+qt3KqmOLDphqclydvN1UfbEY71pdVLmT1lJQuoizpwmfq7gsCrRiNzScE5UnVOSNJf7t2fbTr05qZZSWj0MKKCoEPZkylW4inEr+FmMgxl69rVEeOe5lLhhZ6NXzF1/4SIY9yHvYYOb555PJsm0HYDR5vGOBKZpTGi+8tSX3XHB3KUwdclYFIHCFDk8pE4yXJ9xhBW8WDUhw4MBoRJmN7/MWndS/9ARxjFL9pIcZ3+Z207KRKTq2Rqauu2CZZeaGIKLck+vBD1CHxbQb1mkDemYki51W2iMRLi0a7bFyzvUG1Pou8jdA5lrnRJ3pg4T/hg9j6gk63gVnd1CTaMXTFXuo9pQDtXaHOEo0K57oDGXdXgfW4+aGtk6SEAJVbCIiIrqdLEboolTH47rM1Iwf/s8SEnTgP6EXRUk2Bd79ESqRPKtGYu0aHxBYvIKvDx0FtuhzrRS2hmTsGTcyYXxM2vQ3puiB3GtodD3sZeWCeBVeyyL9BFmF8OgO11QXv3/SxDdvyz7PYhhB+CJLLlodszq38TWPZtu3U8jr7mEdv7GdDBAvqcJloaqFw/wJXwNKB5CmEsvEIGRjRjZ63JEPPDwoOCQPc6MlvfqGpH0a1CLEYRc8Rb/22Pe+UdO+z5dMV67vqc04mBVz6CYWgtsiv3zpg8y/norR/ahZhGs4TIbXA5StSZgBYLLR5yMBaj+lx49opQRzM5B8dBHbPrytjufnwwkzvFhJI6SVVRt7g9oYX5VaIvy5YttYBznY9dWY47KSD3jYqxG5PdZBDL/buo3+Y6P/lBtf7zm6i9b1HnT1zLP4AxW+L/hieu9/UHbeKzV5QopmdTCOqlyK/GrpSn/F8IGmEPq7i7bsBS3tnTrc7eZ+Wtq7bjdAA6YvM9WdNdbXElia7lscV95lSLy/oLTYOP+Cp9P39rH4u2wvY6rpTMndokh65roaCs46V/IAojFkReRMYYRwYzfLAfltHXIRnt38iGvfnm/qy0+HritXxh8B9Qk+6wgizthkk9Z4LySzadtHhxAlXss3/lWqhmN8VGisicqI6yr3CRq8bskNCTEpW3RFsY/TAJtxBzZOUlLedw2987ARcHl1XfvJNH+0u2mgwB+UEEuSWdRfszSQiZ+XitTtVvgGyLBfT04iaqy6K2cvF9p5Y/H5Bs8VGhf0tKr8HlDyUcK/03XeAoWX57Hwx0UI3c+eGntDUghVB8trsW9aK1rK4Hkjy4aZ/Z909bahbdL5k90D0C1qqNMZN2MISDyftBUdHDqUikBbuY0+uIG6Z2eYEFr+DBChWkZVlkwwNCVLyzQ5ZcHPzRE7O6e+panRq4YmtT8iaiidJ+C2JrOYudThsS+6O6adRE/vNmoLDOv5DP87vS9zyGC6L1eRa3cwDlDInVV6P2SVzrhHBPnN5G2uw/4hXhARCKR2LBNxGfJFGIkB+aVPKXPAUtJ9ltvebU2S2EuUanDNLY3DHkOUCoCSDwOfmbUgiawsZkq0Kk0KlJf9yRvo6PMBthTFwlqn+S5wpVmQJoKkvFlb80mn7iZqH9SW39z68JkfPtrkHjKb5jTHdglVocKV2efv4nNsh5vg2TXZg+mPPFOcY8Ju4d2Dzsw/v2ITiYsgHGF+Jkm2pqZSQuwuHLUWsyIrqiaU7dqBSJHrA0JTIt0uOJT11aJ5K2rwBU4pBjyUQbnomziFI/1wLWt5p+TD5UBOUqqs3EY5jIpHup4i0HGAMja6aBq4UUQxvpSJb5NOQW6gOBmrepCC67TFSW4ndeu9bOeml2JN/Ow9EuXyQR9JIrYA0ibJirHq8mtt9VSUcIw20L+AaFZqmoybwGwsJHGHBcVb0+Xr25VoGuHpUOWqC6ZWz3aVpkjOnCoTgU1yWFwjZ62seG/aLS0axqZIDO1L+f+q0GER4uUBxQM/JHHuJzFgVbTMNwjPjOlwdyKtb3j4uYeBAlzYbgnx0HiJU/SqSz1kQvK22sWDc3SLoW2WFg7EL9SmB1+rJyBR//H4zW3mmC+FZEJ13FWG7k76fAO4Sa9W1oCsvvrhhVfHEGtLldi9taAZLd5aFEV6LMTTI+fYClKtBxa0C/alhFaM0SMkJESRJXqAczeMxKQHgmj9d3KDuh/pr7JMBeSW2aCsTnMbCExt+r0BPzZg7Drjh+lNmUJxOujpTadd3ls7atQhSAz/1DA58CXw9ExBx4S05jN7ZgYjra1QMyAl0wWBSsjd+OCf2lCCxuoa/dpv8KWOvrTJpvLBeg6tQyHwlUT4rp6EOZ8A2Fj7n4zA9C743gvRX4nH8S0kRqGdKDdcV3Frjm0ns7L1NiRFtHvvqk2vr5Gfy6ExCXXiT9D37KNMOR7KjZeCD22nQFQjxVMhtCgKa2pPwjWPH4styy6eefY1BUDjytXMjg0UT6VpmOffdA5VjkSR1yK6YNqAT4B+g9tMqasKcNc2extdcVt7bE/UdmEQ/nc2w8Vy1uUYw3ncgLxA6bZx6iPu2uGcvL0jM6Mm9UdWpGEv/9h0RQ90mFA/Tb3T9FN8JNRbuaxtYvrcWxEVxkZbRzutnDJcQN8l4JtkFyli+s/kjOCh145TKx/9frvU/N9oNSAve2tlZZUKFM+VTbZJIFoEM5XyuEpqk5d2VhK4DdGyWXzrd4BnLoOm9skz8ItxJA/Up2VsvziO7NBrP1PosU1hPOezAyylYHA6rmFBJoIxjEePQeVfei2ZFJZSn2V55F2o+/8hSUcy1PkIXzZv38EWVYIc74GgshjIhXAwqg8UbYKWp5v6HRvfuyQrZaJs8LtfvYnyGcro2hgtxYtVdIFK863pcI9tKMu7OyPvtMhTQPpOKFI4yNXr2qYQrL6XmTR6sfswyiPwPA8SkCVkneFWJzwRvw5ZWU6BhxInqhiHjHB2ffrxs+lwi0Zcb2rrbiJpqWYmsrhjSat/KpJWEB9QgoMxSim88tI0ivbDnYH0i1jP3DangPHLEh2ix0M1905bxvtvYZoJ71xE6xcHH8Z47fJfHRKM7c85bPIH3Nr/iMT75/25AhewZxTSiqTaQuHunlWqnM595cB/+YEEhFWxAEr2JMf9HZGN5kbcEnJUopAKfJLN25A2WbxPNon5q36D5oC2WTEsXpQj1l86Ebkr2FZ+WhKn233zwSy59cxqvv0PjSyLKzgcxPyPUp3Ef058M3PeaMrSX3z+ofjQokl4aLAbQjJpgHAZa2hOCVQRYL3eUBKSHaYKPJzczKfBC9wfoEa4bTgd+E1F/g4J4MPfkfm/TUOgXITQ4w4vZhrYL2zVjL1jG+7Q5/38FqIP8xsFcwEjcypMhiG/88xjkv8TdrVfeZiENq37VyOOSaxTgee7R2GHFZawVeGW2sdW40MZ1YtU0Ara1/dJBe1exLvnABkJOfaIm7YdK5Jty0ztIqldlZn+AeRwbyhMwF7nzDOum9IzvylFvdhlrcKu244rTZmOC6ys2pHULQWx4v46wTK0TLne1/942B5OED2WNlfvfzbNBqP0/XDzlfRY1VsTVXHRPw/+/A3wPlorn7mpXd4qZLFjIofPHrKi3CxQtXokVmccPDVHeil/kY9qrRha6bbmyfHJDiu8NsFRDX6tKOjCdCT4j3cTYWVYrdQdB94qtkVB7xMbtKu3cFnrBvtfn5u5n1ay+ThPUcR4dVVtonnx3OEGaXJaEfstjaw2J7Az333v4znIWTG4VUzOkcd1gWueXD9FQNNXLJEpv/xwQxKRIF3HvRrgyie1e9d97fqwbl1gs+JQgNZO2+RvBIwOE7Znc4r0o60m+o/b3t0R06LwdDR+rFmhtf0zURrfl02pD2UAkNvD1IODeLJG8ymtZEUIXj+hAfrwPIQhDBS2vbNrKaQ0Wm0MIYNkuPPBj75FPaoX/Pb9bB0ULE4vbC1A+fnLwEj9Rduex1+kx3Iw3XCsJoaqlG8idlwhff6HoldupWvV1H0gFtP0fM/Xj07zpNj4PbnYmkH03cDLrq26eTN8bQwmp68zDZWsHEV2zDqrHFU5T2BNBY+ae7bTOG6VgRGWwfyLrRuhFDwWCFCzPzGlCu95M2fqAd7aBgFrUkrrRS4kY4DInIPp1zIe/gFaxPbFSBtQ0lLMURXrsvbQnEI4Syv3+lGIqs7xGG918/oG22y+HtEaMcXTlgiqmtPTsFVSO7/WBOHBeJSIQOwEz2TYUBQRJ0BdhmuSQtfeVFKqx87aQq0ydC1f9hxULVTfaaboBs8R+tOAIXS2qzDi6nTq1dmRLG0JKZLkAZwbiT0I5QHV2FYWaTolwyhFipHaVThyytFiqzZBUnHwych2RcufeIO++tz1u2c0TLGaECS80/6ekj6GQCIhZKfbHFDRCVyfl31t/0Ul3SPzqW8XsbOVn3k2AngOIjJp+StYOCZITIA3y3NQoOHvzfjmoYZBrqD6dhz2Aq5Yka6UwtyAedr7ZweDeNbg2g+T7AddvN8sdxSr4ZlDbAKwynzLHYJDAg6/o+i2z5I49wzZgFmTVgl9AKy+XrSDVG4opY6pxApeDn4x+pUR+LWyt7w1rGN6TMpQtjqH/WowXdY6xxD4SnYcS8JNLZnmTacoj+friZHcwuFoqUphao+Rfi3OCmWKAfyPMPXJ0G6dHVe1BBbBSc7UQP456I5t+KIyJiM/jOqjTPrItREJ4AtpmIY/3C146JRzF6GICPwSIpZN6snQUtt8CmNQRi1NbsZiDuVRqa50vw31BNvCPNv95TKK2gawplexUU/hbQcdn1LkowoEHa16/MX2lnGU1NR3g4qCW8p1R2K/nwscrHjpJvtRaz30pMVG2wzubqkXhyvLPkDSmzPsRNR6jnc23CUasNDqzsx8uUSG+YrtZS8axRpOJA0+NTJraREejjISX0pWRQppiDQJkr7sMsnaGDSgc/sGeQdr7WjI+VO9U6TFAcY992GvbqcjeciKQ03UoOV6nUm71oOP1Ubx4VWwPkgxXAY/QfISzWXqjNoqC1J8THc1saPYAghrToumoS27RmqS3uuPY0ffVJFjKVovqH1nND7EwAeFrUHyxn+VKlILo3XGicBNwpD8TGbmG6CelGFhux/Ooe5Wo68FfHLhZrK4Cbe8R5758+izBCTtghrs8dMj8KsHQAESwbWyR20yEd4HV3BL04H9ql0UBgsHOIOXSFDspHzO9vFDv2/C15uRYOGaHCQz71SLSv5yMXWlrSAtwPmZAlXkhRa+pNwQUmVSUhGuYpSlxNSezfvUnIrWrBXjoEOSUoltQcb8zKxyPPfagcyXg0+tuSQVrbNSNekyyYvK2lLKsEA1+Bm/0jJE8mYVYU2Q5T6LK9H1T0B/AOBcJJSCC/EwnAADcF+/Cn8JvUDnFMAYdp8YfadXYGuxWQB3spuW4FGB7wOnaNAOY7wRqk1JELFIERgoGlJd0oJd+pVFTicAXTIDkRBm5ttQsNV9KbXAkc0peT1OhdpnN36WoTZhlrGSdM7WN9p+eJxx0B/fdScW+GDCN1C+Nb1HpHGz6iRvt+nIk4h84a2nW9A1DMsE1f5BN6zDSBoMfcKO/+VpYhenVuzvE65nqvNkoMk+28SSKknqLR3wD76CzDjewCDQMAwtklIv9y8cRu+rBh3B/2a7MvKMr1lDoES+fN5XQ80NGdY/wfOXo0bo84PTwQ32ugMhAqEQ5USDB8eP/DbFJS/gHkKabz5volmeXlh7ug/ApaAa+5KpMMk71z5xNvpoPwnjkGCoUbCi3G/jxQuZIl9mEKGYo+KD9dubMm/O4ozApKdOeUnYBSGHNxHWMTqUJxeLIaJeZo1b7w+Z3BtDLM4PXzq29FXXDPiEVADwNbKYgmjiyN8atC9+V7iCiEjgrlPhd0rJAFP4G7KS7iFEgk+O0sQdiacE5UZT7vAwk80R/u32MnlGFW9QHBXDnGRN23c+Wy+r2DzlJXEyMj2xGxNpab0K2VkXYsqcb4Wwde7NhGFkZ8FOYKAuGxxK5mZo7WhzL1VDIZQC0Ws4iHYemzpeWUmf1s5riNbn19WrelLQB8/8vZTE8nqwH5NH1vULUp9ZxPHbqwjXXrNyAlp/kxvSVPwLW3MzohcRj+XzyZFsrK+dMk+EhrIAdw9Kud0g5WNW51lAbdiiNT8Tp1VNa2G+sCJifWYROrBrxaWgz8s3fulpmrnt21LW+GOvBPxSBLplHY9lXyMCohiUhKkAshnHRWPFsL9WjcGBCjXWcV6cwclEeJQQnmKSa+yG2dcDqE9Uaw8CnpQhoOxVXY+iVjoTSK3W/6b5mCJL9MXDt2sgWcV+l1if9Ml0df81geQtPYRhXN1FYdmMbSMUSfqvSFsy5QQ1DNq2ECn+oYbD9m/5cCMVSLh7AJOYgZ4Ae3PawNW0re9QNw5VRPO7fYigvJY3+XxpS+xmeiIl6KtDXCWhZot/dbZwscEEONHg+luOrqxiFTlu3a9GBDtW2gbYdec2JMoPpAY83IrLpDtcxtwT11W23pvOdGP83C2F9SaN5W1x1XilFnw20vkmjjzlujG9X2laOq3l4ocNOi/btmalTaMiq3Z9KLLquf40mUesP8EfTDBsK3CPIjRUJN/dnLBueNPUB36OYrCH5bZpOX8k5qVPDnBZR1Da8eqTqZHvH0CVJtmc5UlyGBbLGriRm4aOXr02VUJwiC2/Ql+qV/7xEdjxjQoEMLzlktCgNgAHbbBeGY4BMksJRqLMWyovtSJ5BWd8tqiVGscNR+13P49yGkAi1CGUF1fDy6f4Y+eRKzuVJ6xvv3dk5ldWp/aQNOlLbBGmO80JVS+iyQZLP2lkNWXxSB02DI50ivd9g/vYkOb0RjcMv/HkTBL6abOmkNFGyu/G65YWHb3uC/XqFQc2BSfr6w5ervIGz/fhkbig6ilyM5QC72xLgEDcYcogyL6sooptJ8nfqLyE4KV73a770LsVWvBnQIH9GzzBcslHZ6cEgK2T/m3tY5dI7rbDyRjPEFuKRzsxTxLFs6ABpXRMfIOmY+UBSrxAPNc5j5xefc904h/Vf+Ms7OX8+h9crXmDcQMWiqY+8es2XViYjrkfLO1KVCYGedd04h6K/AUh1hI0ruSP4UqcsGBH365HqvKdj6ACz53/YpMdJt8Mmvmojpe9PDoNmjknozhDwH8z19ArDm1bgN/J6/sv5eLTOSOeOQdiO1HqayrsXOg1zjD2Ba2p3TKjMtMWcWx3156QyCnJQZxlGsWNqVT/hJxYWY1/D97i1XVyJrSVbu0ufCRXDGt9a54RK72r2QdUBwvBaJZ1eQicHNS/hER7+mu1Cnpht5CUYNlOxFdjrynqc1Dwm0xwKCV6YHuyj3Rl+pu63LSoqcXSXCYPk2u7Vl4PKLtD7B7qatue0TxmD3wrsGw2FVRAHUJx+Vo9qv1vGQtJtqbDAzqYV6Bg1hfrmjMSiPDxLyOo0YMTP2iv5I7c4BKZZh8cJnl1DA6Gl0yWW0xksnDTE6EWUXBaE/+edtxpR164TB6+ooJSxKfdbLZqW7RY9fruy469mU/WZzSFsP5butnnmmhrMAopN/pNeYw3HUaiJZbWaXThM3IKTYLOVLzhMAFQRPpUXgeVxlq+aLviz3kGUdkQSJM9GoyF4G1n/ScaMrNWKcPXeLClhn0FNWzDJyzuat3hxFzqSBR27TcE0+Qjh1C9XvnMdSHuzLCcSF6qytWx/ZnVwG/keHD+27d+8EwNWLpCmThohwM1SKhiHGjSg4TLZ0ywbo0nR18GsGfgVk3UaO8fn+IClS63G237CxkJOzqU01TZCgDpF5PsRErvVS9TCKaPkuEWrqMyX5dH14xGuPeAY3374stJB+U7XRbCTjyPjYEuYVSsKataSGH2LylGSzI3UwJp9u5XDRx0GC5bt4rya0DLF3S434kzD8pPhhXyiZcvRh3vH1tNsWOiCWBsPBgGcDZovmEoFQTbd5OX1biVwZqqLa3rhb/Al7EWZIO5vY9X25g9qmpdxWhs53LhmdiTLFIyadZ6b1eRY3EKGTUBV/PRYuYFy1y5hvDGLyoGzxHCsprVWLUuDAJGSuqFrFv1gLrwh0Pigur28QrYr1fmp6wI+iBIWkBiw==","format":"google-gemini-v1","id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","index":1}]}}],"usage":{"prompt_tokens":3704,"completion_tokens":4488,"total_tokens":8192,"cost":0.04147815,"is_byok":false,"prompt_tokens_details":{"cached_tokens":3311,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.04147815,"upstream_inference_prompt_cost":0.00108615,"upstream_inference_completions_cost":0.040392},"completion_tokens_details":{"reasoning_tokens":2892,"image_tokens":0,"audio_tokens":0}}}"}, "error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\ndef set_transmission(principled_node, value):\n    if 'Transmission Weight' in principled_node.inputs:\n        principled_node.inputs['Transmission Weight'].default_value = value\n    elif 'Transmission' in principled_node.inputs:\n        principled_node.inputs['Transmission'].default_value = value\n\ndef get_or_create_material(name, shader_type):\n    mat = bpy.data.materials.new(name=name)\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    \n    if shader_type == 'glass':\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\n        principled.inputs['Roughness'].default_value = 0.01\n        principled.inputs['IOR'].default_value = 1.5\n        set_transmission(principled, 1.0)\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'emission':\n        # Glowing warm flame/wick core\n        emission = nodes.new(type='ShaderNodeEmission')\n        # Deep warm orange (approx 1800K)\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\n        emission.inputs['Strength'].default_value = 35.0\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\n        \n    elif shader_type == 'dark_metal':\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\n        principled.inputs['Metallic'].default_value = 0.6\n        \n        # Texture nodes for realistic metal variation\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 10.0\n        noise.inputs['Detail'].default_value = 4.0\n        \n        ramp = nodes.new(type='ShaderNodeValToRGB')\n        ramp.color_ramp.elements[0].position = 0.3\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\n        ramp.color_ramp.elements[1].position = 0.7\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'shiny_metal':\n        # Brassy weathered/brushed metal for cage and burners\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        # Aged brass/bronze color: Hex #C29D60\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\n        principled.inputs['Metallic'].default_value = 1.0\n        principled.inputs['Roughness'].default_value = 0.22\n        \n        # subtle bump\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 30.0\n        noise.inputs['Detail'].default_value = 5.0\n        \n        bump = nodes.new(type='ShaderNodeBump')\n        bump.inputs['Strength'].default_value = 0.03\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'ground':\n        # Soft-reflecting textured studio ground plane\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\n        principled.inputs['Roughness'].default_value = 0.5\n        \n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 15.0\n        noise.inputs['Detail'].default_value = 6.0\n        \n        bump = nodes.new(type='ShaderNodeBump')\n        bump.inputs['Strength'].default_value = 0.06\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    return mat\n\n# Clear and initialize scene\nfor obj in list(bpy.data.objects):\n    bpy.data.objects.remove(obj, do_unlink=True)\nfor mat in list(bpy.data.materials):\n    bpy.data.materials.remove(mat, do_unlink=True)\n\n# Create standard materials\nget_or_create_material('Glass', 'glass')\nget_or_create_material('EmissionCore', 'emission')\nget_or_create_material('DarkMetal', 'dark_metal')\nget_or_create_material('BrassMetal', 'shiny_metal')\nget_or_create_material('Ground', 'ground')\n\nprint(\"Materials created successfully!\")\n", "user_prompt": "Initialize materials in empty scene"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}", "parse_error": null}], "tokens_in": 3704, "tokens_out": 4488, "cost_usd": 0.04147815, "cached_tokens": 3311}, "error": null}
{"ts": "2026-07-15T04:44:35.195596+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\ndef set_transmission(principled_node, value):\n    if 'Transmission Weight' in principled_node.inputs:\n        principled_node.inputs['Transmission Weight'].default_value = value\n    elif 'Transmission' in principled_node.inputs:\n        principled_node.inputs['Transmission'].default_value = value\n\ndef get_or_create_material(name, shader_type):\n    mat = bpy.data.materials.new(name=name)\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    \n    if shader_type == 'glass':\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\n        principled.inputs['Roughness'].default_value = 0.01\n        principled.inputs['IOR'].default_value = 1.5\n        set_transmission(principled, 1.0)\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'emission':\n        # Glowing warm flame/wick core\n        emission = nodes.new(type='ShaderNodeEmission')\n        # Deep warm orange (approx 1800K)\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\n        emission.inputs['Strength'].default_value = 35.0\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\n        \n    elif shader_type == 'dark_metal':\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\n        principled.inputs['Metallic'].default_value = 0.6\n        \n        # Texture nodes for realistic metal variation\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 10.0\n        noise.inputs['Detail'].default_value = 4.0\n        \n        ramp = nodes.new(type='ShaderNodeValToRGB')\n        ramp.color_ramp.elements[0].position = 0.3\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\n        ramp.color_ramp.elements[1].position = 0.7\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'shiny_metal':\n        # Brassy weathered/brushed metal for cage and burners\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        # Aged brass/bronze color: Hex #C29D60\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\n        principled.inputs['Metallic'].default_value = 1.0\n        principled.inputs['Roughness'].default_value = 0.22\n        \n        # subtle bump\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 30.0\n        noise.inputs['Detail'].default_value = 5.0\n        \n        bump = nodes.new(type='ShaderNodeBump')\n        bump.inputs['Strength'].default_value = 0.03\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'ground':\n        # Soft-reflecting textured studio ground plane\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\n        principled.inputs['Roughness'].default_value = 0.5\n        \n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 15.0\n        noise.inputs['Detail'].default_value = 6.0\n        \n        bump = nodes.new(type='ShaderNodeBump')\n        bump.inputs['Strength'].default_value = 0.06\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    return mat\n\n# Clear and initialize scene\nfor obj in list(bpy.data.objects):\n    bpy.data.objects.remove(obj, do_unlink=True)\nfor mat in list(bpy.data.materials):\n    bpy.data.materials.remove(mat, do_unlink=True)\n\n# Create standard materials\nget_or_create_material('Glass', 'glass')\nget_or_create_material('EmissionCore', 'emission')\nget_or_create_material('DarkMetal', 'dark_metal')\nget_or_create_material('BrassMetal', 'shiny_metal')\nget_or_create_material('Ground', 'ground')\n\nprint(\"Materials created successfully!\")\n", "user_prompt": "Initialize materials in empty scene"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:44:35.195596+00:00", "type": "tool_call", "id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\ndef set_transmission(principled_node, value):\n    if 'Transmission Weight' in principled_node.inputs:\n        principled_node.inputs['Transmission Weight'].default_value = value\n    elif 'Transmission' in principled_node.inputs:\n        principled_node.inputs['Transmission'].default_value = value\n\ndef get_or_create_material(name, shader_type):\n    mat = bpy.data.materials.new(name=name)\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    \n    if shader_type == 'glass':\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\n        principled.inputs['Roughness'].default_value = 0.01\n        principled.inputs['IOR'].default_value = 1.5\n        set_transmission(principled, 1.0)\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'emission':\n        # Glowing warm flame/wick core\n        emission = nodes.new(type='ShaderNodeEmission')\n        # Deep warm orange (approx 1800K)\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\n        emission.inputs['Strength'].default_value = 35.0\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\n        \n    elif shader_type == 'dark_metal':\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\n        principled.inputs['Metallic'].default_value = 0.6\n        \n        # Texture nodes for realistic metal variation\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 10.0\n        noise.inputs['Detail'].default_value = 4.0\n        \n        ramp = nodes.new(type='ShaderNodeValToRGB')\n        ramp.color_ramp.elements[0].position = 0.3\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\n        ramp.color_ramp.elements[1].position = 0.7\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'shiny_metal':\n        # Brassy weathered/brushed metal for cage and burners\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        # Aged brass/bronze color: Hex #C29D60\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\n        principled.inputs['Metallic'].default_value = 1.0\n        principled.inputs['Roughness'].default_value = 0.22\n        \n        # subtle bump\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 30.0\n        noise.inputs['Detail'].default_value = 5.0\n        \n        bump = nodes.new(type='ShaderNodeBump')\n        bump.inputs['Strength'].default_value = 0.03\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    elif shader_type == 'ground':\n        # Soft-reflecting textured studio ground plane\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\n        principled.inputs['Roughness'].default_value = 0.5\n        \n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\n        noise = nodes.new(type='ShaderNodeTexNoise')\n        noise.inputs['Scale'].default_value = 15.0\n        noise.inputs['Detail'].default_value = 6.0\n        \n        bump = nodes.new(type='ShaderNodeBump')\n        bump.inputs['Strength'].default_value = 0.06\n        \n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\n        \n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n        \n    return mat\n\n# Clear and initialize scene\nfor obj in list(bpy.data.objects):\n    bpy.data.objects.remove(obj, do_unlink=True)\nfor mat in list(bpy.data.materials):\n    bpy.data.materials.remove(mat, do_unlink=True)\n\n# Create standard materials\nget_or_create_material('Glass', 'glass')\nget_or_create_material('EmissionCore', 'emission')\nget_or_create_material('DarkMetal', 'dark_metal')\nget_or_create_material('BrassMetal', 'shiny_metal')\nget_or_create_material('Ground', 'ground')\n\nprint(\"Materials created successfully!\")\n", "user_prompt": "Initialize materials in empty scene"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}
{"ts": "2026-07-15T04:44:35.243200+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "name": "execute_blender_code", "text": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:44:42.921283+00:00", "type": "llm_call", "call_index": 5, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:44:35.244290+00:00", "finished_at": "2026-07-15T04:44:42.921283+00:00", "duration_s": 7.687999999994645, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:44:35.244290+00:00", "finished_at": "2026-07-15T04:44:42.921283+00:00", "duration_s": 7.687999999994645, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "27257", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":[{\"type\":\"text\",\"text\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":[{"type":"text","text":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. 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rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials 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"}, "error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "name": "execute_blender_code", "arguments": {"user_prompt": "Robustly create standard materials with clean names", "code": "# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\n# Let's inspect the names list or write a simplified node network that doesn't fail.\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\n\nimport bpy\n\ndef set_transmission(principled_node, value):\n    if 'Transmission Weight' in principled_node.inputs:\n        principled_node.inputs['Transmission Weight'].default_value = value\n    elif 'Transmission' in principled_node.inputs:\n        principled_node.inputs['Transmission'].default_value = value\n\ndef create_glass_material():\n    mat = bpy.data.materials.new(name=\"Glass\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\n    principled.inputs['Roughness'].default_value = 0.02\n    principled.inputs['IOR'].default_value = 1.5\n    set_transmission(principled, 1.0)\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_emission_material():\n    mat = bpy.data.materials.new(name=\"EmissionCore\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    emission = nodes.new(type='ShaderNodeEmission')\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\n    emission.inputs['Strength'].default_value = 45.0\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\n    return mat\n\ndef create_dark_metal_material():\n    mat = bpy.data.materials.new(name=\"DarkMetal\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\n    principled.inputs['Metallic'].default_value = 0.8\n    principled.inputs['Roughness'].default_value = 0.35\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_shiny_metal_material():\n    mat = bpy.data.materials.new(name=\"BrassMetal\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\n    principled.inputs['Metallic'].default_value = 1.0\n    principled.inputs['Roughness'].default_value = 0.22\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_ground_material():\n    mat = bpy.data.materials.new(name=\"Ground\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\n    principled.inputs['Roughness'].default_value = 0.45\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\n# Clear and rebuild\nfor obj in list(bpy.data.objects):\n    bpy.data.objects.remove(obj, do_unlink=True)\nfor mat in list(bpy.data.materials):\n    bpy.data.materials.remove(mat, do_unlink=True)\n\ncreate_glass_material()\ncreate_emission_material()\ncreate_dark_metal_material()\ncreate_shiny_metal_material()\ncreate_ground_material()\n\nprint(\"Recreated robust basic materials successfully!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}", "parse_error": null}], "tokens_in": 5322, "tokens_out": 1264, "cost_usd": 0.01462995, "cached_tokens": 3503}, "error": null}
{"ts": "2026-07-15T04:44:42.922288+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "name": "execute_blender_code", "arguments": {"user_prompt": "Robustly create standard materials with clean names", "code": "# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\n# Let's inspect the names list or write a simplified node network that doesn't fail.\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\n\nimport bpy\n\ndef set_transmission(principled_node, value):\n    if 'Transmission Weight' in principled_node.inputs:\n        principled_node.inputs['Transmission Weight'].default_value = value\n    elif 'Transmission' in principled_node.inputs:\n        principled_node.inputs['Transmission'].default_value = value\n\ndef create_glass_material():\n    mat = bpy.data.materials.new(name=\"Glass\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\n    principled.inputs['Roughness'].default_value = 0.02\n    principled.inputs['IOR'].default_value = 1.5\n    set_transmission(principled, 1.0)\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_emission_material():\n    mat = bpy.data.materials.new(name=\"EmissionCore\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    emission = nodes.new(type='ShaderNodeEmission')\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\n    emission.inputs['Strength'].default_value = 45.0\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\n    return mat\n\ndef create_dark_metal_material():\n    mat = bpy.data.materials.new(name=\"DarkMetal\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\n    principled.inputs['Metallic'].default_value = 0.8\n    principled.inputs['Roughness'].default_value = 0.35\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_shiny_metal_material():\n    mat = bpy.data.materials.new(name=\"BrassMetal\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\n    principled.inputs['Metallic'].default_value = 1.0\n    principled.inputs['Roughness'].default_value = 0.22\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_ground_material():\n    mat = bpy.data.materials.new(name=\"Ground\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\n    principled.inputs['Roughness'].default_value = 0.45\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\n# Clear and rebuild\nfor obj in list(bpy.data.objects):\n    bpy.data.objects.remove(obj, do_unlink=True)\nfor mat in list(bpy.data.materials):\n    bpy.data.materials.remove(mat, do_unlink=True)\n\ncreate_glass_material()\ncreate_emission_material()\ncreate_dark_metal_material()\ncreate_shiny_metal_material()\ncreate_ground_material()\n\nprint(\"Recreated robust basic materials successfully!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:44:42.922288+00:00", "type": "tool_call", "id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "name": "execute_blender_code", "arguments": {"user_prompt": "Robustly create standard materials with clean names", "code": "# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\n# Let's inspect the names list or write a simplified node network that doesn't fail.\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\n\nimport bpy\n\ndef set_transmission(principled_node, value):\n    if 'Transmission Weight' in principled_node.inputs:\n        principled_node.inputs['Transmission Weight'].default_value = value\n    elif 'Transmission' in principled_node.inputs:\n        principled_node.inputs['Transmission'].default_value = value\n\ndef create_glass_material():\n    mat = bpy.data.materials.new(name=\"Glass\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\n    principled.inputs['Roughness'].default_value = 0.02\n    principled.inputs['IOR'].default_value = 1.5\n    set_transmission(principled, 1.0)\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_emission_material():\n    mat = bpy.data.materials.new(name=\"EmissionCore\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    emission = nodes.new(type='ShaderNodeEmission')\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\n    emission.inputs['Strength'].default_value = 45.0\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\n    return mat\n\ndef create_dark_metal_material():\n    mat = bpy.data.materials.new(name=\"DarkMetal\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\n    principled.inputs['Metallic'].default_value = 0.8\n    principled.inputs['Roughness'].default_value = 0.35\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_shiny_metal_material():\n    mat = bpy.data.materials.new(name=\"BrassMetal\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\n    principled.inputs['Metallic'].default_value = 1.0\n    principled.inputs['Roughness'].default_value = 0.22\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\ndef create_ground_material():\n    mat = bpy.data.materials.new(name=\"Ground\")\n    mat.use_nodes = True\n    nodes = mat.node_tree.nodes\n    links = mat.node_tree.links\n    nodes.clear()\n    \n    output = nodes.new(type='ShaderNodeOutputMaterial')\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\n    principled.inputs['Roughness'].default_value = 0.45\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\n    return mat\n\n# Clear and rebuild\nfor obj in list(bpy.data.objects):\n    bpy.data.objects.remove(obj, do_unlink=True)\nfor mat in list(bpy.data.materials):\n    bpy.data.materials.remove(mat, do_unlink=True)\n\ncreate_glass_material()\ncreate_emission_material()\ncreate_dark_metal_material()\ncreate_shiny_metal_material()\ncreate_ground_material()\n\nprint(\"Recreated robust basic materials successfully!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}
{"ts": "2026-07-15T04:44:42.960546+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "name": "execute_blender_code", "text": "Code executed successfully: Recreated robust basic materials successfully!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:44:50.708857+00:00", "type": "llm_call", "call_index": 6, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:44:42.961051+00:00", "finished_at": "2026-07-15T04:44:50.708857+00:00", "duration_s": 7.75, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:44:42.961051+00:00", "finished_at": "2026-07-15T04:44:50.708857+00:00", "duration_s": 7.75, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "31864", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":[{"type":"text","text":"Code executed successfully: Recreated robust basic materials successfully!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. 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{"id":"gen-1784090681-NdAH7fBEUmpPhnHIBiP4","object":"chat.completion","created":1784090681,"model":"google/gemini-3.5-flash","provider":"Google","system_fingerprint":null,"service_tier":"provisioned","choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"STOP","message":{"role":"assistant","content":null,"refusal":null,"reasoning":"**Designing the Lantern Base**\n\nI'm focusing on the tank and base first. My thoughts are centered on creating a squat, wide cylinder with elegantly beveled edges. I'm also picturing the filler neck and cap positioned on the shoulder, aiming for a classic aesthetic.\n\n**Constructing the Lamp Mechanics**\n\nI'm now considering the components above the base, specifically the transition from the tank to the light source. My focus is on integrating the traditional tubular side arms, the burner assembly with its wick adjuster, and the gallery for the glass globe. I'm also thinking about the multi-tiered cap and ventilation.\n\n","tool_calls":[{"type":"function","index":0,"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}],"reasoning_details":[{"type":"reasoning.text","text":"**Designing the Lantern Base**\n\nI'm focusing on the tank and base first. My thoughts are centered on creating a squat, wide cylinder with elegantly beveled edges. I'm also picturing the filler neck and cap positioned on the shoulder, aiming for a classic aesthetic.\n\n**Constructing the Lamp Mechanics**\n\nI'm now considering the components above the base, specifically the transition from the tank to the light source. My focus is on integrating the traditional tubular side arms, the burner assembly with its wick adjuster, and the gallery for the glass globe. I'm also thinking about the multi-tiered cap and ventilation.\n\n","format":"google-gemini-v1","index":0},{"type":"reasoning.encrypted","data":"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","format":"google-gemini-v1","id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","index":1}]}}],"usage":{"prompt_tokens":13195,"completion_tokens":746,"total_tokens":13941,"cost":0.01814903333333333,"is_byok":false,"prompt_tokens_details":{"cached_tokens":6598,"cache_write_tokens":6598,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.01814903333333333,"upstream_inference_prompt_cost":0.011435033333333334,"upstream_inference_completions_cost":0.006714},"completion_tokens_details":{"reasoning_tokens":405,"image_tokens":0,"audio_tokens":0}}}"}, "error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport bmesh\nimport math\n\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\n    bpy.ops.mesh.primitive_cylinder_add(\n        vertices=vertices, \n        radius=radius, \n        depth=depth, \n        location=location,\n        end_fill_type='NGON'\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    \n    # Smooth shading\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n        \n    if bevel_width > 0:\n        # Add bevel modifier for nice rounded borders and highlights\n        bevel = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n        bevel.width = bevel_width\n        bevel.segments = 3\n        bevel.limit_method = 'ANGLE'\n        \n    if material:\n        obj.data.materials.append(material)\n        \n    return obj\n\n# Test basic cylinder function\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\ncone = add_smooth_cylinder(\"Test_Base\", 0.08, 0.05, material=mat_dark)\nprint(\"Base test model created:\", cone.name)\n", "user_prompt": "Test cylinder creation helper"}, "raw_arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}", "parse_error": null}], "tokens_in": 13195, "tokens_out": 746, "cost_usd": 0.01814903333333333, "cached_tokens": 6598}, "error": null}
{"ts": "2026-07-15T04:44:50.710365+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport bmesh\nimport math\n\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\n    bpy.ops.mesh.primitive_cylinder_add(\n        vertices=vertices, \n        radius=radius, \n        depth=depth, \n        location=location,\n        end_fill_type='NGON'\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    \n    # Smooth shading\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n        \n    if bevel_width > 0:\n        # Add bevel modifier for nice rounded borders and highlights\n        bevel = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n        bevel.width = bevel_width\n        bevel.segments = 3\n        bevel.limit_method = 'ANGLE'\n        \n    if material:\n        obj.data.materials.append(material)\n        \n    return obj\n\n# Test basic cylinder function\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\ncone = add_smooth_cylinder(\"Test_Base\", 0.08, 0.05, material=mat_dark)\nprint(\"Base test model created:\", cone.name)\n", "user_prompt": "Test cylinder creation helper"}, "raw_arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:44:50.710880+00:00", "type": "tool_call", "id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport bmesh\nimport math\n\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\n    bpy.ops.mesh.primitive_cylinder_add(\n        vertices=vertices, \n        radius=radius, \n        depth=depth, \n        location=location,\n        end_fill_type='NGON'\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    \n    # Smooth shading\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n        \n    if bevel_width > 0:\n        # Add bevel modifier for nice rounded borders and highlights\n        bevel = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n        bevel.width = bevel_width\n        bevel.segments = 3\n        bevel.limit_method = 'ANGLE'\n        \n    if material:\n        obj.data.materials.append(material)\n        \n    return obj\n\n# Test basic cylinder function\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\ncone = add_smooth_cylinder(\"Test_Base\", 0.08, 0.05, material=mat_dark)\nprint(\"Base test model created:\", cone.name)\n", "user_prompt": "Test cylinder creation helper"}, "raw_arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}
{"ts": "2026-07-15T04:44:50.752047+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "name": "execute_blender_code", "text": "Code executed successfully: Base test model created: Test_Base\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:00.009812+00:00", "type": "llm_call", "call_index": 7, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:44:50.752047+00:00", "finished_at": "2026-07-15T04:45:00.009812+00:00", "duration_s": 9.25, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:44:50.752047+00:00", "finished_at": "2026-07-15T04:45:00.009812+00:00", "duration_s": 9.25, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "33385", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Base test model created: Test_Base\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":[{"type":"text","text":"Code executed successfully: Base test model created: Test_Base\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. 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\\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Clean up test object\ntest_obj = bpy.data.objects.get(\"Test_Base\")\nif test_obj:\n    bpy.data.objects.remove(test_obj, do_unlink=True)\n\n# Define materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\nmat_glass = bpy.data.materials.get(\"Glass\")\nmat_emission = bpy.data.materials.get(\"EmissionCore\")\nmat_ground = bpy.data.materials.get(\"Ground\")\n\n# We will build a highly detailed classic lantern:\n# 1. Base oil reservoir: A wide circular tapered shape.\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\n# 3. Glass globe: Rounded hourglass glass tube.\n# 4. Wick / Flame: Located inside the glass globe.\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\n\n# Let's create the base first.\n# It can consist of:\n# - A low wide cylinder at the bottom.\n# - A truncated cone tapering upwards.\n# - A small ornamental lip.\n\n# Helper function to create tapered cylinder (using cone or bmesh)\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\n    bpy.ops.mesh.primitive_cone_add(\n        vertices=32, \n        radius1=radius_bottom, \n        radius2=radius_top, \n        depth=depth, \n        location=location\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    \n    # Bevel for highlights\n    bevel = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n    bevel.width = 0.002\n    bevel.segments = 3\n    bevel.limit_method = 'ANGLE'\n    \n    if material:\n        obj.data.materials.append(material)\n    return obj\n\n# ----------------- 1. BASE OIL RESERVOIR -----------------\n# Let's place the base slightly above the ground (zcoord 0).\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\nrim_h = 0.02\nbase_rim = create_tapered_cylinder(\"Lantern_Base_Rim\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\n\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\ntank_h = 0.045\nbase_tank = create_tapered_cylinder(\"Lantern_Base_Tank\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\n\n# Collar rim top: radius 0.055, height 0.01\ncollar_h = 0.015\nbase_collar_top = create_tapered_cylinder(\"Lantern_Base_Collar\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\n\n# Let's add a decorative oil filler cap on the base tank\nbpy.ops.mesh.primitive_cylinder_add(\n    vertices=16, \n    radius=0.012, \n    depth=0.008, \n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\n)\nfiller_cap = bpy.context.active_object\nfiller_cap.name = \"Filler_Cap\"\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\nfor poly in filler_cap.data.polygons:\n    poly.use_smooth = True\nif mat_brass:\n    filler_cap.data.materials.append(mat_brass)\n\n# Parent the cap to the base Tank\nfiller_cap.parent = base_tank\n\nprint(\"Lantern Base parts created!\")\n", "user_prompt": "Build lantern lower base and details"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}", "parse_error": null}], "tokens_in": 13901, "tokens_out": 1141, "cost_usd": 0.0223159, "cached_tokens": 6951}, "error": null}
{"ts": "2026-07-15T04:45:00.011469+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Clean up test object\ntest_obj = bpy.data.objects.get(\"Test_Base\")\nif test_obj:\n    bpy.data.objects.remove(test_obj, do_unlink=True)\n\n# Define materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\nmat_glass = bpy.data.materials.get(\"Glass\")\nmat_emission = bpy.data.materials.get(\"EmissionCore\")\nmat_ground = bpy.data.materials.get(\"Ground\")\n\n# We will build a highly detailed classic lantern:\n# 1. Base oil reservoir: A wide circular tapered shape.\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\n# 3. Glass globe: Rounded hourglass glass tube.\n# 4. Wick / Flame: Located inside the glass globe.\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\n\n# Let's create the base first.\n# It can consist of:\n# - A low wide cylinder at the bottom.\n# - A truncated cone tapering upwards.\n# - A small ornamental lip.\n\n# Helper function to create tapered cylinder (using cone or bmesh)\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\n    bpy.ops.mesh.primitive_cone_add(\n        vertices=32, \n        radius1=radius_bottom, \n        radius2=radius_top, \n        depth=depth, \n        location=location\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    \n    # Bevel for highlights\n    bevel = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n    bevel.width = 0.002\n    bevel.segments = 3\n    bevel.limit_method = 'ANGLE'\n    \n    if material:\n        obj.data.materials.append(material)\n    return obj\n\n# ----------------- 1. BASE OIL RESERVOIR -----------------\n# Let's place the base slightly above the ground (zcoord 0).\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\nrim_h = 0.02\nbase_rim = create_tapered_cylinder(\"Lantern_Base_Rim\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\n\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\ntank_h = 0.045\nbase_tank = create_tapered_cylinder(\"Lantern_Base_Tank\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\n\n# Collar rim top: radius 0.055, height 0.01\ncollar_h = 0.015\nbase_collar_top = create_tapered_cylinder(\"Lantern_Base_Collar\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\n\n# Let's add a decorative oil filler cap on the base tank\nbpy.ops.mesh.primitive_cylinder_add(\n    vertices=16, \n    radius=0.012, \n    depth=0.008, \n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\n)\nfiller_cap = bpy.context.active_object\nfiller_cap.name = \"Filler_Cap\"\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\nfor poly in filler_cap.data.polygons:\n    poly.use_smooth = True\nif mat_brass:\n    filler_cap.data.materials.append(mat_brass)\n\n# Parent the cap to the base Tank\nfiller_cap.parent = base_tank\n\nprint(\"Lantern Base parts created!\")\n", "user_prompt": "Build lantern lower base and details"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:00.011469+00:00", "type": "tool_call", "id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Clean up test object\ntest_obj = bpy.data.objects.get(\"Test_Base\")\nif test_obj:\n    bpy.data.objects.remove(test_obj, do_unlink=True)\n\n# Define materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\nmat_glass = bpy.data.materials.get(\"Glass\")\nmat_emission = bpy.data.materials.get(\"EmissionCore\")\nmat_ground = bpy.data.materials.get(\"Ground\")\n\n# We will build a highly detailed classic lantern:\n# 1. Base oil reservoir: A wide circular tapered shape.\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\n# 3. Glass globe: Rounded hourglass glass tube.\n# 4. Wick / Flame: Located inside the glass globe.\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\n\n# Let's create the base first.\n# It can consist of:\n# - A low wide cylinder at the bottom.\n# - A truncated cone tapering upwards.\n# - A small ornamental lip.\n\n# Helper function to create tapered cylinder (using cone or bmesh)\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\n    bpy.ops.mesh.primitive_cone_add(\n        vertices=32, \n        radius1=radius_bottom, \n        radius2=radius_top, \n        depth=depth, \n        location=location\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    \n    # Bevel for highlights\n    bevel = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n    bevel.width = 0.002\n    bevel.segments = 3\n    bevel.limit_method = 'ANGLE'\n    \n    if material:\n        obj.data.materials.append(material)\n    return obj\n\n# ----------------- 1. BASE OIL RESERVOIR -----------------\n# Let's place the base slightly above the ground (zcoord 0).\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\nrim_h = 0.02\nbase_rim = create_tapered_cylinder(\"Lantern_Base_Rim\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\n\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\ntank_h = 0.045\nbase_tank = create_tapered_cylinder(\"Lantern_Base_Tank\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\n\n# Collar rim top: radius 0.055, height 0.01\ncollar_h = 0.015\nbase_collar_top = create_tapered_cylinder(\"Lantern_Base_Collar\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\n\n# Let's add a decorative oil filler cap on the base tank\nbpy.ops.mesh.primitive_cylinder_add(\n    vertices=16, \n    radius=0.012, \n    depth=0.008, \n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\n)\nfiller_cap = bpy.context.active_object\nfiller_cap.name = \"Filler_Cap\"\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\nfor poly in filler_cap.data.polygons:\n    poly.use_smooth = True\nif mat_brass:\n    filler_cap.data.materials.append(mat_brass)\n\n# Parent the cap to the base Tank\nfiller_cap.parent = base_tank\n\nprint(\"Lantern Base parts created!\")\n", "user_prompt": "Build lantern lower base and details"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}
{"ts": "2026-07-15T04:45:00.051899+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "name": "execute_blender_code", "text": "Code executed successfully: Lantern Base parts created!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:06.936850+00:00", "type": "llm_call", "call_index": 8, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:00.051899+00:00", "finished_at": "2026-07-15T04:45:06.936850+00:00", "duration_s": 6.89000000001397, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:00.051899+00:00", "finished_at": "2026-07-15T04:45:06.936850+00:00", "duration_s": 6.89000000001397, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "37205", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Lantern Base parts created!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":[{"type":"text","text":"Code executed successfully: Lantern Base parts created!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\nmat_glass = bpy.data.materials.get(\"Glass\")\nmat_emission = bpy.data.materials.get(\"EmissionCore\")\n\n# Let's locate the heights so far\n# rim_h = 0.02\n# tank_h = 0.045\n# collar_h = 0.015\n# Total base height = 0.08m\nbase_height = 0.02 + 0.045 + 0.015\n\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\nburner_h = 0.022\nburner_center_z = base_height + burner_h/2\nbpy.ops.mesh.primitive_cylinder_add(\n    vertices=24, \n    radius=0.025, \n    depth=burner_h, \n    location=(0, 0, burner_center_z)\n)\nburner_base = bpy.context.active_object\nburner_base.name = \"Burner_Base\"\nfor poly in burner_base.data.polygons:\n    poly.use_smooth = True\nburner_base.data.materials.append(mat_brass)\n\n# Flame/Wick emitter\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\nbpy.ops.mesh.primitive_cone_add(\n    vertices=16,\n    radius1=0.007,\n    radius2=0.0,\n    depth=0.030,\n    location=(0, 0, base_height + burner_h + 0.01)\n)\nflame = bpy.context.active_object\nflame.name = \"Flame_Core\"\nfor poly in flame.data.polygons:\n    poly.use_smooth = True\nflame.data.materials.append(mat_emission)\n\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \n# and illumination through the glass! This is critical for getting the \"soft pool of illumination on the ground\".\nlight_data = bpy.data.lights.new(name=\"Flame_PointLight\", type='POINT')\n# Golden-orange light (approx 1700K color)\nlight_data.color = (1.0, 0.48, 0.08)\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\nlight_data.shadow_soft_size = 0.04 # soft shadows\n\nlight_obj = bpy.data.objects.new(name=\"Flame_PointLight\", object_data=light_data)\nbpy.context.collection.objects.link(light_obj)\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\n\nprint(\"Burner, Flame Core, and Light source created!\")\n", "user_prompt": "Build internal burner dome, glowing flame core, and Cycles point light source"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}", "parse_error": null}], "tokens_in": 16199, "tokens_out": 777, "cost_usd": 0.0210315, "cached_tokens": 8100}, "error": null}
{"ts": "2026-07-15T04:45:06.938540+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\nmat_glass = bpy.data.materials.get(\"Glass\")\nmat_emission = bpy.data.materials.get(\"EmissionCore\")\n\n# Let's locate the heights so far\n# rim_h = 0.02\n# tank_h = 0.045\n# collar_h = 0.015\n# Total base height = 0.08m\nbase_height = 0.02 + 0.045 + 0.015\n\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\nburner_h = 0.022\nburner_center_z = base_height + burner_h/2\nbpy.ops.mesh.primitive_cylinder_add(\n    vertices=24, \n    radius=0.025, \n    depth=burner_h, \n    location=(0, 0, burner_center_z)\n)\nburner_base = bpy.context.active_object\nburner_base.name = \"Burner_Base\"\nfor poly in burner_base.data.polygons:\n    poly.use_smooth = True\nburner_base.data.materials.append(mat_brass)\n\n# Flame/Wick emitter\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\nbpy.ops.mesh.primitive_cone_add(\n    vertices=16,\n    radius1=0.007,\n    radius2=0.0,\n    depth=0.030,\n    location=(0, 0, base_height + burner_h + 0.01)\n)\nflame = bpy.context.active_object\nflame.name = \"Flame_Core\"\nfor poly in flame.data.polygons:\n    poly.use_smooth = True\nflame.data.materials.append(mat_emission)\n\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \n# and illumination through the glass! This is critical for getting the \"soft pool of illumination on the ground\".\nlight_data = bpy.data.lights.new(name=\"Flame_PointLight\", type='POINT')\n# Golden-orange light (approx 1700K color)\nlight_data.color = (1.0, 0.48, 0.08)\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\nlight_data.shadow_soft_size = 0.04 # soft shadows\n\nlight_obj = bpy.data.objects.new(name=\"Flame_PointLight\", object_data=light_data)\nbpy.context.collection.objects.link(light_obj)\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\n\nprint(\"Burner, Flame Core, and Light source created!\")\n", "user_prompt": "Build internal burner dome, glowing flame core, and Cycles point light source"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:06.939545+00:00", "type": "tool_call", "id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\nmat_glass = bpy.data.materials.get(\"Glass\")\nmat_emission = bpy.data.materials.get(\"EmissionCore\")\n\n# Let's locate the heights so far\n# rim_h = 0.02\n# tank_h = 0.045\n# collar_h = 0.015\n# Total base height = 0.08m\nbase_height = 0.02 + 0.045 + 0.015\n\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\nburner_h = 0.022\nburner_center_z = base_height + burner_h/2\nbpy.ops.mesh.primitive_cylinder_add(\n    vertices=24, \n    radius=0.025, \n    depth=burner_h, \n    location=(0, 0, burner_center_z)\n)\nburner_base = bpy.context.active_object\nburner_base.name = \"Burner_Base\"\nfor poly in burner_base.data.polygons:\n    poly.use_smooth = True\nburner_base.data.materials.append(mat_brass)\n\n# Flame/Wick emitter\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\nbpy.ops.mesh.primitive_cone_add(\n    vertices=16,\n    radius1=0.007,\n    radius2=0.0,\n    depth=0.030,\n    location=(0, 0, base_height + burner_h + 0.01)\n)\nflame = bpy.context.active_object\nflame.name = \"Flame_Core\"\nfor poly in flame.data.polygons:\n    poly.use_smooth = True\nflame.data.materials.append(mat_emission)\n\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \n# and illumination through the glass! This is critical for getting the \"soft pool of illumination on the ground\".\nlight_data = bpy.data.lights.new(name=\"Flame_PointLight\", type='POINT')\n# Golden-orange light (approx 1700K color)\nlight_data.color = (1.0, 0.48, 0.08)\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\nlight_data.shadow_soft_size = 0.04 # soft shadows\n\nlight_obj = bpy.data.objects.new(name=\"Flame_PointLight\", object_data=light_data)\nbpy.context.collection.objects.link(light_obj)\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\n\nprint(\"Burner, Flame Core, and Light source created!\")\n", "user_prompt": "Build internal burner dome, glowing flame core, and Cycles point light source"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}
{"ts": "2026-07-15T04:45:06.976227+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "name": "execute_blender_code", "text": "Code executed successfully: Burner, Flame Core, and Light source created!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:15.169966+00:00", "type": "llm_call", "call_index": 9, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:06.977320+00:00", "finished_at": "2026-07-15T04:45:15.169966+00:00", "duration_s": 8.202999999979511, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:06.977320+00:00", "finished_at": "2026-07-15T04:45:15.169966+00:00", "duration_s": 8.202999999979511, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "39947", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":[{"type":"text","text":"Code executed successfully: Burner, Flame Core, and Light source created!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. 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The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.encrypted\",\"data\":\"ZTI0ODMwYTctNWNkNi00MmZlLTk5OGItZWU1MzllNzJiOWMz\",\"format\":\"google-gemini-v1\",\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"index\":0}]}}],\"usage\":{\"prompt_tokens\":17783,\"completion_tokens\":929,\"total_tokens\":18712,\"cost\":0.0237723,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":8892,\"cache_write_tokens\":8892,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.0237723,\"upstream_inference_prompt_cost\":0.0154113,\"upstream_inference_completions_cost\":0.008361},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_glass = bpy.data.materials.get(\"Glass\")\n\n# ----------------- 3. GLASS GLOBE -----------------\n# Let's model a beautiful classic tapered/hourglass lantern globe.\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\n\nimport bmesh\n\nglass_mesh = bpy.data.meshes.new(name=\"GlassGlobeMesh\")\nglass_obj = bpy.data.objects.new(\"Glass_Globe\", glass_mesh)\nbpy.context.collection.objects.link(glass_obj)\n\nbm = bmesh.new()\n\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\n# We'll sweep this profile in a circle\nprofile = [\n    # (r, z)\n    (0.026, 0.0),\n    (0.033, 0.01),\n    (0.040, 0.03),\n    (0.043, 0.05),\n    (0.040, 0.08),\n    (0.032, 0.11),\n    (0.026, 0.13),\n    (0.024, 0.145),\n    (0.023, 0.150)\n]\n\n# Create profile vertices in the XZ plane\nverts = []\nfor r, z in profile:\n    v = bm.verts.new((r, 0.0, z))\n    verts.append(v)\n\n# Extrude by spinning around the Z axis\n# Spin 360 degrees, 32 steps\nbmesh.ops.spin(\n    bm,\n    geom=bm.verts[:] + bm.edges[:],\n    cent=(0.0, 0.0, 0.0),\n    axis=(0.0, 0.0, 1.0),\n    dangle=2 * math.pi,\n    steps=32,\n    use_duplicate=False\n)\n\n# Merge doubles to join the spin endpoints\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\n\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\n\nbm.to_mesh(glass_mesh)\nbm.free()\n\n# Translate glass to correct height in scene setup\nglass_obj.location = (0.0, 0.0, 0.08)\n\n# Shading: smooth and add materials\nfor poly in glass_obj.data.polygons:\n    poly.use_smooth = True\n\nglass_obj.data.materials.append(mat_glass)\n\n# Solidify Modifier\nsolidify = glass_obj.modifiers.new(name=\"Solidify\", type='SOLIDIFY')\nsolidify.thickness = 0.0025 # 2.5mm thick glass\nsolidify.offset = -1.0 # extend inward\n\n# Subsurf Modifier for perfect smoothness\nsubsurf = glass_obj.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 2\nsubsurf.render_levels = 2\n\nprint(\"Glass Globe modeled with realistic physical thickness!\")\n", "user_prompt": "Model glass globe with realistic physical thickness and hourglass shape"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}", "parse_error": null}], "tokens_in": 17783, "tokens_out": 929, "cost_usd": 0.0237723, "cached_tokens": 8892}, "error": null}
{"ts": "2026-07-15T04:45:15.173279+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_glass = bpy.data.materials.get(\"Glass\")\n\n# ----------------- 3. GLASS GLOBE -----------------\n# Let's model a beautiful classic tapered/hourglass lantern globe.\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\n\nimport bmesh\n\nglass_mesh = bpy.data.meshes.new(name=\"GlassGlobeMesh\")\nglass_obj = bpy.data.objects.new(\"Glass_Globe\", glass_mesh)\nbpy.context.collection.objects.link(glass_obj)\n\nbm = bmesh.new()\n\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\n# We'll sweep this profile in a circle\nprofile = [\n    # (r, z)\n    (0.026, 0.0),\n    (0.033, 0.01),\n    (0.040, 0.03),\n    (0.043, 0.05),\n    (0.040, 0.08),\n    (0.032, 0.11),\n    (0.026, 0.13),\n    (0.024, 0.145),\n    (0.023, 0.150)\n]\n\n# Create profile vertices in the XZ plane\nverts = []\nfor r, z in profile:\n    v = bm.verts.new((r, 0.0, z))\n    verts.append(v)\n\n# Extrude by spinning around the Z axis\n# Spin 360 degrees, 32 steps\nbmesh.ops.spin(\n    bm,\n    geom=bm.verts[:] + bm.edges[:],\n    cent=(0.0, 0.0, 0.0),\n    axis=(0.0, 0.0, 1.0),\n    dangle=2 * math.pi,\n    steps=32,\n    use_duplicate=False\n)\n\n# Merge doubles to join the spin endpoints\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\n\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\n\nbm.to_mesh(glass_mesh)\nbm.free()\n\n# Translate glass to correct height in scene setup\nglass_obj.location = (0.0, 0.0, 0.08)\n\n# Shading: smooth and add materials\nfor poly in glass_obj.data.polygons:\n    poly.use_smooth = True\n\nglass_obj.data.materials.append(mat_glass)\n\n# Solidify Modifier\nsolidify = glass_obj.modifiers.new(name=\"Solidify\", type='SOLIDIFY')\nsolidify.thickness = 0.0025 # 2.5mm thick glass\nsolidify.offset = -1.0 # extend inward\n\n# Subsurf Modifier for perfect smoothness\nsubsurf = glass_obj.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 2\nsubsurf.render_levels = 2\n\nprint(\"Glass Globe modeled with realistic physical thickness!\")\n", "user_prompt": "Model glass globe with realistic physical thickness and hourglass shape"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:15.173279+00:00", "type": "tool_call", "id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_glass = bpy.data.materials.get(\"Glass\")\n\n# ----------------- 3. GLASS GLOBE -----------------\n# Let's model a beautiful classic tapered/hourglass lantern globe.\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\n\nimport bmesh\n\nglass_mesh = bpy.data.meshes.new(name=\"GlassGlobeMesh\")\nglass_obj = bpy.data.objects.new(\"Glass_Globe\", glass_mesh)\nbpy.context.collection.objects.link(glass_obj)\n\nbm = bmesh.new()\n\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\n# We'll sweep this profile in a circle\nprofile = [\n    # (r, z)\n    (0.026, 0.0),\n    (0.033, 0.01),\n    (0.040, 0.03),\n    (0.043, 0.05),\n    (0.040, 0.08),\n    (0.032, 0.11),\n    (0.026, 0.13),\n    (0.024, 0.145),\n    (0.023, 0.150)\n]\n\n# Create profile vertices in the XZ plane\nverts = []\nfor r, z in profile:\n    v = bm.verts.new((r, 0.0, z))\n    verts.append(v)\n\n# Extrude by spinning around the Z axis\n# Spin 360 degrees, 32 steps\nbmesh.ops.spin(\n    bm,\n    geom=bm.verts[:] + bm.edges[:],\n    cent=(0.0, 0.0, 0.0),\n    axis=(0.0, 0.0, 1.0),\n    dangle=2 * math.pi,\n    steps=32,\n    use_duplicate=False\n)\n\n# Merge doubles to join the spin endpoints\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\n\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\n\nbm.to_mesh(glass_mesh)\nbm.free()\n\n# Translate glass to correct height in scene setup\nglass_obj.location = (0.0, 0.0, 0.08)\n\n# Shading: smooth and add materials\nfor poly in glass_obj.data.polygons:\n    poly.use_smooth = True\n\nglass_obj.data.materials.append(mat_glass)\n\n# Solidify Modifier\nsolidify = glass_obj.modifiers.new(name=\"Solidify\", type='SOLIDIFY')\nsolidify.thickness = 0.0025 # 2.5mm thick glass\nsolidify.offset = -1.0 # extend inward\n\n# Subsurf Modifier for perfect smoothness\nsubsurf = glass_obj.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 2\nsubsurf.render_levels = 2\n\nprint(\"Glass Globe modeled with realistic physical thickness!\")\n", "user_prompt": "Model glass globe with realistic physical thickness and hourglass shape"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}
{"ts": "2026-07-15T04:45:15.208765+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "name": "execute_blender_code", "text": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:23.187840+00:00", "type": "llm_call", "call_index": 10, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:15.209770+00:00", "finished_at": "2026-07-15T04:45:23.187840+00:00", "duration_s": 7.98399999999674, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:15.209770+00:00", "finished_at": "2026-07-15T04:45:23.187336+00:00", "duration_s": 7.98399999999674, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "43191", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":[{\"type\":\"text\",\"text\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":[{"type":"text","text":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. 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The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}],"reasoning":{"effort":"medium"}}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 04:45:21 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784090713-dyLH91hOmDJs7xu0L8Hr", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1b61dd17a15013e-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784090713-dyLH91hOmDJs7xu0L8Hr\",\"object\":\"chat.completion\",\"created\":1784090713,\"model\":\"google/gemini-3.5-flash\",\"provider\":\"Google\",\"system_fingerprint\":null,\"service_tier\":\"provisioned\",\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"native_finish_reason\":\"STOP\",\"message\":{\"role\":\"assistant\",\"content\":null,\"refusal\":null,\"reasoning\":null,\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.encrypted\",\"data\":\"ZTI0ODMwYTctNWNkNi00MmZlLTk5OGItZWU1MzllNzJiOWMz\",\"format\":\"google-gemini-v1\",\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"index\":0}]}}],\"usage\":{\"prompt_tokens\":19691,\"completion_tokens\":930,\"total_tokens\":20621,\"cost\":0.0254349,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":9846,\"cache_write_tokens\":9846,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.0254349,\"upstream_inference_prompt_cost\":0.0170649,\"upstream_inference_completions_cost\":0.00837},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": 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0b2tlbnMiOjB9LCJjb3N0X2RldGFpbHMiOnsidXBzdHJlYW1faW5mZXJlbmNlX2Nvc3QiOjAuMDI1NDM0OSwidXBzdHJlYW1faW5mZXJlbmNlX3Byb21wdF9jb3N0IjowLjAxNzA2NDksInVwc3RyZWFtX2luZmVyZW5jZV9jb21wbGV0aW9uc19jb3N0IjowLjAwODM3fSwiY29tcGxldGlvbl90b2tlbnNfZGV0YWlscyI6eyJyZWFzb25pbmdfdG9rZW5zIjowLCJpbWFnZV90b2tlbnMiOjAsImF1ZGlvX3Rva2VucyI6MH19fQ=="}, "error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport bmesh\nimport math\n\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\n# Custom vertex rotation is super clean and easy.\nmat_glass = bpy.data.materials.get(\"Glass\")\n\nglass_mesh = bpy.data.meshes.new(name=\"GlassGlobeMesh\")\nglass_obj = bpy.data.objects.new(\"Glass_Globe\", glass_mesh)\nbpy.context.collection.objects.link(glass_obj)\n\nbm = bmesh.new()\n\nprofile = [\n    # (r, z)\n    (0.027, 0.0),\n    (0.034, 0.01),\n    (0.040, 0.03),\n    (0.043, 0.05),\n    (0.040, 0.08),\n    (0.032, 0.115),\n    (0.026, 0.138),\n    (0.024, 0.147),\n    (0.023, 0.150)\n]\n\nnum_radial_steps = 32\n# Create grid of vertices\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\n\nfor i in range(num_radial_steps):\n    angle = (i / num_radial_steps) * 2 * math.pi\n    cos_a = math.cos(angle)\n    sin_a = math.sin(angle)\n    \n    step_verts = []\n    for r, z in profile:\n        x = r * cos_a\n        y = r * sin_a\n        v = bm.verts.new((x, y, z))\n        step_verts.append(v)\n    grid_verts.append(step_verts)\n\n# Create quad faces\nfor i in range(num_radial_steps):\n    next_i = (i + 1) % num_radial_steps\n    for j in range(len(profile) - 1):\n        v1 = grid_verts[i][j]\n        v2 = grid_verts[next_i][j]\n        v3 = grid_verts[next_i][j+1]\n        v4 = grid_verts[i][j+1]\n        \n        bm.faces.new((v1, v2, v3, v4))\n\n# Write back to mesh\nbm.to_mesh(glass_mesh)\nbm.free()\n\n# Translate to Z = 0.08\nglass_obj.location = (0.0, 0.0, 0.08)\n\n# Shading: smooth and add materials\nfor poly in glass_obj.data.polygons:\n    poly.use_smooth = True\n\nglass_obj.data.materials.append(mat_glass)\n\n# Solidify Modifier\nsolidify = glass_obj.modifiers.new(name=\"Solidify\", type='SOLIDIFY')\nsolidify.thickness = 0.0025 # 2.5mm thick glass\nsolidify.offset = -1.0 # inward\n\n# Subsurf Modifier\nsubsurf = glass_obj.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 2\nsubsurf.render_levels = 2\n\n# Recalculate normals to face outward\nbpy.context.view_layer.objects.active = glass_obj\nbpy.ops.object.mode_set(mode='EDIT')\nbpy.ops.mesh.select_all(action='SELECT')\nbpy.ops.mesh.normals_make_consistent(inside=False)\nbpy.ops.object.mode_set(mode='OBJ')\n\nprint(\"Glass Globe profile-modeled manually and solidified!\")\n", "user_prompt": "Manually construct the spin grid in a robust manner to avoid api version issues with spin operator"}, "raw_arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}", "parse_error": null}], "tokens_in": 19691, "tokens_out": 930, "cost_usd": 0.0254349, "cached_tokens": 9846}, "error": null}
{"ts": "2026-07-15T04:45:23.191002+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport bmesh\nimport math\n\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\n# Custom vertex rotation is super clean and easy.\nmat_glass = bpy.data.materials.get(\"Glass\")\n\nglass_mesh = bpy.data.meshes.new(name=\"GlassGlobeMesh\")\nglass_obj = bpy.data.objects.new(\"Glass_Globe\", glass_mesh)\nbpy.context.collection.objects.link(glass_obj)\n\nbm = bmesh.new()\n\nprofile = [\n    # (r, z)\n    (0.027, 0.0),\n    (0.034, 0.01),\n    (0.040, 0.03),\n    (0.043, 0.05),\n    (0.040, 0.08),\n    (0.032, 0.115),\n    (0.026, 0.138),\n    (0.024, 0.147),\n    (0.023, 0.150)\n]\n\nnum_radial_steps = 32\n# Create grid of vertices\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\n\nfor i in range(num_radial_steps):\n    angle = (i / num_radial_steps) * 2 * math.pi\n    cos_a = math.cos(angle)\n    sin_a = math.sin(angle)\n    \n    step_verts = []\n    for r, z in profile:\n        x = r * cos_a\n        y = r * sin_a\n        v = bm.verts.new((x, y, z))\n        step_verts.append(v)\n    grid_verts.append(step_verts)\n\n# Create quad faces\nfor i in range(num_radial_steps):\n    next_i = (i + 1) % num_radial_steps\n    for j in range(len(profile) - 1):\n        v1 = grid_verts[i][j]\n        v2 = grid_verts[next_i][j]\n        v3 = grid_verts[next_i][j+1]\n        v4 = grid_verts[i][j+1]\n        \n        bm.faces.new((v1, v2, v3, v4))\n\n# Write back to mesh\nbm.to_mesh(glass_mesh)\nbm.free()\n\n# Translate to Z = 0.08\nglass_obj.location = (0.0, 0.0, 0.08)\n\n# Shading: smooth and add materials\nfor poly in glass_obj.data.polygons:\n    poly.use_smooth = True\n\nglass_obj.data.materials.append(mat_glass)\n\n# Solidify Modifier\nsolidify = glass_obj.modifiers.new(name=\"Solidify\", type='SOLIDIFY')\nsolidify.thickness = 0.0025 # 2.5mm thick glass\nsolidify.offset = -1.0 # inward\n\n# Subsurf Modifier\nsubsurf = glass_obj.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 2\nsubsurf.render_levels = 2\n\n# Recalculate normals to face outward\nbpy.context.view_layer.objects.active = glass_obj\nbpy.ops.object.mode_set(mode='EDIT')\nbpy.ops.mesh.select_all(action='SELECT')\nbpy.ops.mesh.normals_make_consistent(inside=False)\nbpy.ops.object.mode_set(mode='OBJ')\n\nprint(\"Glass Globe profile-modeled manually and solidified!\")\n", "user_prompt": "Manually construct the spin grid in a robust manner to avoid api version issues with spin operator"}, "raw_arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:23.191002+00:00", "type": "tool_call", "id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport bmesh\nimport math\n\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\n# Custom vertex rotation is super clean and easy.\nmat_glass = bpy.data.materials.get(\"Glass\")\n\nglass_mesh = bpy.data.meshes.new(name=\"GlassGlobeMesh\")\nglass_obj = bpy.data.objects.new(\"Glass_Globe\", glass_mesh)\nbpy.context.collection.objects.link(glass_obj)\n\nbm = bmesh.new()\n\nprofile = [\n    # (r, z)\n    (0.027, 0.0),\n    (0.034, 0.01),\n    (0.040, 0.03),\n    (0.043, 0.05),\n    (0.040, 0.08),\n    (0.032, 0.115),\n    (0.026, 0.138),\n    (0.024, 0.147),\n    (0.023, 0.150)\n]\n\nnum_radial_steps = 32\n# Create grid of vertices\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\n\nfor i in range(num_radial_steps):\n    angle = (i / num_radial_steps) * 2 * math.pi\n    cos_a = math.cos(angle)\n    sin_a = math.sin(angle)\n    \n    step_verts = []\n    for r, z in profile:\n        x = r * cos_a\n        y = r * sin_a\n        v = bm.verts.new((x, y, z))\n        step_verts.append(v)\n    grid_verts.append(step_verts)\n\n# Create quad faces\nfor i in range(num_radial_steps):\n    next_i = (i + 1) % num_radial_steps\n    for j in range(len(profile) - 1):\n        v1 = grid_verts[i][j]\n        v2 = grid_verts[next_i][j]\n        v3 = grid_verts[next_i][j+1]\n        v4 = grid_verts[i][j+1]\n        \n        bm.faces.new((v1, v2, v3, v4))\n\n# Write back to mesh\nbm.to_mesh(glass_mesh)\nbm.free()\n\n# Translate to Z = 0.08\nglass_obj.location = (0.0, 0.0, 0.08)\n\n# Shading: smooth and add materials\nfor poly in glass_obj.data.polygons:\n    poly.use_smooth = True\n\nglass_obj.data.materials.append(mat_glass)\n\n# Solidify Modifier\nsolidify = glass_obj.modifiers.new(name=\"Solidify\", type='SOLIDIFY')\nsolidify.thickness = 0.0025 # 2.5mm thick glass\nsolidify.offset = -1.0 # inward\n\n# Subsurf Modifier\nsubsurf = glass_obj.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 2\nsubsurf.render_levels = 2\n\n# Recalculate normals to face outward\nbpy.context.view_layer.objects.active = glass_obj\nbpy.ops.object.mode_set(mode='EDIT')\nbpy.ops.mesh.select_all(action='SELECT')\nbpy.ops.mesh.normals_make_consistent(inside=False)\nbpy.ops.object.mode_set(mode='OBJ')\n\nprint(\"Glass Globe profile-modeled manually and solidified!\")\n", "user_prompt": "Manually construct the spin grid in a robust manner to avoid api version issues with spin operator"}, "raw_arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}
{"ts": "2026-07-15T04:45:23.322775+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "name": "execute_blender_code", "text": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:26.835867+00:00", "type": "llm_call", "call_index": 11, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:23.322775+00:00", "finished_at": "2026-07-15T04:45:26.835337+00:00", "duration_s": 3.5160000000032596, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:23.322775+00:00", "finished_at": "2026-07-15T04:45:26.834826+00:00", "duration_s": 3.5160000000032596, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "46310", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":[{\"type\":\"text\",\"text\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":[{"type":"text","text":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. 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The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. 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bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.encrypted\",\"data\":\"ZTI0ODMwYTctNWNkNi00MmZlLTk5OGItZWU1MzllNzJiOWMz\",\"format\":\"google-gemini-v1\",\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"index\":0}]}}],\"usage\":{\"prompt_tokens\":10828,\"completion_tokens\":170,\"total_tokens\":10998,\"cost\":0.0051522,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":9348,\"cache_write_tokens\":0,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.0051522,\"upstream_inference_prompt_cost\":0.0036222,\"upstream_inference_completions_cost\":0.00153},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# Fix 'OBJ' to 'OBJECT' in mode_set\nglass_obj = bpy.data.objects.get(\"Glass_Globe\")\nif glass_obj:\n    bpy.context.view_layer.objects.active = glass_obj\n    bpy.ops.object.mode_set(mode='EDIT')\n    bpy.ops.mesh.select_all(action='SELECT')\n    bpy.ops.mesh.normals_make_consistent(inside=False)\n    bpy.ops.object.mode_set(mode='OBJECT')\n    print(\"Normals consistent and mode returned to OBJECT!\")\n", "user_prompt": "Fix mode_set parameter and compute correct normals"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}", "parse_error": null}], "tokens_in": 10828, "tokens_out": 170, "cost_usd": 0.0051522, "cached_tokens": 9348}, "error": null}
{"ts": "2026-07-15T04:45:26.836929+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# Fix 'OBJ' to 'OBJECT' in mode_set\nglass_obj = bpy.data.objects.get(\"Glass_Globe\")\nif glass_obj:\n    bpy.context.view_layer.objects.active = glass_obj\n    bpy.ops.object.mode_set(mode='EDIT')\n    bpy.ops.mesh.select_all(action='SELECT')\n    bpy.ops.mesh.normals_make_consistent(inside=False)\n    bpy.ops.object.mode_set(mode='OBJECT')\n    print(\"Normals consistent and mode returned to OBJECT!\")\n", "user_prompt": "Fix mode_set parameter and compute correct normals"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:26.836929+00:00", "type": "tool_call", "id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# Fix 'OBJ' to 'OBJECT' in mode_set\nglass_obj = bpy.data.objects.get(\"Glass_Globe\")\nif glass_obj:\n    bpy.context.view_layer.objects.active = glass_obj\n    bpy.ops.object.mode_set(mode='EDIT')\n    bpy.ops.mesh.select_all(action='SELECT')\n    bpy.ops.mesh.normals_make_consistent(inside=False)\n    bpy.ops.object.mode_set(mode='OBJECT')\n    print(\"Normals consistent and mode returned to OBJECT!\")\n", "user_prompt": "Fix mode_set parameter and compute correct normals"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}
{"ts": "2026-07-15T04:45:26.878758+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "name": "execute_blender_code", "text": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:34.783805+00:00", "type": "llm_call", "call_index": 12, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:26.878758+00:00", "finished_at": "2026-07-15T04:45:34.782802+00:00", "duration_s": 7.9060000000172295, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:26.878758+00:00", "finished_at": "2026-07-15T04:45:34.782802+00:00", "duration_s": 7.9060000000172295, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "47192", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":[{"type":"text","text":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}],"reasoning":{"effort":"medium"}}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 04:45:32 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784090725-hiV7qwT3tr3ad5uiYe0w", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1b61e1aafeacbeb-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784090725-hiV7qwT3tr3ad5uiYe0w\",\"object\":\"chat.completion\",\"created\":1784090725,\"model\":\"google/gemini-3.5-flash\",\"provider\":\"Google\",\"system_fingerprint\":null,\"service_tier\":\"default\",\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"native_finish_reason\":\"STOP\",\"message\":{\"role\":\"assistant\",\"content\":null,\"refusal\":null,\"reasoning\":null,\"tool_calls\":[{\"type\":\"function\",\"index\":0,\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. 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We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. 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UPPER CAP / CHIMNEY & COVERS -----------------\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\n\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    obj.data.materials.append(material)\n    \n    # Bevel\n    bev = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n    bev.width = 0.0015\n    bev.segments = 3\n    bev.limit_method = 'ANGLE'\n    return obj\n\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\ntop_holder = create_tapered_cylinder_simple(\"Chimney_Holder\", 0.024, 0.027, 0.012, 0.236, mat_brass)\n\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\ntop_hood = create_tapered_cylinder_simple(\"Chimney_Hood\", 0.065, 0.035, 0.03, 0.257, mat_dark)\n\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\ntop_riser = create_tapered_cylinder_simple(\"Chimney_Riser\", 0.031, 0.031, 0.032, 0.288, mat_dark)\n\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\nvent_rim1 = create_tapered_cylinder_simple(\"Chimney_Vent1\", 0.033, 0.033, 0.004, 0.280, mat_brass)\nvent_rim2 = create_tapered_cylinder_simple(\"Chimney_Vent2\", 0.033, 0.033, 0.004, 0.292, mat_brass)\n\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\n# Bottom radius 0.032, top 0.01, height 0.02\ntop_crown = create_tapered_cylinder_simple(\"Chimney_Crown\", 0.032, 0.015, 0.02, 0.314, mat_dark)\n\n# Eyelet loop on top of crown for the hanging carry handle:\n# Create a small torus for the hinge loop\nbpy.ops.mesh.primitive_torus_add(\n    align='WORLD', \n    location=(0, 0, 0.327), \n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\n    major_radius=0.008, \n    minor_radius=0.002\n)\nhinge_loop = bpy.context.active_object\nhinge_loop.name = \"Hinge_Loop\"\nfor poly in hinge_loop.data.polygons:\n    poly.use_smooth = True\nhinge_loop.data.materials.append(mat_brass)\n\nprint(\"Upper Chimney structures and venting details finished!\")\n", "user_prompt": "Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}", "parse_error": null}], "tokens_in": 11011, "tokens_out": 1210, "cost_usd": 0.0173139, "cached_tokens": 7476}, "error": null}
{"ts": "2026-07-15T04:45:34.783805+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\n\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    obj.data.materials.append(material)\n    \n    # Bevel\n    bev = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n    bev.width = 0.0015\n    bev.segments = 3\n    bev.limit_method = 'ANGLE'\n    return obj\n\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\ntop_holder = create_tapered_cylinder_simple(\"Chimney_Holder\", 0.024, 0.027, 0.012, 0.236, mat_brass)\n\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\ntop_hood = create_tapered_cylinder_simple(\"Chimney_Hood\", 0.065, 0.035, 0.03, 0.257, mat_dark)\n\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\ntop_riser = create_tapered_cylinder_simple(\"Chimney_Riser\", 0.031, 0.031, 0.032, 0.288, mat_dark)\n\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\nvent_rim1 = create_tapered_cylinder_simple(\"Chimney_Vent1\", 0.033, 0.033, 0.004, 0.280, mat_brass)\nvent_rim2 = create_tapered_cylinder_simple(\"Chimney_Vent2\", 0.033, 0.033, 0.004, 0.292, mat_brass)\n\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\n# Bottom radius 0.032, top 0.01, height 0.02\ntop_crown = create_tapered_cylinder_simple(\"Chimney_Crown\", 0.032, 0.015, 0.02, 0.314, mat_dark)\n\n# Eyelet loop on top of crown for the hanging carry handle:\n# Create a small torus for the hinge loop\nbpy.ops.mesh.primitive_torus_add(\n    align='WORLD', \n    location=(0, 0, 0.327), \n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\n    major_radius=0.008, \n    minor_radius=0.002\n)\nhinge_loop = bpy.context.active_object\nhinge_loop.name = \"Hinge_Loop\"\nfor poly in hinge_loop.data.polygons:\n    poly.use_smooth = True\nhinge_loop.data.materials.append(mat_brass)\n\nprint(\"Upper Chimney structures and venting details finished!\")\n", "user_prompt": "Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:34.783805+00:00", "type": "tool_call", "id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\n\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    obj.data.materials.append(material)\n    \n    # Bevel\n    bev = obj.modifiers.new(name=\"Bevel\", type='BEVEL')\n    bev.width = 0.0015\n    bev.segments = 3\n    bev.limit_method = 'ANGLE'\n    return obj\n\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\ntop_holder = create_tapered_cylinder_simple(\"Chimney_Holder\", 0.024, 0.027, 0.012, 0.236, mat_brass)\n\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\ntop_hood = create_tapered_cylinder_simple(\"Chimney_Hood\", 0.065, 0.035, 0.03, 0.257, mat_dark)\n\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\ntop_riser = create_tapered_cylinder_simple(\"Chimney_Riser\", 0.031, 0.031, 0.032, 0.288, mat_dark)\n\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\nvent_rim1 = create_tapered_cylinder_simple(\"Chimney_Vent1\", 0.033, 0.033, 0.004, 0.280, mat_brass)\nvent_rim2 = create_tapered_cylinder_simple(\"Chimney_Vent2\", 0.033, 0.033, 0.004, 0.292, mat_brass)\n\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\n# Bottom radius 0.032, top 0.01, height 0.02\ntop_crown = create_tapered_cylinder_simple(\"Chimney_Crown\", 0.032, 0.015, 0.02, 0.314, mat_dark)\n\n# Eyelet loop on top of crown for the hanging carry handle:\n# Create a small torus for the hinge loop\nbpy.ops.mesh.primitive_torus_add(\n    align='WORLD', \n    location=(0, 0, 0.327), \n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\n    major_radius=0.008, \n    minor_radius=0.002\n)\nhinge_loop = bpy.context.active_object\nhinge_loop.name = \"Hinge_Loop\"\nfor poly in hinge_loop.data.polygons:\n    poly.use_smooth = True\nhinge_loop.data.materials.append(mat_brass)\n\nprint(\"Upper Chimney structures and venting details finished!\")\n", "user_prompt": "Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}
{"ts": "2026-07-15T04:45:34.877141+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "name": "execute_blender_code", "text": "Code executed successfully: Upper Chimney structures and venting details finished!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:42.536304+00:00", "type": "llm_call", "call_index": 13, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:34.877141+00:00", "finished_at": "2026-07-15T04:45:42.536304+00:00", "duration_s": 7.6560000000172295, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:34.877674+00:00", "finished_at": "2026-07-15T04:45:42.536304+00:00", "duration_s": 7.6560000000172295, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "50818", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":[{"type":"text","text":"Code executed successfully: Upper Chimney structures and venting details finished!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "name": "execute_blender_code", "arguments": {"user_prompt": "Build thick side pillars using Bezier curves with proper thickness and material", "code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\n# Authentic vintage lanterns (Dietz style) have:\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\n# - A thin wire safety cage wrapping around the glass globe physically.\n\n# A - Side tubular pillars\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\n# Let's write a curve generation script!\n\ndef create_side_tubes():\n    for side in [1, -1]: # +1 is Left, -1 is Right\n        name = f\"Support_Tube_{'R' if side == 1 else 'L'}\"\n        \n        # Create a curve\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        curve_data.resolution_u = 16\n        \n        polyline = curve_data.splines.new('BEZIER')\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\n        # P0: on base tank (±0.065, 0, 0.06)\n        # P1: out and straight up (±0.073, 0, 0.14)\n        # P2: straight up and in (±0.070, 0, 0.22)\n        # P3: inside upper hood (±0.035, 0, 0.25)\n        \n        polyline.points.add(3) # adds 3 points to the existing 1\n        \n        pts = polyline.points\n        # Point 0\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\n        \n        # Point 1\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\n        \n        # Point 2\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\n        \n        # Point 3\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\n        \n        # Set all point handles to professional automatically smoothed angles\n        # For Bezier curve, use select and automatic handles:\n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        # Give the curve thickness (extrusion) to make real physical tubes!\n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\n        curve_obj.data.bevel_resolution = 4\n        \n        # Apply dark vintage metal material\n        curve_obj.data.materials.append(mat_dark)\n        \n        # Convert curve to mesh so we can smooth and add modifiers if needed\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\n        \ncreate_side_tubes()\nprint(\"Thick side pillars created professionally via Bezier curves!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}", "parse_error": null}], "tokens_in": 12234, "tokens_out": 1226, "cost_usd": 0.0243036, "cached_tokens": 3764}, "error": null}
{"ts": "2026-07-15T04:45:42.538813+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "name": "execute_blender_code", "arguments": {"user_prompt": "Build thick side pillars using Bezier curves with proper thickness and material", "code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\n# Authentic vintage lanterns (Dietz style) have:\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\n# - A thin wire safety cage wrapping around the glass globe physically.\n\n# A - Side tubular pillars\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\n# Let's write a curve generation script!\n\ndef create_side_tubes():\n    for side in [1, -1]: # +1 is Left, -1 is Right\n        name = f\"Support_Tube_{'R' if side == 1 else 'L'}\"\n        \n        # Create a curve\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        curve_data.resolution_u = 16\n        \n        polyline = curve_data.splines.new('BEZIER')\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\n        # P0: on base tank (±0.065, 0, 0.06)\n        # P1: out and straight up (±0.073, 0, 0.14)\n        # P2: straight up and in (±0.070, 0, 0.22)\n        # P3: inside upper hood (±0.035, 0, 0.25)\n        \n        polyline.points.add(3) # adds 3 points to the existing 1\n        \n        pts = polyline.points\n        # Point 0\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\n        \n        # Point 1\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\n        \n        # Point 2\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\n        \n        # Point 3\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\n        \n        # Set all point handles to professional automatically smoothed angles\n        # For Bezier curve, use select and automatic handles:\n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        # Give the curve thickness (extrusion) to make real physical tubes!\n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\n        curve_obj.data.bevel_resolution = 4\n        \n        # Apply dark vintage metal material\n        curve_obj.data.materials.append(mat_dark)\n        \n        # Convert curve to mesh so we can smooth and add modifiers if needed\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\n        \ncreate_side_tubes()\nprint(\"Thick side pillars created professionally via Bezier curves!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:42.539817+00:00", "type": "tool_call", "id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "name": "execute_blender_code", "arguments": {"user_prompt": "Build thick side pillars using Bezier curves with proper thickness and material", "code": "import bpy\nimport math\n\n# Materials\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\n# Authentic vintage lanterns (Dietz style) have:\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\n# - A thin wire safety cage wrapping around the glass globe physically.\n\n# A - Side tubular pillars\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\n# Let's write a curve generation script!\n\ndef create_side_tubes():\n    for side in [1, -1]: # +1 is Left, -1 is Right\n        name = f\"Support_Tube_{'R' if side == 1 else 'L'}\"\n        \n        # Create a curve\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        curve_data.resolution_u = 16\n        \n        polyline = curve_data.splines.new('BEZIER')\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\n        # P0: on base tank (±0.065, 0, 0.06)\n        # P1: out and straight up (±0.073, 0, 0.14)\n        # P2: straight up and in (±0.070, 0, 0.22)\n        # P3: inside upper hood (±0.035, 0, 0.25)\n        \n        polyline.points.add(3) # adds 3 points to the existing 1\n        \n        pts = polyline.points\n        # Point 0\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\n        \n        # Point 1\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\n        \n        # Point 2\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\n        \n        # Point 3\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\n        \n        # Set all point handles to professional automatically smoothed angles\n        # For Bezier curve, use select and automatic handles:\n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        # Give the curve thickness (extrusion) to make real physical tubes!\n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\n        curve_obj.data.bevel_resolution = 4\n        \n        # Apply dark vintage metal material\n        curve_obj.data.materials.append(mat_dark)\n        \n        # Convert curve to mesh so we can smooth and add modifiers if needed\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\n        \ncreate_side_tubes()\nprint(\"Thick side pillars created professionally via Bezier curves!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}
{"ts": "2026-07-15T04:45:42.585300+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "name": "execute_blender_code", "text": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:49.703895+00:00", "type": "llm_call", "call_index": 14, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:42.585300+00:00", "finished_at": "2026-07-15T04:45:49.703895+00:00", "duration_s": 7.125, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:42.585300+00:00", "finished_at": "2026-07-15T04:45:49.702383+00:00", "duration_s": 7.125, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "54743", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":[{\"type\":\"text\",\"text\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":[{"type":"text","text":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. 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curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\n\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\n\ndef create_side_tubes_fixed():\n    for side in [1, -1]:\n        name = f\"Support_Tube_{'R' if side == 1 else 'L'}\"\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        curve_data.resolution_u = 12\n        \n        spline = curve_data.splines.new('BEZIER')\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\n        \n        pts = spline.bezier_points\n        # Point 0: Bottom base junction\n        pts[0].co = (side * 0.065, 0.0, 0.06)\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\n        \n        # Point 1: Mid-body curve out\n        pts[1].co = (side * 0.073, 0.0, 0.14)\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\n        \n        # Point 2: Top shoulder curve inwards\n        pts[2].co = (side * 0.064, 0.0, 0.22)\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\n        \n        # Point 3: Top hood junction\n        pts[3].co = (side * 0.035, 0.0, 0.252)\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\n        \n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        # Extrude/bevel\n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\n        curve_obj.data.bevel_resolution = 5\n        \n        # Add material\n        curve_obj.data.materials.append(mat_dark)\n\ncreate_side_tubes_fixed()\nprint(\"Side support pillars drafted perfectly with bezier_points!\")\n", "user_prompt": "Fix Bezier curve creation code using bezier_points instead of points"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}", "parse_error": null}], "tokens_in": 26965, "tokens_out": 841, "cost_usd": 0.030938033333333333, "cached_tokens": 13483}, "error": null}
{"ts": "2026-07-15T04:45:49.704900+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\n\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\n\ndef create_side_tubes_fixed():\n    for side in [1, -1]:\n        name = f\"Support_Tube_{'R' if side == 1 else 'L'}\"\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        curve_data.resolution_u = 12\n        \n        spline = curve_data.splines.new('BEZIER')\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\n        \n        pts = spline.bezier_points\n        # Point 0: Bottom base junction\n        pts[0].co = (side * 0.065, 0.0, 0.06)\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\n        \n        # Point 1: Mid-body curve out\n        pts[1].co = (side * 0.073, 0.0, 0.14)\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\n        \n        # Point 2: Top shoulder curve inwards\n        pts[2].co = (side * 0.064, 0.0, 0.22)\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\n        \n        # Point 3: Top hood junction\n        pts[3].co = (side * 0.035, 0.0, 0.252)\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\n        \n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        # Extrude/bevel\n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\n        curve_obj.data.bevel_resolution = 5\n        \n        # Add material\n        curve_obj.data.materials.append(mat_dark)\n\ncreate_side_tubes_fixed()\nprint(\"Side support pillars drafted perfectly with bezier_points!\")\n", "user_prompt": "Fix Bezier curve creation code using bezier_points instead of points"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:49.704900+00:00", "type": "tool_call", "id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\n\nmat_dark = bpy.data.materials.get(\"DarkMetal\")\n\ndef create_side_tubes_fixed():\n    for side in [1, -1]:\n        name = f\"Support_Tube_{'R' if side == 1 else 'L'}\"\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        curve_data.resolution_u = 12\n        \n        spline = curve_data.splines.new('BEZIER')\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\n        \n        pts = spline.bezier_points\n        # Point 0: Bottom base junction\n        pts[0].co = (side * 0.065, 0.0, 0.06)\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\n        \n        # Point 1: Mid-body curve out\n        pts[1].co = (side * 0.073, 0.0, 0.14)\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\n        \n        # Point 2: Top shoulder curve inwards\n        pts[2].co = (side * 0.064, 0.0, 0.22)\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\n        \n        # Point 3: Top hood junction\n        pts[3].co = (side * 0.035, 0.0, 0.252)\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\n        \n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        # Extrude/bevel\n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\n        curve_obj.data.bevel_resolution = 5\n        \n        # Add material\n        curve_obj.data.materials.append(mat_dark)\n\ncreate_side_tubes_fixed()\nprint(\"Side support pillars drafted perfectly with bezier_points!\")\n", "user_prompt": "Fix Bezier curve creation code using bezier_points instead of points"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}
{"ts": "2026-07-15T04:45:49.745953+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "name": "execute_blender_code", "text": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:45:59.409782+00:00", "type": "llm_call", "call_index": 15, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:49.746952+00:00", "finished_at": "2026-07-15T04:45:59.409782+00:00", "duration_s": 9.672000000020489, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:49.746952+00:00", "finished_at": "2026-07-15T04:45:59.408781+00:00", "duration_s": 9.672000000020489, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "57387", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":[{"type":"text","text":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    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0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\n# Let's create:\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\n\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\n\ndef create_guard_ring(name, radius, z, thickness, material=None):\n    # We can model a thin torus for beautiful shading\n    bpy.ops.mesh.primitive_torus_add(\n        align='WORLD', \n        location=(0, 0, z), \n        major_radius=radius, \n        minor_radius=thickness,\n        major_segments=32,\n        minor_segments=8\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    if material:\n        obj.data.materials.append(material)\n    return obj\n\nring_lower = create_guard_ring(\"Cage_Ring_Lower\", 0.043, 0.13, 0.0016, mat_brass)\nring_upper = create_guard_ring(\"Cage_Ring_Upper\", 0.032, 0.19, 0.0016, mat_brass)\n\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\n# Curves make visual presentation far more organic and high-quality!\n\ndef create_vertical_cage_wires():\n    angles = [45, 135, 225, 315]\n    for idx, angle_deg in enumerate(angles):\n        angle_rad = math.radians(angle_deg)\n        cos_a = math.cos(angle_rad)\n        sin_a = math.sin(angle_rad)\n        \n        name = f\"Cage_Wire_Vert_{idx+1}\"\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        \n        spline = curve_data.splines.new('BEZIER')\n        spline.bezier_points.add(2) # Total 3 control points\n        pts = spline.bezier_points\n        \n        # Point 0: bottom anchor\n        r0 = 0.048\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\n        \n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\n        r1 = 0.051\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\n        \n        # Point 2: top anchor near top hood\n        r2 = 0.033\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\n        \n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\n        curve_obj.data.bevel_resolution = 4\n        \n        curve_obj.data.materials.append(mat_brass)\n\ncreate_vertical_cage_wires()\nprint(\"Protective brass cage horizontal rings and vertical safety wires completed!\")\n", "user_prompt": "Build horizontal and vertical protective brass cage around glass globe"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}", "parse_error": null}], "tokens_in": 28675, "tokens_out": 1247, "cost_usd": 0.03607403333333333, "cached_tokens": 14338}, "error": null}
{"ts": "2026-07-15T04:45:59.411286+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\n# Let's create:\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\n\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\n\ndef create_guard_ring(name, radius, z, thickness, material=None):\n    # We can model a thin torus for beautiful shading\n    bpy.ops.mesh.primitive_torus_add(\n        align='WORLD', \n        location=(0, 0, z), \n        major_radius=radius, \n        minor_radius=thickness,\n        major_segments=32,\n        minor_segments=8\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    if material:\n        obj.data.materials.append(material)\n    return obj\n\nring_lower = create_guard_ring(\"Cage_Ring_Lower\", 0.043, 0.13, 0.0016, mat_brass)\nring_upper = create_guard_ring(\"Cage_Ring_Upper\", 0.032, 0.19, 0.0016, mat_brass)\n\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\n# Curves make visual presentation far more organic and high-quality!\n\ndef create_vertical_cage_wires():\n    angles = [45, 135, 225, 315]\n    for idx, angle_deg in enumerate(angles):\n        angle_rad = math.radians(angle_deg)\n        cos_a = math.cos(angle_rad)\n        sin_a = math.sin(angle_rad)\n        \n        name = f\"Cage_Wire_Vert_{idx+1}\"\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        \n        spline = curve_data.splines.new('BEZIER')\n        spline.bezier_points.add(2) # Total 3 control points\n        pts = spline.bezier_points\n        \n        # Point 0: bottom anchor\n        r0 = 0.048\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\n        \n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\n        r1 = 0.051\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\n        \n        # Point 2: top anchor near top hood\n        r2 = 0.033\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\n        \n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\n        curve_obj.data.bevel_resolution = 4\n        \n        curve_obj.data.materials.append(mat_brass)\n\ncreate_vertical_cage_wires()\nprint(\"Protective brass cage horizontal rings and vertical safety wires completed!\")\n", "user_prompt": "Build horizontal and vertical protective brass cage around glass globe"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:45:59.411286+00:00", "type": "tool_call", "id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\n# Let's create:\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\n\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\n\ndef create_guard_ring(name, radius, z, thickness, material=None):\n    # We can model a thin torus for beautiful shading\n    bpy.ops.mesh.primitive_torus_add(\n        align='WORLD', \n        location=(0, 0, z), \n        major_radius=radius, \n        minor_radius=thickness,\n        major_segments=32,\n        minor_segments=8\n    )\n    obj = bpy.context.active_object\n    obj.name = name\n    for poly in obj.data.polygons:\n        poly.use_smooth = True\n    if material:\n        obj.data.materials.append(material)\n    return obj\n\nring_lower = create_guard_ring(\"Cage_Ring_Lower\", 0.043, 0.13, 0.0016, mat_brass)\nring_upper = create_guard_ring(\"Cage_Ring_Upper\", 0.032, 0.19, 0.0016, mat_brass)\n\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\n# Curves make visual presentation far more organic and high-quality!\n\ndef create_vertical_cage_wires():\n    angles = [45, 135, 225, 315]\n    for idx, angle_deg in enumerate(angles):\n        angle_rad = math.radians(angle_deg)\n        cos_a = math.cos(angle_rad)\n        sin_a = math.sin(angle_rad)\n        \n        name = f\"Cage_Wire_Vert_{idx+1}\"\n        curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n        curve_data.dimensions = '3D'\n        \n        spline = curve_data.splines.new('BEZIER')\n        spline.bezier_points.add(2) # Total 3 control points\n        pts = spline.bezier_points\n        \n        # Point 0: bottom anchor\n        r0 = 0.048\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\n        \n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\n        r1 = 0.051\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\n        \n        # Point 2: top anchor near top hood\n        r2 = 0.033\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\n        \n        curve_obj = bpy.data.objects.new(name, curve_data)\n        bpy.context.collection.objects.link(curve_obj)\n        \n        curve_obj.data.use_fill_caps = True\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\n        curve_obj.data.bevel_resolution = 4\n        \n        curve_obj.data.materials.append(mat_brass)\n\ncreate_vertical_cage_wires()\nprint(\"Protective brass cage horizontal rings and vertical safety wires completed!\")\n", "user_prompt": "Build horizontal and vertical protective brass cage around glass globe"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}
{"ts": "2026-07-15T04:45:59.469223+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "name": "execute_blender_code", "text": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:07.624973+00:00", "type": "llm_call", "call_index": 16, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:45:59.470728+00:00", "finished_at": "2026-07-15T04:46:07.624973+00:00", "duration_s": 8.155999999988126, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:45:59.470728+00:00", "finished_at": "2026-07-15T04:46:07.624973+00:00", "duration_s": 8.155999999988126, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "61505", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":[{"type":"text","text":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. 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bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.encrypted\",\"data\":\"ZTI0ODMwYTctNWNkNi00MmZlLTk5OGItZWU1MzllNzJiOWMz\",\"format\":\"google-gemini-v1\",\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"index\":0}]}}],\"usage\":{\"prompt_tokens\":31199,\"completion_tokens\":1213,\"total_tokens\":32412,\"cost\":0.0379555,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":15600,\"cache_write_tokens\":15600,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.0379555,\"upstream_inference_prompt_cost\":0.0270385,\"upstream_inference_completions_cost\":0.010917},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\n# A classic bail handle arches high above the lantern.\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\n# Max height at top around Z=0.48.\n# Points for Bezier:\n# Left side attachment: (-0.076, 0.0, 0.18)\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\n# Arching over top: (0.0, 0.0, 0.485)\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\n# Right side attachment: (0.076, 0.0, 0.18)\n\ndef create_bail_handle():\n    name = \"Bail_Handle\"\n    curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n    curve_data.dimensions = '3D'\n    curve_data.resolution_u = 20\n    \n    spline = curve_data.splines.new('BEZIER')\n    spline.bezier_points.add(4) # 5 points total\n    pts = spline.bezier_points\n    \n    # Left attachment point\n    pts[0].co = (-0.074, 0.0, 0.18)\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\n    \n    # Left curve shoulder\n    pts[1].co = (-0.092, 0.0, 0.38)\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\n    \n    # Top center arc\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\n    pts[2].handle_right = (0.040, -0.01, 0.485)\n    \n    # Right curve shoulder\n    pts[3].co = (0.092, 0.0, 0.38)\n    pts[3].handle_left = (0.082, 0.0, 0.44)\n    pts[3].handle_right = (0.092, 0.0, 0.32)\n    \n    # Right attachment point\n    pts[4].co = (0.074, 0.0, 0.18)\n    pts[4].handle_left = (0.082, 0.0, 0.22)\n    pts[4].handle_right = (0.074, 0.0, 0.16)\n    \n    curve_obj = bpy.data.objects.new(name, curve_data)\n    bpy.context.collection.objects.link(curve_obj)\n    \n    curve_obj.data.use_fill_caps = True\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\n    curve_obj.data.bevel_resolution = 4\n    \n    curve_obj.data.materials.append(mat_brass)\n\ncreate_bail_handle()\n\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\nfor side in [1, -1]:\n    bpy.ops.mesh.primitive_cylinder_add(\n        vertices=12, \n        radius=0.0045, \n        depth=0.008, \n        location=(side * 0.074, 0.0, 0.18),\n        rotation=(0, 1.5708, 0)\n    )\n    hinge_plug = bpy.context.active_object\n    hinge_plug.name = f\"Handle_Hinge_{'R' if side == 1 else 'L'}\"\n    for poly in hinge_plug.data.polygons:\n        poly.use_smooth = True\n    hinge_plug.data.materials.append(mat_brass)\n\nprint(\"Iconic carrying wire handle and attachment hinges complete!\")\n", "user_prompt": "Build high carrying handle arching over top of the lantern with attachment pins"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}", "parse_error": null}], "tokens_in": 31199, "tokens_out": 1213, "cost_usd": 0.0379555, "cached_tokens": 15600}, "error": null}
{"ts": "2026-07-15T04:46:07.626476+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\n# A classic bail handle arches high above the lantern.\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\n# Max height at top around Z=0.48.\n# Points for Bezier:\n# Left side attachment: (-0.076, 0.0, 0.18)\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\n# Arching over top: (0.0, 0.0, 0.485)\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\n# Right side attachment: (0.076, 0.0, 0.18)\n\ndef create_bail_handle():\n    name = \"Bail_Handle\"\n    curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n    curve_data.dimensions = '3D'\n    curve_data.resolution_u = 20\n    \n    spline = curve_data.splines.new('BEZIER')\n    spline.bezier_points.add(4) # 5 points total\n    pts = spline.bezier_points\n    \n    # Left attachment point\n    pts[0].co = (-0.074, 0.0, 0.18)\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\n    \n    # Left curve shoulder\n    pts[1].co = (-0.092, 0.0, 0.38)\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\n    \n    # Top center arc\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\n    pts[2].handle_right = (0.040, -0.01, 0.485)\n    \n    # Right curve shoulder\n    pts[3].co = (0.092, 0.0, 0.38)\n    pts[3].handle_left = (0.082, 0.0, 0.44)\n    pts[3].handle_right = (0.092, 0.0, 0.32)\n    \n    # Right attachment point\n    pts[4].co = (0.074, 0.0, 0.18)\n    pts[4].handle_left = (0.082, 0.0, 0.22)\n    pts[4].handle_right = (0.074, 0.0, 0.16)\n    \n    curve_obj = bpy.data.objects.new(name, curve_data)\n    bpy.context.collection.objects.link(curve_obj)\n    \n    curve_obj.data.use_fill_caps = True\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\n    curve_obj.data.bevel_resolution = 4\n    \n    curve_obj.data.materials.append(mat_brass)\n\ncreate_bail_handle()\n\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\nfor side in [1, -1]:\n    bpy.ops.mesh.primitive_cylinder_add(\n        vertices=12, \n        radius=0.0045, \n        depth=0.008, \n        location=(side * 0.074, 0.0, 0.18),\n        rotation=(0, 1.5708, 0)\n    )\n    hinge_plug = bpy.context.active_object\n    hinge_plug.name = f\"Handle_Hinge_{'R' if side == 1 else 'L'}\"\n    for poly in hinge_plug.data.polygons:\n        poly.use_smooth = True\n    hinge_plug.data.materials.append(mat_brass)\n\nprint(\"Iconic carrying wire handle and attachment hinges complete!\")\n", "user_prompt": "Build high carrying handle arching over top of the lantern with attachment pins"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:07.626476+00:00", "type": "tool_call", "id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Materials\nmat_brass = bpy.data.materials.get(\"BrassMetal\")\n\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\n# A classic bail handle arches high above the lantern.\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\n# Max height at top around Z=0.48.\n# Points for Bezier:\n# Left side attachment: (-0.076, 0.0, 0.18)\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\n# Arching over top: (0.0, 0.0, 0.485)\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\n# Right side attachment: (0.076, 0.0, 0.18)\n\ndef create_bail_handle():\n    name = \"Bail_Handle\"\n    curve_data = bpy.data.curves.new(name=f\"{name}_curve\", type='CURVE')\n    curve_data.dimensions = '3D'\n    curve_data.resolution_u = 20\n    \n    spline = curve_data.splines.new('BEZIER')\n    spline.bezier_points.add(4) # 5 points total\n    pts = spline.bezier_points\n    \n    # Left attachment point\n    pts[0].co = (-0.074, 0.0, 0.18)\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\n    \n    # Left curve shoulder\n    pts[1].co = (-0.092, 0.0, 0.38)\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\n    \n    # Top center arc\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\n    pts[2].handle_right = (0.040, -0.01, 0.485)\n    \n    # Right curve shoulder\n    pts[3].co = (0.092, 0.0, 0.38)\n    pts[3].handle_left = (0.082, 0.0, 0.44)\n    pts[3].handle_right = (0.092, 0.0, 0.32)\n    \n    # Right attachment point\n    pts[4].co = (0.074, 0.0, 0.18)\n    pts[4].handle_left = (0.082, 0.0, 0.22)\n    pts[4].handle_right = (0.074, 0.0, 0.16)\n    \n    curve_obj = bpy.data.objects.new(name, curve_data)\n    bpy.context.collection.objects.link(curve_obj)\n    \n    curve_obj.data.use_fill_caps = True\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\n    curve_obj.data.bevel_resolution = 4\n    \n    curve_obj.data.materials.append(mat_brass)\n\ncreate_bail_handle()\n\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\nfor side in [1, -1]:\n    bpy.ops.mesh.primitive_cylinder_add(\n        vertices=12, \n        radius=0.0045, \n        depth=0.008, \n        location=(side * 0.074, 0.0, 0.18),\n        rotation=(0, 1.5708, 0)\n    )\n    hinge_plug = bpy.context.active_object\n    hinge_plug.name = f\"Handle_Hinge_{'R' if side == 1 else 'L'}\"\n    for poly in hinge_plug.data.polygons:\n        poly.use_smooth = True\n    hinge_plug.data.materials.append(mat_brass)\n\nprint(\"Iconic carrying wire handle and attachment hinges complete!\")\n", "user_prompt": "Build high carrying handle arching over top of the lantern with attachment pins"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}
{"ts": "2026-07-15T04:46:07.679798+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "name": "execute_blender_code", "text": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:11.581287+00:00", "type": "llm_call", "call_index": 17, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:07.680803+00:00", "finished_at": "2026-07-15T04:46:11.581287+00:00", "duration_s": 3.9060000000172295, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:07.680803+00:00", "finished_at": "2026-07-15T04:46:11.580280+00:00", "duration_s": 3.9060000000172295, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "64982", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":[{"type":"text","text":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    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{"ts": "2026-07-15T04:46:11.583803+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "name": "execute_blender_code", "arguments": {"user_prompt": "Inspect list of all objects in scene so far", "code": "import bpy\n\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\nfor obj in bpy.data.objects:\n    print(\"-\", obj.name, obj.type)\n"}, "raw_arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:11.583803+00:00", "type": "tool_call", "id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "name": "execute_blender_code", "arguments": {"user_prompt": "Inspect list of all objects in scene so far", "code": "import bpy\n\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\nfor obj in bpy.data.objects:\n    print(\"-\", obj.name, obj.type)\n"}, "raw_arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}
{"ts": "2026-07-15T04:46:11.611405+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "name": "execute_blender_code", "text": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:14.421008+00:00", "type": "llm_call", "call_index": 18, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:11.612912+00:00", "finished_at": "2026-07-15T04:46:14.420009+00:00", "duration_s": 2.812999999994645, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:11.612912+00:00", "finished_at": "2026-07-15T04:46:14.420009+00:00", "duration_s": 2.812999999994645, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "66171", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":[{"type":"text","text":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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{"ts": "2026-07-15T04:46:14.423016+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "name": "execute_blender_code", "arguments": {"user_prompt": "Ensure there are no unwanted duplicated objects in the scene", "code": "import bpy\n\n# Clean up any duplicated objects if they exist\ndup = bpy.data.objects.get(\"Glass_Globe.001\")\nif dup:\n    bpy.data.objects.remove(dup, do_unlink=True)\n    print(\"Removed duplicate glass globe\")\n"}, "raw_arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:14.423016+00:00", "type": "tool_call", "id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "name": "execute_blender_code", "arguments": {"user_prompt": "Ensure there are no unwanted duplicated objects in the scene", "code": "import bpy\n\n# Clean up any duplicated objects if they exist\ndup = bpy.data.objects.get(\"Glass_Globe.001\")\nif dup:\n    bpy.data.objects.remove(dup, do_unlink=True)\n    print(\"Removed duplicate glass globe\")\n"}, "raw_arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}
{"ts": "2026-07-15T04:46:14.457220+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "name": "execute_blender_code", "text": "Code executed successfully: Removed duplicate glass globe\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:21.209965+00:00", "type": "llm_call", "call_index": 19, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:14.458225+00:00", "finished_at": "2026-07-15T04:46:21.209965+00:00", "duration_s": 6.75, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:14.458225+00:00", "finished_at": "2026-07-15T04:46:21.209435+00:00", "duration_s": 6.75, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "66834", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Removed duplicate glass globe\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":[{"type":"text","text":"Code executed successfully: Removed duplicate glass globe\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\n\n# Let's create a large plane for the floor. Z = 0.00\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\nground = bpy.context.active_object\nground.name = \"Ground_Studio\"\n\n# Add studio back-curve (cyclorama shape)\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\nimport bmesh\nbm = bmesh.new()\nbm.from_mesh(ground.data)\n\n# Let's find the edges that are furthest back (Y coordinate positive)\n# The default plane goes from X:-6 to 6, Y:-6 to 6\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\nback_edges = []\nfor e in bm.edges:\n    if all(v in back_edge_verts for v in e.verts):\n        back_edges.append(e)\n\n# Extrude back edges upwards along Z\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\n# Move extruded verts up by 6 meters\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\nfor v in extruded_verts:\n    v.co.z += 6.0\n\n# Add a smooth curved bevel to the back corner\n# Find edges connecting to the extruded verts that represent the fold\nfold_edges = []\nfor e in bm.edges:\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\n    if len(e.verts) == 2:\n        v0, v1 = e.verts[0], e.verts[1]\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\n            fold_edges.append(e)\n\nbmesh.ops.bevel(\n    bm,\n    geom=fold_edges,\n    dist=1.5,\n    segments=16,\n    affect='EDGES'\n)\n\nbm.to_mesh(ground.data)\nbm.free()\n\n# Set smooth and ground material\nfor poly in ground.data.polygons:\n    poly.use_smooth = True\nground.data.materials.append(bpy.data.materials.get(\"Ground\"))\n\nprint(\"Seamless cyclorama ground stage built beautifully!\")\n", "user_prompt": "Create a large seamless background cyclorama stage for realistic studio lighting"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}", "parse_error": null}], "tokens_in": 17253, "tokens_out": 762, "cost_usd": 0.01206765, "cached_tokens": 15311}, "error": null}
{"ts": "2026-07-15T04:46:21.212476+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\n\n# Let's create a large plane for the floor. Z = 0.00\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\nground = bpy.context.active_object\nground.name = \"Ground_Studio\"\n\n# Add studio back-curve (cyclorama shape)\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\nimport bmesh\nbm = bmesh.new()\nbm.from_mesh(ground.data)\n\n# Let's find the edges that are furthest back (Y coordinate positive)\n# The default plane goes from X:-6 to 6, Y:-6 to 6\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\nback_edges = []\nfor e in bm.edges:\n    if all(v in back_edge_verts for v in e.verts):\n        back_edges.append(e)\n\n# Extrude back edges upwards along Z\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\n# Move extruded verts up by 6 meters\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\nfor v in extruded_verts:\n    v.co.z += 6.0\n\n# Add a smooth curved bevel to the back corner\n# Find edges connecting to the extruded verts that represent the fold\nfold_edges = []\nfor e in bm.edges:\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\n    if len(e.verts) == 2:\n        v0, v1 = e.verts[0], e.verts[1]\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\n            fold_edges.append(e)\n\nbmesh.ops.bevel(\n    bm,\n    geom=fold_edges,\n    dist=1.5,\n    segments=16,\n    affect='EDGES'\n)\n\nbm.to_mesh(ground.data)\nbm.free()\n\n# Set smooth and ground material\nfor poly in ground.data.polygons:\n    poly.use_smooth = True\nground.data.materials.append(bpy.data.materials.get(\"Ground\"))\n\nprint(\"Seamless cyclorama ground stage built beautifully!\")\n", "user_prompt": "Create a large seamless background cyclorama stage for realistic studio lighting"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:21.212476+00:00", "type": "tool_call", "id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\n\n# Let's create a large plane for the floor. Z = 0.00\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\nground = bpy.context.active_object\nground.name = \"Ground_Studio\"\n\n# Add studio back-curve (cyclorama shape)\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\nimport bmesh\nbm = bmesh.new()\nbm.from_mesh(ground.data)\n\n# Let's find the edges that are furthest back (Y coordinate positive)\n# The default plane goes from X:-6 to 6, Y:-6 to 6\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\nback_edges = []\nfor e in bm.edges:\n    if all(v in back_edge_verts for v in e.verts):\n        back_edges.append(e)\n\n# Extrude back edges upwards along Z\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\n# Move extruded verts up by 6 meters\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\nfor v in extruded_verts:\n    v.co.z += 6.0\n\n# Add a smooth curved bevel to the back corner\n# Find edges connecting to the extruded verts that represent the fold\nfold_edges = []\nfor e in bm.edges:\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\n    if len(e.verts) == 2:\n        v0, v1 = e.verts[0], e.verts[1]\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\n            fold_edges.append(e)\n\nbmesh.ops.bevel(\n    bm,\n    geom=fold_edges,\n    dist=1.5,\n    segments=16,\n    affect='EDGES'\n)\n\nbm.to_mesh(ground.data)\nbm.free()\n\n# Set smooth and ground material\nfor poly in ground.data.polygons:\n    poly.use_smooth = True\nground.data.materials.append(bpy.data.materials.get(\"Ground\"))\n\nprint(\"Seamless cyclorama ground stage built beautifully!\")\n", "user_prompt": "Create a large seamless background cyclorama stage for realistic studio lighting"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}
{"ts": "2026-07-15T04:46:21.251788+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "name": "execute_blender_code", "text": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:26.877692+00:00", "type": "llm_call", "call_index": 20, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:21.252787+00:00", "finished_at": "2026-07-15T04:46:26.876693+00:00", "duration_s": 5.625, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:21.252787+00:00", "finished_at": "2026-07-15T04:46:26.875687+00:00", "duration_s": 5.625, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "69634", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":[{\"type\":\"text\",\"text\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":[{"type":"text","text":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. 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Create a large plane\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\nground = bpy.context.active_object\nground.name = \"Ground_Studio\"\n\n# Edit mesh: extrude back edge\nimport bmesh\nbm = bmesh.new()\nbm.from_mesh(ground.data)\n\n# Find edges on the +Y side (Y > 5.9)\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\n\n# Extrude up\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\n# Move extruded verts straight up\nfor geom in extruded['geom']:\n    if isinstance(geom, bmesh.types.BMVert):\n        geom.co.z += 6.0\n\n# Write back\nbm.to_mesh(ground.data)\nbm.free()\n\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\nbevel = ground.modifiers.new(name=\"CornerRound\", type='BEVEL')\nbevel.width = 1.5\nbevel.segments = 16\nbevel.limit_method = 'ANGLE'\n\nsubsurf = ground.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 1\n\nfor poly in ground.data.polygons:\n    poly.use_smooth = True\n\nground.data.materials.append(bpy.data.materials.get(\"Ground\"))\n\nprint(\"Seamless Cyclorama built with safe modifier-based bevel!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}", "parse_error": null}], "tokens_in": 18039, "tokens_out": 572, "cost_usd": 0.00891225, "cached_tokens": 17255}, "error": null}
{"ts": "2026-07-15T04:46:26.879692+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "name": "execute_blender_code", "arguments": {"user_prompt": "Model backdrop using modifier-based beveling to ensure robustness", "code": "import bpy\nimport bmesh\n\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\n\nground = bpy.data.objects.get(\"Ground_Studio\")\nif ground:\n    bpy.data.objects.remove(ground, do_unlink=True)\n\n# 1. Create a large plane\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\nground = bpy.context.active_object\nground.name = \"Ground_Studio\"\n\n# Edit mesh: extrude back edge\nimport bmesh\nbm = bmesh.new()\nbm.from_mesh(ground.data)\n\n# Find edges on the +Y side (Y > 5.9)\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\n\n# Extrude up\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\n# Move extruded verts straight up\nfor geom in extruded['geom']:\n    if isinstance(geom, bmesh.types.BMVert):\n        geom.co.z += 6.0\n\n# Write back\nbm.to_mesh(ground.data)\nbm.free()\n\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\nbevel = ground.modifiers.new(name=\"CornerRound\", type='BEVEL')\nbevel.width = 1.5\nbevel.segments = 16\nbevel.limit_method = 'ANGLE'\n\nsubsurf = ground.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 1\n\nfor poly in ground.data.polygons:\n    poly.use_smooth = True\n\nground.data.materials.append(bpy.data.materials.get(\"Ground\"))\n\nprint(\"Seamless Cyclorama built with safe modifier-based bevel!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:26.879692+00:00", "type": "tool_call", "id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "name": "execute_blender_code", "arguments": {"user_prompt": "Model backdrop using modifier-based beveling to ensure robustness", "code": "import bpy\nimport bmesh\n\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\n\nground = bpy.data.objects.get(\"Ground_Studio\")\nif ground:\n    bpy.data.objects.remove(ground, do_unlink=True)\n\n# 1. Create a large plane\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\nground = bpy.context.active_object\nground.name = \"Ground_Studio\"\n\n# Edit mesh: extrude back edge\nimport bmesh\nbm = bmesh.new()\nbm.from_mesh(ground.data)\n\n# Find edges on the +Y side (Y > 5.9)\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\n\n# Extrude up\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\n# Move extruded verts straight up\nfor geom in extruded['geom']:\n    if isinstance(geom, bmesh.types.BMVert):\n        geom.co.z += 6.0\n\n# Write back\nbm.to_mesh(ground.data)\nbm.free()\n\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\nbevel = ground.modifiers.new(name=\"CornerRound\", type='BEVEL')\nbevel.width = 1.5\nbevel.segments = 16\nbevel.limit_method = 'ANGLE'\n\nsubsurf = ground.modifiers.new(name=\"Subsurf\", type='SUBSURF')\nsubsurf.levels = 1\n\nfor poly in ground.data.polygons:\n    poly.use_smooth = True\n\nground.data.materials.append(bpy.data.materials.get(\"Ground\"))\n\nprint(\"Seamless Cyclorama built with safe modifier-based bevel!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}
{"ts": "2026-07-15T04:46:26.927738+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "name": "execute_blender_code", "text": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:31.207930+00:00", "type": "llm_call", "call_index": 21, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:26.929242+00:00", "finished_at": "2026-07-15T04:46:31.207930+00:00", "duration_s": 4.2810000000172295, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:26.929242+00:00", "finished_at": "2026-07-15T04:46:31.207930+00:00", "duration_s": 4.2810000000172295, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "71899", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":[{"type":"text","text":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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0.65\n\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\n\nprint(\"Dark studio world environment set up!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}", "parse_error": null}], "tokens_in": 18626, "tokens_out": 290, "cost_usd": 0.01241445, "cached_tokens": 13433}, "error": null}
{"ts": "2026-07-15T04:46:31.211964+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "name": "execute_blender_code", "arguments": {"user_prompt": "Set up dark world lighting to emphasize the warm lantern flame illumination", "code": "import bpy\n\n# Let's configure the background environment lighting.\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\n# we need a very low background environmental light level so the lantern's own glow is the hero.\n\nworld = bpy.context.scene.world\nif not world:\n    world = bpy.data.worlds.new(name=\"World\")\n    bpy.context.scene.world = world\n\nworld.use_nodes = True\nnodes = world.node_tree.nodes\nnodes.clear()\n\noutput = nodes.new(type='ShaderNodeOutputWorld')\nbg = nodes.new(type='ShaderNodeBackground')\n\n# Dark slate gray environment mood: Hex #030406 (super dark)\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\nbg.inputs['Strength'].default_value = 0.65\n\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\n\nprint(\"Dark studio world environment set up!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:31.211964+00:00", "type": "tool_call", "id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "name": "execute_blender_code", "arguments": {"user_prompt": "Set up dark world lighting to emphasize the warm lantern flame illumination", "code": "import bpy\n\n# Let's configure the background environment lighting.\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\n# we need a very low background environmental light level so the lantern's own glow is the hero.\n\nworld = bpy.context.scene.world\nif not world:\n    world = bpy.data.worlds.new(name=\"World\")\n    bpy.context.scene.world = world\n\nworld.use_nodes = True\nnodes = world.node_tree.nodes\nnodes.clear()\n\noutput = nodes.new(type='ShaderNodeOutputWorld')\nbg = nodes.new(type='ShaderNodeBackground')\n\n# Dark slate gray environment mood: Hex #030406 (super dark)\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\nbg.inputs['Strength'].default_value = 0.65\n\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\n\nprint(\"Dark studio world environment set up!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}
{"ts": "2026-07-15T04:46:31.248015+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "name": "execute_blender_code", "text": "Code executed successfully: Dark studio world environment set up!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:37.013972+00:00", "type": "llm_call", "call_index": 22, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:31.248015+00:00", "finished_at": "2026-07-15T04:46:37.013439+00:00", "duration_s": 5.76600000000326, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:31.248015+00:00", "finished_at": "2026-07-15T04:46:37.011930+00:00", "duration_s": 5.75, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "73246", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Dark studio world environment set up!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":[{"type":"text","text":"Code executed successfully: Dark studio world environment set up!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. 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The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern 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"error": null}], "response": {"text": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\n\n# Let's check existing lights\nfor l in bpy.data.lights:\n    print(\"Light found:\", l.name)\n\n# Create Cool Soft Key Light (Front-Left-High)\nif \"Studio_Key\" not in bpy.data.objects:\n    key_data = bpy.data.lights.new(name=\"Studio_Key\", type='AREA')\n    # Soft industrial cool blue key (approx 5500K color)\n    key_data.color = (0.75, 0.85, 1.0)\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\n    key_data.size = 1.0 # large soft shadow lines\n    \n    key_obj = bpy.data.objects.new(name=\"Studio_Key\", object_data=key_data)\n    bpy.context.collection.objects.link(key_obj)\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\n    key_obj.location = (-0.6, -0.8, 0.6)\n    \n    # Track to constrain light focus to lantern center\n    track = key_obj.constraints.new(type='TRACK_TO')\n    track.target = bpy.data.objects.get(\"Glass_Globe\")\n    track.track_axis = 'TRACK_NEGATIVE_Z'\n    track.up_axis = 'UP_Y'\n\n# Create Rim Light (Back-Right-High)\nif \"Studio_Rim\" not in bpy.data.objects:\n    rim_data = bpy.data.lights.new(name=\"Studio_Rim\", type='AREA')\n    rim_data.color = (0.9, 0.95, 1.0)\n    rim_data.energy = 28.0 # strong back-reflection highlights\n    rim_data.size = 0.8\n    \n    rim_obj = bpy.data.objects.new(name=\"Studio_Rim\", object_data=rim_data)\n    bpy.context.collection.objects.link(rim_obj)\n    # Position back right\n    rim_obj.location = (0.5, 0.7, 0.5)\n    \n    track = rim_obj.constraints.new(type='TRACK_TO')\n    track.target = bpy.data.objects.get(\"Glass_Globe\")\n    track.track_axis = 'TRACK_NEGATIVE_Z'\n    track.up_axis = 'UP_Y'\n\nprint(\"Studio professional key and rim lighting rigs configured!\")\n", "user_prompt": "Create soft cool key fill and rim lighting with constraints tracking the lantern center"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}", "parse_error": null}], "tokens_in": 18927, "tokens_out": 728, "cost_usd": 0.01422, "cached_tokens": 15350}, "error": null}
{"ts": "2026-07-15T04:46:37.015979+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\n\n# Let's check existing lights\nfor l in bpy.data.lights:\n    print(\"Light found:\", l.name)\n\n# Create Cool Soft Key Light (Front-Left-High)\nif \"Studio_Key\" not in bpy.data.objects:\n    key_data = bpy.data.lights.new(name=\"Studio_Key\", type='AREA')\n    # Soft industrial cool blue key (approx 5500K color)\n    key_data.color = (0.75, 0.85, 1.0)\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\n    key_data.size = 1.0 # large soft shadow lines\n    \n    key_obj = bpy.data.objects.new(name=\"Studio_Key\", object_data=key_data)\n    bpy.context.collection.objects.link(key_obj)\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\n    key_obj.location = (-0.6, -0.8, 0.6)\n    \n    # Track to constrain light focus to lantern center\n    track = key_obj.constraints.new(type='TRACK_TO')\n    track.target = bpy.data.objects.get(\"Glass_Globe\")\n    track.track_axis = 'TRACK_NEGATIVE_Z'\n    track.up_axis = 'UP_Y'\n\n# Create Rim Light (Back-Right-High)\nif \"Studio_Rim\" not in bpy.data.objects:\n    rim_data = bpy.data.lights.new(name=\"Studio_Rim\", type='AREA')\n    rim_data.color = (0.9, 0.95, 1.0)\n    rim_data.energy = 28.0 # strong back-reflection highlights\n    rim_data.size = 0.8\n    \n    rim_obj = bpy.data.objects.new(name=\"Studio_Rim\", object_data=rim_data)\n    bpy.context.collection.objects.link(rim_obj)\n    # Position back right\n    rim_obj.location = (0.5, 0.7, 0.5)\n    \n    track = rim_obj.constraints.new(type='TRACK_TO')\n    track.target = bpy.data.objects.get(\"Glass_Globe\")\n    track.track_axis = 'TRACK_NEGATIVE_Z'\n    track.up_axis = 'UP_Y'\n\nprint(\"Studio professional key and rim lighting rigs configured!\")\n", "user_prompt": "Create soft cool key fill and rim lighting with constraints tracking the lantern center"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:37.015979+00:00", "type": "tool_call", "id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\n\n# Let's check existing lights\nfor l in bpy.data.lights:\n    print(\"Light found:\", l.name)\n\n# Create Cool Soft Key Light (Front-Left-High)\nif \"Studio_Key\" not in bpy.data.objects:\n    key_data = bpy.data.lights.new(name=\"Studio_Key\", type='AREA')\n    # Soft industrial cool blue key (approx 5500K color)\n    key_data.color = (0.75, 0.85, 1.0)\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\n    key_data.size = 1.0 # large soft shadow lines\n    \n    key_obj = bpy.data.objects.new(name=\"Studio_Key\", object_data=key_data)\n    bpy.context.collection.objects.link(key_obj)\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\n    key_obj.location = (-0.6, -0.8, 0.6)\n    \n    # Track to constrain light focus to lantern center\n    track = key_obj.constraints.new(type='TRACK_TO')\n    track.target = bpy.data.objects.get(\"Glass_Globe\")\n    track.track_axis = 'TRACK_NEGATIVE_Z'\n    track.up_axis = 'UP_Y'\n\n# Create Rim Light (Back-Right-High)\nif \"Studio_Rim\" not in bpy.data.objects:\n    rim_data = bpy.data.lights.new(name=\"Studio_Rim\", type='AREA')\n    rim_data.color = (0.9, 0.95, 1.0)\n    rim_data.energy = 28.0 # strong back-reflection highlights\n    rim_data.size = 0.8\n    \n    rim_obj = bpy.data.objects.new(name=\"Studio_Rim\", object_data=rim_data)\n    bpy.context.collection.objects.link(rim_obj)\n    # Position back right\n    rim_obj.location = (0.5, 0.7, 0.5)\n    \n    track = rim_obj.constraints.new(type='TRACK_TO')\n    track.target = bpy.data.objects.get(\"Glass_Globe\")\n    track.track_axis = 'TRACK_NEGATIVE_Z'\n    track.up_axis = 'UP_Y'\n\nprint(\"Studio professional key and rim lighting rigs configured!\")\n", "user_prompt": "Create soft cool key fill and rim lighting with constraints tracking the lantern center"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}
{"ts": "2026-07-15T04:46:37.055151+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "name": "execute_blender_code", "text": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:39.979790+00:00", "type": "llm_call", "call_index": 23, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:37.056149+00:00", "finished_at": "2026-07-15T04:46:39.979283+00:00", "duration_s": 2.922000000020489, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:37.056149+00:00", "finished_at": "2026-07-15T04:46:39.978281+00:00", "duration_s": 2.922000000020489, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "76011", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":[{"type":"text","text":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    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{"ts": "2026-07-15T04:46:39.981791+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# Let's clean up any default lights that might have been present in the scene\ndefault_light = bpy.data.objects.get(\"Light\")\nif default_light:\n    bpy.data.objects.remove(default_light, do_unlink=True)\n    print(\"Removed default light\")\n", "user_prompt": "Clean up default initial light to prevent overexposure"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:39.981791+00:00", "type": "tool_call", "id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# Let's clean up any default lights that might have been present in the scene\ndefault_light = bpy.data.objects.get(\"Light\")\nif default_light:\n    bpy.data.objects.remove(default_light, do_unlink=True)\n    print(\"Removed default light\")\n", "user_prompt": "Clean up default initial light to prevent overexposure"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}
{"ts": "2026-07-15T04:46:40.012183+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "name": "execute_blender_code", "text": "Code executed successfully: ", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:47.004325+00:00", "type": "llm_call", "call_index": 24, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:40.013695+00:00", "finished_at": "2026-07-15T04:46:47.003767+00:00", "duration_s": 6.98399999999674, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:40.013695+00:00", "finished_at": "2026-07-15T04:46:47.002260+00:00", "duration_s": 6.98399999999674, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "76679", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: \",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":[{"type":"text","text":"Code executed successfully: ","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ","parameters":{"properties":{"text_prompt":{"default":null,"title":"Text Prompt","type":"string"},"input_image_url":{"default":null,"title":"Input Image Url","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hunyuan3d_modelArguments","type":"object"}}},{"type":"function","function":{"name":"poll_hunyuan_job_status","description":"\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. 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CONTRAST CAMERA SETUP -----------------\n# We want an elegant product shot framing the classic lantern center as a hero prop.\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\n# and the side profile (support tubes, arching handle).\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\n\n# Let's create a camera\nif \"Camera\" in bpy.data.objects:\n    cam_obj = bpy.data.objects[\"Camera\"]\nelse:\n    cam_data = bpy.data.cameras.new(name=\"Camera\")\n    cam_obj = bpy.data.objects.new(name=\"Camera\", object_data=cam_data)\n    bpy.context.collection.objects.link(cam_obj)\n\nbpy.context.scene.camera = cam_obj\n\n# Camera Settings:\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\ncam_obj.data.lens = 85\n\n# Position camera\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\ncam_obj.location = (0.50, -0.68, 0.26)\n\n# Track camera to center of glass\ntrack = cam_obj.constraints.new(type='TRACK_TO')\ntrack.target = bpy.data.objects.get(\"Glass_Globe\")\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\ntrack.up_axis = 'UP_Y'\n\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\ncam_obj.data.dof.use_dof = True\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\"Glass_Globe\")\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\n\nprint(\"Hero camera built and configured!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}", "parse_error": null}], "tokens_in": 39579, "tokens_out": 544, "cost_usd": 0.039197166666666665, "cached_tokens": 19790}, "error": null}
{"ts": "2026-07-15T04:46:47.006338+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "name": "execute_blender_code", "arguments": {"user_prompt": "Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target", "code": "import bpy\nimport math\n\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\n# We want an elegant product shot framing the classic lantern center as a hero prop.\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\n# and the side profile (support tubes, arching handle).\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\n\n# Let's create a camera\nif \"Camera\" in bpy.data.objects:\n    cam_obj = bpy.data.objects[\"Camera\"]\nelse:\n    cam_data = bpy.data.cameras.new(name=\"Camera\")\n    cam_obj = bpy.data.objects.new(name=\"Camera\", object_data=cam_data)\n    bpy.context.collection.objects.link(cam_obj)\n\nbpy.context.scene.camera = cam_obj\n\n# Camera Settings:\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\ncam_obj.data.lens = 85\n\n# Position camera\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\ncam_obj.location = (0.50, -0.68, 0.26)\n\n# Track camera to center of glass\ntrack = cam_obj.constraints.new(type='TRACK_TO')\ntrack.target = bpy.data.objects.get(\"Glass_Globe\")\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\ntrack.up_axis = 'UP_Y'\n\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\ncam_obj.data.dof.use_dof = True\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\"Glass_Globe\")\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\n\nprint(\"Hero camera built and configured!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:47.006338+00:00", "type": "tool_call", "id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "name": "execute_blender_code", "arguments": {"user_prompt": "Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target", "code": "import bpy\nimport math\n\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\n# We want an elegant product shot framing the classic lantern center as a hero prop.\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\n# and the side profile (support tubes, arching handle).\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\n\n# Let's create a camera\nif \"Camera\" in bpy.data.objects:\n    cam_obj = bpy.data.objects[\"Camera\"]\nelse:\n    cam_data = bpy.data.cameras.new(name=\"Camera\")\n    cam_obj = bpy.data.objects.new(name=\"Camera\", object_data=cam_data)\n    bpy.context.collection.objects.link(cam_obj)\n\nbpy.context.scene.camera = cam_obj\n\n# Camera Settings:\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\ncam_obj.data.lens = 85\n\n# Position camera\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\ncam_obj.location = (0.50, -0.68, 0.26)\n\n# Track camera to center of glass\ntrack = cam_obj.constraints.new(type='TRACK_TO')\ntrack.target = bpy.data.objects.get(\"Glass_Globe\")\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\ntrack.up_axis = 'UP_Y'\n\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\ncam_obj.data.dof.use_dof = True\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\"Glass_Globe\")\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\n\nprint(\"Hero camera built and configured!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}
{"ts": "2026-07-15T04:46:47.056725+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "name": "execute_blender_code", "text": "Code executed successfully: Hero camera built and configured!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:46:51.164537+00:00", "type": "llm_call", "call_index": 25, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:47.058232+00:00", "finished_at": "2026-07-15T04:46:51.164537+00:00", "duration_s": 4.109000000025844, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:47.058232+00:00", "finished_at": "2026-07-15T04:46:51.163530+00:00", "duration_s": 4.109000000025844, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "78877", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Hero camera built and configured!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":[{"type":"text","text":"Code executed successfully: Hero camera built and configured!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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"}
{"ts": "2026-07-15T04:46:52.492177+00:00", "type": "image", "tool": "get_viewport_screenshot"}
{"ts": "2026-07-15T04:46:59.290797+00:00", "type": "llm_call", "call_index": 26, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:52.492177+00:00", "finished_at": "2026-07-15T04:46:59.290797+00:00", "duration_s": 6.797000000020489, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:52.492177+00:00", "finished_at": "2026-07-15T04:46:59.288798+00:00", "duration_s": 6.797000000020489, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "347660", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"},\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvQmwXVd573nOHSVdzaMH2ZIseTbYGNtgY8AjkzEzhDDk8YKdANUhlc6rdHVVp9KvUql6IanqTiWp7oKEJBAITjqYR4DYBAcPYOMJzzaeJFnD1Sxrlu50Tv/O+V/99WntffY9d9S9V1p2Lf3Xt/7fsL699tpr77PvOeXTTjtt0aJFXV1dLS0tpbxSLpejuFqtSgKI8imBiXwqhj2y3OYepuRoyvKJykl7tdRfKlWPm18jG+sJ1hq/eeWD6APnEzCOOQpHfDTtK1qecjgmapIE75CGGw+KHM0RH9Dhuhtzfhz4FB2FhqAD0Xx+NNjhajVvfwTMeCykPkWPCMFrLCOLXwfFFpK0YFOEEWR4BCoOYwS6k0QlSSBRjey4DGs4yTGKMYzGezwciYthhTd+5IGBgQMHDuzevbtt8eLFs2bNwlOlUlEtEH0nY2CLT1GC6MLWaJIVHY0VJqrW1tYkKoTYT4Rj5bGRHTlt1DtiOWaHHMiQhBF7HyvFgVKps1rqK5dqM28qlEZHM8rHNu1ja62ZHI/fWJrxPlYcjwKgHGYzaU4jp5GAukxR9/X1SSUSGhmRPOu9mO81FhcU1tjsspxYYMWzpK2tjWcxbhpgZ+/evW4WgGasEVKT1hgOo7A7sBNi4N4Rg+gia0ROYy1ODCarZYnijC6ypsZwLPY7fiCOBS9JHk74WJLwCvJQzEwGkjQLzE5wV/EoHMykjd8RaiAsNZI0Chgai4xH3T73rNbZp9kIQF29u17qObAzysHY9ApJs6W1rXPJReW2mQmtVB04uOnR3JUzrm8dHR0zZ6a6aB08eFDBEwnLKSrZsfR2nFbunJ/6pT3Q13rgZYLMdmE5V15TarA+K1fyPmfOHJi19d0DU6zRE6Rzzjln3bp1+/bti/LZs2cPprW398iRI8uWLZs/f/6GDRsOHz6MfNWqVZDXr19vy1F3YjBHgquXfBEShWFr5AhpTkwY8mK/Y+I0GcuY2DxRRnrKpQ6eu4907z6C46jsqR7xqJMD6qbjsWTELk6UoiNnLCdkOHbaTAYgE7BrVBy/1N0b18osJ9dXloaj/fv3s6yxbFK2b9+eqzgmQq2xxNBbX2Nts7Oz8/zzzycMFtgYIbGxXCMBLFmyhKsRKiyDCFmi4dMF2LJli00BoF199dU8wnnmmWeiPFrj4Q5OkcyYMYM0Eo9uXXp6enbuPO6aSm/umo8ujqjlwmEjkZDaQnOywhhhI5zYMU3yWNuROUOCaDwxVRvJ0QFiB2yywZD2J5KQRBWb9aEcu0TGsThC8+Oo3ZsFMpJrKku2xF4syYJiThJe0pS1YgtZj+MhaSYGgs+NP8bTjJ3IL8D4wtqQHhML8LVIImcRO/fcc/fs2fPaa69lA9OOGXl715Kuiz9VammXqRkdrVdcsJSb/Udf2Lb1sZ0HD76WuFCTFVir+szl13SefZ2ESxfMPH3RrA3bDry+v6c60LP71Z/5OUs0giKxaXSsluwVWSqXL19uzrZt29jQ6sHE3Llz2d/SRT1v3jxG9/rrr2s4e+a8qXXBeTyWWL60a/G8mdt2H+rexXa/VO3dP3Pj1vb2dhzxSgvGUUERI8TDPQByapZWksDyy00CXdn12fFgCiM4pRB5G8pean2TJDbL9Ec/+tEzzjiDjfiXvvSlOH5UUIQmXTb3L730Un8/Lz7UCiFytLhCsI+XhNEuXLhw8+bNHFQi4Gk/cZMFIhYhW5PE7u5u7J9++uk7duywcZgYQRcvWS1LGAuORNZoOTZ8tsAdCMebZJHEOCIr5gJSxtHlOk1mRcACBg8dOpTLzxUyZI6TL/YMgQkNk8OA2ThAqxOwVLhYQtPt04IFCwryZt0TBYZ7nvPEnePE7TnP3dHlYKkecfxYKNBVbzGnQL24S2aJnwJznLwUxzBWvQ5ew6G2pEkXUlEqlA1LMDVcg7neJYx1NrZsr0PKkoeUsCJx4n/+858/88wzv/KVr+zatSurwplOL8vL1q1bWbXNYRlkgY5nOpcEFsnchShZY+WFZedTn/rU448/vmLFigsuuODf//3f7Z1hUhgahQiRn3322Wy4WSdZalhFX375ZZMNli5d+od/+IfPPffc7/7u71oIwBQBMBAK1igs4ITKukrM1Fq78GUtRsclg3W1+VURF1YXiAaJIfYmzdg1GXASnpu14xGylIRqWiIfp2ZBJLke4ROhg4zqEUvXNJoRx96sPNfvGAqLPWoU2bEkARQbScjj1ywOI45lyBERZLE1jcJ2miF74DqvzzvvvNtuu+2Xv/wlayDqX/3qV+PSB1kbTrrKnXNPWzyPve/6Lft37j3SP1A90jvw6Xed273j4FYbzQCvkK2zFqvzrRef9t9+/dLZM9v3HOj573/32POvNtT2yqP1DXW2hSxc3uBpByuz7CTJw+WXX078LHGUNWvWPPHEE1q3O9tbb7v1wkvXLGa54kT/xXNb//7fX9THDTBvuukm1upHH32URZiRPvvss7KJuze96U1437hxI4niusAWWl25NQETQy1X5XJtURZJDWrr8BCdhRiL1113HRt3vLrLKuazI2dZ/8Y3vvGd73znn/7pn97//vcTjQ8Sh+0Tn/gEVw4uOWzHr7/+eoaNa54bEf2FF17I1YXUAPDyhS98gWsJ8X35y19W1x/90R+xVUWd2wM8Yo0r1s0332zdJDA3MQJmwAAKgCQCbrjhBi6r5JT3+7ktwTt+devD7tzqEdx6663sm6+99loC4DAwWHRJDookh7Gww169ejUZ+/SnPw0z6hqTUvVyFYT8rne9i2BWrlz5tre9LZtetLhbIJNcJj/84Q9jUweCAMgD6SKHEHBNAOTn4x//OGOxr0YA9REXfKGrusCIXdeS3lzprVbbmJH1Y4SGD1lz2inLAZwooIDwrixNfBgFR8ddPpoRgF1gCit+BiVgC5Kk2Q9tHUdqFXoASR3oQ8AxTGPiySMFNOOFUfzgBz/4j//4D26kWcQ4qTnxoyKnNmsXy8Ub3vCGW265hRXmoosu0gKChHWAxLJ6sNq89a1v5eSNusWYlefHP/7x008/fe+997LmsIhl+RoFCwsrMAUvLBTss/GbJTOEf/3Xf/3P//zPbBcSJQqAX24/tCnXfQg2TRCNmwpuVFiWk/QWNLNOC8iaeKROQHXWwmSTFIyIUONYIo6jiPIhsfKTZMlaNlsQVezSCWuJTuRGtWhyYY8R2PvkAR5aASDaOIpsbsdqONFLAc4GILLDYCyNjpHlcMxvBGJOGnEK5OxJeML4zne+k4eVLINsVArIew/27jnQe8WFSz9xw+qumW1nLun6l/98daAyRJCKUGbbWlvOXT63o632QHn+7M5P3rimwF3sYmH8yEc+wsrMmvzGN77x3e9+tx6NRw7rPCseeWZFVeouvfRSEd7/thWXnLOIO42evgHqqy5adv3lZ6qL5Z1Rc7x4esKizQPrs846S12os1Sym+VDS+4B2NGxBY0esxjvPvSAWoHkFNDN1vaTn/wkm0ueefOU5Yc//CGL9dvf/naYV1xxBf4iH8xnClxCGD97bgrbUB63+zESF6177rnnwQcfJHq2odryEjdjIF+k4wMf+MAHP/hBQkfIg3w9p+f+48orr8Tdq6++yvDYc5PWyy67jNnAJRB31vUI6eUKillNEQaiFIvA7Z2eW9PEAo+j4OORC9s73vEOjhaYmypbM8AIdyZk41e/+hUchsm9Bxdj7KOFx6uuuuo973kPTWxS48W6ETAnfvrTn15yySVMZR0n5grZ4IDhAsWkcK/y4osv8jCMDIjGNZ6ccNfBzoBj8d73vhfXBMDdITcD3C8iTIwkTcejwz2smnMevupmFO2rGdBTKvG3qjlvhDWjPCk5ShGhcQh0iinM5IgkTZguUszWIiTyaMf5KDhMPpoRgF10rGkmRkRAiEeNzu6mKIgjVVbJsPKZOyIyoI/IWMRZSV555RX4kckSwVaYXSw0jH/sYx+DQM1JytnKWsfzC87iz3zmM/RGRfb6MLXGKgBbxtFnP/tZVjbsYIFeNsrU3PZTs4JhB2sq+hAWFS4VL7zwAisDr7Xo5UgM6uKBOqsKG/dvfvObDzzwAOpoRWsODBUWTwrPDv7gD/4AO1zYuG0wAcB6qHWPAIhHXbpSUFsSVYaLNbQ4IbGAZcKjAFyGa3ls+Q4jAUmQ9NqvmGoK18c0WMVeOIPHuME/yk/MUi7Rrscc5LqTUANxnSRkzCMZK4PJiGJu5cIHizPIOAE+rIBk4E6IrCXuss0YQLYXSTMDz1WMQow4sAKQ64tNIC/g/fVf/zULIE/cedb5J3/yJ/rEjxWMbRJrCM+bo+7hnv5XNu296xcbfvLYpg9du+qexzY9/Py217bW3i1RYeOkB6bsoY/Kjv375vOXvOm8xRevWvj8+t1PvFx7f29Gx+Cb0iJpaaXmGEniA8FK+Pzzz7PLeuqpp9ihseml65jpOmLd5qk5b86sXbuWdxSffPLJo8e6fPl5S/r6j31y2NtXueqCpSXeI2hvX7lyZbTD42w9Gmb7SqrZ+H3/+9/XTQJTgrSYrPxwvSBgC1Hh0Ovotwm5D0D3Qw89hKGHH34Y8KMf/Uhvq+OVp+DcN/BUP1m1o3oWc7/FQ+JNmzYRKHtf7JAjjh/Z4epFLrh4cKljk8rx5pKmkXClYevPgysuPLjDCFrMAC5C7Ol56C7deG+EnGsb4d1xxx0YZyBJMI899hgv/3ALQSTY5LrFSzhcb0gQrzSxsb7vvvsSFZocISJEiwsto+AQ6hLFgWFvzS0NRvDF3Qt3LMTD5Tx74HHB0FCk5ukXZGpuY8gttfhJwDhlR85nK4x03bp13Jnxohh7fWjETx4gcFEkb1w1SR2uOZrZ+KeKhPfd+VvV3nIpPWwnYgDZI6gZlcg5FlGuZhKvDqsWa7Ca4kScKxEhW0dy1kgSwHg0o1PlhHpKTz9nyUNjRBy1mGpzACwLb37zm1nNWJpMM5mVl4sW1xiMcHqy7rEdJz/s8jlVL774Yq4W0lL2pMjyEtdYInEwPEzhxRgWAZ5rsB1n787FBsw6+Xu/93s8ho+xsclm2cQvT7yIkyWLVVEPRHDKKopTwmNhYQHBAoshTLRYVbCWPIB3hCxiDju6A2OBqABYwKYwBlmpcO1RJFqjbMpsYpxoFbBq9xpknYqZlSOhC8XElISxN9d4Ikya0V3sijhy5E51NqoCrcTICWwmQcYmI6I4NnDsjdic0YPoMVpLvA/ZRXgFEUYvolFHFwW60fUE42aiYhQUAqM2H8xSwzLC6X9XvbBB8qWBPQz7KB7Xfutb38qOaMn8mTe++cwfPPTau64667v3rY0Edsw8TcYLb3ZEufB5Z83/p5+8vGjujP/lo5e8unkfT77vvP84dWL4nd/5nUceeYSopOKA1WTJYnvGaknTg4oLO/s6RoG6LUgxTFsJUK8BjOiZjl4V4TEK6zBrsrpYJ9mmyhT7eCzHeNjLcZn44he/+N3vfnfQ6PH/HHdToi42/twt4UMfhq5atYrN5d133039ta99jVX4eAu1FlcCxsxOVAMmBby2Qab00J2dJVvkD33oQ9zNwGHPzc0T1wYeG9955508j2dXyr3O+973PgEIXHXQwiOWucAA2BPDJAwuljz75zk0xwAJh9PxkIu/+7u/Y/z45QJDMJSYDu6ldAPAMeASCIdCSPxhFptgksVlGBUbFNCcI1ruZJh2PJQiCZgiJEbBLRRa3H7wHB07YO4LMZ4YIZncFXDTScx4xAi3CtzDoUUwTPQYp3RJINZ4KQizqJBVblKJ4cYbb+QSzm0Gl3kIfByDOz5tYZfAtTzxO7Waze/ds4eJkUpIJnN7JY+1kpPNfEyaewXcNCeRZwkwLXRgltjO1AXOgEZHjUTCcRqU05i1L++xjhwpEpsIsSvB9REcdwuJCkU0zj6eKbAEsTpx483ixlLAyaje733veywXnNc89eCjQnbtfKj4i1/8gqsCpzBLEOsbKwAPGlgQWKBZCqRYsMayuOsvpbiTZ9fO+y1S4WEE1rLLMhIKHllO+cCQPTo0qeCRVYvljhWMKxNrC37VxTJCVCiqqZqrC3Zg/qReEHJ1YLE1hy6eYmi1R459LJBAksN1wdds80cPfCASU8g18QREU02Q7kJLWLWNJM0hablGbG3MgcNL/DJAFRPsOitx16QCSZyxqaERLaBRzObDMW5EzsoTlaQZ+QVdkRZxroqFcXRRaIypiKPlE44dmIFCYm25//77WfpYA9lixRUAzEd8bIdYKFhV4OuwLpjTecPlZ86b3fGtH7/MOyfsY7tmtB08Mvhnk9Dg8xo260ncN8cpwey46+ENa7v3tre17jvYu3F77TmCC/tJlkE9ULCQsB05ltm5ERiPJ3jFg50eXY6cEfGyA/s0OGDWfLrqpfrUK7veedkZvJcvsx1tLY+/uIOnjxhkAWRLRip43sr1goWRjSs07PB0A/tkgA09j615mYKF14EB2Ov+/Oc/TwI2oXzNNddowUUEwBPgLW95C1b015+8jMF9Q/LXkDxfUdJ5WkN87B2Jj8VaOSUUrhaESHz2RJTKsjLFey8oKjuWx8NgRQFpJUzbTMg0mS66uUm6pOKaXmy6WRyAvVtLxi23qcRpbNpXNNK8X26BOOTcrsgORgA2VWAnxjDeuDgP7nUqHLYk7dUq5yvTRUMrjrYZTrGFie+N45147xPgkQE2M0Y4cQ4YNx/hBB99AuYOn/BYNtm7c8PM2k2Tp+ncS+uFEwWvwJIB0hXlNE2QVqyTNZYVlXt+tuk8VaHm48dIFmbhxSA7VLbp1AjZ7qPI5RM+qzHLO3t03vfjz1tZRlauXMkqzSYeRR6aJAb50EDW+FwRO1yxWOExpQc6XB1Y/FnA0VIXTVkgcvbxus2QBDssyNSJC/cCdPQFLC8+vsW9MlJQx3jAsjZKmwXuJr7LAzTw6AwmOCpHkvUbu0YcHkasa5D1NYUkMS25YU+2YRIwiyQbPP4270//9E9zY5aQPSSLDPF3LFj9kf/6ByyHR3r7732im96rLly6fc9h/lZ17zPf3rPxiVwj2mTTNffCD1/+lutWnT73iZd3XLRy4ZZdh556pfbklG+V2fKf/93728QIm29cEwMrKtsqr6jsZvn7JbRY7vQElo8NiZNHxiyh7CpZ2ZCwhMLZM+fqrmUXfunDl1ywYgE3G+WW8i9f3PE3//Z8/5F9MzfegQue1PDeOOostrxnwRNq9r045dkHu3lCYtnkAQrvVbIUsz6z2U7iVBMVVmldbsC1VyRxrz6GwQafmhsOduoA5KzvbMe1oReNSwImhFnHGQx3EkTGA2BoHDae2cNhTZcbMSeyJgYuHrp0TaTfCfDF0Mgq9Qh8Na8lJhMgq6JZgVygmTCaZ9ra1Pp+d4c9LODcjiA/w3LUiOwAGhESufhEq6OvOuFkm/aiYZ6owWYDG4GEsbDcecG0Bd4aZ/Ubw6GxdrHwyj7LL04xztWFT11Z2Xlmb9cGrOaoQCNIFnCrsxKyYmgBL7gw2I6ArIGxxtMg3RJwpUSOEWWASw4fMjQzaoywcZflZvhJMBPZJNRcd5M87NyYGwkbjVF8j7SYFo3DREt1lBdgeyngjKYrG3w2vPGOYTTxN6nbzDAxNVYjzbrLxgmH9YqdIasQbw1kCZbwOIBlgdhaO7qWvPk333jJ+U+/uqu3r/YqC98Mg5wn7o027lohFc+MpRfPXHPrssXzzjl9zobtBzbvGPzAsGDjzmN1FkZcYIHVm2cZHhqrJXtoVl32wHrgzVrKswk8MiI22Txx9yMbNu61r4NsKZ9zxlze89my6yDv5bNxrn8d5B08v0CLB+qsnFjjs02GxuLJKoqcGOjiTgBreh5UsD5jQbnCArrHPXFHxEgogILCCD1IaM2oFFgbfRfBJDHXA8xff0fvblwtxMTmOoqDFS6uc40kwiR7Se+JavIuGDeUteeZ073ooBcfBXGaz4RtxunRjHpxGM1YaMQhEgq9uBg/L428j6G8yecRGu/IBitdx5xrRMk0h9XcmC6uCm4KyIj23EPmX9awAzPXGjbJg6w5EpnNGq9daRpsiJMgJ2fTwQPi6CKebJE75oLAxGEUuWQP1sM0KLA5Jbpyxxsjn5YjzR21jn52vD76MS0RZ1VibzOL5HFLVtuMlvbj/t5d1gZ69lf6Bz/Ki/YJj2JJa+c8fofJTR448B9Xmv5DOV/XS+THuT7+p6BkBA73HjaorbabBgPlznLr4CMJCwG8ZVPu2x+9uJfMJMHHLq2olhjIlIYMTjfu5k0VoJEUz6ExHEucK8VmYRKVaphZRfXSlRt8rlB2GnUVxzMVe6fcc/fsUc5Nu2eFe5FQaHJwi49vca8NTnKgwSrI6TGigoTXj+3gwS2gucszAUByklxJaLLAJMmhQlXtkAySmKd608clOSKjGa9tNkqOc5swGzltJG9kv5Hc7gxs2aCR7pSTe4y5kZ/Y8RbHpoDFIU6DZCAaQtYUkuzospLE2hRqZoecG/wkHzJ3EelTmdxhnFghUeYGoFuQeGOno0KtIi1woq6jEmsINBOmJK4TI2rKSG4XQhlMzJpsuYG7GgGGjMdip410p6Kcz8zm1L9nJv/7NUc6JB8XAyzFo+AMC7iGA451cQi2k6VFd7GXQ6yjHCd2JEwzrDxwjpMr/YXM5BmgYqNWITCAw/OskMTNyKELuSQGsiMhQ5aizRqIkFjLFVplUoEYqjATG8CQp9/c1tAYHQXMgQD4cDBeig90nAkSQnavtbISd0XjEmYlJo8TkEf7dVPDT4Y8TjFMvNk4Xh1HJBoyTY26ICrILtDAIqNLAcdavZJkbTaSm2njBu4qAAnZTQAe1aRWQaKJPWQwBR4nVRfjcjxgjWtwtEe7PGQN3/wxBzGYaBz5cRt32i1tncymSKphHvtXB79DJ+0a0zaJoMgkgPOBt4uS6NXUbj5+llELM0x9NRPJmAZ7AozxyhRDdopOQATj7zI53Ey7s6rl7S2lATrqMzMhEJElSWYshxO7kllBM5GM/yiH8MB7Dkx+Fohkhg+hNpW7OaMZMoeMYzGsSR6PshKARIUmwHVuenAnTuzVfEDiiZGASB4xZry85Snv2NdV365HbHYyKzJY3tRk4AqSwVIK7l7GaSzxiAtTG+BUOHonTjXpAqs2Qb3UKsgB5utVV9mUnNqXfxuZNoCRasiMyOkCa9QMXHgKjVfHTgELU6tYyKyOExs5BPUmA6fpIsIUrRkvY/Sok1EwRk1y5OCkd4o2NV4dWWrebOGF/jgWdUVJLm6S5rzprJEphMc27hiavfLtc8+5oae/yo+48icC7W0tfMdNa0v50I4X5+/7OTpw9DdGssKCS9D68hYOHucqHIxSuPTyor26uCTrhctsF3JMQcAUFlDhm9G8WUFi7META474u6g4EvdOe0AOJ8MYk2nnpkDSjAGrq9Eo6HWXwdZqdWml+jp/f10dfGrlrmh5OmFNe84LTjfmefYsmLqD9dzQEGKTw0rhrNd2FszYqT3YiK0ehcKqtT5Eie1MEkCECpJ4yAMDZz1UQtTl3kkS8OjD4HAkk5lRM88ZtQYOQWPHl46dnYqgpnFNLXNvJokVs3YsEVCN39i0+igBNrm6UaIdIvSoIdCkpigGQCRPOUz82SEzCobpw61BaeBgD3w8xo4X51A4WzseQBKDg5TcNUDFxnMB6rkFXfkap4mXG8zYCokcg8nctgtGzeGmACw0OJq82r8IVbt3koAYuTC1gCJkL8renQx4FPzp6piPBY9cE7WLlt/adYJCg/wuOeett1y35gcPrn/TuYufX/86fye7eefBK85f+v99/0Wx+XZ2/USo3qDnN7H4YjJ1rVmzRr/exxg4nHwzmr82mO9N45t0oCGnl++n1JeIIeGPefkWHv5il+9J4PuD+MIEvmXMK7sCk33VurDxfTpReAoXZMA5NIBsLOBmYkfy4lkYe411PqtpYWJ8WM0tpdIZA6Ud5drPM508hZOFwompPyfnvBiTZA43gdlJ4gkDMI5mo7BRzFEu7MsAywsSjHhzo6UjqkR30wAzNB1ujaV2uTu6o6WL4VOm6PA9GRKgpoUMnAEyai3yYLooGr5qJQccAZmRrmv1TuaaIfgy5zgZO+NlziMBUENTmbpH36PTcJjkUSLMYGvTPezwdNzp9cABCCkIVQsYy5SEJA05tYUCkrgGqCS0sW0Wu9Coc8d+NLqJCHJsh+xs5x5u9XKAVOLYHYbHjqQ4gVYpBviCoFog4mJdAoDgOsaGnJlJkQVduYqtjaAXj2x69RRP6m0KggZg/6G+//nAugOH+/RVmttfr30EcOBQ7csiGSRbfl5c4Tt0aPLdNNR+oM5aw52HhMgpejDPEoxQT+IRYgGm7htU8634WOY7g/h2fb4JPruW1Y3VKmiokKACjslTEWSnUSJRBhoNzWSOY5ZjoQEcsJqqJcnqTgYJQ9rSUjq9UtpZLuX8hflkCHHcYmDOc9Lq6FMz/71MNPKpyRBrMbMS5AiZAOrKGvTcoCs7W6IkMrN2RiDBoE92FndOf4XKxWDIDIzA3aRSYYAeI6P28AmStExYBjQrYk0ASVN5kzCbwzgrhC3xABN5YkTXdWq5Nhl1m0pUpmhTCYl7HbKqosnPuDx8wNTKgAaig6gDhCQ2sxiCbmMiH+yxkwGla+rOBB10DTCpPed1CtBLQuIEcCoSxSnRZCAaS260cbY4D2Jq2oATC5bnGkz41jVopDViOT+gwQ8iEfz3v//9XbsGv9yG7/Plyx/5BdIHH3xwuJYZINYIGHDsVRmszJnVfu7KBY++cNyXBF954dJ/3/gcX+X70Y9+VBlM/PHtwuzIu7u7ecqOxWuvvdaEc889l18G5Zvq//Ef/xHh9ddfz4N59fI7UnzzsX6AEAm/NcjaFH+zw0YmL05DAAAgAElEQVQA+KV3nO5moqMmsaZIrFFUM4JG1szkGCQcSwRiTRLIwFRfp5LxNt/klnZw794yDffucS5lccwS5xpNlvtGK352CmUlMmh5tD85scdLcnzzj9A7+8kZ9phExWHirNeJj8GYAZokga7sofQsMpCuQpJQuKCW2aSOTXSzrgsMjqBL89zDxwIrIfF7L4tE00PMEbiYtCrkVumNw2fsKjEDYlJH5tiOS06xCVAtYEmuO8WvLmHXAGF6DXKNJEJFQs1M8M5eFmoWp9otTTI6NwvmczYDSBi4dOs5GMyDrU1CQMxE5bEoQgkLotUwmedWjCrZJCgbBQbHqYvfdfqHf/gHHna/733vY5byc07EyTfBs+/l8Te9/N4Rr5rz5e78wh1Pt3/605/yW7O8K37XXXcpJH6kCQK7Prbf7I0ZiKYE4LiN+/7DfS9uqH1FfCyP/aq2j+cM0Y4hdgnzlgvmyCNvuUCLG3fCJSDC5eV1yHFN4be+EdJLkX2eu+dehkUY8esxOqjUBgpbTeHiGqZng5mWAIRzTzN1WWs0gNySwDE0OJpgToiu9u5nVEo7JsHe3dPJgJzESRVxcbp0TH1kAS6JInKdCFyzObmYD7mnTKI1nZpkwEMmA/pkT0Lq6TFSZo4Kw4kgd3TMBCVBZC3FTgVA2Ct+NKKuKJkSWGPx1YQUETaTgVQ4/iQPlk8DUD+ktanuDICVBK4RXC6FNVLyAF9MyalVrBWBtLK1poprgI6Cmepyc1wBvuQuZgCPjIuaJGgmqKlIpAJfiuMa3gQY13ByHWnU1OSBkwIOOI4azLFLDl+uqeaFuHCRR+kijK5pSm4hQCWJB2Hz3rNMOVJI5MF+I9N+R+kr2jSWTWquVrpO8fD6s5/9LLv2p59++sYbb+TJ9d/8zd987nOfY0N/33338YCbDTolBsPC/pGPfISdPW+mJA+1j610uOTXqtYsn/f4i8f92NWbzlvy4w2OZxDwt6HcCvB+C21+NhXf+lEomnwoQKa0UxebX93jqTwBocXmXjuP1atX8/NUaHGHAQ3hxRdfzG4e7CwzYG5NONk4qNq+uzcOT15Uo5t0uQlwgQyOipMfZ4c2+WMe8wjZu29uKS2vlLa1lIb7hw6aV9QJIEgkzAfJHbMkWbkImj+umaLGtjB+gOVAKwKvq+GaE378fE1ay6wMFMLjwHGpZtnhEJCW5BowYfHHeRWxA0CoSWJJLvBEAgxrXmGfNdMXKtTJTzMec8OYKkINkOMeAyYJTAkdBclJhZMZmVMLM6JYCJ5moyHQpa0bOwBpwVQSSJfOnek0PTQWxqWhxbQw/DglYCpvABKinET+1MVKgsaVOwryQGFiOAMG8FFU9kyQEXMAcmEgAkKKMwnO9T4xQnkvjoH4Yx6ygWksGCm2k1VEgvGnnnrqtttuY+N655133nrrrZ/+9KeRb9myhT/4xDJ/z8k+nl00GDk5Z5/MkrV8+XLcoY6QZ9k//OEP2WDzGrlOZIQqx36AiTEsufp3Z89b/Pr+494iXrZwVvfap9d0vPjrv/7rR7VKxHT//ffTZC8+Z84cfHvdZKutDT1dhMjrMdbiz1JfeuklafHwXzSa2pRr88ELQJbrffoRZM0epxNg8R3xxw5TJQ+arzFaSagN6GVSn8b77i21n1aVPKokOM4fYdcGicrUanLm6glTcr8+tUYxVtFqB481FiVWw3j0o4vstKnNsPpyGUGi0siaaHE6gV2ikYnEyWWJbJAToprIGCaJL46pNiI6xERFHjQ9JiAhdopfYWoDC4fMlUJ1rUv+COJnYqjYI0Yomh4Ay6c94CiQivrRGDwiGrKyMeIMn6i8aVLZexyXuwx0oNU0BmiPKEmsycaUS4hTMVyg1GluRF3kzgmAhPCQmpVEHC7BfmZNL7ti+FySPv7xj3/729/WSQcZIZhepVo2qZEgT/bosoyQnb3yD649k4CqUDqrB6+//NJ7Ht902bmLX9C3yuw4eMGK+d1raxt0HvJDlpXdu3d7MHwnzPz585Hwd6g45p0e7c4h7Nu3b+3atbrdp3ngwAFrrVixgqfvbM35CIAvq+FGhC27hkqtkMiCdiTWOmmBDu0kHL6nBLEZC8RavZpmpjUajmiq4bgJMOZDoLP6SttOsu+Zyc0YJzN3dGRVN8Ccgzq9c8nTQOj5kwA1qZkk1CwdfBxRnzK1KjcnyJ0QMWmK6aYJUw4wEI1FkbPc+3NLRqd9/JQb1MgCZrycF1GXGUJCqCmSK125x92cCIRjHe0nGLOSCMiL64Q83k2NNPHCNZfCcDQiehUeZIGEPz2aDM27rjgikkA2NEOQKyeQwdQTkBN59LEACMc6BqzA6NXBMkA+3Gj1CDUxjsFsQsRJEqIsJepTrqk0krpGketkYYYAoOm4gM1H4vX2jjvuMPae1hJUpB4ltiOA5ZjYY0/c6W6bMb9j2aWllvSXSquHd83oybwuU7fHaug4Ek8j6GL/ocftCpEdfyPjia9p39QsyV1ihjt2TRHV6BoYZyWJC44OnDiNIKgZhVES5Ym1ETc5Rc6slPhtpuM+IRqxuWmhyHHhlGG2sPgWLDonZKyaV55dxU1HKFoyf9zMzrEokRGyoeWVLu1WrW4vJxsgJyqkl2wwVcjMNE5LnGxZzNFH6IR4MigzJMeZiUA41lacHoCcKC0C1BqsEhLTMj3G28wolBCYzBZqmtIiMy5kJppS9iLZEglRtB1biwACTdcCIkyGmuCVDdWEpOHEgMEqkyHgsY2Bh9remDHwcXotIu7pa/eT11xzTbzBIrljO6phWdOBVwwE50f1wzIy/cjMBnZjWih1SjDGLPDA3WUJKZUwHl+wmwaoCEeJ7Uw2wNqp991P7d2TQ6PtO3MmedCY0JpvMn80hWKNel08ePVKrNGVzKI4tdyVADcTa6NvEg8rDAsLpvBCZsbP1+ijnTALSguZoZAQCtPGl6IJC2NIR5pp1DBj7Wa0IAJjsVC4NryjQoFYmyybGFFaADW1o49ak51Z1Jr2mFRQSIuAxqvkOD/TNQkecgQMVk3XSEiFOUqLzymaIrgGTNfinBgwUmVAAOwyRZPAkWXvzigUv0EzwyEtBbRoKjJrG/cbb7wxbtwLrIxrF2FxS8Gl1GtiDHRcXY+HcQXfqJbHJgeog0dydCdHUxIZaYTHY1CT0ya7MJ67bx3+36pOzuGMbVRs3znJmSScWZ6NnngGuU7pzc4uSagFUMyCXGuTTcjOo7b81dfNuOxMtjgnPh5yQmbIj1xzfMfqDgfLKlg2EM4OE4IDiKA28+olCsHIJBm/mpC8j7eXU5/hkAodzWQ3j5yDovw4XZMEKGDVil+BIWkUoSdYffbVKphRmKtoR0pO5KDLbkd1lJ8kWKmmzp02JIHMKDnUkzwno4nQM0RAI3VyssJaUvg6yNhxorLDdYLC/QMBKeITEolTEQGR0MwNLFcIH7niF6B2ieNCGJvFmBi4fBZzTs5e/ipkU0vprIHSltZhf8/MJM8YB50IWdeos3MSYXYKSSXKwQj5+xMuEtz76VIRBx7JUT69MXmgMEaSw+LDHQ6ATcaps4z5EJPA9NOnN+SKLpJGlnyt1XxTGkWgVnFXnGBgFSUffJSeM5ndNUkA0TJ2iuNhjKTCn2LTVH40OtOmPYjH1IMlGxTNHwlpwiQ5KmaOBtSdHFc1Y00B54aNsBkLTXLkAnKcNjSJmJrkePGJfskP/Chp0t0Uoml01Aw2G7bzo5U5IUgLxUmSIkXrIGl6hUSY9IoWIwerOBU0bS0LaudUVjrBElY9omz+xaAkC2pSGxA/GJuSNDMcp0mA2nPCXc3YGSdO8wMZpwAmuVnO+42tpbMHSt0ndO+eHCaaKmQP4FrJlCSZXYnQs3H0E5LP8jCuHYbukCf5MZ2w8Mit96lcRPVr0AinU5Y0r5xSmrEgp+leAc29rJwU8QYjHAjsLUidLzaJhZOhqSTEPRkZ01aM4YMhkB8RlNKTIS0aI+OlZKdH3K1GpraqZE+p89xTGp03yzGOsO6kBnTJltDkSQsUJ0OmZIPUFPJIRdBIxZ8qw8wOrUlJcX7ITG3zevz3vWJZWp4Jo8+SDwEgFo9CLuiSRM0kDJPHCmB/fDfuHo8ijk0wqWcnwUQk0YDY6xESInLVEiopEUuiBQKspi1MA0AGNLppMJZxGgJ79w3au/POTLoJGcKnJ14EwrG2FYQFc8xdAtQqqANsZOIB3rk9Jnida2xMT02q5CjUrqL166izBEE7+BN77DQPCSYCsIpGAU6GQzMJ200AhQkgkFVsRqIdmO4GsUPqZLAZ3enKIQ++D9QYvcPQAVLaSRRlOiXB00+AOgHZwZIKCyFrXZIWyXGiABQzTwZQX4fSDT2Z0RkHoDgPyo8ydpIkimHmpoickBmyRKkn6bitI70oKlFiiiNMHQtMepVP15Mnycdt3FvmrGg9/a08MogDKMbVvv2V7vtaK7UfRlWJAz4qq/2rwRtwtwTmOy+Ru6vGO1UyGeD+O7kYZCgno4DzimGrFtjcWjqrUupuKdVe6D6+1wlCnp1vlkQgTC1gC1MaMBZ9usVlksK80lZ1Sg9qzIN3lrBMluqzqfYiDWVkkyHOxgSrqSFELEniTk3XJ/ZCgneKI2eZUq4Ij6JegAgnba08ePgcYnYV8ZGqE+VkmnxCQO78jEJFJUlyfGsH/ugRF1Y9goEoUfU92OC7gjJFlmx5BGanrgqjZv3Jxk+ilCsBE5wlwCSZWo5trEAcsjA1xlVHYI+celErZili8ycnGPy5bEbCoW096/ryzMXDDbRyeGfvloeyWhjkG+P5UVU+WgV7zuEr7hjoWrJkCZ9Q803w/u76rLVGEn1Wy8854agRZ6rLyRhTaqqPIsYfzxwwXbF2M1HJJkESy1nRN7SUzhio7mip8j0zSW+0dgrzIJkkcP98Mv/2ajPTQIlifrLikyt2EixZupEectLKvuenJyRAGFMCqpuJZ3JyiD8+XNB+y/t4hqkyOYOfyKhIVO3+L+zAyJVmF+kiEggUpQswmtgwS8FCBGrKrHrtwu4EXBvANMda4wRwFLMkLwSsqaURUUNTIWMC4xTPpDXr4ScRKkVKl56TmuBcASycJMBHlniEBbLhMXAJfdw1HDezKo0kdqqTkaaZ8iKbypu7TiBo4/GbrkwE0dpSWzgoLeXKTUt/2dV25KfbL3vHkqf39M3edGjJmxe89MjuC9bM3jyjtffubVf2VwYfzJePaknXNYPkV16/+tWvXnrppfz00p49e5Bs3bqVn15iid+wYcPq1asRsl//zd/8zQceeADyV77ylXPOOWfz5s3s9fldJ36ViR+AXb9+PZjfZ0WXV3VXrlwJYJsO4Meb3vOe96AL5leftm/fvmjRIr4Jnl90eu2113QUHc8pMCYZiOeS5nec5RHLnSSa/Q7ATZ0PyOPBUq851moSbOY7Ivtr3zNz6jsih8wYZyJFW1ISrtdChtSaogSmogtDAGsgAtSacpZ7mJ6K7CRIF009MQVYAtk0K560gNPZZzT55HJIxrwxJWmUkzY5ycCVqJgQbbZ02yMyHGYXhWRSJDTIbUJGXlc6DkjoWrpTpWY4MVEKmzwoY9RIaGrstSlYL1NldGMbpw49CUjMkh8VLWKx1ypZrUgbFj7qrfYviqojiNZ04BQGcoVhYWSOLbbHrFnFjxyQ+1a9dKkLklZtm9V75nUD884rVXradz7dvu0XpeqoFsDjXpVx0B0t/W+cv/ara99/qL/z3h2XDVRb2Kbv7J13ZKBjV+/c9paBy+a9+tjr55k/JGAN+uIXv/jggw+uXbv27W9/Ow+uvve9733uc5/7q7/6K3RJx7x58xg2Egg333wzW3B+V/Wd73znjh07ZsyY8b73ve8nP/nJu971LoTs2j/ykY+wWV+6dCn7DH60lfl3/vnnc3vwL//yL+9+97uRPP/881dccQXNIQM7eQg+bSIQLk4CHJ05ogmrjudVNBL5UT4xmBNC3zPDOzPDfd99YiKcbF5q26j6V4Xo7w65mT+xR7BRfjRddXn2RRpyM9PY8xaggiLAviK2sBEgXXrjSBc/nSPT+7Ynm4pkZcgSJCGx9fk1eKFCi6RxCXDvSZU3hp8U8oCkIHv0Mtul5aSx9uoecljztpGXKS3XBItDUD6VN2224NCkpjAbC/ZY0c60xEoCQyMPcYBOmtc0eiGLQ8ZUaCqxAE1LEWKtVEtid6gjsUGDqDgJseMntiRjSBipipKWxC/d/rY5B970vw/MPkO9vadf07H0ilnP/NVo9u75G3e26T/b+YZ9fbPwpBrQU6k9j++ttHW29l06v6mNO6/KMCp20ps2bWKr/eY3v5kn5aeddhoP2jmd1q1bx+N2bPKM/MUXX/zRj370x3/8x1wOKSTol7/8Jbv28847b9WqVbxC8+STT1577bUrV65kb8Fzd3btSKCx4+/u7sbLz3/+c+4Nvv3tb99+++2YPXz4MJanQeHc4PAPzo6j/zAuoEYHyJ4D6k3kalJTOIuoZcFgGqRLQ2BB4m9VVwyUeOu9d9qMapwHwpTwl88wf9hOaakdQ7ealtQUL/rgJl1ooioqzn3PW4Mm7YwhjTAoGGQUPJ5gUIRn4Rg6moSmRpZ2tGqf8hz9clsyFh8tI1c+J+F4k5Bqk7heCmYyg4USFZG4eKRIoFFHZjHGafL8z7NuWHaKvUzRXmWAOruCkTf2WEw5D000snfS5k3TONZKjhNCF5ONZJI60eiiIKEodTSd0pMH1NNQG7jP5Th2csV8O3L+f2HX3rd/e98e3gaozlh2Qe+yKzv2vmvG5v8ge7YQFYfE+Rt31pCu1sG/N108t2NeV/urW469Qd4z0P5PG24Y0jTH+J//+Z8/85nP7N+/n335RRdd9Ktf/YqH7osXL+a04X2Y5557DiOMDSGbePjf+MY3rrzyyqeeeooNhN53Z5v+xBNPsH3/tV/7tfvvv58n8ddddx1b9scff/zWW2/lAfxDDz102WWXPfPMMwh59wYXd9xxB3wkQ0Y4HgSGE82qSW2gIYsjYfGMJ1dsoThtdICpNUWKtWIMJyfm7v611tLKgdrT91PP3ZufA8wr7pzhc+oxP7WR0kSVEbCa1KxKFjbjQpOWmuI1C9yM7iTnMArljbRwtlIImFOVMj0GOE75Zxoob7JP3vwwXnsC6vFwrTksy+BYENIc0imHVYUIKeAhVQoIw1XHY8wblr0fVfAYZO7lBgZhuO4KIp9aXbVDlZlRSeoYEfkRE0CZKmP0vAXEQvw0NYrscOpDrFUMWbj58ZI6io1bcSpmz8GPFSCZA6WOnoUXY7ClrbPcPmP/i/fMOO1Cmj1LrujYeDcXUF9Do1MdCGqEGFGXAc3yTTfdxL5QHZ2X3F7unAfmmfqHz/zZdzZcDz5z8cz/9rFz/58frH1p0wHROlr7P7H83n987SY1+7c+0r/5fuGk5nBy+R/lQxSiv/zyy9mpMz8S+2Pb9ORLAM1Egl9JYipjMJYLuDaI5GLMZYzbmGLOqd5GGWBfWdu7n/TP3TVd4zQGq6maBBrEZLKRQs689SrhOQxIVpaoeAqTgfpFrXZVI1csg0rXqcw0mQEuaSTQFzZtQ5McetIKuAYYN3KnmUwvQAWMfXAjlSkkZ/jKnvJA5LqHnDYDHNdjQdJqW6q8N5SUwImZJHEOg1UYuIGSkASjps4UsMC4pitrPMkeATsqB5aEnTUyDSTVzkX73vZn1dYOVpk9z/7bzNMv6Vx8DuNqO7B59kP/W6MBJtmDxl2694Gc1/lP3HsH2v+t+2ob7ZrR+jsfXP2X//NV7d0rlfLGQ0vc2wjUrLfV/vh1lIeHMfA0HWuNHDEn6FKdgKxQRiTPBiaJ5QKeZ+i6S3YmoJ54jxMwqAlzwR3rej13n+J7d0/yOKUtVD7dlU2vZxFAWEA1fAmzilosOJE5ATkR2H02YmZ1x1bC6E6U6xEPhK0SBXUWMRKoD+glnHJjGXESsoqNDiVyFVQAUmTWScgzINIIloRauXUmAcLU9BpnAzgZJAyfszWOlBlI9piEyq2z5FxFcgFGXbkt4Ez1Lgao8zQOhIHrRAZQ1KXsUVMiuRGWrtRd25oB6k4yQJgaL/XWZL+3rOcjTQijo+gsBjBGag3HdZNpbJTeySYv9+4u9+ypzlp6ZPuLPTvXtnUtbpuztLVzdsu+dQWhZrPHGyha7pTA/I17Z2vvx5bf/w/r32XTHW3l33zPyv/3B2vXbz1UKbVsPLTUXbmARZYj4VuEXA5CgqAIuDZQF00XSbBsCYAm42Q2SO5ez4BEHnUnP84mYfLHPKkiZPHgnZmz69/vfgI/ufA8BxiTKGFlLOIkh3EOg2OBqWaiMoZNdgAUTjGd1zx9x+MY2m/G1Bh6JM+jtDakhYTghRg5+yfSyJARshZ7mWomCZONkwzT4SF3kVDNSDAW8BEBqCBXcmiCVYvMVKSQSZpYPoGZxHsMTOFNzloXfsdG5E4jQDlXJhuNSINt1GvL0xUw8OxuXvOQ2yHdFzF2ZVLTMubKWAQzI5iuqdO4NPDsikeuVMghD4kg01S6rJLVmhq5qlZnrv3ewUtu71hw9sIrPkXMLW0zyv1HOl/74QjiJzlnnnkm75nnb9z7q63P7F1lu927jrBlP3BkoK+/dgvV3tJ/07JffnXtLSKQYucaCU0uS1zmSTSHAeB3Fnn6DpOJbss6NjR1eFSrN3aZnwu4F/nQhz5011135fZOaSEJTFbbKT2cExV87W9VW2p798287z6KILS4uMYS2PYittBA89k1wCtR81Pd1k4IIGBuxRmm9p1s3z2EExIPkTh1wwpgWFq5Xoa00IiAvHYPVH8UykrIeij72Q1BHFFuDJEwSlxgny6XuHTjEXmuX43dtYCmioW5ihJiVrQCDtYoSiM0Xe8VD7p0kc8hjeTaH9J7QhiZl1zXwxUmkTSvLkUiT2Ydcg6xPhcCQKDEZNJs3svUZZIHFZIAYCCqAWTAWE0Nk0wyG2N+oKFOEUF1bdbWSxSewuRNqSONMRvkUMUnuGhw6hq1yUmJKpMQt295YFZrx5HVH22ZU3ve3Xqge+av/r71wMbhhsrF4qqrrnr55ZdRBB93fzNoq0peBhflfYf6/u87X9lzYPA9eAi9A213br7WXpVB1boO6beQkPB96m94wxv4hnX88X0vvE/P16uvXbuWKc43PC5btoyvjuGvSDW5IXPYuJ9AaOMA7MDkK9vPPvtsBR17hfn6djHZwfM9M/fddx+OsrSsRL/9tGnTJrpQfP/738/dBX/eike+0yby+TIcpgjh8ceyUT6umPnqd4vH1dG0N669+4oK77uXB+qLKbOORYGBqx4yA8xDOJrnACaDsXRFGNLOVCcwTH2SxvadHDI/k9V2WAMk+SPOW5OKo3HBWORFk6RJj40ykETCFGK1gYycM50HHADWRkpiwX4TCwltyF4IFHxJEZxYcJMuOaV2ITCwORFbaGALljQJis3mGmEGehJqgPGFEMLOvborwiTOIb1HQqKbG9v4CWMk8tIoHuQQzDdIYkOeZBICs8XJlJHISSxM2iYZ0ILvZb8gVPKgwrSJMwdhgVa2y3NSXYpB+TQZm7iAOVzjtjCNgY4CA8xmEiH5JJnac8YkKJ/xwMXeCcALqi2rq63tpTK7jn3lyovl/o5NP2H73rPyg217X23b/VSpkq7wzUTFBpXx8pWJDLz2FT9ejge/WZftfMvAmxa8/MSeNZjbfyj1Qe8NS5+4Y+N1dkZascWFHBBfj3nrW9/6s5/9jC9i53shIfOq+nXXXcfG/YILLmAt+Nu//Vu+mp0NMV/czi5527ZtGOEb2Tm1qB9++GGsXXPNNa+88sonPvGJb37zmzxZR52tM18vc+GFFzIMuuI2GjLf8v7d734Xg/xyE19f88ILL/A1NdQM89lnn+Ur5Nn6UxMkXyTPN9Xgne+p1MYdJvcYWFizZs2Xv/zlb33rW4TKN9WgyP3A6aeffvfddzNG/lIW74wFOQE7CeMBsH/qfCaxSZ5pxpIlNDoW3dXqmQPVLS0lNiBMCcqUTi9JGH38IzaiTScnMoU1hLO1IJhGXgpUkoPYyEJCyzbHxEXzRrIBWGIjyViQk0AKTJZm7eARJp9poAWBOlsktyMDjECWX2oVHAmYVgxsoZiW9OJCitR00TTBBg3clQuyFrI0c3CkTIqDnPmpXiTMUk9UheQ4szaHlMRBQcbLaKzJnQfSyLu85PYm8ZgT5QXq5gtIKyYTOeqaoiaziiql0Yt7xxwoP4pEu3AkFLxTy13ESBSYVnvOqYmJMztw/NZn33EPlaGRzzhFFTDMqX55ymZgrCQ6gtSkKGuTWUFKkw09ZMoETNQFpfKaauvzLYPb5vnVlksr7U+29JUHesq9e8o9u0a2a2eYu3bt4lsT3/jGN/Kwu7ZxZzzJ4PnCx+93X/OBMx5af3BZqVxa2bX1vu2XXrv4mb19XVuOLLp60fOzWgffFp7RXuK7ZpQmLuSJKba5fGMjcvKFC84ZNspweEDOE3EmK1vw3/iN3+BbIz/5yU/yO6myw/c8PvLII+zpIfz5n/85D8UJlF9afdvb3sau/S/+4i9+67d+C8tf+9rXvvSlL/FtMw5+7ty5MHmWz96aOwF+tPX3f//3/8f/+B/QaLIpZ8w8uWfPDYFfd2K/zk814UUW7r33Xm4Mrr766q9//etPP/00cu4N+GZJzL766quoEx7NBQsW/Nmf/RnfGY8vrxSO4RTIzYATJeAaYJyraKGnlgA1RbMXYNqQYEOptKKvtJG9+5DUSU9ofuAkuREZeUHvkDngpKZwanBKYgqc6yhXOKTxSGjewoiHE12M2EiMORdjGTlXF/XSjMUqELq6upjh5FbXe4XnGuD5HyO3heYBARRYoAuCrOXS1CtaJAhHSWIkdjkGA8cfaRYmQMJFXMkAACAASURBVBzrOiTkusOEj1ATVbp0FdzD2FTiqKCpGFSLVmwktzeq5/qKBCzAiZJclShsRG7SFOokjWKb2id5PiP3prORL+vKKU0BauwkQpMNZJaa4q0YWjTNmUJAZ3cMmLEwUdmBOBX0crKP1fkefU0/rERlx6WJSkpjVpkzyio1KqOfQudU2l44umvH4J5yZXG1patUPlhicg4xPxWYa51TajJJAGynKUyM/Hfcq6Xynt45D+26iJ9KxdmWw4sO9s94ePeFvPuO5K6tVx7qn0FM5y4tVaqllzbVzrb4oN0p4xk521w266tWreJ73Llj4IvY6eVZNRtfnmEj56n5DTfcwI80sRbwmHzhwoU8ep81a9a6det4Tv+e97xn5syZqKxYsYLfaWJzcMstt/BhAbt5hMq13bGT5kdVMchTcx0MPL73ve8Fo4sXXraRFr64T2CbznHizRzMIueJOzt1frmJGAgbCbcK7NQfe+yxd7zjHSSLTw9IHy8CcW/A0Ox32gNNHYbpKRVxFGZTQYYh+HwQcK3DhJYJWQtjK+EO8rWW2ndEbpyQ75mJY88OpLg3yx+xpDi9xb3NONW1h4WGp8Uc00bbd0w1M+RRckY/HOLEyJBhQKBoUKrdFHAXQMWxAYQFYn2UO/gvpnh6pA2N8kyGE46bkGXWkiyIHMVpFTVRsUTqsRnVk15o2d5sALkSuxBwJJDdBS62b2Y0EoW1LefRTacu5EosHHJLYp1ba8lpEgZNlRhPxOqNRo5qHPs36fWQE/kxhQzKMmUkK8+opoKokhjJjsvKdMWkIUfCdOVyqcQiwbKMq7auQb2/tqcBRGuN+FaMYFjkqDgJMWPxLHV45FOFDFMkV7rgq5h8CmQzEKeWe5VMEsuMRejEkk/4R/Na+9cqxYA3ZBLq7nJlSan1cEulXOJ+rHZ2yKnsyKPtl1urc846NGvxkYHe0p7XZh7a2a6u+rp17G45/3vciyNTb2dbafmC0u6DpR1rG36POy+rsPHlTRUu52yIzz333O56IRS24zzJ3rlzJ8slP4PKjzSxFWanzqjAPJJnz60u7jAYG3JNZbb47Ox5FgUNPjty7hk+//nP82YLiZ4zZw5dPF/HPgQOBjGw0WcngReYBw4ckDXiYXfOfS1GeLqPr+985ztgmPCJDSa3BzytoUk8HFoi4bUfJDSJHHfNJGrEHGIj1OwJPDKDmh/UBthRk1qTQ1259kUoqNWVqzsJhbzku7JSe+4+mr9VHcNx+RAU2GyGU6A++i5NDw60ATazUXkR5FQqPkeyusMNshkLQ3IgaCBZIHkyzGhQ0z4OExxPE49Ixmk6gcImAMSRepQbsyZQyDAcliyKyIli0rR6BFkOEsdmIBV5iepDYtlPaCOwIwuKLbGWNB1/Iy8KKbeXLrLq3GI5pjdxpGZBSHIELddXtDakEVkooGGtoNeRyKnjiSoRx9iEZYHkCIjsZpYvF9QqNoIiWmpyjug0oUYim2YKoC5wqi7IACmlkEAVMZ1bpbdA/VRXowyQTxJLr9ML1pzUxFaSwWbqEFze3/pim3aGbNTRKZ1RKb9eruwsVw+fcX3b3lda978mC7IWA2hpq666efvc5YO/mFQZaNn8yOKdz86Ra28FayHFH2CqzL2g9kXxwyzlw9tbjmzNVSIyhsdqqF6WQmUhlzxiITtp3onnCf3ILDh95H1kFsZJi7sOMhbPPc0M1ThVwA7bIBuPxwgQztZoSZhVH0MJQU6Al2YCbnLv3kzAY8VpJuzRcwqijV0R5zotJtCrp25s35nGuRaaFGIKZpPTBnIsKKqZ64uu7ImgMy4rhxyNiGBhbIIllzBqGds1EpMNLATkGmEhpWhpJWDW9IRmUwLyKw6SSDYhCsVXXWAKgq2ZFnUT3CSHSGw2sdBk046K7TTqdXpFIMPJUtxkGKIpGOFGSR7SoIxIPQnbzcgpMAiNwhjhCAtIBUnUxWNSIFgSmRHLCLRojSYcJBRlGCAtusgwJRoRWUZsSkYS2lg1iWcM7Y+ttSbHiFMVMiwgRcalaTyGA2wypClKI3vOIZhRuBYgkyIAVMjwiv5S/0Ble+0PU2uFbcZFlfaHW3qZ+j1n3tC2j437BnVl69Mu33Pa5bs2/6rntNUd29f3LTqrraOz9YV/Patnz+Dr3FLB+3GvyuxeW3sVJGuuWMJrLTznzuXgwLt2CBHn8kcm5EWXEe/a8ahjMDLXw9XCVyz2jjBrin1Pcj0+Oj1q/8JnllALR5A1NXkkjvaEh8RE552ZFQO1b3k/9hHU8WHpuFA77IjNda8lWQAnVzdhDsmBgIo8RnKCzUnsF8iTrqyLxFTxqOnljpqo9Lk5M5mSWHAzDioKkVO8eqoLiTl4oZlEwnkhiUFCUFN2IsZsblPuEiMIo+tcaxIm6tKK1owNYiTRiE3BrO9zass1+WENlFl/ymFTAjKiGoltgmNT9mVKmBqCsIB1ozDSTLCimJFjSeJLclmwHQmj92hZtCiJjpDbTuQgpGlJYoSZQ/GMJcNMY2rxlXnreiwA+0piKOZYC5vGUpGEmmIjwo6ZJpjiMwUMmdpaEdOFnAECqMWhFrAXAZmKQikip1glYsiWGyAUBwlFRuyCLoLXn2VLHQJ5Fg1+tCmtghprw+LblBy5mQzK8iZBYq1JrdHTlLS4l1NCyDCbCmF50YGAeaJCHf1gR2CBDJAKCrpgStYICXEhn2A4qrNk2VG9nr+ErLZc3Nqxt1xtr5TmVEovzqi0VWsPYeuTMld7ULhgzUFiaesov/zo4YHe6unnsqpX5608vP3J4zbusI/buOeaZME666yzSgcObN+9+xAHOJd00gh1jOvHunbsGbdwkwnwVICv86p4QuT+5UCTvk7RnAEdtXjWIaHJRpJd+/L697sf+7pTq4UTVXx6bMQS0bMugplBaN3YZTsCcKIEZqIVm83g6GtkuJmhOebEBRGylWQDxAWD99CY87qoyKbJ0CyxKYQU+BRh8wFI1JRibtM21WvLKEYcrcUuOBR5wULEorkGOIAEmyN1RxJptowwyhMMzWHYHULyo4WCFckXZnLeaIWRnbqrwcrWLExCciSSm580pZ702qaNAKwIOfqiSYkqxlaRhKaYqqMRCNFIxHQldmQt4SQ0MsxdqJio8wSKLaaadDHDrQ5QYNQUcaglFzBZvdCiRLiuXas4psI2FQFd1gUQDAddEssj39iKAIQiC5tTbCexnzRt39bsJUqE0aUQvO+UUKeQZ/35mXRFgJm1kEiyHKzJCHXEappPl7H5gBGXxOCI7TSvGOO3loTMDYqFAMIjyV401AUZGtnONRXVJxtmOBROGZ01ueFpUDpTINCUJJfcvFBGqLH8XKnU2d+3qNpyqFzZy7syfbWoyDO/P97e0d52dOnAeC2MCpvqSue8/pmLejq6aovMslUdm17oOf/qWVo/Wtn+Z0rRxp1X6K9cdtp7f/u3NnZ3P3fvvSsGKisvufiFnh6+8mVMhpoJZhwFJM7WhTm0SGoZrXepNqcR0MBJNwRqgXHKxjiZbTS0KSHnMMW0JM1GQ4gq4ljCn2pvaiktH6hubi2xd9c0cK8NDinJEhxb1qa7sG/FCKTi3uKmgywGSRixmbWfDQaOaAKxLvYrU9T6e24uEmA2mnaRVZcj5OLkNiWEII6NJE0ZsQUD8y2xYhYkHJpwKJIDsJblyLVreYwqkohgOUDYikkzuqsFEfYxLEfawaPCAxf+IIfe+sW6dhmWnbrGcXtlhRHtREyvFKktj9gE2aE2zUBd0U7sEpZNcWJv1qwlESQqtiOzjZii0ZuoR0mWQ0q9xeQ6oo875Ci5X8KO1OPlxrsKW47hGXPIiIpaQKYcp3TdtCNJ6I2EiGXfNJo2YiCObUaOhFmmVQRyCQojsRYVHSfqFAbOTZEJcYsJE4L3l2DrqktN+4JgOxGboN7YZYN0RWw7zYBoUHaiRKE2Y6d5zrBsEoxnsl1gIaZacpjOtpkTAJR51TpxwGpmvRMkRdMGkCWMuaS9qzJn+ZHKQPnAps7+I7UtpUpPqdp99G2Z2glWj6q/v4+PoSu9PS3t1dpOfXHP7KX9HbNr5/eRPa29u7sOdM+de/aecktp9sLWjlmDW9YD3bVvgknKce+4609Fxehsb79+4aK1B/a/fPAgCWBt4pEDr8R84QtfeP7pp3949902VPCqjDmjBzpUPmC1Q3d0wy2AC4Ncdz6QArVchpKrcgKFnDlM07hsncBg5Jr0krAxCaMZU8PlJHyahKqAk67sEHgRbVX9nZljV4ksqT7BmslA4i42HZWEbuItYjkXJxtIZEacGHFXAtR0DXDJ+mK89FJT3KudhIRRLgJ8gOTCkiNRk1oP0pjeMmXLwwWYklkbt18BDIpTYFlRJQQblNxmadpyokIzmrKFqCt10wpMybiYMhXtRCymOLIvier6k57B32f1tpIuWzYWP2sHptyJUFyLHI1n+epFXmw20iJG0c1ovMBa5ItmSdJUVOpNDCJEQq0S8VFZbcdDYYujnbr3OjJFTYlznmYcgjDWskKYUZ5tZlUSCeq57qBFyzRFI4gkuJZyaUZrqaOt2tFandFWHqhUe8IDXLYty7qqS7qqszt5sFWusKLua31ld/lwf85wFFvzIXksHgK6yrbjV7Zjeq0ljnWjXFgZgFAQkrUKOLJjJqDAaaQl2PEk8pE1FXBB2EOaZT6rxMAYWrwWDGkkErAjUwKqIUhoprJH7YJH955IUC6dfsUevv6lZ08bf4Q6Y0H/3g1dO/hD0uScCSF2Ln3LqjVP9c1/rVRuObK79dCOzsO7Onv2tlWPDqhjzsB5H9jUPuvYO6V71s3rfuBM2VAGNPyGG/fbb7vt2ccff+jor4TytYx33XUX+pwq71+58sGNG3cc/eiwyY17PEiKI3uokmMWhnxs9it6ugCqDSJ/qmMeTGoZmlQD4QAp2+MXVXTh+TBKp9Fmo8hre/dKaUPh98woHoIxkLWkibAZj1FLGEWN1E1L5Mg1BHEEqFFkYYUgeQRgmwWoSJhg6SKkN5Yot33TYq+1EJpgYQQQmOTEPOT2Pdd+NAUWR0L5TbSyhMRCbEKOwdtUNBL5kRzliZ2kKzaFcyOnywFkVSxJYsOUtQSovdFhbWETnxt2tBONxDASjmPIBZCjI+lKEu1YN5ItNDO3VzRzaEJzM1GRXEKwmAAKigKqc81KyOUTDrpJUa9q5jYJp5Zlck6RogjoAtQbFSWPEtGQy2nS5WbsndlWmtNZ7Wyt1L7aghLO6faW0hlzSktmVdrbgnSQV+KcXNTV1jWzpaOtzNc9UzDBF5wBaiNpmVVt76y0olnrbeWbM+pp4x+i2z/QtW9gRk+19kl+e0tlWcvr8/o2Pfna/ke6W9fvZYk9thBF7HEpFbnDhx9LHKnlpFqFXhHItkquTXGsHoG6qC3MteDeMQEF8QzX/piYGtLI0XwPznCCJEsuyh61SjIEaAlfiglt0jaXXLK/c27P4Z3+2dLSnOWH96ybs2dd7evLc8vi1raPldvurPRua3zv0TGnf9lle7uWHar0t77+yuydL8yp1t+Nx6DSSM4B+a/K8DbqOatX/+3Xv273XGKFWXoe2LDhjXPm3rt7lyRY0WeFJucCHyd6fWg5qUSeWscsd4BjK+TwTKrH7T5MzQyTKaHD3Qw54UTFiBPasJq2Q2BSlCQ2eXK0vqV8RqW6tfHe3XYwYiwjWYNwYlc2YKtAU5EK2LoR2IIUqQ04j9Di3JRQcvMNZJmmCGoa0zQ2R7rRoDlWj732ZaHNuss2G/31qi3DFI4AbOMy1YwEFZu1TSu6SwZFtpcsEM12AOIkdqDFLtuxX9kRLZIjjl7Mj6YkjBJUpCUhNU1qlhQKmBdptGgzc7SDt7qBzcoUTXcJSO5ex2bFRJKV2yBdtmOAMDqK2F2yqdrWsEDRFY4auSSiga1lFUBugSk+vQZWjxKwmu7VllFNuojEf+GKNc7WMlWlNLe90tVRndlebWthZ1zqai/t6yXCwS316V2lpbMHZnXQrIdd+3cw/rYyO+zO9k7+sKEmqUv1YX0NVsozK20z+1pqW+t6DLUttpaWgWrrvv6uvZXO/ir8eq8oNV65p9SyrtLeU21la15XPL4igMLv0D2mM1DaXFrYVl61bNWe21e/unHL9gc3lp7aVttyMHwbFY61uhQrcoFcLRsRiAlHgiI5V9rBciEOdWLQBJnKxiO5zCa67hoZsGs5dXNk1qQlU6OxgG5ihMCSAgdJ9EKThKNoJqmm6bQnNqPu1MJzzz6455WuGPP+TTPnrzlQsHHfOdB/Z7Xy8YEZd7Qd2cHtcP1vTElf/Jy/d3/bxgcWlUr8nxZSRyGT5Lb2QwlKPSITF5ZKP/7br4uBkK5HH30UABPMl6K/47/8xs/+8i/1dhQSP7yJRmztFBhBBnRQRqA4jVU0/TTAOGmdK02/2JVkI5mfsYkWe/dufpup/tyd3U2iS9N8AHw1LUSSLWIiF0hs2gJAhZNOHJrRIxbcFM5tqkvuZCfWskmvCG4mpiRHCM1d0Y6FdUuDeUgINK0uIEJ0KjtsJVlAeDbJby+wm2E37wBEkKJqmbJlhInxpBl1bTbGAN9ym7URA+yYZpuWiEZtiTkAGwFQIsfYwIqWGNgUEuwkTElENsHCaARMzqXOVdaXAI4C+Ucusl3YWuIx2nQXQIpWtyRyjKORiEXAiIWESlO1bUYv0SZaKgwqGpHQTIOsHZj2IhoSaBR1ITSIeE5H5f3n9S/t4hWRml5LmR15+7yujo4ZPH7nqNUOXKXUxu65d6BtRltvqdzRW555hIdovNxas1musSg1R7Vn2HsGZuyt1h5XD5baTrzGYGO9tdrWV2WfJIWjBP1L+IU77OPZ49Xi3mBz/8Lu0oI5S4584MzXfv3Ixntf6blnXVsfP0cTCllVyynNAnOCXg2amSUwnzWloamX+cNS4weR6IpjIzYuPnIDdWWZUSW3NxuYzIqcqCRNG0+AowI0qZJYUFN2qHVmASiR6SbXJhypCEOjGcm5uG6yZt9LjRWxQ/6bMZJr+UQJWzpKldrddVpaOzh7S371Je0ulbaVK99t7fmN/pn3tPR+sH/G+ZXafeyrLf3fbD/8UvnYGzJZxURS+1UjL+Lu42+adm/bxh/OX3XVVczy5557jt8i5adP+Q0jfo3o6aefnrl0KXJ0peLdhi2cDIDpGCdc0hxNBsbQFGGMrbVmxhXTkstXSNTqNV8SNY1FhmlaFpsjLQiSqBkNJro0VcRhQq+v7d3LG1vL/S3H1jI4tgyGTJOShESTwumgMwIsDmDQTYN/oMms+tWMXFnItRaNyyOK0aBxNGthop7rFKFpVjQzmjUtqkiY0GgiVw3Q5ZPFne072dOXeERFYdVybV1HYhAVHYkCiF3RWi62EF1iczOxKb/0yoV7I98cgGkSJs2s0HayQLrU7rJ9SeiiyKZrkwH+e1aWdH1ZivJvDlo2JQukwkLbzAXRSEIgKuyoQKuHmcYZVeAQGHW80kf7HqasSVcEdyGM2OoG0jInkdOkiyI5IEYIntlW/a0rqjsXXPtK/+z67rp+XtfeKmkZYKs6eFcelIZxsT6qNcRacpQ2mf7lHmNfZebDRy5oK51//kXdN128/rFXX7/vtdatB1o0Guc5m1KNAwLFmUcopoQ06/1parLWmELMH6sDmIHxlQHsQGBvA6CXYqAmtc2qy5HQdJfJCCluGkhoXcsFsnYkt6ksSCzYjk4xDAKsRW90jdyZEScycy3bQqNeyzFFwb4l1tVNVBwsTF0LEjJySSAbR85E4kpfqbUz55giPxpmw3D4o9Wft/T+cc9cv+5yxUAH3/X+f3Tse6Gl2eWgjRRks3Cwvf0Nl1321L33PvDAA/J/2WWXPfnkk88//zxN1uzuxx7zrr1hgNO9I8lb0hzN6JmaySwfjbWJ0U1Op9gEE0PMj3CUKEhLpIJQILEQm2C0rCgQtcCsDtQqtimPufXmavWM/srWcqn36Md8uTSZsmtx8BIljicKs9boVXiiWQthQpZEfLrMFLAkauV2yU5iLWrZlNXVG5sR2yBC4ci3xEC6tiDAks1PGnOB0Q6S7btPBCkmxhNrjjlxbV3JRUNIcQBgawm7S3w1TTNZIKl9jUy0aEYLWUeJHZpwRMt2SaJec3BBMTliWVONHBX1Slf7FTDnC7dP0OjlsY4OAZgiswY2KDm6DFy1u2iKb2AjWKYLF9EgvUnTxnNtqjfWOIpNcCODkQknNmUhK4mmIobPyHnWfmjhFd19C2rqxw6CjJ2q+cWMlhd6l79QWr5s5d7bz9lY3b/x0U0D96xr768MHqMkpU6ZDkTsFbY8OVL0Ion8RqaSbSJa+kYm85mlcDRXJYxms36taKBIzHTkAtRJF4rRhe0AzARQdLoJW4WmVJCoEHyMPxocEmMNI6JFjMTyIY2YIAuOMLuNpIv8U6JHgucQDA7maDC22QxIIm9GpYhTLR1+vbNt5kD/4WMfhXXO6zu4fUYzZ/2t/TMY3pa+Iy8d3r+orYPdyYUz5/52/6z/tWPfcTc3jSPwpv84yo5Dh956yy0//MUv9A1u9PFV7mzcRTpzxsxH7rmHPB6nMyGNMc7+hMQ8AidcOJmpI1DMVRnB2ZVrR8JGhyDxEpsRR8uYopnbK6EI1BQkcZGSMFoztkEBaoqWLWEzC8DGUu17ZtbXvyPSNIXhJgCDiRBJJAhHGnwpqstNcRijD32uqWjcZsWUKRunSaHLvQKyEDE0a6lXkixGK5IjtpfElIwkvShK12Q1RSMDegysJ2Fs7OJqI47MUkvR1rLu5AKCOFY3EEGKpqlJbS0BydEVU0ZsKgusboPSpWkQLZtmYJtIstZME1BUidCOJFdTpqhpUqwCJtsUmeIOijmJUGeQaRFYHUCBibpwpIHlTq5pwjGOTHmMEpFdqyvqYirhq9ccgEKypJG1aCpi+OhmYxOHrrec2X/+mtW/OLIsieRUM5uBbf3ztpXntc246OKLN7xjzdoXug/+dF1b94Fy8mK9c4sFcDx2kvgAGURfkR/VRY691qKLBYdiCUdcD4ZlgZrprdMBTJGdXGsyAgeg2mal6C6p4wsCGEDJtWkVYlBJLNtFBDYlMKQKNDuynSG1zATYI1p2mrUZVcAQYvLVS/4pJERNxcbYORBIAJIX1I0i9zALdHO7tjw6f+VNO3r3tPbs5e8/qzMX9ZbbWrbePzeXHIVdpfK59TdkTmuf8aPXtzxd6f/tZashnDfQPqNUPtTMxr/RDzDxoOsnP/7xTZde+sOHH9ZyuH37dvmeN2/ezW944x0PPRhDmTDcKPvjF8CIj2vzIXlOS4UmEzQ7d5s3mDDHfAhZg1kJMUQhmCKhajXjqZiE7WMt4JqzFKySqMgyXZbLCxIBE9yMZGsB+MhqbWtp9UBpXdi7R7ItyKZ07Sgy3SVm0mUVesFJM3qJdoTphS8sdePYNM3W7MUAvmlRN8GyIC1qAZuFTEmaSKDVe1KQMEWLQr0ww/adwrMZnRQiZG3adW6Xw0A9WoCspiN0M9KsLpqa1rWKXMemOJZYUXbEt81IE5M6EWYl0UjEVgQIu64LjlUKUrq2IEDOAVwgeQbGtRPvnIASgpNi+8iFE0IiF8ceTY4S2wEgd9NkgSiXejQCR80sjS4LZd9Nu7ApgLH9wkd4/sL+Wy9deH/PedY6BYbMAA/gn+lZWSqtWH727i+ds2Hvri3fe766fk9LT/gmDRlR2pPkqyt7vOw3y0cCnwKgmJkACJboiqNnw6jQVd9GHvu+IITayufuIOF7Cy6zakYXMRIwBVOy6TCyAAsws3IkNm5CFuQqWmi+JFmDZjYC0ULE5hfED0cepUgqKFYU0FGAJiZCyDpYAgk/t5kbWC4zEVb6yuvuXsZvnfKNkHTt3Thn72tNPW5nDT36NypV7sw4fjqETLjae2PH5l3i8Ljm4NdBMpNYl7u7u50d1K+cN39mW9vTfb2v79uHEpILzzvvv95++9///d/z1rvNNPl1kOaPK+AQNnMkmqQVhxqNxKlToBVVCmj8coo/6xCtScVcmwW6SVfSbGQNmgoEgGoBS6KujojrRiCqCNumu6QrubFAVIkS60ZQbBmmLPCBFN8RyVvvxx6/RCsBO6TEsoMM3GN7iII4seNe42g8sRybVoxObcTCaE1CcVB3l81Gm+qNEts0EEcGLRwSFNjkD5tYqVmguIJCc4SymZVYLpCNRBYSj4lZdLOKUsmqZyVy7drGE5sOPlq2VhKh5FigeAfA5Uq7aoRWBKNLEUAO1t6iLh6szAdYnb7YjBzvWuBwLHzJEEeK5icGs3JJREt0C2JImLlmbROyw4CZNCWxhQRExaQrNud1Vn/nbR3PdlxzoHLs6+Ei4eTBx+Zf/phDf4Dizm85dG77plmHNjy+8dCDG9u3HTr2pxS5B6LRNMAa/Nxe2cnOAQWQq6IuBxA5CCk6I6g5HyHrFEMOoIgPUJfkCCWX8dwapuWQaSYqUSJyQrC6gPkGCcE0QLEpM4tpikpkmM0EKXJBXWCELi2JXhgVXv04HDsWWePRJrh4UFn14Ur+r955Fwy0vXrkwLqeg/PbOpgiV81euKml/wude9O7kzzTRFjbuNPFOPlsOv4AE0JmzVnz53/ytttmzZ697t77tj77bGXJkid37qBEa964e/ATMPIYgPHI/DrsrJ2RGbSdBDRpDRofTycb98RUtllgvFEX8liwqXUnGofgpmczQIWuCNQ0PwtkDRV1uWkv7srqDinJWkNFBukaseX2UvmsSmljS1V7d3vBeMTZZkHAiieqC8eAZVDLfYEp0RLFRnz5VS8Jic2okpU7vIRGs1FinXm7i7oJjvYVWEKgKYM8X2AHT1p4GN/ItXSZzCLYoJr2pabmPFjNrF9LpJjQojVh6siJ2KYAVgSLQyQI2ho2pQAAIABJREFUYxENCYRYi0/tol43o6MEw7QEvptguXOvgbrcNEDXj+F1Q+WuxLK8UMtUI4NST0JCKEXL7cVdMmj7JkSVXKeRIJxLw6BdCEdffAnMH7xtYMu8q7v766+22/1JCY5Nr/zhh/4AI7ejPHB6665V1Vdf2bzzF5tant9Z+5wnt8TDJwKHKQpjU1jHMdeahVhICl1IXJuJNQrLkQByAc5lFws5R8S0uoAsRyFGYtM0y1ExzmUmQshZFUnsvcCgAxBIrNmyDQ5pKgkv21RUshNxljmkBHUKh4M7KwB8maVudETgwGxyFM0zHerllfb/s2fu4Des16Xs1/+0Y/8DrU19DxQey7fccgt7RH0MlGzc7YZ9JIPo5a+U8uaTN+7mNwNwDa3J1DRpMLHWKKGN5ImXJmloNc+MLtByM0auGTasV2UwpYIuQCHJuJp2ZCCP1LHQS1McA6tgSkLbTJpSV29W3XayIKpEDDM2I84aKZZIF042Zis2irn23D3zvru1bBkJWEYSL27ahZm2k4BoNulq1LTxRgTkTZoVTQapC6KNTNuXisOgycykCaA4BrA5BonBRK4m1nh5BnVt3xOb0UKxr2gcpuJJrMGJRiK2ei7QyQifouEDIpOmPEqui3qyCYAfOW7KjhQTAl1R7uGIZsVcuXoTjq1ZBULENLkosomHSfysXdTRVMRwKLKgkNSUxJgmWAR1UeeWqJJEFfkJjS4bjzRhkyPH8ajXnFvP7Vtx3hte6D0ra+cklBw3xXPGH/oDzBL5Vr15LQfPb1vfeXjzD57vf3gLX6SRsuLRUZ8OSlZuTQicjCoSQqZpgoXIk5OxwCxaWddIpAKg6JE8QC4wzsaRkrh2U8zEqYW2Y75Awk96i5u5NjFop4l6I3mkZW06QrpycVQfEkcjWXKjCJFTOO7azTsMAJvh7PIFGeOm0TTOOs2XlFuqMxaXeMOm53UIV1Xab+/rWl6p3ZRuKw/8Xfuh+1t7MxO8gSXc33zzzXqRFMqePXv0N2H59AZS3nrn1Q46k7E1oA9PPOY2E4NJc3jB1dm2ICALOqjuat4sKvqKWSyAKeiqzhoRR76ovcpIAt8gq5uYhSkvUrHHRhYgNOqSL1tQGMMynhttYjY3Tnux9+IgE0eN1JHX9u79vO9ee+5uWqLuCBs5dVRmAhqR3VXsDpoJuvxkDSJJXNs4oKBgMGtNfMmjWXlpxJcWfBOibjaGyHSvdW2t9kyhXGbVyi61cKILG1ScBTZFSHzZIzlxsRGA+PYiCUIVnZumRUXhGKokKAIsd1MSNVVnrWUVoyThRxcYjPYTpo008osuhRWMFEHmEkhJjFgXprsstCQCMamLaaiYU8yMrtES2UKaxgIi5Nq8aHH/p65dfv/hS5u86MZxTUt87KDmDy/0B5jPrUt5AH9h+4bl5fUPvHTgkc2tG/cztY7T9OHjYOncBFDQpk6OGk0K56NPSbtGLi2AhRGoN0oaMcUptkao7BopIhMPgPPFNgWG6xQjjfxG+QjMKs7cOrFG5EhcJypJGB5vQlMzsSxhVqXAXa7ZXCFGKBwRLV/mcGi4v9IBsnBYYGDemoE5q8r9B2rvsbd3te/4ZflI7YOkhdwzlkq7ypV0lSy0XouSV2X8cJfIhvuGBiPUtRNHWMsmNBtAk7SsYpRgRE15VNPeR+YiGokYR7EZsbqiXwcJLVvcC4iRSy4JjxI5ChwLmipRK2LsQ0ACoI64oClatFOMs8axINdJV65lMXNduEt2sjFLEr0gsVauTQtt0xIDxe8mIDfySACzdz+n/tydT7Mckr3IgpuyaVpiKjbhZL1HOyIj0cLhLnuM6uqNEvuyoiScuU2uRHZtU1kAh5LrN5eMEHIxH4PSFU32LZQFNTllWG1Zx7xNbGQZPsW9alKTDeMkYJPlUSemT0+0YpCJrpsyLgsWJsDDlDz6TZhq2nVuL0IIic0hj7htJoqJC9FihBGbzA6ex/A0uezpKmOaHf3/7L1psGXXWaZ5bw6SZc22rNlpzYONZdouecDyQHlgiKa6IIKho5oGoqGb6IAGIiAM9BBFdP+h+cEf/mHCgSFMQICBKLAb45LtojxJNsYIy0JDZio1WEpbsqyUlFLmHfo557n3vV+utfe+J1PGZbvuCsXSu771fu/7rbWHs+/Jc8+VnCEEcGiRMhKa8Z7WqFUplWvigmqpoeETv+CMtf/97Wd++NgbnlufLXOnsQMb18PoXpT5AkfpZeKyPY/fsPfgvYcOf/ZLp939xC6+QbJpHGLOcNvguVGP4LaEiMus51KmIjioJi2cZFUQcWlcMuEzNXtDfvzvE4WpIPxEKo6dXhk2wNwkVnKmSAHT6izBZtgoTw9RGySMacofm1Wq15zmDxaQIK8OPs27cHtOMw/NtsprZ162es6Vu4/cH8GV82887Ut/t7RyNJEJoJ2EFHDCg/tE8rZTVX1bciWQyNDFRyT1bbsp5CYrsokAIi6zGY7pm9j0DG2RAqRVKTF900IeA+j7AXeAHBQA08MxtcRJV8eIaolEPHxAZmswuAoGD+pEKrPTyrGooOaCaYioA5ZZh8GCKjWII9LMRsf47vX1y1eXHuRvM233nU2NIDo10gyZSp0BTSUMK81hzzHSMCut6ltSjVSms4lMk6UhNWEdKUHVN7EhZKjmWJ3QlOLxncdEnt25nxKhccNtvCLCLDiNuzCYPqZjidpBFoSPINh4gn1EWh9PSggBvWbIAZChTTPh0AatiddcaTJrPHYBlUkwwxAEvOz5BM/QA2S8F68KzSxTRuSEWWlg4zUYJqY1bg3pJ2TDqWDPrqVfuWXprhe+6cja7Dvvd5o7sHE7Ht2OMl/gKL2b4BdYr9778BnPHLzz4Wf+4p/3PrvSqtTDTXZOiU6pDYydGzmjApIZL3MzDAEwKBvm4KzpcLhq8p4CQe5s9ceSJncRTUSkkVv5FYdjGfaNV52KYBIl9/Gata1sTx6MpPI6O1Yt5LGpmi6WPJhCkONigywH5Ryd6nL8kjfNn9q3/hlnffcL1ve8cM9jn+9Nozbom8K+Pg/uejROzXCwxIlgk84QstsRHDCtI41dlsbQSO0bBY3SA3rcpGSIrGQiWjhFsA4TTKKg/0qZhjA4rKYQpoeDCgaTWIFTLoo4Q9eSZU4LypfTy07kjulHpM+1PB3BtEERg3W24l42Ed5Yu3ptmW+K5N+2ECHLqQhmyBStfxaMVIDMwTrDEcAEyJzmVyYpDhu1XrMSev1GM2TikKvFGNOUqjzGJJ5GFpjrl16FAIZRA9CY4gmeVzg/P0PElL6HSasE0weZGkmoKQ0ZQSNyHA7Kzrzn7oNqCcKJRYKJACoheJDZF0Zkghk1gcyKaxmpBJrMZtYhBzHPIrwHz2EiLj92Sey9JpR7MjqNZjNs1OqwMms8tfHU/j/etLLn8psPHL8wwR0w2/NtdqHMF7hNUjfNJ+Cv2vvoi5677/Ennvj/7l5+4MjuvAHP8crJkONYg53YRiDkEDz0iUckBIG0JphhiklEcAqyXD5561cRXl+4iLyOxvSb+ES1lDQxi06tuQ7B2ZwoNOSmjDoMUx2nDEa28g3GqE4tglE2N76LSG1LhsABsqUMlJ+5+Lt3f/WLjcXqOVftffhjpBC3T8q2AP4J/8DHJzSefvrpxmBahdPonHPOoYfWJDZDCPiplqmJisNJCmQ2hZ6mmiDKGSalAgRrY8qhnPSIEM+wBxBoiffkGqmYlGZIJFJOscDKYbYOY9qDhuYw4vIzZLZiyxhUYKqPk2u6fYqpphVDi0gPkt6UMfMo+1xpYDbKZ2I5g7IEaRDom/QMq0Wz+XBMrAo8r9+3a/2q2efdZ98R2SiHn6p6zVhXQKIujWDlWA80mfTNbD/UXc0xZeOqVTymj6aVNHZVv+okjiC59DRyexBBU+jT8k6G1sRDbgCy/NIOdyT+5Yosng5znvRZRkjJVECVTdC116kGR6rZpSiEn4j7wNAIQzj2koMDZPZSicOkzRS7jSKexDlrw26QLJMNTNYEjSlp2aXenYifmUGZJ3h/OaoeptQGqOkptRLETIUZ66RnKpFkCexDy0ozG+twmLpl3/GLX3bdZ57deWrPPn1DwdrS8r3HL17adfEFLzny45ccWn/ywb9/6NjH7t975LmNa8eDVQ8ZOIfSc8a+1i0/swBmTZRWBZMYfiIhR4GIOFMVjMnCqSVxpdCqFCc89zoupYhD4DmeflCz5vaEeFlb0zMrIXGHsWYYDKch16koCBodgpKjEJDERGCCI268pg9OVd9o9kDxMXJcYjH/GeqE3zaeTa0cpeeVjx9q59/gvr626wXLq09x4DyaWUstgBTXlf6E2foZ97FvlakJPT7zzDPPOuusPj4RoaamQSaSvs91bfRpcMBN3ycaQVxyQ+jjfaRJybAywcZ1cQiWk6Ecg9GpwDcLmx+gky6o/ODB1WV2DEwIjqXURS1iGgsTe1lFpNEzNNIza0Q+zKoPIcNKBvfMhuDQAurUoCA/7+5bXTo0f9+9ksVN/b1mTan64ia9kitekMadfYzZFFbdx1IsIDUDaFgImAU0HIcI9o0pgzWRiCn2EUywiTR8aIlQGBcU/LHfXoUcNQDNegR1NpoJymQ40eaSG6/9DS0WxMH2VbM61lzJRiqnxp0lQgunghpvyKHFFHIwwDOKYPMMURPBTVadrWpgnzx8GePW5y8q1Aorhq8yghOaoTVezVBlehpTak7ISrvyvLWfvuWCjx17DY+PjeDOcLsdKfMFPs99O335+L49X9m3evddDz3xwftOe/Tp2ZsLHCwOpX30m4PrAc0soCFkqjLFDdOhdpU8oclUz0enT28iY5rQuDxpXE1WjhrXKdeURib2aiELxvokWqRLbsjh1PggE4JkZyuuueJBWaaS24PM9mqJIJvEBE3MlPGU18RrVphqOlw78/LVs1+2+6lDm8zl1fNvOO3RT+xZf44jpSxTpND6R//NrK3/k3LCR2XGHtz5wsfv//7v/9M//dOt1IKYPe+886wgRQRQSrBJRACzGuet/5mDcJEfhcguyByV6CZSqsqDw+dpmrIVj5rD+gH3THVlzgJNuhyD4KZ+4mNqY3EFB3tdJjRrASerz4nEKTG2kL6eRfT7UqPfCBqvmhVXMvdF/q6qn5mpcXF0BPTq0BupKdUiiZXQ4ChEthLGgl5oldljctl/GiDNmi2SoFmpGVlbIhAqZqgUtDhGx4hDs3AnWMmNWhUhsTLrFJhZcul9fPehMGrOJsU4QSOhhVCBNHoazJAdhpkhgGBlhtMAly+TrCRC67GR5uf8RtDhTOjEvWKoZi2+z81sQDiUarCfimmAdjAlMxRISJAHDr9Wi0Xx3nzlxLfRrArgGIUGQCfxCc2kW16yIkXkxWes/+Ib996+55Zn1k4zvtPXHdi4fmroBFzmCzyBcqoDPj/z4t1PXr/7wPJTD73/C+ufe3TrAwUe/Qg350CdrbihNcPKRLmZjRdAZuU35AwrR4VMVcGKx1JiCuAteXRyb/HpcEw5gg3BuMFwaiUNv06Jq0I/S+QUZJMy7R5afOtC+txtS7VaEqtydAgGw1w997rVc65YPvbk0vLu9T1n7nnsH3YdPZxKBKRwgGxVkxc4fu6il8AdEjB7cM87HIcPH+5fA+D88i//Mt8a+Wu/9muf/exnGzOGvON+/vnno0uhtp4zGEE5cRIZ0icyDRYhy2lckE1EO4eLW1tYFbf4FFxlCTq7oP7gB9xTcNTiFTCtb7UhB9S1x8WC5Shbp5ILGJMNJ4kTzMZughnZgOg3ItYmzSVwPQjso9ADNRtajKJJxPfd79+1tPn3ube/dzeyqjXiDGkwK9lIqs0UcYIZhmCcIYC10/seTOJm1aGRXMgAWgTHQKMgLcEURsRgNDM1qOzxqhzTIQMSn4nOdyCyDJ0NJ/o8vrMJ3O7yaY1MCZTSouoEOxVlAbO0FNBo1qFMIxFpNOE4lR2ozKoWrGylRaRyIKSAVFuzQhZQAABCw0EkTHEiPbnPldwzowmAw5HieIEp1Sf4KgXBoT3DJlLVKoamPj25DqNch2JzIcjZvbz0c69bfexFtzy6ek6V3cHZga2TI6ETQJkv8ATK8x6csXz8FacdvGD1/o/effTzj+w5NPsGya3WHFknPL5bpBNRUgZpzoZTBQmaUhPD76dOtJ2NIhtQpRqvJr1nKsjFRauCPho26YPDQU2ZCDIbWYKD5EqoFoPkKAsW4VTNiidyK20QZ2mIpH4F61DaoMIS3+N++vx73I89wcZUThS85/vBpxoUO8v9kDb7A0y+jPHI/sgjj/QP7rz/cdNNN1199dW33377gQMHqp+Yd9z5mDu4LqPiwaleZ5FIFlO3rybqWyOLY8Rr2RUjUofgkLfVD7MWT9bgkAf3o0dnn4iK47b6SlnSImQ5Y0tItY2U8dTczE4Ms2+CCWamPEEzDKhrjCyzCyojyxkPP6toBBN3sdU3uAE8u/u+u39XtYrLrFLqD1bbB1NtdCKODg2CwF4FeyIZAmyuHZEAlcd69JkiN2oym2ENQh5TS9xqmwPBLHE5ijjUK7IBUQuQ3xMiGwtesbgzUsDgG7qNAuk0gk288VVcJhgyWBBmALPsrYJjsiHP7IekCPa5yhLvpyIoUJO+PxAQIh6agpENGJQ12HNUawqAFmZ84SQIzjcO83jBK1SdUq3RjI5MffusGu9nB5X/22uPX3ndTXceu7zO7uC6A9s9jZf5AqvC1wvzBvzlex67ann//oe+/OH9yw89uev4yBsRngn4Dp4GmQ1IheEHZKoh94SGOc1POjRwyIlHLSCcRCqoIjDnT/Ibn66BxlXmhTaobw3QYiGtj1dH8KBaOKpty4EQ3+QKBnOrbCpsEh32shGsIoO500HT6W157U6WRt6N6QVxD00wE1nkM+68of7rv/7rv/Irv9LkOxz7y6mITxiTyyycaEo2MpYY8hiopuAM4xWX52Ohe6QGhwmmhrGaE+dw8mOSfwOLrMQHQSM7vZyGrOC2Fo3vtEVDjkV2abCGmhV9C8uwcipWMOQGyIx7EreVlRk1huIoGInOxvvuu5fq76RktmaJUWNWTfoKHNonMVIC+jQub7D10IuTWIEuRoIrn2AdyiTCOelNpKoNYstuRCIbYC6ygNQ/KJggZGRVRidxQZQBNIIhSzCxyeKFijd0meJyawiKhD9X3ZBVPFMBVcGlEanBMAFV36U5Cz/1G2FYRRzWSJUNhkZjWJlGDNbZGXXuArnyo1ZBszQS62zwXHJjqpEdtAi/P9OqBZifuGgY+WBR+SpX/Zpb46kzwALoKw3cRL7r8pU3v/Lyz6zelMQd0O/A8DmxxSvzBW7N/wugF+8+ct2eh0575sFPHjz60fv3Hl1ZXpt8hyFnTs6HgL66kJlqaHXKxBoJOaCKy3QKPMiBLy2yY/yaXpWrY8XcHrnYaZBpXGh5jq9SNaXibTlowh+kOVXVKh5MkUBiZiuu6eBe36xtUyLeCMYdBZv7Vr3Mpaf5qpd7FykwiQ/KRhyCzI1I/cup9TPuvPX7lre85cMf/jAHjKP4kpe8hPfjybn44osvvPDCO+64I055cK8VVDxR0CJTjVQznFCAmSLHaKoxG3LdoJoeQiWPySYe/UQmAE8S+Reraj2YknoWsQi5kdrWpeEzjF3dKOJ1OCg7VkMsoqzLoEjIDViQXC0ahd60KXjCwvfdD/D97uVNcAS5gJVNDxB73aI52KTJpIeTSII1IsZONYdN/QYj5VZk2KtlrwC0htkMk24NuSsl3gNliQ9KNY6SYYYcMKY8527sG+kNTX2q5aID80U03OgaTmPR11D5jYX7QJCtcKpR63NrwXW2YqRQJiK5Ue6HRGROuKsPk6ZyIhlmSpBhZAPMTQ+T5mw4REIAOGyCIfeAFDaB9zjcZO6ZtCoojiAgmKkINrgqyIcZ8kVnrv1vbznn42uvP76+9cZkTdnB7sAJh3ZgU8p8gQPEr3do79Lqtac9fMHxQ/c/+sSH9+869ORu7g45vgG9bT15mA2zieecCVAq/Ea5Se9pDcGzPSLy6Ru7JquXjQKgIWfKrCgDeAi0lwOBGyY3N1qyelAV+tkUH00AKdM1y6kp4vSaZhig3bR4Ck5WAFM0joI9cYA9mk1zWwiGFiyIbAPUbIIMk7Vh6jvuDJjIg/vZZ5/9G7/xGzysf/CDH+SF7fd///eZPffcc3/u537u7rvv/smf/Mnf+73f+/M//3O18uAeM9UyFKQgszK0JoeZEjQKDAeVKy2yKFRsrswx8aozgcdka0p1J76gI/8i7NvtpOhiYhyrBUFn9Zq2CDkKjWZPqGUPzio1NtXox3cCZLGuqBYwkcVUc0ebJlPY9L2mpnO3ohivVVdEL4AWAIZ38fG1h+d/m4mU2phlWGXNbYJVDZxZMK2vGQLxXpxqe7Lu8ismEiPj6Zu4zCYYcgUwFzwiqlkV2GGV6rHKMM2qhOgYZEibq25t/qAFNO5yHGse35tHQKZ6i5nupvKgYILQkp5gIgGh9ftGlrN9uvGxYx1xALIZRjCRHqAMzdbP1kiULSMLgVNxM2yUGVZNyaY3TGlVmQJ4D97rFHL9+FOVDSaX5jDBxp0hsjQfVhievnv9p1+9dOjs1z6x+sKevBOpO7B1xtfoFi7zBW7N/wuj5aX1i/d87erd9z/3xEN/fdfaXY/t5k842cbOh5wwlsYQEHLAYOGSVeiZzlbZiEjuUybIya0gBRBUzWIqp7rUkuTU2WR5gdgr6B3ACzAicUxiwKBsZlWAE5ApQIIxAkCmOSuoKcHJTSSAqawITMuUgDXS5j4bXUNYcNgrm1iVufkYhOxyzAIDZv/sSHNCTP/UU0+9//3v/+Ef/uEPfehDahl897vffemll37hC1/49Kc/XVOUVrdXU7bye06dFUfNdPqTcpGc3N5RQePVq3FnOC1VK+/tqnKwmiFnyElTg4M4QYDWyka8EghWjlNG7EOuQ8rg7MwUgNkx/dDilYiJdTjIqQRT9LKMMesqTrV9zY2sQ5X92gqwjSmAOwBIIhGDs4t180MpRuzDFNy/vnTVc0v37Zn9bSZb1ARxYRZMzTOD8tQSHABTPLhAptChbfht/s/nmORuhjf+r6C96ayu4TRDaHpZczPbDGEiaFVgWkNohhAso4kzVARQRQjW3chUgDoOVQY7dCqyWjDFIztBHt/54Zlnd4YyqxS4ilADKTPdkQXGBRCcrSbSJDL0XX+U9ZLQ0FIYcURSRuKDQBH54JQxSDYYcjhEghuQlxmVsaA1nAytmWEtI+IBELI6giFHGcCOuWmQqYEjCCDOEQyNSBo6aKYNcgjSsENZ0x955epXzrn5iZWdp/Zs5LcqWF9a/tLKefx31pk3vPP1D/7AUwfve/SZvz2w97GjW+e2ZyDngIvMiecwcYeQaTUoJpg9SmTO3SAbTGJAsgC9iLMhS1CWKeMGxbVPbiWYG8EAyc7SE7ePyPzK2/gDatKY4gLk4oop91JojaYK9GQFD4IQAiwDcixMrBaVUxNhMqQ8bhQAmrkCsmxe+OC4SEsPn9lZ/onHPQQBs02E4YQse8X7tr5zpD4PKnzyhYLVMXdDfPAdd+f4uphnnnkGNl/TzkP8e97zHuIcGBamurT+HXfj9lavZVbSDyutpgdDqHUn3rjEgjj8wWGvMy1evapgjfc4a+zterKRM844g99MBTcu25a3rUWvUC2a2WaYagddxsjJaoyID+rIr1Mm1kg0K4BG41L0/HZY1YjUIYK2ekMhUjUbjIIEpeyJNMpk7V1avmJ1/eD8bzMpsqBy71gjFpAbIlNVtuJkpchEGiCh9oM6ZjllbxkT5GrkQZHsKpxtynMqwUXEIdNgSgajbERMvA6phBuXZJgBtVox7+Byl+MW1zy+O6t4cIYRDIBTMUPI8ptHAdVqL9PdmybHQmWGiVTBihWHBgjZdGhGMiSSMqYr0UIyuJJ1lNCbEpGsdUqSn96STO85xLH2JdkneDRVzquVWcS99mMHjYYREYMxBbz9yuOvfuXLP/fcVTW4g8d2YOBppaVuUjb/385/A8f8Ausle7768l13P/jo4b++e/d9T/BPp629p0dODIEnjFRwZkM2kngrOh9XkUFmJVRM9rR+Qw5/rIzwASgrXsmJhOls4pUshum/hiWFK5Grj9aTByMT4rEQyMSIxn2AJm5kodEogDsAU+CG4JBcZwMGaQQlNLNjsuFLIDdM/vgpTQJBp+hpPGDzBG9QI4LD77g7zV9RFXAf5MEfNsnbvpiZkoICiFfcDJspZrGTA6BBsM9UFBLvI41sMyRRNQ6kWIXgZmh6IwJnrEV/jFDjnGopo7FohmYh3sRr2VUZPDiV9IBk9ZFM9WpUXvl9YZkFNORG1jrpvfA48SSk/kglkQjNS7E+i4cwCBQkMY6NUZNl2fCJ4+Wsw8o8trR+cHnp6pWlu0/8XVU4WULFKLBGelrVAdeImNXVMhq+w8YFcatFoU5JJmhjyCwtSxsUNwgHpmVHR0AcweRmCCBYxSuNKYf2rJEGmaGJ9pGtAKYukGlaAOQI7J2Cz9BIdBp9mNxDWSA/SMPMHa9miRNBQREiCUa/AmZh+huWc+7WdjU0Zn3ipGamJsgmQgAgHr7xFFaHYvpsiPuTuGoOnULHlkoYShAQrxE20Fn5yYpFBdZMOrTEVaOnQaAFSwtZcXpK9SbAW07q8JrnbhOvH6SJC8B0APpN2TdesPbOV11667NXVP4OntgBjt/GaTFB+qaZ4k9oPbTyooeWXn/2i4/+u7ccPPPZB/7j3c99/MG9Tx/feDbwHKPn3KBqAOeh5WdKUINZn2dsCAEhK8swsmCCMCXbEwww18SkN7PN0Gsnicw2hHr5R1N+Y90kRhNg2ckCcAerL8qUwcVY9X2Un5ezdeFHoTLVd4qaFJFJAAAgAElEQVSL1BU1jtbGWpSNTg9MjH6zqGaIVyJxNJJ4tYgswYZQh8Hcl3ivnSzeK3/Tm9704he/+Itf/OLnP//53Mp4dmff4Ks8A9t+qwyP7L/6q7+K1t/8zd/81V/9Va1P7DvuWU9DiBNxcUNg6AIqs+dMR+I+ZtGkuwXpm9mJYa8f6z7LKVfXzzYR3uTjONGM90aJZ7t65T7SZDWmYy4NrR9Wo7EdIE4j194rjV41g+AqJaancfkJ6PsC+giCiDc3oOQyG5wCEgnoZUMGSFMq9WfIrEGuMP6u6v08u3emg0ZmDU5hGiMrcQOzTN2dSh8pZiUkEk4FcBwqXslgZ2swufBzxiY4BrjbIhKdmA7yYyo/WWNk+DPpzVOlildMOkOaGxi+ss2QIAvkwzMAbqzZc8n2SFWQDeyloCUomBdyQtlVucGQq7i+6ADS15QQYlpnGwy54SdSmRoRsRI2RPFpC8lkQaNppL49hNgBwskx0sie2ZQEGWwvOVMNgMPpx0M8AOb8Rrv1L/gEq6yYqs7au/Zr3/2C/7z0xmPrG+9wNbI7w8Ed2LgkBudmwc35zf+PEr/hE3uX1166+/C+9QP//NBXP3lo+d6v7uJsq+dGU5HnXj1/QnZKfsVEwoccfqMsrQanmdUCZiyiUNMrWUKdTUpA5Yun+UmcFkeKq5KmJj2yXJtcoQaJ0CKiKT0NmqDxqsPkJkhKMCCEAGcrreKam/QJQviNfuLk0o4cOcKS+SVS/lbSJZdcwk8dLP9jH/vYe9/7Xm9rPLXzTJ8s1La/H/Hm06233vrjP/7jH/nIR5LZAO5x2Bu0xAwDmBU3hEhVJsHBpVYFE4nArLkVR7wHlRYv1UKuw+CaCLMZJleQ2RQZr4Ypoc4mtzJrsOJwqgLBXjZMwZhIH0eKpqY4vVLO1kSwDQKAs5CTEuzpaNZ0n1f90HBBymHFRJAlEvHQmKrYXCI1HWycPtisDMNPBIKc2vP5aP4q01UrS/fuXu//UTC5sQN4BSliAcHSMuSGpUJ1rFLi6uIeRqEnE3HWDWSYPczUYBY0bjHkml5Ne74c+GSBq0VPNkLlaMqs4j1GsJJJ1w4waISCIqHVGpxSxPdC/Cpxzl5P4EpWgZ6WUlUYtE4ufDF7opdD4rWA8KPm2eJQkUiFbAQdj37VD6e6aCoZnBaygDggywQrYjx2AnqaHAE0AOnRB/hGuAq60Neym6lwBMyiyR7Sg22xC5kLx2PHFE/w/jw2P56zf74PLYBj+bP/au0fd7362Or2r5LJ2gHf0jtwfH3X/pWLDyxd9JLLnvwf9t2/8tTD/2n/6ice2LOyvvWiwwnGGj3BXGyNGKf3PIQQ0O8MNFqNK9XLEuGKCFlayL0FkZCrflVOfFbE5nUaApHoh6mR5AQBPbPOhg+wMetyMlSZOJF8UJ4L0+d4H9OrZsWk1GGKCVA2vSCzsa4i4pSXqWQRqbipgVkiUa7MSJmSX+f9gR/4AZ7aWemnPvWpW2655c1vfvNtt9125513wuc2Rdx/OVT5hH+2iGID/u7v/o637vPJmWaWYb3xNSX264EPp8aT4lLVN1gj8Q2/MsGQaQkGJxFQZSE4rIIVhx+FOht9ghVDZkir5OCAaAo4j+s2KtJwMkQ8uALFq4W4KSYpxOv1w5DGLMEkSo4yII2CwRCMRHYCaKFLaA6VIsgwGMCw7kymYFYcNUFjoWxSMsvrPZdEchVsZDM0K8MxI+L8dHJgz9JVq8t83p2/q9qkmFiD1FDF61RqS81EwDlGITRAEXoBKXUbG3KGkGGyLfO8rRM7hAZIsxhxQ2iG1OApB5mpLCqg4RP3XwmreMU9n5SxlVYXRBhSDAoWE6k6VMpfPuEVxd9e5W4b8iBQ3KdJFYxIrvpEJFAMDcxsQ2gsPFvcRnMrARGGKAgy5UrVN9i41KFkFAjSANoBxCgAIu4QJvpaUOQ8ddZVmkzUxmYjFSZGijRSGQIwlZ+qCNIINo2gH3+Cyanlr3/50qiI/H97/crRF33nV46d3aTvDL/td4BfYD28eu7h1ZtOP/3Gm1/10NuuP/C5+5++/Uu7Hzoy+wHbU9dNmJ9iG+fY4MnGOdbw6+6RktOVuOQoA9SsfU1PrqApoA4h2Gp6JTRelSau5IqdRVzAYmnBxu3N8rZAhGstGH6vqQi3UAS58caCRK5WeiKDWTLtJdAL1By0q/wxGhxpITBUvNGPRcOvNHEe3Pkjp08++eTHP/7x/fv3k/5d3/VdPMfz4K0X6625s8/NpIgxAOfLX/7y2CzxpjiZKld9aX08yiFHkMgiOAosLzhqiQTUqehnFiAhUww5e+iJJDcg/Cg4VQlMRS20CiBr0QTrMBhyrSRxgY8LqOkY3wBpKRLAvtESgcmwkR0cNhaVw5SCNQiOS+INrRmm7ArCSVBlhiy8Fh9mQGpISuVbVZU1Yo84oOdXcZm8735w1/rVx5fuLd8zU6XEGqUH9OLJigsA2lgx4TfAx/EmODhEnErcyZgOMg1yC7YYh1nOWAoE3z/ASP3a91nwacT7YownxSHF0LKTTVaGAZBRCD9qDeAmy2sG79fy+/pgWkPIEGUbkVnp82thWh++ZIuJVAOQIiLZPsXXYDh9unzItHlpswNNE8PXQgCHKSpPI84mzLPb144oDM5GGRoEhpyQ9DTEBfbOggGZinjPT258AfM1zRaVYGgBnLeshSE0TkiOLGQir7no2Zdfc+Wnj10S5g74r3AHnlvf+/nnrti1/LLLr3nsf7n+0FcPP/yh+5bu/PKe/P0mThtOM84ZNyfAIbMAg2J6gcHwm2HSITMVi/B7O5jQFI8p/GqXS8n0yDpMlsPGSx17HzDADu1NJwsXc+tdolGLY0wjkilTUKMlCI2yuVTpCTLUEa9Ki6yJiitYcWTDd5ZhgFm177N6fjgmZliZWDBLL3jooYeuuuqqd77znbxRzjvuTB0+fJjelcqMzsY/ApJJiB9rmsWHNwEWfBqYl7dR6ISa9dUqweGPYQguQWbFyW0AHBdbNRtOnQLXIcxpl7HZRkRHL6GxzWeWhqB9tVZNLzE9DSl/nAXLHxO3gNqrlvoBishphkmsHIN9JOQqHllAqm0UOMdSPxxpDSdDxSvHqZjWITR2NbmC9L2IT6h9PCmAGPEu+oG9S1etLfG+e74jsjLBStELyKVlsQ25GUKj+AUvQHLlY7SIvvW4OWIULK8pI0PPT8i1ZbYHqJEiuZllioh9psI3QmKmAA4TdLHEt10ssmTR52eJKguWoAVMPjxDY9t5sxZxcEybRIfOIuKR0muQWYPwGULetn7OSZjsJA1yijFir1rVNwW+rd4uohA+6Sow1c9KC4chKzXY840ognVyqbNOGa99pDBCnz6RSjNOJGooJxjHmiI5b8Pf8JLlt33HhZ85fkPD2Rn+17kD/ALroZUL+O/882/4gTc88GNPH/zcQ8/96RdnXzxq4+wSeA47zOnnFMMN9ub/oMncDGzcwerQLHsvZGYZkkhPk5xhPb1n6ie+dkdZUMlEIBvXyKvGOEbOzj03fmneoCKpRIXBPvom1pRM1USCiUOWT9+XTan8yE1vOhzuZv7YkIgggpJhJtIsRLvUE1oiDaEZwqdFHxCMAji9gpD/8i//8qabbuKT7m94wxsIcjt6/PHHK82sufDy1i+nMuZtdV8D1Fqw55dTzznnnFpWk5gpjZvZsWFd9hjHuPryF7eQ3yhPmA7yx+wGyXgR15HzhsbQxhMDUpw6ChJMYURoTDnrEAIgnG2BLjVFi0QaQYbYbSsbAmuRryzxgHAqUJ/eAlJG5VQcZg2C4xKCkdQDp4pXXKV4DljkzI8d+mNSyjprv3dp6ZqVpXsm33evxYCpJ/uJSHwbmvrM0jw9GsLYsO4PudICmqyZ+ub5UNfV0DJEHDx2/iDFrL1AjLLi0XHYBJmd1k+64vQ+RM7UN1/wKkfMFMAePmCs/iaXYnh8h899lpSsTqlKZoogvWDB4yXf2kwHE8TXBjbCLI2gEfRrY6oWE6w+/dj5r5oW9OiLEY+I4rXPlPUwZUt8DHiwMEU/1uQ2fCP0J6tf+WrGxeEZe9Z/5U27//G0Nx5Ze0FjujNccAe2Xr2GEzbnN/8/zPpmjfLJxyv2fPmapXsOHP7qf7hr98NPLa9uXQpbRXte0edc3ZobQjBtTuacDyBeccgJBgzJz24aNKcAXAi22DmLCM1bhzhqDIMHgQr0TSJkp2pWIsouKB6FMb6yPFDRJBNhOT7KExlMTDEQwHAGaXFv+MqSkniTnngUQuCfbfMp9PPPP5933Pn7p/v37+e5mm+Y+e3f/m3edyeLf+PNCxkHbuPBHYTQKT+445SCBoF7kal+GXXK9WdhAeH0oNGvBL3UrPFg9UNLvIJBfVPSA2YbOm9oglVo6ndIz8mUntf+bd+3Sz2xMMIwFsGCZhhapMYAiUzBF8RoW75Z6RfhV07sAjJLpK8/kYBpfmYDNKKncUSIA+jr2htxhxxf+ZGaALw5w/fMHNi9vDa/c2IxqB9rpNRvrCcs4EOWr47kims6dwHPQIJm2VdOxVW/xsGxqAA8q2boXm+wmVrwcTkW3sW2PQRxMXFbfpbGekkJP74hVMCsb/nw+M47PXVqDEefChWntzHlLLlEXAJAKY8avS0LbIxUgDNGiKBAPmT59tGch0942Q4fi9DGABbhqxz3xgiFEODo23N6o0Zfgi7xijiAIE39LGHX8tIvf9fq4fNez1cE9hY7kQV3YONMHWVvzm/+f5T4zT3x4t1PvXzvwV1PPfhXX1z7/OFdz4x+bm7jZGM1nHJZUz2rgwMq05RMRaGC2dk8F6+9D69Garq3jpz2TlVCVRYrUuOVH1ODIQMiXvlVZxH9yo9XDYKjHxACKWyFLxmm8xxPYwd6slnSwILQAJlKsKE1KSmjgohQA3/w1EoSlPm93/u9r3rVq37rt36LyptvlVl+xzvewSO/FXzlK19hMVUdzC9jvfSlL+XR/NFHH33kkUdysEPzHfcMe0A1WWFAT0uk8hOsIMtDDUxT1qHMCSP4cAYJc7FZByE4LwnGa25EADR3nx4mQytZpOcbo/36fckaRaEZwqGk/khNGDUKDiufiHaZSiRlBNTEYErqz42oSavD6Eeh0W+Gg/rJ7fXhE0xJWqOpb+NunGD4jXI/hFxLiiBAsvU7BJ/G31VdXbpn9pmZRU8M9EmkKRhlhhWnNi5v6pefrIDQApotShzQ6Dukpw1uUVwCFIFPpAarSzA0+XXJBslVJGRABOUPllT5wf3jvi4QAiJOMFtEsMYj2ADulljMPvw+//g7mjalNkdbwaTnwAnoaZkNiNrgrDQ44VM/zeLpmWpW4TDB6BNJMGo9gI8+fa2HocxGwaEWM/V56zWbiPo9OS4NX32ClNRnNWSG6BuE/G+uO37Fda+889i+nrYTWXwHts6/4ZzN+c3/D7O+RaJnLB+/es+DF609+PcHv/bZL+098LWt0ykr4NQSB+QsJW4wU8mqwLPdLHrI9Jy6NKcaHdV8NlVnEf3qGMHYEdFL95AlEKQl2ANoMjM1xocGJ31oPYhU+DUSfg2KuUvTsnswuV3Q2DGzdG8UDKLQxHt9I+E77LMk8GKRN90bKd5oZ4qndqrNFFmzB3c/20e0eXBn9vWvf/0NN9xw77338mmbffv28VuuH/jAB3h8jwTA73GvkYoR6cuFQLzSgsf4EAZ1lJqYQtAmc3O04c5Q6ygA0pjiWMKhJyhz216LyifCsPEyqBrvuPPgXlOmXTjhKElOZPuUWMhp9Jthk14t6lRvZ2SQ31jUYWqr4oM4jtXCdKeqbFWAf1I/3sCvUvGtmg2GkwNRcxuaw8HPzPQuNZIlN+LNMHbk0lJS4gKmEhHTa9EL1kjwBD/KAVqgTyQKme2BHLJoi19x8lEjBaxsAEMwyvbOWlIstq0tS6hMgmjagrXzJQHMrZWU2jQlYiX2ioBl1qmKoYWfkkKomsEBVsiQApIyAeRDiMIEGU6WkF2d4DMln55cW11dciWEbzwWJIbZAy2Iq98QeruXnbf+k2+86BPHbuIzzQ15Z3hSO7Dd9pX5Ak/K4puNvHtp/cLdT1y7674vPfrobQ8ufe7Rvf4Cq6fogidqFuWpm7tKzlV1cgkzBDvc1iLigsqPvlMZAqDRb2uRlOpSLWocDL+mgCXXlIpNaUQYNpxKiL7ita+0YG/aue/N93XWTTxCxCIiAEvSjmHPMZLKBbxS8MVlYBOTBeCpPR/7UZ/gxjvujpsH9ze+8Y0XXHABf3QppfPBeT7O/sADD8BPy4N7zDJlJCUmHtBMbcsnEY7NLaZ3tWoy1YgzWxsEhhwP+jCngSlVOXynmmENLuLCEvh3dj4qEx1AtauCckhhCZUPnvBSbVtCBOHTsKBPUNCIZCgzwyarGUrmUvHUSm6166WYJdjHG/EMB2WrRZgBbmwsAkJoAGq0/lgQhGlfU05bWr56ZX32d1Xn+9roN0MTPcMHp6Jc7cC0WhLDmh4csIhFvADwG80620zhQsRgraqm9BgLguGTztC+kpU1Tgr8pEDLAmuKmBSzKj9TSNHk2DMlH6AsPbn0TVOEnrutv+6/7afgVI6dgtEJIB4MYEgKdQL6VVRmcGOhb52tFmJTTtbC8qKmUWOnr/rys4okprYKPDRwbHUqOLJEwO4SuKaAw3/RC1b/j7efeevxNzy7zs/XO+157UD7mtGKlfkCW9a34JjVnL3r6HV7Hzjr2UO3HXzmb+7be3TFM3G2Ts83xpzn3Bxyh2GKYJYLzRsLEUGui3AaYDp9TumAMKsFOEOZ9ILwGzCY0nAyrGSCDBWvFhXLSXpAw0m858fCqSaRIYSaLm5oleDR4RGFRhwmjQPhP6US6QVTg1PwTayyPY4O4rxSoD+3mv1JdR4L8z2YoaEwq23sL6fyHvBP/MRPvPvd7+bRConLLrvs0KFDuvLWezARPypjlbWsLKMGBzFMSrFBqCXKT0SXnM3ExdL6Gno7UqRFc5oj35RF9FFjIVQ1KFuDqcQfp9jnRr8Z1twxi8qpCwTTEIwmQ8gZmliHi1g0dlqoXKd6rBEW+Zmw5wxGtHBKo2k7LPCymQUeVE5wbOFjdvDJbY54dakYJp+ZuWZ16e4Fflc1joLoZAhIsALiriLBrG4MuAr56jfMmCaue7PwzPag3yg1J+ycqtc4shOLgk+DMMhhihpsc+JGhyYWJqqvY3yroCKZ6pfZRLi6+fwM57lfWEZ6Q7DUWrBlEcGlIQ8O5TMVkbgACGYYTiILLgQ++0Z6z1cqglbI0JaU1BYgM/1mxqzg6YWHCTCdFFqkABkGEExJ4Mbi33/32h0veMuTay+sIjv41HagPcVblTJfYMv6Vh7vXlq7eu+jly8duPvBr37yoRcePDK7dmicgdwKuJQ4/WyzE3feJPS9J/nEqW5KCJUP3pSfeoiERov1dD2xq1mmEAFUneDoh1OZoVWQlBoURyRTqIUvUH/aJSlVJ7jOgmkcOB6GAXI4gt7Yk9KDkDM1UVLILqcOSU8i8a133JnIL6fySnPdddddeOGFn/nMZ/iEzY/8yI98x3d8x/ve975/+qd/uuKKK37zN3/zXe9618GDBy0l77g7pEeWFc7UN4HBECzLITjNUznx8McA+uRmlmEwoM46RY9FODU3wQZUkWaKobI1biTKTXriNeUUfjMVWRfS26FcXcQNrbqPYW8uJtqPMROHRlXVPVODAL4pdZYIQ/saF1MV+rEQZNjzibiQwSmDsyI2zxwAaklxmNwxu6aq8MfA6cvLl68uHdrD76punbGIW4ageoGxQK2evWPiiXOLyeFAWfHMChLUjhTuRGCHDbkfUhUKpDRTytY+BPcqC9Fowg4RUrKQ6FQAh0bxMG3MztYwvzkwlSERpGwqEIFAllNVdgInRY4W4MYxEQA3VVx4T6XfLkWaPhZU6xQRWrDA+hOE4OqsxPhYP9ebdaQM8iWQDtAoEasyq/a9F6s2PQvpOYmoz6FsNHWHBqAJkqUFw7GFhCnQhd6UH3v58XOu/Ff7j1/U0HaGp7YDWze14fwyX+Aw91s5yuJevPvIlbsfPO3IwdsfPP6h/acdO/FO6XmYJebcTqQB8HM1ZapmeWlkCnCyFn0KCr0sNIP2uoABTg2mMJWWwszy5mC6HHDE5UTTqQyj2QDV7CX3Kc7GMWQAZFsjmyHHgjsVLSI+x0/f6CCHrxQuAHo+MEMzHdn6jntMBbNvIaQ1UQr6pV/6JT4n8/M///PnnXfeK17xCiTe8pa33HXXXXzqnUd2vmny/vvvN5Ei+N3KYAARvG312zQblzokpS9DQl2kWCZF6lUTp/GES4rp7aoptF6kmkKow4pjUQFqLIRIlpNZcvVqHNWsfU0JbkBEAE4FNMzMcurgQuNQDnKaoEtoggxjFBCOa89QMOFIJaQsUo9e9DTe9ZyvY+M8J8KwmtbZ1NBwKr/B1MNeWRXizgoUqZhZvn7gS7vWrzu29s8LvO+uGuKINGtX1j6mtWyqIm5h/RpNqb0LqQp1Ftw7EuTmQCJZzCZXuwyjw60NmgvpZ0OLkWT+xdAp430iBdD4d0Z6mZ5aGUa5B6hZVU54OCkAHDuCwQIiM+NN0148EX/7nJ/SeYLn7tx8+Qw6tPgmiyBnLz0WONoyOwggZ+3wBzkEodGc9Txh6EJq1pgjFqZvu3YJkHGhr+K4M2yCRPz3aOKLLASR1JCqLBsFV5q+OlrJW69Yv+7aaz5xdOepnb3ZaV/PHeDa+8rq2V9ZvXHX6ddff+PD/8+199z1pSP/4Z/3PHZ0Vz4B70moK2evJ7DBOhXc/NgP39zmIsoySExumAFOeZkkJWCeOrs8JTAEGAxHkCCACLQUxjCzTplSewg9XynT05vF1KymeWtojayJEAEbCZs3PRPpBTJrelIMkl7JBLnt0PLhGSKsgldD16Isvc/AcTFRTXuU4fAWeTgAGsq80cNn3OvmkDJbyPd93/f5ogWPP92U0+Lmm2++5ppr/uiP/ggeb7fz1P7e9773yJEj3Hx/8Ad/8M/+7M/g69q84+7ynOp7ZpvgrIhuRxpOvBI3JcMxII0egn2VmhCRNkEYc2SLOZZ6aRrHBBNR5GR/M5UsXUyPrHYG+76uJQUE9Hwi1aUhDJoSpLH8hswwRgFy4NfZPjERmQypqhEJpwHS6E1ZMEsRzvO6kLjHgkgjiIuXTxNPSgNOX1+6enXpi3tO+JaZahQsQBYQF9X0mnAkhVbX0pTBbCJiLIhEmWDVTzxZAFMmXEJGzUYKwFwjcuIlQBNgD00ctWkAHxdT9JJf7RpTU7DTcVo/arj0/MYRciLe3Enh1uwymQUE975uV+/SMGMB4BxWMLIBZjGsEVJs27rEFH4tjCFT9rFgGBcBEdriLlogmBTS1RdkSBALh2Qx5JIkQpPf9Fecu/rTt1zwdys37/xCarMzz2e4dUMZVinzBQ5zv72il+x+4rq9B798+Evvv3P3o08vHT/xDXjWWs9VTmPPZOPpx7Yk5ICc9okM5kKTID9Zg2SZEexTMmV65YPhcxUztYgLNFMW4Vdfs6pFxRameLVYxMXceCmbIp1Nzyw3Yd95MYW1c0eip4VGhG+EjBRMvsjxwQcfJELjPsZ3y8QRMGvf8z3fw9s/Muovpzobdbx9i4gneF5sPvGJT8TVz7g7JAuAWobB4TeRJiW0QSCZKUB0GsxsphqRsXhDyzDK8XUqQwTDYcp4XAIi2AOOCq/i/FzVT8UlU4mQlUPDbDWqOImAJqVOBUffCOecT6JNHAsiMQpY0CV2AHRSWONSLUyJESkWVqWm8eBaqmNwdWGTMwyYMIpLw4k48YpPX1q+YmX93j1L9QYeo4BGLTvWxJthjACkNGpjQ+Om1OU34v0QC5ZPHAXTLSB9HAFpzHIoFz+aKtfC1Me3BxZJnIZjssB9/TUC3x0jpcaDIYDtDcLXRfFq0UdM4cKnYcHlX/lxaYBVaQqfVgtgGL7YnsKMj60lWQEaMUxKjAIkx5Q4jSEpCUZwEKQw+DUFHfljgLguZtXc3kiXpFTC2aet/9Ite//x9FueXju9xnfw89yBBZ7GNymb/3+ejt9a6WfvevaKXQ8vPXX47kee+ewjS19+evYvfrnWBtfCCUzL1OyC2bzeEw+QVofykxKdBpDSZEHos8KZ02cdHIPVqE9MYaZU9zFyUgAzp5EHD2YbBZjJdaohOFt7U6pFZg0OTsUFgMW0Cwq8StIANMgc9yeeeMJ3cIj81E/91Ic+9KFf+IVf+J3f+Z23ve1tf/AHf8C5wctE/R53jPaMnTHa/9AP/dBtt93Gsz9P7dwBb7nlFn4U8G34LMlyM6x1VxxCBfPit456plwSQ0CCUaOS+kqfOMyKkwggpV4YkdUow5riVI1EPIDZikmpLjUXPOjCIUTBVxelFvSdMKq+MQWI6TESy6xLqBEsnBojVKPgqlwxhFhHMIDZBVcEkyylYqq4w8Y0U/zwGQt96QVVJ5ip3iWzgngF6GJiTY/RzHLznnt0aem+XUvXHlviMzP12b1xqUM161qaSiJOXEzP9eIlUGerbIOTkoWQDq4tvtHM9mLnvw8m0ujXIcpcBfWKjnIDGFIAvTc+xGMNENubWHsSMZqzth5wK0GsPhgmmH0Whxn9SBlxpXFZZOHcrGmshY8aZsdilEpqAZH1cNQdSGIDamFoUm0iMqtRxcxSG31csvbGog4pjBaXOiXWovbwHTZGiNB6BSJJGSMkC03EoZkCdvn8raV/d9PaA2e8+umVnaf27NYO+EbsAH+U9461q/ae8bKLr/nqL1y3/9Cjj3/64b13Pjb7A8ycnNw5m5uhVweznsn0NM9ngjbq9sQeW4ApzMIHJ6vya3DmMW8NwSFMwKCjWbpUcnSqC0H49oLQKtCOXsekyHG28sEJBiSLCJieBlfikz4AACAASURBVLPxTTAgykbsExQkGBfiCVbA8aXFlGPNong0h88x5WtgaIBLL7308ssvZ4pc3lv3V6T0Inf4l1OdpufvLvHUz1dAksmry5133smvqzZnVT4qg1nd1ojELNUTwZqhvYQ62+TWYU1p4gyZrUGxwegHONsMk57lJNKDagcmJZvTmCYXO6ZielK/maoI6VUhQZWrUTCAFI9ODU7jugOkK9Kn1GJMyeogV9znqjlR25hvHvXiXo0qjmlSEhkDMWU5SPVqE6ZM0U5qq/3MzF17lteXR09gakA2lQBYDi4nZcRyWHJNIWKbV90eYoLw6b25kIgvjWBAv4fw0ZTczxqJcoakVE1dmO1BNNkBZutyMjUILIy+GlmJ/N6LiFmnYGSK+tUFrwzjyPK5dxNf/LdX0SHLyvtNQIrmbIDWDLOcFBBgSu3h5+hM0CIuMAVsSk0MDkhurY3gtg0XjaoUWXXJTlUCKf/m2mP7rvmOO4/v29Zih3CyO7Bx2k2lbVI2/z/F/baeW15aP3fX0Rv3Hjj9KH+BdeXvHz19adfsmyI5XTmHua7zgYiJbfBszzlPLm2CzxRkW2g1ZRDDlyyYeWznEiOYyTIY34Coxcipmc12RqZUiyYlBOONBUbGm6zUFkAirdIqDk0AEzDPGEgx8ZlnnuEQyyTyMz/zM3zyhbfIb7/99osuuogvd+cz6pwGPLjzehfZE35pr3FlyIfa/+Iv/oLTCF2Sx0pk1lPNXp2G3A/V7E1rxPUkUocVQ1CfXpAUAFX1L2yV0GCUXQiAxmw0GQbXLIM832Q2oNLEdSqCGtn3KYmQm02uOuIaSYpgTHnCF6NGBP0JC8jMIjjNgdaYekaa6JRSus8s581h+sFgZnvAOeDDbizgaBpyhorzk7FPhwzDmQYy6ymnXTXVNzorS0v7d6/fsDL/u6pz40xNAGrDJQU3TOLMUrwHsdaQ2lwj22JDweLtI6gUnCYeQgUaweS9anqyjFSOOvQ2p7h2TsqILFNygGIEQDlD9YkAqIeeKRrpTk33JMao2QREVLNvTJnlWZygWRYw5gXHt2G4QdO4lTMcI8+qLy+iYDYBsqsD6FX7RooUToMJQuVDyw6QyJBWCWKD9lTi9sZoljOUFZ0Qshwi6DCkSQuoWXBgzlkz2iKH9frzn3vVNZd94vhLo7MDdnbgv8gOrC8tP7H2wk8+94q9u2943X/z0A+tH/z8/V/7z4d2HXhi9mkKLtJ8kzcnOfeEwdsCUxRv74Vgb2RsXczaIMOxD5mpYEEiAvi+uGQ2BN1reiziIpleUMlNRCOD4FnCeG3o9F4EvUskMSC+NSuzAaERqUFriykghJRa+WFqp6zM1772tXyv4+HDh7/zO7+Td8n5ShheQ++44w7+VZZ9pvEmr0yyZt9XYGvUVbRnzXXYYxQWuV0mUUeHYKztayRkQGqDlmISrMweRxmQWXEUmqGOGoVjbjOM4MQs4uo3ZIZOcUiSjv60BUwI2YRe04iO8dWoJ8drTHBWTXeR9Do1kuWkEhQoIL3xme68ZThWQxUPhszDSh4OiGex4VSQAgAnZYRFjKpgHPVt3BmyD1ngxlLHd5JHyPuWl64/vnTX+PfMxAJlGlXpwnIAWIQQO4rnRt+s16zFL1jUIOMYlxg1GwKTiO7cbp6PUWrWi17QODpkymOUrEFagtZJCmAsRbuZa7lvuOcuEDV1Mox+gJts1iIbjhTvuOPIszu3afZw7HW6NyWLY4Q1K7KwlNEDCNYzO5M2T1RXWtdbE5NCkBT6UzNqNhw7W/USE1/8J58smdpIZGjrZYm845rdf7/yCp6ZBmd3gjs78I3fgePru79wbN+dS/su2vfE/3TVoae+cujW/Uuf/dKeZ5/deH/By4EPOHBig7kzcH8QUy2RpmammgjDwYtCZuV7EalpSp3tZYmYkiluL9umJAsmXhpFIaDGwRjR6qyYKUAlhwMgpW5RTzNdBRPha8SU5VWFiJvobW2QEKZAfmOHER+M+dmf/dmPfvSjV111FUw+1A7nd3/3d8GIc7h9aWCIy8Y77lFR+mR70vuKiYzJOuVs7fUdy2Jt/U3flN49S2CKLAlRDhh0tLwo9CB2AXAGs7K6xlFNUqiN1lsMRrSj93wa5BjsfXl6WNwoIjo6DBYMrogg7WSN4LMiEuuKGrs6lXp89mI4t511Pa1GMCKlRnrc+DrkAYLcXn/C1xX1KYOOuPAdkQf2LN+4unzf6bu5OUFLJYiAldKR2yJvyrrPboK4F+8jKJDu+aCyHO1ialDTEFjU4KNk70JEI8/VvrwYAYJj5KJQoCnVK1RTVoQRWYAa73G8UAb7x011qeSZ8bz1vhiRSJz5mjKGKQk+tZkFDdCTowbguyPpOet4fGfD/efyPqVG4Lt2yqOlvHgBgpOoKXFSCKLQrzfkgGqEwmBKvGaum+sFsCh0albFsRBUIyJZVEOrQ9RoGOX3ExhKEOzetXzu2Wce3fkLqXXXdvA3xw5wpj6ych7/nX3OdW+9+aF/++zBzz3wzF/ds/fZ+V9g5VbAFbFxGu/ezY/3XLac6lwXPMRnamIpkG1yBq9cphKX7N24ylJDWo0Hb/ps3OgkZzYgRhAIehdyFgWzAEbkGBQTd1a7ShOnj5ER+PGKLFPxanDsIghoNIlUmjiFZSpAKW5THDswn23hQ+l8NN04PX/nlG96BJBCtfQ0hmjOfvXKxsDXsM3Aov/P/bFJ0KMJMnRr+lnj4Q8Om72WPFvN5q056QL43vqbeDOsXmCyOCQ1WPnxmttuvCRAMKsyt8VsXYzG7CJSffszJrQxkHQI1Ss4AELIBh0uaAqZrCjUeqpFxXDYvT5r2pd6uKEsWFXs0HTb6z401TqkF7ghdVgXNYYpDCMVcKe5RrFZjT7vn9yzvHbN0ZW7dq+tjJzSvR1nuOJ1K4iEGRzAFNaWZ5arSz3J7YF7PsGMi4Ce1hyppOtbK4+j2zU4FU4FSrEogmbhWwng3je7x9SCXtIoj1ZTYhegu6aWRwrBRV5ioXFDp7F1/MspRvlRTVn6GAkyZIoshlmU7rXaiFQwX9PWQ3+dqlgj1wXOs7gRmBWAq683ZIwsj1laFe9x0rOopFhJUhIH8JRDD8EGVmfv8vreM05fGvger8jsgJ0d+C+8A/wC6z88d/Xy8lX7rvrK/3nV/sOPP/bXdy3d/7Vd+RNOnMzcHKySM5xrkFsfgAhTnPz0uRyyGCP2kL0MMwuoKWJ0aOF4NTHUy3jNqpHE9YpI4o0Ow0wJqkudbaSSZXnJMp5Zs5wlCLAnbqIW8uuqTex1IjVIqGpgyb4EJJGfvvxWMcT/8A//kNvjW9/6Vg4lXxD5x3/8x9zzSeS+5+uaWTOpd7zjHRx+tKgyfzmVicVbfjl1MCVOzKbufkeYbTalGVrhoMVgsHrVGgbJ1V3yYIVjucTNsuZqV3GfzttpvrXmVLPknm9kbCuqV8WDWdWr4sZ0zKuhVTuwWYBB5QQDUFOhRhqLwSFGxMmqBUTNqZqovuWdrBcXD2eFWdjR0BHUXruIM+UDBBFaFGpVDX7B+tI18+93H3vrGE1S7JPL0HURwShxcY1kCmCWz1I1PojjyFZAYC2JhF+NGmsP1kldWZaHjlK46DjtC82tOCkvj2ZzgGLUA5dMnGaWRabU7EkPsoETZGRjASCFN1bgc7t2XTUXXIdxRIR1OTtICLMCUmhENEol4URKYE8KTLAt5GlQs8LUsfFFthKYpTwrTHwCwHdRLzt75cfecu3tz149Qd6Zej470P64PKC1Sdn8/wBlJ1R24PxdT1+794HTnz50+6FnP7R/9gZ8mWwh9wqe87yyuEB8iK8kryymAF5W9GBaaAtexRNZUQuIOMBEXRbxUqRKTWeF36RgTWL6WlJwUgKmvUyEHH4i2WSmBn2J8xBIk0C/b9++iy+++N57733sscfU4RnbN0ciu/z2t7/dt38Qrd/jLmORHlHe4ccPspUBHNaIUpxMCz4lyE9WbtBRroQ+SCV4JSuF1axBXLMqIRYBdbbJqnYV1xQwW8fvFDfBfqhjfONlpOqPYTST1etPRLgFeOZVTioZs8PrFI4yXslqlhz3WBPRvfGqJSWrB9WrzqJPQ5MmZhZgT5DdwCLNIbMBVU085tUzY/SCpaUrV5bunn9HpEHJ+DaAYYIwqTB72OsPRsyifmbjFVBTNLKPV9wrcwwnqxL0imNAOERorCteAeH0wCM4cVxIUTm5DJssjWIXkBRBk9XMDg7x4sSgt0JApcWoAdD893HeieH1uKZM41ohIjT5AUnXkX7OmtGm9zCJEcQLjIInVQh6Vcfg6pWsrD0KDTCLvvfqmUS0e92la/z6173Hd/5UarNJX8/hCWfzgPDm/Ob/Byg7oW4HTl9euXTPVy4+tv/gI0988oHle7669flSz2178rwoHHKf8VZDnIuL+8Yitw5ybV6G9IKuqI3LCrK+ArApZo3lmpIeGukmEpxounCribKJpCTSYIapLWBxr5qSwmIa8epeaeAJL749hvfdIxIvIry9y1s2kQKw6tmPZfkHlzp3UrgvaKz63JSrfqqsuwAhcXBeDJJYLSqWQC5efTzpoQFiJNCrYTJUjV7QEAaX1nDqcNqrMsExBbi0hrDtMGuEKa4R02ukrlFcI9N2E8wJa6a4v6Acu+gE9L5MkbjI5nNY00ihRRYcX6TSEpQ5T1rIq9aJGuui14WpgErTCyMa3+9+967164/N/q7q2PvuTa7p8UIkBMuuw+AA3qHJk3HNDaEBcAa9GlqsAaS4cN88IB2yXvQBjUKGnhtmJTgBFGRdc+2t3UhK4+jQH3s4TygVL4NJGQNW5dmVCrP2Pqt6SVvQi0Tfm+FWzhM8t27/LTUW7vCYNXtIkXjlp7tUG4WArB01Nz9Z4VQQaxLBGDWHTMHa13SxieCJwpKFlGpk+U/JDkMImBM3yFect/LV1bMytQN2duBbZQeeW99z4PjF9y9fdP7lT/3YvgP8CaeP7l/55MPc4mY3Uu8hgGY59bLlquRK4aN3XDIwmeIe0qegQDBxyDQuZ3qD88nZZS6AP3jBztI2H+gbzRQZQSMMSaHOEBrAbFJcsoSZ02Je8FUAkFK9iGfK2fQAmy6bo43/N4n9LJEJL44Ix4V7O0cEGuui54WSp3biOtLHZfmd73xnjlzzURmmeHlojge/6/oP//AP99xzTyob+6iMZpYbssCtkZBtCmjIbGtTQ0MYHNaDMUgwGFPBqXk1C2FoZMLXKV56OVSpYVu+BK6feinWLH1rzyz6s4LK6W4wfVVoMBtCOq2J90P0CaaPXWWqU/s6C55YWsOsQ85sNMmlWnxpdVYMgcaJxNZ5OnmsCfbkiUi/IdrNbQd8lZKD9UnZ8ZmZa1eX7px8dtdX/ZTNVrBGvfo+tAaQBXnxa01rstAZy0KQBsE+juSyk2OncWgNMEsvl2zf0BzOnTcOrhsyVuRgOkGX1heJqW0wkXUR91g3qx7kGyQLTSqcTtl03jjTuLObyG1Enax6QgeRFDlRUjNFFhtCH4uGkHi1jhfBk9p/14WF+4+OrTHNUHfXRXBiJ//Xm48deNH3ryzNztud9i+0A6O3wg2/Ml/gv1Ax366yvAH/8tPuv2j1wP/9H48/8ezsBpKVLnKtwed68R7iNcujoIlKEQyQYE/QJmHiWks9AFOMgMmtRdbZmtVjEml9vIk0gtNZIQeo5tK2tWuyyK1LawrLkCywuVgA6LndmZsbYPgBTM1+OVJeogE8tf/t3/6tb+ATvPbaa2+++WYe2fkhIBxBvDMEuFp6gWXVROM10mNeKvLCiUJDMIJOncoQQGtSpodVp2E6NUiwyEzpu601j5v8i9W2NMqIsiAvTuYajA7A1tRP1tiBbph1iJT6tYeQuNiUDeP5ntcDVwUXwXrBBFC2rRaAkbMCMEtjM/Mj6CIunFfTRVbHlIQyWdlJC3DhCQ66swpEUvAgpwYhH9u1PPu7qqtL+/csr5547lcdcGPt0ghWWhUfxGRRZE7muvyerzI9ew6TxJOyI9EiAU3x8RX01lTIlP/a2+T2ZCOQSfHAWfkYExqNWXtA/fcBhqb3ZUeQdYEtcvE9cSFksZOI0yJYgVP2xOPFPRmFBd8FID2JiFhkVp2FV18wWWHCcdhw+mG8PLUgKNIwEaQRtK+z7D9BsqiZvk712HURx442WORLzj7znp2n9n7vvqERTu8T72jfUPdvEzPegP/cc1efs+uS//k1t/6/H+ca2bppcPJz1XD+s1R6gcsmThMT55/sGErgmcTLjVnumbRcUPKT1QhqxyxxW+UnS6AdPXahkTV4ZwghIF7qTNgxVbPqnhCvs7qHHIAFmEQIsctsALO0DE2pQ2dVUNPZed7WKyA7wJ57l+O1wJeDqpOsje9x7+eaCGZveMMbXve61/F2O3LNrPYJNmsgTsrgGWAK4rSszSBDg+5axAHR70EEt73FIx5NsT12xLUOAaAXvaBOgbe1a/joc4X4xZz6QgjoLYzEJaCRXXAYo2qaYOPOhnDsCDbxBb0qTQt6G8qAbLjMuMwNZx2Lzdlc1SqePsEqE8c67E9mZ5syagq7sbhdElkFWSxWZctoiumt+czMweX1607yMzPoUKTbW0+V2I25WxtnpkccnZqetTSALPcEMMEfdKdItiVZFoD+tlIkJqupZ2wI38Mdl4Zp3D7iLI3KPXAJNon9kCyC2iVrbNthxppTHRqNSBJ7/RrB6+mnn6ZCHt9J5PG9SSQIv/Y1nSJjt4ippZKS1RmpmhXrSwQa2FdrME2awL6pHII7yepoDCEk0fTBXp1qF9ppZ7xw6SR+OyB5O2BnB74Zd+DJtRcun3/Nay669zOPbDwHc9rnGgF74aR0r6AQEgfUz91xdfPWLek0UnwJ9rKqKWCkoiYfRwBB+8yGXCOqmWjPrK0xctjU4OpIdJZEQIZVgSnXLgEOkUFmslRLD7nZTKacNUW1XpOIwfBrFrkUxsfceRfM2pg1hQ9leFcnbor9CT/x6J3+i1/8IskcPCK83c4Had71rnddeuml3q9Da4D1GRSzVMyyYI2ZEsgEO7QnaG5zkBqvfkiWdgBaT2giMQWwLl8kGs70sHHJMIB0MPr2UcuQKVrizwfENEB3+zhqoencfBv3Rcpj29FPy0lCpLFj6PVDL6j6p3YU0CGR3pWm1zp9jAQc7udjV8+WLFOQYbXG1JOzX3VoPXh2eem+3Us3rmzzeffG1yF2Ombh6INtvZcR1sXTFT20MU4Tx85DoF0zyzBSG94nKnMUiHv4+tw+Apny5ovbenzPngckMe4eMos8BTvPsaipHztBhnH3UTX7ua2v+hS5yLZUO94IIIuPS2LNCy0NkGoFY+7okFv5qX8QoMaKzKJnSKvFmEWwAtw9BLEjEs6gkcGUTZE0UozoGN8AspRNolNnc17vOWvnwX1iq3emvuV24AsrV3//Kx78p8fW8mWRnPy2Zi1cBTYvokGOKVynXqoMSeEmlud44txbuLKIO4tONfKiI6hXONIasokEa5xEKlTfqWbWrBQQd7IawZoYmpoMndWun01EkCx98dKOuBEWDs49p0nPELJ8IgD2k3de3G3jPK/z9hlB3oVhq/mAukx6HOGM/uYWd/9f/MVfPP/88zV7z3ve88EPflDc91afuHvB0PVQUCI9J5EGoOlKjNd1VibxMXHi/VTNrRidie0edNeaOnGpO6DpzPvEs7naibclJMUC7PHS2iGcqiOmF0TBOptgZieAdngFxDdg7rbVsZOcbczWIzhhUadQIbHWGZcA+BUzhJ8FmjtxNKsdNBKnyXrFMUPuZalTQC8YEyTO05gF1zImMHY8u+/ftXzD6ux7ZrhZ9mRNI5sh+8/qiI/V00sZ4fDlqTHrdSr7kNxqxyyOg5d8+D2wThz7E2Zbd48CC7SMFNO7JIILsllg4oMAJi1TPoXjEiPBPDB79eq32kVhh06/wCg3QB02kzs44gxrGZWstWVw4GjMcuun8RaO7+JU/iAmPdsCIfs5W/zm8gOqgu5MUSo4iZXTY+2Ik5XEnqZjfANIcT+xiyOavUITueCctafWZ6+CO21nB75tduC59b2Pv/CGN+37x1sPzu4VtsHVNVNetvTE4fezBL3ouDnQHHL1cRvkAjSFOPccLkOGTesFdRy0q7kmak2vFyCJ2g2aRsd0qk2kqWcsTiJZpldOg92QBMPXMaWGEOCUQzDvtbMQss4++2x28rLLLnvta1/L9w2CP/CBD+DCOzK+HWMK5NGPylDEhRdeeMEFF0jtP9eeIgBj2+dKapU1S5zVBhBnDWS5fmkRAQT3aqRQzAShSdGU3sQMUQBXHXHtIzW2/BAGgVK40BpCjaQGAI0p7E7NsXdh1bRZBZtNDiOBjpw6ms5L2OgatX7oxdbHa0Sj2AlIrAvEjxRdBVUhGALpkhNcBLgDpvd8BekFEFKbdSbe5w5G2EwSc81Ts22QTFD9o/xtpl1LNx5b+sKetcW/Z4Z0qkW/OlYjphjWvs6SZeJs8ZtHoRJ6DA1HsgQ9oYk0a/chVa8wGSaSzc8sO4kIB7HSMjsGIJNIFo5oMnQTer7W9jA9cJ4zOYh9Vh+xTnaGqZroDoy5h4wj2IfyXryP+LtJrI53a8hy2NN0J14LAFOnW2qpWX6vUCMkWif8/khVZrDKZPkTkcciswAIcsC9po70/VQVCb7s7NXH187OcAfs7MC3xw7ct3LpW6+97xOHeNybvaDnMqyXT79SZ+HbzAJ7xXkx9grN7Yt7BW/GkyuTuw2t9yLSSGFEltb9FJGkVMFaauX0jpl1ysTGDg4RCPZVhClacjNV4wkCQg5A0y2VNpjI0nxv5dWvfvWVV17JkzY37Y9+9KNgnt1vvPHGO++8k48w+c8d6iC78VGZONU6ThlnCziiHPtaejRxhKZv7UNY8EYcPkBflTMMoc7q6BSYht0pO8YiQC97ggAsMiSSnZmbz7rkTgOlpjnMsuc0XOCnsUCwufElyHXokKm+EnQyu61vCBqRS0SMSNyl6RVxh07VW0M0pwHp2knTq/Z9uo54kXgKR59Ednis1Fg3q7aMPC8ydAfq8vtSifAn1O7ZvfTylW2+Zya5KYAImCu/L1VT+34H4JN4CptDIlk8iqkwuAPW6drFnookwu9Lzbp6gIiO5NZEfekFfSIREyEA3IdBWhN0rzj6KjSzGeo7s9+87pgCcywAiLj89EnsAWQS2VIX2xMGI7wqsC5ONt7LAfAC0K8x7v0U+0kj7noHLWoQJi5EyLJUE2frLzvQpJjl/pMoU53KHMTVkcORYcioEaepfPG5zx1Z3XnHPduzA75NdmB1fdeBPTf+dzd8+n13zN50z6o8/zPsAWRbc42TSPPyBzSz0SGXmwzNiBd+3h4mK/ecud7G4weYRFMA9S5hPMEYBTBFy1BZfIkYHys1KdJMId3Emei8hVYBMxmSQm5dwphjI0gKNyIdVSPRGx3M66+//vHHH7/77rv5jPoNN9zw0pe+9LbbbjvrrLPgkELPbTw1jH5UJgwBmfxmKpkveclL6ndBOusWJMVFkpLDmakK6l7UOGsbvGtnwYIMzUWNRJaXQ0iEVpXH8JjjIB/fWFdHyUxpOjcfLYBX7gW/AiI1aOoZYz+rY/PlEKeK2Qca+09vPaRYUgQXBKZX8tx2+IfU0OJFAQkuCEih2olEV5oyqqwPxEQsIGVUTo/xOtlzABELiGMj677RDy5k2zU2ag6PLi/du3vphtmz+zLfyjC4A0lMAdYAmV0dvLKS0gNzBzdHd/o+ywi5bA4n4UmZsjNo+tDvEsb0+wKIcGVZc2qow0EpHb2WBw/WYBZBE3EEjJVKnNbLUiqHQ5Ex/SaOjnfU+kwMx31oyBmSxWun77jz7jvD/rdXQ24AZdPQZ3PoOY6kNxzd6Zu4Q0oFkEgbJDRB7IigljX2jknRlJ7yaOwn2FsHQKnZAsqd8MIzVu5ZOz0KO2BnB75tduDQyoVvvfKifYcee+iprd9dzOq4CsDecwwSmbi4mMqslxX9tolo8v4xtynJ2PEQD6ZlKrKpTVDj8Gups1K6O0+y6hSJ3ApmfvMnsXne1i2rGdYCTInpxOY0IiSSpV1KAhCp+uJGlkQiWvPU7g851113HRZ33HEHj9x/8id/og73T9alyMyOv5zqiwGh+j3u3Dff9KY38SlJqV/4whe+9rWvKcEb+PVzk4Pf4450szx1Bntl7UmEw2JkEkQnWcEBmYJJS2Li2wKy3D6YYPtePzpO2bOVC74gJV3A2cwHm/Syn1U/r2R2VOZNJkaMYgrmeLFMmkFp2/bbHhGUFZmbn3DOWUC1A9fhmPtJ7U8tgEQW2MjiaCXEde9rOCnHqu/+GLGS1FNpYny1podG66vts5oIWZg25w9BaYIM40vk9LX1q1eX7tqzNPsy0XJ1NPqDQzd2OivW1Z1SEazLnJtvXwCOkGriYGE1qC+JgCSC+7IHa6BUGhvb86tLj2ti1k6wZyZiAZBp4FQbwjQgS/3mNCCLKXNn0pu4qpFInLuBNdSpCUwK/xpLLo/yufOP8fW1J4UGUzt6s+KeSK/GoTTxpPYHXxJjyksSEZpSgFTC7tGq+LzYjX8rqIX9X9992qdOe+v6zncR9gfp6xo54fVjWHmTsvn/YdZO9GR24EW7njrn8H/63c9y5m+fxjXi1QSVa6deJhPJsytws0FLVgXBVYck3sfxVkA81+wguSaCNUwwKcYzlODQPokAIrboTIBFEsNpdIgTwct4vS81TIb8BiqP06Zcc801r3nNa3x7Zf/+/Z/61Ke4S+vCo7j/TksKkY2/ycQgNkpzT//IRz4iHusR4rdXjxw50hC81RK0GvuGU4da27PI6XXWxIox5VSokfgGMAvGyF4yiXEMqDpjuIr0HGZtTHmF2DMkztbRZ+FZO0vw7LXUlQAAIABJREFUubwXNFKrHePUOC4ZkqtRghkKYAJozT5wsTV7G81FQOx6UNPxdQjAblbHZqTSpjGJg1vUWGdY1bJMfZtNqMyKIXtkt602pgJ6WvOGfUQAwdUO/MzS0n3LSzccW/QzMzWdRfEA5NHEvU5VrDXk1ECK91xzK3kae0SytzEVZNiI4Ov+s7copIyGNjgkkYZjRBpaTAHBckjxATGmSG3rLoFS3dtt+XjFFzKJ3BAAtlrtZmz2/xoHeyBYJlKLFGk6OrxlQAqvBDzB59331NO46Eufp3yqpRHBtCGPDS0VC0+hVEvEpia9Q3TiqxFTFIDOgqbQZLo/iuxdXlt+wZnra6On/Vj9O/GdHfiW2IHH1866/vIrrjt46J7HZ1cobaJsLxAJXHdefQxNpCfIMJekUwwlkA4gK5wkEukbs7yjnDgXJndLTZHi0u6vbgsgpdaQIYLgbU2l6YuOBTs0V9z0mbIG6wRHTUJoTXqGvSNTEQGzDzy4G3nlK1/JE/Wtt97KJ2T4FdNLLrnkzW9+8/ve9z5o3K55VLAMhot+VAZq09j0t73tbQcOHODB3b1wVfS0ek5su7YoswYOXoaDAPHEg2eWm68KdVNgxr2JV5FabeJjQC/t0GQ3jcAHmKWX1vasixceMTSA77iPuSwSj11AX0NTz0mt9BRqqJVYDMeUxTZlbKtMCidVEqf5msYax+AkRkowuA8ESRycis4g4OD6xMZsU0z4KYBIrcFr52RNn1mefUfkK1b4XdWlXDCNdb8JWlMtW4QjrVaVUscAiVQ7fZGO1UDcZ9NqijvDMTvjEHAE4z7NzKw1IA7g8gTQMitIpNbjlEaeRYubkosUjS2iAYg0u6F+emugx4WbA2QSGZob2jRItdBIzLrMQpMGtq9SvFrQOG95fMedt3aa3EquGBcagh4X8EQitPl+zJ7IBUiRaIq5M7n5Kzf1AKpXg1FYxLRmuT8mXnbOruN7zpv9sshO29mBb9Md+Pyxa/77m+7/97fObkRcdDQWyuVm6xctIbRZwvxSpd9M2vjFlT6XSL1g56kbHbnxHUzkwvTaZJZSvRGRrKCzVaQaVcGTMq2bkMSqFsys7kZwr8PkSquyURA0U0kMjZfF3HuffPLJc88998CBAzy+X3311T/6oz/K+/G8hLEbbBE1cKMmcSbCR2W4XQIY14/KRHcMsNH8EMCPUOjym0+hcWPN8UhwEUBl0FinxUynZBMFz8e07qznuv1sd+bNSoAC+fqy0gyNTJftLDsGYNvHyHrFUZpDjx8R7appxb0y6bSxs7/nEzEFkO0l0jCbMpoaVDgp0+hjamI1rTjMpgbi7NKpnYSa9qtAM9YBKQBACrleVw7r7DQmkfRtdwlfW9ReuL700pX1e/cu55EWHZqEgPArWNC0poAtID+lZJZ47AS1Dw1T8CkcGvTJ9XIDW0n66AdUd04G4qdsynEZPDRuxVgN3kM4H2olKW8CILt4wanBMtxeqtWUYHVPsHfnjsTrB1uUl5CeMxbhZNAXgqsGeIxwtHngwLW2kGtwzKXGs0UKOpWt0LryxZBfccHKv37dK+48tq+f3Yl8fXegfZ0YUN+kbP5/gLITOqUdePlph/7pjs9/+MDW7zJyUXCR0nMVKCno+8bQq8k7kuTBm2GT5ZBcG0NyTSfy/7P3psG2XdW93z63EeolrgSoQ7oS6htwF0zZ+CEnAWJj84KbykuVy8m3uMqusj/b31zJy5fYrucq54PLVamUnZfCGGNX8mLjBGMeDjYmIIOQkEG6alED6q+6q3vPOfmt/d/7f8aZc6655lp7XyGkPQumxhxzjP/4zzGbNc86++xrY8nSx5pwOlUwQM/pwUHaGNcRFQV3iiNKSGxib0Ip943GlgVILfsuZBbUxghYMh2aEQb7wgsv6HcReL373e8+evQoNvwp6UMPPcQ/e/rXf/3XHM5cto3QhfvQhz6kGyQ+fIq9MTuGQACUj7lLA5X8AUkUelVHRymJqy4GMO3egyMglAQ8NgmBmQoyRb1o7DjHWDxa/CyRMkJZxndsuhSXOSDnjitAU5LevRZkVsywKVWEbszLizsClrEmippGiDQ00oSJLetChTARHdSC0ead+/4erp0AQT2DBiwKihvrfFpjKgDpo4EjvWOXhFhxTgFLyfNg2jJQE1nCWbuz67dnXw/v3W0/KECYcHHD0qTgqDpHUFxq3d0nDBZkLQkPIYliAgkHmiq+BcqxDyfC4qgl0U54Ga07N5Dz5eRUEKiPA0HpjRmOrCoyEYkLLEVM+oxlQy+CbBQ0J9yHYD2Oejz48zPqggld1BTI2B5Z0Rkg4eil1uzYpkVoJOw8mAMCQQlBXAqCkuBUJNF/6l0nL7nxxx85tfjHSZLeTXONGdhbJb2gwSSIveabjuYMbM12f2Lns//2s6dOLD8VluwLbyXt3779kgeUI7UBi77RDNk4khVUG9ZdfQIunA88brzTuSX6otjnJb3OK2oigqO4qjFAqLhjT4kGjYRxka/cqSFAcVMEQPODDMH/ABOWMSjGvFXxBVtdQB3Cn6I2t34goluLrDMdS0ISRqewHZUdcZXsrijgVXkDHS1zWRG73Cyzgw2xlCnJIsDokhszNh5+jlzRECsZDlAUuViICLKnxpegiXu0XEWes+ho5BzcRWiXllgYt5hFG8WiZrBRH2VxQNOHz8xOmx0mGl9q04hxo6zQZkIXEfGaEBcXguKbD6eFBoniPBobl++Z4Tsibz3VdHfPaRBUiVJOnIdBGuwjHLWSYz4HZUIAzmFEnScq0ihyYAjErfgWCRCI3MKWB4AO/TwViaO4EUhHIkExkJxYVppagfqprM8XVpDJQcSQXhz7fHMvNDLGkdKYKCwZoMaIfOGFF2rgcicVYHpx9s0aoTU7si9yKypNWAQq+HRRYJXg4Kgc5l3R8sjZO8/udP+s7Kac7gzwwCis6dMddYM/zwB/e/3wW2754DVf/Mt/6b52Ni9sIiu7Y2L5jNZ+d1cuzPffwpcdZ9+oFzh1ZTPqoBC+fOVVjMjJQ1EXjhyn+qYaNOh5EmnvqzYmgjj48MTAxb2yz2uxsh5HBkuNnjpPFEpOIf+AIUcTEAea6BNkNCDzoXY+O80NHGRFkZ7PMXqwchRI4WuDFDKp9WdMidJNBYOcQK1vFOAd5xjectSyUL5QWh+j0Mvk4c78RZCW0CTak1qxV9xYY0wz0pBMLaGCpq5GswSHoBQ93pIuN+cUyjSSPNtlUACOuOY8Z1H+2cBQosGMSLCvDVoEvGLcxKVOg8FibxqJb6WJi3wrNrFLNKilLF5JW2iwFH2tjPiDMp93v/cQn3ffurv747vCD2xGyGkQFOasKJ+MNh4U8CVRySZSHpSTPgRosFuV5JYNGHHwhSpBEer7XRzExwh48RtCmsQVArV7K4ITNRg3gig6PBksL7MRKNEAeZAGviyMom8CFZsKRKJwt0xTadGokSVgwAD12DAIZz72zBTFyroAmhYSjlgCK3x5KRsiUMTBmAJh6sS3aG+lVhHIxbg2u/S8s+7e2fv8gPUb4XuRAfbd5m5/uhL/wMm333792/7hkee+8+JACB0OMmLrUSTHDag9q/0b4din7HdqlDjKgDr6RnvLuGjPohF4Ma7tJcgScH2wBCVenKvSg8lJBaz4JL400ccuvDgu0CDQW+c8d11YYk9QHTV2Jy6l5aOGuGuwCIIVCK/VOXIZAkzgw5lPCCzV1HBo4kXpfgFBkTavzz///A984AOPPfbYz/zMz/BRG5jdcccd+TNeWDFADoVGZtTIiisNMtRRqrZAE0zCUbuLXpfGuLaPgmgIQXppJCfhaKoYAc6D47VxFIgClDUOKsFNG0iwCwKzkPQ2NvvA5e7eIhPGiz7SwKslA9iw/to5JzR0ZckHKCaqizSISNxiV46WaPAirjgXsxHtzcECuWofb4QS58Q3JsRy9EJ+hY/EHdi96dTsGwd32S1Jb70JbSIy3nxfJ44xFWaCoI9Hy1hJoB7MPEHxVZ7llYTra+IFOEnWz0hFswoN4uJLAWRUXIzla85KArUEmCAI0wJNCo6k13EHkxMHhTEFX+JKjr1d7HkROLWaBJWZBPQ8EniugBDdKzIfmKGXRxRfT4YvxZhFL+JKL4GaCZKGoPZFiM0cSgwhzP7FmNTlNkWNjImrJGADlFnRPOecM7dnvQ+7IuZGucnA92MGeOl+187NP3/z5//wK4vPRrM7VIrD8TbBRjK7z0r2kUrRV0oM3Isj7mouw+7deWwWDSTjmPjaxkIMxOHAuWRfTkjuvjQB0ZGLMQRkkNTo49mCi88NuYgJSgq+UlKDSVAdj8KUgRAwUEnCuZn04osjvdQUesWZBBrWvQZZPAnkY62EK6+88sd+7Mf+5m/+5qMf/eg73vGOZ555BrNf/MVf/PM//3MeANGYGATTTGMjKgjYqIaN7EVatW7kGMf0RdhBueiriA4dBQNCAF84Yywy7moR8MVr0DJmAGM1nSu5G0cClCqwHlrFpthlJjDHIMcxDXqRY9OAk6fJCI5rPu6y4NASSMgqcQlkQIdAiATMKjHQTKEEoT4v0VHG+DbSTpjQjPdgIWsI1CoxnGX+UYBjW7ObT+77nhn31gVgYat7oSyLOcFMvQhJQnyXrQdKesHhECBXETBJCC5oMFBtAvLVem5MtaNDnkJcNBXfIhNcCM146cUXGQ1oEhyiKMzDLq7g9i1aRiWBGKZq1kY8fBSUmgI4mDxREKK7ZRwpNPsMbBkFjnoKV3BeCIHPuy5iQYYiMwko1ZRACGtiXCtjiKIMAgVwUo1Bki4FdY2QIMvX46V5zuHdA2ectXNqQbsYdKPcZOANk4HvbF9w9SVX3fDWh+5+sjvo2CNsB22ZZLO4iWA55gEvinaTbIpmdqGXDatmn6/0tpEgWPki2waBpoqjJAK9nH4U6TneOTo0ZLp40ADLOZB4yYBahUAYOBD2AKpJnfi6GWHFWbVcjGb7KNiXo5WT1l3IDIGfQxiFEGzZgetbZbBgVE8++aTTjcPP//zPf+ITnwAOM8Hhzz/Eeuutt/7VX/2VA/A+hn9OFVChJ7XNigLJkn2xN1GahgRqCu7RzM1ciGbIGnKibGziy3iJLnvRcFPKnIA0JJaUeg4aIzoQUaKvgsbQUU6YkG1viVFxMR5MV4UJccVZGcgzUyEDLCUOuWKsLuwRVLPovaQTx0jGlKINvuz2qGmXtUJkLybIFiKOQ5sP6cKyj3b0zeVzdmfXbs/uPDRLzieFNgELEQECok22zSoa1GV88RqcqcjENPAF3IvEqUA5yIR0UUjXoGXCn+gUZjmOF03EqTDBUkOeNlP4giDaGgI1GtVQRdbw4UARSWqKzCQngxpsKlafr4ImNdHRUMNZHzTi45ggoBkMZwNxpu4LbUsJCQetELocV9FjnSC4qSFfes72L/+rK//p1Rut3winNQOLp2MtxtJk+d+a7aZvfAbO3Xrlmuc/9+++eGBnvlOTDasthlKlER4vFeyXrrVzwMbY63DDC6V8vZ1boi8CL4/odto4cs5zdlETiKCqFZ3apUjDceltGXIEkS8aBHyFIAM0tuRfNdU7EWswli+fcefXntIrOvrFYGj7ZJQFt3M+FcN4fvmXf/mnfuqnpPzt3/7tY8eOgcLbFyIZa9pXkou3BiNZNbAI4q0Q1DKTYJfJ9yoWEEMzeBRyJrFXMu5wEA00fVC5I5pK6NweMuJDl2VrUIqDmVjIoYRQNyh6WQlzyZGAexEAN74F9OQHl1FZMiw4jitlMQ+2l9DxmG8SglIkJzaDTS5VbIrKtUx5MJ8EkGOC9TknsjjXYJLYFJuYMWRKo71AROOVra37D85u3P8dkTJoYUJEHW1FYnWl0oU7Qp4T5UoT4dqU5KJsq7ceK/bCmYIvjgixyzQUPelSdOZIZnAYFRpj+TLkOYV9oWMsZGYThvNZXbz0QilWKMERgjiIWIIQmxhgj6OyPYo2jthDBl8FooaJQSS4K8aFHk8X4vIIwIV3QmiiQUUGUMairfEq88pD5CCcyMS+uNOrUVTCxS7FuujM7Rdm50b9Rt5k4I2dgRd2zzx80dHb3vatrzzW+9eM2oPaVsVdn6RINlJGX2no1Xa2l+0R2InWI0R3mVFHg0Q2VN2XsfiwlSU1vhyzHFkKgQFFVKl1RCTh3IxxBUgIvASV9NorETDTeCXQK0dC86kbffCGM/niiy8WKwyeeuop3vfrps3vPKVX6G469X4elBiJA/pXfuVXfu/3fo/vaOffcFIXzj/3cz/H+3V/eDG6FGUFc60oZgBp0ielhpHQKGKihH2yCPoso564Ck1Q3ImlZrSJNIpkCM0KKHZFnLqsuLHO7RUi1oSmKU1uP1kDjUEmGCj0hOi4wLyFnmlIkIs2mOOKBl0WKsjMFJuBumJT6RqkLQ5FJlw1dK+q4Pd1aV8QXYu8mJbE10ye3919cGt204nCe/fEpdgkV9qSffsLMiq4I1ATWrIEfmLhuJG7NMVAiRJLTRaO8k0Mis0ll44GtEk4vjEosksRQUpwdCa0h5Yj4MqYXkUzZRS6AKRLNU1k1gOWORmFloEwG+suTcsPKAIOgh3BdLEejWTF0lWYUeMFMfsOCgTlTQ1oXN/5/SGPj4q7aQBrAshkiclCwBdAk6xH7wY8f/aLNl5q1r3Ue8k5O8d3zmmx3NhsMvCGycCdJ6/9hXc/fNd3Tp3o+RGbTUTxeOcH2OJhzeaKXbKJO9peCLKkV0qag3sTG0qE1eEpvbtiFMvqxZ6jgOK4CMTlAUQRjl2ioNMPDfaAcHpTCxPHyoGGSwKLI0VQ6qVWkWUlD0THhohC+OEf/uFLLrnkmmuuQfPggw9yZf/bv/1boLjWc+UWPWq8DhU/tkEH79T9NTRLGt1/yRGfjfGre3WhjDZRFnXVUQ9RsiMqUd8owzCmgyaOsS7iiAZmTFt0Lxr3KZPQuZlpSIgG0pCuvrRE47XIkYwWRw5rMnmXNdgARW3NWCEyqfiKTJwdJouMeadVfItdQOXMW8jAhCVK6Po2LgaVspF5kQxKQkNeOc/TUon7It8zc3D27lNbdx7a7Tmue72VKy5VRIypk15u6hKxHIh06SeWPoPcxRp8WaW4K+cEVbFBIhCCotWiFWLmiWW9CQgRiVVkjh53iDEjFFPCS13iQA0Hv9SpR3RvDM1ANBb39gniYCZ6kyIauCBY7kOQ3qPGftQWw15viXR95znS5y4m1DkT+JNVCr5Fg9xFGsXCUdPt0E5L7vjOt259d3vx743kvRvNJgNvyAy8unvw8TNu/MBVX/t/HuheagyO0TZsJZ11cmF7aodKsFkfoNyjr9z77AXr3vmpsPeyj16OOJTA2gYlG587LodPBPchIGOoxl67SxBIPEAIxO1XvcJPBmt8bHBXFASZqVcRqSUkQd2kN56cN910E+68K2dQ/FkpTD772c9iAzIHNWedHfckqxAwxe53fud3Hn74Yf4V1kcffVS9xPizP/uzD37wg/xTT9Hew47KiuyhVmzUJUtkCbFmVcEzQtGkDGLqOTFolhs4erKAoqUIzIksJjL2knoSOzZdEcGyyNBEsOxeCTmZyaGBYtQJvpsm0GeDAfOlLaTk2HftQk6GDUDak0B9c5SYkTGYT8ub8k9OEGBlYkkImn1kWDBKWu5S18zv7rt8v3v8vLsIzIksjj9kkQTNMhoODpnleavHVS9eYq6zrOgCvoJKkGwyvALxahmVAUKTcKIzZUYrErBSI6WJI3PNW2Rk+RoBgQI4mUGwbyIApXVeGXjioqbAxdzjNbHcpWOzfFS4l9C4tA88hsaRpIGZLHX0Musjo5zgS97wHfVzi8OJOQOneDhFQXxMBgRkVgu1Bq60UCfuF5598Nuz8vMusdw0Nxl4I2XgnpNX3H7TA19+/IVnTyxesLLLvEGSDeWBYxBPEo4mW9rXxrkgd+lxpIBAE71K7iIzjgKKjGWDPQJkdEHvczdgYgCU0dQlQNtHgS6iUFDCByYca7jLhbMOGspe30kVwYUgfPQqMRwymIqFMQ+XT33qU/zd6bPPPnvs2LH3vve9vCXnvTtdtkGAT/dXt5CjASi1y1133cWnYu67774//uM//pM/+RM8FfXSSy+FsT/gbvtRAkEBMRv5qik5IaPQVkKY9I2KaGMNxE0JDp1QimaKLnfkvmmLLrkMc81T3hU1CQ3TU96wdCoQKNPIxIgVOUavmEGDXphIyC0Z+2SeOGrSTSZJURIuIUNTJTFraeKIGeEkJC6RhrklNmKO+4Ths1q4ErWsGZGMNd8z8yCfd9+e3XdoFn9qEZPicCJzDHQ4TNtreDHwuNMjuGRx6FszuGM2IWm4UHDXSAFhamCiWgIyxUmQJTW0dWrjLpycdkUDApNFiPpPDoQWK9cRE3cKTMYS0Ew5bx5dBC/K82R0v8MkLpdgHFWcIjUxKLqjnD/sXmXUvKzCjOt7xTgHEXOig0AvTVHKLdH0kWHgIoxB4njowIwH8Mnd3t8MJ/ab5muaAaZr8ePhaxr2zRPsntktH73+7//Xry0+0aEjgs1CBtin7JfB3RoNuuNp+RavxVcbVtkmqNypJcc9SxQKhwl1cXYwpii6YKmLllZGqD536XFBkKNgOYh816WL04mXgDKji6Me8AoBunSyCVNDBkcu1BLoRbj88ssvu+yyX/qlX+Lfv+OXmT/wAz+A/dGjR7/xjW/IQKOW3L3Pi9AKQM2nGL/whS987GMf+/SnP81XQHL3//3f/33gfvRHf/STn/ykzXLBI7eATZQJT1Bp5sy7vHsAOWCiYc0VCSdmbjq0BNyJroUrmxhaMgZ2T4Sx0XN3FiVMRIZeC7Y0HwluymDU2I3ZJyi6OVhI7M2EzCR8EssVmxAwBwvC9JSZDPrKTEUmmvGxqRMBaiKyXYvuzgZCHxkc2fCNl+9IW7LdYUJBGWvbx7QgqzzLD/Sz2Q0nun9XtXdNGyITGBGx+sh3bAIfeaMhtGS5M3GM3cosSK+CgXNW9EVP3LCkEN0CBloz0CA6tQTeakhIEJJmy/07cXFT+ESHD7L1UVBCRCzqLePOWFqo2kWCFmoce5wmyYmLZwdfBq40YkPTXYlLsYkvBdrckgHheSEyiXGFj8Ix6Yrel70EUE3FAlyZj+TP7b4L8tx9P78WITbK1zQDmwv7a5Tux7ff+v4rr7jygccffG7xd1MOrM2u44Ldp+LeohB3ZTxv8Y1dQtZ+xAxBaDLDkoIc9YPRowGOLiCri7rI2UoZyJFR00SmFhlqWyYCNjw7KNLjS9Hv+ujitOHoq7jjFXtFgLRwtZCez8nwipw/KAWK716/9tpr+XqYn/zJn+QduiLqfEPGt/arw/vvv5839rfffvtXvvIVUPjHmCgf//jH4Scg1wxAMgOwINka6Z0j+44S5G4Xmsiqo2ADBNOQQI48/mi2FhkmdTIygInIqF5L6CKIwqkLmVWSm8257N20cgNrQFiRsPgIh9rIFmJaIjEM1BWXvr1aBNwZfuIuPrgXyTgovqx5bCavHBzZI3X3nIxZIXBA4B6TIHlw7C9szY4dnN22/zMzg17KiQkQndQpIkoJrhEkF2Fx1NgrNkVHlArKNU4TBw7RmUeKXMRwHn9xNSdLnK3SYIMBXrgLoS9QUY8LOH3uIFMUwu5oPExFlHt96u0eBbloyPmRGy0lz+ksKFlD6siVVo5y4szkCNZgE6PL3b2DAgPnNRXMdX3X+zOnRe6mYSHCOjogGIyaO+zlzsCVfNzPO2PnxIGzYoiNfLozsLmVn+4Mj8K/4+RN//Wt3/4f/z597Hq/gMbx4aO1cd9pb2qvsd0o4OCLhi4Km1E3WpR1wjrBRABjlYpLYhDdxUG+6A2yRF0cKToobObh28yOuYCv3XFk4DwlRd6jBseOokfTfBQFR+zRX3XVVbjz3v348eO88uCL1zm9+ceUhKDE7qHpe9wF993vftdUbCGBj9q8+GL5H8/ld6N8mj6x72sSvi9E4uJxSq/RRnclxamxkODEJnlRsqKyURYfEMiyUx99B/ngxZNs2ldnKlAcvvigL5KRfRwscnSPzBtl3Bm7hpm7iIZZ9Rl49vtwckdrRvE3H9wl5+5JfhyoKNSHX3SJStxpElFkYpflCh8WHo7Onl0ahXN3Z+/a7v5WNb5PAFAFEATNiAWUkY+yJ5vGoNEMd3x1QkW9ZeIyRsw0UprqkiBW3Mi1AkWsnQyYlJbrr/kgEFRF7oSmoJFN5FBnggvjknvEb5TlTghFFwHTMEiFD9GxZ/HUeRoqEfrcwVSRPTL41DQVSDWvlCj6LE2C3NJU8oFqWfwLQnMOYqL6hy/d+aHbbv3WqUtaIm5s1pWBvRtTL+LSZPnfXsNNx8oZeN8Zd/3FP97/te/UXtc6CBuHracmu4/DB1lKvQ5ARq/9pcNNJ4yOKRmrFzMVgw8KOKpgufTeuwRHd5uhRI5dkuvuub1wIiwIGn7ROFHiyIFJiij24vTT8QVUYo+GPxml99xzz8VFvY4OAvd49HxVjB3pXXzGXWNzRy703dpzy4qG8Yi9aFFXjE3JQ8Ue2bmo+PZ1gVB0FxNqCUV3QrOOtTSLBoNKuQ+ayUBkEj4k0O5Ky4oJMdqgICYQUNyiPV2UYoZlX3cvYlaUxRRFe/GhllJChV70zWWmnv1TufyZj3LluIZi+LhrC1jZKEBbN5hR/HERGT7v/sChrRv4fvdDM85acaN2GaQBc4Y/ef0re/AhCRoIEYlO7XQxNMw446RXbWLyquTflrkAMmjwR6DYIJky6yXgQsFeUwZz9BMIAIIX7i0JhBIjJZDSYko0uf6KDIDWtwjwxx0COGosLV6ywREvat4nIajYvctm/w/zMuPz7hTceTKRB14m2b1FUAgIkD3sxZ8UoZE7lExSZKgT5CNnvPr8bO/Jl/RumpsmFeXKAAAgAElEQVQMvBky8OVXb/jord++87PdJh4cL3uKE4MS9xpebGG9QEHuw5HevUDpTJNLvj1zMvhGdyFQ50FlaeMcCg3RRxEQpqGUCjVjOPQqtkTAgDMqPiYIzelHkRnZo9gFBN59c3enSIlGw0FAgzu3diYCpboQ9n3GHX8u6PIxbl0AlLfI0QZoChrV7lKT8GgIQWmZP7sjEGvsUwcvk8GdpghEWMktlIDCLPdt1PDc1YSBQ8FLddG9j8+EDBTxE+Wc0YJV0uWmxn6aCDiKBOcnpgiZ1W8CfSlKoNxkvenqZs0oQUxAiGsAZWwim14OTtcqBOCvY9Qbh+gqeSxp4IOxGD7HpWfWfb/7tM+7A6jbpzCTiNBAQw1DCoK2m0KrV7TRcHWTPgGpN3GnKIEIdWP3ElpFGtwR7A4NCk1qu/QJTB9QYwkYTe5KjmS6ALQBgmhU+JBVEDCgRMdBGWSmT/wxjs8VcQA5ISNMHOUrF/GfQEBv3HHnl7e419cATFySoZF/qEJGfJLevuY7Lzj8xPa+51Sf5Ua/ycAbNQMnZ4eOn3PzB6/52mce6P7CksJOZKOxKyk6AdjsaKjp4pjK96nMqHGX2RypVmEGlCwUTjJ6lcS5b+/r2BEBuaABIXHPm5hZ2ULAxhIIAYJZkSsbMC4PzcpEwDe+reAe6G9ax5dzDGReavDxQkOh0biIxbUcwNhFBvb90gS45AvazQBTJwhQ65FjHmWjOiYLexg4tt3bBaIkgKYhwc2Iac4IlFUIgA9CBM9lc0CwbDMywMWdWjjUyYhsuUbBNBDIYRHZfBAkF81QQr6vq0UPeLKQTC9xFw3qJEU8tleZRNDyhURo0XCNUMyD3FchgG/jRohkYnL02lVpEUnkItvoJZnPu/Pvqt5yanb3oe4SP6rARwUCmkQ1AXF0mFBY5NRWJlHwUgb6DBL7pMkxh7sQ6AJNtckk9kShRD4i0BENp3ni1dcESgRYh30XxwqlOZeu0jKesJBEm+ELAag+qlEfKeHC9On1jzLQEdqfouibyOIsAiKTGNSbuPNuCHc9jbjNAyJ6doQMcp0SQyAD7QTOOevgid3FZcWBNkJ7BthmTUutHXFj+b3IwDdPXfYT199/x5O7Ly7f+bL72EecZvxaTFuvzgtjG7AN2ctqom9xx8bnHqFBoAiBLgqyhBjIERGiPncXQrRPZAwiAXHARkFljJISHSMlu2MAAWdANtTRMZd5PlKkx52jWBngjQafo+aKrycLsDxqKWIyZ7RHqbu4m6JIuCloeAhX+RK5yCbmMeqjDIk42thVlM1BAjUcYqCYnZxSjglCdMkNBjUioFRgbIZ2jPjIka1tRiXBXhLyiNYgWE68aIqYKMEq8syNV9SYhgQ3DctK8I2nL0s2zgX4gzl2CJGMbjxCjlDCHKQEARA8hJxhXQM+hVVEjWUkVnTELJkylpCuLEIoelWUx7dmDxyc3Xxydtfhfd8zAxMKxFRLQBaUYokMBCjI05Igx8ZblwgUayOIW5KlSgZEgHVIYSAVy74uLUIOXKAk21JkBikRlwx7CHZvFEQb/mRGc+EUWYhQZqUsyR1LEOhSM9oPynKpDyEyiTLgBNUrKB5LvCrys2owrg0YCAVYcggassZogyiccXD3vLPPOTUrv7aIlhu5LwOcVr41DFxM+iA2+tdBBrZ3Dxw7eNN/ceU//PGd+36OZSuxnUWQraQyyJd9ZxvcI0LswgZ8igS7KArPEQQZyMYGg0KMIgRxWAygeodOYtHkPFRE3HWkINQ55AREo4UAyLj7ZTyOHIa80YCGolNz0iqEKIkMlt2vHfkPbXUoiQlXbOSc6Gl2EMsP1Oe90uAex69wqoWQO8qeWgLGRWK5Y58GDpXnUwslj0KU+hLSR4AQcuwzyPW4UKSX7KaNhUmtYn1RwKaoH6UUE7nkfNAriuo8S7jkynYC+LLekiujKYkPdT5Ss8IXg8piGCQDVDFEdDQTlJJjr3am8gCaSjSoywwhT0LdhV5osIYpJ7a2Hj/zwE3buw+ccRAtXRBQDSUKyeGVQJ0VOIxichoZAu4UwolbzBgaCgRQmgaW4qleunBHIwQp22uYg0AaESKswc1HQkSWvRYSeUDIEaJ9UYY2hSH0cci9YKJCF4JqvZLRKJyNFj7YaAgQQJ4wlfDHUUNAzgnTG0tuoFngcUVhyfECPrepaAD3LJCQLqFhOTlF55yxOzt8zua7ICuZHNW1eCaxQ0e5lY3BMF7ZYqNdbwYe3b7o/UcvufyB7377+N6PsmwlnwBsHI41BUWvPTXIIW493HWqACUEagogfWg2wAYvijngIt86hwqC3YE1iOypTckZSDjY0r5FQWbqmo9gUaHJEcQk1phxmlEwRqaLHOp4RwM3FeHv+20j/0jq2O88Af2CCy7g2BVcwkNNJoDUcL5jI04ahlycNTX7ajn29db1EICJ14FYJS51SnJppJogq0l08h67wBSs6thl2awQZLYKB8NWhI7TvBRtdFXyXEwgY98iflEJHfRmRSZ1KERjwYpPPQS9uEffsTLzCAK1iRURRINaJdoIYUL2DCIENrk1CPAhOcyRBNGTnhoaRFR5enf7le2da1/h31Xd9z0zEa0ug6OJSDgUvWCiQi8CTKglk0kdVQCioauIkCuxxFG3xhYOOYKYcDLSpei2AZxivWT3RkETgWYaB9zx1XpGVpacnBhIHKjzRGGv0zUZRXTvkwGEOQjmkFv2sRIlgsKcxYAZGo0oB+nT4KJ3TvwGg18W4+5XUH0u0psVgjQkgdkUB2on6tyztl89dPbm4l7P54ReX4JaN20lBhCGq5htulbOwO5s687tm//NrZ/93X/kjWoBLu5iNpfOFuy8p+TjDZhAyN3noU4GGdNFSezzZjSTIyAyo6t+yhVZOToguK/IoQUhHwJphAZ61QKJZkke6PKLDFx0uOmmTde+G4ztEohKEwjelOgDi5iJB7VkauL5Az0VnEoXCJWHAUPCN9Y5FL3OtbjlNnUN66bCoehLUBX16pUSY7Gxs6SkWd8ngDaNfARckuo9I0XG3KIvMmlckQYI+YSalcDzEJEVCBOmIw6Eqcw5RAPJZuUu00PDFYGzKRKzWYvQyCGBggDMKWSAwh+lgINSZpAhOWgog8SOz7rvd7/15IxfmQ4fpQmPeZNYRNHPMCjgQy0y6M0KpchgL5naBTPf3a1sFxgpCJ6LxJEul6TLlMRqFQ6++CrtSaC8CaWYKxmggYOmr5ioHMcaxmIOyNBwV6MgL9YVxxTRaUZHmpQ6KwXVKDCewIGnD4UkxL9e9fQhRErIc1IFVnCgYO/puOy83eM75ybum+YaMzCfm67anXiWrJHLBmo4A8/unHPz245e+9YHv/X03oWk6Ka9rCOLbcX2REOT04CiPUhd9JUSM/caAQ1escs2iaAQVkYEK6Mg+woy5Dno5DKNQx2BXhWzEiUfR+h1QGGG3MIBG054ijC7JEgShORizYR95CMf+cxnPuPvrLEZoH3HNAHoteUEQSlwoosIhFAp9qLEvTKRfV5Rn+RHrBJltEcWJcfFeMUfYBL8vCk+4pb3omE64KNSNBhUMihABs2iQcKKJghSgoagRLmOvn2y3Pt6B/WKC4gK9gjU6KOvKSV62bCoZBBd2mVhakbsJT4gU2KWMKBLLuw1ClccKdHTNMIogc+7339odtvJ2df3f969CCJuxdnnXSlnCuuqmKgimpW4sC/6bt426xPEitBwSAiIjJSDxOCgZ9K0ZIJPBpg1T4eIUefMMYYV+pwV6aXAIe/KcRKNOODOQMBXiMQGPhhUWBGaXgaC+4RUzMPuffS8EYGILhDmj+QgyfWdmt8AAwJse0LMgVGAhvvl5289dWpzcU/WwmlpMo/CbZ+v08JjAzqUgX8+ed0v3Pro//SFrVPbe5srbsMIwGwme1DHiKY76YqOiay9KSUI2qE06wgJK8ig0QITAWQKIEm4YhNLn0u4m8MEBNwZhX7LJyYayCBUpCoQuQ+mQiMCv/uMO4U2jeI4UfIk+Nmf/dlf/dVfveKKK/7gD/6gzyzXw8Y5SnpNFMFyYgMl0qqfMyr0Eq+kyejafc1kTmpxBkn2BAsfzPpqS2i0c0gc1YQDAyFiJJZbEoUSl0W0kTsGUTlWhoBdLJuVNURBdizlSk0y2bcqjFwXEvDcWDSS2mSk12VRxHKEQQ1DYF/4h+BBewyIq0IGul03P7lgpXnBAJkCMvfIFmIYC2RyPnV3v5nvmTm8xbIATTzz4RALSsXxoiSZ9OZejZq+ezPpEh/lLUGDEhoR47ZHVjErMkwci00ccWdOSaaQi2ZSVoiRCmxAa5nBJAQulMkDgZWywSiQgUoGot5EmXCQjVIxpzN6WkFwMoXmpAFLySNilqxhfjTFkt8p8SMZkzt2WsWBQCTz8gvPeHB3812QSdZPb3PfLK/2xDm9RN+s6C/tvuXEBdf+yKXf/PIT3R/ZM1/aqo1bHjNnrnuSLe+QgFDcVRFyBK2ZojtK7ItdCoEvO10yZiqV6Lb0sQMCozCHiCAltQT7IsAqIshGZnXC5hYDoZzncvFudDGMUj57380Yl3PzN37jN2655ZZPfvKTn/jEJ6yvCOJNHqHubEZ7CGFDjZIas9hrGZuWh6jti4JA1CVisS66OF9iyBDWQqMYq52YaFfSVcGf1qWI+CJYZmElc6osNS5Tj1de04hpXTEpkRhygkmT0re6GIUMpnHAyzQ8KPGhBlynADK9BJKN+FBzVdWG556HrN4JtTgQq2+YwhQN04uB+LeZHjq4y3dE3nNol8umqUabQVk37wnbxMSIS9IocSAiQ41ykJjWQ+PNuzgiQnBBBEERsSlmDL3IUGsSEzQdvsWuxLLY9EAYNSWxccbELemFEoXpoGYg9IKGnJgNNnEhFQwEkCKNHKFITAgYgwYmUO1ksOS7jYHlAzz8ayFc3yfslN2d7bPOOPzK5rsg8wl7zTT+UW38OnzNOL4JA91z8ui/vvWBLz964uT24v2IDi5SMWqf+oxiq4KgTLJ5VYqJjWeFDbC3LKGdBr4+b0WDesFg/p8EOW/KWMQYBU8ibNSEhphQ547WCMFNJxONmbi3T4ghiE6JKcVLyn2fcc+xuLX/+q//+oc+9KH77rvvT//0T5966qncBo0GqS4oIhDMXKUpOtaVgHgy6pb0Mp6kpukx211kqGOC3NsnTB4CgEo94URGPPNALcRGcc5DiIz1xYxJGccrmdBWtk+KY0VBqaiDQENMEs5qwoRkKhuRWIwyKLfQSECgRFzVElj5SotrBJAZHZcMMUxAkqZuitSJvr1JLF3R5NIlzk/KJUpCT81l5+z52ezBrdmNJ7rviEzPThsNCRoIQ05GLTKuE24mZkdSyogShkPBF/14iQZCfYHhkFCKxJhcUgqCWI0lIy8QWmjkQxMTQEiFQCIBZDUxkJAjSEMqgNLhPC2lGoiyIRlAirInwdHnvLrlkxPDEhrUg5NiNAvA6s9Vub7zz5SwrXgZnw8ccApeFoRw+MDumWedO3lVm8ZGaMoAie4moaf4XMquaD0OG/VpzABfkHrvwdt+4aYv/Puvd18uwp7y9mTLUxQ73859nCIC21AICPluVTj0gPehoZ+zWA8NRzQZHxQIkYNYORUys/EgZ0PFocldCQFBxZa54KDuwgWlm7WLu27tH/7wh48dO/abv/mbfbd2sDxI4xIjV7q3RWCQ+chxjOwTHMaGZpGVuYzxKleiejhHJwqlYswjh9dFohcHZYTTJCTEaJJVajFRUKcrKut8EoS6cd6rQFrE9IImmwRWxOgqZgwlCKusMfCJmASlCawuGcqVGYo2cSnIeqmJgPEqawwEAAEZHMuc7GKZiTa1UketT/4IjSaCu1qE57dmDx7ib1W37uRrOVoc9tuIG0H1IwQjUr9ouEaQvN97X4s8AEJKBy33uYUGvswdIEqpsiSGwaoT53QWD488HAh4eSyJ72DTNEAogkASkAoxrSutQ8aSMxzkgAsghGCBIQ+uMQGKErXoSQmCPhAlJqBRBgnIAEvRWGUsHKFc2XkqnXfeeaBpA5rAnM5eZf0Zh3cPveXw7IQVG+F1kIFwcE1/VfA6GMf3O4WHT118+1Vve+u9zz3zyt6jhEH5vNJRoJNK+squj+eGM6M9KQTJBrdNnxAtQRAIxuDErj53WerQwxd6HGLUuFODQBmEEmeHEIiag772iiAgUOJYZIYysYeeNVHA8hAjUTfQsY8zmnft3Np51/5bv/VblVt79JJszLyrT9MNJXtOJI8ZDV48E7ZFWABbzIq+VgpEs25lIogYSguJASDJWBKDaU1gKfiqLoKIkokxNSsyAYpw1MVwVnbM5sUaBBRyFA0vvEG0CCJZNHL9oAYORKcgMK0IJAQZRzBV0FCWrYGRMgpw+vbYIB8M8NUdERkmKokjZNBEStLYjKuM7+5WjhK4u99/aPe2nrv7klf33whrGggMhKIjiQRGs3YZHO5kJERoLY7enpEbMpc8QNqnMolFSkHWDwAeZmJTbyonmtwkIQKUASAVfDkCgk0CUo/uXhx1b44g3UQuiy0lYE8hdTQRYi8J0ZZRb+walIHyWDBGTsCNAC+tIgQrEbDXG3f0fPydJi/jB5kcPHRgc3GPaXx9yftm+PVF7Q3Phq+GvHt28y/e8vk//PK+f4/JA2eLUdTUrtSWtNKWCHPbRRX1yN6kuHOAqBdT6xP7vGlLEOogMqCOIIrl89M2MgMcg2jfJ2MWQeJY6KL0OUqfH2uRJ+4eZh2neyllHjYF64Mf/ODFF1987Ngxbu1PPvkkXby3uO797z5y5SWP3f3A/V/6BhFsHwWYzfnvG4DTFC2jbBfxJh3JC5Vo3CjDJB9a7lvnBhOeeaJHnbu3aCY7wk0zTRQvkRjRxCaHiGgtMoFEiRp6fS7i07cf1LsiZ9whkIOg5IJCusxQllBFoMCKhSFumCGj7BvIoB4cwlEPWmIANxfZExoNMrdMsRLUBEosVJiwcVqY5DbQePHA1kMHt95zanbXGd3fqrqIDHULN0ZB5mECHyOMFfBlajQ7+DppylWCViEGAi6Ns5PA0sSRwlgIwbhyAzSD3DQjMBFaEWRQSUIIxM9mWiSD9jLIuUHDIMobdSMaZhoCIFrzNNt9bamFAQLrpIhQmVCBYPDiiy/izmffGSPX977Z6ey3Fr9tN4GNsMnAJgPKwBPbF/745e+87ti3v/XM4j6dZIb95ScpXWw9nSo2G3Ws4e6tCg4niXAAocuYFQEzWwqBAw13ZIHIALkRBDMGSJH9KCZxLBCIIIJNOMCtQkwI1INMyh+VOXr06K/92q/x16gf//jH9f2P57/jyI//tz/9z3/5+Xu/8PUrbnvXB/67f/35P/o/tk/ufa8kkRSPmWAwng8xaEllHCFQnpuoHyWLD7VKn+8gt0qi+zATPQQSjZpLavt6UUJJNYKXERqvkiLaepUJN/FRCE1u5DY2NINKVkgjAjSwnO+y7s2fblRoIEOtXsBJFLellnRxhwBEN4lGDrlZBIl5E59oD0+K5zR2adfQFZWjZJAjk9zX3HJiGM+p7T63u3P31u6NL3efd59MRaPgPG3/KUKUmNnIDdkXxL685cOMGrIKJkzIDAixq10WCB94K64o5a04pzEEBGDCcACZxgQv8qllH0HIEgXwGE5yHzdAcBnknANKozzkTHL7ObVFlfTCTTlBDxNKYjDYxIW/XsWMt++QKf716iVn7x7ffBfkYCrXYMDm2vcgWwPkBuI1ycDXTt34Mzc98QdfnJ3c2fuzQDYtwXVSsdEkFOnoWGuxTNxx8YkKCEUGxXA+SSKIEDhGpATBZkWQ6BtljN2cE6kxsaUFB7VGByz0KO1MZG+QCpO9izuR7EAi/pdP/fv/8+N//vIL3bF48dFLfujnfvI//uFfvvgM/2zL7P5/upv5vPIHr++EZRE/Hkv6humlesp/YeLprPhrYWGgrCWWjBkQoJSLODGJZaUJCO4Vg76uyE2JJTNAoXeNr7ip7oOSXo51m5ZeEXMtAcccH1bo+9ZcywTV+Th0NEPpQt7Iv5rYiI8EZAiw0liolIgwQQaNKMavI2CJgUnaGI1emaMBqi9vts8FeTUu/twdjbiBIyZqRkuNcZDbq/PPu3ffEXlopbs74eLdXXyoVSIx5D5uGOvuLoPEq6Wp8erGjFx0IQr6BbO5nJgRXZdmECYvfnwpOgpGgUR6IsYUI4AGMRUAERLafU1FZ7GBPMoxAgqE4QhE2YsGyOImnkmXmnQhwESzrGbRsqLUP/hd/OvVI2fvPL+9+S7ISvI2XW/2DBzfOfPAkWvfe8W3/umx7qshdSC0HybsWW1bTgAe2WQTAY32fmNyhSBfQIRD0yASbFaEjb0RRGSKLrmyAmJj05Omzi0ykSW1oSpCHxNcuo/EcPXh3MToiSee0FlMx9uvu+LG23/opWdfOHjoILNxx1987sSLr8QYWwe2dne68Pyzqfy1EIIGE4NF+0ZZc28QmjiqzhE0fuUi6V3lAmSoPhDzQbBsLzQmhpLHCVdMJ9ZmowQwSW8jiCjF2rHiiJQ3UbVBixBBWuyjjQZCDQi1CzZOGgILwCW6JzLu7WlJfGOTS4N/ABAlehFcy9gMaSKrqS7qCGLlWIG0MPAE2SCmZMFdEuakFp9l8ogSm8bmBbuzd3bf7z7i7m5WCBTIMDtaLRqR6KlupCGzVW51DgQIMukVPeslmBUGSVdsMhwG1f6bhOhr2WmJsczKgu0R8tRhprQ0HgsRzTIgjIjm4GoRK2qMVRsEbowIJWlBtn6sABNAyElMSwuIWSHwIw0/LpITTl3I3H7ViXfe+N5HTl3cgrOxqWegm/tamfcPGdUANn3fowy8ZevUf3Lqs//D3546sd09UrWh4MIOGrsZNQIQjAMCOJRkcHHbxi4ZU2OgIibSR8tG2UywF5kWR0JjZgKAqAkNjpdpZMwE2L60DHIDpHuSAQGPZHq+861Hvnvfo28558ztU9snXy78TT63dn5Byefg9XerGl7jIwRjxRVFNSVzdsMEZuRFGiXITSnrNe7JcOr2Sa/pgeMulHBQjdJ8lH2b5QIuq5AxoCOiAZMiwQZoog2yi20QGucouoySoUHeXCJP8QGNhFCw4eGKMAo/GgMo/KgclHGRl2vdGEDDN5KUZhBQBiR29bs72QBEczSnufcY1GBFz3WRGyCMiPSuktvniHxwdvPJrbv2f8+MWVmIHEyM0MgMhMucRkQzWo6S9XKBQQ2uXrOykATSLwFEL+lqaUIAX92YJ6TXrPCFCRHNRPmhtqbOB0tyyyyDQ36mpRcvfGHFiBQambjmaQ70xmK9BM2L9hEylolBS1MgjKgvvWZlwbDiRur4vDsv4GHCx99RHth6ZfMJDmdpI2wyUMzAid1Dj555609f96VP3dP9Qb9stMt0MqBpPJfky9bTdqbJjtbJgKyTQbWayCpyjHXUiwxQ0SsaV+RkRALBPo5I+NQRRwTk7uFggJlBFuzbTjwziSCgARLJRA6SIz3kxUdlIpx9QHrlePdRmWLhRTJv648dO6aLOxMT39koDI4IRXeIolft6BhrAEWXdiU4xix6tdATSD2bRfBEqUElynozp6dVQh3zhqxSRzsdvdwVICOe1IRQvWTUrUKth0oC9XhekR4RYZJMd05MUTC2YKpooAqZuIAnsBJgTiaH6qMnS8YCGb28NOEcpK7hlGFLtpAp4mhyXzg4e/Tw1o2ndu+d/7uqsnTeJBTdE6XSy7goSVd7kxHBSpdUvGIOI8ggPZOZnFscAYFM35qJ3JD76PGZbHqZplUyI189F0lR46A0v0qj6UGG19UAMjql0V2Ngp5tY8kk4FokmmtoqCkbsXKdOMYmTPTXq9tbZ25tbu4xNRt5k4FSBu4/ecl/+q63fubYC8/zWcl50UbTBuRw8KGBRsoSTKfDWEUGyNrI1MjgSA8IGsmDtcgY0CBCHnSXgUAW5OZvK5DVBRmdny2UsNFZh28c0VgyEUQ41EUCKClKOzbd3/Xz1rNx2NGMi8WnP/1pjnhu8Hp1xHltA4WhacFdFQEEACsGLV2MCjPNq+Sil4hV6EHGaS0irKKEmEoEQQMfaRJ6bkb70yeTPYZPQYAVgSBgQU0WgJaRuJ0+MkVkZU8196dkpkTJ9IoIiVIDpE70o5rQgAxxI70EQSGoK/ToJfPJoBKcwabIAEXJjSsMMcZF9J6ZzU7O+FvVra/vf++eA9Y17Gsyg41g68bqhaGXX7TnwAENHEEVRxftcxl31nbftTu3L2q6tO7uQgYaCNFm3lObXxtjCRmGqTWc4NhsUNBSAQQEL5u+BFborZcMtCFTGZTmF57JADUKfOlipsiwB5VY1ps4nsvzbaf8VXd1303vJgNvqgzw1ZD3zG752Rv+3//tzsJ+SQ4WHeY6SYr7V13USQ7R6Nz26SSb/BRNHGMTFx8IOiLUa/BonD9EMFOJIOIjx3WRUZRIJpeJq6IuXCSgRBCCMhZ999149Loldg/KzB8FXKdg0KXPgPzm/IrGGlIcbTTjoBcZxtwIGN0tK4qbowRxo4YMNKgTd82H6qSr2ARqlbEkmKLHxEFMMgaQQZaATA7j1TxBwJdnfKIc22RElUkXMdNLwJU6EBDoWp0M4x07KLjlhwJKLnOgQcz0EvKDTRwhU0nOIAIGMAGHTa3tkLgkCUx6Y/N497equ/zbTKvf3VlvFPPR5KqOESX3JZBlSXKw0dTnji0aOBB38EMmFYZKIG89tAZWWYFME4VBgenktIwCGzGEg+wRGJQAxXAsoHyZpsHk1BmSELiBQw0mdW5vnnmXNAxBvtOSA8jbztl6emfzx6l9CV6vnqN4q/sHlQpTvd5AG7TTkoFvbx/5yXddduWDTzz0/N6HhB1JR402MhszNrHRVqW2fV3A0kcTUD+pBpsAACAASURBVD7BhFP3jb0RBxCed6ChVMFy8JDBxsZCBociGXd6JQ/WWBYHhaNw4kiNJoZuJgIunKJSgo8x8t4/wESDh5CjJs6VZnwSV8wGu8iUHn4QpWCvuuioRGsMucGEUSQghK7MlumZYW6vFPPw4xfiq/NJ6FWa5kY+mRpqNFJCCY0WEDWs4NaXw0qItXRBCT7kBxqil8DSS2lJHUNYyyIEh3Q5IWJFrSJ6UKJpGWM0CXMuGdDO9YlZvcle0GWlgiMmC36BVWTINZfkUNfD1Xu7f1f14O4tJ7vviExHW/XMGZJh5Qc/hkYp5rCK2v2cxqAoLcujD4rQpEXXXGSlMTHu+A0x1ALWSvbiSXBamrrpgoNAULvkOXQXwpxgt03sohnHa0JijQwghZlCg2BwG0RhkCGTDsIgTsSMMr5KjnYEOLG3Lp9/5uxB3rhvrpL1NG16NxmYZ+Arr978czc99u/+ae+NXkwMRwolaixzCOiywW6l9JnZPgrYe1ODw8Gu3goOZi6YIVPjxeknATKU2BUj1uVIXjiyRy/wurt7scedJvQ4u6iRGekoHCImyenQUFnreAj84PKRj3yE+uGHH/7CF74Qu1aRRR0EBMs0leI4YdCN6WsMCsIErwQcYsKBVSQp2po5aspgLBAGbZLog80ucfMCODyXrW5NQEnuBEVmZlnHkQCrh0fgYIjVDWAFSF4bOaFq/QSBAeqGMdbX9JRD3Mmnt4MYUiuZ7eBkmOsXmW93KVrCRPNleiJs40aGmK3Oh1cwD8/4W9XZN7LvmYn0EoZQJTol5pB1S1lxHZIcQBrXs1iJZ8IQYnM63ScG4encjhJw1HW5kU8Obobw4dTFIGasy+D+HOYIUaM1rE2xSp7lKxxhiicZi+GQBxniyObCcjIfQuALjvmgSWg4jQgL+cDmFXCSpE1zk4HeDDy3c9bRt19/6yUP3P2dcf9uDJtRRwTQnA/a7MjxHOuNGjpyHG1k9Ahxy0fLALAQiUtRQ9ckZFxQRpDcMdEYBL2eFDJIcLrjZl6ie5GhzHyEJjjRPZcFCMLij1NzC83B+9///r/7u7/Le/s0INIV69xSWaNWsYFn3ZppArB1x0hPMvYI0ZG0KqFx2uqwxd4EtmiTK/HSEpFAzcoTPdeix5MMgZKDnG4NrAjhWoKDmica6MXc2sbuUTNNBh8CeRSUFAWSIPxoj2yGyuTq+WQxM2WNS3rOcY9nzADc/Ap2MitAYDL5TqnsUR8/tPXYwa0btmf38t59nlUnkxDITqP0fbXMVv9ZwjjsAggojQS1YAKiR11kSHJwUX5kacdRgnAYFwKBir7iZoYIjohAwVF/dAQfZEoRp0Wpay58AIFSiws2UFIdSaLRjxNKNWhQbQSUGfbio2s38ih3GwuH45FxIVPc5UAoGbK6Dm5NT2CCvGluMvBmyMA3Tl3zi7fc/2+f5CjoPrxHGXsK2Z4zhK2qI0VbErR6DnXsyEWWcwqL5zt68RnEiVF8+gkcSur1KRGNK7LGJRDOMZNETxGxiru6NBzJk/nULu6PPfbYkSNHdM5W2BDbP11hpoSKnOSKr7pwZ9iDZoMGwlE2Y5071knKdy2U+jKge7lqwimiaqeuWwvzJy41Q5v8qMuH36iZ89p7ikcv7UaImW3fSKNXLrOjgPK+yg0GNSapC0piL3ooRTXpzZuY6cKUd43SCMcuIqn5Ve0uhJjDPI3agABGl7Gy3FlFlVQ7kxYUxZQQnpntnNzavf7E7K7ek6OJGlCsZ6aM2vgtnuKWMOROCYiGhsBgR2ESF3temZNq47SQyW2Ew7iAgg88ExsMVAZJ6hX+hBTFiMQChy0GDnziKsoziQZ73PsYgqMzNuLEcIMyyMw4geAjue6Sk7Q9wwEBPtSS3WWB7B8+2H3m1ZqNsEoGWBybVK6SwO8L31d3Dz1x5i3/2VVf+Q/3dr/6067X4TB4aiUD1N6UEgSghIMeKGSV6EWXeqMyyriAI03dMnrZHhfJwqHu4i1L7iKGqiOIn1yJwagUKaz5ANU4tIHHL09E/n2lfDBRQ2zOzagZJcMVe0DavZypxIWHJdk02nIuRiALkNytMiKDkD0o8W33IgwfDxaZCSYKZTLPZPgTmjGT4mYQWCGrhqplGWgHQt720wTwk7hFHIWjKzdW9mCoWRPhIkijEqj6BbcPx8k0Sb5JOl+QuI8iCYIulKO8cpIaly46olq0UT4rsfhb1YcPzG49Oft64bsHcsheDSE0NOokXJ5Jo4heviBJETYrLkiYkB8fIw5aFIokUZohUACKahFhUMlwcIcP9eShae/AirsyEYFytpEpKK2pU5I9gPmPAXXHpJdw/DhBruKsAYsZymiMJaVCki5cSDVepAjj6A7Y2WeeNe7PMqL/Rt5k4E2ZgftOXf4T1x37/MMvPn9i71sx2Gg6TEgJG23UiYQvRY6Stfd1pGiDN2Y6hoaP9r6QJ+NASUND0BnikwTBpchQverS0HSUoYEPvUWvXJnj9A2NKHt/nFoM8Pzzzz99/NnbfvrHmKUHv3zP8Sefy+OtqNHjLQHR+KmlRxA9CzSTpJAsHgajZi4J6qbjWlMUiMjigz/2coGVBWS6+NdAYIW7+BdxTrfSK5JAXgoKCiuKkobQzgSXBKrdt2g5T+Giyg1aSHJJYqSjTpM8EBqGpjOl2BuTmRh0qdy/LGliszolhsZVSQspCVpsVpLJrMFHlESviFBX8nn3h7Zmt53cuvPwiDWTYIokHPwlPBigFCtmgYKmkeSoO3fCJDbJDBzINoAKTVOTHs0kY1AhCRQLCV+gct9GDSGYd0AipaJv38oEgQJP0YASTS2AIs6gspuY+fYfpJRD5clEwwKAEsPsiI45hYSPi4cGmieOXp4fW285PNv3T37npDaaTQY2GdiXgZ3Z1kMHb/zItf/4v9/VvXRXYaP53GCj6SmJktMg2bbxwMQSd5lhuQRb/JdejHkqIXTHSmaQ2CfNaG+cIqXEkSYRKXiZvBzdhTBnlHLOoawBgeKmKNFECVTssk1R6FCWOIBQdG5DGD1QvX+cKjjm6aprjr6yfeYjd9531Y/cdOrEyXv+7iuwKAZrV84ztsga/xEn3EULYU674zcK06uq3Su3FIeO3HJJkTU1RQxZAvSIWPliFn5ZoadRHmWNGnEzwwRZ0+9FQ3MtWSKK8pCEqzRznpCRkhuAGK7CTWgVAo1dGheUlLroJY2TGbuKMsMBZ5VBGRYQ3bmtgadWpjUW4EkpxuVaAyUsMbD9BIHvmXnoYPcdkcm/q5pAJZNOk7iqxQGSokRWKYn7qCZQJITRxatbI4J44i57SOoj3WAiU4rJHASHCcjTKEVwJYdzEjQNk16n0Zwxg2p0zGVTwnjaoISJL0WUkoQrmaoTAtDLSXJIYgzUipSgQThwqOHWTduMtyeLOU2YbJqnJwMsv803Qp6e1L62qPefevv7r77k8oee/Pbxwg5ib2m7sXM53zg5u+02393QZCNTaA5SxoatKjOgdIOnqf076B4NiOgmOJaFD7iOdwQTw0WjsHEiiJKUHl1iU2l2WViy0tAUGqU5JO6QJGhUYhlJitLAR2V4F/LiiZevu/09N9z+Q4/edf83Pvvlq9978/1fvCviJrKjIkimFksLZo+GUztBmNBUoHZH7FVIkxKqJggI0FMhv8w6DJ399hCGGuWSG8NHSq/pxMZUEZKu17IpnkqjOUcCfTwxZmjevdFlrEwI0Op5EDdqlTwE7kw3lOJuyc0aNYBwk5gABT1CLGl2staqlqLGiFwfbJEk63n1qyTIzx2A3u7Np7b4nhkTTSJCjzLIE0q6Aq64DBRICSduJJPnM/bOaXaPEHsxZSScsiIlADU6hBaoQZ7M3eQTSUMWJVb4KstAPEk4KdIr8zjLhIjNmOqiLEpAQQnHlkQVcVBqrzE60HZ3uh+c2Dz7lkKf50a/ycAmAyEDd+/c/LGbPvcHX1q8qu220rLYSjuXGg07btk/8BS2exQA8cYHii1Mr44R4UfjigwH7KmxMQ4y4MavuMeuIiUMRh1uAnRoiClRSSDRjhGjgWUZDFzcX3rppWefeebM2VHcLrvl6peee+GpBx575KvfOvnKvn9sVSk2NALoFIYXlYkM+7pBYl9pQiC5Gyk1yhEyBXfNJcSQRY8aR3IqJXqgnOJKxMEuTcOgGQZiRW0heokYXZFkNBgrA+XBjvI1PQmedNCMiTx2TU8jU2ROiri0afqgRMFMdbQnImWQ51pWgmKx/IpLfc5xBE+NDsw4nAmy7u7Ujb7iibEFOb4wmz1ycPfGU7NvHtrl49irEGMbknDduRtZ5WbQQ8kC0J+rknM0ZqW5GJx3w8qSqySAxemz5aDA6FgDcfqUySSfwqnzVKJWuXMrCoOiQIkmmH1DGOQJCO6YAQVzmn1Qg3qSTCFRE67vzqQFwp1x+NCr293vl7plsf/6jmbVXTTH3FSbDLxRM/D0zrnXX3rVe97xyNef7LY2w6Rmh0rOR02XlOxB37hk3+eSg0iDFwVZUNQgUKS0F3rZSLAeSx1E8TiCEo+YPij79gkxNFAUWaInXJ+X9cRVQYNA3Y1nfndysxEK34GL+znnnPPWI0d06mF91Q/d8PBXv3XRVZc+/i8P0nSJ2bFyUGDklQdGizsIjJnJoOhX204B7mRBidALKnI0iCn3QbMWA7gRXZbACtlCRJhPX8dN9jlP+eb6CLIW2fTMNsKKgNhSM3dKb7SZLBNx1AAjVbFVaFYCgklKmMCKoelOM8E3cYGDrlmRp2zG8iTnQLVfuBMmsZlDKaUi2Uc1n3H+6oXz4YYTW3ev8Hl3EeMYYdeQ9sFjwVT7eJIi2ayYKyYIBBZVC6uYXsnigGwh+QQO+HlKc5xEQ6LwghXu085eA5JquOmiDJp52gABPaXOEwMSpelDXoUVgSh9UGZowVQ7lvt5XnLm9iuzs8h+Z8Pouv90FR+hWUidZu8GPzfYa847N9UmA2/eDPzzqzd89KZvf+3vTu0M3572ssQ29AnARqawW5O9uWddlSKUr3kocaJ24cSownSdttHRoRcN0oMz6B4NEigGqF70glII6uhFlw2iXrJcfIGpWGK/98epORCaxx9//JnvPvXqF1+CDr97ZHQvPfk8rzH43Sif7S66NCrh56ntc8FmPundrDs1yoUmjLGp8MxAGJv9PC5RwMn1gxolXdwwRtC1z4tDhMVwLE/Qxrr0EQaHMVKLcG5GF0VJQMgNrKF3XcQIB6t8PYhkkaq4edJNFZAilGm3C0C1rFIBEjTh6eTAjesRBtTm2U4jsQRk2iVSOElK+d4bpV3EqFUwRkhC9zWf7f6Nm+7fVV3xe2bA18LTABOqMbpIDq5Scr5KrhxRi0o/fRXT0kJVjloJWldFKAetC/gKqsKqiABV0kIXgg1AQ0kBk8FOJsaMUMAh7ZIdYqyAOzTAoSDQNAJNaaitLApHzto9vnPWomtxfe9ai7Ev3bfCbb7rnTsMQM9tvl+q9hENWpKWvXXz/TL+Dc+pGXhl9/BL513/o0cf/OoT3QN6wr9S552rQ5KjDy4th4NOKh9T2uwcUBLcS1Oa9iHKRcTEiiMLAT3KUWiCkjsInMaigZ50jUITlFmBCVQfq9ofp+J54403vvzyyzC45557rrvuupdefumS9/zYRRdd9Mwzz/zRH/0R+vZkJZYQQsMsUghEkQb2FhgDRVOlUSUganKyr8IkYipNUWOZLhcrLcSkI6Pn1Rqpk2yzaQJxyUO7r3kiJF7SaEnRtQo9KDF3Cf7Ypqgy+0DlbPOsDuLjkuMMehUNEihRVW17msoh2UBQ7d4orJEVgchYZUnUqYon3BBYCUCxxSLVCTKfd+d95rTviNTej/lB1jdpMkYNE6oTWOGL49irbTGQocQnt0GvrkGqstR9dMWDy1DwSSaRHCqxCVXoqaBHSHq743j+SiXvSiwrTbECpzHzFarkh3HJQLCVuHnXuW/ZPTFbPEoXvfFEXFzlF0kgSmezzMm8saV38zny96NGQ0+nvDSSdsuS90b3hsrAXa9e9bM33PfPj5zamR3Wl4OzEyvfydE3eB07+GLAXuN80I5jj9OVPwW834tnEcocyi59HHK9WEmvc0asKlAyMxRNGUeehkJAP/acjy6GEr5G3ftRGb59/H3ve9+XvvSl48ePM1vXXnstRDmIzz///PaTnZAYqyBrqIRHQ63RMiT/GCdmzkiLwHxrJC3GdRsYUuBGnVvCjUIs6rw31wDSaJn7JpocCo2LjImFxnIfVWzWm7GEarGpoKYXbeBJ0bJObh7RbJQMILGoR3nJGEcKKXKTXy5512nFTkNmdGyfFT+5IVbwAcoMxTlh1UgVM9xZ8x6jYCfUfM/MIwdnP/jq7I69LxDbg1FWqfdUSwnm+bYiUYyRLsrScMp/cQeKW7IS0gKhfHoNRBeypCGsvlZBIARjRFhljOIJK05s6DFMEQYzz2ocS1HWMiBd9K44RqAoQMFNY0SArRIYow9SlQGrlHSB6TFGkKJ83hmzE7tnKmJvkpM1qeZy1Q34FqO+vpXagX2bCr23qIU+49f3QDfs1pOB7dmBhw69+8PXfPEv/mWm5xe7mMeiTh4ubxMeavEQ0BEBV7b2hDOHfY2jhgolnV1C693yPYmJUDBMroJCQ69HSR08h1JM9O3Hl108QKLrCO29uPO2+Hd/93d7BrhPDRCvx4ToSDShKJZE9dUcA1mayj6s8Q2iN042cRUdQTJNGEpGAKplSho5AthomZuJUsdyXlhAiQ3gFNhSJ12DTSAHbUYZCFBUi+Bzsnu7qwiODb4ThpOjsbTYvX2rwmyZ7tzXVM0EKADdzF3aNeCMuiKbKoJlM+FXOpyY09ZA5Kx0afFH/VgZhs8dnD14cOvana0HD/Oqcl9xYvdpqw1Gt/q9VhFYDGQ+rgrlk1FLSIiQ1cqk47IWYjptgFK4hIObYkitFYtAMqV0VhFIF2PEhsHStPsEAQTwx96SFUjEnFg1eczTC2wlq4M88aUwxnZil5w7e3HnbDjEjA0G6gwW1/eFrUYRs8oZulKKm0iszQiqyaHvZssoZNxiucc4D7nXt5G+zzJw7NTb//Mbjnz+oeeeerl7aHJecT/UGNja/CUkMrs7XvPUm5wGUrKPdPSp6RrjVa7d4BgWKB9BVjpQn4CXigyQoUpBsAtHEBo3WwTs8TImJ5hk9HBrQbCNoOCz7+LOBLz44oujaEFCv9RuHw8uzLGprCIos0JQcl1LMLgHpWS5aQMEXJzfqJ8gs2iKIQyV8EzYYiZ3alFaV8bqrEwvESLbSDUuQZBZhdPwCYcvSVtL/mFI0b5FSMYihtSNKxazda1YxsjBBKWYJTGkVimyLSaWJQGxCJX4tjeB4jIE1CAaJIFdki3k9ll+AN7duf7l3bsPr+Fmw2WUjDF8SvtwbGm2LAaUpIuiNaaRUk9YcnjphwpYTXA3PQm6cJN/7fFk3ZonxsRSM0FwEwMoKWMrEtMYSZeI5XGVW7GFAE3bIFBgQi1ujBEDiGG/IjHcKSKGUF8Ybz1r9sRu9wsgsYWPBSetJuxf4ImvOsc9w2vBvjd9yef7KyTm42357NDmzl7J4vdr1x2nbvuvbvnc//z/pW+7+MwMhVFxUPDRDDY451g8GJOjoDJ+LHUGstF8sLS7R2S8OBykAYqTRzJKuiQTxQUlMjUlHlwRExkbEZOl6sSm3sQlEuMck73iVnxNFQEzRtQNCTYUftl64YUX0hQhBqA5oKvvfNRIlO5KVHcBBbibowQx1gDkCHmyoCEIVnUl9aMiTjaGEjkxVTEHzYLZIqhMjrUux8jWPA2+pLlvWaNcV6qByoM6ei4MstUVbXV6ECM64STkTFo0ZgsIb8q9deWLkjKWqrwqe7OFmG10E6VGo4kQZ8uEa2T75IEZP5ffdGr2jUNruLuzj9hN0EiSZuYibLYijFKElSV8EbQltTc9nIgzStaFW2ijHCNVswWBd1esAUiOXQkxOuOCGKui78IdjQdl8gYZoLBEEFunDoHSyBZLE8OlMpuDrDDAnZJf3xk4Bb2Et5x1cOvAQR50c6Z7t/ZkIC0RbbOYsm43zE+Gecdib7B39jft9ToX2hOii343zuWB8Dof2obeWjLw9M7511165XVHHv7W092NkQWjNcNGEz5bjH8nnho9JwanGTKnGbseYRQH7NndctFGBhNl41GTxMKLAgJQEBNtoamrnV4kJkCPHagk7mAzusCNU8vDxBeZYhBCU+SCvvtWGZJLmpRf+mwqZ+4ZWEeIaDBWhh/hKl4d2XlR9Ggpbh39eaEL2nW06F6XIRbzWDd275Jsl19k61m1/C4JTJiiBFmCDb4ngthqiUAgEqbprLazxQWQNQ4tQRNh1UnGBtmyRSdMaBJFTV0RBlea2eKFLF8E5UeEoSQcH0zFiI1KQLg4OhWNXjYzYbEFhx/doSdAaiUQwS6NAt8zw/jX8j0zRIQSxxQjhY84i4Zp06RLhOts6eWgY22QOo2ucURFM0DYTaApaYkN9OgVSdUYiF6RLcQYJkUrJEEb1QSfQsbwakSDoQteyNRiq7UKMQTJo8hEYxFT0oCiGXvbZejNU9VVLNqYVcEy6q3d7QvOv/jgrPutlEt33VzuzUU4+toD27JLz3yPz72T6+y8ozONyFJGjcFeJ4Imfc67jyb65eCcxkn5e50MeUOjMQNM/NdO3fgLtz7+e/94kD9TpbB52TrF40WnBMuJI4jtiYCGD9JM2O92AcTHKXEVoo88lnThQkHQFqeGg1zQYyMzQjhKH2CujxzA4SCSDfqxaCJpYjq3ac55la+Oh/g1Bw8Mh9T1XU3VcKrwoJfnVrSvyILqcjkvWPJfaoZqQU1FRF9BI1P1yav45l1wKy5BURU9vBDMliYyJWeLpoiWx127xoTN2SGgKs45YduMEgBkFoQ2yjEaw5Pko0FIbttdckvpje59Mqy03voM2vVwwBh6CNQuCYLMqC0kBmqyaDVMmRVt2pUsM9C8hXNHs0VIeudM961euHFqrGXpPtN9R+SU75mBJxMntuYMVZQsNkbKzK6YOkAYJuCrHyBa/PygDpRYibNySy2DRsKAYA83Ccl8jW0yj5BheUQ0NLFAjKbo1dkCwhQkaGMpyZ4xUphN0CBZT46CUlPkDmEh4EvRx21lQNNoV52//fKhtx3Y6d6exNLd3Wkvd/TeQ2jaYLStuhNiXhbNRXu+5RYfL+kizv+Hnc0XXq+3/+wfxQA7Hyzzc3LAeNP9fZuBl3bfsnvh1Tcf+eZXHl988qQ+FDYZJy0FMzY7hySbFCW3Z/Zp3TfvxZEjSHpOAACpaXIU0AWymjKQkjrHkSai4eu7MiHo6vPq0zsQHCigCUQ0Ei/ZUEe9LJPQ2MANMwS6Itq+CXj++ef5ZUeEG5RB5Htm+Jh7tESp4pASIEFe6CqyjAgtMjjJOFu8ijYQo3jyog0hKBBGiRC7vocybJ3hhMacb43wGvPWnpBBwkCxNpiCCVs6yYCaoHEt0MQVDerKhDBJ49wRN6iuuB7A4dxZ10jBIW9Q6lsSdFEY7+B8rXcWeO9Oue3k7M7Sj/ZkmIKBF6QEOFCKhLEnb3oSdNArFE0BK6QdjejKsHiaNmx5FKm3Ha2PO2iAMMxR3IpoUKLQxW9NYcuOcFYtFB37lHhRWGxavVDts2zRw4cCGty0F5ApcI4ZJiJmvGAiXCXiHGwPDctrjuw8vXURUJAR4BxgUXXXdxd0smvYI3baJ+x7Cnfxut4uQvf/Bfay2Wn4v/rmwve0glbCfknHd4uYq2Vn4b979oXOjeoNkIG7Tl79X972wNe/u/3qds+a6Rkk21M/YLMXOI54mGLIrtfn43ucyur5qTZ/tMx3lprsYB0UZZ+qtjvU5s9HuOnwwRxAOFf9Cp06XBI0bX91yQeD2CwAzVWRg7jZct/FPT51Lr7msove+Y5/+dwdNi0KQOPFX7WKHDZoqLtM7P9xR09c9RahRinJb4Jfd4ceBa/cDEp06dzPeydoQFt9mB3dZeFhltAAn+GvHiWBXaW5JNv9N8ERz9ee8GB+oOqNmnNOCIPGIhnETHCKTUAoY9cwUH2EgaKwE4EtRmxXalux5HSXancsWj53kFcjB67bmT1wRrf1oKfl0Y1/fD41I6vfaEWV2QQQNEYKGSmhRyGZ1DnhZEnIRTVduPBMWsssmJuEGCiRO7rLkw2BgVBj0+V3mWEooWROpUkQxja1MMgbaGMXibiRXtx5IogwBHj1g5LXRjzIyeRYSrYXHx2Y11906qnZkXlEEtLNr8J1eVkWlIiLm3UnLWwMuJLQzcNWF7nDnf9PV2Qvtg695c89V2LR5CyGfaY60pe0+6z29N3AN+UNmIETu4cfP/OWD73rzr+6r/tN+3zHjBsmLuxxCm7sU/6elV0JFO8+OOtyLB3F81286MS4eOxgo42PHVDTuJkDaDqgQCMiJedW1OAozhBAppYvMkVoK3IDP70Risrb33XFNe+7+e3XXvHi089/+65ju0P/3K1HWxyJlJCeQLcPEOpx8pSRLjHzknsRmtI3ncx3yxBy2KKm5WIBTXwXdHUs7scSYaYcgZ442P2Gr12ryJnFLQYJ4cm0iKIhT0awI9ljZqmVZ+stEMgZtrJPYIUw2HVNBGisE6In4ZxkVnjSRbOSZJ9ZuddYjTLGYFs2hQkryWoqoubxGfK/u3v9Szt3c9MbSyWzB5MpIHXUws9MhhUiqeMVa26NjFRo1BQ3h7GCBY48fpgIIYSeKSIgXLiZBQ0WCGc4SXIL4QSN5hROwQduJJAfVJiIfBljqPTCn4RoMSsoNfZ48fCONBgUlnFoIdo4EXD+IvWyt1/81IxfNeC771pKCOKqmDlNXd8RFhw61TpKfovVgb+EV7h1RetjvC8FfUZ1/YJ23WjT+wbPwP0nL/tX1x/7LX6yRAAAIABJREFUjw+++NL24vY47bQkTexTCgLnA+/gOSuQOVjYldoO1JSWxxCOWAoNGShvK6Ohby8xLvT0eEUZ0RRCB52QMUhs8ohCw4wuGec2dQ1e3S89oQIDAcnhHde/85Ibrjr3ogve+Z7rnnro8YuvvqzTcwAtTzJ+pnj4n785eKF3eIbtnFo5SoAhpWOx/IFGqRRIzEIcyKgQ6zLmQcu3aoqqOUtwCJNEqK9LHG1s99Mk5GwjbdGgjmsXJivObBwLqWBBjgKMnCNbwYIGYUo9yZFDnywcdh3D77Np1MOTAiD3nhxNgSCM0AiIGUlj4XHqtbtULInO/oqDFWdcUFLTpDZDcy5i8j0zvEu5+dTs7nV8zwyxGGbLu21xNvOcs5IMGoMFdvVFwvCZCFbd5B8txDYmGQ1PNdaJ2K5CEl+KTs5Ru6w4rVDiBxVGqmXMqKEdk0wsbHiPni/yHFDTCgKAGmxu06656oLtrfMu5dda8zfe4dG1hIAnEVUjuOzx35OWPuv97/57sKLt7aj1xlojWrfvN+XNm4Ht2dY3dm/9N7f8/R9+pUsC69a7nm1LaUwNjvKVPYcwhwmyDk+6OKBQtgPGuJw8asJN9LTBp6F1o1q+/uN0ghvgAKJ0HaPX5TnYIksMVqfxWJzufaQH6Xh3/tU/PPvYk5ffcvUdf/H5a953y8N3fJMD2Bv28tuuPfojN377a/du7xR+tWEQC4wTWm72CZhR6I21jYWg4WHAjLprRQG0FnrFKPhS6LKAzELxZAhZE1xEGFQKf9BslAEMsY+c7Q7hWKzvEzAGjQH2GYzSg1a0F1WlIk+ICItDgkBzleQnZHTpaRyseDrVCZSIYQNaI2CCkDc56Vh4q1zI4EMBWbUuZEovSgkkASGPXtfwt6qsuZtPbd3NDblu2tarKyNkyJ6TPKe/OKhEUpyxqXMmaTpDV8meiYsVcyHB+iiIqmu6zBBBhK2hFzSMAYwg02TGSMZ4AlXo5chEx0u3cwQxhyFFU4Dw0ksvUee+7RqgKHp4j6KXhHj3O3ae2LocltBhRc9rnfMsDxgu6vnZ3w0hlgglUzQYRP3a5MVqXeKRVokhnjSnJ/wy7ua/mww0Z+Dx7bd+4IpL33nvdx5+vvtjUx9KHAu++bCLwxJe/P7Nq5tQ9FLwpY6R0egGD5Rew6PhvjftZIaGwHVe9dGLBCzjooMODSRpggYTEfYZiIDGgezeIjh1MZZSl6QlQeveMyUqNR/52n1PHnv0tp9637Ev3s2HfXgzL/2RK9/x6osvPXr3saJXUckp7KQzSGyUxMQYJi50IScGp6kZ6RVDiC01vaplJoY5ZzQebxFwlJKIE9aEOUfCxIUb4xWgao1iFKVoDIgBo36azPKgaHcJAf5iSK2CHqERn40B2hqng9CmZIbOdsJqwXjOtsiZ7On9YuI4rUkITYePgz4cE0aQjQQT1togb9CjLpLvA+/TP9V9z8zuLSe3vn64dfoSKNaGmatLP1owXo0d5WSqgIDPeNfyUgAa4LD2wBQlZdh5hipJVp5bODML+I69bScJdFOhocfmzccLZ/QquJgzXtCgyN1oEjADkK7B5Zc45k0QKBAYtfy8PAC87Z1n37d14Wz5sRc07MJusfM/Vl+s5wNkCmIRJTQeu4Wc7WnRzONBwODaqHttd2yETQZe8wx86eRt/80P/t///ee665yLjgVWLjuXk1nbB6V2FgKWcUnbsSjoqKFLx7L/npVrfTuIkSMHGOqsyJXYa6cj0FvhrC7Z48LRZ5dp9DjxQKAU6anLdfkz7nRD+YJLL3rqoScOHDxwxjln6qBDf8ElR772H75wy4fea4hEIKoLXcjUHpUypYFNGJ6gmNEk6IpNExbbBE2cK1MY7UHwjEb9KnJfoiq0K5zXy5BAxaRVxqs9g0HuCBrZc10Bae/Ko7T72hIQF06QuAIrqbZ7RWC7sjsiYMV4sIvscddRDjGGc8y2JouaLiVZQgWWW9267rJEebo753dvO7l1Z/XuLs5KuLkpz6KtIdClyzHCWjYdIJRpl+Mk1aaNwGMMniCbduxtl3HXeMnPWhaM1t55550HN/iDrxqBLmLpGdlIW/TgNi2BeR7gQGF3wIrxmoZTjWAvelUYy9UXbJ995OrtrcOLy66u7DLVrT2X55tlibH478JqecShVUQExz2twt4AlxEXI1pz1JiUNUNv4N6QGei+GvKCa9/z9nu/+p3F10DtrdX5aczO1X7xYU4epm0cdvT8z1lPEIKzlL9nFVTjZ/Dy/EMDTBEDEFgKGg4Z6ty+rgFNgJiBI1hkoCag4Qga2VNQ0HR5FiBdaDqluvvqpx/+znOPPRl7Lz56SWwiw9XQNLtBLK9fNNd4KUnijm12kzNPa3SUEo2yDPPYO0HmB039omeCb9EFhlpbee8804ubbt77mmmKSVOqlV4ZIEugJttFL3FmzVR6x46L3UgCvRPq7qadm3mFwI0ybU8WYTW/YOa9LRolmc2MMTI1f22pi86c6d4p0IKW2wC1xlWtu/t1p2b3ch87sHfMQVXkxbm+QiJJze8azxkurFw9IVNcM+hJNTXFNDR3XiHWS8ASenqSJV0TmowXQBhqihsR4IwLK03rRNmmhpW/B52BayCNmEUzJYHxEmsUwyIaIxVVvW/TjKChVPJ5+9W7Dx66odsNi7frGbZuqkxgtucUUSFUyxmZLuQoZLinTaHFtjwi5itv7+tovBCz0Zw2PhvgN30G7jx57U/f/MhdT9fuTmwW7Vmypcccgo6ICfkDjSOFgi/nmD5Ig5JrvZQVTOwpNsArecSwu308Vs4WIxSFOF7CcQzKbDKgzhyBzEfQfSyZKAMX9yK5RAlKX9ZIBGES+1WaAHodJDgaIbVK0ksTnjlVMsvjKjeerAFQX3XUiABbLJesfQLveTNkGPaNes/uNZfEnMUEwzy4sv16oA0TSJqJaUtImGOc7OfEgCZQ3IHWuLB1U6yvQ7GlZiySoWoBGVbUYst+ZB327cp8RHUNsEAx5DrDIkikjQFN0I5zkd3efff27C7+cctAu4jQogSEtKxxyIyXVa3P4TjJFph6irM9yBBLUgc9SK5lUgTIjCBEQBhSYK5YyBhQw5AUYVn/LXMOODi0PgNiERcaCcM+e4wpXts2w51Ukz0KBi2A579l96ajl96xdf7WbvfvmbInSANLb35Jdz2PIPWiyzE7gViE3is8xbJbOzZYYrPP87Q25hG7Ic1LN7xugHsEOkLzcc7/O7JaZGOk18b8zZqBV3YPvXT+9R86+tX/697Sv9ORpYUjSDqd1cgs3VEHaYTEUW8cUHKDp7AfdVAAi6xiF+zjUWm9BbxswBGKO10rMiQoIED5ZKsALhjP/6MhUFOcN1PtEghF0DHGwh3rEoDNo04GBw3GsFUWaAoK5pI1BAxOx1gm08ZR9KhVEqhB2pr+xOs1aMJW5E0bIeYWmQI9r/i1sAIzCTQWNmHOgtGlBBzAVa+yTvDVUhxLrM9egOKmbHsIkTMGbCiZ9UHJHkC29rp2H2jAcm3qm2hzlmBuoop7Qvs7XCV3Zze+Mrur6cw3Xq8ghmN/ulCSk/NEMWAOJl3r+gUaqdM6RBicwd5xzjugDRQg/GjBL44RVOiEMwXOE6aex54Y4gtIncNgL5QMCJr49CVc/Dvq/XGLgDmN97xj57mzr92d+Q9TOxM2PYdZuJoGcaHmrEufgLAVMc4jCQqHrIFEIWdyujR68C3Jikk3wmXRc3GvvdSjWTwyl5rNfzcZWCUD33z1ip+47tjnH3r5+KsjVpa3OacNDykIsI9QUk8gw/rnnOFQReAJxXkILGh8kKbvaTUYxYenGeKCchpDvIqA6CmEMB9lBqU1RQGD7he4AlUGi3aJkmR3AYe+3H3avYHsU4howdE1H+qdPM1GU4jBHEX7uizCmmmRtL2idDmbujoNtXZBVEXetBHMGYFSYY6xpmZd3IgFoJZlBVOcMbBgYwgjmzZQrMbJ29iwFgDkjghPa0YJImzaCLhrvyjPMB8cfj0i7pxia5wXTQqYCAlzZ9sJr3NTL3+ryrDX9R2RYCp1xU/1FBPeQhtHJnr1q7aGDEOKVk7j/LIqmEQKgkYBbRWguKDz2oku8NeyvOcE1/Nvo4otzGHLkJkXhgy+ySMoLaNqMQQWTAFG98MHdz980/n3bl05v0foqk4YXXXnN/PFO3g5QWBu2LUk00xZMRBxdr1w/h5f38ViWS+H6Bu8BpYOZmm++e8mA6tnYHt24JsHbvvY/8/emzRLlh13fhFvynz5phxqnlEFFAoDCbJJAkSDsm6CaFI0a2uT1JuWmRYy004LmfQBtNVCH0GmlTb6AFrKJJmoVhubLVHWxEgCRKEwFIDCUKjKOV8M+rn/7/U4cae4U2QliDhVecKPHz/uf/czedwXL95b//f/9DdHPbRpLzOQLcaOpoYOZqVCnSqqQ0BD2J5xBqKNA4fH8MjEY/iQb08EmBQhhuBTt9eDZCBPR3F061TpqjD7qIyO11RjHc0F8voXPv3dv/wGlupk4AtlnW+pD9CpHg2hLnvS8MAv1dCexutmL8qqmpHTi0JKneNlhY+HI9jYgtA1n9oFrUo/5IwikqnCgTRgUBiZDTSwBb6guSVsxKShMHxIE6+JZIAsq1LMVdMbAABDs4ycQ4cV3qCwbKKZg0KSGxlqliz0psihFT3B1lwoYRKnMLZTk+93p3xmNv36SN8RiTaw6SP+EXCYCgJ11xXOEM79Tqm2udRYUMhEoxMiBEELk/CyqIRckQcwBacQhgj5lGCiGchFhczwSUEzCoVHplNblbRWBUMoEhAMg+4FhfBxkHqUFS61xKqQvv/hC/PLpz57OT3y/DzPytlw2adlHB0YsztXlNcgtwzfu0TnDUnjGk6lJTzNvE7fIHS81KVqnDqfAmXw+jgNmkHeqN9i1Ciw69xFoCICP57f+tLLzz//vfd/cts2R4VECxYDdSywlThMdFBoj8fZEmrEb7aFDE/cGYJCtB0fH0PoFO13/gRC6RQqmBRsBbaUKCAPYYgQS73WMUJvG4R2kipYOltDI8TDew+ee/PlGy897TJ+rE0n126cff2vvvHg9j2YJycnTz/99Hvv8XPvYuFIxR9qOgQoJISbWg6nboRMmUBJS8ny2DpOpU6YKoyCKIwFA6UOOe/wmj9FWtA2erOwVuQCgCEEW3V5rocgQWc5UF0VogHwAkzNyhFsaAgV0dQ9CpsBnW22REvlqFLSoNUSNVAVjRx17WopGEKedUUQtLoKvf2aTDTHVpogpnoCs4iWyPFarqWqetOeuy9JtZq/Z6asP5a6oi0BAWMPKi0eK5IEkDBSRtk4rEMK2vipbixIoEJTMAGNI2WXGzgMwWt0IjMKSADgNTiJJAqFh1BH2AOMYk69ETZ60IBCORsaehOKGF6DE+V83eiff+b82/tvWPrNLkRv9q7TLBBR7hBYerYuavUJGm9nj91Fm5YiNPCHvyIkAa3bwoj84hBdVPHY2rq8gOPuZNvE0Nm/XdlFYJQIsJj+bvLJf/Hm//U//n/8lWLbc+xuSj/l2Sr1HaTTDI62eb/dxCjOMQp4OCV4usEJljJ74GQ4kBgINrQJp+AFfpoU4kC90QQyUiidUgjtgayOpB30OvXK2t//wXv8X+C//VffFIc3MX/6p3/67W9/+2c/+1lYUhfoKWG1DfSClXKzDmRZsg1HCCPohSGKOMyuyAHJ9VnQNnoT8Lo+QzMcQaWmNC8XBGLgYyYUdtUF0w48q+jChagLkv2aKCRjiO3RQ4liLvCKITRv1dA51lJHFSfLiEsInOhke4KwsGaIgMKtUMujNmHhEEyTuTZDmmXI3fl+99++nP5N1XdECjZ1qkTIqQFfiZxfEAckQ+RdOrYfbXmhfzxDd0AbJSwPMBD8WDMQwmw3iT8d19QMWZYpkgAJURmWVLiZBir+UiD0E2dACnxdzJsVqhcNOI5a1jkgR5kd9ACSSH7uGR63//Zsesh6skhj0t5E+2tGGNe7nK3UPM3kUx8YZ3lIRTGl60VC8OiChqBO6Qotj4dVcMGazqq8Bepdfjxgd1Z+HSPw3vz8H7/46lvvvPP1n9mDWnY35x5bgN3dvMHZIDphwmuN0vYJJmJkVmrqFIquTgRj9fusKOT84bkzBKcHl0XBYhu1IKeEJKqgqds4HqPKBMOBJD5ec6xBS2cKckryzUmq+PJ37+7cuVPWVccBJdDPz891R4YYxsJ2MIcQGOqRuDOKkgY3xUAXE9m8sFL5NjRL4e7du20kN8oIOTXBLCzW8ixu1JYKlBWmvf3owowTc5BTytoIOPgp5a6Uw9geM55qKNMoxG7zjDvqbM1AIy8vDLGXwnBWPtunbKs3B3MEs59OxlZGHiZLqLCKeiPUwHHfYKDz6eXe+WT6fb56O1k2hJwuYl4Ie0vwRBINON5SfqOYZofDTcCQ1/JgabESiDMlusCMJKUZPGMZ1W/G6wADBqMtHXfU2afRQqHiFr4goz3e7EsMb0OgkOgRn7DSZlQqky7408Plf/lHZz96/p/PJ9zxvm5s7ayIaDh7vbWSlHypnbHFX6sBnxbXawJW6CgQaj45dQ7QEflxXXFmPzlwd0ie0Aic7j38zL3/7b/7P/f4yoEoOlhocmhQYreGAPsDfuyS4DcQHBrqdZXVT6Mbhpe7UKgHE8Agg687M8vgGwBwpCOPLXTWKSwjaeCgDZ0SUMTsh4w84ROXv8RB6omxKA26ois0isNsMTx6RyHQWfZfoaEOomBLXjCwEg8TRggKQwY2haSNksBs6PP3agwMqAIvhNxtbXS2lEEzFsNQy1EFMUedVXQV1gDKtbwKo9o3BbK9fBtJIIGTWgFnUYmgDnMQlLo1U7aCJDrLi7Ms2ZIjAIJUOSQFL1piDITAO4rodHjhrXXa1Y8m0ezxpgXAaQnTAP4lv8I4W3zyPt8zM9rxwcZhm48yQQEbbelHXHAB8CwAAtJvnzKWKeONEBrKExch6kSgCpxMUKTFgV+EYENjkQIG6gYTINRHcdg1/dwsK0cPAJggTDfrRIzh2rCipc2g5+D/7M357Lk/WUz9euOEQwLneGOFW07oMTwN+6jIqovpy4Scz3TmImm7jD7nRAwFhjqw6V2dcQSanyuZziepeJwsBLuyi8CACNxZXLl7/qkvvfy1v/h+9jVh6T5lj9OksM03HjXNKOKg4HxDLcK672LfNQ8v96JQOjleyOCpkYnPx4c8+isv1hBICXxUE5cFkibDKalYexrrAskQEKIz+44IOlSky4Pc8603esNGe2R1kiChi0miFp1Kgpkmde+IpNoG0jhegCHACmYzeDlSBsCouq6ycEtOe4WBX6s51S8l1LhMHUQqM5BG5xD3GUsBg8CLZiFBOF6rYoP1hor7pEfD9aQA2D5K48wBL/TyCmCJCb8inw5soPvl2Q0KwQDOutw9w52/oAd5udAA/t3p5HJ/8plL+47IsVIJQOqIb38osWBUGAgBbOEHOTFnrj/88ENOTkWgIUTtu1CltBhi+FrKo26w+T1dMFPQTBNiiH5iiHImfaCeiAyoWJkEOXQGeBGSREylDvzr1xe/9+k3vrr3AvKWmHtybp+TEZ3k8cZYZe1oxQ6RWbHKmXuGwTTXFtOyXrJRbgCaTsbbf6JrNX0UHb7CzfBYu+6jcGJn86ONwDcfvfpnb33v/31v/tAfM2o3FE5dtolyOXrjUOoHO44CTo+BOnXUUHMWCQzHETrhgJ+TGaj9QOJmRACFSuIH+q64Tb/yla8E3H7g0lGAE6yUuZFW4BALIh0CE52aJ4i0qzfdD2elOeDBpybZgoh5kjCAhTmISiV1zBFxhgmgstZj3dNUQQAixCBS5KLT3pRmYKoz7epNoxC1gbNOj8CrRgaCuoA8wNO7DZzoLMx7HdoCPwUv5CEgnSBXCX5vAoXS2VtDeSDrk8IcCbxqACOZA7fX8sAGzjPzyTOL6YjP3bGF4+AsHHSghaku6gAPYCWmquugVuqsE27JFx7WUpugATgK+qGpLdx5/HXfdNK5DZx1OgWeMAo8BAXfdTOFI3XDg3+wN/lv//jw3Zf/5YPpMUxffOpUIAp0MLVK8xGr1oqyvjyYTicMkZJdqwV8VSOYaTFqRUqz83bVLgL/ECLw8sHP7r39b/7nr9uXMDYX9j6bnZpdoP3eLN+mN3QizJXUsL8wjYxqaY4zp2CIVJskHkm0cX0UbpCCcMtm4EQnRad0y7GIaXj2gaH2w5olUVqXadEVpaBEDkRd6CVw/bKigp60CZI6nKlYSjNEMw2R8qGFnN6Gz0gVhrRvory9cIMk8IRcROpFrNohtlKFDTDad4FK71A1JIUdtmLNaPVvxI8AerpOfTNmTJMbNcsAOIokaQqtXCjjx3eYGz1qtpv2og3fKbKVdm2kQctAxORFyAs8XS1zzRjYQLxnsezzPTMNOoHNHOnxM2I0zYYfmiDXM5WuodbsjPsRF1YmanXipdOk+Cv44Az8ir+Q1LkvnVpOY6380AmANodz4I/gR/xRlUYenCynThfkv3hz/vCFf/pgek0RyJ+yuyk9cTdjPuW253KCh9/+9J3lwBHLdoSwrJ8qbzohXtZj3SoumdHrLzireVnV+TA3n629mMT10bvWLgK/rhH4wezpP379mf/1u+///L5dFg2FjaCzKD0ZOMcoDaOauxp0cqRgiCINSGKozcGFjMTQwLHMJ8nRAIcrAyXNeOp6U5xAUpIjSJU6kRH+AA8xZuKuu0FJTMQo0IOJoomBCH4zgZ4hc9msvNyrGMGvwy/klfgZMta9GMDKMKKrkhD+ylFgViGezJGWY6WSHkw0VxrtqirFDw1Oqc2xj/DuXFCpu2JrkCeYLH7qwB+wIWSLutPiRxsnRacMpgGhuqQTJJRKYdBSOCbohUCMGtpH1OJXsjXi4rfcfVr7PTOVyMUELWCER8gFnsgDT3+raMiZWzBNWJggzKF8rGMK2Kji05Yoj5BCw6TInQKMjU3hZDfpLQHNjUM2CkgnS4W3LqwrYWMU+HXNQCCjWaAXWnWzZm0icCIc7jcM+fRT8y/97ltfO3ydZWpe2T9bsRi31tJpuJ6Tr33JjAkRiCx9XxEGmbHel2TzSeZuvYz2ktI5z1/lPgbwG99VFA3oMrE2eNfYReDXMwLfWn7qP/nUX/wPf9327zGxF7TN2RGcG5ylEAOPU20uxU+HHlY4TzhbegeV4TyWpaAckPyaE4SYbY6pSrs6E1BCL9p0bELDF0ejBD746uqTuMe5LL2qZTiFkvb2pkdPMUEi/ES/jGp0/GUTXTmaxcIoXKAUmDQDf+WosvxHxYllCiHYEMLMOqYEDUFzRJxKXgcmxGX8pBpyAcDsZOEfCBs9rNIhx00ZAI6TaZG8xhISbGpNRA/8Cum4M/Wef0fkb9V/v7tOZOoCfsA0PEEXVOpRJkgRU+4OjK4nuMBH5OWI4g9CepmjgQs1XQDAYyuxoqi7Qk31BA1gEAJYnxKUTpRThoSXsXiN+4W3BGE3iBfPFv/5F29+6/gPLe22bDoSdG/Zdeh3YJq+i87Teh+GQV/9ytVdu2X0SfpurWLmnqbsKR3ojLBhSwtRWjTR3rs6/WgiY2N2ZReBX9sI/Hh2/YsvvfLG2z/6+/c3/BS64CKLPw4l9r7SMx0mBclCU6cQdfBRVT6CEIiUD0O99xoDOZ0omOP04wkLaGFy6YgZMNoTcRdrCFAp6KRETAraKj7jzhiEfKxVGoAK0a4tq1Jdo2cYwkBQuMNSQ21oQSUcgT8dhU5CzNSmzIG0plDfEjpQlYaDnKJbFg40QQ/NWpcpJ7raENuYrIJOAAs2kREBWjGz1dMiI0e+3wJoDkLLJ9lCq1UkOtTiQmEKtgd1SJYJKkphI4ijLdB7CUUogiDNQueICtH87HJ6fbn3vat2kGshoR/8ij/m+h3BLAAGjnsCsFBBCKQIiAjQQtBFSbtwQV5Qp/yUZixQWQCPB2pqOqWBQRF+iOgCFUX44SuqdTdNjOpEcKqguXILHO0v/5s/Orz/+n/84fR8XafFMwmqN40lqbTXpaI/F1hx17rEjuHyW81WNQPS4iBtoApdBSLv2b3uIvDrFAG+GvLN2//7f/+vp4vV6u6J309NOzbZHTpYOA0o8Kljy+ggammDgZzVEu53fVQaIoPnABROPTmqFIMZ+EOgDn8KNZWBb9+VFv6HIjiUOJSDX0egCPm63t785qQNoxSUF25EmXMPal3Atd6oKgcCtceNJRdUF9QqntQ93rcUVJWbiluZ35UjPcJPBJgIhR1O4B+yN1AyFtTUNSDFfKUuyBFJBn6INhtB8uF4am4IzeyTu5TTwVRnpQvCg5jwFxY8OuFTUj0DaU6r3rk7LuiMBpgOZYG5A8LL2acul1+dPSDRHogwhhNPDlmahbCEQA9Cqji+WV2aESlRnKmbJ7HSIqOIqmIy4jkAQtAqz04jEAtJJ2p4IReaN7Kgsq1QW5lnVzq4kYkqYGi5phHYm07+6y9O9l/78m2+/Z+ytpC5FDg3nG18o7KH8dEsPHFXP4eWqcIgr9ZIibxlbwnEd6sSM1sUGyaqqja96yWTgslAEAeBKsdSpWbH20XgiY4AXw25uPHxL7z0nb/8oeUDQ7AynGOH44iDhUNAO4hzoPksaraIzjhJUGv7bth3NcqcPkgDDU5+mUpQYXLAQqtIEgAUXFCzoUYshYpmhH300n5sGn0NKpq70NjjWmrWSW/ur72qmQ7BAZoKgei0t45GVXvhOiVlPhFgksp8OOGF6FQGJCrMAkTaJVqOl/mPmRMuCI/qACwXWEUQaR7wmEE2m5MLyCgpUXpBU17IhTZ7qcEKwzkIhu+m1IRCCmYFVuBjOoSfpvBTt3QBkERAo1JzA2klxOSadXqAigsUAkWhKReocRDwaKAGWGja5L2EAAAgAElEQVSwv2e2mLw1n3xjvO+IRCWG2LMAaBmxwAPBqChqUgszgdVhOOJGQCcRGzchBjCOoxMioGo9UBemAJmWhYFoIwKazZajmsXAw2QpAqH2n31sfvbGP/nuwSsE3u8GT91Xq8Z41uWvvppcKsvX6aJpvX6F6HZBSgm5EXSsGkrOERUh1SagTD3pMJKVYF11hZUTcc4IofFhGgcfMTRA1OnZ8XcReGIj8I3Lj/35W9//m/cWjxaWu7c/T3SustnDNe2RuFMQ0N0Bn9Ng4AYBmAxhkVMLerhadPI18MJJBo9OlIOfMgRtQJVme/AWMepHACuU9tOg2aJmuGoI1IJNNYRKP/3pKGZ93NRKylELoTpcUBfIIXIPhkZbOrdUx0Skc5Hib9gqDMF9BMbFxtJCbfsFttEFVMmjIMYCLLUAEDFELUpY+dQooeZMYdFKraagfUDqYJAM8YCc06ROoAcfhKglHVSmxcRR5Ih8QQD8gMcdTLdcLT/dszxr3O93xzswAE+xrXMW2BRcQABCYnihorkojMU7dDJkxHMGQxQCS9116t2DVZW6wDTRJAjoRHPBkX5NIqPZB+2IagEDQgJL+YPnFl/+wme/dfCWI9SkZOk7nORCy7v81flGeUZumbn/o60DWpsNOsvY1ZHNOce2P4b3VN0abrq6YkiOATEJx7Gfcaqy90wbAcxWmmMRF2a1sR13F4EnLwKPlgc/ufrWl1/96//l7+xXv+IuYwtTAu/qVFo/Wjk36hY8fJ2rjOXgoobTIB+2mokAJrUSbqmWIeEgA8FDQSHDdb0iwKl1fHyMmJjUzXgaelHO8Ipf0GwYU9nV8ooCfZSCHvc0e1gLrV5cHfHmK1js3YwZwpdUiVyTI+FCKvDk0EDFCy361IsAr63V1Yuu8i0DAhhlAAV5eRH4g5AXG11gaSm/LKgd2LQd1eWnT+FF4AeAIokL4QVoma/Rt0Mk2UO8BjnY8JpFRUEVHH7pHuVEA8x4oUkZYuUn/K7qcvLZ2eSrI5xYKyAgBDPgIWIuVt0+F+Cnt9Py1nyNfoIRUnQ2rNtwASK82LgjcBC1TCL6Y9RAAp1SS3i7rts6L1DIRfgfvnH5p5//7a8df34dofy1u8PyZl5WiW7WpZD49WIcpe1qZrSNcilL5Z0XOX3zh2ekLc/mbXAWfkBAqUa7GXCOaNspmp201tzZenPKCMa4RtHZ4N3LLgJPcAT+/vKFP37z7X/zg9u/uG8/3AMpa5hDhuMLmpXMdoawY6LXg3M06GBBLYeMNoi0DYlKG7UyJ4upL3XbEz5Hq05XIsDzMmqgBrMH4KHXoBCEYZyJEkz5prrlJBEaJFMNo9Bga6MHsVgK0Ol8QEcJVQizNLU6g/mRE+GFvAZ2ENAKL8SIOKV/RIWokhckFqlywaZWzMf1Ygh+kFCASp3qkRfUlOBLhomgFORDRoR6R98UqMU0W7jl0gUAxy7yEIEQJQwvJOjIINBSbahqJn6Kyvmk4XtmmodHL1MA/tQFaPwiKRxxOaEKtaP/TIM4gx+1Imw9ra8oTYcWTLi8kUjVsiQ2yrcUkFpiSzQq1QKeKLkTa/sCYUrBC34b9c8/Mf+TP/itf3/lDxeT1QpMwEhJjw/P2MCK9L2UxyPlGbXn31YlrfxZvbJz78igubRoDNFKGH7E4WmhaFphBmEId+l7FtHdy5MeAXbDT6avfezG33xwaZm6CutZD6G164OZ9/d5RWdcNNw7pApo0QHSR10+JtSy6dDJkQuHTmxBlE+nfNyGV4brK0xQi87T01MGcE7yQWvp3zA+7+6TuGOSQuhRgktYFU0T25SIY26l8ysToDconUc2DkAtaCUiL1QXBnX1ArXDXS5gaN+Mmw9fGAV4CGpollclMGZNAu2tbFsSzOFIpRejAyYyWr0juqbI6zcUU7WAr5uLVKyBZt2OngVijiCQV6XHkFYRkaGwsAMSLlDYlZy8EMGvJBTb0feF5e7T5W9dTr66ugsq7WdMrag4ncQFPP4Sz9QLvFZ+2aSuYx9WCJeSbOiOo1fi2hqpFyBnjeFCwYvVmO4UOplZgsCsjXj2ohZtWk6sCnyhBDp6iQz84NQRV/Yn/9UfLs/f+Kf//vDNmqw9hkr/hvTdVrD9M2E7L63pNFxSeO+Cb+z69D3tyqxa+s4gG2eLVU0jla07pf71mrAQjUJRrGAGwSCj+Xp407cruwg8uRHYm8zvX9pXQpUhxpbntOFwQAAO67ws2YlTVovOYLZUxf7SxRHyaNCRCF9d1BxcQwAzNv19Vn0xPEw+H98GcHXirmOCWiUcEIF2CtoJetc3CgVVlU2Mor+yqxMTPchnPjitJRJKsDIw+lKF2rrfTA1bPQjhZ2A4wnJRE+QwFaWxvOiBsDAkRVXoEmzV6YzEEAhK5ZJFnpVWuf/LVtpzmHrUtpcPSU2HNrDo6AovRsyoQrkSoLHioFnAC4LAx++EnBq+cql4Ah0AOhHgJAtEIdo6DWwW1ufdP3s5+Xr+u6oN06Hd3SYNBSdiJNnU0M0Y2veiStkwICvXdqoqZkQeRZdiWPCCJhPHyVPgx6h+BNpYEkPebAg8tTZIwMAL1gOrogfg508X/9nv7F974yt/v/8xz4qZoCw7Dv0lAgGbR78DnFrNas7LEnSTslzYc+ssfc9Tdu/zBWHpu3J6H57n5JbYJ12e66/6lL5ntaMWdNUlzHbUo65QFFKYQQDV/nPbZSU7zi4CT0IEzg7u/PhO3UrPAMapyGnGicEi59CmDMQfatGprE+aqQua2VNRoovhZUl6AxtDInmoEw5tGwlORQpiqOX3WcEMzTlJqYRBr30yNU1nkWOwpKkBWjcSMcbX9dLVuxCRiHsbJSCJUpAXPHmBDL0KUEFsYBPAw5ea4KWOaP5i2eHL8CUiT2VroNfl4SAMzRDCHxzkEVDpNL8MSZWU7fbmSDN1pQaMpiVk5ALNuumAT47CroshoxDCCaQ6wHVWmAgKq5Qij5CUF6xb0N65c4eu0QGjcNxUWOB/ts/PLqdvLidvH3h6lftSNx11YSnwCQhJ9pCctaAwmsSBXZwuCTnCpIiICYVgRigxtoGQv6MDxrrebABv42kJ/vACWo5QU8rTQRwQ7gr495+f/avfv/6jW//snb2bRMM2qyWtGxICj5sd+F7sWbr/yxn26jwX8QPAqDw1Vy/3qJ0MdvwYAynvXzGzPN67DZjLu5zJiBCf8RTHLOAJmfSJbJ2+Y2Nrt3AGZfeyi0DfCOzNH17OfYO10MBxQUGQm0g5jx+ErU7CBvWhhJNH1x9HE0yaMUoc6uBsJBCOVFsHIEPC1sbhdQKojb8FxDmpx/AY4uRUcGJg/x9SEIWNx3qYaU/UnUTwoxS0KeLUBE50QUBN4ltwvlKsB7PBaFlbeAFBr+pwAYIiR4iw6LKSJ4FTdkQcguxOVNzcTwLsFANQOSNYxuELvZoRCHkR05EO3EgzCj3UGyU7CQA1zf8KY7HImqGw1ClyRF7gKYuK/U9dGKWmxo6+QZQKY7dTKACTFiGUBrnzo+XimfnyzUer5+6VTvVgjp67x0RA8JMN7Q6AMREqPUDGEDS0T7JjVBsikuyYO2ZEvmhq0ukIpzZqDsCsUjQ3yx/sLf+jT86/9DtvfevqFx5O7YvVsuIfE1FWzX7NuQ2vftKunqmHpMYqMbcMW9ryfF1N67WeUvpezONdWTl9V77u/M2ZO4bCH+Ks1Z7WMO1gyo8pzYIkw6sdsYvAExGBy0eX1bdNE7q4gzgiRsng49TCqvYLmkW3P7jqEKMwAGMoAMOn1I1qw+dgpyCJTpL4/YP9ydHlbH55cHF/sje1L9ztYQCIPUZthMsBBEo0y3/JF86vfoBRtaXTrS4UWi6yGzUE3qm0cQRJhny0hbjhCxggohawsiP6afjogIkVmy12SG/9mpTUEe1hzUWbGWlpujnDbqmkUowgxB7BHQpi8gj8FASwDtFp8TCKues6qhJhgUl+hmadQYUuIccF8MuFEAC8OVPvBZ+Z2V/ad0R+rd3n3UPzRgKoAAZP1/UmRzQjYSUc0ftDbZBOUxOq6ggizDJG88ZUuE5DgY/v8gU+P7oFLaGgDqIg37UJTkxwG6GWUjn8tYv5f/H5k8vnPv/Vg9fn/Coq74SR8yNI8joaPcmOXLdSUzBdgafvsJKTVcPr0neNwrQdxfH0nbbScGNaQyozfCgXlXdZNwUxS/ezETmpLkvIs76VLEPK6XtmDnkwy3LOMoQf/ZXh+HfVb3oEHj2896j1E/dysOJk4CzicGNh60YoSxY4OruyreE7ovIeQQBJSnvNBUOFZsBDp/IKBPCi95Y8OFkcns32rz6c8gh3wW8MHE5nV6e/urmYTao/414AVG4Ca8gloZBFZNEPLfcguOHg9Pa2jFb6K/kDmaDVDJFLpe4APgomoHsYShX2GN51COZYcNQUYYYQcmoWpZhd1Y4iz+pn68ZO3qgzHJEvkteMFBxBJzLtNW80HQKYA4bMBbMrgQaWFgUixgowG7BHgh5KygQKyaXqMuyyfEsOcZBmarxIHUGDJkXxh26pU2Lv+m8otP9d1fbKgcp6I846i8oD5UisLgg5wnQzpM4R+HpXgNi4Sw5t6GT6tCTKgOs4IMcXagoy1OGIQAKYI47S4Fed8ga+QsHCJs4FzWdHy//0t5Yf//in3rn6udvTU0t18+yU5SKcodmSXU+pV6lr9FUTrsufn9tqW624Fd/YxldAXCprRnKfdXkf6Czj9pzeiRxvziGkArki3HCW+qMEdS6TIhY74+TzghjF3pNTFAoIxsdH3lfMVNmO3kXg8UZgb7J48Gi2sB0ytHAQUdDCSVXO4FnwOsEQ0DaRvO2LxoIAxyZFGsqaG0c3dQoAEmjm5JQohjZCIlM/urjcu5p9rG9+f+/yg4MHP5suV+9/+CmlPfXok7gTJsWxCbuDLgRUYWUUDlDwpKBh9Osh9IOkbC56NxKaWmok5YVovODrHfhY0hDlG62PKyBfuDLDhSBarvhx8QzRJl+oKcwFtbTFzoHZrB9Jduw2po90pH0eDHIWP0io5Us4AjZ+0xxHwhcEmD6Sy2bXevQqYR2ume1GiekQEnYKWWAhS+sBMh1C7s4hx++qjv7cnSAQZAorRO4o/podmMxLzEgKaSMdmgnFRuH2AoDRuwLQ1mnW6kKgky94StHFVqe5Pc5UEm1azOCBPjlc/vM351/85PPvnvyjr+49j6T2M72xlozO0tRMk7a4p+/Z9k9N1NBI6hG+p+6rQyIz6Mm4Z9cOwU4XFPkIe/GU21+BSJen7kFEHu/KrHv1kXfUuJwl+hnfEOTAnVkDGSkfxQMBghBFkaHJsKCDqNW169hFYJsROJ5e3ntov4+OEU4PDszh1lBCYW2jlkMMhSx7KafurV9KpEG3MBzZ6q1TA9ETByaadfCuNE8n+8fLKzceUfNAnd09u7f38JeHi0d7NJtLn8QdBLradTpQqxQsAZoCSupCV10Tx4YnDZXKQbgRRu6HvRaUmCdV7zQkNmTRFAwVmhgtcFo2U18K7rgrq/XUUmFLMZRjrjfsSivCT61TIJXR6hoY/21gFkiAsaQBqSYuqCgjhA5fwIAwiVcbXxBGJ9uwjXCYaENIM/DiuGkYJfw4SEGMZkRS7lCnw3VytdGcjtpI/5i/q8rfZkq+Z2bjkEoB8OMINUUCIlh1vHEaFzbaiAaaRz/uUIhmPXoPd2JeWDasmX6+oBmFaIYozGxlPFsyUcXvXt28Nv3yW/t/9ObFO8e//+/2nrcZsLzWLmal6bKoGdF3IcY0yRBrzfNjWqtt1YhBYrXpuyfjkdYbFs/Ps/Q9bZoirX0ReR5PS8ehdwseckbIuZRvDufI1S3wK67aCPlGIyBpySLDSBdw09m7HcTyobvXXQQeUwSuTB++d+cRRwX2ODd0d3P4xG3YEgcLm6KjTCuZOo4g+BROPLR11VwJAIQUYeYexDTNsTQvJnzuZXnt5uLg6sQy9f3lozvLBz8/WjxseWStIG9O3IGOuAXPCzEicHIJgkIvjolYKe5OMQGjBKhsGeDBlBc0MRdMEf18Ge54AUb7pvyili9qggeCWkWrsKCT6StwxmpiVNZ7KGRgWtAQ2iBwhN5+OUczGDQTEM6CZrFOvcwIaNksPGYOL+QRixwv7t27B7+TzlQYzByFo+y7VC20NBc2o+ZFTiFDU+CpKbhDXdBTbirOxASi3DuE45+ZmX56tvzGgWdajboATz++UAqCxBNsBV8QI9S8pyoID2zK0PA8OJ2amBccYeGxpMdd1ahFIdEAPGVgBBh+7XD56acWX3rt4NVXXv7h/if/7d5ztrhW+95XGlNqea7NGlMjQjR1NJ1DBYeluDrwjdVUJLkpfbfVbZJpvr5qeq/tCVgAtA5ol02IjCt4jtGEVg/j8W4NvDfRZcXFRWa15pqAIMYbeSey4BgLYwqkCA/d2vhdYxeBbUbgcDL70LN2jHBuUCA4/Ddm8KxbCqcutVYyNaeN6ALk0Iy8sprgFCS7NkNPaAYAzEoYdcr3Dpf7VxeH53NqO8aW0/m96f2fH/AZGIag2Qo7dL+z5lUC5+HKqgIOsFIALf42kic0M6ljXTO4gcLwyINjv5wLE0dU1y0Fep+QIi8AE46EXzDDEegnxxdQBey6MJbdkS9yCrpue2jP16ntzQ/rPTTgC+uWBaZarqGQghfMCw9rRfRQ3jyEbTj6+w3hBzBpn7Y5HHwBCUyVZlTNvehkEhWcZsmuve/ucyhOPn05+Ub+/e5okDtpLV+oKYCh3mgIrzmXhmfYZUNo5v0AmlknlLJAgSNHWGwQ0eWu2GKjBBNCy4MVonlMu4bQmAMzq71fQMB9eri8fnXx5q3lJ585fPX5p24fv/bu5MV/Oz3hyTpLDQGmJKuztWfu+kwFYTMrL+AHnXNMkdOZTAt/JenWZXs1xtqypnyYnix9NyM2UOYMiDV0wQTtncYs5vGWpSNvJ7k0mCGN1xjvzLoVlFVkbJgZx3dfADUfnlFoInamzEDvyi4C247A+d6D73h6mhqKg44DJPJsBFinKtAsUY6yrqeWn392AHI8RqbX5lBN4VXSqWZgB8LKrWSZ+rX54dli74gn6pPlbEqO/vD9g/m94kMi9IRmfAc2Rb7Dr0SSMvEx+yOajFFhGEQqFDTCo8QiFAYB9DqjIVMmstn2l0Jv6guwu66Dgra65rgBSd2BRjl2FZbUnTownfjo7xHwjSZYPHogh2TBHY3dkjsbgTULsKrZk81rGwEKu4sa18IdXGYguR11ZUgjIM0YuvZiC9Pgoe46FnlcYKzmKB0utfxBMZbf6I+ZMaQkmP3YD3YKNaVx5CdHe/uL6RvLyfcPlQTiSlawBZXKd6IZC2zmkXqInkqjBJlQU9IzqjA7NDNPfNJbhg6FTHG/DLsSajBZ6mAAM3XzrjnaW948Xn7sYvHs6fJjN/dPTk7OT44vD2/+fHLzV9NbfzU521v4IrRE1CaIf9nWyo2x1TR1Iio+OePdhCgfEfKa8RU/BGoIk1RSDpGhMdFMA5NgfKtcUk1jJc08fXdJx8TUsdsY5f/cQ2usCESrHHc2Fcot7Y8qi4dzJJMuD62TfCyjVu9sEtLYK5kdtYvA2BE43b/77u2KbJW1qoJBXaYQrMaxbgROJIqUc0BJefMZhUybEprBTw6gIcu92f7J7OB0sXewmO5PFo+ms7t7D39xMH/AF2G10WoyuA9CCpqJSTzbkiOVWuy2oFT2FZgolY0Cf5QmgQB3nSplGPQKQ4jhMEXuQQT/sRFcivGF+e2NanqoKeko92blDs2WU5MqaUljeqyIoSqdoNgtIMHEiLODKgw1rOaWvpfF0BnpNe7gAguSoglSrJDxvTIj0+qEAWFUNSzvMp6WHHSyAjem1wSNIl/QLHdisYkoW0Q5cdjGCgTwkGwS/HJHHqXu/HC6eGG2+IR/3r3s0RAOURJsAtJp9jcaFX5CTSHmqTtaM5jeqKROAKi82UAzhLTVSXblgwrNLGw2DjGhuTflafri2ZPFrePpx27Mjo+Ob14cn52cPDy8uNw/+8Xixk8mVx5NjmZ8t+PCzj3+8Vd6yddtNvlq4iICGDie1ZyUmLDgwLRE2MSNg4CPdNo0hhqFTdm264meZkIalIz75bsKP11tn75jQ6aTJ/H+pkApfOTyOSFf3WGrAG8ntPkTvISq8iBdOUQDEasjUobHXFNXSlcp2/F2ERgUgaPF/bv+4VNWWnpcs/x0FmkdygYydt36x7A5ptKu3iCwQmE41jmjIGgOPwOnB0u++IVMnc+37O1PF/f3Fw8P5r/c4/sjFnPbdEMKjoNQIIHNuY02RawQE+toWQirNLaU7ySmcySd4HQ4oKOk/DY0OjV/bYS7yqAcYHWjCktWYpK3NeWrqmF4ndrh/N5G8ZeiyQoYaKNobYjeRraHOSKmFCRMDyRY0mxpas0Ufx8HE/KFpIRETe4MtEJk4l3BQFXl4YQa5aBVlyaImibg8UsuyC/osoY6jmCjSgu1TqwfH8DA3viWQ/MidzCUuiOPytZ/yGOPyeQzs8nXO5xtZTXVHAADm77eMdEE4UhqAG1ohsnyJjKdpinVU0mjDZ1oTtdJpWQn5v50eXyw/OSt2bOnk9dvHjxcXnnp6WuLw+v390/uLs9+sbz+weTgF/ZV++5p9kwmdzt//mxr1K5pcndL31cAyMb9CbxlyjbvCFqGLp43s5QdjivzlJTxvuZXeozBeP7zl7RjA60JytP31b7J+f7q+8moPEc3Uk0bodGWfxvtaTs4/NWTcmusE+6LsDrgyspG1fqT7xEct6NMhQEGi4ZBDdJpZ9K5K7sIjBiB2aPLh5MjvlOX9cX5RmlYaHQpZ2Bx6jqGwwXUMKQ9VFlHnhMmTm+lK22U8Bz96Mbl4dl8yvk0nSwuJ5cfHt5/92Ax803oKtjFKOd3WWmFuTbKG2RCT2VM2l5u6W5vMNamC1U4KYWS15HNhAnrKLMVSFAbaU0wxyXkDh7JKfBDyIveHknVuDhDmxBGs0zgkZxKuxhF2bidtocc6ymeljR4KCQuFDmFHjjyhfOCj4VI87iZTQqPoLEO2x8W6dhKGvw+RZbryC9coLDesDLWGyc2Dh92Jz6VGIYwgYry9McFqUepZtzpun/5XVU+776N74gEGGBYJ6DdOJuaICQp+KtaE0Qz9VE0TNJ3YsL0IVYWGMJBJ3j6/aDjYG9yuLfkUfonbi6fPpk8e364d3R268bJ3cnF7enZh4uLHy5PyON/uiTuXFwNMHFbaSMyWQZr0guyd7vXfXsm6TtdKLT01xNYy9sVRcs4LYk3dZbrpvm6Qm1qo5ggkq4kmK0Iu4kNge5os6RS5hsnx+KkgTO7Ppo+8z3j8WoqM6bjctR5t5B6sBxzoTKtrloozHtMo25VDPD6t0ZiwIJlwgbKSadX1Gr4jtpFYGAEDha37z7o/D3uLE5dXqzKjyaD503+/vLoOp9Tv5zu26ZaziaPPji8+85hw1c0AjuuA2BzIzNQud/AMBqAPCbQaNZ+bZu4g6Z9NiDV1CoF6OCghJ/0bi9hKpju3cQRxlL71bI6I2kSStxhkqh76/9IBmp2Ch6BxKanRYJeh5mxaKauExjCb1Aud7RtcIoSGJQt6fdEh1jvPRYA5Ezpmm+pqnmO2JL4WMiAW2puKaZUkrql/Eax1CNofsqhgwVHiFLXHL3O3I/8I4ifnk2+2eJ7ZuqU1PEBya6nxB2DI6w3uRarDoIZj2adtgKfUHMeoq39eVvQUNckvJpNUFHKYpxrfGCF5+iH+5NP3ZqdXT144WJyce3k/OL04OrZB8vzD5fUZ99Z2vuWb2t/q+ZBFOoYT3N1OpYtwLH8UbIubAP8n/2gmZI9ei/pyXRbPmrnsEeVVzus7Hm6E6bWD2rvzegA4QPRa1LBbEeYcYG28dJhI3Oeo3JVLknTxJJeb3rb3TcXIEzGZP2fD1rL7lecLJE3g4wyPVaMdKfUVGQyWi8KFNFISzlE9EawjN6VXQSGRYB1uZhfDvnrS6zDOF05aYHD8UUZZX1KFTo5Zg+PDviN0r1rl/snj+zPGi0n/F3Syw8P7n6PL1fvE4U4WlEu5GAOZh+NyZhVTBJmLUkyVBkvjoAohcHIU9oEGvcQKwwfq9lVebgjImDgCzQ1EyCaJvcrDyNHv19lFABhKGD0INDDKGpCISKaIC941EN/eQizia2xVmpBP8q1GjEBQU1RrPBF7pBX9ZsUMEc2VrA7vAmkBuW4kJYwJ6fwmhLMMoHyLb37BUAz8jKY4MgjTVAwCx7RyzyOla+HFQjL3eeTt2aTb42Xu6dzBGwmVAcCTm2coxRbM000UL6lCSV3l/LZ7JLH5E8dL0+Plm/c5POaVz71LI/Wz8/OTy73zn85Ob+/vPbB8hofdzG0SZ7vJ0qNBxw2lk/W9GZsP0s5k2iacP6Plj16twPW1oy9iTB1UpmQYjAVCNrJFo/eTV+exxdoWbZT3JP8jRAln9QGVmDQIAfyXgfp/X5LGGUimVjStGF5pp48cc9kPZc3b1YP55WX55wsfTef3XHZt16AeW2cpCtjK1JEhi7WqRP2bMWFGWgxrKRh7souAj0icDSd373fdGG118ly1e3AmaA8WCetreZehU+87F9Z8CH1g5PZ3iFpOjfc3uzOwexd/kjzdKDyFFHcCIEczDB7I0+Vb37izq7moOfyptYOj/FCMBANXqE8dI5L1CnHkbTIaLjT0iNuVh7ijgs41SY8KaeZxlkEVOOdhKVEa4haTHo1p80K+/VihciMkrjjCzhVgzlcY8Ggn0IWEk71Q5uOQhXmUs6INMp17mjh4UvMkawg0HLhlVEJOQq7rpmyqjJHygEMUe6FI4+iDhnAUBhVNxBJujS/zGYMHIsgd+czM5/q9dw93IlVhy8CDMEK5DoR8ssNiRoAACAASURBVG0cX0QDK/0+2VIXvfMjfm108uL54ta15fXTq89dP7/cO71ycnZvev7B5PTR8sp3llctsTQn63S04OvgWWWSdUMsw6Qv8vZsBE+85mwEzjEy94rfWYVnI1Xb6NUT9+zROxzTZcrTnD7DYSyz6jCFNetp8SL5/Fm6KVLJ+f7qy8SoLH13e9Y0WID3rpz2hofCE3JDtoGQ+6hK03dl7sYUoCxE0Ybww6Hw+RmYDskhFmgtd6nb1bsIdInA4fTyvXv213787B9ymqyshipO3cjgW90a08nBNTL1+cHxfMqXBc+Wi0f7szt7d9+/wtc1rgxMLAtNlWNxlE0QyNluSjBQO1D5KnGPi0qbOfzBkqIz0FIoTAldiilnXBr9FLmGZrmmyaCmDHEKbQwfF3BoQznwopkS4ZHcUZd7s/Kobmyq58mhcYRlpoJ3AQwvWHsUcqOYqS09iZRRrABgrGktzBRuXr16FV/wS+6MOE2oJTK8k4nojUiQpEYSiVMUlGv5UeMFBY/kVFe7IOcgZvaZ6K5jN8rzu6ov+XN3PjNTV3BBJfySR6o1X5Vj5S+R2UbYUU7YmVMAQFcCaGBe2Z/wDYwvny2ePj145vrVi9OzydXT+eHFB5Oze8vjO5Ojb07yn0TEMZPeYg2q23RJ1SqTrBxjhi19T/95rsli4o0Lf9zcf2eVjNJkLEvPiFwbVmTCNJCMZ1k5DUtv8yXKPAadjVyl7z18zoZkiTgaTT1FfD1Lz+wbVzk61HrKnnS5iOfhFgp3U5m3DQpObgBmWhQA89jdVhchkwZ3fSXOOk/XNkIWHE/ZEYJGQES2vZ25Gr+jdhFoEYEr00cPHjzieSynOicY64oTrMchVmkqVHFcoxwZOGt3B5n6yeLodLZ3vNjbt79ROnuwP7tt39K4nqlXqE+VAx4JgbdtMrighMMcNewywGuvgbylcg1hFIUbc+173BXcVBFCMjYYdoUCQqOfg1T0dWGFSxoE5nR02am0tzddsNJbT+VAlDMLxAeCksrAj+WVzlQq88TS2mz4BQF4uSaPWGYUXGt2CgHGak5Hd5NdxFnQVTlelFcgXlBQRYEQVJI8fBwdNgoxAXgCu3aEDbOEU/JLavg8Ors1PAqnhhmx0cQkzvfh2goalLvzeXf+riql4JSENUfA6OoU8um7moLpgU2U85aAyLDAGqaV4+H6lenx4fSls8nz55MXzg+vn59OrpxdHvKJl5t3J8c/mxz9hC9hpBRuHz9XksxtIN7ScPQrryz1JAzDlGSnThsLXCxpXrQGORAtgTc/LAOW3qT2rN3PExsZB4sew0tf0Jl1S9+VZ3sgMm77F4NhIz1LzodJVSl9N7fwyaUjm89He5ef8mvpO/L4YT6aR5mGhONdioS7vIqGN9WT40peTYUVBPgBB3/TkVf+JUFzLM5bMRMFO3IXgaYI8NeXvuV/fYlTi8J6Yw/rhKepddU0vl2fbiJkUX58Y//g7NHkYD6Z8oxjObu3/+iD/fnPDzdm6nWmUuVKw4A91t2NKuKAaUWGGjosBqS0FyajdE/B3/A97qT1Y2ENNEEIbjTbELp0kWSspl8ENEWxCD0slG08CQv9WAx6CIEL+dWkQ9+UwaHWXI5lKAXZI/jp8I00+lk8TIFcCxfwiBXFR4zgBHOjtlQADWwk6pT52Gj80jaGEH4R4GlzJOkZ6ihvVssuA4A3BoVdUBar5MgpJismxabH9xS1hjBxCPTTX2k0ZW4j/Y3J+unhZH8+eXM5efsgezwTTqUY+tGoimkdUW2AQbk9X0mOYiaLNP3V8yWfe3nz5t71s9PplbPJldMPJ+e3J+c/XV57d7JnmV67wq3BDPvd0W5AJ6ksj+XIbh5mEMDhubm92Blv6467ipuedUdxDex7V0VluabV2V7MtySvdskpTVd2aiyTtP9Euy6r3HetcNcbHa0IDbE03cHGmDLfOJlLJpo2M9o1ONBI1CNrbyR89hjtw2TZ4GTz6lQAWxEeJfsWfWJFsUBYNOy9gr0YWteV0E6uNOyoXQQqI3C6d/fHt7OFiADLhluDworSRQNnYGLJp1+u3Lzcv0Yya+t2fm//3nuHk/khn2C3pv+FuEpsXZnc7BRGAZ5DmHo4+MCgyKhJZPSMBiZFTBwJOkZB1P/8ON+3lcNSFb1pgAKrPJzQUHROQ4SAebOeSUTX4ycIcaeVJ0dwynxLnAI5TrEyCqHoqr9TBLDYSb5OWL7gFAtaG1KScPCIhGMbX+QC+EIA6+D14zMRuEONlZiviJgmSzu5h/5tg48Msg6bnMKvgoBWYPOSRoaBlN7uF4wWmrzNjg/kFLqam1oPwpZKppP1zsQ+M/PxR5Nv2g9XRy4Y0qNxls2IwcEvyuH+3uHB9NZVvi796MWz+TNn+9dPTg5OSNNv/WJx/WfL0+9P3KUBe3r9QBo5OKZudYqvKw/MJkBDz5AlwyHluCzZ9uxd6XtBFem5CVDoMA16Q20vNPM0Pd130C4mK1brOPSsuqB9JdNI2ShL3avS94zvEB0kEN2gcQwzThpprzDU5Tz4q3cwSsoNORKCGoQ54EoTvnhodPey3oTpPVSKFzFREdvM2ED/DwmjDVhCSnBX7yJQEYHDycP7FZmdLSJdMZzVZDiM1L1ToWKdxVc07l9d8n3qB8cL+1rGPX77dXr/Z0eLh7Y2k7KY+y/oKAnGHPopiUB/MsCzCyKD58D3ndFfbXptyQQcbTSQ1ylvStzrEuv+GJORIAMfyBSC6AGosA6MCPqlJzSPSwC78nG+Ii7vsKimTLtn9tZzq8DGdVPa8ILCfLEkKFpY8oIajyjxbegMkQDMbYAJSKOEEb8EGMyBljOFDJjFiQujWAnNEBtz61S4Kw1aCr5QM1YTIR+lyvvt9JRAV/0M7JdbtzSk4FTuLGmwheglnS+6cIf5YniDIfvMzMR+V3XE75lJzWFdP/HosfLDKYirB5Nrh/bxdB6ov3p97+zalRP7+6M3Plxe3F2evL08n/N5kS3urdSnMWktubXPqxSuXctg9WzZ7XqOCmU5OHT+hZFca/aVM1aoLV22ytLcqHX4E0tbGNmjd98RxrJiWHLaGcYySybcsxgOG5wl4qHF+a7VIZiEiWRi+Yi86W1D5075iDx9T5jRKwJZ6fOQeQgtKFmWLyQu4qRpdzzWcjahICJeYNgbJWg0KhpO2wgjDL0NFm0admUXgfUIHM4f3ruMFbbe5y3Oagok6YQyeM5McSTNVzTuX10cXcyo4fD7LrN7fEj98J5/AkcyDTXaKAiQp2GCtUpzrBWLHr39YCOgnNq9qU2yU5wIU3QvCw9jpS3EhJwmYuCHQBKxFH9t4s4Y5FLR0NuJENCoBYIahzVPIwY0BYb+8D/lj0WjX17ItVCroFFvya8wtCWCeVfBQQi8kyF5xJS1eYiOGLtxe/FHP9i66mcIvqROhWt4l+ayNDFB2UaQUY5aAitiuInYXHIQhfzVJNwBvxwZy5Cgbsyth3gEVJDrjVPBr4iYnErnq71FcveX55NPziZ/m/9mZvuxbST13B2owGuQ1yLM6umE73u5eXXx0tn8+bMlD9Qvzs+XR+d392/8cnn+08XpO5NDy6HyTH0t62qw0bdrHP2W/QHa6/wGJywGypZ/Q7HuLLE0FXnTR2WP3m2hoyxL3l1XZkoGM7O+w2zZoM80mnUWT56OrtEZIBd1wRx01tHyRaPy9N0x+8ic7686AOB70g0MJ9ebeZewuEg8ki8TOOfTpsqi5hFEsenJm47EeaL8R5dZ218sVlZWn5+xBuMjdDmRMl3ZrtpFYBWBvcW9Dx+uHoStOkoUl7ju8cPj/eOT6d612d7RJQuQv0tKpv7gl4fze630lBRnDJ3DLGDyGS1vzGlV1w1pz0dPQb9nDWsZNto4riiyTpNRLTFImzQo4WSs0pLaxB255sdXBfeAlRb1KkDUFKURMQrhTvpjYEsC/RhtKdwgljoFHZLMBHTZrxB48gmmWEWrShHDI1YVk0UKUpiyJ8cjEDa8MYgp0xwBO1zTfGnpN7iDft7pbhRr0NDcxW5Hf4/1jyMxWQW/cA211PClHy+aYfTrlSHl1v00VI6SX5oyav1hJq3A8KtyYFfmD/Lcnefu2yhMK5NLATb60ymDhsNfAb9+Zf7K+eXzp4uXrh9dnF09PL55b//izvTiV5amX+F3rAxYzezpEGKek9PIxMcqnfV7zuf5YQIhf3C9OjGjs4KlvrUOnd2et2eZJ0LKwf3RO2m7/WTJosA4k1sbTsM1ZK86AUxD5PHIW1Zr/9nIJJq+h7LsWMg61hkSMyUYciZHmPGNGZYzm8bKRnnqbv0+1QjnfNce828d5mpmE/m2JYsJAzWc2ov4bHMVeCLgixZRpn30rvpNjwDLcXH54HLTUy+eqR+ezfg+9b2DxXR/sng0vbzLr5MeTB6dagVyf7HwRokmeuI0JufZtn5MRPKgvTPQF7RJIciFv/ru4kCUXDlqdDFYBUwQyCi+rtx/rrEp3Iyq01+22IODfkHqNLbONYUewKET/cwEpZP+TsJhq9OoSmH8Io1gviEQALwKS5lCgo6trU5HJaqBTFyQLzFraipumiy86xdGjcJEv+EbXWujH+tl1xiIaxRMNGCji8JwSW7E01UAtShnRfXbAuGXJlHW0QnmmDIdT/30b3RHuftbl5Nvjf15d1yTd+DnYzNy5+Jwdv3q4vXr8xfOli/cOD06OZ8cXXwwvfGr5cWPl8ffnxwslHt1uaRs+Svf2uhtL4Fa/QHS9p8XEdHM2e1eNwzLXLSX/B96Pd2e+98Ssmjzf2IM2lJx5O12Yk15sp5cVZawR8qu+8t0ZndZaLJLbNAnZ9BkWAybp9Y2X1lxvnVgVixwGGprxYi86RrMD4lYf/a2ImfipnrXCW9llTljoZA5901NI81u1qN+mh47O0eIjBvMaEUMXS5jeEt0rmP3+psYgYMpP5av+Hnj9GB5dH12eEqazv/TxcPJ5e2DB+/xsZo9X/cRq+x5hzIW7gVO0egbSLBWpY3lrSsGDreM1nBv5WjTsa9do2NfHHTqauutPB0o/FipTtxxCWfkGEIxkmEqA6GQR/Z43BgwNhLCXycWIVaU5WDuWfEnA5VK4nFaZe9HyAQYj0JjxchBJovZLCTooz8x3bbX+KIFKUPQJEZah0rvmMERMaCTMErziGpDFXs7dkHBNcngDt7JwRjVnkA/8WHS2w/pJIl+ltBGeJWuaVTzWCJPfJo3cifABWHL3SeTIbl7uAbBZKm2Y2Qxf/F0/tzp4vWb09dvHl65djI5vvlgevr+8tavliffnBzZJ9RZqsNXK/nWKhUr+DdO0/SjyRK3PLNb3QadTNQNsxw80slKjYpT/rgZcUdkozxzny/5TKddm/ETdaFdobYUPm95PkrT4mZ6NHGyCw0hfobEBLMcOeN0fpHjeTIuZ0xJzvdXWXau+VfZjLTdRlgSDnr3QatPzODghzsdFQPEksvBz6ZWbZNB0arkq9pAsWHpRDCClkYsaBErFTvqNywC+8vZr+5x7xzwHJ0vfjk848Bjh1qm/ujDg3s/OuJjMLaMGguriCsGETY2Fw0ENwIrsHFQh87QzwrnomFkcFpq4W4CG6O0R8BWyBaEll4kpRMB5FvqbxY7wLZwy7zMYAACw2OZSUGgOW1ugw5f8A6aElbwiDLQNWZCqyrUjkukgMua6dWsMXEhCaGl84R8xIUgA4m6jL+ZI++oKUiqtjnLE1kNJ/64v71ZIJj6RcNmtF17mTiKO7d6x49rI27pgLTtEPHeO31vgFPyLgBADHEN/OjXvKc6x6I75e46pvERPFqTrBDKdLI43l++eLZ44ya/Szp9/vrpydnFvYOn319efLC8+PqSL0Hgltp8UfVzii3iCVm/0cmowjbVeWkPppWp5XlmMqIduTp4a+TDxgZJAGZgPCG1pmPml1a5z7k/+Vw268+sePa5cohh4vi8MX06VNaJ7Jyx0fn8BmCPsI0KTndCY0HgGcsaOLOpeXS1JukZts2sG7VeCrjUZWzxIn33WOjElaTZ8X8mYotWOkUgYuoyFqSZ8WrVk7QNj4mzuy1ojgPpCJRHEhmDF0yjXNuu+k2LwJXp7PbFvfNPHC5mk0e/OrrzvUP7Hpi+Rccso1l+W8rg9RCZBSz9WCyk4MKu2y3WOWJtnj6zD5SlMJBLhBrO8OveMr+yljaA+k6E/VrqiPmW4khMIUQDDBNER6VyDnqD10D08yUqA5VsHI47+IUtJejQeKRROEW5d+9ecDZqe8wCLGsAA7LOriYLGQp06oh2TsopK6E3prvcOwoHGAVg7dUykCLXIMIXCGKiJnxmdnt7DfwsntR6e/wNkihUkXf6MDryOIXFcd3hXSi5OzojgA3AenSRu78yyX5XVcNT16CxqxrXOLV4VH5lf/n08YJM/c2n92+eHNw6v768en5v//rPFzc+WJz9VN/MmK568plVUtQD44Yhli/10F/IqizpqinexcGj9LFJqKavBdsTw0YDKMm8tJf8X8b1R+8LPXr3CTSwcsmiA60H5358aj6VaNqyom1qLB/NBmmxiS/wPjBLlsXpVZshT6fNF/8nNc7Pku81jotZr3nhyXUxZaeHRSqtCkuekK+4KWcl4/F0/xVZi6kot0dECI0z1bZAERkVeoHFgz2znwcwo5FYcdForV35zYnAxd6Dv/3u+d0f9vwgZV2gOIEp9HKpkWGzyuImrRvSiY9CXV7ca+hnVaOfmkIXNdaHJK4oGTGDt++rRmN4uNWnmLKiQITFloTFLy8MgQzYEBQckSrmFVpz3FJ5VzGmFotdR9XJ4wsKKSCnqOk+WSaEL7xJCO/qlDxpfPDjEajyScteU5zI4OC4qV6qfyDNNmMDN8MreEdTC0PTx6ypWYlEXTGkUmYgE/DpQ/Gu2sK7mMrUO+JDkxODenvrc3u5u7z70f701cXeJybLdw6zTY07LEsK4SJN55sZX7++eO50+vKNg6fOrx2fnN8/vHl7cvaL+Y23J1e+o8+9NH8Is1Ve2nVyEvkstbPMq6KkB1UIBFExoIJlWaMlcuWuClZZaDNHqmtsxHC5ohRXaaZ1Gddzyuncf2E1W62ersdQEdluyzcdr6uU3VJM8zHziGUQtAY7OowNdJnhenjuwFez43zXnd8thi5zM0YYS2m8O53RLiaZVWpuUANzmTBF2bSK8mZUZZ9dSqEjOCjc83dM6OE/xcq4K8p06c2Q8XflNyMCp/t33r/H1bCVB+SEkLuGwipTssTSojnKAkMnZ0esXzXRHNfBKBOIwuEZ/Npn3APxKPgqlRBrolzZJSYY0iKmZoWaQhCpGzQQ+uZkq2Fsmy7g6VJvI1yQwX3gqZabCMgpwsJ06tvQdfP0tlIw+tiaMXHCrx8UyEH5OK5HRGzjchrR94J3oRnXKP3OjoGJdWBoIFpGqeAdTXTKNWrl6JVW6OK9wcZdWTm2JVNRGvgzLtYkTmllQuCUHAT525PZc7Pla5eT7xzyzYzLW8eL12/wuZf9p86v8MGX/eOzD/aefn9xyifUf7z+zYwt8SOWJYh5Uth+YCdJnCJP8vzKx3kWSOo0uJgKrYhE72CtZQVmYwW/3G+9liRaT/KLo6uw6n6wn6sjw4S7OouAjfF/nkyaFkswbRXYi6uFQK3FMNenRRJN77XK+024b9FY9yNLxKUp5/urr1CjsvTdFmzSpGVACik7+F3GM3brTAiGu7/mexBoWWtaCyWabqGyoJgi65G4B43o2A85rJgOk6pJ3xloSJHblX/4EThYPrw/sxyJwrxz5G7pAbnSX/STBmAIc1x2neIreLoUGIgGFmlZCfpxAQG6kOlkokEYW3JBMKhlos1GWUvcH8PjdmKkrBqUFIUMQu4JscKnusHtyq5QVdk7CrNNsggMghlLCrtw8I7C3DNbeEepw8PAht66UY+TjzuApKakdoGNjzhITddW30ERSbZTeZulePrR7lZWXb16NUyk3vXTXB6Fzq1ON+DLibXmjho8mkFbmp71aO7KOBs4eihO3SAzpAtgnUzgUbo4abpz9p6/4N3B3vK5a8sXThcvXyw/e3p1/+zK3vnN+wcXv5rwuZfTtydHl/wBbdKNbtdBta8WZ2UuazumWrgVV9mS0il05k83x1KfY0j1GU0wtWZygXFfMYGFLEX1rBL9gUEPcM0i3lsuSVEWKpbxOWNJGUgqfRUwNhPNU08flFdmznT4IlkjPKBwzN+c1ijPQC1rToCpp1OdOWX4tDTkDg0r0o91aLdklTUMjrE0yl+9K2vntHuLvKtWA4kKAmWbii3dDJxhCTLfWTZhBiwrWh7GzBdKkd5kcNf/6xuB4+XDO/mXuDPvXEAUFkLv9Lo5FJzqFGTY7UqvaWKxchQw0qsBeAgXLoXyQPfAFG4vgxdgwQOhgMmvMh44q8SdMUhTKuV6M1FLiWBB6+0XCiO+IxrFUN2c9XahMLDw9oa5hEON6ZCUayAhc/Wgdo7qiDEJVP0I/KIwcelw4OEjBWYd1Dp+qucjp9PFmYLBNfAzfSzXrb794NTAhCKZAhiLxkGU6+0HHtGkpsDceGC1xyAvthcoALObKuciXZ/yDth4p3OAgakXN64snj3hF0mXT59MX7hxcvX0dH5484Pp9dvLs28uT0jQ+RN9l4vsSwDSgePQa3uoi0o9Io4R0uN1tUqcru4IFRCbJVLpoAlylsatnwkh0ItYmyZTLEajicxLe7EkEbv2T01b4cDk6gFv+ndWXSjbB1ovcsiGuWfW52os6GU6vPO8NIMQzF6EeSv0yWo1hvH9Nee7pKfiMQKfAZEJWhxEWzyyFL8iWTfYgb9MoCIXMBA23flEJKR3edTsQKHDaR1lNBU6xpouhTSXcZUmvyv/8CJwuLz7iwdavSvnWAXcEbQ5rjnGITiftVRWQsMotElhIb3GIhzpBobEfFV2tgdmCot5S16AqqWJVeJO9jnw3k1v0AgJUISGmgnb3mM5WQTDQC8CeYFgtsBPlPhtPP3UHo4WAStSfuFjYVS/JprHUtUegJYjNSUdpemD0wMSqnqMSq030yhvbwLJWKIpKiaR3ZJyUqP0Moo6ZY5LY3oUE6mDgVBLlE2BFdwM/rgEVrBeeE87rgmdmPz0AEc06arDL4iCRfK1508WL13M37w1eeb06NbF6fLazdvTi/cXNz9cXvvm5LD4zYz7k4P54nA2408oFVSN3LRcpl5lwQ8kVw+a60elPQwxEw02UunutDQT8D4mCu5h3T0soBD2TSakK3+67Jk2esx3zivee3P+sOvtG2f4ZQQvCo2RovK2VpOlmFpIEMjYYsujn/JdFb0m4oImPKBouFRJrZSV+caxXN0cdHK9mXVJBFcQ0ZuCbum7KScS6HYii6jHzIAlTGtS8s1IZkZvtkMtYh7HNHRlWhp29T+ACLD8FrMHs/rbUvcRniq9ZjEooR/Rd3RSWHdcFtRYJD2DM5YJVCm9RqEe2kLgxZZMxPuQMIFT2f3UPm9gDAV56jQQgKY0JEDII5AOeQJp8y1/OyUfwQwHqEw/3j148AD+nTt3tgp+q4GSO3ihN77hCEYpI+4iIoYVghYmRidQzrIuYA4HsZ5alINd39dhgs2JL6mqcWnwc8R0ek+rVYqDclZ4cBCclQ7Cx8S250IHJTCGxwe/KHpSAoFO1WjGl0pH9qeT44PlaxfzF88nL1/fe+nm8dWTi4eHN99fnv98fvO7y6vfYSNvmsbZ/t7+fHI4m18ebO25Oz5wolhSlMcpDRjM4Of9Va+bhCyxy86uquFj8OxgTN2o05n/vCCT3YQ8VYMJT/5SXpk2xYqh55kIrJp2K/G5GfJ2HryXkndijWi+uLKVZkyPngh8zPJPs2xRdX7AsPVun7indHEtxq8IDQeNhdX/qa/MN44n1hYetxujvMsQ02UzBDbXlg0x+D7WJUxJEBIVxyF4n6fvBsYUyigaim10ulpk8g++mxU0WIGgF8Jlqmkbvyu/5hHgXRufcG/jBGc4hYXB9Updd6q3UcVwrkKKhKWKxaarkF7uEWo4hVShjfJmmVAoLzCBU1rnzQPb96IQj/COH5ujGQKjWeKOY+l9j5PoVThEhxlGUnqAwwSjQs82CFzCwzaacYqCPKgoEIzCL5hooBCNmJJUIQKIpZxt0FgZrlZKqOVd6LT584KDvA43VKeBMBLbut6x+HinPYNCfJRH1FgfxUEpDM1jwS7oYWsQq/IG0azhIyV1ELqrgyxp3qql27yAYXiT9x5d34HIKdzEQWpKrMk6B/FC8xuAb1xd/JNXF//olZP9s2c+nN56f3Fxe3Ly18srJhCXSOstNd/fm9pz97Fz99hqORK8wF8DmXOMXpVq7qp/E2XKPdXaJDikPzWBh2Aup+mW6Frp55BCtMkR2fZ80sikiWE7DQi2fezdHr3nOJS0Iy0nlMJbDp5l6vZiqarJryZLHuZ884sip3Oz4vWtzRyI/J9pzkvCz5jOYccgoXR8NWqtyxuZGDvMxCOnN0K2/FUueLRVWTDNWUahxmpv5qBcV3SLq11MxDI1TlFFJBGroxO9O/LXLwI8Gvnw/v2JvhW3BXxWhRItzn89SeQSZLc2D2Xx2I2YZ+ooYUjd1VY2gXD5qm22uLFXXgCMe5wao5ig3jiwUgANFBykRkAO3rfAWiHhsfc6xEu1uNSyJ+mNQYxRzQQmervRrDl6CVl58nx+raI3DYS8I7gMae8jGtoLB7BtE8Q2LZiLUENULiDJh9iWEGJlLM0CHDMYauEzI1udFAKIXeowOjqBclJepiPclAk4KsPfhKAHndtewOymhtxd3lEDAzAQ1HKw/SRiQrn78cHiU08tvvKJg5u3Xvzh9NW/XjzHB3YUt4E1z935zMzBbD4b8txdh3YgCkLgLJu1TxUQhYFom4ajfLsm3ElfW7kfNUK0oQAAIABJREFUw9L0OmdMO7aaYpXF26SMtGxUpL0axAU3qT96t3VoH51xEUt5XTOjoL32sFmD/xgZhCnM/TR+TsOn2A5LvuxGzL61oXIHDJz/kybnW4ebs1cDaziNlY/Im942XHTltIsrROaBg5aCXJFphBNdK8LsOZosfbfBGS8f4MDQYKqt0I1IlopF0LIOB1WmpXJX/9pF4GAyfzjjorRPsXcqcf6Tp+l4T/MW1pKufu04Fgzy5WSv2WKYQFX7NwnNOgu9ACtk8DKqFV4QLjRTH5GnpBFIheHbN58gQQhc0jbZNgrvEOTPNpRLJ27rvU5dgs7jQIKIm0MwMN/bdqQZXixfrWAJa+6o2zs4MA7NIANVG7GyTIOPcjAFr0kvKxmRg1Hmnd0yos7UR00lHK0uzG1pjbHNr1y5MvB7FZuDwNTYseL7XT5SM0Q+0kuRgxDNqup6eWD6yumDL7569OmXLm5feeW7i5e+ycP1nsrqjEyUu7d97p5azxIZT6hq1ecdeYqUt7fw6rmRErFB2i2VCwVBWU5LsWpNICRHJNySWQnrFcqZCk9K9aKU08Us1bRn73p3zKrkcZal5C6iZHRdHX3+IB3nnDBXLaO1yY71HHQ21kZYoOy/oSXTkKfTjtF0iq9M3KIujsRo2BsNYxkM68xTdnV5nwXR+l2TNUQwigFZTo54oXifjBV6rIkKs2YUujPSAmV73j8/Q5fTVimA4lTQSJimXfk1i8D53sNf3LMDv3fh+qBwa8QHL3XpU4vorTkGShXNeJMQnJAZSLB+N2bwLHtK3I8MAQa+tzFtT9z13eFtpHvLYGXEnYirZAZK0IGEcgGT26Mk6HWeYpePudf1jsvHTQo6w0FiqIKnEAPNhdqBeoYMx0FgyM3Qg2vaSG18RJJJabncw0RXAiSAxFbXgcjLQdXoUdjlIHXqI0fVth0hd1dW3cOR5iF6ByXvtD3xBQc5v1Ifm5U09z51vPj8i4svffz08trL31++/JeL88k82/vNA/v11ubu6c5TupIfQX0M+dmouPUZ3nKMkqkOOEtObhybCTBwi5PCdrLMdCMYwmIeZAkqlKWk/sLBaQuSf3xuhgOWT8aQZSeQ3YLs2ADdXfaCNstxTZTRkZxrea9BclEXTPSa+X4FJZGMyy/0SLOycvCscdx1E/D0XJHwpr97iVReYuaZeSS8aDKPY6T3OsN0ZVm9Ed6qrkwFKldFQYxAQaggEfHMaDoK3JWaHfWkR+B07+537qzPfWvIbEZKrAc9TaYZz+a0NFrr2yzIDUXBBHZ5aoZ+muNaQZsyeEwoZQ39ENyP/SzaZ9xD0WZHe0kAF3w9hhJQMgBKTCcc5TrEopCgE3dmuoeVj3yI1g01BTDU+MukUJhUOBCjg0SnzI2uuaAQK8KPU5QwKj5dA3O7x+MI640FtnEZ41S4KWDUFAZStIEL8UmbTDebRZOe8kekgaFzEFS91eKmlGgSVaM5lqse7W/0tyUA/qTp7z43/8rHD89uvfzu3iv/z/yZ+cIf6lgKV0wRWupsKTbjGQE/QODz7nxykTH6V7iYCs2WqhMxArh1Twx8nZXCSpA/Ba8KzQR9kayzUpTr31amablsC1Q4p8fI5JjmKEMsCeZCms49EWfHwsr5IiRGrdMre7GmTmMnXKFRXthQQeccFLQDqQFNtazk6ftqxnK+vyrnRQ0w7V/mb96kY5Wdp7SLr6Xvvh6lRKOVuvt+s8q6MpZbW2/Saxl+MSDEB6Z02A8+vGFtpD2M6jNXEq5IeLvy5EfgeHr/g9J3QdbBZtJ1j0hAN0hhumnCRwBJrmAIrpWCTJ3+lny0cXNRAg+csS4vIfczxuDgCwVfoMM16+hYtv4JFvAAujmlJl7IMCv4A6FZgYmHhK+QoHd0cExxIA1Uh3eUgh7NH86G48RhxHUzEHOP4eEmjshZXKOwN6h7KGwegk6sbENzg10shpshBgYt2n5giM/jeeje8DH08EVE6mbEWW7iaUE4bbJt21tJBwbNR2I+dn3+H7wy+dzHnvrJ/qt/v3jx/vKo8LeQbEdakhCDhhGFtYn26WTGkwPmZb7Y8vfMjOtJTRzwyJemBWwVtBVVM6wj262Qi3UcthJvN9Iegdfl7krSXSNHA6+WWcrlXLklkPxIyO4bP5UtIvT5GtBrXueG/IxhR/idu8o1bRg8cfMc1E2bNgNpvS6RcXu/SIkplE+5ohUfjjJhiMxzExUCPM56PfOWEldmfJeJ9N0IHLYR+ueNrMq6Qkb2TZ8EclweEyJmejIZI2j6myXE/eBmCgTa+xyvizvXA54Hd6V4Rz2JEbi6fPjT+0fxKw0FiEylLk3xmd72KQGSFDRwHjOc24dmQf/AJniUdwFSbxL6WQFkZD5AAmchAdbVGVYQaL5My35l3ypT7hiLAzjtSRRCyyU9RA8TCICbkPX+0A5qw0qoHZ3ASpv4IoZpavmbwmA4k/QYoKZG62jhrOvdyG/jJs4iRr1R2xAB9BPq7VnBBRW+j0kLQDOoRTvubOIFu2Pbb9sqrchHLVpozQje4Wbh3GkzWQystLJxLJZvXV184aX5H71x9uj0lXcWL/0fi4tCvr6mJE8I1phtGpYgWLF0gxf+ZS/OTaoZf2TNn7sP+l3VRGENKU8yODUyPdi5nxpqUyuOzPVQ2GKIrZ8OVvpBIV9Wgu4EqDJPaWapdIo0Tx4RMnP2Yv/s0e+Cv7zln5vxkabF4LvW9dpiZymmB9FSXfvPk3NnuTkEYvvIvo9wYbWH1gJnIA2q/VNRFNO03jjr6bveReRpOoPNo0zMtQVt2n2sp91qpJxV1m4ByaOLwUJT2IpMQkSgrDjEvGEGDEHwc3KNL5W7+smLwOn+7dsPJ8p6Of+ZRi4UipDShEnpDRwNuozQGVZ0KffWWR6IQulsaYU1SyFZT93UEi4rD05YYWBXX7aSuOODzZV/Eh1AOKACYrAybXxMXHEJHwYS5Do9couuRvnFPp4jpqN8vlZVdMlfPIUIZksCdT1GtVQeYp1MrDx0KlWCngY36WVRDtmoYauBYC2xzEaxIk9ZuiLAD0GNCRYYVgoLoAFVvy684EF1v7HtR+EOixl5vFONjxDwG2azvX5JooqIEUzUthl7drT87NPzf/za4TPPvfKD5Sv/bvH0bJad+BuGW1ZQL1LeghLOh+Sv0lCtS7n74/jbTIaiGkO9h2mPp7K4ZDrW/VxJrXu84o9LuZUkoUu1D0SgfD1XaDvUV/ImrQaGQZ5y8moh4p9lrgv+ZC5a7NYiatnjYBPNAyk/fJfoSLC9k6bshgFxh6DdpM2FEisuqt0mxrBanirjNj/8n1QaZOu2jDzhZMDygVnS74J4bhqELmiLVfavIkdnhA2zGBQJs5vzNS+CYTZyYcvMiQ8FwoqHkzdR1qGWiUNZR05mDWeHzh3xBEVgObvkA2g8rGUj6fDnFuDqHH3KuFN0rZD7kWOgf8SbKwIaVpRbYyU4LEuYsTjp6p2CgpyCUVmBoCnvAkmBGJS4A9oOuvxD9xDSLvewzYNDSm9/Clg/wiaeUliI+MJCgQ5PcVb+Uo+CMJSPoq2rEvPTi2YTUn5pGTGnndzsJNwV6kB5XMNHOUuNNtCqxOYsmKCXIc07qjCkR5Mto5XWY2zlELkZE4oMjmCFldz7Z1yVhspM9gvvQ6ixWO4Vh4+wv35j8Wef2Hv+mad/efjyjxbPfXt+tU64ms/s2Q3vnSJya9bKNmv10Cpu6FrrzHJ37qH8ycpa92iN1I2NSj2DzdzXwCx3zKLRrMAzpGaRgb12gPiz6956KuczdzLX6seUdnHOqnk1ddk/JIz0FxJHlmj+6N2+cIZlY50WW/0zR2w1uaD15pn6ikDAM1BXuhJQk9o3QZYgB3MAoRkvpOnoy/n+6kZXHHMqH4GgmtaNX96V05G2m0/uly0XEZK2ufVhKWEBMK2mTlvP4mNRi51ovZgya2vpe/6GScH1Acghg6TNhwbktPjG3JUnJgKzhzNmSrmfQHHvcNFAb0xG+znBRcZAjCqN5nbGUD9VDaMEHl+4znwxZz86GDcZwAoFGOX3CQVsbRN3EKOLAgFuCorYOQoTsYOgFLVv/+s+sAiYEfewO7eq5BH6ZYKwiih4OmIT25q8EXVWqtKcytVUgHnER+Z0256mRgfSYMad8gqUWnyMdQstptxU3dJTYkJKve2H7kDSSdcjJqmnDKeJaxR0licUPmHZ9mIja68L2guniy+9vPjd1y4eXnv5u4tXvrc8afpITGU47E7PCu7EI888MYjXXKjt6yq3SEc8xufulrEwgSvrlscEL/h5BhuM1YAWlO2FdSstBrURSeEIos1OvvWaNXh+HCLZmFxh/hr9OSHlrd4k2JrxYJJcKsT24twlv7CqT73bn1nlP88es+6SbTwT2IzIMCSJpnWbKcUgwxo6c+wDX4VL7wdsOv0fL+t86zCO8nGTqknf1eWivubyUPGqRN2CFY0SYdZlGUVZoW3mKwrT6+GiKw4rd4H1kpVYNrRNyLVA52SW2Vco37EebwSYlDv37vANC6lZrh4KHLJHLgJktpFEoZYLDivcaFiBwIrspmC60mijaKVhAoV61AXH02HLh2W3q+ZmecBTsIJ+Bc2CmCTYxcRdgMgbgAUNVmpsMAZFQISASWk2rF5paCM5RAaoYOuhAXiaFfkoDfJOMSq4qa4ehjoN2UbQCp7KBL7b6vDQFTztBHij8DY8KhhlvlgG1OEp3uEUTSQh6Bru6VajlHqkZJc6ZZZpfKTIxwiyPG0DlYDw/AD5NsJl6y05KMcQR0occNcOlr/3wvxPPn50cPHqj6Yv/9Xi1mLe4iMxniPp2jbTolZtGP6kryWszWIVCQiDHkvunp2uhM7XL05aymKQE383e9BKQhqrnU0VDLRs67P06N39zJx15zwzHOBklkFbnDbhNY/NdBZZoXD77Af/5Lv+0Gq2MlFnI1R7tsmE+DWY7bzYgBCm1vpEMtDTd82gx9QHxuQ6a2glfyMZd6CmM+f7qwC7KbPu/luHkWlTkckABljJuKwHio4sl99EWL9ZU8bv9lW5iqRtISN0BiyjrEURg7qZTlXt6MccgcPJ/N4jsue1xD0wcAtQmFXlltBcPdE7FoFOqcVKj/cJwNOtKjyoAqeWXIoQTuF9giRTmeE0VixkedDwCJ2ye3Dt2jU6woZE6eONBcwy4pDcSHBVp5o3yvcW0AZvHq7JQBIxnArCJrld4oIGJJutPAm9hZUnSLjsjhZTtMcwQdhtM0H9Qsek6BRgOOuNpjzFr0gT+2muG4XaNAetExvIjyUaeoihPNXShY8MBU+pQ6wrwXsD3Nn4DqGr2oI8IDlDmZ23bj768mvL115+4cd7r3xt/sKj5YHnDgVxbxZ8YuNuzsU8Acl3d5XSrjxAeL6zPm7sz8wUXMVYZtSOK8tsVpx1IP1bJa9Wz2j6K9000rJqrdXcvE9p3vDha41NCiv7tUwwFDulUsyY2fQmITaOTQej0cDzd/uad7vKcx3gs9xYI62Op1thEcL5rsYy1synhJ9p82C0wJkbb/EqWw5RMLMxOd9f3a5RWb5uHidNWsr/iYHT3gedidtrCOQEGkyiJqH3Zex9SfpuYUK/RduCEE04Cqtx+EKIbDKQMjiKZzNtSnflsUfgfJ+/vqQprbXNxOlq5jrjsZGams3aMb06uHQorBYuOBSQh9Cs1MT2psS6QrL9hai0CrVoGPFJfwFnBA2QxA1bB/fv39/Sj/6xgVcFBNtuYlRzoGmQOdymMG3UvQHwy3yP7U8vtQEpByNtjSH42GnlxcAtEeBJ56KfFTRQmFkKGqRTnnIKQIgZdD8rbUYRWx0EbYT7ychTxh4fH2vRwtEBMfqGInQUojq65vCdb3V89mTxxZfu//7Hb9y/8tb3Fi/9RfkjMeWn6RvO/1BfJIhVnpAVu1q021rNn7vPe31HZOEUajRq7lhCtBH8ZolGFcSt/ES8ccTmTp/VlRgptVsxzkC0K6U1VFt3NBWWNLJqPMqQBs5eCPxivpjuQXOvs5DpMEmKU2ZEk+MnkHph0i9+RtlAI9f4aovlQzKJ4A8gTFWeZQdk55lSDmR3w1xxSbkfTYuELTp6vdtJDcqVepqNTDafEC7NmLr0PQ+ohQy9JqmF7RSjLWbWoWlwruLLiU+LLgu2F7V8RNZTScPclccZAf760t+1/utLXG0UZpX7lFrN0dGyXpSFc8dFYg3TMon87Th3n95LDLGuCxoNpGQytI1sBOSotaBBDYFbN5agbC8VwCjQVYgRhqjDEUUwmnUIu/JZXrzJ6Tqqhzx+FUbJU0Ja6MLHWC6FIU9OE5AsZbZlS0iVzmo2cbZhg2GCELU31BJPWQwYYy3v1FlNbnjKcQO9jc1f8AgTG39/tDCkZZNvifm952d/9NrVk6dfe2fhH4mZZcmP38u5GtZ7JDY5b9ArCtcMlJUVt1hZYiOnde5eOGDd243acwEDammO+TMC6Fxt5SuTMDB3X8/UAVwBWayBhirxF5iy3spQtlqypNHzROJtE0caubSfU7LlyVLtCJZP2QgziUM+O5ZXsom1g203O+UEApaPZtEwufUVb5fwaH+qKcJg5vJMW8mvugxw3ie+t+SvOe0CxrNcmmamJAaZazFEqbbJOTNJ8E1h1hspubE8XHms3YatcLcavd40BBCRsSfkKsqmwKxHxDM655uKXXksEbgyfXhvw6c7iziYu0Jivb0rz/bddMplBwgucd2wRUCD23oHgiE9VFWGpiU6WPdKQfEz7queYRSgNR/D1NhujIIqaGpFgZpCmEYxtBEntjbKDBeQs3oPCh1GNf3Uw008Zg3hQtmunC28IUGeIn/LQxo4DGFPPobEXZlu159TyVlq/MWLmNwGZzHESngMy3tcQwd7y089tfiDl/bfeuWZH++9+u3Fs/cujzwNwG12r9VZs2E6B3atDKyogSrLw8nd+RbSQ/6u6kF8rNNz18xNma4FUNtRskTAWCcsmlLPyAwZYnG20euuZoKa0vYT28lQGzB1MmbI8tRNHul09zwTVSbtC1UvBJ+f19p57L+yas/f3Z5n49nMaH6stv/ERCoIo7zLRlrofLCryXQZTtMr3aueYZRhkFZzUW5mJnL+iikAQBUGa/ooV5Bl9hmd8s1pcweeIpgxxPSetFtpei6cJvLGM4trPM+3dGD6JQyAhe0FAqq2R83ofOEW+cMiuBvdMgJnew9+eT8+VdZyUCamG58JjHx34FWOKqUW+crJPnOh+3REQ5V+sgK5VenakqGtJO6KVKU/DUxGpUWS+K/CvEIUhiNfZhZkxmpiayxV0hOrSl7LEdU90tZxsY2uDR+VsKqWfpylaGYhRjf6ESrU5MrZWKVylv3c0lmJxfDtucMUcFyCFqKHFU0u9Uvny889M33t2bPTp55/b/Hcv16eTxa65hOtI2+jZnVbXVSm/HJ/72C+4Ld5SOJpRmYGNW4hvChslYMOM5wZ8mwpNHkcqVZeQZmzA0ru0TAtLQAIZ6vQmZ+WdC4sCVSaC8zMcd+/ZMGLPbuO+eQMgrnWVWywBpMuf0Emv6RckXVRoM1Sknca1zhWeeeg2LqyqDJVhtYzYmEw+FYiWYfOJc3lLH03VtoUjQfmsoutaM/a1WGmJOAmM1O5BRtrKFTRaYqsGC9pOg9GfjsomLx/MnnClxdaFSGlN+ebsCvbVduLwNne3XdvxyOMPnaYJeW7XEbxyZb2txKjKLESGFiX/Q801N63LRnaSuLOY0JFv849Iqv4KsSIaVsRaEo064YHHyWSD86WCAzVrYA2FhlOCWch8Jfi7hbfkCD5eJxqg7yfjLu78pf1gB6cwuX2aWsP05hQbHuM7TQEL2KRY1GTq/lNJ3e4s2jg/Or6dL+TLxLmOQS/xaFfSW8eLn/T9Xz9yvx3np39wUsHV26++oPlyz9YXszWvxGsqNCu0yKvRbvPmBZqW4qs7v08Yc3wzP3vqvLcfct/V9VwWjq4nlK3RN9ZzJLXLKXDqLu6lfijtOdy6OiSZ9K16y5m15xU1u6yaipDJfwEn7tqQWD27GOy+Tp23faU2v8ZsIzjOSfSrmY9jRTTrBFnk7dhKp5jKvgJN+8d8JohxJb5m/ksE9lc5+mtS67E0qbJW4+l0xmdv+YpvAXQLXg8LAgaETl9luEncnoj4L4lKxwlZibTm3uenbGGI79KGePQk1B7r4fXxq115Ip2r6NGYG92/0Hbj8RuMKzUiEnjiRJXLU2uwvIYBJRsqAsx8jSthLJwJUeG6MKQPnHdVUOl2jKzYAgBDMEsSzZw5CxebyVxx3DELiylaICLgOqU35VG+WP4IAGo+AxGy+Sp4C8hxtNwFmKjj21kNippIwC2NmLNMgV/JczM6n1O+DI8hW2GoV6Mssm3uiTkL7V+oqf5LfvbBm0bGQJIkZU28kNkmKN4NxJ6wt/ggAd/EeYLNz737OyLLy1fffGFdw8+9rfzZx4t7At0NxdLA+xGri9NffWjyj2bLZXHREaTdKEng1SJLPu8++Xs8nBbJ2qAscVgCWIlkJDqQBSOJOlFv+dbYxqqw4TF/5+9N+uSJLny+zKzMquysvZ9y6qu3tCNbQBwGutInCEf+CTpVY/iZ5B0jo70YfSkNx3NcHQkEdQ55GhAzgyHQxIYAAOggUYt3dW1di1d+5aR+v3vNTO3cPfw8PBwjyosVpXu167d3bbrHh4ebkNvLk3Q5PIZwJ7G5UrLqjU+wx/CLAvNMIiw9IC7MfxzbeYFYVPn2KqPEss5DSUhWm+Tct8iDSf39S9UovGVfDU2zHUOMdBJ+XQMfRhOefoeSQJZXdXiEq/vLI2flKnLvfhngbXQGJT8dEBxkIdEaiwgiUyNKrRCY2HTORVv4gimBBd9QMPvS98RePls7Hfl5xdPD3q+zjZEVk2PU+VIFeEAvvN6sjGnOoRQkOk7e9rT5xRbZU+K3Cl8BONjtUoMBjJM8qZkVW/bjEfT3cYI/5gDgNJsVq2trxuShKZ6M9IDisv4iMEOeGQd87p5UbJnViPxl4KbLqe9v8QERoZByYDeq3iUzJtfOKLM48Jl5FNwxHNc/KLMr6hZAroWc9Mdv9bX13E5twcH3d+E5BGBzX1b3zu7/Y1zez/feIe3xFzZ3r09a98yiJQXITUMpyS/b8Czp2Ytmr9ZaSbOCDNQz7tvv0zPzGQt/YMhPewaOk8TWbDcsgZvpajXOTUpFm7DQLrKvWtXPlqwJ1njeLEp3YQyDFWdCqx9hri8vcLOClEhDMjoIU1nLUt4J6UGEFjjEC4tWXI/4INltrF4Ml3ID21znVyaZ9lmbpAW8UqyHS+MajLbwFQVgbUyRgy2ihxy8uKc8vO8wQQaaWqWIHWORl3oHh8SqSojtOFaqwgFUPVCJKH3AsYDS7UBDpy/P80dAeL88MljdUnfhX5MiYen774lec/2qw2Z+aUCwtE1xC6PIiRT8M4vFRzjToHBZWBa0V66Fwly5sQdHorv7gAeNVfmezzIkpp+I5ukYcMQAU3yHXAf0eXBzVvRTh+77zl+Hhh1/QrsYAw2eMFrAB89HCmMM46zyiRQXPnAOyvjwuhzf91lV43NkwYzTTi1gBFoge/5jY34iIP0b97FYHC29hqVJhgOro++eXr0X761vnzwncujzb8aHZr5h07zZd12aI/zsMc4hqOW0gDGs7CORYKmM8yTqBU7RkV83r1JytxtZCwMDO/HZmHmbeGypTrKD1sWTYfJLrcU0pLMdFny2pJhApkbnMyu+ur+k1hPD4SkxD/UBaEBo2R8xJKoaaR/Kdd1MvWP9xJHmL1qJwQp+5Rp1uagYSM+OQfvMJ0g7THLdtNdp/wIbTKbYjXFwbPkjNH9Eom5qKsMzK37E0HWYMFxNsNas4c6+muOS6O771WZg3j1inWfQAspcYSCmgXdaqEqhthUC4P8fZkzAvz60oNnejaBPbFbnpAMoAcpfpsZwAV6Pu00IFlrOYIcaAtGrEvGDK4WGD9zOpW8KwFIxgv3iKdVvZXnOyYlHk4wMXFHUCqQAqPAjw0+4KR7WzJuiCq6mn2bVWnyl2U4uQyAvw0uz6qlmd4j3EzTY2ty2QGXjLMA9GNf4UUg8ns0u0EUs4uBwXESjXvqCWtOg78zzUyPEtIcyEX1DtMR7R/WqmpPLvvATgbjb20Xsybmq+SeNb0l5rvnVs6eOXNl6Y2/Hx17/nLiupFpn9rj7NfakzOWIUDbvG0Wx0E4r0YkThLhz8wsKHdXOjOWerphclhFNew0UyfZ65RTjs5c0jWFp2vz9Ey6Ijn6qwY3NT9WyAsEujxiBaoWElH4o11gOAWsnh3jB5vsqfdoPyY4nUwyyJcKjUGth6ANQJ7NAE3KOD49y0w1abMlWUlxdFHInooMdMGys7BaFdPo2qkapUgdU65a/k1TFBbqctScleuxgrPyWAiP4mTAbCql9TIg2Pb79F0d8urLvh3PP3+8RcbJLsMOgkHsL+2zQbgoDHoKk6E5/YDAdy4UUaiyYWkKDVDwgoJV5BUcPU/oRU9yGWkYjxZ3Cjy6KGAmBVDxVaiy4jYhyAucAC0NRWVtKtCSfWFkyV0MRilV95Ej/pYGgUewfRDm8SJZMo+QWt6Sy9D4iMcvyky9XCu/GYmKZoK+WnEEvxjxCMxdBgbjZri/UM6pFC2MjTzHnVNgAzs2txwbDGko/egCk79tTMUpLhIkYXn7/IGt72xuf/XckXvrb1wZnbo82phsoe3nk5vrW7SVxx24nmJWrG5kkg/Y0U2yY8invG1WmTX0DYIWmruTRvrMMpd9kLvbFoUayzujCOFicvepFtpSEnoAZ6O/U/lqCCxsLSRIW9CYnQAtFdWJNVRd4ZPPc0oXbqPR564fQfDOdsUyrUkJ0jgN/kia45Pd1tWBLSH7AFxlzLjllpeIt7OHeTr2AAAgAElEQVRpLzDmfE3VEnHioaZSpq4g4pTFTOuZA45oSt/dGAXeYiLAZ7hV1eojk5ARMQoAxdnyKrCIrWkS7Fy/P84agY2Vx1ceK+ZsrBTCq9xzxw6iXbvvQMAm5fuUesuSEBhn0uuSEcWm76p9659JSBtizHNdGOzPgaNoVmuxk+IuJ2uRXDJA4bM4EL1JuvBX1yteoAYoSWlfxaZ52NsrghLn3bepXClSABAnI91ZOmOqzcRuYVcj+FU7yqe6WSLwwcExx1ddZvBNdT+X8DrDqaN9rHtH4x1et+nlDq4h2deLDryzsjACa2+6M7fxlIJAP1Z7eVZdh3Y++0fnl795ZnXH4Xcub5/996MDoy3Jt5KAiJjzLMtZc2YXy0pVsCYo5j318hLZnEaLvcHoXnL3K1eunD59Op/C+UWJO4A/3ukypt7lHjxNIpRxmt/Dq5JOLU3F8NDG5L1r2nszwQW1C6BswIrqydPRJRYbpY7bvjmTu9ZZGaKIZ77ZisEgAUpBTYd1Z8Kb3nAAaY11snO6meEgUFbY8HZLFHUVV+o2C2N/Rhly9BCYkLIHIZ6cqylk7MLbnwsNlYQ0QN1exRhD4vIaRkBYlKLuUfUAcvTiOTtN4gCF9iocWgqhv4faRGDn0vNH2XdTiW7KdNm5qHpK7ZmJh7+vrRnhnqGxG7qugXZ8zMZmCoBn1e4Xx0khYoDhMiW53D7pgpLiEshqcl36TJwySetMeJKYhSW4RK1qdh4jLKeKq17w3wM3k0dOjK6Wr5TpILzKgsFVZAPGhwXO5jSMLeQQogZpDU25qNcQ9o7mSME8jvhCsTmlJ3yaHe/RIzQSf5/JPYqtFYWD6ZrECZLLcn7GYVNVsWvH9tdP8q3T5eMnz15aefNno8PPt9q9JaYqq2dMKWW1oe4Dvhj1BTRNufbraTSt2hsEzZm73713ly8K61OPIjHBYTe7xnhQUEaCVsZ3IxpaRb72+bzGaXO4xutuLtRyaTQp1sq7awkypNsYs8+sJtBmoqWr/tB7Ic00yBfI8Ml6Vtps5gZAvhoHyBxGcG6YzfWB0nepwkZZITfMXI5u1lj67kiZEcmMSTl6JiC0GpXg7C94AH1CWoLvWXvK9FMeHwFCRjgUxgSEqCacLKYQNF8YHRDMH0dv4+gVD25oCKGONdH+vkyNwP6VZ3cej90ohIW4UogkRzYv9kp2Z8pUad0ILNENj7Wgy6vdRE3lynW5jykjpUrSyBEh+E56MI/LSKjqavOs6lQXROBWtiLtgyiFBsCHBUcKMeo9aVtYZjY1MA1eu/tTJbwqgnkSXOJPwXeK2+8dzWgGqHrEAKi9rqtSzo/BBtajIa7r3GuOyUicxS/eblTrdSLrALx7eOt7m6Ovnj14c/0Ll7dOX1jatc09BW2KHYR1YGHb1k4cOat9GrKZPuxBS1V+1Nx4TvY1UhWNM+Xunsa5bQzgz2599u677xayWkDEiIGR5kgLjrlIlOPO3R+lniDCpSC7O5CV8F1MnyrCM+W2ykTn9ktwqFk2SW20vbW8xUPv+soqa5f3q07qpXT0rNLUhq7TMNBUcJGiTNNCFIRBLaHYpGlrbmRqeXY1MX13P8Ua8Xa21VdQzLuDn1k1a43CRO2wxU9jVj77nyoKrIJgMa0HZIovGokIfhMSDmFFMSRB9DXTAWAvEmIBpppgxFol9kKoqf33pSECe1ceXH2o3cp3LuJKIZZskXnayhbmN8VzZIPYDk0o9RwaS4a+AZ904dfGxgZVUh3Ps4E7GN/AknThV2+J+0Cpkne/1j4rHguquEeAKA1+9tXUewc0GIaX3gpASHOvwWNJv14jfzHeYTYfyEztL+yhcyluGEe8dpen8qao4pEzJsxwQC/Ry71OprrXvgAlJOoIDutCwswDHFkffe/s6INzu5YPfuHC9rkfjPaPvSVGwbd9bx4d03l9y9TYTmN/OtNcFBP9CnNvLuFjzCF3r/ttJiVoocjvmJHJhMuXLp8/fz62znDOZ80MbJ1IMbjD4Eg+u86WAYdMOSxBWkAxZdIzXZt7E+OgcAijJNTA0fZID7Pz4Dsdw0qLA5FWBMpfTQ1IrVhaS+Sj8JJl08GQBplkjRpvoCqMKbT26eY6i47JxwJVC7nMMDFNl5MVeHfYJMpwM94VxCq1mKa7sWpWVAo8COrGHhuCLFGG1jIQF3lxelhFiQn5IQwbQ4YQoxcSCzfBVXytytFhtRWVUo2G35f6CGy/eDla2bm2pjCyQ4VgVmg9qSXG/ukx1fY7e0XYFASS/Z4autBItbSfTuGf1mypiiY3hAh//Ji3YeoRGvBkO+iaFIRpgqe0I7a3xB3r57QSCRRP2gCSNACCQkne0A3DBSVpcYBuyFWXWuev4ilCkteo8wFd9Xp+XSUJKcIl/BBVdLmnSThVSnI8GUO0e+lchCeZSekQANb6LG0jHKsgw2t33Fmwk9LGa2h4Y9SciTuPxPzhqa1vnt2xefLUpeV3fjg6Ntoq7uuPeWEb3himh4p2SpUgXAGhKDC2B3t1uKPp09jwvhhOkUtW7s4PyeodkUWQlcKRX9SV27dv79u3zxeBuvYpOA37ReW41mNNYYw9jc3qbOjrfZ7ik5oJl0ubKGFiQwvpJRIT1XowyjX9IcRNtJNAW4KWeV+k3Y6AwLwgYox0amLyVcowVgEZIsoIFUZSlVwWo7U6dI1SNCU/JlZlXCR3eCJpQScjqRX0ckEq5Y7zW806yjChMRKYM8w8CTFhQaDJEWx/Fkt5FBE6J2QAFCOTE4LlS4e4jEBGmnBZIJN10eRIj7iFVDEF8AKZh5iqeKwS4FKTmn9fyhFYfvH46XOS9zK+tk5g/Y47WyeFTb8h16+VMBPSdbHn+rra+WqBQWFbd1jJPVcpWYJwCpT4RZO7VqKZv9pP4k66ia0trfHp4YmLw87o8wc/Kc2i4Eozqply/lai39dnOphdKu4FR/faq32pa+M79rQhm5PGvfZp4xrdU/d6and30I5M1LUfkx1UJBZ0+RRNmAQkxx0Aj+Ne2qTpSU4OwDjTdEu89DSPxPzRuaV3Tx24t/HuJ6NTF7fXtZk1FG2v2gwbSCY3RdG2idqu6XKitHguJHRUVAhIUFV2anKAHqEjfDSWmuavmufuvlx6uarcfe3lCKBZOLfmb9/+7AtfeK+ZrLk1ZIdkKsMXhdGzItMVHdew0ZgvDMjAAjkb5CJydbPxz0iN9a3HPkMpDHAliBQdostqYpEgIPxWE1JDWzEtlPcrswwywuam8Elulk+K16Rm+OCVCC0dLvSGloknJInJzRTUXEQuWgtLJA3M3tNmg1EESxRAV6BhotVPVROiyWdtpQwdGsOLnkBIm2kESQC9IhGxKcjVONRCFbkMkHtOKZHULMg+MkWvInsC6FXVRaymAIeKV71B8n5fxiLw9Pnjlll7zsZ2RpXNmj2UyFMdIh9wjUh24eiiAJMhTO1PbPOCEBPQ6oF1xPZ1tZCHK8H9JO44Vptu0hN5ca0eKQ/B1KglQxOAQI9+wgwK0MFPnjzpoIKYYCpHeAHcU46dHe9gwythqXXcu6xztjqrI6ij4xaTuGMb3Zo62t13g1N39zhicYrn9mZy7eCu0Z+c3/ra5p7VA2d/vf3m327v4wnctiHVNmYb4DQGnM1o4Yoq/Bxr08S0au9FmM9Kjq1UTiayfCs0p7SscN9alLvz20x1z8zkgi9euPjmm2/lmG6wZ4Ehy+smogWXJ5oQegRQZ6H0w7xRHdOfCZNTyqlKAR4j76vi/lhGOCbSkro4vsfc9YTRZgxWqslzSS0R28tkCXronV0ByZlPJpy6cZhzYccQlW0dBlicRaN/UAczsgFsGJuGRjFmdKUS2YN2D2qIbYU4EOnk8o0w8ARXLG/GLGjqMYHAZbgLUMY46cyf4RU3wcjKgBTNKN/MSdpUS0WcsjFYkvCGkYmUuC8rxKmAB07LArDojD6HY/it4Xf+sLI04m47GXi3SHhSRJi5J+UptSf03aRN5cqvFiBmMy1t0GziXnxU0NrZHncNLf4wBQJRp4E0X+khccdDdxuA0Htxq9xtjhDMb6vLpF87B7FDrJJ3DbxVxyF2lzG1L8cbDOjWhGF0Vjde54I9+Q6GKjIp+F51HEqa5lHXnhdFc7o2VRfzEBVeIOYdIDxOV+v4VFGzEjDz29x0379Tb4n59tnVw0dPfbzj3Z+ODr8YdZrvtduf9jOlGrYzahQRCjky14CySNhOaVAPwlzOpCM2zzpUSmm6BaCVz1O/q3r37h0+yfUPcycZ3B6vjK+P3sg1pkzdHfb8MSnpXV2huiTa1GfDpCAcAkKbL1zBX04TO1yG8kcRSahFjMLH11ZHJJQshjFu1lG6zWxOQqulSwrVEKpi5J9PMYPDbJMS1jrRSqkXeEEXFBFfexaxcbsAu99tttRSC5kRWmosw0ORFx4csyEQyx1A/YnX0nPMFhhgtRpfoAxyFAN5DakaIvE4MrWOA1Jgigt2N1x2mLRQRXcoHnfva1Ch082uKhw7w/yIEn5nzxvLL+4/UY/OUwiyp3bMDl8Gq1nEPPJLvGzWFJBsptwLA0hdD55N1o0pcXWuIpDCGPPMwVV0ltZlI0d3nq75rU0f9B6I5H9ns5oZh5afay/pct/dfZrcawAcp5SIczmvJ4z97Q3Day+wuO/uMsOxjePQwA5Le43zULqFbQybqiXv9ESM1+5+UtHvPE+KqgCqm590/+rxl9/d3H7n9LEbu965PDr54fbOpfmizgAfHyps9TZyZhg+VT8KzJgY7bSWixTtA0KawpPVKUnKirk9bmzWOhX03H3t5daLyjMzjKXLly9//evfmCqkPcGYoe3ZIqUSHGa6JTouaiw9jGTpLOLOF8yz28oA7K4uGV0HpFwsGCW3DfSI1LFkOB8wlkWCFUuGUTSZTFoz/BPZ3G/zSBPLDEAntBqeVN0EQVnCmNnllJkVoS/A5BrGCIqKuANdcllGu+EFXQ45oeX5wWBvjXg7myPg5RMKoky1yS9OUUBsTZSRQ8IDPUwWChMVkS7XpBmp0YrMDAgdoCraQr5urUU12O2BhlABV5F1djRrJc6rvhKGJqskMhf1O3hcX3566xF7zJRHAVtGhsWQQmjJcckZ2O8oLXnbk7l8jhR6EBWeUpu2/tW5YSgqXZygDmfbm+2UUxJ3XCJw5lqYB7Chm4I+ju7zwlIW1M3qYWd6dHHZx9HfKOSe4jJRrn0uqLOinBH5efVVwTjrg9i9Tr7T6Z37Gl7/FGwxTqEOFzpY6xcnHJOdbTodRYv8LKhW3em9o+9sjr55bv3R3i9e3j73V9sbMzwSk7xN27W2NsPS/QEqiDpD7SZwO6rORlQY3VcSmNBidbzu0XGXTO6+xkTQd1XHNrkPP/zFnI+2V3zKXZnUWOCj50VPh84vEAXxJEgjJS/jtbylF9jWJUkqqZ1VuC+6wVjr9XoJygyTrum+eUhrU3ieeufuG59B8tyMPfhuia2GH2J1tFwiHUHpn4XXd10ZmCFVk232LxlvfmmPTpiJgA39nFA8Sp7NnIlsLhkVJcoCLwGSEuUVlMYUqlnrWCqvZkJhQoymyL8D0iicKiMtsDLMlHjWbjXDjeNDz8omCQ4dQEy9CGVIC3OEPeiOj7AR/s4ddi8/u/a0Z6+JvG/f7MV9vdWRucA2nQwlSciTZodJG/pSlxRVAXQldeSZOIsxHKuUtZjgg8/t5hy9lh8n0VfbNASyWyrWbEnyPc/VYCGIqOO12UO8pbvWpJIBtTQ9InEQ3ynopQC4cPCUNLD60ojMpKIvmQ1ysD+forWUVd8hc8dnzfjbqKu1oRuSSeef7sG+sbb9ndMvPzi7duT4uYtLb/3N6MgSn8a3LdWVIvLGc5RkO12stDmXBbThMRoYSSR6T5pL+s3z6L5GZ5b1dDa9pKOu+qKSu9+5c4ePUHgNcB35vDh3ZVLneRAaktUa9S2Cs7DJ7osW+11cvWrsraJCcqZhJsb2vB3USYVF2U6KXcAss8Zq1K0oS9S73ikc1Vo5ykzllGq0nF600ZhiXR1P5U0iorTwuuyAmXSqhtH5ZL7+JpVIZQlyRik8nGa582aUEphVjVCoPE1Hlrw2shyvO6QJaRm5KDMAOXJGvDLKm0LSH8Nl3vrBLFFsZbzGgxQEiw2mGjAe9VARpfeKWS6TYotz/64c9+94ervy60t9Oc/eSg5GqH1DL2XbzVrgopDIORm9w87e3EfIp8CFOo5ebdYyT6vLd3XIwVkwUwWuerLvdC15cqHoa45CTjw/TH45a0aVK8VaChjvSGA3niOl2qPspiTuuYRB4WTPQFrkfEzTXQVxmOT7EDagawixbWS67+4+R2fBHsZ8td/bCKzSIIrxybHaNARme/TynaMr//SNl2+fOnR957sXR6d+MWr+odNq8IlDCEULC6G0ba2OtL2UOu4aHLtn77m7pzXucczWouHasqvxqTGsFxS5u94zw0+T2IdCn3zy8Ve/+ge9SJ4ohLEuX9WJ+J7IDCqqWUsi6QIwxSwl6sLbgcfSJ7M9cyWXkxYetce+NzCnaoDH5Hq2lpaRBjZvCtNGJx9jnkKqMdx6X7an3lP2Hsa+zwAdfadSx2nLcs1aTQsogCZT3o5ZZ+7HhNVtmnA01vIowHkNHrd9AmMcWjqbukRn3Ba/iFcFjzgaxqpID4yh1ZprYONSQByQaUaFKEPawJM0H4GQlQD4lF7HADlBjKnNDhOlCEJGgT7BXlXdpXgbR5OZaoHM8L8Lh43lRzeecBdYsRqoEFJ/0oF8muLJam2c6RzdibR7kRB02+KTOnIkf+CefHq4zT2pw+w26vSuw86pMOFrc3HQV0f65GkvzXsOLi/exxwpLXM1JAzXVVVHsLOveEanNYJRRBWvAXCHknqcIZLgqj29YzCjd5m1AlHkvc+llzuuXrfSsutrxTYj6TtfUJrJ5m89uWf0x+dHXzq959metz5ZPvtX23snP8Ju6ShR1/bUT/D7kdIiCkpSbB9tQVsmiRtIPJvzlveUKVM9z4kScjiA98zwsDsL6C8++og3yfg87VGdea5eRyZ/PvVUn3sYfPTRr86ff5Oh3mwtephw86tr1pJaTZ3GufyLzrvv+D+j09PHuPyyFbWlaLOomrcbhlvvRGrkt96RWzykZ0pQRbOciN9VVdV70iCPcaCxDqeVajn2hgoh4TSx1M06H0ZS657UM3vcUGxUBWWBl4CAB5lTmoYsm7e60TpVkBnIFA9LzolDiYhwObIRkAOlGGVVl6AIUsBT3OOxKhUTkppcJkdrkXxv+q0/rm0/fTHioWI98jHcDuth9IyFBdOXIM+nqVI87JCR5PT1PDPyKUgmg/eMF+HD9azys/iVWc/ga+M5ZfFtHnA44zqayfpqJXBEsFaa9xn2UBIBwbUgdI/ycN2TjMwBjO+m0d3niDSPAHIouF/b67nSRcKYNIS65H7qffedqctMW9iTTihNBgzh5vqO7f/i3NYHZ3fuP3zm4so7Pxod2mKPTyEF0DZimotZoGRVpZgWVp12mECuTHpmWdN0NbQrWWmXu2toFUYn/wtUg5bU5BNw0E5MugD4iupnV69uP3u2f//+HN8BDt2eOEO3l92nPuco5dcB13etX792ffPsZtI2CVAky5ZNop0P71pMhgaC+V12vpWGWZhcTRh4rRhDMDhZhuonB+Otd245aA1PI7iwGg2FmxBoyCMhA2jXP3W+zHGCsCO4HGNRIjxlFts8D2Kc046JT5oLYzIKgU4V5mNGKTxsKWyRkrNR6WC8Xk20qio7tzandA8ivkxgxAnpgEQb/RhgBsUBY9oVITfUq6IAytJ3wuoFPEDqKmCndHyCUxXgt7Usv3j+9MXWc5L3+JOoJB6DJocunJyQR0a9d/ytbgNFmM5NFwzk01TJRQd1UJcL8YKBDMdzueRd98SdkA1qdzIxAXQPGjniBsXx3mfEkSaKT57EMg+Aih6ltbEEX6aSNbj/WuXotY4QT6JKN9W2tkQy8Lz3U7is51+LSxS6gEsFn+Et3WlD9qWjW//0ze2zp47eWHv316MTz7bXlriAZbBogPrOETef6SOoRmF7pvaZdI2aTqiQgma8nlQIYXusE6TBkBF2AZGjkKa9u4uMtjyMk19duvRHH/wh700ofVe1LCLvIaUHKn72NSNv99ZJRzmYZRuTyCbh79y+ffrMmRvXbzx7+nTX+voksgKPZQQ0Ds8CPwckacau/nfIjg7SEghSvaxrYkOZsE3dLZjFQ5kdOk8n98bTSzaw5ZHSRO4e2gxPFlAV3xLNVkyAvtlqmtUaAcVbQuNuou4WYxKl+EBv7Rm2aI+QxMQIR5yfxRZugY83FDWXrEdsRGnmWmPE2zniqcges8sYXHzAmAxl46JS8zgMgjZNXEUhiMqQ1moRgSKRFoB5GSWYyuyQTxafO2YA3CqmUUeKB9yRORyavMEof/sOz58/fvpSXYOX/jw627Tfnyb77NHftPsjk32fJdTvrJMS0EQBM2ikUZo76Db06GBJFL54OoGDXDCkST1X4j70vUysxFwKAA4wDvAKwIM1aPegaIgMrNQrU6vJfY+AHxfj/lTbOhAwh5la2N+SNx8AzkKnU3woA7SUszAyXPNR2pdGBsB/+5Xt99957xejdz7mh07DDyfFbThsyNqPWmpsS9cgbgZtDVJmaLJXX0eX5Wp0Ip5nkNWCFPkMLcZeC9q5SP7hpz99/4vvj3bu2mHPzEz6XVUN82ykJ7s622fLSH1C1uwP85fMkjF54uSJy5cuvfnWW830odU6zBKjVuRVIvEaVkMPadHzeC5ziMCDNomizDF3HZWzeChH0Ond6jk7GKty653Rx3/2PUJNpm7E3mjeb9snq1GAaQ6puS2JYeRqDENjESil8u6tRVUJb7PzkEUxZUI4LZGOjpTbqbvwPCl3ogJPXSqMUgmzJc0WGdEorFIjMGsdg60teSNzTaArNSaPsMu2rD9gc0yiMUAapTuExxcEP9r0cYO0UHiBVChZKbO9lsNjDUb5W3NYW3r55OlzDdNYcN/3aLZ7f0PLPA+uIEQTwcqk5Ac8hYBDzBG43wuG6Fk4JwcxzB9o0drYOrEpSWuuogKnnMadotoxcSc0caA2K23bikBKsi+xEQvvcpr4nuhAoUnqcoD+4HPhHDMojPs+CABcERGm0FUcB1W9SOHJu5JS8BQi4ECKQBoAJfqWVaK34AswOgsvOgxUd5xxDpC827e29bU3j/z1yL65WKBT+xRgdo4pAmnGPHzMjZzOMwuF3eYzBka9pR4kMCBDDjKLqM608nEIjVl/8CaZvfv2Hjp8GCNfrNnz7vF3VZUuZKZnwyHDzgcis0Mn3rp168SJE48ePXrw4MGBgwfvf/75/gMHWhoyk8Z8wYMxRSAPyxS9Ih12oJYNcCvVea3MDHkeUjTOw5/JHG2vbDECuFHJDfUV3qTAl86R6XOOnJ5/YhCL78M+XAOBJY0+PUUjc4I96nGbUcly2dBmLgdlia8AXLRya4rE1RY1W65sJAVZ5LazdXqVUp4bM/Kz1hxGepalm8sKjs0j5zUZHmQFJLZOBII4sbkct9Sc84kTQm+YAKOPtoiRUe6SxT+0xM6IhEb9G37Yu/Lkxv3nS0s1r0Zg/6WwIc70gkWClO+DSGiZ9xPV0gUD1UFDzUbvez1pBgXYDZizS4lYigAyS+6D6Zi4Y2Jn++gVSkrRPKwcKQ1Rhr5k/ZyhmcqORkyaStaBoCECC/YRSzrY35mFeLpGHwA+Blyauj8+79RZfpWROZ9em1htHQLDGPYJ3Czcx0CanBB7BEpT4IsnXn62ek57eFOhE7XBLKwvMZ49av5M2mZXaYrhRfQjnnHdMpXU0BSL17aN/n308OHeffuuXLly4MCBixcu/OEHH6jfrPOe8+4CRo7l7ovpSHVinI+tgra99PTp05s3bvzZn/3p40eP/vv/4X+8evXqvv37kdOKXQPUcqkSuUYug7+QoRFQoika20AFc/QRrgLZRkR3GkzHmdYOWGbNtb4ydYsEo4Hr/lVLzvk1ds7LoxVuJerZGSdQKmrzzzowVEJ0rU2iFNDQM+plUcknqZN1Y9GwYUDjGLIagQa3AqdZKCvrC1Qp3bZBH8iEB/TIGU7yJEyOGJiq1FyGcm5ac8oKXEndzXf54QBSzSlElQBkSzo2qU3mlYeuzx0fYBZtRVWA/Zdx8FicU6uJ9JaxJqP9DT6cXv3sL24o+JMKiaY/XsLOSEyUy1eenyEZYDd0CUQMAo/bJJnNeNMQXukIJVVsaGaZs9XzYbzrfAOeCHjBcUopEyiZ1yVxz4djSVypCiUFaxzAGgjMqnk7pqRoiKpbO6dkdzxFwN3nyDCqDk2I51T3GrKXIsDU9SAQgeah+Rr60sYkhk21H30A+NGFQNYmAt88u/rp6ESt3nysaGOpJXr9kJ46yNyQQLQ3fKFehkynvwDS3ax/n165curU6V/8/GcHDx5MQ8VD8HJtVe+Ziffd+9M8URIDFZNarjr3Pr/3yw8//MEP/pKsHcYf/OAH3/3u927dvHn8RP34rNUak7PUlelKvpa8B6Qmo/ad1n7Or1MB1TCvj6xCPtL7YcLT6vQBubll6qjWUIgzQhIYI9sjMhA+c+Jd7yTvJtuJaBdxkcSbr6ZakHbamCAWSPFo4IlRsBdFyO5aF9pjU342snq3nExao4U5Y4RdZUy3zQFring7uxa3xDJn60Az1zJuhbYmfVdgy/hIn52JNgL4M0MVJ4tyBPAOMWFeJEiA1Ko/LLZmNdqsCgaAIlkGqyG5YaJTq2zMm1IliPzNOx0a3bz0+djPydX6gKN+X5IcgLtpth7q0+kUvd7zgaSRSwLXWE26ak3tjJxVI+57BNAIL+b5BcBUA7ok7sS9Kp3ouwUAaE2dgTX0EGWqKQ0ExH1OCeBNUJUAACAASURBVA3Ca5vc/tqmSUhYKKkbgPEdYvOePVtlEq/jYWkm6L11qkmzakwRAKDAbn5rDDgAZuivRpRsRjWdssjxg6f+yaBHwIOAAbMuTPt2bp88dvzC9vrUYaHde3xHKQWh92rIF/LNv6LDkpeEDYO7yBimepVYDWijcZyjh5rP2BktHdPrGdy1q59yQ+nggYNnzmzev3//wcMH77//fhoeiYH3zPg7Iqd8VzUxzA14r0wdPNxr/7M//dOf/uTHUKKT41/8m3/93vvvM7HZbNKtsnpzLIghkvSioslwDUA9Sw12jk6ITrrKGtn9ozTMCZPPj+0VMnV2wBgDfi91ewfJeq6WNnmok8ic00ByVDZR5ZkrJI3Kc/S8b6A3EVJlgB+gsG7S+PL+8pE2BsMhHm8JzN633kGZvDJoZBNjKS/0Z+Jzswox1hyuE3KyWrzJ85iYwSbeMm/pyFvDyDLVlpynFFw8MPuoMyDYSIP1kmm2MZlaBci4YFQExFGwGNanuELpEeaYihEjJQQafIJDU+wBbwL5m1V2Lb94+Pnde888TFNsx1fWCrZj6IDJJNkWyeaH9p01ioJeNKKXjXjofMA1uo84izowHh3MoNBEFcfBd3B/bO1wuVOPSbHr5ohiCsY5wHGqkJkI0Fi9VJhJwqzEfN7RrBGvUxxcuPuegjCrRuh7j1sHG2ZiySOQBiIRaAgCTXBxnEnRPMRMDHpzuKuFFASPAKbiHcsTX8mYs0O/dmLr1vTnZOaJzVy8pUxac15bX5LJylRUEnYeoKRxHlEteXFAnWiLbEuWtPa58x6E48d1W5pt4/r1az/96U//5J/8k/sPHjx5/Jg3tJTEhtx9gffdMcDW8IlefvLxx3/+5//iwq9/nZv65MmTf/X97/93//yff/rpp+fOncubSHfGgmCBKA0FIjoe11L7mLx+KhWV/YitkcKjLyFTJw5kjKTpK9urnmXWkEeUiIE9dp6qGsJyTsLF/spey77Dw+8akaJXGMO08LzRK6FLNR31zxaisD4BO9LU+ugeC771S7AlmlZzHu++GgIJtdS3GA1jVK7UU28jccfhUYn4DImDatAhyg7yrRpa8yY1R65Ao5CU/iy+yWsBzhZ4s3S/uDmCERle1popUhKKzSnCGxxwwPsgIVWNlVJTFPO6nw8uP/zlDR5w3znJ0Hx/xFfPaN1pUna89mTa8ZOE9IJnX6agEZP8Bnxzjje/Utz0DxnIQPxeHhiUdkvWc3umJO7uJH4mnnTzG90gvQNS63DAwhS5C0SZ72Ald/LLRJBuDBGgDxZsWDJp8QDDgDj4+uJBwHdmAkdKS3ugRw7HlvTzk7W3rY2uUhBgQb7Pw1xRilIbmZNoGp6TqbKgkXxJ+8dCiudm1u3a6PjnG+PQylG0SDdxpyGw2aDXRu6hr3YAo51vc7I/7du37z/8h79dOfweXqxZvTZcryh3ZyRHH6JZDOl/+4MffP9f/j/ccY+44vyzn/3Dj374w7PnzrFUbmzs8QZFQeOhtoyhRVlVWcvXF9K6CKd6mZ6ZUXpIXU+/WP6pkTDawXPqCoSX1m76iBKbBlTkBrQG3Xqn8FNNuvWePTdjtMZBYq/5YUm8vNQ8De7CqcFssoVOQVBDUZNSIcTGMRohdLm0cctlmMAyu9VdfilNpyXi7RzZqcgqsy7QqBYwIpXjnJI843C80M4debJz1qBYZ0m5ApUwrhpiM0s1dUmKqga+cIqmRTUADqOeRjNG7KEWmOuajPg1P5xZvfm/3yo/J5Pvkqx+DXkqQSg9P7OAZBp7KBjpz6MPlMWVguBLKKs+SRR9igHz9OxY4o4mL3GAaWxRcscYZsPdvKz1BJOwobZpCKRHgPjSqej1Gcjgy4MwhN401YcQPqtMjPE4ACTeXoLgIU0yX3OAIOTXKljbMggsRlz7zTNTNta2N0/oOZnXJETa6ovCsNDAsE10cVcLrn9iWliY1zOERtuOPQQOSkUxN3KwopxRtG6vPH/44MG+ffu/+Y1z/9/f/OTN82dPHJ7YucrdX+h3VRf2zIzcIetQmlI48Dd//df/6vv/ks+OClQObW//2Z/+H//T//y/fHbr1hvnQ+I+FpWcuBZ2lTTlWmsp+0NqTSv5ObNwz9TT3QfP1IvbW2V58s4mUDs3wwjTiT9KGH6eFJK5c5XAT69Z8m7eKFVUgu5HTxrdS0W2yCE9WQyLuu2qAfY0MlXcfu26XAg0jm3fmae6JWMQ6t649LGjjzk8KpEVeHGLXRhRFZSKbcAagcFG6/ICpaEtIQ/c4CMfQYIhirVzQEiEohDTdx855gtNavOS9rXQDZY5QGBBDn0g2P57GJzdCUSBoFAp1aKO1/K8++WtK/eVibJRUvAAV8hKZ82XoEcIS6XfmZ6VvUNs2MopMJLsoVe5/HzJtNuPKCJAQXjVC79Kmd/NHe+9914yHQBlWC+HYoljiUaF1WlUWVQhrN6p/Sr04DLUkE+xUaenrzziYPgsmBh4NPIg9GtGkoYBwAtQlDQC+EzDa4+DB4EjGCwh7BrLsRCNnLczTGwR2Zm9G6N71MBLv3sQ0mCgCn30PpzbB2FON7+7+fLA5pfvbB9ssLnUlHbvEr5DVVkG2wubiR2RPP4/F5ltXzl6SNh3xyE1BNm+xarCjks8KAqFAqONtnWB7/79zz/88MMPPvgmS8zb508/enD73suDB3br61m1Yrhpu2OLV3Zv8xFVLcEQSHyzHCXI3jx79h//4z85fOQID8Ps33/gxcsXPg2SapaI58+fffFLX+F+0u7du2eMionxiOZak/ThgJKfUxWRgHFPfeWlHQG4r6176npU3f9PHw4+cHxSTdUngmKACQq1BNp49EFZtGaQ02u8CumnHEBgEFm0ijSj8aphdEjVWiBqqG0UEn7RNIlRWx1Z4IE9aolUOgcmnUI1grFJ6NBk51inorqLEJwakoRIEVpFY2RG7MzOVUsgvYEeiSY0CvR6iWCcerwWGV6n8/rS8933fvl313emhMFTps5pDIysMBy5bcr+C0AZ2mO3mWijdGqeUDUGFvZ6CgBCUsLQkCrgFGQQuJswNhBXNaJllSsAvwioNlcxGDfPTcSqwDYYrGxD1kyDEAoBojilDQkdqldFUIJvFth7K0pn6rxuBigKWRyYG1TRSxni6qibkb1z4R0zhKmSJKcgADjSx0N1MCSWWQGmFfOlc1S/ubl6ZXRyVqXd6C1JD6xsLEA6emCmTT7aF5ZGJ+8wbgil2ZwPNxoL722czJB2JVsNYCRcv3GD9JdBSAbPKnTu7LmXW9ufXr+1/8iZQ7tfjJOHmt7v/uLl6vbWpN9mquWaE6kO1epn+QiP9Ozceer06W9/57uIZR598snHv/7oozt3bn/0q1/dvn0bzH/8u7/70pe/srGxwTsu0+o6sw225KbJODN7Bwb55x1edHISY79f6s+pO87vqY99QJ2IZwGalJbkFMapN/QHQQCBbI/i7ju3ONnT7C4frWFmxHDSkwpq6NAcsFlrg1qtxXy33i91hG2GYzeYS6bKMDPXBVZbE0Y+6I52jH1qCID3RboTnsgCXtx2bWnkUVjAZFWaXYbihm15k9SP/Vlso4+xMSKNklibRMLq4mLgsIYmQztAs8XTUaqFKiElzhQwDqvBqo5xSo5qLiqlmtpfn3J67c6Pr5ESyqkeC2HxPJNlk+KpcI/ya0WxlKGU2KORY7NS5ZGWpiMKazunDZ57I4r8BFEoxYxa80rIGVaiMNpKAgaudlAKS17cQE0ReyC7TSI150MOnUOChZ15axl9bHGklZhYDMbiwHDpPOZqNb6eSHz3uQHgFqbB0HvMUwSYgR7ehGkP7NqxffbUsY+2drVnccqWGW0pU9e2PUcxpfOJmF17L0pjHHxfz12oD0g9toXxPAUO7/Hjx+/cuXvs2HF+CxZlbBBvbB6/e+/uJ483Th5YWlutubP+Qu+IfDnsMzO26nAI3mmSaK1gyXDPeA0hGwyDmUn0xhvn+e/4G9evX77ML6he+Psf/vC/+q//G14NeeJk90tNqc2Utgjq/CRyEE+lGY958Uv4mVKwLJf+nPr8WkoSLKqtPQ39opP1U34K5uuCSus7yTs/2RQLjc5rbDR7lwotjyNvgKBVhwtLEWlRc1wIVBomATt+qnPLDRmjkxplxGbiWItX3IyYbpsD1hDxdpaNKiYsZNSxKmdcft5qMH5wjrKNTKTEB1GyP/COI8daRSYRIVyRMdGYapNlKBmZgg4T0RV/gFRT1TEcQ9Wlx7bxmlG/BoejS7f+/DN9Lj1Q8WyN24ukZAxy8toYj4EUKvieTFeVsvpRUkf0aAyuUXDJrlP0pp2pwmdI3BHaJuvtN6JTlXooiaYHFO2E3gv+A3Swhy0q/2ZqBwkdWJL9HXhhgT0PhTvuEWhIzbvFp5uFiQurMJVjwvQLIJyiARH3IY8DR+ZDv7qapXX29Funt64svdEsfHKr9qdUYm4qREp2ARLBby4wqw9pLUiM3eKAnCShPnrjzcyyDz/8xR/8wddW11aPHjvKINy1s7gkO3Rw7/7tHVc/ubRx/AtH1p9UBfLBTfptpmprO0xukCZ9XmftQMgYxtYTzyCYRCdPnbrKq2PeKA9I0nT+f+vb3/Zthhe9e37fzqQ6KlliAzbO3DqifnCk6Zap05l217ruFY39aKqV4g6qJ0qBr6H2casckBJTQdW8H5UILpHR2AtnyCaVv5eKhVXM4gkZo0joXN8CfKkMMGSSkLeImGLbqbQ2TByjyd0KdptqFxOOUmI5royqKWqnWEptJIHM8XiqVuH0Z8IsRoaxKg1BftbqfIHSsnNxqydEbuG1ipChwQHpMEwEpFksnAUKPQbU1KAQXQw7IU5FIix2eV+I1MVkTYHO8K/wQDR2Pb997WFxrTiQMSwvFCLh98LJZ4ZLHpILrhSNfD3J+wi9IL2PElm/gCfYZC/oRXJDxjJD4p5GW7+2TpWWIoUBpYQMXlpxj5LIpgqcSvCqPJ1qWCJQZhov/pK1BIGBBc1MoUjsSfjQAOahtC8txMEf+HGBCKf4eABIWsBAmaqLAZiHXAR2eLrsu+eWL22d7mCkp+n6+NwizP5a2mJ7i/u4cbYhcRdrIPHjyrIa/jYr9RHgZvVnXNyk20nktjSPiZO1Y7j6JRuW7sqO5a2z587ef3z/0tUnZ04cXNtRvqZV7s53Vae/I7LOIE0Du8EY49Z29gW6bXaRXevr/O7Sxp70DVTJenD/Pl/hZ5CTrx8+fPj06TPXrl7lJTNRT7ez9Scm97dEuB2WqfMDpWFNWFamvsKvkhZWSmlRWwTkg6Gdp2HM6eQuyNZQMwzm2wsj7V2R4ZUzBNOniI6WLvoRVvUuQ8M72ZZKB5Gpa4AYCrWokkcGChPXECK4TEeJBCky2Y6hSYItI1ZLfRF9hUyIAh94M0phIlOQb9UQq3KTSNJfQUN8xhsCVaQIrUUYYxChE43a6QPVzFw54jhVvcBClBVoKw5k/SLsWHWs4kyv4Li2/eLKvRe8KYsvr3fY42a1mLCwzuA62z0rkifWswppQ+8ZhVOi1N8Gk9IMz63byOlMQ65Cgb161z/JbJu4E6kFWJzMAvBI+ZWH4wkiZeiLntyGBcN4V6sxDRoIGLiKQl1uWsv7GiIxnhHp1xizmlcKBewMcR8SiG2QBlk+lhooe2xqNmmSovUdo1MnTn440kNvzUUbqBfOtrv6BusLezNv762o1naOHQssSWkMBLoFJjP6sWZcipTGjGeqr2w2pLPf/d73EmVmasIJ2L+xtnf3rqtXr+06uHlsT/mpd55338nz7sV7ZsZtyiXZsKOZ3fTZ82e8x2Yyac42CSbP2D5x4sSlixfOv/lWTrRz167PP7/Hszxra6sPHz5gh1vbufb48WOed8/JusAxx+zCG3mqmbpepj6aHAzLZEOGFYUMfnalqPHMrlGfjxzLGaGnJl90iiCjkp2BTcLfF0n27i0cnTBq821E64QPFlMuUBNHXPqXlpFS1W20KQA4OZ6ojAmrz0pjdHqOZpTLcilm7Tg6NgctMX0vWF0aHovSdoB6jGQH+SKwpBp6g/0cYSMzcgurxcR5q8hEIUBiYhwtvi7JO4f2YtEI66RwCrJ1QQAcRpS7mATKPZktg0PjWMWaF3t4a+f1H159+vDhaNeuXfv37yd9pwxtAr57IupJLVUW2PmVkld4eoBACio45mL9OgEyFjvwVYKcuC/YldLP2MYRpZ7QI79t4o7FHq++bMrlYBPyKTnSA8dmUIpgTjMEjDEL1ph7kULhZvgRewYaKK/E05Y5NL5TmJ8cU4jgHSgUSUW/ANYy62aaO//s7dHF5XerZthCUqwmWvNZ+L3Ec+IKe0Oq/9YBSiYUAZVxZyuxmMn3dtyMSe3oLYh//vOf/eEHHyQT6LLxHSG1COAO8OaZEw+fPr/0ya3Tp0/v3KFXpGl7t/KcTYPcfWlUfkekTePCljhh+BasBt720r79+11CxyN+bm8fOnzk9u3Pjhw5ihCy8zu3b+/Zs8d/WIpZSeJ++PAR1vBLFy+ef/PNjooyNjnRMsSRS5n6ikfMULywJ3+ZeiRrOrtSKNp0bZOgWdpMF30Y+20Kr8Y9JDr5auBZoyFDAqknZ3gfkSTqyRldTlNs4Nms0RZnCAkIgBYTpaaaTO69D6sUCfEYTbLPBQbqhB0HfLQnIW63kfiAdRcCj1CywagCLj8FFqSN01TxtZiYm5vXUhVCYtZFWHjE8ydyYiGERqIj/WzZeESm1hB9C6iEZ4xGY0aljvZVy6uhD2QSlCoa/DqpeIRjbbxaajP6xRxOrtz8T9dWsYokjcJj6KTvpNHAydThLElJLXrRgt5ZlbJYpeyCFayNBMgoqGNPh91tGM5Hl4xffnGSrlXQ2ypxdxPnt8/HGEcsyKVhGeFw43I8+9SsnZGzd4MZB1VLuolq5iIOFGLrgF/wwUIofrNy02Y3W7Z6KDwazkLXU3oMBYFl4DHoW5rUCxlKGcYzJe4fnNv1n7eOo52Vm3/sFF60a6bKdOMi23TK3iiwEIvNzt5k5oJ8GwNjgQgOdlc3X4SUPVj6k1tYgm/cuHH40GHfWkKTlCY/SuShund9Zc/myauf3V1bWz92YM1Sh9DEd1VZnlg9X7JuOI4Tk6dOEh8pHz5y9NbNG3v37ZtAUsdWi1teZlfmpvuhQ4eZpNxT54hf3GP79a8/+sIX3tvYvXH3zh3uuB86fOj2Z58dOar8ft5iScxky4k+/4unX5aUqa9pAM5TLJZTe3YeDbW8clOZYCvjQz5XnMSlmjI9CQIkwQAiIlwPmkbLQzVWcE4zyLi9aspBhJTdCXxiGRytshVJxHnJqXN8DkfPTGfeUCxoRZPEuw8FLueR7XU0VXykstDasoEc5eISrD8DQ9XcUoIdxcfMO7KILvy5/khQztGD+ZJVNBXEKAhLB7pkYqyGvjFMgN0cO3rcFe1I4IyqldqMYNADZjy7f/vxSzdfqvh8j0Ias3fvXnY9f5v2oDYgHDNQCsAmSwymZtKsWp6sw0jpnF34hu5r4DxyZoqPe4ebBLlV4g6ZR2cmNSig4BvHxGjhahWvnCuxLwDAWcZc74o8DoqIFeKACo4McYqrW8wFQ8k1zClhFlBFKfMnHxseis4TqY3Nuk59Fa+QRy+epl6umkorxQfGkd2jZxtv8NuI9Ip2zq6dA9+gOXTVC8d0t3hcou1Ovq+xSYYoNATDScdlZLUGzoxqVpAuY9xOmkEktbxo5ctf+cqsYuk6JuWZY/sfvVy/dOkXp859YX0lfga9vMR9d56ZWa7ed6+oYbQzrng8/cWL5zuzr8NWCFshcPPM5ubVq1c3NzevX7+G43xy+3xrdbS6/+HDhwcPHqSX+PoWD7vzphme6Z8UllbKIpEJsbGgucCiqW+U2iPYHqTlme+pR8nNZ+lF24LXRlfXWq/HBTvj9OAcLTaUFlX9VhPDSeuLEvjAo2Bamu7jlyPxwOkEaPUwAsXJsKk/C5oihjl1gR2DpCLMZDeiNCe9GuyDU3XrhDEpoRKIAw3IwDeOz5CWQBtVyvmVg2sUcYjpuMAAhybP8iOOs+iFVHuELZ93pCXrCFYodUBgBmQ0wdJE6QIhoME7QtalLvGgO8ZUR9/MlNDqXZRIJXKocnDl8cXbLEq6250XdnB+JZrN3Z+XI5Vi+8sJBoLze9JstShNYbDdVQcwlB5zDBSRGBN1T2aQ3LDL9+W4uzA9ccesqdZA44XoYB+whUgHeKey17r0SnIsN76bwbkXxIEg5NGgFbH5eMrpgYlVCbOAKkrdyEF1oYLiI8Q9dbjHKdTGfpxFbxvKfmnodO4HpEvfajR8mnCkfOvNl3dXT/FiuvmLbc62/80va2AJ2vdlqY7eQ3bMwGkGjDFPI+6xnRFlG3ONSN53/vY773QYchJoScWeHU/On3/j2u37K8tbJ47sdSSanuu+O8/MTPldVV46yYhiB+VnDJfmTtzRS/ZPnJ8+ebK9dvDh/XujpZ1nT+ylStbOw+78YNSevXshO3ny1NWrn545s1kTlJlQCi1PvzAZcISRYV8n3V5daXhOfSb5zcQ+Eif1bjPvPK3Nem1wmHifHYAJ0A6isQNKMylU7aF3e/BdO/PY86gmxw82kMURAOXY2sYdwzGHQ9VogEMpUUR0drZ5Tt1clIlZm4OOkdZQN4TqBS62RfaYVieaINbyZuMTr5AZZVGVbE/fY8RSEH0iGikifKYbIB68CLCdQSEnIhPgSr1DRCJ7bCIHIwucQRxoEI0lUaLPildFE5bJ8V4Z7wCvQTxE2Vy9+Zc3J40lvY+ci3l2On6UjSMPzyzmpiR6KWjksXu8JgIkXRxBkmwMEQfX4sJJ3/0hEdQNGnyUTk/cSaBLPhMXxo2XFAsPEJR9WYz8+RPoZF57AL3tiaGEPgXEq0SAgvE9RmMmk14tMdHwkswgFBQPiyNL1UQ5NIAN9BfHoRUl+ahjPjOJEsaCMRaN1ATwjc31n44O55j5YNsm5hPRgVs362ynnMTrZiXjIvEsU69Cqw1P++UknUPhpTF5EpXwkAw/NWo/JhpRk84xm6FdthOLwgWBp4+uP3m5dvnjj4+fPL2xE00qbXJ3JPHCFybj855uejF0dxx85x9++cN3vvCljdXnT548xj5+ockWOlnu49x2zWW+GTv7bX77gVLlQe4me4A9p2551avqXV80POyLOxLZMJotFBwqgyMZExqzk0D+KHZCki2/bFXpp5okT9NFy6ErE7WvjjrRGG50aL3UDJUBOSyKSCNVXlycmxtx5XOcp8HicrNpimNAje66HHKnygyx3TLijEYuxjbnzTASpUbRBMZIG6s6R1jUCqal76pYVLw90hUEBaViHNkyQNp9M6IdWJEl7AJks0l369QYOkbRt5KAwOJ4q9Q3OVt/x1PL1/7h1tgDz1XZLBf6/YrlZRYEXqrIratBv73KQsdW6wFhRdJiFX9fMsWkamSPGF00xNdWIhYYG3qUn4sq8okcm2CikIeDKiGgYBAFIFH2C7iifmW2kYZe/GqghMBHA4CTeTTopHmikaQ1qB6iCZvnVJ0PDyxEIIUYNgcEGhibQz2Ev1hF9zGrhxCOTIKJX5QUVTxFKQsWmKl6T+8drR0480LfqOun+Gbbj6xZpFT12krBIUwbnzzZxtQovS2dpGs7bU3fqHW2xlwvt5du3bz15a98eZKItHDK0mqwCjZFjNru1RdvnD158+Hy3ZufnD6z6Q6G3H3y76q+ePli69HW40ePeWVbIbITxDMw1+48JrjHDi2vbR5fenZ3fffu/fv2Xbt+jeyc+1lIPXjwUJJ98uTJT69cqb76PRFEwDP1tOSy/vKzR7hc14Xeu3AutoOZucxi66k6q6In/Z2VD3sApNcU18Yj12jzy7N0eMUfZGQnpLEw64dWWZ+QiGRj09n+3FELs9isauFWYlrk6DlsS77Jkp2h2PCesrPE+WIW1LvnEp1AklW3CR7UlE/mg9NwDHxBSMbrGNwThZkaGiVcqKyaw6FJPeJkOsse6COdw6HBVaQEfxzIcnh1Vly1rMfdQpQEfN4TEFCwReZEAIJUzWGQparY5i4r21u37tx9tjUlcXc9GKnvrmbfXu3xUWSGckrWSSfsDkIIDto9wYCAb+N469yuTxeAv6Xndqbu+9OFVijGUgT6OGWlqKdKXNDanIRVZPaAwAyU9iBoRhGl7xGmYeEzhIDQ/V5mFDyFHMmuYgrdq25OAUmGEI3SbElNDQBc+U3oBsp+m3rXWwoInYiK2o8Fx76hOMGrb53Z+nTp3ITGjmjtt7bldeSfnc0WTm3uYdMwCbYX+YY0TWI7qslSpHhy6zAtKCz2iyV7SOZtJrUri9urao6LLW2MCe4g7cS+0bOdxy5f+/zogfW9e/RZcMjdi3dEjgnk26K8VZ3n0Rml+w8cGGtrXXn0dHT77r21/WdPH9F7heFbP3KEn0s9t38/j67wkhmU7969kZx1wVDyaQMfl/NNtXFVpUyd3yrl0ZexbWicfrymwDG0Ypoz3jhcLXg3oN40enAwztd4bu9vGCs6SaDyygBGjL0vkiVBL5the/cUE2Lt9tqCLL5isqr7bXiLvAVAMj1p9GZV7F+qGoGllXLB6R03dkQaxeaCQfWUQV3ihN59S5gMCMQ6KaVOLRFv54inoiycYhhBMeVWi6ohuc+bIkk6xxQ8SrNs22KvcMZLL1SYNM/FsWwS4N6pP2SZauoYqhT1hKwS4LAokj+RwGlKTXkVeJ5ybMe9X94iPWuVuCdF3HGnsO9zHwGbuRmfLE80bQDcRwhHiNukH2SSFNZAtmA0dkhX2lhVpUl6ySpp7Vev3sGbVOIVgaAkDK6iL1UXBhDlBetlHKCUz3TIuohJmhv9hntSAF3dpNbh8A0zxwNCTADcAIgpiwnIQpzkZwAAIABJREFUcC53lkwcKH5lm4QwWVoGhNARzHxyJSEOEOWvbe752ai4bVki6FZt2Di7CaxyaW8xrAMGS602maklDK6pdG0J6CNCzbEtQ090KJTepeVbn93i1Svr67t9PwU/ty0pwEssUOdP77r1ePe9K788fXqT2TnpmRl+L+nAwYO3b9/m8+nr16/zshdMau8rvtx7vufB7Y83Dp48d4pntx7l+/TRY8du3LzBs+xsEC+eK5OoSobm4sULe/ZuLPGtMN79EstsmXrkys42tF5FF6sjLf/KjOkM2uQIHUv0agJYiJ5FbxCZnTIQkarxWfmy7rxbDjj2FmZim5J4n0mwqHt9bFsiKgne4SATLNHjVdlvGX3t8FCrFUUUQH/BUsePHz0+EKg4vSoBYdjiQLsaZPYYTRCS4WsxnrOrq4Oq8fRdzZHEAiIhrDocjEUXA5ayq1ENZkSBjJSJpQT4GDOmsXVMXWDF4oxOdUOCHfBQJ2RzVSI6lfNr1/+367Nl7UkP26V/e5VLeuzkxbINe2LiYutMOy9cLffcxA6AFi4bCIjfMSSrbqM3l9ANRosr8kwbGNXdROVcq+SplByVYCLVi44ksCWQD8eWLDORIZ/iKSlAGuUeXz7TmUlaL8SY4dp7kdZBiEfDj85OWDCpwwzpoH2RLDiFm1OjrSFigwTiZN48ASGSTF3WjiStBJzcO9p14NTz9ncfS/yTq9qKbbObTDJDi3aUULRXsWv57gcuATlBhO1coRhr7alCxykbWIiuYLISFMoyv21Krvz+++/35Eq9mGMbT57vOP7x3R2Hdz3cv3fDcvcX3LVO73fnNTK8xP2LX/oS75W8cfXS5V/96NHDRy0T95dbo9v3X5DuH9g/OneatzrW3LjhDe7czmdIr66W92/e+qLM0L5evbF//dGTB3s29q6Mym+fqHesNZYu9vymNUdPhN31hiFik4ShaaOz/RD10dxuWPsMtWwTPZqktRjWdzJBXveudc5zTG2G0cOwMdpkUsbICNdCYob4nlnAWl6CLyZh3C9RZxTVnpB5YcLK2ipBxHiTe2N0sqo2fQ9CzK0SDU0p73ZeEWeUQYu5nBFoURGrUQdYjBZd4hZhW3zkT/zzLsgQDN0oJQEux3iDfkXENCroqec9voYBVLvDAJSESTBA0U8iKVcNN9th59NbVx6UJ/5MIkgsSd/ZW/nqPOaRdFWzUFq9IHmenTc3jPi4ItJ3CmZQcoLhYNdLUu0ZfIfMikB5QACaPqNER0OeMZyHGDc1r2qv3b3l6AVGjffKZwvtBf4WUBJhouFHhi+wB6TDYJonGq53HgkdeJmo1QTax4YHBBixNkbCpUsHLVUWBLrkapNjvn1mdGXp7KTWefBpU+0mRJuD9j7CogsA31ZMVAbWifb+bfa6ju/1xlk4MNHPIRwxxBcvXHz77bcHcMDjXwjmV0vP7xp99vjgJ1evnD553N8R6e+Z4SbWzRvXDx46/Jd/8RfLGyd279JsZ2oXzBOgJy+W79y+s7Tn9NGNe7sOTfnNppOn9OqYc+fe4K0vo+UXuq1uQ0SZzTbPqWtnefH45Z4j+/X7P0MUW7Veweiiyy1hGpsKtQ4q2/KpwykOkVrKlkiNNoV5hoCKnD+K5Y+cc4z2QcTpj4dnZKQIRWo8aQazepkD2ihEYRYUS1qW00tBRiN5KuMUjisdpUL6ZV+pabxKq1lnWJEqRc5xObmIKzRCFPggLFKF7s2qouUP6xQF0zUGW5uhY/rudJHezhZ+C6P8DNQFIBkWASeJm0VCJXazPYbeO8iNC30Twi9zReqdYRY2VSFoWfYsP7t6+9GL0VyJu+sizeNpOuzkq6v+7VWy27QL08pm3Wbhaml5TuZi0fVKnp/BZfIu7HEfc8OqMEZSvKehJ0Rir9I5BlKIJrUOip/ngsF8DE93uKsMXAq+THXnFbrspg4U1RQTtKAiBSSl6a/k8ozuGNTrScHEfZomxWTqIJkktg2egDNda1ciNs0/2Nz9s9GRNnI60Ohe2ZTtMEh1SmJkG4SQBngt4SomTGjxLvaBV+EZEKF9NeQBc2tRzqEg6MgpehrPhfzP793jyRAuC/tQXRVfKErQ0Y3nz3Yc+PTeyoEd9w4c2M87Ij+/ffPhc721/dmzpwy2F49vPH7C+w1G+/btZxdkaU28OfD58933bn68e//xk8f279jBr1js4nU0d+/d5W0Q/O5STglsb1Ifrewa7dy948HTezt38I7IHcu8Wd6Wl0SMOiWD48jU2gsQhPcR7tnsMadYScre2doSR0gMSKuebK9f4mpUNwjQgse41UkJZQAjhuFt4laU6dFfovUBL6goKZ2HDAYacoCqI4U1pkRTiIgo8RbYDIrxC2ZmLSUwsbtv7pXMtr964phGJ5ogJCTPgVfIDGPVEDY89KqHzvJsUZvVMWU3MSYgpdrGLpe0Iim0MES51i2qIDy0yoUCo6asxLYMlbqB8HpRo0GcY3eE7glVWk1CqWq4KYejK/c+vEVmWL+STGGua8YGklG/ocajgOyPfHs1GljH0B+O7T49P4MZqB40AUiG4126Ac+VA0pxP3c55SewVFtBTkzcWeJrM4ykezggd6BBC4FO7sFClSMFPx1o4K1teoUu19rTDZliAvv8MelmQxsuuglTObYhnpPGY8IROcSEjkYvpds46WwMk9MXpqqE4xujjUOnX7zsbTUsqZiUtfs+4KmpbRZsGb6nlASMV510HDepNkn1JPq+8OQPthfOKM9iAQ8uhk0yOtDsNMOJR8nfs4dkZlTdLHiK/Yyoc7tGt2+//OT6/bXtRzzG/sapU3cfP+JW1e07d/iVM553PHjiJMHg4RaeO8/FMf5v3X36ZGnvwfXH5zaP82s93HF/+uwpnjNZeDv77o0N6JWpr/CBsu/1pBr2PvXR6sljmxcu/JrfYwJj8VLqkeRzGXPgYMdvxCYhrQBb+ZnXrYj7I7JeVkyk2LXPZ8MMDnRw2XvPE0cLgmXyWK5hbvumPivRpkooeYAm7Ki+h4TpID9F68HOAQjCPBE2wpY/Fk0ee8sZgwjHlI6yNGSxQM1R8Vb3zUiVHqehWhJsHSXRJRrwISWPvCXKoEU9HoQbS6harysbz8TQVKTyahNt+rPQK4xlpFGGHiHWiCxh5JHHXW0yB4KxYHoPyRiJVxGPavIiVMcqoS1RGvnEw6kdN/7PaS+CnMgcG1he2IXdClZOdkayTb+ByI0P/+1VPjlsaVKU2vGMFr+NjUnc7MCehaW+OE4hDujN+wSTwDe4X5+45yI6BqMrGz1K4KrcmOQ9DYA/fvQQN7hXldOAIXav5N4zJrk7DbZNasonADSEgkJYHJjE9TrgMZKAD2GJDxUik4Sji5JmAk0UMIlgYQBKa1V/Z3Pr49G5Qc0I67fp8I0I0IGYmk7Wnxgmk0xsse2DTplIMFiD7Vzav5pKzNSZPb6DJfIENLHHtksXL55/881YM6WSlxAJ4He5GXg1DYmiA3D48KH7Fy8+WdnYuX8nN9gPb68c3bV+5I//+O7dOyv/9t+defZsdXPzV0+fsSx4R7wc7bh26+7y7pNHN57tfHr7we379z9b4n7r4cNH9KOnO5Y8U99eeiEn+Fo1j75YIlKy7ejRo/fv3z961K4H4srsNHwI3uJ1kCV5HatySslRz1GtWGNzyA5qQqsrjLlRhX42RP14mSAD3TIkdugEqgpaOoKe7GRYu/XOz8brfZF623vOywphv1db7L20ygRLSbV+aBrFBDKHoStVXW4airmaHPagmpdjpuQ0EQ7d4FVVZI9N6EiRnWkvpenGIIqIFzMloxQmCg7Cs6q1Cp03xf4RJ4Gytkhj55SXB0aQRDBWUmvgVROSJEgAxgpCc6wp+qFLpFF9E/wQbQDBG6tVxypiKZqkoKasLI1Wn9y8/aTYW2uIJqDY+PJkneQ4WZVzkENToOSLNOBbfns1l9ANxhjP11Htz89gRjdRM3GhjgKLR4NeIEmYeuVQnzmRUU3lnMm49sSoJl5KrKwkRpzxkjC9A4SP3K53sX0JpEc9Jj7BEEtPUyZNgL70vuZyasNCPzYMYAbSQNcMU2PF2GZRKF0f8pzM1zd3/3jA52S0uPM/rfJT7Qy7wXS6VhT0UbHftOLojcj2ori5uVTlERGy3Uo0KvHstVmO9t7Dfdz8LjMp4mWx/mPgZco56gz1ixd+ff7Nt7hZde/u3V/fefb2T/7uyv49P921c2XHytPjx3+xsvzdAwe++423f/mrXz1+sXb9xtWtHYdWn95bfnLnppL1w2fObm7vSA/Bs6GurGyxNZQtr9rIEzi//PBD0n2WJlq1NFlPY9Kip1hUXTVyLkzMeCTEu7IaFUuS0rI8j7oZZqh3TwfV0hH0ZCdAeYi73PkR5PfetWjY9CWnhUvJIXuOtwPI6SwvV5OyeIUoh2PVA0jNShIUETVnCZS1lGrcS/RO4MRGLdts1SsRBlExTS9oggQ30900Vk+aNbTdjJhdI1wsbiHRsGaFwPRSTYwWJ/BjNFY3JotbcNXWSoVd1EFkjELEWFhkj1SZehFa0OkSSootgMPealTq0aI6VhlvcursuH/lyUdXn7wcFa8izBprQJYF0irXwLbbPldhB2dRhd2/vcrDMw0beo3iOVCopmAzOzVi0Ivlc8irYc3Dgq5yPmBP3sNGtpD6riSlPnGHaBJDiX/OqvcoblAcZrnXQBv4V2przXYDapsWj8SYPCwYQExmGvqLt3kBGieFZTEXx704WB1mRzdGuw+d2XrZ5TZGblLMSFmU2Th891G7r+k5ZRmeTlHmmL0uk2bnmptD258SDhNkYfG8QvXe7GFzOnb8eFmgqe5Nx4RIPHr08PZnt9959ws+rg4eOvTG3r3PVr71Vx/+53ffeYvP3nhk5dLlSz/60Y/ufXb/5a8uXti7/8ie0f4jDw/uPcG3SF0qS8sKm3GI0gRNE9BnzpzhVfGnT58J7UpRtrnTjyUTOAZDa8jPPcw0XGxkeM8V06jJbAXfk6kJVMMNA1TL5JgvT9A/jsZDywxxVKCfZGJwnY3GhhMHHNP3jk2J4Yw8Vi1YSvYSgAhNNw+bwSEXlHQLbSmiIA0zMULGhH/BUulvKi7HidWPGtShVmILlBWaiLeztNtwkCQ3ImBiDh4JCAKkldRcOGMUc4VGWboTqBPUjR7rCLhEY7PmqMPp5FjGlQxVSFW8V5CRwznSxMsC76T6JjWHcnzl9r+7OTFpdCLPWzhSRWD7ZD0qKc6MQ//2Kj8KQeF3DEs5bkHaN4Tlrou7ISSllszPdVc3pXP0BX41ZCy0JtUQ16qu6QO3su84SB5GUHDAAVeBlal3aaK6sEur3EdMSqM2xy8GRjt7v0cGjW6Jh+IVWrUY3xu0+DhJl+wemV7CQlR9sDVoH6iJ4c0Uywf59zZHlzs9J2MrrtZuLf1Mr7DBYLjvPWUPRGeUE9rL9D3WbX7JxMGLkoVU0gbmqvtVX0jjF4iSyhzwVYVjjuwR5rF1fsek9ETKsRPHf373zlvvvbdr585Du1fuXLu8Z+/eCxc+Gi2Pvr706N6+jfU964eOr6483LnUx6sheL/N9v1t9tTiA4flZd71duwYVzKLL4TaRsBMMbeMybIgTaOOveV8ElUjwGfpQOFwk1nTZrNdNgW7Aoh9CcM3HrjtqO/x8+yMzuaWK7Ej25QWGv4lzQAePwOsEZFqjrDVnKsIhgQpu62JWyAqujQYXDDXQy5KcgVZzbRUqb3dU2aNnVjAG/tYso6kRGlCkR1y6aAmVnOxkYmzZ/sx/TavxRgj4DChCEjjVGsAnNR0GNZ7SzEpaNQFsFvYXVDMbYiwOsbbdFJJg6cEV6tgjizduvqQJ0nKiSybqRfkU8g18w0OxnkKAnlgBgm8eYbvypPUstqAnEdme17PsElC/PkZ/Gqvmth6Xoc68hbC0p4XlpJq2BHiltck7nRAL0HHaO9LNKXBgWIK1tc6gJO9qHbfZjqWcqmZeGcl9rAQkxQWB2YaE7MqfQ3pGQOEgvHgthGEFBnHQEBrPl778oJQ0+NJdV9i28jBndJXVD84u+s/tXhOJi5UtguaJluf7eBb0yT1TuIsBfck6qHw2kS0xfQtP8vUJVs5QioBRLU2tqwhUbQAOrK5ZE3tOXRPMo+pwdsYN3ZvbJ4tv0L0xZUrFy5evv/w3pnTJ/9o9Ld7juz+ydGv8lr3t996562du7Z22Jp/f5LgWfHKRPglpk+vXEnXD0yu9V3rEjSA4y3ssxEwRXUcND4mwrGF7DqSsfGhpEqjfAxZx9U7jpGmJWKm9F2WiimcCJpXhJAH1EfbXPGRtfPlHDCZW4BaTOSuQDH6YAc0AJywBZzSdzVoPmbSZHiklqia4ta5l2OW1NA6yuWbabJDSFUCwmn8qDZLjnOCYJ7jxWcGJsoxjMdeKBMVuyIRK0hy2XUXWXfmVBAR7AzEQopTMTbL3aiISC1qDe2pzXE6EmoPL0cviHHAibwvwDixI0vVle2XW494axX7tX4AlTTaN1CXAzx08sbr3ikk0Pz2KroW9vIZosGuTSE/wXGqaPfgeKDyI5H0NMaRcM0ZFtMcvsCKdqqEupy454lUbs1UOKVcaTbimPflJA+rMtPwqjYNjeEDkYE+hSlFJo1yB5Jf9EeCf3cAJqFPflz2AePjcugIoCsN1KF1VeXjcppovE9m7dD50Yv652S0joY121fuVLOGSQdf3Gtb2QRene81W0qtkc1IzweMRo6OZeoTOdkO2ZXGU4VJxA3hm8TSiLdds8fxxvj59NMrhw4d2rt3nyt+Pnpy9/6NTy89uv/o6ZefPFk6/8bJU+d2ri79XzffXXry9M7zm//oGx9s7N6z9sX3l37+i0ZbZ2xUj+qu0saePVwb7N+v18jcunUzvL6mb8dnMK7U3+p7z2u8c9sNmnb6SqPaktlXM8tsOihFaz/YLFXzZFNdmXkc3IKAndwE1qXuNv/44qrdMUczAqBNAFXBbo8mbmaaEZUtNeqmOW3SJFaaWxUnk2UUKvI21ByXjqK0/NraC5ogQWMKCv3VYkIYjcZEeSqe0Jloi3RK34lRptphy9jlLPoUUCk2AwICG4hVlBMABHknWpMbCaP6IAXau0dxRoR1lcNedWKOFGdJrYdXHl78jF+M2O14z0fZyCCYMzd1dS2P5GkUUtiZfnu1pfBmMhZeCu6jnaOn1M5CHFgGQRINaBoy+2YVk1oRm27A6+mdEh0GEZQSslTFuJSMIs5tdXM5Ukr0LasupyVx72Q47h/HzCnZPxnBlyTH+nrihwyQ5cSJ67cPSCM7uUZkGC0Mx85jJon6zQKY1elC8XtneU7mrLY0lTBs0uhJwBQH29JNETN0szaesLO0VmWBSWuKJlbXpGtCkCagWxvYhlBzfGbP6wXzhvWLly6eP3+ed79cvXbl43s7T+999nLf6pE9x7/4pY31taXTd24/P3Dgtn1Z6Pm+g2Rbq9eura2srK+t8YxovdCuWPrFw8frZT6+fJnvquLp82e8SD78VGqPjs9io80nhTwkQZ7NxOk1i6ROtIqJbeGvZG1Hqa8n1YkS55GffTLJ1uhl6E+dwl9oJZJ89KlPSGHSs67E1q5Q4tHcNbc9n5RIhV86HArzVou9DAs6S1URsxbyryF0nmLIyyjF2JoPrg8W43GrTFeFjTYfNrYoi8NLFS8pRML+AlPBm1TJTguXEeeiQxjB5zQFTBCkwVYOhBgQ1FkHWHOKcgkIWrMwJYLgz3ifqSeseOSr1VM7bv6LqzurKSk5j9+AW2T6ji7/7VVePoPBJG8L005kPIcmaeS7s57gDZGse3eUjjV33PE/9VaiBulXEt7qxyGsJAoLC33yLgFVx1PTJIBQpAsYp0FIh8h4SCdpWQB+CAOQSYdyTPbXRoYAdoh8kjkPwAR4JUOOmOC1Bwffv7a5/qOto2kfa+tREde2HInOujtkMgm5MGCq4bZ9aJdyk3wETeVqZT9SJLgfYa005kTotR04x80C82CZ3rTwkx//eP3g8f/3X//fu3ftOXXizKl9h3aurZ47eCqJunH06Fe+8qXHH13go+QnTx7z1MrJc298dunSezc/+xk3ZXp1nwfZeHGNq+btNDdv3iSD37E6/nME8zqePGsGPFk1GjRaJwdfW4R9iDGhifaKBpz7z/ISbfDQpb6vc9fTNmWNatWpgmF7Q2L2U022lii99KQvVkMe4VWEASCwyMZz2HQJIaKsaF+QG2PIrN0mk7HlyGmwS3PHJFppdKjlrIFMJ1meaMbx42JEKUykKRjBWIwkx520C0pP4Ys0nQiYKrPJ6JSpB95xpGQrW/NWaQx9nWEsQKJJxFE3RqgowtY3DgBTEgxBXuU5qSOja5/c416b0WUHv/HMprb49J2kgvQdU0mgKax3nlJn1vUPsnfjLHKJFRrR7hji0L+yCRLH7rhzF9CzGTfLWTAOJAGaIKE3dBpJvUlsLcjHaDM5NISFkhMTGS420lhvljCp9RU6jklzGu9O4YJ/1JB8XNiwSRpnBRjSDPhZuWalJzIeHI6Jl2HD12vAHF9/tvPg20tbRVOiKQMtSMosr2fdtrcsGNpfGIbaQcxgO/XrbSYNUFtZhllklNA7g3Zl6vrxo5gLLI9WXzzdOn3y7Nrq2oF3v7pr5y42DMbS40dPr129yr61k/c/skrt2PH3P/7p106fenDhwi/ZTQ8ePHvu7DeeP7v6+DmJ19KEH0/tFobV1WIG7d23787dO1c++fjsuTfK0mZzvMw9oa5xYwOHEx0ak/UqteUo+QSskgyEwSasVMYa8rmB9JTFKhG2gGh5n8lz8RmvhRa5VgMjkD8EjvSqd0ktHp3RtAJBYzyK3KvO5EbIHBps/glWWOJcFHVWhU6BM7mC64qESSWnKKWOrIKTuY50VpNToQoylTIDZjSuy/VqQkftZYxlzM4YZQQ5brKxuvgiZSc6FhjTp3ljukGiJTCYMBGNIa0ibYks2OmYRGy61Y90AyV1CYDD+JMACLy6Z+nplRt3n21V3nhLsxX2Ux7WIEEifYd9AQl01Cxr+Y4+VX/5DGb0/u1V/PL712gh5aPk2tFIoPxmHE0LyJZXyV28b7ADy1BJyc1K9g0KpAE0qJZJwnE8dxljvORpOoPDI5PG9CRpv934GBtdxgC7s0SGsEx9yOq1isxA/ehhYUQlZwlO7aUvC9y3NkefLpW/XJgYZ9uMCrbpkPYDrfvTKfun0Ia0tE06GkePmdGjKdNE0f7KnG8Kp3J0+x8cYGPl0YRReObEOfUDSYePVKUwwNitnjx5+uTxY8Yed8G/f/Xq0sMHhx48OPn+e7dv3/54564l8voXL7hV8zIbnFVR82B4KeQvfv5zpkCNkDnGnAfEB44kGxSGz7TedksgZr7HEVdj3XAoM3AR2ov4aGZ7XMJ5Nt8RZHkkJw+2p4QW94BhL7RgcqCou2kQn47W04Jo8lU2ByQydYVl5mklgLcmUZeIckY/1l20mzK3YqxpckWOyFgrqoSU2hH5MVCaQ4nDGESFk3aSpEhZiBIm5t6iLapGX26ysNuViFyu+bOeSP4agRtAoJ3DgDEy7z305U1hLnhPpO6BxroshMU7TzYvL59ZvfXXnxabGsjawmaXp++kVUlILX2/SH3E+OQJ737o5durrGNecIHSfCmSCEjfKZ4r9utdLk1Pp3i+hTJg9OXNC4NLqfPC9LoieppeSdcw1k06LGbYMSteVdjbxJmxi4V+dHoi4+MSoI2EBhok+FLRQDNcE9rxa57ge2SqwWE4TQ0OBH94ds8Pt7I8zFf+4Rwek6ylfAwxUEW7McVup3G2XIJdxje8PnTO6gU7mW2hfeieWYZlNXgPI2n6kmXqgIoLU2F7h36jdPbC+skrKfVWysOHc+579+5+eu3a+vpuR/LNskFzdzaRr3z1q7kBY3DmO/ip3aZh40mKUdvAMaapnGNaQ8XXmR6HXZ2SibghtNu0kkaPR0NoZtbuoj3ls9GpLFIKbKCalyxfJAsMWfDsEEtk75riNtuhLNb1XDlM3gNaHVM2rkqxIIw1mSqp1Zw1CwKmcpLAaHClcRLCIxcCqYrSZfOyzBEbywRyVXwFvkqZYYzW6E1PnspLr6SZKImb+GduWgCDz0wTi02emodoFGQxPhYouZd3hsdWqt0bJBnkZBxPbl/95Z3pibvk2qsPPX0np0LOYvIoV82R2+0UVPPtVTZ3Uvn2WzxxYDdnOZX/nd5ribPYgBD/5GEg3/VrR+4wmlxl8n+RwCK1o4tCD1HwkQjQx/SuOipGY5G+Y8Yr0VvrI8ak+DgBtvWVplc1cqfwFV6zufb2sxr7PTgc8+DU3lCvOlvCHNn17OXet7e0472C4qPOp0DP6uPeHcRawhWzi4izHcLm36zKNWfnLAP63miZ3VDfsjtfynG4ob6ke+pdMvVGPUXj8tqB5R1jESN35y4FFEPcdx/TVFiRQb7kKkOpp9VKGFqMtNMQyfSNgT7aUTHIsB9TVVMJ2if7XsMzjgpbtTJFxaU+guMsqdZFu/R5d0hV6BlljRTHWHfp+hN7+KsaBc5p8oxQ7NBaNh5g24cRGsbFeJP0USTIPZ/gurWHFNY4Wh48kO6XiZZtcr1SRKlG4zB1hjC6cXyV0jAWPROeVcfS9yimyM5RabGX2uyvIAApaSJKSAPMLrR5RUCBCUyGyA6hw6QyFDA7Ri8f3r3x+bPiubiMYyLIxkr6DvsrSd+5d0Yhu+AZQmwgwZuU39Kach5sdrMnetWuwYUgmXsZHFENph1rK6qwZ+AeyUcrjgGIcMzn5ACy9fyPp1loQT6K1DPj75Lvlnj1aO1w7jcbSUwIjl8aOqXHZ6DLxKoxdAQju4pfDGaqduKTxo+bpKEz+y8p1Lrznc3RzeXNuu2hlrx/pM+IeeVamo4Q3xMkbVIwOt4uAAAgAElEQVRSNq5J0zFtJeNN47VC8Dh+rhq+M9T7icBkQ5Spr7CuaqemkKlzT503Yqti26yAIcvqrg1uyJCl50q4GdXtvnt/PUEmodzd4hKCYxbSIf0pyX3OYHX9HNlzJqkLKK9bD7yQv9pUgTGGJp5n128xn3jVVC9Pk9T7SKcMhFyWaMvAI+XidufdssswvTTKtR7YIqHAqy5KAeCjADBS7v6GiiFsloz5axiTKoKaYpIl3c2roahHuRbZR3FuVQLCsOEQKLF5nKCKr8WkPB3hpsdiqhBKryw3sWNNEV85e584Q2ocQxJrFxvVaPy7EjXJiDSyqIwPTu+q82vX//0neQRmgBGY0nfYSKZ9EMwgYg5SUju+0E+Sw1vnyeB5Dbw/YIJI39ndQciGyHnwFH/RRZJDIXOYdPEwq4tF4p78mVXE/PQ9XjakznCrCFwKVsNwyQfu/O7MKmGR2hnBhChp9JgwmF7hZdus4RqUvhofxs9Aa83XN3f/ZPuQFmlf3gd1bIJwBkAaDBNIKmjbhMEGq33zrVDNgVhQOOS4ZQFzmFpmrWbquqFuW3KZNKQ2wzq7Z/VpWa/V/b47G/pWfCeMkw1rjekIw2d7acTHDcoYFqCzHAOlkh1GfllMxzoDT/nShIsHa1IrfzG1NZBDH6WcLLeRGdYondw8T/3MSHUfSJJ3XmxN7h6fmzG5NDL4odYyo93GPAqxtzTdlx81IdwTSKsEWMLVVlQNo8MYCkRWJAxd0cqsZSroo1ECKKooG5a0SomNZYIqvoRRVfZzsBUo6LHQ1qXveCpvmv7MUwtvIhQXkoUca00VA+RYkb4bsRlns4MIe8ecW/n4f7061/015LzC9J1NnPdF4g7fXuX5GSyhDJSsK3yV4vl6ukk6/0WCOgNx3j0VdQtCYECHCxG6AUZKPoE7dAZXQh20Lyg086khOHmajjC/jMm7G4L5lPwGczNyKFyLExCPSTU+A7l3Zt9obf+Zl9urWjgH0tFCrO/izYTaN634ToO5vRls+xZd0GzA69xqmbrfz7ae5NGXLRbVVh7J8bRVLtBJN86fd8cCHlYbVHm80LPsxxI3qbNRxKR7Vb2PXqWT7Xqq9/hYFygrcvctROA0hOLQiefedZtAuR+1t9Lgi4Ane26ojffQqSZCC4U/9+6verdU04a4vJI22i3qrhymmBmGMGQWaT1OgwU+myhjMRFKEsaQuS/WHvLXHN8Cdpnusw0R+WK+lpnNJ5BlgipeMkUVKIMKRSZINoKYRWfEkal0VuYfs38XbL1jcQvZurQlZAIc64Z4s0zw8SC1FmoABZ3wri1t3bl14+HzuRJ3iTOBZMwAPDyDZPIuNlxrGfZAjkcuhA7uwfHMDI8YkME7PKzicek4i/s4jj1zuq/OQIrfzx/Xsriaz8AGfThJq5L0LE2Hq5c0i2+m8gFKg/bfiCZCRPEQJYNbxgfGxLJ4YGHaU3ySRoYQaweXLr6aLNL3b53ZurJ8Nq7Zry7+bAZa3Mdctw0vYGgqtY6RdqwU+gYQPotNnkHMYARbmd7SaHu4djs9pL7Fo5+FR7Oohw9GkzMbWyvqqTZ57o4RpfvuraQ3EoVk3TwrkuOKQcxETw4ahQ3ViGFuZ2HhUKrKcgmMTbyUkHpoKgEq8/VZV/BN3gxa1aGBaRxEkMSAtFvv9rp3fZ7CP+YITcals/d5WFVipq4UwJZlA8Qly9Q7ohdMUZtTeT0ijSASZU0OmmQxVlqmIpzFLHd+Wy0rbIFMJwg4Zhxy2PEFMsdEJpNhC6/iFeVEVs6RyZqMJCbi423hQkVeuxwLvocohWIMEJnJMsdsPspioxHqvZUL/+aCtfV38IST9N1vm/aevjNoyIX8piQe+f3cZH769iovn0E1N+N7NyDpqgLY4+7jOwXbKFWyZowSdwRRmumGayW4pagRdC9DpOlVRzCgQ+CqchaJyeMDjGrvRCI566cHMLqERdqf68JmDOh9BKYQMYpcHSo8PiVdTJ7cngXA/JjFH5zZ8w+jQ+gy3zkuQG29Cm0KjAHWeS+21daTzoWN8qtC0hZRbRoe42OPY70qBmZ48YvfGWKkznBPvV5mFTtMBFjTJ3hVWBBz96V5cnfbPCwHlj6FMgynqerD+H9lS5DbqfRweqiKoHWAQojE6fHxsyL1yrZeKVdpM06MMFJz9qzREHkm72RaYPXgO9kTE8ueh/J1Tv2sRqLtwyUu/Jp/tiwbgEXFiDD6YnqWqlKIQA7inFBEwNtvOnZxCpKky05O+iuVQKaTfEmtEW9nwxeYKKqEUVV6gi7TGeGiSRk59iBDSbYiYFFw+ywYwo8RRKEJWQBS5/SIUl+4BKDDzy/9x2uDbJHz56+yNSuerPsgw4Xm+3Fop5D+8duryCB9X3Ae6KkaBnR4/4ze485nB5nviwax2+96pjSdiHsBDzC0QQtQMb8LBMfj48uTx4c01IF55COhYcmbR3JLXrzAtfnnjEVIh6QXyZSpVzLQ+FRPjEMDJ/eMdh08/aJ4nYgW36GVFvJLU0q7aNdNrRBahVp7pI1J20ZVxGIwpjl2AZtX8YpG9LOb2YtfbJscxh5tuMNIltypYe2cu1vuJdttAek+hHz2vcJVSDNgmGXQ01MNI3VwfVek7q9vHmhkZGLNgBmXAA2sMLiyEyBFfggipnrfjFZXPinP3Fe8LdO2qIsjhT8H1OCrAtgEu+C8CsaRTUNIPRGTUWeY7eidI7cEhTS6KiKQaUmjUX9ewBuvtVtSE8UUlCVMVrXFR4SB2JoUf2KcYEu7A02k4yyC4LhWWScYQ6bWBBB3mU3YT6x89ncX7r0czfY+Gfe55dE3aG6fkb+SBsyaCdDp5JC+7/t2b6OlpfIl1PHbq7Dz8AxySIbJ5tsy90FnHvPlED0/gzyqeDFVsB61mcnPqRKnEii3ik+8eNAxlLKYNH2qeQsmqI1/ChGtbo+HiJ7qvbN6FzhrAHHNPzCaiZHIpCg5I44gilk3q0dMFQxY5HT99ubok+x3l3wMpL6eKQ5tiG29zgjDmBrHsAtU8RnJNHAuZm0UsnI+IdNMnNBuP1C6xP1BdrP/n703DbYkue777vbe69f7LBhgBjPAACANgNgIYAAQAElIFsJhOmSHTMkKM0zaomXaEaZC4bA/+4vDHy35g0XLCoYsh8IRXhgOO8zgAlAmSJFYCGKAAWYGA2D2tXt6e92vX/db7ubfOacqb926VXWzqrLq1gDKmb43K/PkyZP/PJn5r3xZdTGgBaa+Yog0vG4HrCiNEnz0wt23dLkq2HdXDurMFIsjqh6i02QIyJZhCF15QBSmqwEBqhcyJV1JSECk1wUf1mwmruYmgYLaxVZterkukFYKE6R01ODVL5oUNYqW2da7lFpqKBqEMSqbBbSIjxgSSIp+gVJKufjqJSmRhDbGEJXEZFANET1NpnvHTa3aQRm9MruXNUiGUWQisYAzyQyw9FXJRYpUIwxbpiSJOpVpyh5nSfqCvkvczFBDMUP1rCaiW4xRkC2ieqRs79HZ87/3arPPwEj1+nOkfHrSdxwiSdaNPaqaih9o4LdXUcsDbzB4O0tTUVelYjg3DMTaZXQIWlKgqXG+Ar5YYwE7sA+MzCbi5BK3ywIrm8sCo7U7ss3VbpoBBzOgoa4iICKAD8ElNhfBgOaUr9VMG30MAB/nSwYLbhPkZg+o4S1r7QwloOdkdp+aLf1Qjk6SYWqQKdhp0phP9+oU74p5Rhb1eBYoEKtkQIG+gqz5jEPqvHw6Wq3YE+Tx/C2hWz5IFSiuldXgrYsPtvz+HO+IxHWm8UQkTD0uCbELSNMzcUI/tRFCelVmTTmJ1KsGlLt/sCKqUrxHja/YApsGy7HnnLZUS65I3+N+i7qPLwEg+pKYHDJmkpeXCDGNSzNVQCd+nf4RIUXWAbmkiEb0eyHJJUHm/sR6oZeoE7E4qOMmZOL0+FsXVa9FJy6R+rbKrLkyMCQWXSUlIzFxC7HbZa2mZ6cY9VflIhBdyrVeCkUX0BJZUToCLo92mn1qhqP1sYCWQDji9MlIb77dO7557fXX9hfMhLoaDUbGWOjZfWddTnEz0glmAFkQAOn+cAFt7LgTeO7x/PnzVMH5mXDq12vCgCQCXObtJy5+gGm9Vg8JYLWRSZU2CkG/AF/ki88hedRZS4Qeop9qqShfWLwv9j/j6014YXm7ulUidTODR+FIuFMTVqK8CbWZOh84PTt9z0OT6dI2hsyfVYNMu7pw6CQsC0mF4Feqkmo/a2Sh1QXET7yU1IKpR2vebNRfnFOKVUn9dZbzWE/l7wYR8LLJ9t1loOk7IoUNNdjhGSZZbRtlrlgFDy3i7omZQsTU5pAwobPYgAzggiapAWV63hAR+i44yJdSZ4tKkoEkB2fkvWb2tndJVgkiGjO+IJ8kxAPRjUiboWWS0FypxOJyKf8tLkVCNMi0KP/lBFVNXq5ATjmXbAXFWolF9NjlukgkhrXasIL0VclYcVRWLw0f1BglX80SKi952KQYCA72L1FmIZAQXiQaj3/P7OUvP2fLizO7jQgLPQGOBD2zFd96l8R26KJ7ehX6jgFsxpv/tdF4rUMBkPMztqUIP0wZUP2ZA5RCOgluxKCaRq7W0VprK1SE/SlEKigpKAI4BFyQT8T4pLqk/1lWQ3y0wLBOZQELHeFQMtuSKDVtLfhTOzU2XRH6P/fI7KX5u9MVMbcyuYqPrA9IWlCXikr5FS1Sjmealy4L1Ve8rK/gKlRVDLI+W3yyp661saeexdRXLJHm+3fDSvEACTo/ZPVCXd0AkYmukJ44wHJs3x0D3L57nNnmtxC/AsrVtClUnRoLC4yWDMtENIB1qwYEUFpGBQ2LRo5/KaXPiEdlzYkTCIlCGAJDUzm3nHoXUeQMbPcpOjQxFdHyEbe0OApUCVdG1FMTqJpkhojIStCJNHPSWxHNTbAWSuMkFtHoVWltaobAanqsZqEqIZOsR+aqlLBKalrctJSMKNV/gI41EZpx3N0ziaHbvZOtvaefura1szNk37dNikKXsRwbNyNOYGm2rehVZJtLodUELOHV79TS/tOrDAkMoGrou4HgKIoXcVfo5MNouhthNWk62tr0hrwOpjl5WWXTDagUAS1GiSJdAKFsS+vIG0r0PoE4+IMYIOCUm4KCqhkbblTUaV1xWVarn33k1BPpczKFhdQ9zUdtnk4tT4WFS2TSF7JcWx0lygUUlbXI9/YlUa1w9MUTpdqA2XAw206IvGWi2gtVQFjbwniai7+lq+lygSsZ5FnVk3FvtDgzk8xtIa4GCYdIW9ZC3XEV9IJSTL2WLnEhEXVpDUSoU/tpYzcw1s7iXlh4kiIgReJ/RKWsSSQ6UxIEWX59Cz5BK1ePYUjTkVBNxjVEmaRIkiOZxKlCbvFc9yBEne5SJLSkOHq+N7nKTL7Sp1gm5sQASEz+JUMko+3SzEggMz2W0imZNqoiA8UgIEUutaKkcJwk3/pP+yCKx2nyrZ0So4l6ofJRAVX6wPSNb7zMD41yiHTC4miHiptboOk2+ABMAFPoEdbiJFMnyw7PkK69nAS22Tg12tOr/PAqRvLqcCPTzda6rN1qBBx7/wwIZBB3jLNgvAoNyFkIu5uOHe1DsAxIrSuHEkCZIkOJnvb3b5TUMuKtUJg2AhHBNRZ8wMr92csu/UEL3miMcbYFV55UyDmZnYsPTyery5UuLIm53qLiHeogDXtJpF6WQasvaXSbcVmLZFUprjPB1FWQ3pN3v4Rg6hFrWGNAsXl1c6X59H9AG4ThSBAvX6/2ZFvPu/OKg3hmq9ui8uUVgra9UWGKnI/ZqrVpIRMe7afN27DqiOZK4k2rdkueEEDLjMrKVxTVbKEUwtzlfTPikmxnyJiP2qqoIyEeECeKPtLlS7sn2TPJdFGvBHTZzaVfi2Z40Rzg1VqGh1opluqYk6tkiGQMIW2R5Wamk6icXSdF0SiBhpjmSEdcUXRpuCGnoAqwi3+xskXaMn1XYdE/mM8eOHz6t1+N+KHb9w1O3yGBjhhAnBwl0JYuPuAGZCHJLQQdHJaILqrJj2HAwcEBzsejq+0/vUq9tN3uarCRiHQMSQQbEHyCCwFDm0bHaszHqvEc80L/agwoPq1IEih/JSlJQNggYU0ZE+oSx6JdFtC51p1AgCKM21AGdFbPZx+ZvTx7ZMk8ndnj6X2xutgkvSQZ8iJbPV1Gb7E4hqwqhC4WeU6/6PIv6qDp8stHq+fUQ9TVDRDqdERM0wUp+7fgMXiaT+8u3hEZT3choC2nA6OFb3nZW06zk44HX+TxqbrEE5i+GrbBGZMZwQabHHx6LVNDzUTqFQMSdHi9JVrAppHI+CgFRVFp4awsDEnyHmeJwVKrTUZypZfypfSEK3ENZb3WRWTJpaaLuAwAqdHlauJyAUlKhswySQHfuDVQq6eI0GYxaDlEIFjfJgSisuaOmp6dYmRcFEdcnIpUXECL41q3SUb0XfIQA7vlvJi+q7AIPTR7/RvP7h3IdvsihKLvMChjCKguxTMR3ix9x8nscVVePsPxdwBhA94ccQFTuBjuC1DARRXWdtNNurzHnSQCeYRwla7RRN1Uukao4Wz+7sBTCHmVGE3HTgIyhg82wy/bBCrPvO6kg4+5lzNJ/KmMRyHMTZQrvpEIg5CxYHNTQwYA1Effefq70/vRbyNNJ1mpTSIEnYUt2sBnVEkDmoOq1PV6zmSV+IFSeSPFfMjrX4LWlK9MbdjwDYxMO7LK5lvpcpSmROJ8pfinE4sinkqVu08os9F9dyFoNgOnm1H+WgadchYgMlj106K56oQkbpa7q2kBcchtaiLDJigS1J8iwDw9J1ITQ81lVFC+FjqISXdAStg2jN/2rtsGQkSoV6ZD6XzX9CiiHNucghSx0bozkW4muIJ2KZ+axHdup68VWOgqipl+NU5r08auFohaiTkJgaisfJEe64jURJKxzKKgigsnN+BUpRSP1FiL9dJuiRbpMa2nqHH66dvvfu+fvJi9IrNEAjfrNYG9Xdbu1VatplDEkQQQrrMpTI3QdxSyXlMR9qBwtcZGU+DrBAgkv70KIYTNB7TBgKKBtBTlYLXaFnkd5EZ2Oq3XVw1qMwWAnNslHQsb6AYCuRZp06ru12WOZTeCzr3e6vcz9LVNBAHxBxz8nE8Cah88M52feyTaE8msRqfpiFNkCpROlHrLBapnHgxqhI8Bsqc+YIbSxYPt9MZ/9midUYKcGLNOrsn83Pp1oVrkxky90NjCzOxWnGyN5B2Rm+Xusi0J+6hgvjRKyFxUVpVUcuyNc3cawhzCYmQziTSsgaBeJXrBOhNuEiM0M7OzTYpKmHLh7eiQ4pEKSZBT7zSN9IGcm5EpKII8bjRNlyJ6SUnJjVNEoc5YplDjKkKGZWlBvYqSJL0ASdVcJLDQtSYWmYSUtdhav1woklkRiNP1W00yNTETV0A0yfCUaVtaqvBqRaoyQeW11VG2oC3oxNXGxUh/aPr6157duzNe2m5H2AUgth0uVkwWuALqDFUgWEEIaCYHdWpLRbBh4/QdAwggAH3HHl4+4/hkqbYgjDcaVSBuZF38OD9k31TlywfLiYZgMH3lFFE7vBOkHFcDprybm3Kqf+ykwYqxB1y0zHoN72QEFjvWjx0MXg0CJYcVBYAIoNxg/vijk6vDd/a8dii8qssRshk/J7MzycbUmZxkyZAPfj6XpSI2nmmrEscK1z5MilbEcDpLapJVOMYhOY8bMjFUKaU5ySkp38vOnJlxnrHecl3x1KnEnxweLrJew6qEUUUmwNWs1lJk+tXKAhrhbmzQ6aMWGbEh9kq/tkd9oeW0FklYTsQOdsrYeJdFhh34heKoxmjxkRxYfky8KRT1ia5HcRz74nRVpGLEEmpVXpQualqOZZRZFvC+sioENonpfKcgJhVEMnwJlxZZC3G6fuscsEgRqVirlIs0i4BE48tkfJFllH2Vvs9777z7+D9+3osZGn1n4xm1jr4DaZKDQm3JbSjQR6YfIke9zoaGqstUyxK/v7/P0s/Tq3yy++5/f4I8WKGWhpTiVF7dk2lunUQgtmFTR4l/WaoDIAIRK0Xtxj4b9Sp/C7sjmYlVO7c0LXvFKua4BE7iePaqQCrFsIKpO78yp8rz7U++69S3ZvellKQuYw9NJa+9jBx7rZyXgA5P1yivIuuFZFMtefolzdRTGmQVKscOUgoCXG7WhpipC5/BkhXPCNrlRWh1g7sLRclrsvIc/VCYVCxPtqilxXk2IgrMKC4eJNdaVceGGCvRxL/EjY2vgZEN5UZn1DvUEcWkDelEJe90oZyd6elyLVvCEU1nWqUHdFRYJKaeRmd1fMjUG09cGl+0zxVcaqYpXUpKXMRlSLJGJ/JKR9Fg7VVdEatOaZFaEjzb5UZlbQ6w9q5IikxcPhGVeTROjipVSUmLsxb0ff7w9OU//sH+4SR3u93Z5CKQKNZNaCtgQhWKF0FXKmwEyk6/2w1DcrMsbC0F2mi1Pb0KDgQO0uRxS+x0u3uUyhMrqIuszRB38PW/KSluwGquEfRMmi5DOQ7cojVnQ1xJ17/xIXMju2nGXCDayMCjUixJdlD72OEPeEXBQMKpLDhTMdvzLv/hc9Pe+UfhYGvbhZOukLTMQutVZRZrKVGWWE6/2MvUGXccUu8Ppkw43mZ7CzbYInrCuz8CmCF1MQgBacHUhVoJZwmgvrKKznB3g0YZkGGlY0WxaQkg6R+lkJXBrF9QbCgxkGLCqEW8sPLAUnHwnKlcixdWSyy6SiWCrt6q4vLC3flLr023NhRRFV/GdFOHxqJPiKmMVCp/TxCWvzBgtetUXpQ6oVREJ4CFkancMpdWBaokCIDyz66Sn1GOjHuSF+LRhQARR/mWqygl0i9fi5Ro6lim73G2loiy+vPt+fi+vb/4vWe9WDuIsRrCQcUW/c1RPm3nm5T2A7WzFlMv9NIYJqtzy2ZgAwdmqJQ3z/D0KlyCpyhJJMWwMt+tvxO6GeJu1gfB1LwHUIiYQmDyoZ7WtUFseAspcbzT4AIrCwVstZ3WYQa2tT/Skq3DBudFpCddi7gB5c/Uk5qJf+qds9d670olZl5GfpyZx3zcSlAcmG7KVAdCi5epYyWQ9eXFL7rfU8lqVhffm5hK+jdeSHhFFARpIev2HafG3+p+lhkntf29ce6uUIlLsBrLjmw84bcNhM4MJcdGeBvxk9R8lapD4RIzkRSn8gwlRFW1unCZrlC71CKJ6Y2qo/CaIHky2WI2r5Ji551T79YCybCZ2D4Rz4yIGpnLJd/ifMt/GkR14tIS14GptgYagJEd0hgqjyh0ZEb8ZTKxgGEmeZnpsRqdL1dk4iJRRQnhuG7rPhB7z/SZ/+tJXtex9KvesUnRt+0WG+6shinmwCVZ0HdQZiNMuzGloPFL25BV9j6CIhMar3KlgkMN/PKr/fYqsGBGQEA2QNzdUFtp7PoEyhpH59Ok8Qy8B1DKcj78jz9nrK+ySYk6UPjYZXAZYlYXKBEAjeA0ODBdSvsRrGK0t19vskaDy/744OCq4FpJnS7+8UdOf3d2j7ssijC1yqplItFXkXxDeQsb8iqQoy9y3jQ6ts+eur6isTpTX6mIJWXj3D2sDcoatH/R68jESsNXEswh+VzJaS+hfe6uk5R+OJ6uEDB9bRYKG6PKvTbWI4ZAcpjGcImXqFnlbYvA9nYqdWFbTMr45qIasT/6R6VicJSnaxR7ASRaiNq0MI1kW8cswqeUVmM0Ra4sEZ12k5x9aSqtpA7KbNyc6oUJtWJWS8TOxdjon1MamUEzl3Pjgvod58lFQjIlI7RdJVQ8viMgSZIlrXexd3Pvlacev5zB2mEI8CXDjpW6mICCEjyVIkbfbRdcKmg3OPrOgs4iTmitfgcXWLm3vwNLwJfPbIC4cyfkD6K7vTPQaTxYUNx6pU5PAK6NwzpKulbWeUwSruJh1pEm0BfRnNqiQQ4uqxobzK+CzzUPn5/2z71nMs+YE7OayzzNzGozb1Z+O2ksg2nSzIvbeJk6S6ksjljYn40Gcvhn06Y2DUj1DW+jC5F94mbGbioBRvGNDJMkusLdm/xd1ZhgCVmjXsUpAyyBYtNjxGzbrBnKuJSrKl55cC16MAPLRWblmDm18L9y+qMC0ooFrYxUWNPweDaaaJyem7HRQ1zHgc5CVBkPCxJRxKWoi9eTZGIynXh8yXfSbHVB06Iy6Q+re7lMWqbMtdWtplPMWLQ1fqEFGce1BSsNUUH5WpSKr6KUZRlXCQBSyqlU4UcPn/gfnuJV19GWKAgY+0JwLVlXe5Y+KGL0Hd4MYsGX1KXK8i8cfWfzm7g/88xXmZsDs4VRkL0KF7+9CpicfQdV9uLNqlxFHhkbIO6YTsMybaPZFlzuKgQuq2Ykz4aaassWt3mlbCkn73zFUpqDy9X4lo7gXSDmJmXgYiSnPIERHryNn+YHLfqPxBNupnqbYaMssTBNmjNLNZyIFYOJbnoxz+uSMBv1Z9x+LFnbrBHdgMKnRwBFl0P5sCClwkLVAcKqv6sa7P3uClaEmN7W+CKGnFKosPjGPef93b4Zzr1oedR446ruMtP4FnCySYvaS9RlrZECxFJb7y6RjmYWYjTJEanMkzM6XUrN6hRxRL9Vt8ym+h/1JPi91CAWrOwaiVBcgPxUiMuQXKKpKSXLl+iJOlY0KpIrAiQ4rr0kvkhf1hHrMSNppKiM2su3XarKt09ff+rZl17dl511glUNv6xJMVlbO0LfaQjtYnEPsu1r+PCJTxqjwGGKNZPr+fSqU14Q2QBxT44boIRLOfuMSNX0FaetIALWKbpWINxcFm03NHyqwEUIIManya/1FR+1yPjb4Kmwmhhm0LSAxqANhJOIMXi49Q9YhU9L6a2PPXLmyYxzMlE/Ziphei3KzixTPxH4+5x+kftq20rkBQ+Dmbzta5NBHH4zeCy1erk/3EIJsWUAACAASURBVJXu0ImgroDRjt9SwXAXBkQ4fRU1yfvda+y7C2KRd4MZbXJYlrPHpgs3H5YrHE5azJDOr9gKH0OEbEmQWvKqIb3IDAe5aWrmM6KGcMI8K7PrjYyTr0TUZOME2PJcD71D3/mPZSvuf4lGBS2RghrR75iUi9fFcclfxGMxvpNmLxcgMxWU25duakpJ4tLVTYsFQPmSf8lgMjQ3mm1igThdv/W+glIqtUiJZays6CCAAtBNL+498b88v7O93WeJhGprVrAPR9/hzcQ3tftO0wj16TtuZ7xC4Ct5lB/5vKdXS8HdEnG3uZXWJs8x0wZ6sQWavooIxH1T3pM0Bljy7h/IsoCfWRHgIgAXn0klQeLU1YTaUrZhQB0zDC58jOD02DRRqmkUQUNev5RqkQm/89xsdP6hsWxUW7AZM77K+fYSyinrnywcPXqiNCqkp18S04KuTv4Km5LE5dtBJKcBWrmOkZibuEGIs7Vpm6yzy6Qjx+Rmk3XfvcRvM8kYBETpxyRedTtVRrrS2WZbu0678kZhj+sES+QvHGzh+2v0Z5ixpkQJe/xF6eBo2ihRuzVXeDtOIuU0ak13efQ1zyYzaw1YK5a33s0N4nXMrQDJiKiWoSPKLS4xvdZL+XCXXEiwtSOdanliqUjk5cZSZb8xThTHdlJDSoPlaOWRrAnEBSXfSmlM2XkyRW4KohsDEXjvyfe/9L3LNw4S074oCBxYUnnFCmtrd+i70QPPdtLLkDHsR54bgJq3N+7pVX68CS7K85bmaZ7GNNVVRp7sE1OwyQJI1WywZ8OKxSDu+FCxTAu5uAKwWEXEgcshRiJYkdsOXElLWmh4ZhW0FzMyszITk4iZ/cBV38Hsphw9mZVWSPxM9D6ZEk2TWqLVr2SpdfYlmDqOx+SNCxbuqTdjxjozV/LbNSMFutCFeO0kttlXmqhrdIO7b23tFP6uaoSajupoaKeQXennCgkduZMJYkbsZULafKFallMztOxyegVg6xSRiVyGjUxjZYJOSspb12y9o3gacXdci1nMHAwWTXW6rmrNi4GCM8YiSCzItjipZaidegl+y2a7wpltkVytNTO3UqJVH7mDTH5occ4RaUTG6k3mLgoa8jpxRYkJPYuy56Y3b770ra++1hQVTDWfhdXoO2ffiW9k3xaTZO99OoVu2V1EwX6okXXxsHWHYVIt9bkECgLb2WfPnkU/T696Eo8wvWV005FOLCBYrxBxDaDxbv/YJW4kgqlJwzZiA1DY6SirPROxdgyj6sTM1U6dGbXgMHgwwykjT5PoNQtmrSGGvKev56lNpZsZqcTKl+wKfeTh00/N7q2ggZFjM26Fsq4ITN3IepQC7bR3vziJtRFbAdaKNS0gy05pIrDWqDyEF/ODLOKLhVxWvA6MF1tINj5sWXMcd4enxWhHHtOaedGEIDwmrz9j05r8jswo4x4KmthsdlexfmWaMPQbGCslsdPOEDNc87wUmBcVbb3LCBS10t1G3q0S47ZUFzmixBCwoZyMYIdLFJv0wnpPL1EgM81SbywVEKmlIDVKv/O1VGpJqPSFU7VK0E1XJCDEXqPAoiFRcJEusYSkyczed/Bn/+13rVR7nyyy7EhCgTZL3zED3gypsBfgOPpO1xtfBxEIhktvCCB23AnU6P/0ahXiTquMP9EMc27aT1jbPCwroGUNgbKqFpuxdjW90RTHOA0x6lLMfH++p1HbmJKcVY1WVKx81QxAw2fMNus1QMPFbQ4t1lYn1ywJUss7zsx2Lj40nrpzMiXs0oYzE7tZ2KusHFKPfvZI5KHp8j/vU68RVrumhrJNFs2D0tbdhWXKCBaXyzH6pQuAbNwMYTfz3tFotK3n92bxob5ltNq7otO63y/KrIxeCc1TctsIRGgvT5rDW2Lst3y/RPOefJmfqWk6JRKLxjFn3mVXQv8Exsv9NV0pbMTXbYiIvA1ZjWGLpfApcekC0ZeMc6nXafa+IiSCi6Cag9N39GOfGm1Ru5AEFwwQqxojk+nWWkmxhmteJPn2w+e//L0rNw6rLE+ujsoR2CCBJT74M6OlTIJUcBcB2Th16hSOgkmk8AmhLaWnpjA18vQqZvDjTcW/vUpFXss5yKIuHjPiu9YqGwH+5qKknYMfxSZhBh1TLFM/NxM0EEuChiXJy/qVVtOADa5zq2kIVQozuAV3xtj4EcgSU1Gougr04N50DZ8FMp5ZP/fw7JX5uzyFM8SYdaNFKiPTkmRPfbCYYqINdd2jyS1TMsP1SMlyocUjMrIOkUS1BaKRUyFhu2tGMRJlux+lX2hFm72THIlSr+I73traPT7ml1cYNpsFrX1AMtubMkOYZzSM7W8CBV6Z0OcnlSiQjlYlzWk99a8VEFhwCU3maZRQ/9aS8uGQjGPyt30RQblwFLYp5EqHhTBVrTMaJUJh4+GyGDd6XyDFVbtcRReaICr0P7mykCwQpyW/tdaFncmsGnG1TxotgQuZ4KMrS7NkPmm/pMRr5qJgIl0Sz832hpe+8ZVXd7e3ey1sh4lNWYF1lmD0Ha5MPEuqqTT6moMPxsQAAcph5yCSE11TdWfpxQCeXsUq7iLst1czf24og7jTBkB0vksD0LV2Nz3Lho6m8WeR4PdSwGVM3bUZ/3urgLaYw5z1rUQcaM7Z8D3+dLWpMeMajcMXH9pxksURzsl87OHd787uKxYryrVZd1kiwdR1eeDoy5TfrsoSXS5Y6ypeBGspqV+YxSffkmIIlvwKn7N1DJOKi2XZTNlNjZqUOWKJLOLl25BSlH8p9EAwF4kFaMvyh2ya1XjPzLKyWlctAOJln3qISApkrncMRC8FoYQEkIjAhlJZRY+MNqG8RqWzNWCnC250kmLJenqG8qLDxFy6DG3+jg5zl3+xDnDXsaHc30a/gmHMVkYwktI94t9C96OCmpG4FLsXlyaULBwVS3yp5pUyCYGqUTNR7UaFHHyJ0VloRMYalsxNFNRSo/n0nXt/+t99cyw33PpbpyjY4L6qkPcW6XuS5cLToOwOP0BwgGzqfgbvsqdX2ce8cOECZnD83VlIRG417IbDUilAaIJxwmvhQ8m6NxWnvfZGnsoG0K/gRiDicMPtgt8PVLawgwXBiuBww80wEtCSkwVu2gXLsQ1T61vyds7J3PPO6aTONiRmsKHEE5GMHSBTitYOU0+1PwAeKY0BLtcaZW5mNQXpU2c02kL5idNZLRLzk7Vg+KpXfhUJo1z0egyH4+3FeXffmpqRE0ACDeFSBiZwU18DNEnaPGsWQzZvhRBNfCltyRJqmXizUogPJr5k7bBEl06P88IZmDvQy8Is2Ecjw0aqFIj8QteeaI53nkJERaJh5NJJJEiumB/lWqKkak9Hl6kvzSVtuUxKqMqlKVRzVb3EDJJIWyQgX0rT49xF+vuOn/jdb1+5fihbt9jJKszSvNkjK1gi5H06haFhCWyKeNSgEF/0lNtdLd6MNkCQZ/+OmrFkU9yVfiFgBrvvoAGbN0uEuEPnW2CcQIYFIfCvq4Mm0zH+Wug/gjFOK0Xx4o73V94FSZpDA4Nb4kADOpRTCwHnK3A2UEU47HCt1i6zxAZJNQ2U+twjs5dn5c/JsLpA1qNz6ixr9A2gMMOG76MyTevE4q9LUS4tM5oUNUqX2SYc24GGcly60SpcXWsizGc1vEMXfp0SDbSqupLPqq4xuOFs6Rq7yw1bUQrkyOFIFTdI0zqqVjy74CQywYu1PrdgYSFLa8MSANFUMUohSsusXIvhJEbFFleSaOmajV4Bm84Q7i7vi+TfojIdrCJAkWU2rwmxCu000aolpV8taG8vLqPElFAkGxcRixuaIswsqUDbo+AYQFHlkcBKbv++yaUXf/jtb7y+dOCCxY4/d8PTmiDNkUV+XzAEgtF32GnNVRhGQaPoNroTVf4sFHkThjdjDCbVtMSv9RlSgECgFWfOnMEqDs9s0poMA1tJsuFYUJVxdHqagBjyBPqMniso9a+yDDc+HW6ABgvH5zzBQTjIGRXP6grEsISG1BmouM7HH9593OOcDAtN/DJ15l3+55xmf8DjpLrAqJEk2ixcYPKPZ1Z2s91aqnjFSzOLcQZ3+vHEZblVNJw5ysbdck7RVUQ7hcSAWzbSReWz8oS7d+TMTHRQomS71oor/bWmq7tZgaJi1XonC916aTo4KvhJvVrj0ku4CVzCeDNgy0iKVUTfUo5/BPmK5BcJqljf9u6ou+sBbT1zrNB5SURHPG4WwCwTdCuyqAezY2avNujHovAibRGTXK4CDbGFXolZ86UhWoU2fkkiEohye73t6eHFK3/yP353MDPIloSX9rxZuOusgMuKS18JeVf6DmlmOSb4q6Brobms4BQx5kYX+BdPSRqBMfpe1pKUqjqXVH379m0axdOrS7dcdZQWl63JgYqVl8pdtYQ+JpFAxFTR0wR6uk5nl7LqLSoMaHYvK1OgBnDDyyvjRkHXC5vFhIYwUJk4KpvxjrOz0cWHZ9nnZDj6wg+UMpVwAAavAz7uEpJMfblaBCPfXE5v+YoBIqtWg6b4qDa6Ke+TkB01taZJk4oxNs/vgtOKJev2mJe4k/iUUrniFpbP5czMduH73currFjCmrcYPT7ulVWVTmgUFk34WgU1UkzR35yrRg1r2RLjS9LqFdwMCp1RSiNKTxCMeGt/RBridOkxIcucnJEu0+ojAMQS2QSPKC6Ztl4lIqpaLdeiJikzn6vFRpJLENWmZSHkqtOIGNbg6hYZZg2TqqJ/Wrd8RAL9+eynDv7sn33r9uGk6E0yypknRlXrLOiu+soRW4I9d98dIaE6aK7/5rqPeUbfzRKsKnUj4aPfUwZmwnn3log79K4OB/Jsko8Yp6hBHI90Y47xBhaYFw08Hy0hZDCg5RoLrHZoFMiQRT8SiJjx2A+YfBKKC/4E5n72Yc7JPBI3fD4bTFlFZPIGPP7nTJD8lqqbcGPBjn/L0kVfhzHbU8uSd8nyr//p8IlX0k2iZmPBcwQ1aii4KF1Z4Gr8QisVqAS4VgKnMrc3uO+eaqWiUMpVdD4zP4f+0bemMaW3JJSKPs4caytZPKC4OUoz9+BuKRCwBLs1diNQGZNoJpKvpUkpTrdfWQVvduXMEuJam5BoZbm6CEvPaLqYTISvqJdk7tFcaYVciDNIXEOW4StCsbB8a+aSimRuiLi0Cj1io9wq2tVC8fuPH//jJ1565loRa3fSUFVaC1Xlc+P0HYZmNxJEIGzOSCJYCGUnArxNEzn0EzZO31si7jZckli3FqdquKb1K5XyVwb+3GB0szUbMivaICaZ9qwmGnSgZ/MTn+C22QG8amRzKcwOuE1qjvCsbrA9/+Ajw+8Pzs76/ECvzqKzUb8yU9fFzVYeTwOaE5OVbLF0la7Hp6gQLVlzooAfxtHl75zkZaE2rmws59rZhgmuDl4OGv0CsXKVjWF00sK+u1/jxH1wqHV+K6xM3E6UqseZdr86HPzrIl1xFWGjQnTCOG1ivOYN1hVgFsBSBEsU9kXiinx2gs0T+Vvv2vM6Imip8Hf5M512sjRdAQAHveHlkpwYkgU2Oh05nKIi6iYqnmW4NMeVkPqWguSKj5Vu7JKW3AtTq1VINTqVylXv/snrz37/W196Xh649AyYavQd0mxxz4JNiGEJah19h5kYhtCSsJvra41P0ndq53JtkbACbRB3wDVPDWt6pjbqIrgeRYaq4V6uX+3Bi8yyLSdCCluDxadpDjp3k4N5OCUPrPgU//GTYZrAW3ya3x/0BtuznXvHW+cmMhsP+tOj3pOXhx8///yTs5+Zyi+D/PgE/IRRpUufV6M8V6doLKhuqcKjGEKUUnu8LGlWyMPghgxIsCZgA48BKV4INmRQrJZ9d867z0c97oDjtErf9bFVOJRNLRkQz8DidupI9Wta0p95QUWrlmRKNp1ollBLNQ6p/FNHaoa3lUNSwa9+IxHNR/K1NDVFCShWJsDMHL8vUpuuu+7SgJjEKxQyqcQR/ZYr8RRppcUlwUwmJhcZ85D6VlLIRN1nVhmXWT8ihmIY/7QxvdOz/Yuv/d5vPTWyXtNc3w9MhUFBDFgQN8JTnaGO2nF0wqxSmF1+qxGj75iEMZDMNul7G8SdOyS7VQoOKqOCgD8RTDm9SABBPoNXF1YhljMGwuospc1BB3p2Pw1omNRQZ3nahgGMhM0is9bU/nA+PDXbvjDdOjvuD3ts6EyP+ie3tu5e2tGpUhT8v7wW9+5T//qHrj0++tzRvPZrLqstrWtbUk1A1sOikoWZUlAHZ4JwsiC6Bq4tnKiZUh0hqbSA4bNoRcLIJqIyv0VAZdzltGxMQQPlHZEn47Endy/T9QWV5mWBVGJdkMoS/dVw3cs2qSUV6fKyprpXNuxKuK5bV2XMFtS+bo7IKirDWQYReUWqs4rGewling2NSENkh/IBTli490Xa1rt2hKtWCuogVi3RaF5gIxPWYoiLK4FBbKlcLjmUWqlCuTipaimlsk18mOb59uzop2/84X/95/274+p1wU038toZugdKYBwPYuA2YUnvwjEeTCK0TN/bIO74ozl1fcek8+gt+zS1aCa4viyuwgZosUxrue0bA24WqJpmMg6Bzm5y/DFsGh/GAHcRfDZdkY9+YLFugqmPzky3z0+HpyaD7f583JscDca3R4eXz3COMi/84fNbL9288h9/9ivP7nzmyuxinphvuiwU1add31p85HS5SxrjY5Yue5F29UC35vlUmS8Trcz5Am3liKuwsDezDC/QM+zWeoIY04wpJfE07j4Z9ZZ+V9XHY0pWlCsu2FkwSBbkK7dI8xk6htRb2oQip106yyUHdEJOsYt8CVNLWGuolyhArerdmTvYCZNyoov6xNzIZGQT6cqu2WmhHn3hu7ZHJJWxI6pILHbcxXi9m9BvuWJNiIxUzTLiE2NRVwyR5V8cpMSiSJwafatBSypSAvUvB73Zo7f//H/68739Y6+j7cU1CkuNX7XOHh8soli+ci5UmEBx8KPG1f1s0jEA9LtG382wyg33Kdj/4he/2OgOq8FarQrKQjT5tGBdiKMQdCz4NHBJhl+RxQOqGbOkKMQFf17xvN+oXBt+7wAEMUIeep5nQipbUqpgF4xhH51zLzsXZiP20EeT2Xg+vTs6uT2c3OEn50q1pveOM7Nf/+To4G0/9+rsQVtCypVfSMv8v7jaXAwj8CVGZbEJC66JXGJvqrhUhVwfYyqorVYklDHiKsIRxIrKNwOhjKkGRdJbeVZ1OhoucfeKSr2KGR8SvqTwpcpsGJZla7pojJun1gxx1xJfOVfAIwIwuXx3bfHIoJU5yqWTA7+IyYVNZs5b0pdUF2ty055oihM1uoSBE1uyNDs1FinOjaXKfcPaf+bwq//iL578/edKHG33rIMtNggGhApe4VmkWAwEjIUjhk7YmjiBR6CgnRrowmkLMKEVWN6QMYKSBya1RGjA6q1SnkYabMEJGNEM5RZ0Le+ud8p/zCJ0pzF12kUcvzHXD4Xejxlc6eb0e9sXJ9sXxsPtOcNidjKf3N06ujYcTE8fH9U65X/5zuC3vjH92x/60w+/9+NPzt6frrfEtewKJdeKEkVriy6tSrGD4WYpxYt51ph6Oj8lHubSvH3VmDDaS2qpY0Z0q6PQCXIr8Ja0JYQG/yoL+5pnVcOcd8+3xy3xGKLI5RrUNYdh1NRxm3xIyuREf5fwOfGVC2yZ+tbK0ocAUxGZaJNDJkwqWhjs0omwQsLcxVfio7YiKBVatepQeomK2BBnkRDKOFGjUpX8pwExvtO9mp1qJURbVpk4t9r3e48e/7PHn/yDMg+k+lcEZaeF0CqKEDf7/Ys7SVgfRJFLNEDWKzAWCrINGsQYZ1XlCPZjDI1q7l6iceJugyAPArclbALWbf63WXlqf0LSwTbF1O3vCZXHz08IblEzlanv3HMy2Japez6dj29vHV7emY0HyT31nZ0AqOyf9P/nJ7b+1tF3fuHDN78xfWw8D/AnywBmrVOxWO5yJG3tcpk4JMFd/sRGGICeOMhQNcDkjkxIRLzwhwEP3fK3/LBKU6Z5d3j2mZmUtpKXxq2MIZVyPQEb9/U2vqRd5cQxxt9nyqkulFb0kIhO7MRgYIwCE1+v6GgLOOmk6nRWZid6WIxdMji60HR+hIjWUgtVqf+IW2jryZbS1jMaky4CF72dsCw+td/kin8y1tafnInK5IPrPXtIlYXhwZMXXnjm2//PD3khTKFcjUza7xgz8VLHGSDrEBhKwXTr8H5nfh1jnJJQEaPvOFD9G5tVk5ol7jYUXK1c0lUu0Tqsob8muEp/nCLcw+HoSQBh6v5/0HirQIFjNGEq59S3zk1P3Xs8ONXvMWHPeyf7ozuv7c54Xie/QhAGcz5rmkSFv/PM1tNXX/71z995cvTpm7OzVRTq2lKloHeZ3PUk1iC9g5AhFm6NidVX/GZQYJgbGhW1BCpWbIyu7loTJMB16Frcq9pGJc0igyd4G2/cvdZ7ZgQ+gtRKtYqfd/UpDJWCGRdL5bR/KZ5gY6pqa3xtVgCpJEJQiqWrNK+UgZ7OcZVIaXfRaMTGSGUfFkMFWJ21YpslIU6HjcMcZetdf9BN2qwN1+lEq9WydklUMqXpziJdrhZzD+lUI/9pSOVa4pq7Eau1JsIPjl/qv/Tl//W7vLwuqrS5Lwy2Pea1r51xm+sYA3Vpgr2YMXSSvXmm1L1EcIicMaHouwHY4Bl3gMNWPjHd4OAWxEJwdHwU0uDTp08fHBz4CLcgs/aMO9BhM4GI2QN6MEiHZ0Aju3Cs3DWHuzvaWJcrMw9vyROlOxdPhruyoc48ON4fHl3fnp2UWHLMjZmVnHk1I49emP5Hn9h5/Z4vXJ1XelxVloUS9q+1dq2uJX9zvqh6yVpOWFtbgwIsqW61bLAab9XOHmVKWixe8711BBNsvKfWulGiKZyZmZQ6775AMKzvi02NI5NouE+0IXviRbgcgPFkU6Z3fRpZSaYOMlED5GupLcl0plX/U+8JTYspMMHXpaLleVqvlioXFFJCkpQIxbkJwXQU1t574Q//wdcHR5OVKtOyga/Z52IFh5G7FZxWQGNIpyY6kSw+A9eao46q7bzKZum7WWfGEK/w5wXQMwCNBAYj7thEQDWfZiV9w2V3zpTzZCptDsjAclzFN3mVuCtLlw8HoN3ntODlnSLuNsgrjDRe0ajvfpkQkR/Nm/Lil8Hxze3Zca3JKzg4F3fmv/lzvcn9n/jh7L2lJ7B4LfX1syy5YjiEJsVmueGcpUbSEO0OXWakrDU4ryEB0xU8xXB5JQ9YRQVVjYNT7FXLFus7IocF73fXlR0gs+nOsrLaV+btZeyvXWWRgmA9FflgiqwWVJ0BQTBjCqr1z8KadWS3QJk0L/6XFEukMX8sgoirA9pR+JjaWJrLTEZScd14XwLVlUVyEbJT4/zi3Fhq8f3Q5JX5C7//D762AdbujIC7EyAwGE+nQeIdj3cyrUVgFNB3DGhig79sKwDEdt8xBnwKiiMpbF1PEyl+0Z/9Sa9O3IVgarCK6RuCEU1LMfu6Q5TPnj179+7dYqQKQAyeBR2k54AQoByGmLcR/w7OTevA5e85w93Z1tkJB2CG27xoqDebDMf78pbG6VG9H3xZtp5brAq3yMs60lfDfu/f/9DkZz7w/u/MPlL6yHsl7r60fCybw8hdJJTkmpR1DrxQsrmYrOsbql1okgLpAOkeOA3fZZWBPr3vHoGnxIweLKMqSId3qrMqGhNhKHjEq0pZbDJwxxhVmJFVVnsQ+YrgaN1RG+RrqTmJdMki2NIsMRVOfaiQSfJpwSQlvjyJLtJVzvRZkcVnSmiREZVZsjaVm7x85+Tlu8995be+cdL+Xjtm0Apwg2sSd90UfPVMttc/jmF2L9ER+o4xwLVK30kEQKwFwExCiIDvGXfrD3NlQ8o4OizTRvUqfNS9EQ66aoml0ITNsnbrDz4tgA99BkSbtcrAyevEPDAbTS8wRn/2iB8onfWHM36vFILOOfW7b+yEZeqp1tFH1lmp9DqXnNz5358ePXbjh7/86YNv9j876YV/XLV4pl+ArO5YuS2URhWflTUELljprqaaDSmatLxai8qugYOFzXZWmdsmzrtvH5/MR6Op7mcq2RHMqvVF/VKd6iwbUJ6+LGKGnLhgzZDRhbExXRnmdeyRWxCcTFoZRTVBp0Owi5ool6zLVGR77QKr5MrtELOdykUR00eSji35EpnoHt5ZGqWrMLpFgSZpQpQqyVZgkRrH1L7c3FiqB2ufvvDlf/xN+YHAwSzYWxqd/ryIkXUznuUyydRJNIaaTMzT02g6HWoH8dmJI75Z+k6HAoiBAz6ARgo0gxQiXBabl0vckz0BmuiyphLxBBcN3dlux2bzKk/jg4g5DK1qACRYD1mHEQ9SURAl5jFBVNVX4jprsDM/dd/x6Ox80J/y8NDkcMAPlB6/sl38RGl9A5Ia6DX721YysX4c7v4Xb4yOv/76rz72B0+f+sKN2TlfncKSGYYy9adCRpJKLHYzubQlIyDVzqs1ZVwrl+bGzn/C1imzX9RYgc+HJok9XTpN1Lg92Y656IfkAnKys82+OxtMcPeFxOZijTpPhWYJBcy6KzY3jPwKL6wFnm9hGVPWefXqq4BDZpE6nSUkJuGI0jQHgxEcoU8iJD/VJE+tilsjEmUKElK/qlFDJCZqRO/iBicVJ9dkRExF3SVXErTWdKJlxbkLFS7dIg+NX5y++Ef//dd6hxN4hfyOIQEOZi1JCQe5hGga1zSimVkRicZQMcbiQaqurITV3Og7pwzW8uPKtXgWBBBsgLhzL0GvYxhY+ZSNjspQhg6AaLoyjmK6lLKR1TPcZTUElKdpu7u7d+7cCahzVRW12M2lebD1Cp+ElLC5e/FNVapIo5cMKoxZtbPRSjOV80TpqQeOd8+PpvNjJszpYe/4xs74zmDeB3moaQAAIABJREFU+kM2SfMaPUp076n5f/H50Zv3fO616QPJSovieJSuEsi4FScln+zN3KUgVabqJXU1XUUp00LZE++cGUnPQ3q9aZ3i7kxG1Vuyvq0Z2p0rCj9aqXvneDzeKjrv7lNnQJlQzhPKpMge8UUBTz5XMFxXV+kCBQo7ik+BxckstxYriIJL/M9JRWDF6cxsFjjWKthreiJTVLjL+EK/JdWClVqNk+LKRqKZQkt5WmihW/MeHL/Ye/HL//Br/cPEQkmttrvsSQfTtWRdoxOSY1wRzlBq85FSMI2N02XXLGiY7Xa3z8SomoAlSaaNMSSuRZUu6P/SL/2SMTbQRIVrUs2I2VSqU2vWWFy8iSdTQQ9HpKVEbGVK9kGxPfQQwoRisdZyN2UPx10G27Ode8fb5yYygQ76s6P54fVTvbvyG7fdwcemm+bsubAz/5UPT88/+qlnZ+9hj2d9v2cRZXPCqGwmRVqvt6JEp4gpbahjj8BoPSA3Rx594YFZHXs81JcWadwe3SpecKJ1KO4cn4y3RgXPqpZuYb0CXeGm9FPUEH04YR2MKuslVA+epV3lmqqCFF/TX0rW4xueDHziUb6UJRdRgkYTp94lJ0rTaCzm9KxEkmLE116azJr5x81OvK99/uKX/uHXhkfTyJSovH4ZXV5LB5NFVuPwHEgC6UCNqjpLoamqac+qhZVT2rSHugiYCr0hZNqMAFCTC0SZAvR79YdTMzW6xE5tt2PVmTNnDg8P63ibgKVD1+410YkHo5BAxDXcM9KdHW4zGEehFXme5NkoHzFepi7n1C9Ot86M+0P+EtmfHvVPbm6f7HOxUADIhDzHXci1FcMYhlPAfYtVw0eD3i+//+TjH3jvt/qPTbJ+oSk1JYvXsT4Yy1To8M9Vta2lYM9mDUi1tJQ9CmakIGNPOKW60mUpeyrVUK5QE9zdjWBxxJLOuPY9M+WaV1t6g/1l60kKQxvuiWaVxDdRMkhU8EHRRuecpYbIGF5muoajn5EJtBfARrE4TxiAhsRLZriWCuJKFpEEMCZjxubFV3MtJdWkODH+HvTmP330l9defOIffbNf/DQqS7wtYTCWuPSab2ylCAE5oZn5DzSuUZSVXcGeLDXB0rCHVd6aGUypKjIYUc6Vv35gxyTkV1kQCnPPuIc1fePaQM3fX81a0KEUgYilCEkP9EyD07lxZMyA5lYpmPro9HT7wnS0O+lv9efj3uRoMOaJ0ktnevkTCPaAfEfAwQz6nXutRu2ZzHr/5zPbL9966W99+vCpIb/QdNqqi5xPL2QDzpGjxHJRliQ12pCOKC8eYhGO8VJfLBykRVQhtQXRFUIJ9ydBRr36o3ygcKl1JVvLs6qnjscnW72O7LtLfy3Oo4VAvEBHPKwFwDSOmkSy/hFDBZZgLtDaaJYNmSAuFMZO7TD20qKBBl7L/lhci3hr5LELx10kMO3qJolulPT155roBVYoE2HPghyrOYroXcMqSEnAiGOUyZh1ydzYXpVKCsUZGDz70NFXv/KXT/3us+vXJtvhtlWMHSir26lKRpSrR+cIKEVI5oaKm2bsgZ4SZ4UNpbmaHmsmxtjr/mDM1fS4UnQZ2vgE6kz+7SQzI0bx6YvMs/iNEHcq23g3ZGJRnGgcHeNNDLhpBfA11JaCkVNsZxO5GBOKKLOPvnVusn1+MtyZwtRnJ/Pp4YgNdd79wpM+ngF7Mqcqz+JNiJlJTfcaj6te/srV3/j0//fCuV+8Mb9AQ6RG1ghZIGQtkngcWO6XruP0jXzbDNWpXkviI0ukrpMKoSJJSgLMFkCLurGFmvyqqNZlMcmMfFEhzMHRWI2fMUgd7WzpeffucHcxvam5SHEU4PiXGthSbUawwdWUPRkVrk+q5kLr9ZaREECQFxglSFEb6WWUSCH9p6okqho10S7EmWUulupmsjzJJKLV8aEmRKWU4asakRU98VlaE3eJqlf8y2QyLy0xLUTq7uzOT9364688+ZoPa3eq7blMDkSkqCSWGV9HkqxiZh/ZFOKLiqja53YiRG3rddjtBITbIAKK9WWWJeBRIGldXp89ooGATug7RNT9zb+RozJ0A+03f1xu1GauwJEz7qtPpjqIzSwMBhpCC5Z37SgRfoazOrco10/93s7FydaF8XB7zp9w5uP5+M7Wya3h9Gg4L+32i5obfR50UY13DHxwDEaRd4kqgjZ73r87/7ffPz/3yMefnT1SQM2xh46rUs1PRhmhRrqYdgclzOlah/l4kaIYOU1p+0sW6NqZGZotfK1kK/JGmLI0p62iUp8uyzOgofS2TTIcrTFZfVPHnqhX5Gupg+J09828wp/l5VPZuchLnl5rUbt0SZHChL0mSrpkJdLzUiKp89Mb77n5L/7Rn+49txftM1oB/08WGlY0I4VUDFzCEBte3QrMwwZ4I2ZUJCEFqqtmgQ8U0RMWW7ipCgJJERpStdrcchhjJAR23Qhx7xorhQICxvHxMeDSeDc+DGKymkA5F37N6BpENmy4HS82O8rt97YvTnbuORlsM9kw4nvj21vHe6PZycB/T31tRV2DCIObMMlGo/kkfuiG/dag93c/PnnkvR/89uTDvAgzEy7knTNnCrSfuFmT3GYwDXc3PF3jypuFKNMlwM3BZQKZSGaWLUqMGE6RSGbeqZPxyahD75nByMrcPbmCBxyt6kXYVRXiTNzrJTbu2A7KFYabbbjJewqvqFAKbqkLkKNYIs8YBT1rnRt/UtCiLqJl4vIJo5xYVFciS8vKh2XJJ9v575y80H/lT/75d2Yv38peFxbSOTH4H1xItGllUCP6Lke21WRbDVkE7eBKq3XnVGb0HXuwalWkBb6eqhSI5HxRKrX+JXo74gQ4pTF1tttxTVgXd0Id+VNAarDWh71RDf3BfOv87NR9R4Odfk//IHGyv3Xntd1GX6aOF+FLmaOl0cY2rZyut7nAKqKB7DGsDpnxrPdPvj36/JVn/q3HDh/vPTbN+oUmVFGwW76UWGOaRtL0C8W0RUe4Z2b13aLu0msrRLkdrPJqMdwURwAUuHKQzFOg6ZnYF5bIyzyS8+4nJ116zwyIwG2WSVWm+YJCcjg3NDxNbaeGvzh2E9ORoimweqC/1CUm79dtSwX1QoZBNHMsJhBiBElnmKh+JnAaHj+uqiX05IyBIcIRKlommq3NQVwfqlQ0fFIQKqGO5/id3gmPon7nye/93z/Yujsux9qpDMJnVcKCYETUahVDjWwZspQNfmKGe8k6Rm7wLwAOBMwgLlw5PosPhkYsScdC3+1Op7FexCAKT9xpkjW1nnmlS4MmAapHcIUNVnbcj46OXOK/iqwiYBNJlM4PTmzNt85Mt+85GZ7CN2UqGd8eHrxyWph6WwEHpSv5bKtCr3rwsSWsPApRxMY5EcRpEW7p8wdB5uw/f3U0mb30Nz92/Xun/srtefS4qkedGxOB8DXNSnXttAVUmLpQzEKvFJOa4BM1MBaTusHdIxz15sf8E6bh1TI/KS9VWUJHO9vK3bd4EDArfwNpuFoWCVyYpzODIBoj2biRVNQp39aGCww1EZBGxXeQdXXldJtP30hf0hbpURsoUV9zITFpp+bxl1IdzvpTTZISs3YYN1KRBZphLUNCy8eMXHQtxU0JnxYk99759ffs/+k///rVb13ajtPXfGMHayiMEzlUZO4QkQ6JRwya1BGuzBKJSZjNHQUmdYEDAB1gsgUMUJjE5UZYruvv8EdlmjhO4MxNRswpwZGIpdPBFpJixM+dO3f79u1U4mYvu3WAu98b7sz4zaP+mTvDU0xCnH4ZjG8Pjve2ZycRtu3DRbcydH0Ibmu24WxQ8LUmmWciSYRpkYBb1tk8ePDs7O99dnTtws+9OH1wtbEdoYDOsGhVc9e1I7pm2sLJQlnFIbsGEZBsyqQIR+mUjOMxRfBWAb5u33eNuwuDSzAsYUNxE90yFCe0971kUnvVFtVUzSQphdZ4QS+qoHxeNZOsHrVqEXWVR2MiNhgfsAArUfqOIAn6KR9LkaWLSJGmJeNaCvL/8PjZ4etf/e1vTd+4vdiXJDMz2DrFJ01m6fGnmCy4tsBRKlNz+4n2V4IN0ncDk14ETBZxkDGToAGktA8INYqPffGLX1xLRPyNQ2FzZAsECWK0DgVQIwAlnwUWUqSF30wtMCAza+PEfbg72zo72To3hbKzXTAbD+cHu4e3ZtPjxbSRaXmbia3dBPo3KrPjcEjczJg6qhjbFoo9079SJLeHvf/0E9Odd33mpdkjqSPv1GIjopTCRoWDmCTo4Yy1d++spUFMCgtamyZxn2DGF1Hz5O1EZ6YBfUfkBs+7rwAhfgmYkt6dcdemL3mOAl+TdPkWQGP666m/gpivSVmqtb8tY8kloovYeC5pSPTIqsQoov/sQz4lxYmvRJK5Inp6dvC+O1//yx+8/Hs/GtzJ/4u31hmtQaw+RjGlfMmAHogphTZITFMmYxKsks82TWI1twUdMLltkEGfCBsxydVP7YGJO12+2khXX9kIwBlTt4JgV4EPQf5opjvRVdaGJuSt1wPeL/kYCVPfPj/eOjfrD+WnOafHw/Gt4eQu736J7uDfKizZp7HNyTiUnHPSm3imTZSp4R3WDLg7v9D04Q+8/zvzn01y94hEhK1sE9oictnYGk7v0FmbaFlunc2ZFDN1jwYvQ9KcSbkorMtohbsvo5AySQe2I12SaQu5B7opTY1edrDvMk1SOBd8tlFMMpTTd1U7Tnwg9oOk5jjNfTPTJIKU0iq1fFx5MsmUORkt0Ju/a/Ls1uW/+N+eOP7B9ey3x6ACUgtNAlLj64Zt0rYKcRTC5VjU/HfrK9RSqggtxSRa1yhxErauj+36gGng89nmHQWgSb2lsFsrjMbKfkNZ3IVAxCoy7PhcW2+BAJ3dtQPuroEFZtfP4tDLzr3HW2chK9C8/vRwcHyTd7/knn5px6pS7arsS6Vq8RTGMxnSNkvik4SA96g+NpxMe//H97cv33n2r3/s+hOjXziY8/yBBEYLLK14J9Uk2/yk79Z6VEwurRUMex340ehvwNjmNFc1FohC3XfpjpB+KJxF/lCIg5jk0XdVW1ylHO93h7uPt3q80LhK+ewyhSgIM4+PWVN81ZVlkcJ/q1PAbKPqpWJU1/pu4U5Ypq0DuFU467W7ZGmFCSNKFhNxGa1RMyTqNEQX8e0cbSXIE6t6ZkbqMgmZFMmRgvE4E3WLFLkQeC7M9t5396tfe+by7/5ocDRJs3all4vfSArOZVnd2Ouklu4cfAc19xJ6W3wd+PUjtqyjh3sVfxaOSQjTW5BMyvoXDGBwfRVOQ9nRiDyeAR8yDaDQBBmiCjrDGdmFCE2mpcEtGWzPdx845vQLR1/6Q5h67+j6zuGVwXyymF8KKgX/gtyNZGFSQ1j5NMf8E/9B2DknZN1tuvsoCStDD/3Jy6Pnb9z8zZ//4+dPf/bq7J5Iv60KYSurpw30Vo0Spm5eZitXYuWrV5tXabgs/Vh2mvJSvSGhCE+hIHbnljPSc5LzrJa+6xhQxt3ntX5X1QMFmh2DYn5SWEZujjoGlFjdIZNiPMWkjfP1uGflW4aMTOsV6Hs0gcnstpjLRJlo1BktVsvDYvpTTRGB10N/hgSzEJVH40xKqSobc4PZ5KPTb+29/vR/883B3tESZYcgGl8qxS/Vriof1EKgUngtpDnuzCqqQpWBOAV87QxNs2HOsk6oZiSwYBJ6jL4Tr6anVKmQR2Wwm8bn9S4NI0CD+DQTkcQtmqCwSQg6+GQqQNVveH/QG2zzisbx6OxEEJU9dWHq4/2loZ6Eoji+QT6aZxjzhTlJnkDYdDyTadFc1NwYf171z8xj7mEtWavt7Wdm/+HHescP/vyrs3ewHqyV34gAGAKpISk26sK1EUs6XqkB5WnkgqzbOp9ZLIRHlLIq04rgiSXfEemHQuSgYqxbm0pZ3kGgNmySQirox2u94blhqzI7NSbZmZnFiZF7aTtTknFaLJI49B57mfmaAqTSfIz6k3dNnjtz49u/89T8O2/IL0GiliWJddAm0sz1KFV1E5fUDj1gKQy+tV/HWmBhscak1TW6QC1toSCo4o3B8US50fdGd9+pJSRxTzE/lIMOgYjhCL4QVh3UBcCGzKL206dPHxwchFRaW1fxHU6e+v5wPjw145ePts5M+sPefNafHvVPbm6fwNTtZj+vpF96F/hoylI8hzHW3GThXBQ/oWo8E/8kpMxIXeLnjQ7LVHV5lzvD+a9+5OTR933kO7MP8ycW+FzR6Yg8LQ2kqzPqzjrjvntkvYvsQX3PzZOrfRJBqhm5vRzNsqulK6YIUNxvhVZb0Zq4WOF7ZvxtjW5/0FoAe1zn+u8OOpV2X5o6r29JZQnq07LFeHYQKLUa2/2dZ4FRVEa+loovLmLyAyzLQYrE5fpbvcm7Zy/s7n33S98/+Opro1lvwJsHwcooE0tSjO6i6vZjrJJGXdYukW3ahkkYtpa+I+O25Ar2l4NYTkfbfU5DFaE/2Bl3c0q7lbHGm9s1ZLonvnQVBngKtyYGVj519UfzrdPTrfPT0e6kv9Wfj3uTo8F4f3T30k4v/EEbH4valgk+y4M8A5hg7lrNRfGoRm8nPFE+nvb/6RM7/+b+9//qx+9+b/6zBzN+xtazaHgx5emm1pw7Inzz6K/J4WusrJGu785NjmuFWrXowARTFzy1Y1d6dyXBaQsSEZO699ec+P3uI37lQZvpj0JEfqQAoAa9JVGsunUKC5PkKIh0oT9EpR1HpugIT69qOghU5EWVgIr4vgxXGyoR1Dp+ddhGQ0j+8ChYsQCBkx18F9x6W/PJu+cv7t743peeufMXl7bm/dPDLX7wUN6tTj4LjRVUIzf8wV2EvV6dPb61RLk1W7EEVAGKsLqhBgMkYAzGt8ZF6TKAgmZwU0G8iXqrE3dxQQ1EwIUo0BA6cndofsN9z+HhYWs+5F8R3bkqzD761jl9ReOpyQCmfjIfH47YUL/7xg7H1n8yg3lXnbbjmQxdp8dclM/KOinLgKxcPGzBL70wenX/5V/9zP6TO79wZ7bjmhm2llVtulTJh9WYx4JI7yJLNia82qrNptikEPEggXbJnOWrpazGLpjaY+LRWB3lFAsKRzs7uu8+8Phtpmii1TWq2cGBdiprto5yWOn9ybo/5pRVibw0k6+qmBpE3cSqwn1OgqMLNjpqo7EaZelcaXDRajn4LrNm/0z/6J3Tl3dvPfPHzx5989L2eHZ6yl98J0tvB4dNsdawfq1S0godF6QINNSsQltHrAJVo++OKIMY6z4WslhvykiqtuP4ZhVmBMHflJQg7riaEvX00RfjQKlzMgFNrKOK/jPz6ihprizn1LcvTLbOj4c7c/74IUz97tbxjdH0aGe+5rxGc0Z1SzNjsqxBOKqNWyIyUdZ4qW3ZqtuXB52nrw1/+6t7f/dTf/DG+c+/PnugORuibXXZwosW7ebqalpzR24n1Ln5EDzNXRe3QNHq3zQSa/RjGYNoYdUa8eDZ2Sis7Lsv1WuTRlViuaSq1EXUg1pxqYKNCgexKoIUQ1EXooFBrAqPm7q7Dsdyum2VEmeV0Sz/XPkoK74DHsyn75i9+cDk1ZvXL/3Za9PvXB7ePeH9GblPNBolhV9BlyHNTu0GIziDUdJOWSXYz+cs/RBlFv2OvEsQSwwr/kxBPBR9LzrjztByNy4GChUTbAyn/KabxL1zT6b2exxSP3NPf7Z9JDPXZH5ysHWyN5qdDDa+p97BM+74GH619o7ZOSoRiuCi9mefTEdN+W21S/4qp0Oh+rZ9tXoLSp3emv8njw13H/roj2bvs6WiQNgzK2LqKl2HutER1jWe9bYjtinurr0jS/tibXcNNpacleNENhJptwczgMlstXtWNTnSN+5p7WKVCUxGolhFcinXcpsmpUplVF6U1E24Kmy9WyMj31Wsk83emZ+8rX/lvsnlw703v3vp6InLgyt3eDNHiamaRQc+BgtMenuyio3EMcnOqLAgbsQAxjtMnU9g4cbGzDCrunOrY8iw8Q1W2FmTvtPYJeKeOlRABbTcpz+ACcsQ3kjP5VVK686ePXv79u08gRbSOa22dWF66t7jwU6/p7c8J/tbvdtnD++cyK15l0I3b73yKDIuR6CLgdActU33o14mC+bQLnWgnJz8az81+vmPvveJ2YftT7YVzANM2S1SYhktQhW0LBfZFEVetiJ91aZVOtblI+P+ZwVlusAcO23xRq9bsWoFi4ImC62c74wnJ6PhfFDxVVoF6mtlySAq05ZalZUpvNYwGf8SxPq2mtCKa2mrSn2sxSpfm/U9+G3PTx7uv3bu+NKbV649cWn27I359bul6Hq6jrAbt2ntVa9ZDWGPbd5UGAlmnsR58rb27FaHXB8SW7XppcvBWzCszk0FrR7ZzYpVTvOqnaPHlK6RGFoEOjXvbEr3CY+dbM23zk53Lp4M+HmcKVR9ML49vP3y6eTL1Hcg8TY7lq7gJ64APol32cxOhOEKBFySvkGX6+ZKw/bNn748GEyf/uR7Lr9w4QtH821Pd6E5IimkkjlBF52grAO1HUSsaavk7xXgaqgavtYfQbE1lS182hoZuUdT9SleRcojV0VELekfb2+fOj4+2fI5716kN3CeDCIxtWG4ylutRCfDKp0BBP2MvPK1lCxBnR2cH8BCNzBoTLkRO+xNz/cOHpi+Prx7+ZUrt37/jf6Le9PDic6xJZFZFbenHjv1eChGQrToRPb+WJcbJV0+fN2BBpslQHEJbd5UOAMyI1B2AmSGTiSChZlixYlLO+7Fonm5dJjhkiewqfQzZ87wZCqe1KAB/d5wZzY6M90+Pxnu8qea2Xw2HN8eFPxAKcZ0c2+7U1bhVARGKQHvYi5gZsfFF+t2g53qpRq4zCov6VaEmAU42Le7u/vQmfF/9VfPv7D7uUuze1drVjoZJQurbCW0ub1dqkEBGUO0MmfytZIwB7SqFBprhPVehIG5Rqxu9rL+xJjPq7rwHZF1raleProfXm5OdXXBShqiZpaR9WCq6yhSs/K6uI7iumV1RK9VsjM/Ptffv//k1Tt715578+DJK71Lt2fTMHQ9o3KWRbDq2hoEH2XDlJWaxSjD6EpJNBMagFpKV74xYL2meDfhKkvfASQAcaer0FPtvqFSP/oWOn/+/P7+vq+0t9xwd7Z1Vt/9siOPiM/GwtTH+1vTY98JulMU2bV740xUWbp84E7M4QxRC908fN8pt2c+grKDG6ydHy5gV+bc9vw3PzO8/cDnXp+9nS6W5UOWH4kh5jq9zYhQhDbr86sLT6sJiO2rZzQtI8nPJpWqb1iJyrxF/TiMt7o8wcTNpWfvCHff3pptyLfz2iHpLUFWZMJSnvJjUsQ9uwdXN90+rxP50/mF3v69syvDw2tvXrn+4vXD710Z3DpqjK0vdaS8yg/6Xpb2Leto5AqSzfrIqsQKXrkCBj5KCGgI0kYUAhcO1qkdQFpHG0vd7dCQAMR944QvzzNCEXeY+vb58da5WX80Y7KbHg15RePkzsifqSctZLDRT/h0MrELcZsC6oy0sq2wkemYOiOK8blqQDfvc2wW2OBxHUMbvo4vMez5wQ5MIhHvAlLAvLA7+Bv/2uQd7/7g0/P3d4Ez/9hsuru7oPTfK+ox9dXh01ES0xgXpb2AoGeL9HsVkcIU96xqodQGMjfej2IA7V6h6Rs3LLszbIuhg7f58T0Yu0oPDa5eHF+6c+vac5cPvn9leuXO/O6GlnRjyd05CmJ9ausj8bJLJAVpkS1h8AFCtpNUTUVzl+92fP5YIRBVbf5SOZtwl5I6cFHZKn6gdOfek9FZtuJmvLFxejg4vrlVfPrFv7mAXtkw/1oqSLYwidN2o5VEsNA21OGd3QSkGMMW4CowgNo5BgZ0bLHz6iQkAZZgwDLxkXvjYPrPvj3/5TtP/PyHb31z9smTeZjBXmBVcRaErJvc3YakQZfZBNtTtz9Z0IolDhSarCcNEMS6+pSjHd9PWlsh7ga+kfWCLvBRXvyOSB8NDcmsdbAG6o2gNV/NA3YThnm0VUfYZifYTCvP9u7eN79y+ujKrZtvPv7q5IfX5vvH/NE9U7a9RKN6tpfcnQ1Bug/KDkv2PMmNK9IEc0ha1FxDYB38RZq10tOw1jqSVhNYuzGMSPEdS90ddzqGQDWtNc+zIpzA6IuP/GBnvvu24xF76ozCYX96t3d0Y2dyd5B8otRHj48MVuHTxb3ioye4DK6MzuCGCZ2MCaU1nJFDxN9+uz9e3Yn319CQ5EYMA4c7d+7QIh7hYIQz+qx1dBzBgE0axiL4M/dNf/3z9z81+szN2ZmGoPBUS683SXQ9rcgQWzUsIuvKT9M2p68zFIZKokPzWFeoKqrpqWyYDn3De23TSgNNgR3OzGy531Wt1rhGSlVGzN8aqUJOw5U7DteCYf5NSEp2wTBseHBw9b7p1fHB5R+9vv/stenzNwe94TastGtLEmsBMz+Ut2uGQQCgPZmGmc10OjgjwGfSAZqOYxW2ZRrWdNXF+osNY0WoS9w7e06GF0HevcuLlzKOWLGJPtie7dw33j7HoyNzfoN4etg7urbD61+K0QySS5dgVaZhQfRXVmL0uv49GEMRVUlCWVOnKWR0VW5aQwVbNgxezsEYRhy+TYuY4wA205EY18zgyb9RvvvC7O98cuuNe75wdXZPQ2h4qu3mprvgKYTHWA9fuukn13EozSHjgrW/6ei3PHdfgFuOU1a70evos6o6ZhvoSgFXyTquVtFNO+tjeYfLa4+qIgWcXB/Mxw/13zxzfPnOzSvfeuX4mavzm8sn15mHAa2DqxKG0bbk5F/U1BbzWJJwfmPnMAQCl6xfG8cQw1jKQYwObRGP9VWZYeCTWuXBLQBx76CLAEnygHt/NOfdL/zyEQ+Vwtrns/70qH98c3u8P7TFej2E4SToDBhYqifCqa/NgO7pAAAgAElEQVSuCd8lVCDZeBGDkLJEqJ6mBW8gk1E33Yw9b/7uVh30dSUNWzehcOm21YuLrhp2fnv+9z/bm7ztEz+cvkcfUy1W0FQuHEPORXQmRNhGFi3z9c6YiZE2uDoDW2RIkWHVyXqqlVW6obPPqhYhlmp30aW4LbgoX6+CT4Zu09ilsbkwUmwL1MyF0nSMU7EX5/v39K70bl168/qNJy7Nf3R9fpJ/xJolzxamrq3mzBUYxlLOYpFu5OaubS3jWSx8l9V843w9iQS2QcxIsfuKZNbG46v0HWtrEXccF8bWqQ4wlGHqF+87c7K1N9qd9Ld683F/csSLX0Yn+6P2mXqq4xlRHXQOjMQZ7O9ZKYNXL+l3CxRhEBKCM/VUpas0NCWwqcvgvQmkYGu7EaDKX434pBZSSrXRNKRuw4b93t/+mcmHPvj+780+vMEj7zgMzSzVnLDCbms9ZYQYxq9YdS+0wlsqNnupN6PNX1EVtIurdMout9T8+Hl8lqxi85ooVrk7wVrtCYrtUguXenMpZ9MXlUFbZ/h27+TC7MbF6aWjm1eeeu3Oc9dnr+2XoAlMzoDWQRZkxy02vpGMr7IYEUCJtYzAAgcZ7dp9BW6Cqa4346G2znvaygcxzAM07hKJ1CLuwVlLZRD6w/nWOV6mPh3uwNT785Pe8Oje27cOePfLPOOwTOV6AhQEtG5uHuMMecSdLAYe440IEOA6Flpz7s6CBixgkuLHZb3EmDqfFARSsOVIDE+XksL+BFWUVWjymXc7cPdPPTT5G4899Pjgs+Pehh9XrdauCqXEUcV1dYMyVX6ZEyJpTp6S2vhllw0zcBrGbbmf/Ppj9/hEuXuVsn41VJfy7FARC7uzvtbkxijy2prXCniCtlbPoDc73z+4OL0yuH3p2s29H14+/tH1/t7R2nLZAkzRtnQydWdLbCiVIQnhA7T27yvcokbtxtdTGNh9BYZ1DTQsBzSsah+0FESpS3oT0PgEz1orNyrolZT2di458bJ9cczPHg2257waZ3Yyn9zZOro+mh7tzKfyC0ewoPFRrdY11BBAa0hzWLVMRgR0Wi/jK/jxpro7bNMCagMW+HEF4s4IZIIwZ2COMHgxjF1C7utAnoPsNV0FtVSRmhZ5quMbr4+Oxpf+g8f+8Ae7X7g+kzfStB9wpJqtW2uz+Koj69GgSwy9RDSpSo7xCL1PpnUibsOwadD8m6oTv94IKbbNrwNVeuVwZ1u5exefVV3ToQYoQgpvq/6oy7rV6+8P7UiKVdG4rlJhfz69r3/9/tmb+1ffePby4b+8MX7lVn8iZLsWwMzeTLObosgFQDAH2mrCItUORWbFMXKZXNQyLbQdd0Ajt1NbmVjezdfO0Jt4GojBb6vvuDOEUNEa4gzY7Xsm2xdPBts9Oac+np/c3jq5OZqdDFb31HnhBnuWNDLTYzabmLkPulmTrHbIYpKD2sAT9tOBgKfZ5NgBW9ImeHaom9EobyMQhB28RNhiZy5ji51hma6j0nXxCL24M/8vf3545Z7PvTaVX2hqOVRhYetMtCf0ZHdSGHvWSpyVtqqVvsguviraesoGbXO+SqMz8DH0/RCuAVuVCjr7rCo4LDpUZ9poXGR6bw3UKhW1mb8K4JWqK1FoAZpfoZ350dvnl86ML1+7cuV7lybPXp/dPJIXyAUPrKHQVkhRcrAEr6WaQtZQhm1DhM0ajmEsaqxipZqPVSx5rO8V9r+qQeFfit6kaXaP4V8quKSRB9QaKyNSnbiDdaNbsP3BfPvCdOfe48FOv6cM5+TW1vH1rdlEt8QKseHN1gcHB6W8p1BfyExPnheyyhxdDBjb97V5EJ/APxp91DLHkPXJGMb46dqfrszugoFgw97E8mY00nm9I13A3SZtXI9FGYliZ7uwM/+VD08vPvrYD2fva2AVW2MoTK/+U6oLsi6bZjkkIye5wD4bEQUCG8tqmU0JvlQJgutvZloBrXxfald19llVYe52o9gJsp7261b6NF1pmOv5jN80fWD2+vTW5Vev7n33cu/5Gz3+3th06DINZRmFvgekoY6v16fdpqqdPwuU9QFAA7r2bcOXqBprGYbJ2yHSqxP3Yk5QFhqWhsFIzqnvsKd+qjeXEdYf3+KHhLcrvEw9+UqZ0pY0XCAwbmWspbsZHrggEVkuVrwBL2nojryMmdmyG8Qt26A4NXlTYXE+LbN4OmMisIPsUHZ6JNYX8pvuRnPBTgZH3n/5Ayef/MB7Hu99atwLfNtQ3BLjg8Uymbn4rZyB0c9c1HIzMlWmE62GdGo3rml+Q97i2hdv/mbtrDuhrEiTttXr0V6vS9x9cT8knpzcd89CdcNpHR4Mq/42mh8/2Lu8e3Ll5rU3n3vz7tNX+2/eqes5FfA3GtrNrXfbTqpsG5MPrSMAvu1GVcAnr4hR5Mq25akNks4OHW1vwTZqsR3VvM0+EajWJErSbdXKRqX6PV7RODrDE6Xj4W5vzl9XZoPx7cHBa7uzk1ojDdtocC3bmixcF7cytgGFUUlXCCqJ57nLZKRNw5L1vqXjIEzgpsLIOo4HwsV/GQBnO8jORMAdZqPNxxhsKyDu3CD/zjPbL918+d/79NH3R59q8xeaGOTwl9xt8mVcxDmVrBvgkpl5q1Nr5lhUqVU1zo8X9ZWJgQBo8Fmm0DrZiEkiJopVdxX9oW2rYkNeU6PfVd3e4qUMeTJNpuufLhRg+QvGst8L6J3lx034WyCgDbdhjx+WO3jb7I3enTcvX7n+B6/23jiY3R3Ty9EGSqDaSqhh4mUtYIYnUjD9ltAYThR7sKqsbUDt/j5P8Yb+Ms/SSUXYBnrFy2g4PHw1QZycbWAYnC85hGn7Wv0Vd9y5MTLX9G20yg13Z7xJnW314Q40fT4bD8f7A46qz45DzqT0OiyKM+6lbGtHmL5pdFcb/bTdAi3CAyx4OhnQ5dH6dvApqKU7O+5sNjiEAZaBwIzmM5HRF7zekSIcZMcNChobMIs+ZRag6mKd77kw/fVP7b524eevbPoXmsxO3RWIPvDqIuMLM4sKFufZvkRDyour9sjFi9bAslZJzCR9jsGsVZYUqGdbs4i3ve+u21u4kk9n1cMt2QMNxLt3X7E9P7qnf/Pc8RvT25efuzr+1qvjywdyrqtTgcWC1aGFbdoKrTbb4McFqwMrna13OKfPOlLBjMwiZhs1srxmCmwwEUxYvgEtyK0FM4PbX/ck1RSpSNw9Gd7w9Gz73Hjr3Kw/Et4wPYKpD3lF4zQoU0914enTp2FRHexv7LRhEKS/XattXElf6tYI/mTBCfhHPLvVX2FAyQ0Sd4B1IDN/xQAvqDADj/QCl6PHuZOk9+u83rEamBjvea94/+781z42Gz38c89O31WtrgqlkmTFMcnl7cgsrc2yu6jG7nGVJSiS0C1lFF4ok5RD6/qvULRGZhnbWunLRFuaflZV2m7VFd9wJkxKRHUQVCmY0NFYtEy3NmIEBlzo7983u9q788blK3s/fPPkub3+/rEsrLZ4MTk3UnENpczA2MbqAA2toaapoqwOWJjasGO9I5AOnp5ssgn7WFgxo/jWool6fXRiGOZV7lawpTif1FX2johSVYg7pfKowPDUbOfek9FZdoNm/J1qejg4ubk1uTusefrFB0cn0+UnU20w1BzAxv6l82ozdQeaRbpM3HG5NmcQeso6C2RYLaxqIinE3GXefQV8nXmHIQplp7+cfJuRPNtWbRgNer/+s5NH3/fBb08+PG3+D82KZkzX16KzCfA2zlRW+8ileN5XJH22TQcsNG8TfemAa+K8u0M5BMRd9roNHOmZz8/1D8/19i5Or97ee/P5ywdPXJpdu9tP/aYp80eX+TFLAAs3a0HBIpLw0FajGMbyarTEVj3WO0IXTKVbsQ04ugkd3UrANuDy7DPkARzhsnzd6QeTKmfc6VpHPQc78923HY/OzXjxS3/Yn9ztHd/YObwyqPBEqTOrZoRWdcHh8lpRwTbpp7izUdvcTTAV5Zm98XRajbs3t6GSAplxWHOm4F0xKIE0cye5WfQYrfiPG7MFxvBK43/6ndHnrjzz73zq4Lv9T9yZnyoQrpYV83QprTvrfVKKHG+jLimGyUGHam1ttpSYlndgRlCO7N7UmFbzkk8idAjE4+3tUycnx3XPu8tcHrUqKMp4XW7PNutTHtrbsW0+v7d/+77e1e2TG5ODGz968+jJvckPr82PpxANjMzwJRDjL+1MdP77FB6tDSbC9Eu3cmtBxJ/kBau+UJHxSPgxEd6XIG7dmYAx/DUAw7p5V0ZvEoAOx4MwFPATR+GsSE2AyxF3XqA+2J6deftJ//SRzCyD/vRu7+j6qTuv82b1mpaEKW5TXhhdDWjxNA8xPDW540tn4xYNWPSWUcmQsDvvUBaHBVmGQ/wHkOZe71it7Xb/gAv5FGdd/PPXRjeOXvvVT978wZm/sj8/7VOqWEbnBj5YbjOeRhXcVpfijKW5uJKmcuW+IosoNFVfGb3mcnxaIbfgktAFm8Us6drO9GWMLT3KbzNVPO+eQLm5lqV6Nja8E9/ib3LLErJb0Tiaj+/v34Ssn51euXXr1jNXZo/fGLx8k3eulzj9wizHdMefN2F7BSxqIziyRrhbi42feqcTIRgEoAA0e0aLxG7yY7oSC7G2mz1r3AzoADDVs9gMZTeQyQrleGuOyvRHc979wi8fbZ2Z2LLL8fTpzbOHt6YdYeopIOzWh7vGVHpHLjGPaWV1QqG/YeoWMJURjhihZbO7uVfhQGBg1HT95GwFyHQEIBNxVVSO0Hfo2d/fp4rmXu9Y2TygY3ZedbwChW8/M/v7nxtdu/CZF6cPFYjlZemOb5S5lkQuyHFIMpBnWul0epZuLV2slQKx96p9XbFxYUeXocPKHX6n2Gff3aFMn7boCV1GT7h7PTR4G8zu/O47hlfPT671jq5dv3Xwwt7gR9dHL93qn9Q+UsJaxtzuuVvRykhdVMJkYhNy+0s8VdvWL66VtyIY1yzeP140pvUYJIpWYF48KFu3IL9C61kMo2ftpog1N7gTUkuauMPUR6en/PLRcHcyGPZnE54oHZzsb/FQqTF1s6Z9h8vHaimHJ1PhdsGRWqqjxgXD1RwO5GOiLoQPlQFJZGUDO07cK5hnOOO0RIDFRlRY70UnmwF43YULF6wrK+PfXEGaj+/Ztop/LVuD3m98Yrr77k+/NHv31GPXWci67rAuiJtPZSodegvPp2JfGbrV/Me3QKNyMcry54vYqztgXm6fdwu9la6RvVneEZFBx9WZkZeW5bZuRV/ghI6jV3brfbs3Pte7/Y7Bm6eOrx8e3Li0d/cHN7Ze2R9dvSvPQYZduGGozPx0bzen5TYJKGQDNJgljGbwudZNWS+QAb21ku0L0BDQo1ttq7t9A/JqxN8MZwDHtobMo/mj/nC+fWa8dZ5XNE4HW/3ZSY9nSU9uDiev78yzOhfLGrImD4tS6ZgXlpaVqr1YmL7EPGTAHZ+zIdQpMLs5wRWjupprOAOywxmQm2gaPcjfdvC33d1d3shegRmvGt9QSrXmj2e93/728G/e/fonPnD92/OPZz6u6tgN1KbEAY0MIiSUv6Hm11RrjsRnTT11ilsPGsLxXzAiezZkni8aGzLPF2zOuy/tuy82kmOYfTU1Itdx9FjM8MzCoTHfnR/f2997oH+5d3j91q39l64ff/3G9pt3tw7G8Mhd5k9erNcEdrajzHYP838HWQFWsVSxamBnQ+bBNwj0DktV2UUQyo55oNeceZU7HZfrlHkOZ/rR3SiSCHqkhL0dNdD6/+5/9tnDWz021KeHw0ymngIXP+vmTZjZ2anfTE0xSLyNvuzmC+YNvY53LublzT725z9b5MCZ0cInIeW9oS4ZilB2qoOy06emFhuosaEpuL7l2IlDAmAFVV941+Sv/+x9j49+8Wi+rZjKRxVeU0j2QK+QAVQwPGSR9s1T/5X7GZ9bhrbMK+zCfLzbMi/fgoIcflrhWJ5VnQ9oXcUGFqivn9Vp9KR5NtNG0PXns3O9O/cPrr9tfvnw9o0rN+88f23y9LXR/nh7PJf3aTBJEmhUfWR8NLBwUFe1qc9Hf00Z23oPSKvcahgEZ7Sxdjg+WrOxwYubeXSuz58RwtbOekrtzM4FOJt5YW9+qDF9VKa4YfQfRZq4gSiu1zMX286ePXv79m1P+eBiGABEBNNse+pMGQRLqXDYI7iRBQo7TtwB1rlf6qaIkQPaDueCNtbMslMxjMbV1ztiG1NwwPm3pqmrxau5H7DT3ofOzv7OY6deO/upqxV+ocmPC9F9YLhqdldShEI3a4tj6u4YTKn6GgMwTLMbM68USLFwPCfbmXVaCHc/grt31gMxuLO29Xqj+eShwZVzvf3z08t7ezffuHn8w6vz718bnsxHvYE8nEfvwxzaZ1fW30xiTM7M3pgRe0CHvlnObFuKhayaWcycRiJtiaysJ7N2dGIeOjvL/TAPy9tZfEHD7rVwZgDx8Sjk6eJQNz8YUI64m2/5GJrZ/U0n4rhYyI9TNl1RUj8zAgEkSQQZnJuQFEjGqzGnpIZG4x3vX7yfYPD6j5kgiNGz+BVVw9dtjshU2/3+9Vw7GUp4tbVRHFq3x3hc9dc+1h8/+LmXZ36Pq5bne+BsQykT3o0nNmJedGZdGle37YFvLcr337oeagTAdZUm8uODXYp1Il2iwt1PTo44O9tZftwl7s57YE7Nj2VbnZc2Hl+5dPXGU1eGVw63f3B1PJ31udeHqYAqU0dH2B6Di/mZrVlMSnV9Ry5tZSnFPmkUczVQM7I85/bKjbVFIRT7rGxGXkGgAMDm/A390A8+gRov4jPPksx0V7w+gKgqTdxLeVVmA5pL5NwCvtvoX8QEsvh3NK0hpRwFx+oygPglAJb1yOY6lPkI+mgLAFZB1om3DCCV8npH2oh3MXMVN5b+rTCki3UGzMV16eJMAMmyBcCqww0yl7ftYe/XPnL83vd96Fuzj7p3WKctrMH36GUsSSvs1HUgcmyjzM6sB2xfCACbxT+EhSUBU6ylVetcC5kddmU5WbFOsqQFAcX13mND5g170/O9O/f2rt4zvzo7uPrKtds/uNZ//sbw0h3h6EzOTC9MI0wdTDLMnAGbHUpVBXIcqmofPWBoJKEYPcTAmakS126ar6fMxjwqbZRlpWosdQkscAbMKwbQX6dbGa3VOpf4l05Los0AzFyF09I51ygpQdzxFUMkR9vmk/mlm+DnZAQjZeoWodsI1dgtGvJo0+axUwswjzk3lMdXaBQQmZsRMahT9rS5pc3g5yA7Ps+rirDHpzkII4l7+AhvRAYAmTLAltoNaj7tEqgJPlb9G+8Z/7VPPPq9+cfvzHcW8l4ILcRzY4GYca7+ehnVNz3jnfXo2YBQcK00h970dNfloo0ZtFyNvoeEpIarS+6H+Q1eM5N5Z+dkLOfdy5RKNbH5y5YGCdvq272Te3r79/aunB5fO7i19+L1uz+4Onhlf7h3JD2IpzHpEYizcNjUBzUh4jmZNI9VugasZaXr7LEZzM0jx8zVxtcNapu3081r/hozALDLXYx5GOlWugqQ4NhAbYsjLQ1LigxAdFa7/xHb/JtEM6pV419FTclKy1VGncBqAYWMDQJzUNiey6i1A0m0tH0rgJqZlE+qBmQCbrYRS1zbeYAYG3B4nnV2iT4R/ARmbKuXj3zLMubPnPYBZKrmE4MrDOovv7j1+u1XfvXT+0+d+sU7vcC/rgq/rfLYa1tQQudKcHcl6zRHGiXTU8NsVUGQenx5XRv2pHtGJ9VGWLFOX9J0mlW1AvrpeEfeM9Nt7i4Lk3R0A2HQm53u3X1gcOPi9M3Jnet7tw6evz7+o+uDyweDO2NqlMPETNfb29GOL3MIJDhpCJfGnCrMLUk9DcVtNe/y3QWMkzXRDvYwS7MScUmPd2FxpFMww7oYw+qQ44b6F7U4HqODLjbE/CuiFFAb2qzjDTmwAUgtdDHeWIEwlNhxBwU6yR+CliVBvPKTqdZbTEZEGB4EG9thm2AMtSFXCGIqnoQe2h5EW56SFNo4sYU8+WQ6TgiAdFAyMVQcM9hi55PhREXV1FI2tYxV0xOq1CrazLZBXm306IXZb3xm6/L5z706eyCUtaanBDMOW7G3tjUWxny9FaKeZbSNj2xel52apaXBNIYwnhmkApsNVFkYhViFos5zd3mkKhSGw/n0fP/Wg4Nrp0+u3Lm99+be3Weuzp/bG9w4HEzjuTY5kzBJwjaK52FWE6aaTk2GKX/j7oJGdZPVGNpssgAyGLIoF6Odalo7lxjJQlmNerZjIaTL848D7u4Ix8a92zGPWqxeKvXnXcDuS9yRpIKOk044k+eTqTSHacWYOtgxJGwaanRsUB2BilrzibIVNUfcHdogz6iwaagC2jghpfxd3BMBOgXKjjCnYgwEz4KrYkwTmNfmyF+1ATczwMkyuLDHoc1US3uDWHh6NP/PP93bfedHn5r+dO6R91X71qWsocXrireQD5h4sqvIYUtKMt0JtB9JWahctH0rimpcsbBIeCkvAXdYvr5Ui75nptv77rW4+/b85N7+jQcHV0aH127c2nvlxsnTVwav7g94vXpyY4TJhFkXrwZ1ZjZCCqWCS8oyH3ZzU9bMxkLmQywMMh8WQOGZBc6gjVUObWZyw7AjFq42BIMxssu9bBZCX1cxNLSBnTWxlG+v4lAnhS6m0zMtXFWLtb7EHedubqdz1bIKKVAuLCTklcW3CDQZARsV9GJiCcgrFyyd2tG1QedY2xJchxDk1gKcbUhYpTLfh9gzwDxgLOjltW1MCbCZQcBUnj0130gJlL3EQrS1v4tj9VoTcGzQXp2krC3IMJxpddmmZcqPBr1fet/ksY988Ikej6sGCwzMIN0RzKAVRfHUwXzSUVP1/mdxd7HSgs0nlOplETaTE7dMjbYBrtr98+6lMOTR0rcNrr6t9+bk9t6lvVvPX5/Jq2DuDk5W2DiTmC1YTCOsCLG3l8bbZpvNsqJio81CJswgC19xXXm52AB14xOcMSM1dZuFJAZc+PIsqZzOmkLZ9hc+f4OTFuLbeDhlO+WZWEhfr70FQsb3jLu5lD9G7UvSDantdiN5fGIM4wG/36xXYQmzQ/vItFYjg4GAqxjgDIngEw2dyARXv0X4A1vsWMhfacoeZC+uHQsNgWKx+rnUYh5u1dm07rO++sj4mzeZ9X732dHR5Pu/9JEr3xx94agXoHf8a29fMoGe2+3tIDkWk7APa9vxxmodgW1rLFS4aQz3hO03BOyOdraj32Zq626hLJLFGA57k3t7t+/vv3lh+uadm9dfvH70J1cHz94Y3j7huQs2kmQvyQVUGX0khbkxyL09HYgeGAmTVfDlwFleJ2IW0vD2TzmCCbzFehBw1NkzmmIWsrZycgYw88QySraYBLmiOVhIQ7rJc7AQtDlQbXgGce+wAGMhzsBgwcJisupF3M2xwprYhDbsxLnxHjOYxjP7dMrLO2VMZhcAXWZ6ZqIBDuaWy3AtmH0yNbSfCMHlBo+OYIudv9I0ZIB5YHDlScBpAm0pHt55BjAumML4zBMom/5HL249v3fjNz//ped3P/vG7L6yxVflDUA+V7PaT4mHrZiTNIn0dp439W5yGq70tbei1gSzOzqBuDVhgw3htEzH992TGHKzdqp3dO98777+1Z2TKzdu7D13bfIvrw1fujU4noJi+tEdyhp9pMdBvaEJnGmKZaJ9ZuzvxjTceCemMq/6F6wgCRRMvxT0321BmOUVeVgdkYBTdwX784pgHk9PcQtEAxtypLyqi9PNw3F1oDs4ODD8sZZQXLD9XMYgdxQYzGDBPGDMtMHrqAw9YU6TqWKDiTTPAl0CCaNLaCqmbtCkgqoZctVoVoHOsFmAaUMuT63N8oY5MuL4rQNeefZnDNjrHaHsNCGvjfXTmZQZfqH80DrFDDa1QTSzNRLkEdUkXGe25n/vM4O7b//ca/O31z/yTmPxt6T+9uL6dKmeC19jwyaNXMBRhFI3LFzYmhkTI3VbndyNdXqmZZqIbV1+VnXUm57pHdzfv35ustc7vPzGtdtfe+n4jZvTm7Nzs5zno5OzCvNJO0SQSuESLIJ0dz7YG85hmWZRCw6ILZ0srzSvpn7WF/R0GUYa24UbDPwNrDDGAE95Ha5IVqduMFKubzcYq8MTs72Ie2WqlLKj/iUG24xDxAYAXUIgESJiP5RTv5aGNHSfuIOqTaxJBBzg5OL6oI0npcZAUr7pODCWHWwwVKY5moaTmOc0bWTNIcOIJTgnB3BgD2tzBRh9DDi7Pf+VD00efO/Hnpx/4C3G3YWsS5CZRf75hUUZP/lgUv4mdvjAzPI80s7YrNYDXTvvvtM7udi7+Y7BleHh9YPbe6/eOPzR9f5rBzs3j4fH+vI3qCd/WmTGY6S7JruJBeARCD6ruIryInQx9lA1c1qezMbTAzJj2muY29IZ6n7AYFyldBuHLmlAQBiTatfGjbHwCT6E5Tnm/2fvXnstudL7sO/TV5IzJDWckeQoMBLQieVLDFuau2QDeTF+Y8TIiyAIAgQIkG+QfKZ8iby2ZY/mJsW6jcaW7diSZobsIdnNazebffLb5+lerK69d+1VVatWrTrTBbK6dtW6PM///9xq7dp1nusdMPKCUwvbz7Ve6UPA2HUZYp8v3LU5LOZqqsDucbCX9dkP2wHdI8Nyu8qslFcspN3MYm4hqbrDQji4ZiuBuavQZv31Q3xXsO4xCTPlYSRSF8kh381e3dEWOh7LNeSTnUeIH444RcQeK2TmpDcudv/8v338u3/vN35041vtP/J+tfY36wlqfKEvE5zZzSZOVFfIM1ruhdHk2f9PW1/9lvZMz1Uveyr87qMV/67q5RcuP/7yjXd+/eJnjz/4xS/e++Av7j38k7dvvfXhxUePP7cKYUT0Ts/vCoCM0894xHPgiTFo9y0AACAASURBVCoVAstZlkRjNtBytQQuiYaE0xIfzCOHUlNZMm2QszBGSdf40jsY63CNMoBAHtowN+lZAKNBcN2tjDM71mwGRnKGQdLxfOGeXyeVUiMRYEDoCzSYGKZBYHrw4EEpARYaZ6E6qYi0CXOmzzhyMC8y74RBZCZ2O3yTRv7IWJ6K0X7CLDO7yExgHDZaWkRwN1emnc+Uqtd9iadl0hR/58uf/R+/++U/vfPN9558MZ2ccFC+nMt+DCZfWvRhM7/9+JZFBt/fn4yfukSPbhI9DdTyMBbQpeZvVS8un7y2++DXvAfm8qfv3/cemI9/cu+zP3n75oOHNz49/Q2cYB7xJ0Kl7Pnee+/J45UXL4axjtCn6Bxutu7VsTcYKY2y5OXq9S4mwg4h5bvhhNjtUv8YLMxv8l3QsMApjY6t13vDMkj+spCQvbmmfaQpGO0Jeb5wr1BuJuhDH1ZoY/r56r366qvvv/9+fvtVWnKwpuIUM7UBHxqMHub2rQl5lKkBISEcv+3wJUyodnSEpU+amo8d0h3nQ7Bqwf2UstgXUkWBUw1mnv+vX//sf//t2z99479/6/JLk4cqVbhfhZN9SFnCKkoJeYBSyTq7flm8n/HqyfV8zOsLeYD5mRM0WvR595uXj798sX8G5tVPf3rvnff+07uPfvz2xZ/eu/npk4sn51IinKMgdsC748/JhT4WMtR2EmU+F2eAmH05bjCEa6TPHmypAaKeE8kHhIzClAQza8fJOuQIOXnwUh2j6DzMidPGT6aOFylsgJ388Y1JSO1LCZk/dX5LQqp/1inczb2vGZ8thaaqMV/6XstNrLgPlJs9dRb6KL7A3N74DD2iTG+u1YXsyXP04+HNJHVkKfceHM9C8tFelU+GkEw9YHdAgLUi+yndD5E81XLa+S/evvw/f+fy8a/+9o+f/K3JyRm5gd5YGZ4V69N6j5ttspDHpilZr3fHLypkd+DOsTmefZqGew0hn0k47V/PzLz06NEnd26zy2kjdHvd3D15Zffxly9+8aXLt299/NbP37n/H9558qdv3/z3791MSHbbHx6LMEq3QDstGfrIu5UgYk50ceD9DU7aDgdZ5cyhkKuIMTxpCAlY+aXbUjIFuzOAXW75ozvjwPEpIQe61L9ESAUGGCd/P2AEmDN4UWKh7zQMroqYI+TSwALhTOEOIwD17HWCWCZK5UsEdmPOHzYk4T8ikRWFCYJV6wIB1lD5Tq4He1CZ4vhR3TdXuLMivz21t8QeYfSoXjVPBuye0omVAIATL8y+phg5c6F7ofCXZvfI+//8dx//g7/3m390+d89vMx6/2zq+/RA/ZJXID2r1PXbdyhRVvVlOfk5iqw8OU8MMqvziTH7p9kh++yfnfn5Ge6lQF9EyJk6Pt995m9V7+w+fW33/hu7e69+9vajD37x/739wZ/fu/iP7938+Ue5b7tCosRnI5cIw4sPI4w2MmO3dtdYtBSXvvCFL8jIz+u02idCipCTi7k6cguVJgJdgr1BmdUYSK9cZozFX5qGISEPLfbUUMnadVk6YYUMJCSnubB8Sqq1zkPjTOHOWKP4GCuikVOlrq/IEls+VfkzbuKXqQCpU7gn2M34DPUzvxDoQi2Gph82dc83dRxCskxJiL5Lv94xR3dixAZ2Rs7bfYR/g27fVYe0fHxpxtXu3/iNx//j1/6LP7j57UeT/kITSInalbx7nKLKQJtu+4WOh4U8PelJvU53mXFFkX1VX88Y4lnXZ7gvA3v27dozcSr/e/Vb1Ufe8p617n55+crFJ29cvGdZ/c7Dex46//f3PvnxvRt//cHN+w9HGICoop4AOOzFlpzwIlr26g9xyRvY5KNGvpxEHKWo1mzFCXAS2tRzkk7L9xhEjeWYHNuo7DJpOkIyP3Y4jKRmcZuUb+1piiIHhAyzfBbqiow6d5C9NQ6PoUW+xCCmpC7RCyvTiv5hkQ6vmrdlG00C5yOZuuQcQDshb4ooFu1z+m60jULTd77CqL+CRv21tAjY2TwBAvZuGGKTsZK0lng58y5kk72pPZv73b+69dGnP/vfvvb//OTlf3Lv8vVeg7Mf9yw/X8jFIu/+7x89/0eRzg61XIOruJe/zL+S3e6RnP6iTgZD7qdULOt7+9Sz7AzzTAEQD+/eGXje/cblk1cvvF793defvHX50b2fvvPBn917/JNf3Lj30Y2P9++B+fwtjWcFEesit4ozgsyo8C5aquR0l45jIscednfeGx2E0IhgZ2VYtAGlhFMBc9RC7KIiGRwykL/y6/1CLwAdA5NlNltykA2zKk54NnsjREiyBeOAPQQz7pECZ+osTfSp8YNxYJIkuc+pxjXPD624g5WZgvWUQMw62oRliyaxnWq/0PlN/DIVVrYBMEeBA3ajgd0Gc3bPsGyjBjnaOBazj15a/SQFLRTZ+543FhXqi5QyqKkDeftTYrQMZpL5rJunlvMPfuWly//rd2++/8bX/u1nf3PsaM/Me183Rr0+doQ67cnJK0/PNXDpdKfSV84J+dx8z5CPe5K68u/vMerO+Jzq5z8QTu3+6PYtf0xE61uXj3/l4r3fuHnv5Yc/e/f+e3/97ic/ubf7d+/eeH/wPTBHpxHho2p0VeIQZxIRR9sPn1R8aNArPowpoprFl5bD3etc5TgCOyEHgmoFSRLyxJBbD4UBJlGbLYsDIlqQ82hZXAHD/CkCTKQzb8nIpi/YbfmDLN2yKTDZ3lDhflh2aB+FS8T9U2a9NIi98Tfxy1S4AW2yLUI+bDpidyAfxz00Zn48JH3mgEW609cSO+dJr3cU36vFTdxFNKHLqAwaSWgJmoqgmgZZ9L2QaZY4uH1z97/+/c/+y7/923/25L/pXTry8emrG/dXuEDzVdxezhNCNld9MkuQ7iU+vu3N1uX94vpQs+Odi509gWax8UsMdPfJo//q0V++/sr9i4/ffuud9/7jO5/98Vs3/vL9Gw8fe5pm9JaiDQKimhk9xIkOgpjBD9cvnfH4h/UvAfZE16qn5SCJstQiV77ogY/20o3Zh+M2rIR30A03y599oZabuMdglnIQ2OX0+rznIx++ufqNpYB8/lEZBsqgI3azUbCSO1/VpVuSbXI1vLRs3fEBOFZOyDOULvIt23RX2YLHPFlwBEXvRWYBS8GJukMZHPImdTJsfhryehmnKX/pqpmORUzK2qczyx18+tnu//6jm//Tp9//nb/z1vcvvvnp7uAt+1fFTvwZU0TsF9c/3/Zl5Oefmjwi8fPVZqMC76F9XlBwRhWyl/gK+v2/64J89azmXtSWthu7J/4W0q/d+PlXdj//9ME7f/3Oe99/+8blf7j9+zd3H+8EjdHlLwWjwKKlNHFYWxfRPhLQ4V26QlkNGg8ftrD0Tn1oVFuaiWpsLPKipbudte4x8u1B9hHbkS6T1onw+bIRLCocgoX5RTnX7L2QhG5jmXQE7Ipy9lfcRRBogs/mmJS8PRw+n4+aLQUaCJKz5qQT5soJQwAHO/wdsAnWDPnKxpEj5wT1x3ahdfwGiGMfzSVlvxkAuC2CSADOooqEubJyjoUxsz3dZcpq32CEVF/9G4//l2/++h/f+saDy1ecSXZOmAGxD0rNgbarXXom5JAiqwnXmRjme7QT9OsurncE6x0+lbN3tuJHGHkPzJd297+8e+uVx2998N79//zuR35a+ue/uPn+o6cs377cfe3RxR/eufw4j3bIR+FCDwqWCjhnUTFpOHuH9qedRABR14OI0tDZcZZuQAYBeaF7GOAb30aLKHImqxN3XJWD5wRpo9xsQc4wexTIsMDv2qGT5HS+8dWucCLlGfkncDGzC5Qu/uk//aemJ4fNcECMepE5rntLkamb39bEc8+Z7ddqdrQghn8gbw/5BP5aQpr3qJw15WGNkgcjtEjACE9NPb8gDvDFbgcBfi+InJp61Pn5co6abnLjynLCHPJ/643d//B3X/7oK7/14c4jtvt65/Hu1qPLW59e9tcstQ/VMJWOJyu7ZMeNyAnHK8T3eLbxgMQwKfV599PSVy4++srFe35aevOTt95+5/1/d+/xn//ixl+9f/PhiYdv71zV7n8wWLuzXvVoZNvDwmUYhFJXyRD+fmUFz43qjHxKPO9qe+7CGh+IEfkIUEXmN6CwY09NicZWatiychaR6nCQ4ngeTjFwJoF/1uxxxEfUn6V4H5BqziVCErW+nPz34p/9s3/mphZAPYwqJ/LJ8G3iAXfaJTyZb2zAFz7ALnwcBtDJgMzsKACtdcNm3lhfkTNANKzINDmfYV/vOw2+HUliWJ3Vr5KTDIuuHzB4Yc7m4Cre7C3/yy89+fpvPP7SsyLhC3du/cort19+KfH/tA7W5arO3D80/PDJnZduPtrdeGV366XLW7c7P17cN75xccMtwNMHbK7K/Y+evPLgySsfPnET+HS0QDvmuBr5KfyPL289vLz9yeWt3ltXum2eNj3yz3OD768/W3g/0natU88CTVcj57of1xLt/LzL43nz8rNfuXjwlYtffOHxvU8/fOs/3/vo397b/cW7N97+6MZnV09wnRXyqnbf/ejO7pPnzSFZfkSDUiXjWXlONcC4lGT9tZf3o71Q7O/ZtfCXMcgp1ItLcxDj6eKboSLbHlX5FFD55+FJyEVDaL4wAy3hyQhRPNCm4CWwAx8FYB+1NEZOYqxVjeQjEF+51JQTpP1HZUJcKAs01ajNx+iw5SZeKQPM8GoOY+PeC8WOQ3zGnplWEI+dpdfeErsUwgGssrPJ3tWjH/MLzTBme+Mk8B0cHbb4SerQq4UvKM+qdvjw69kuZxtQP0qWaMnybTPBj3vguzcvX7m9u6vGPrd96e7l3/jik195uVONf97pOWO7e+vGV75w69VXLm4+vW2Mq5RIzdIdgRcBvry7fefCO+qf3SYQxFdoV3cN+/ZM7PHljcuLOw8+e+WDy7tPnn/02b3j1a8+r0a+2j16cufjy7sf7W8wrrark3F0Nf/nn582yPsH2nrSuKNFv+e+zec69q+283kJOV/aPXrj4p2vXNy788m9Bw/e+Yu3H/7k3o3//P6N+59M+WkprDwz841Hux/c2T268fniehHLL04EVzpVE4Pa3zS095128XnHDhhfvY4tSNLN0tiScax4qb2sZNKFHu9Js8w/ICRR4blcHSKeTKvXu9rJ2qg/ZaLdluseU1bhBMyxJjpNbNMdL9xXqd4m6MAyiNrg30wNq93je7WsLmoTtX1/RgE/Ie1y/txlOXKD6ZSMeOxeyjnW5VRBnEK2cehSTaOjYkehefRSUycLej1rR0FoJ+yiANellBXNjX+K+lKzDI9z82LnzuHWzc5yvw59HS/u3H3p9pOPf+PVJ196+cmzm4GnA1+V4c/V4i/fvvi1L9x47ZWdryQ+/25g/+rLaPb5SQXhKy+9cuPunZu33GGkQa7CzdVdxLsPX3rtzuPbF48f7e68/9kXHzy5+1n8CPhp2/0EtquuV6fcY+xuf/jk5Q/2LdOAe1H3zZ7tn4q+6j8MKUSaLsXl5asXH//qxVu/dvHzhw/e+dl77/+7tz/7yS9uvvXRxdXr1acPHD1Z/ku3bvkrwX92Z/eh0FPoeYy5Yp3oz+u5Jyc9et15T85Yeo/S+WibOicjpOTk0BR8rrCfu0wwVjvRKRJTnRw6Vrxue3LyprK1Jt+EPxCMHJG/O+O04yBUwC+YRKZJMtyr2m0GkI8X7lspNRR8nLOs5Q1zM3B1uFgMZx7o3silOoU71uQD5jfnL2/3rDTFa0iuErJPMYh6Jtp40CE8OoiakxqPagp/W6gpZKPgaLMiJ1HffhynacSE4vcYSmm3ATcu9kvpT7erfBk3S/uU+emnbr5duntj57bhjVeePPfVRKdfdHdTdfPWy3/ziw/dYNx+7k1jMUPU7truD6yCvfryK7de8hde9hkkRrgyn6uPT8/sz1/ubnzw2Rffv3z50eVztziuXdy4uq/bt9r//+Ry3/L+k5c+3X8vsT/zdNvfXMQkn5/c3x99Pu+zloP/Xlw++dLuwa/feOvLlz+9/+67/+mdj3/89u5P7/mjYBeZz8AMDr+/KHLK3A6iDuYJ33i4X3cf83dRz06ySIMoygfSqKUxSq2+9B41sQB1NJYm/JcOPjkcCFAQG4A0Z5AKbUQMuJ2CdJQAgf8++BR6r0N3diFAbtoEpIHDoulpH2e/853v9MzLSXMXTzZdGkodCyURU0oNOGoccYTdR7BmrxGvT43QqzJPNVv9vNqLIraFJGFXHoyBm1UcljZnFpCqDqNYMY5jIWPOgAv1JSFN25Stp/IoK6UU1WwxSM18yeki3/Tkb/AjSAXYhRwK+By2CP7YJCrfLIjhjd3l3/jCpduGL3j0e3DzDYaWv/qFy5efa7mPD/G9wFXvq48XN37l5bu7l1+7e9udYQog+1b7nzfsOzi5P/9kd/OTyy96QOruo5//4t37//GdT//s7Zs//sXNUpW6Kdgh/O2jXundrMYzMz88eN59L2RjW2gxkPSp5oV9LSy9W63jUAE1ow3Jwbmcl03jimC2sg41TZKzvSKTTshQKQUsVK/3JBfuojRdKJz2ppvzEaQwGXCoOYOD/UjhvpUFQppX/mUquMRo1uMAKzbhI9OGoLoQi3Ms4LCvWBN6HV6ac8aYfu0ELo4n8k4eKvC3N0KExYjgkwes05EbT17JriNhzBK2PRDBwwUgHy4A/IHGi0oOUg7FrhadpcjgDL5g/mb8gT/ZBB/4lwIB+7ZNhCnsU3zY929eXL7x0qXfHLz10Y2CVsLyAyj4Q169OIB/vGfmR3cue79VLWJXZQehFLsaDlMRw31NCoSys48aLTII2G3MIDMFj5qiSGMoRZhqVsKkJurD90GaTp46SC6gMR8cdsNTg0w+r5TSt/0wJVCrdoBTPEXC/7mvRQNKZ3PIm4x7wY4V5IxKESYhNg8cjtQFtVtlqOKQMlxVi2GFMAs2Y5WCfKLAIDYDoiDGqRwyxgq/ufbwjJKoKzkKxHQsRGTgAi1UzGQQwYfrjK4WKx4zVxE8Ge0EScIF7PWF/0LFCvajJG3frfDOUEWDgZLos8uLtzPfqZ5BSVAAnwhBmYbnJe8/uHPp/e7t1+5Ip1oUmg6OQuJPamj2/vvvK52jfjrabKGTYZyiUNing8arNzDKfYByMMf9F8KzO2zcBhMVtgOFJgqU+NTRLNMFurMUOUY6Z4w8FZZQZNjigwhNIIIYUbE/EKnyp45cvGeh1ydyc+9kmx9JXwSLnnYBDcsIKEzBOGZOtCFUoUHaHibTPjJWhms0UR5Z+YPoon1QoBf8DXWYS5zXrGXXTSqzn62ICucwgESBM+Q/SkFSsP5B2AMjmembFSRnorFGfmjDA7OHF2BBGx3DCwbaF7kkKfJWkI4StcjUYwcRW4hqv6iosRJJNphMoyBq968/vPjB3cvGn3enIwNQZwygyiB90W3pXfnuUdWIFWO5G9XejDYTBf6Jbr5fwQBGiXq0MUjJP3xHdLRj5ZOAxTuDPxT1ioF9IGIh2lQW7HC6EOOoqIeN1z3DaG0Wbkg7J4cmCozGovqPysTaPlzW1TZn9kgwdMhpPNwGKFEmig50jy0FiOG+OVeNrxnEcxqv24aoQBi4584RzzIDGzWUFZrMyK4Zy8aC8SEPKywMU6ALcy1iADlKzWmzCVEJiTJbhJgcCuZgMr8vgbey6J4valCgPXxQYBv2gvkw9kYwtdCqMuudb/AjUYerzGkyi0JcoCwFnnf/5sOL7zdfu0MsUBVXReABAF311Dv8bQPNJl9CAV/QPb5fOuoFIapcw00mT1SnI1EFq1jYrjPj5FlC1Ig8KPBxH4aqB6Ic+cmmBmCKMyuWnLlmtglRmTFzzR8qevUo8LG/4i5mjRo3X4LiLTn25BdB0pxFRrEOyiD+aGgoIrbphoNgkVmKDAKEqJ4njKYvRriQet379c+O0E2Q8BEaRtme6QB7dpYWGsxBdWn5U4I0Ee7iRmgTt0NQDWDbdy5yEhLURxMMM5Z+wpg14wXaL837qfFNbVFNNdbC0topIeN8ErXITwhkBARFrEZTWaP69GL3+3cvv/Xw4nvN1+5QhScDYIcDIIjelt61fPDggWhfJBQbJMIRGVBw1gJDVAWxjkc9a9h+al4NW+XmzKzx6BpUEhXFEjrJawI1ai6yAROk1hocDJjrqGGXaByigpRnsVXVzvAsyRGOpoN+4T48VlNXQTCKJ+0jLtMCiLCz1dHI1NXmqqNRbxZWGDdRnmLnRb2r3Y+u2iI0oO+oUXbbX5tjJkdr+xY0iqAQkrDMXoJ0RoNNWKxgLWQXqduW5oWpEzUtXDEGjgBnJmFryhE4JpE2YQMB3eTbDCyELxgHNT1HKGsSUbv720zf38I7IkEBVZgMxwEmrZnHZlTPjqchhoV048oRbKPGEQR0J4CDUR3rN6aaYgBQ4GVy9QUYmDGxwP0hSTxnNvEtASu1EZV2jdsAbFEv5vCaALnHCPNgzE4e5uVuy+cKdx3G+kx3rMrHZ+2e2UmNgHCgsY129pXl3NZ08AFXvswgVbIzxFPPO3ZZMCwH++VkgeKscTgL5sM+tiUWOAKaoiNhBmoUV4WVtUQdq5pQSDX7sR3rtxep5ewkKoSbveVIoiZp68OVOSNzZd6j6ra4ZdKLdliolvU8765q/9rD3Q/v7tp/z4wQAdWzVQEY09K7LMAZM4mLiBTOOzMp6I7TU8VQpjx1mjG5AJbhMd06kw7Mgj4Uhy/0WFAMpCpzIF8MDF7zkpDFliJztQDsgO6ot8WdRmAOf9k5fCEH6ucKd92MMjBfO5d46WFGoTwVbA6Iyuy0QefqYoc8q4tRUADYqjkYH59//fXXeyOjAEH2zmvJi1pgoSdk5Y+wqlwNhzsgwkH4Qk5ECFi0R9+hi1UGLWe6KDGbrYCpgIJgwTFgWULjeSVgZy0stmVgk3lEIBouMXlBEKFX5AVcpBGqHfh96g/u7r76aPejLbzfnXNBdRjYgC6W3j31rv7wqOQAnsECOrDQqxQHep29xKcMGLV744GL4XEuqMIqPyafRWBUA/hHpUiYYRYiXm2iIMY7POmlJh5WahRWCzXmXJIsZ0EB64Vzfvn9XOG+kHxLDIsbehqZ5rGl6sT5VSLyEmrWH/MsdHwjnorhyd0ALRwHEWTWhiHmW+FkNU1k3k2UQZN1HNUxqhNEhDsEC2c5PZwCd/LKVm63qNmaGaAgPAK2rFRQsg+clTibsFhmwwy4+Vq1xaFZDpwhqmx9aAbO2NARQakFXdTu/irT1x5t4++qAla2zTEDMcfD7hD21Lt3vYM98RVxKc5MDkpptFMHLNZ9JjMIjzvVrJHzgGWWooEwm4LD0rIhItXr+ZUiYNEqI2CQ2BMSytJ6dcenFzWZAUtz3L3UzjHqEUFOwIZ3jJLt88LdKJtIJ9RjPeIIc7dn8bbGK4zGDb1nMcyodyY+8lhhkcGp11EQ1uZjtGc8Nm2O9l3opBlJYr/Q+GWHZQawKm4MQQQcSGvwYGHmLCFqWfWXG43VtVANHyXiUGvBSlJpPGSF2GwpEkyz+a8Lb2QEpmuL6sTVIu7QnaXI8X7d/c7uGw/9YnXn+ZnGN+yDNKd2p4hmzPvDDz9kOcr3VCYiAkHGWVpZsygxbZWT0QS9lC7qtgolptCUiMDmNGT0wil+HWBzgr7VujAzwEahSFo4V5t6eKJEBJEIltwBKWG0mT7yeeFOSaoOz7rW1dDW3kZVRuNgE9/hroVY2XlBzchYiAUV5sV7Y/wIAcn4yk6aM5qpWUJOyxbawBB6PLOIMAIxRsIjEJHp8PlTG9MU9vldVmwpJkBjlXRiXkCF7jlECNlY40TtpJMB4hhtWh8ZaNbCpYhLikUyx9aCVKdkULur2r/1cPfdLdTuPEuwdXusSDgb8BHxxhtv6CIohbWfAmGh89jnley2eFQsLjAwQSqAkHaJAizytVmEJrDMlB+bigFjgrdUIpsp0kB3FmhzX6TN6tJGvh4gAjvyAmy1OcvU54X7gP6rXGIZtijLmAsCunmue7yKeKMmDS1GdWmhMQOKt0HJhfEg+4DZrSIweTaE7Xxpw/kD6pwacQ4pPE6e3krhLtLVXHSPCBu2B6ix9YEMTdqtLD2Q1pdszb7ZPTkF/0KELe405hh/nb7W2ve1+6Pdd+9sYN1dzk12C+pDiLhDFEmuihvaq+AtvTsvgxy2X/RMFAxRDR+VdtHZxw4ecIkJrLdIbZOcwshnq8Cx0hoQswWlHSvAqPYgDWmJzSpG9Z3fOGUKRJy9MYsIRlp2q/2AtE8LdyXyQKP50ueMQEPWRmiNKcB8IX60ozbtu+JRybdyEhc2+KvXoc3iz9rcVlRbV06ojhJAe/jzi+jFSWsSEam3SCIZpfW0xsABlHg3rfvZXriIdCj42DiF/dlepxrorso5FeJO9VrrPKtbaEVwmkbhFOFNhxkRqqTdxH3Rft39jr/NtPve3Z3jxjcG7/6tV7HhwjIhyV2FfNcpEOQ9M0z9/v378dhMTQVJwgbO1kA1RRqYS5gNaR1AbKDlwKUIUBocOsVArwmXQtpGFrPPyh/SslL49Ez0bN9pDSJZcA1GODZTkFa8JSrT1dfHQxme/uVUBIwd/XCssWfoRrFYVqeeTeo9KmVvZPo4U7OC6Qkw9mP7GTpxweExwhicwchYTeu3bx/bLia8Mey8e7J3HFxoGec5xXLFaG/q3keSgHdDjlZ8GbvLRRCHjh5Kkz8OxOXJYy7XMb7DXcsU6dXjgiQDySKqya2Y7kuXT3+r2v47IsPAOBpHALKPWMDF2WThhTPa++oGj8tZ6dGR475CUjt6tbWTIr8UPKq+1D7yRf1kgdNIwQPO2A7CkdEWRSm44A7DASoHE9Im0+36l/M333zzTYhjvU5ERrO5YnMcudDUoCRGV7gBxTj/JlZTQoU9ITP9BgAAIABJREFUyg18oXGIJ/zDyNBBSEsp/o4GFsCLjsP2bZ6hBcEyLWd1FcArzB3CC/9nbvH0SydOkfxiRbEFDmKsKMDYqdkDkMf26rXnFxS3hXVJ+SizlTWzU8bQE6aRj6QFCATKgnBWOxRwDVMjxdRXbrHPF8NixFW9tDw7xeoNHl/s3r65++rD/d5xs1uEKaiSEB244BqZ7ibu6eXJGR3DraqpSUKSHw281WTIn4i0Nnf19sNGDklK8Q7NIkbpkj9RkZamZgbhnpvwNUIyPwKT3FYEBIPEmF0uigwegQ7LDDiRu3fDmDKdKqVGGscc4agOaGIiosycDkZFQElCLnpA2pn6FhSPMCluksrmFigeZLeI4renBeeqMxQVmkI4X+uea/ALwbc1wyaSGGefr9eKLaUQZmw/QYaea1TgAtdI3xC8FrCLf6dxiikpECN8hIOHa5xqeeq8XkbYCrwfX+x+dHf/t5m+394zM1gIOkDNudL3GLAdBW+0V7tLOp6cMewp7oqfZwzcjfVayWZRxccvOyBRQaRcc3AYe2VwG/QoNWphvqyQ3dGIQaStfIXIhkHHGo/C29Xr7DEWrtjY/1mhhVKGkXkcB+zCu39Uxtxlp6RJxFwjgwZG5nZwFoXMBv5OmzfFZjZevVnchAFhLUnCsHBBAEQEHY6Zr1V25y2xa5PEYx8pNKeTzR6QnwcKHM1K2BWMtJIHFpxcwjW6c5U6rlarFRGYJQM5s3bXMlUkXdcoIknmIODlbgXDY+a805pFKbxQfOjSIUzZ5sMS66yGmqZv5V6emfGOyEbeM4Nr3oEULHCoiFo9QMJ9xoZfo1ktkneE7t6Ai36kC3sw+1bsQTSDcFTniQ5EUGG+aywB9ambjSXmmj9mRLNp9hDUYGEfp2qFF/7Chgm8X3EPz5yDghGoAQUHxqHGcoY1X9o5mk7oS+CjIW/CUPldeDtGoj06ejdm7uad0cYvh4Ky7shtRoSuhN1j0h6q0G3QwnGXDq5B4LGpbkUtWC/Xrm/D01Rm7UphIA90j5gbDbS0rWjzimC3ylxyQOB2LjEDCMsfAkgpqYKOiJO4KDgyCTkaePG7CQP2jLv3zHz74e5fr/eOyKhmQAe0XuI4ZBxfgtvYtR5drL5ZNmL2Nb/mpRF3U1zSsayZHSJT5AxfM440HUn8LB1FJp0zCHfjzuwhbjbmDFWhr5jAHkQzMmdiK0xF9YyRhdYvBhQnJAEY8NP76YGmpy4xfe5nFA34wz77DSbLU+OMPU/oDRU9oR18xqo5tj0i0IGU6IiLQ6sihnUOxgrDmuFyrC7Xo32vOuzSIa5tSMcofbryNy686HZYWXbj1Sox9xRovJJLspZI0qeatXNebGHA8JxTCke8ojW9KL5omme6G/paw7tlrLir3e1rvmcGF7xmAh3sgSVMQNiKu46+PGdOtmoWztgm3GxUEy8m4iBRIIoPftebjiuLMWE67mxDKHKF4gkjVO4S1TCBWbLt1Oxsho+gQ3v7U82WPm/qvQEzCMVczmQsidzitQOdbasooOj0G8ocgRtpA66FJOnWIuhgc6f8hCNZ3tDmlVdeiWQ5LFJQPNymnav0akEYoAUjIQzMN1TsDgO4nA0PzzvtKuSFtQjH6W52rXiVo4JArIjhoTmNW2jDsAlsuXSs63EQ1IQKA/GquI4ElpjtxwpcXJKcAdXr3g75zUf7N0UuWrvza+kgMoIya/LXPvqy4QkIs4dYepfTPfXuYw4+89uwPTITmNhNmQRGImT14hU50TTh7mg+VtNG4GsEbhDho+pAm/HH7VzPJFLIYjOUOtq9/smhFfcoRMhto5gt/LO+lN0Zt1VDhOSg66ow+TgY4Q8Orgg5+ehhmkL5wiK1r/xjoCRAhQNQMFHGWWGu3hQ9RsiQ49sqSyTa90Zr9iMrilK4WQmTYBiBrY8MnthAtk9Xmz1gNlH3NCthTzACW0PJsfZgJEjhqr282Bt2oY8xL4Qn16YLCXZqWL9V/f6d3deX+a0qRtINLQfJIfGUnOl8hL4o1MaG4lh6t66suy2NuegBIZkiGxYfxgpcXLDECEM9dUOLKXIS2IE2xWUoPmASmNibCMJQtUE4WAgfIXwRBykL7/5FQt0Rr6r0/Y4luURo6G8C9K4W1+k40l6XEXT0WDvUVwPWFoHp2j8Vw0rhY3+IwxJnpjHSlYRPqYPtuydbPiaqKNayhOpCW4pajD9S8llPaUQp1ktUKmzFKiI1AFmQOYohl0yZT+PVkx8ZiKouXF2So3Adnoz3zPitaqm/zRSMhI8ws+JpPZIOk1D9jDVjsll6d1tVc+k9Ccw2yHxIwdJncBE+QhICnGUkBJY7BvxuaZlHjR8C05Hf8T4fR3VfpTFjgLCNwM3Gilts5Srl7XMemAjN5VYx4hySCEnCnJbttAlgR8kjiiHFXi/62nKK9ZhCY9+52/taTSgcNW805l1k3oSPJZW52QRNR3URfZCiC2RGMXI4ixGC3MNLzZ4JmSnejoQwREr4VxQiXaPlMhF82xF4WBJ5YlsPzEgT8nHvZiPcBCmRR5oyGMKQeStFD2v56GL3w7u7b/mt6ku747dHwyZ1dTXcxD7UX5QRDqjWiVXzsbU7YeUs7HhfJM91nKFcgSb8jtFWtopwE3BF4BqlhsiGTfiQfFE2R0k10JjTURPCERMGWq54KWUT0sZTi4yQzPmlV03h98+4475N4Q6BCBwPz1+DM2E3FJHzMDLBnxmcnysYR/qPEvMawJKjgvAX1VtO41FtIrxGF/AKlN3ScNRQvcYoJnOp0XqDL/FRiOB9q69AAA0pjNxBpIFTqSsQ3hbIDAzI9kswuMSY7EEBwYzlkRi/rJsUl5nNMB7Ssufigy8xoNrdivvvfLL7vbu7T8f8WkoK4ClE4giV8zurYMZcjzGMxQQ7viK29O5Hq96m4uPYESa0Jye4WDLJF43JKaGYcU4sxWmA3HIp3CUCqgSmfmtL76xUNIhs0nMTH51nyQ2CvH9F6ATv6lJS8xjEmT+lrSnV5LlSbDXCnPDKJcIrhDymNlme6Lho5JopW4XuXVK4RsFivSu8WGCiDbkeq5hvWl0ERh2nhKeXeGrL6Y47EWNDdTCroKliRTTIUXDdNkS1sQo/ebdKupW4wXhYBcm34n2emfGGmd99uPu9l3Zn7f6Kk329zpYWil05VheuB+dMV+2NqYZW4XnqnTrWoXpXl/gILk4XZWVx76OFaE9sJidTF5E/SmEIk7nUmEUEGxiE+nBupBQOUsBIqlOMB8hatna/0fRzq4cWIEmcgviwcSNnuvmM/Bw4fJh4M93YyOn1jtOeimkEovlidEEeOxpSlEpIcaCvyFIhDppFLGAAY6Vdsb0cLIRVkxkp0lKQMs1TWIXutjnmURlwFQ/DaPY3lMBESqyDIiXk5DtObugGiSUDmVVww8r8Tpsu3u9u3f3o+92Rsk8qz/70WCPGE+ECztPCKaWsQzGqakvv7AF0BGYVRaIcRgRMjC+XU4DMGTf02AyQ2cOKpTC4zM668nOKljaGofgE+DQXLttre4V7Wf2XHo192FKqi1QxLZB1RRUI4vWOViMiNHSvzj8m8/xBKo9AZvBmTqoxB45UpxeH3FDZkalj8WasToYQwoqPnAYMUuydCVLyOU2DdA/QaoFnvsd1x1z0mL4QFjEayRCUDWeJbIcU4tl3QWAY+7JxO7+sJTyTYMwUmWlgXRwWPd6/Z8bz7p13ROIFKWAPm2nQyJkK2SbX7vDkvHzB0js1fbGzKMIxOBjNaN7JGWHvCVc3UcvV610c2HDcbzhoJ2h0JTw8ZhjAqbn0Hs4iswRKE7yeYejOmIFskEOlap65+eabb64uRKbCoOdRk90pc5YizfitkEpae2RjOiyVsU6wmK5I1LfKbhxRTOKJEqfbYP4xnG1bsYrQN2QexlYbvCRSdERK8LKKssHdsMzz2Sw7ApkJXFZmvHSdhW2XJcXgxWUui2pvNNYYKa13vvJHXAcvvMbUKYgdZR9rsVRZWcg50zEzIZTkcwap2dcz7vdu7v7R4xvvvHTr5tWf/yQ8XuyPklJTtlNzhevNsQ3xQXfjeBwr8umpuUqdj3TAB0flBf4SyYUY+7xycH9bSryj47CBEGCUzEeHqnaSzJGRF5U5prA33UxSGKFBGKFtUZmHKdhXMhsq3HkvsAA3rNUqV6P4CEYjz4WVYLqUzJbY3VibyAuqxRQHC2lqZCFgRbucoBecwX4oc5cXShn5KqLub/cPG0+Yd04XMgv0bdrzKb2AVqqmDGeBQBhb1B/QAMup2aedLyjzNAEm9IKDcMdWJ/Sd2SXxwneIQQb7HGdZUebJKm9I5qg/Lm/funfz4rc/evyXTx4/elLeWSYjOdCRRwfO9gPNhi9RX+SxaMUgBY3hxvOvkjnihgPbwIBRLvMa/hLJJcdZBgacfMm8RIXSlchDMk+eonjHJLORh3EeOzVekMJUDFv25jbJjPFVuN5XNWPhWLE9DuI1PSvK0J16D9+VuzpgHKIS++gZ33xqDRhL7J6KsXUFeHGcEIBS1OVxBi8+At/Ho7ykjise9GReUZL8qXvmnd8xWkYwrcxLyBxOOlbgtdqTmd0y4zq1O3DCZeg72V/ILACqGzbxpWgwS2bSukdq8DmTkBAvalYEJV78tvGHt3fffrj7Vxm/VV3LgHvzwhnCYRuOe1czPwLBe2bYmKfefeEMmcyO05qFzHH/DPzeICmUKd0O836vcbWPhAmcCU+qavPOmShkVt2x8yKhw1DYMazgOdnYhjUKmVkg8yDzQrMMyLCs6Q9MPOFSBK8JHQt2Se5qTGyxDBQOjE/mgavDlwyuZDdCzb8FHXrNEXtYqeWuooYXxfgpyS03XZGRmRCo67v9HOGZpYBlnzkIBbXHjvacZZUkJ7YqGpotzo4iCSg36vk4Hx1k+CRSJLloY6L5mZ7fyb62w0JnWJIVr0aCb6125zJgBAskDyuDeEfkP/5k9y+3Vrt7NomZzTEPFgscmdH3z+r4pS1H0IhCMLxDNPMxgrYzbYZuBsN4QE34NiU8ZA2YwtEc86AywzCyoWyHUxQ/I2bahI7Ia8XHHxhwY4X7gCbLXWJPDIKvmmJC5THBc/ibqCRAFHm941hkJgg8dopS7fmqLaix31ZlBgRuT377UoBUGEfSFV7PysxlbGFL1SLpKfVDDI7Mf0+1afC8IABq+7KyBTXGhMZhUThzLgOS2cgbCiNMOu5h6uT7UwiLYKgREDQgyXA0e/qOSO93307tTi/GHMsrc2p3QFnMEoLu37/vAGinIC1yHhemsMZPZlbtY/u2TVQ+CGrSzoG6CICZgxCYeVhh4QLCSGYvxhC3UroPu0zmgGObmZTAATUZxnaf1n5Zi58m09FeNZMuUzAdMhwQht1P9tUY4ahGhyeFA88CiUeM4PXXXz9s8OLMITXJw4HmavshtUsi0yL22SK426WFY+HpKNRR/cQlqhWvOOfoLrwuUQTPEelsX8YMaoFofupFjeLD3pjsbVFqQM2qF53iLHRjG4jw0j+I6jtjlxpi2DKFj/fM/I6/q3pn92j6N7uZsxVrxjxATet8TY/OjSxZ0i9WWZryXdg52mzOSWOahQPyGonGsf1WUgw5ITO2Dp4DV5G+EGYbYrWDgToYNXgJahjSuqSIz7YIIMQugsPwIJsp3PnMTD8fBoKtxJbKjlIE5JgU1tPrHeu89GoYjSWC4PCMA1cJgxouGtRw5qPUnKomB0Z+cWkaAjwxPXkS1NgbKqjJMfhp887sRTxy2s8cp2Z3pi6HiQ8TJuUvV6ltv4JL62rpjQGEhRz10wmK1OlCZvcbhJ+G9lghmaKkFgbpbmGaWX54sfvBnd03Hu7/uuqGandQq3LEkPkWomTH1/vvv++5MmOOZeFoe44T1DAG1JA2mjnmjD7WsZCjso09CWFBgNjul5qNzD2l+ELccjh/aCEppmGBUr2+K35kGNw5VuKWtpDNvFUm7sDKWl6EzrhP4qvMhWdC3FZqIkZmtAFjYpfx29PlXu84MPupS8LWsNinOpY6HzVHUIMmw6Im2BnIcNAeuFpKtrLjbFFm7Ei64T6OmcpZasqCNm00cm70+w2OkO+PeNHexrTEsagzKvuFeYlhqzzvNMNIvYDMsAlvSyfLHiAFNQIsx7kKafuMM2c674h85+buq492P7u5+6z8onNZ7T8fjWFAABT5hv155+ePmBm/VsPZDGjY56/nftIRL0YIuw3H6VGDstQgd9y121EhDJsgPXXWFm1o/jCM5I9hLcCnQqSbBnUJqPk4Q1ou9O0NdQi5lq7BYr6HU8g4NpqDGLLG5J/LKWqiU4NbYjc1u/QLm4Fmp7pfy/M9duCT75zY5NWbg4WOxD5cV2hQEcEofAfU4TWxb1DUUyLxdyoUiSSnpih+PhLA2SK4y47EFr2KC5M/INuI7wryXTh/8OVaKv6WWJ4MdogdGacsJh9c7P7wzu5bnpnZ1PPuYaXQLvJUVTyDbundgLLqKAtJ7BCJAQz3Fau1j1uF4ZbtXGVvcVcj+m0i1wR0nEUci5fpoca2iYxDThVd3GMsJPBmCvfJPgBBxirtOWC+UXOUjZujZDP1i9c7dhHDji1uXcIzV2SnK1idY8qG7nWmGzsLxxGAwneEUUE/sSN1jR1t9fYMTFLfVuEONNEf2of1DV6CHW167KwONQHUCkfFbkG2ARlKiY2dqAi5DMPr+s7A7NMuqd1/eHf3u1fvmdnQT92lY2iXulOSR1577TUrYsr3nOWwUfV6lxdskryU2N2RFz0WRsRzLrmoKZZSgagI4kQsxJiOS41cYRwuD2QGCW2ws5ayk24DCxSmciFH/4iY9rYImrLaqBFyZhnbhgxKdr2sDWB0bPfr1J76yQ9/CYv1xqnkNakcFHFORXkOxTGLh6RFwSEzgdkeq1t0orKDRxBDSqzfhPtEcFsiK5QSntjEk7oi9ZYadulxiM3mJ68Eh/tEzorgtrTAMb7n3T3p/k8+2f2Ll3YbMm5ox52SveP5WFmg5eMDS+8p+8jIky3TFPpaBWjZAQ/BJDbbjsWLNmNgyj5EhW0yCWSJJHHyUK82z5DZFt//gL2gkBff+c53oFNwxCWGork4eLjg1J1Lg7g/cxK78LLvNljlmOQIsxFevPBLmlXEGDtp8VzLGxEEAQeEwc4SUaO42GNxm9Y+7mGWACRfHrxgJwgSK3OyUURYtp0/SyMtJ9dkK8oPbTf8Yhp2uE/7QTthJQYSe13zTsLkH/AFjplf2Ilv6bupHPfJl2RUy1cud994tPu9u7vWk/qBVrxSMCmYteMVbWnpPQgyrSkKus9GzZutAiTfvA/oKn8iPM64A+6jDcmJnQr68nIsMGLYnhgodM8fXi7Yxop7UNVTmPTgQGScj2qjNTpZmFt/vu0rvJ781/5jIsgBXiJcLkqQKZhEwdBfhyPOzELqVzZBUNzu4oUAo+K4LkawLcrpEhTQlNb1AZ+gSyQzILNqN/8i4fD6xYQplu6iGlOTMfJt2QmBwQ7w4SIPQbbwApqurqO/zeQ9M9/6ZPf7L+22dUvNsK288MoixQ2rtvSODt9yS778nQctQZAxDS6AO1jalQqOz6rlyuI3SxMkJEbkILyfTUDa2Da39M72qCaYiBWQnx8ltlG4ozayrPhIcx/DPsBRBIUJ1jbchW253Senv6D0S1WyI4jWKZNVJgjspjbpMDutXeXGcKsmFYIESnvzwmpOMuOVAN9EBdyFl8AyVrNiMwYEAfaQoOCuWcm7IHeP1WQA39wtB5zlWlwcAo4d5+lYOcR1UT117Hn3P7j77B2Rpxo1eT6KG/Z/CPhYecNTDKWkVruzveWeUCUte1BNzomlYxWc357pxs2SvDkf8LHyRJRD0wQPYie8b3OAK1ZpLaRQeSbgG3gdJGrjqTW+4VgyY2ex0X+suSzdHjfx21Nhgth4alDIsyCAGsJnm0UDOmofGQ5BTjJKmxEq6842iJEveaaCFZqFYZN/obkiXgRHjkHEUOcTtF3A4cxoK9vnMLnhRDhiDAQ7SpDzCpEtWvhGJSc2RhDH1DmOciGcyMeIchoM07rKVe90f/fqHZE/vbVrUb7ToMCTI8B5mpHznRTlEkHhWfJyYvP0/BOvsAeDx0qw44mjrNENzgFaNUtGhyCWoty0efWyGYdXOlgDuYlzApzMJGcn00xF90YL93Dd8EAE+xjVMIamqToR4zHd3LwSkrRK9rAnYl9RsyU3Do1z4ibtEkF6RZRklOt6UY7kY1it1JadLCG5MRNHqEl3UwWdaKNGHkEfIJUIPjGNEIyg2DRJTjRAEB6FF/sTQzZ6mkYRydeNDxPQIbAlmCT80RuqCcMu3UXt7v3uX3+4++sN1u78YlQ8jEqIHzlAED/qmZnzUVXL0UbG5hL4mzdk6M2+xFwFxyQt91z6rgPmwAG+ucKJBqJcpnbCYAy7LcApTnJoyJ4TIvneOzIBqtCs605o4APBK/XQXEGAaVMQ0l9dJrDg3nsqBr7bsqeEAKUIH/inkw7YmS0uMTi8HLbptn9xnIkAO4FqZuPhZgjiSsERJ1r621s2wEO39XxnABiSrxJbItkkjkbJwONsWJ4Q8YctZ+mr1FQc0H0TURE7DJu00Pbco5slX9A7XhqlguPHMzPxjsht3ecJXCyctcB8AJDgKPnRWbsyIB4/+OADEVK+Hhh58iWRkNmYZVshEXRWHonNwkeFoxygAIJNUxi5uAcxFZGQ5PaOc+RppA0LEV7YZKgwSqo1C3f+hk5bcAn3o7aOkmHvHaVwwcYEVrLTwrtiEHA4cgSUw/PbOoMgYS5kPsXRtjS6ftKyNMHRnmriIz8qHh9PgWaimPdUg2bPM2agFc9SA/ryo3ClmRzhN37uOTBXm5dE8sYfdhfJw5V6dUZIroJvE9hTUqndf3D1fvd/tal3RFKHe4otR61FwOFHUTnwX0ydUv/wvL5+eMaD7t+/7+Bo4j7sNeoMkch29q5j1Jh1GoMlJC+SQVLlABDbciqwE+7JJGBeRPLlRO2NzHRJLuAQfpTktQv3cLnwFnC71bD1lOl9RP/ZNr0uS3+EtY1gvSX2peetMz6OEOR+KaYTQClbZ+r5szAqwo8K5fMnLTICnFmUfeZoaNLepj2tRUb7zL5lm5FZ3GnNSXN0FCuXrt1ZI3DssQMoy1o5gp1tQ/ItVgb0alNyfoQm4qHpaAZ1HndH68izZK3bwPvdf3R39+1Pdt/d1N9VBVqUNTCXgODfjXhXpeD0WlB2s3nhG9492lqcIJ5OeJKzpW0lo5BcbIGw4wnIoClufXWvWTlIQGZEaxxMkHytLqAGGsnJn5lGFy/cCSRpxcb3bIQjaD5GuuQ3XrQlScRuwoM4p2SnezvCDyMTNIliIbN9xMrhXg1eZV202FasDBgj6NgPoBo0qTCCJk5eMzKeEowYUlRmxDk1yCrn2QlfHhWOcuTEDiO0aSwCQKa4QcaA4mrxkXMUnNOGwIx86fulHAm7NJHqrCuhkqls8X7Juvsf3nn6npkRqTcHxIXbwBwvHmtxwGxsZ2nKl8iKO0IfPHigdo/btvy+Z1sSWLXAWsi8rdgYkguMwkt+bEzepLte9mchKt7ApMxjwgJ2cUnGDhiSSxlxy3Q2qi9SuKMQ5YRwQCBCMNxpRBrBNhaF4u35HiekiAJloWfjist8dkDA9mhK/sb0z3ZvswGOomBqU7wBqTjIKVOnkQ1Z2jDFoyuCAyNXuAR2wk/z8QriDUwBTCnKfqBN5iUEdRfXl6ZJihKOxKVM8dppJh3IT+BiNvWlYqhBk6nHFoLa697CXcdY3N6/sfs3Ubvf2X26fkbNEh9N4p6mXMkx8G1ZPbMbRZHnF6u8Sfl+KgJnj9dvGHVkqQjTH33JzzAHPj89G8c0ww5Z+DV9lxQqa2xlDB5hTp7iBpMlwdRGpBUSo/oC+8AwxQr3VAKCLMqLVAUOTH/2EpuA/tlmyzWghdRIKV5Nx+UmqjYySG1BEys5RVM0qCZVqYkGyt9SUyw3DjpSNRMOFSbnPC+wX27qmSOzIoGyhag9VpHAfI61R3lhhPo0BezDIX4sIHXaM5XKdx1dh+JNoJumqb4yq23yCNPmnd/r/Yvdv7m9X3f3t5nWzKnnNEn1ujomhRSwizC8zMG5AcZdN6ZfqRnWkzNW5TA7rv+51uxEwlUBJ13O9WjletSRpypguMEq4t7Z4r6ySsoAaBOPLW0LdpIDU7BiMGzy1I3HrMLd6HGnFeQFzWUZIv1aS0rmhSDu099MLqtazdGuavXzxXoSifWk4xcH1RDgQaJkgG/Pb7dSHJBWEKgGVNmJws3t84eNpCUAUhxr+gZr+SMUaWnqyJ2rzD5TBaBVqN1xlMqLUg7FK/mpoAr/mSBU7r5fd7963v33Xmru/e7wjHLilEMxGLBjc4moaGqPv8bSu4xflhca8VDWToWW118OtSZ2VMDYIXw0gJWPLnGoltVhJ9wf7A625apQTbAT/jC8jy7cEWbDXxTrRwc9pH/yGSZSGXEY8V6TuvnOeZB9QLVDuAcaF7+EpoiDRqbOWKa2W4cVR3LpAYMpgDMYgYb5LT3jEuMzMPYmlC8x+KJjipLAD/yHJ0pM6dJI0hLfV1zdGIZr+GpguFAd1mVKwVE8FBsT7IalxbCarV217u63qv/4k92/bKN2T3dWHOosUxowGOW7gyWA9b26XOmFMw5MVHAKdmIpcHgZteB0ZYcS29GkIgprx9RWlrEDdlQKCAvZTFmou6OBXVZi7Wyyl1jPF+560tkWIxqipv7FA24Xl94x1YZf79hrP/wRbsMNlrgaTMXU1JnsXWBfRf4imBBelGk8oZJQmQtkW5cpkb0ICPUHoYW1jV58qS/GtBmFSPHxqL8gCFMRALtMTZuoeC/WDvON3jKRHOywBWwRZAwV9VYFpliLUka59oGEAAAgAElEQVQ1VjNJFUHJe2Z++OwdkWVwHy9WqtfHMsVVsSxOHvXW8YL0exj89ddfj6V3j9Bw/36LGZ/JzODpTosZw1TtGgGQzJiy57OlvLWaGtAm+Xa/8QiDV3inUHO8cEeVprZoN9a1qvFRaiKIiL/09TPzso5aSsJT45CWRZLcAbJsbNT+VPvM80YwbGbj1ppFfGmwcE9uBbFTTBEb8g0Kn8PydoUPwMOJQlMsKAHjjFzVcqIlnpzE7Oc7fg7LZduIvYSH/2Thw63cuhjBOCJ5WQkHRos11JozDggz6pLa/Q/v7r75cPe9u1Wfdw+3IiqyuoXIKOGjcGQ2yyEfS++eejeLUnuUeMONac1Wl7vxGJ591FV1RbhVNwCKigChxaihVm8ckYHwNNqc8AzeBnZeE5no4jvf+U4cIYlT2VyzaRfJbEXEQUzW5Z4coKY/qEFTdPLPsppCEqRL5HuJKgaPqgJNVKBLQfmNDJPN2XcgsBzyExCOqiLurIIpZA2Ms2nkCc9hF1oJGwCtyCXCxxfZyDIgsqSrsm5VRM6jg4Twy9UxRyctdZLDijZjzYbKEgSycISpYbcqJerhOMSQOzb3h5lCkdcud3//0e77y9fuKEaWPbeSE4u4ldEi2hQZ7ZDZOBMVQvGl9zrCn1Jq+HyQxbD5FM86bBzCKw+weXi18TOEF20Y4XaFx8stOtCE6VPD5yUKzclEyqMLBUSaGpnWYq5768kSDnRk92UDihRli2ELhr+jKpSV/OgUy50EDqtebvyckYOs8CzGJsZlQqoZinOmaLBNpo6tSR5kgd2BRDW2gmxBHcjvo/lmf2NA+MimZ8HkU6kE1Gv1hAV5BrOJBdRDbB9c7P7k9u7rD5eq3fkUWiNn8ayypZLRIK92z4+uhwicPeNpKGZm6R3FtrPtMxsk4Ve85+yJiqbkWcM3V0l4yByt7HsjN/UxCU+qzS1NhvB8av9jsmall0cJWpZ1FmldSvRXr9uXHbw7WkSr7pkJx6mk0DeyVLXaiPwTBP5l7hIlReA2k6wixrMKFxTfSvlITv4FJUEmqgqRUB2wuVQURItsliE2KjyxVUXoYD9H7dYlfPGLRNbRZqucDJGiglxFgDmTPrix+5Or97v//t3dcejHj46m5er1rjjpronxFy8V0kRsz2sqlA3K94KvmAvhmY3EQf40Xf2DcC7ycMNMSUJ4Lsltt7jSIdTTmvCLWs4SVIbY++cClxi9wTE5HraYWkHfG1AzcsxAg1OXwovi6sz679QUL86XQgDLLErkLVhSiJ5sYLsVWMvlI5pkymAfwgJCNwDGsTbdk6VMpcI41NloHgWO9M9ylF9d8CMYYkQk7JFVAc/8KYgnCGR+aZA/bJ2W1t3/6Pbu2w93v3d3N7MagAAciF2zHopvPLjzqbu+IjAyTqFD7W7vuMiYBomaZBW35VZxfwW3acU3zDksNGix3I1TKah749Dahk0BJ/N2pTdC5Y+MBNRk9rTI8R+nVhZo0emw4il5FmaJfebrHUfJOSr994r1aV40Srzr3Rjpo/Afi0aXLz5f1u15Jv9ksWOlaqS9CC55NxXHE18MY7j+43q+HF/oCb2lCYI5BVsDP1/rqMCssLgZtunIC7YSDAUBmZWl8d98lRtp6f3uf3hn97uf7P7VpHdEqv8oThfOVTYYZuLDSFRgYv6iYZNnxdL7gwcPCr7Hgsw8V8ynBf/NVHlOs7i/MheyZs5Icg67ofK3hxuLZbot33uwarWreMg8vKiU/M60W7izrZlOKIDGD1upHWGlx9nSHwdcAvR79K++/CUGUYfriaVFvWbjiyaCbNkMasAuX1upJ+ozKxlwXvZcf+rujPyLGPZOsoR8vjTebu0LdklIKu1CsYnjiIeQ940o+beoAhtTuwv74s8mMO8K+cHF7v+9u/vWJ/u/q5p55yEeRmKVqVep17vys3z+bltaEv6F5bJL7wwmjIcWyxkPslAGNBCVRYnYRgZLfpjtcrfusYBvIzzky8IyUy+Qql0Noo71pvLuaDfffPPN5QylO9PYY+tejGCg9h0YkBl98MEH+voxOD4kg4HGC10COmvoDi4z8RyRxSUi+Sje2TRrkIJD+bu6tH8M6vmoogkOQVlNvszLem3t43xUQqDNB//oyMMnw8Us/4SLTfOvCOL6Ds/V7FVmw2J7wadZaZk6aW0clsxCN1EZz0aNnwoKu62A37OKRxe7+zd2/+jR7me3hv6uagqJOFLoULYRsphNROyl8Rdn1BVmUVExXR97SE77KObEaGWDJ/EiJC7KF5ltjN8stmkIrNgLmwEULVYUw9TEAKOSnTGr15XBPZE0aHfFndA9cc+iyVys04T1+xsKZ9tXaABiishJDohHI5Fui2ZdAauCU0AY4NMG1BFfNt2Nw5/r34WbUQSPImaaFuv2AhoAq9W+4WL2tDY13Ga6GD8lv6HWhXHa7MRWWkXAmTZChV7IipsrULOTbqhHn7zlzEwSK2hxdAo5KJYetyi/36r+2e3d167eM9Ozfh6BMiozsGYXVtkSOQP/o+wUPGkWBbElQrBYZywyMuMX+Q07P/inRMaV6lQdDJ7xE55310+a8/Fn2LCCv3215NUVG24MiQHzL49jdS/1jtst3EclHkDHUzF8qZQL9ZAa9THFOKZczW1GSXi9G4Od/YzSEWW2sDoOPL/4GzV7r/EE+XsjrPtR1OOJS8c+fEUlAS6UFUwVhiK/MdeFcfLs4r7at8GnTYIyXgZb5iE2HtWR/PBvUP6j0vZOssZNy2/R/U+fvSOSA+zD4tXfeMHXulGxh/Opj0wLBewfCw5ONStyniV72B0syixf7wNq/rCCjwJujvzEEBgjKgJhvkijRoAGAfjvJqylpxrQVpHf3Q7Gze7nVTl5s93CPdPlWLn4nu5UejTU/MhYbVH5xR1bfZ8pqG8m/gVnrD8UsxHgUGbqqPx+GbSugzMkwxfKTmdMlCEuKFsoNxCePXBn+7LyVxtN9Ld0VPBmZo7kKIvYCM8cyuBP8iLrjnPEntyX/NRUu2w0BVh3//FLN37ns5s/ePmGR2FwQaPJaNTvKP9CPvCvIDlD5WueahCX4qHkmSon+VnRqZvbwynERmLYZ3rZ4QilzhCA2GARhbYYQpP8DnLK6Dm4MRisIdqvJvJttdFn3GkCi2HI1Ovx8geaM5HI5XMQnNBXNjK1tGQDOoFtwbozWzTZBMKmqxZakP9UyGNdNgSJcV3Kku4tHJCNSZ9SoQUJh2WIwF3KBUARjobWOpSRX+I317CazV4ti/80NXlZsEYYxR8wHWQOJZziWuP8ZJY5cp1mITYESrlAHbHD0Yj98GJ378njf/ThZ//pxpMt1ewdmCDPhZlcBRMSrpm6yT05A70i1Qh/MaaRz3qNXBaTjvWyDlrlD+FPMNu2XCABQexwhyXkN7LvauLe0tMioxINk2h0xR3ZTDAh2D3ghPFtgm8WaN69VOcY4sSDnekwenRNxdUKwWJRfUPBRadYdHDBrsuCIgBrMSPTOmVdi4o0anCmJWqP8udR4y/dmP3PNCHdsWYjKjaPOtqiWqCAzWyXglh0rPzACdaihmAAoJvjaFahBPk6hdcShsR+JAtozAFhCcEOx8RaqhHBHsnr06v3zHz7k4vvvnSZe791OPR6Z2jB+JnQqHXrOfLi2o/r4gUgnpyZM1T05cLGRA0VDkdLpQhHqx8eD+U5PMPyCYkC4oVRHbZp+QxgIxHbl0oE2PQ4t7A2aom9h1K7hfthvoGdkp3CXKLIF1I9LE59FNQYnwLCgTYEYI5btMJTCl7L8wgS8uyDNcbTZmg7BX6S/FSD9s9zkwmFb8pGFFw3IZFfyikVr+vzxYSY/QQKJogarNmbFG6C5IRBDrvwWYtSh7ngsGWbZ0AhT8sdiGhQwuRrWEv1eldO74j84zuXX3+4+0G5v6vaHb/CMeNhQvCv5sjqE7x75baDtFo0WVNDsR8qJIJkNMPibhOlCCHDi+FfjYLJaB925BrkB3iXgsNmZ89gTb2uJsFjvI79bJeBBo0W7pSEV5KbqtyP+fKEqMPSpYUOzMIxolgnyVgP6cm/kJAvhj1EIFizdwl97vS6hnTYvuUzkk1E55aFHJCN/JmFL39Bls1ofE18tx8YudolktQpfBfSSNZHASIW8gKUwQd9C7FG7Kh9pYCFIFp6WJJL+WbBwtJzZY6Pr6j8wHvW1/bPu9/effXh7oebrd0VXm6f1ExsKROimc3MFUvvprbICPA5A7Ic/sWKkMXjgrVqusyRPPoSWP0Gk1NfHcyfYukRII8F8sfBqOn4WrwxBQjxDpVR3Y82brFwZ+WYJm7wHYG7zh893dcOnR+YmjokOYrdwMmkwkCbF5dKIQDtIM6AAhwHi6Am0k2jr5RgM8ehCJ/fbskSdAz4QrrLQhNladoaX8J05r3HTK6X6w5V+UYBUXAKZsnjgt+lWWMY5jIjLgqqUHMo4LMioWld8+aJYOR0xABmfuXnPTN/flW7W3dv4n56PHmsVOFoy9d6/CT9HtYZ4eyJiFhq7V/O/owyxGEtHJlHZHdtqCHkKcIRcMEXGpIsTxT4c2RE5C+9UxZl+PL0VFmVWyzcQYPj+AtKNI+blTxsp7SKmk9Q0xnEpp4fXgeKlSkivuhzDIFEHL4Qt3QBcUyEZc9x9TDLZadZcnTedFg1RvVANQqGxy0pwtyxpd7K+X6uxM/3BzIH4Sygfv7K6E9wkHoRB5Oa7mauKHzLJr/R+s/oIOXL9/bzk8sEKYI4HUfV692Jonb3zMz3N1u7i0VRdWGhq9qix2a05mid1aRjnxfgaBEqmX0qS4RTPujjomIvNDhF4BC1LFNcaJZFhyU2LfgyCk5FVAT5msWesnMeZB9QpLnCPej0hENoPiD6nEup5jNIlA5l4+l2XSuhWhaQNOzMA8CKZVHOniWOClq2qchMHLbSPSggLSJUD0FcVA9b4YW01g42minDTqLwPZVmhm3pkLjh9gtdlQIlS981LzT+0sOydiq4/ZDalp4rjS9UynQ+hsel89MO1O7+NtM3H+6+e3f3+WOs08ZaqRccEIGFyoakmOF9ajhTK1vPao813Gkm7PQiD192NW4Cz47TYINwBNptVwWFezz5gwh0dEGW4yQLOrpVmxZvu6MNHDf0OkhYxBPJX/rSl+gcOX5A9LGX1HxgZTE2yPJhm4nQMHaos+0xuihtZwWY36AdFaJ0CO4c5xOHcUTb5qOx1gj0tW1XBVbE3cRoAMpA4XGbU4fAzG/THj1WBVanwqA1BpPHrUucQB1rdWs54/x566jA4yJamo7TsdtSxHlH5Hs3dl97tPvL5lb8cskBBVhEJFad26dEO8nIpOo8dQ4z5l+HozqpTdQnA8RRwabldiMSCkKFODiEov0zJLfFN4F0Ua/7RoXYnopxi+7Mciqwk/X9j4bqdSbImuP1jlFvzVebensNsxdo58+YRjBvOt7uAS0Wtb8BZEzNDHCnDRkE2d7Cw0DfdIlfGcQ+ndncQfjFtgJ0OB3koU1y3i269VYmtkUELVRC25K5Jy0VeNNZdwins+d0KFvL/XvCx8d9nnzyhBaVS66jwkw7SX7Cy3RLuEPcJBMM10qHaRKe7fXBjd0f3d59Y8vPzGABPiotNXRlCzep2f2ZVSFR5R1op4CZ73ShghGivj/LWoMNqBD3MLSekNxb0Cj48itkDHq0e/67YvKVWrNwj6ROedYcJRq5QeBMvgK9lrrLOkKYA5cYB5uYM2Bv/F+ej6uAhrjEnQw3M/0wMDly05RhISy5fS0Qd+pGixueLRkbV1CltVC9VU3xKFaOPqrR5a7lgEm2WOJiUdVwKzuRoMQX3AeWKlYSdzCZGTAzNX3/6pmZeM/MRmkQV9OCQmVbwr5ST83qyRkHsSIwYWWKClH4GrCULWUaQMFm4qqs4Q6ktWWCszpKBxGLol7HI0/k3Wc7FmmwTuGOJCGGwR0+yO7kBOVBZouinx+2nHuK0FZhkCgZ7ZeeC+O4szcR6styt5WqdwBk9hyGPdBmrUsEE3aDO3KeCr7SkmauriXn/HlpF2rOH2rFEThXt2RESoRNfneKuxWlPTq1xCHNK1mOXt3ESSzI+pCfkOmSgrqjz8dVuPOOyJ88e0fkRmt30LEitiQ6zSEiMZJ/IJLEW/LU7u4fyJDft9eS2zIDI9S5Z+vNXuRj4E8FflGZiAnyS3kWmkVRyHd/eAr/cOo6aa524c5MI2yd+qOnEEFkDqAily1qGmlVL/ucji/a5CAQhXtOywltegUfd2UVE8b5ZegCHKG5KXy6rpfDHcfE+NbJEpG3vuguSEqQYmy6T6ZUhZvzgtSTlsybrlSgESo4GFupIE404E18al3u/Fb1GtTuUW/BM7PqmGnJ4XpsOMoVZhxL7x6PnrwuEENZ/aXLtnw5gUns9r894HdKdjIT9ejr2PkjEhHhYOlatFLhTg2q2tM8lE+c9Q5YtqeFeifTx1QxOBMVw0YtNWnU7AFgoT02rwyrw/RZtihpcOGmqWJ0WPIVr6IAbisKEFNjLejzkSPjbpTrhRZ1suNCWNEaCLZRii8kzNhhiZ2KBgdHE8/YMddqjwjmRItNBxBlVnzVnmNOUa/bU3ys6y1HU9Tu//jj3b94eavvmQGOMostLWpOMmnE8MOsxwYsB/hFowbDpdEAjzyCOW1l0fqUIogAFC0c5DjFqXGKnw+OuN7Z17EjIu5AyECL4pKkARcvCEJbEefwqZgkRPdAyx5nznCqaNNU2OqKff2OeyxMVrBLX4StUiPniGSujVZaOdpVaBP0BYaHWSdfAH2FP/v8Lg22VDOJRYtG5LJaI05ZEPQRXl4xPhVUCZvmgvASPOMMjcqCVm00OV6tZn8qJCIOWYm+U82qCXw4kdr9D+8+fUfk4dWtnOEaPKL492nJ+9QtCutTaDBjDyCIKn60+sUvftHHUy0HzrMNhsQpTLpd1wYUj47afXXXhqRvQtxLABY1A+D3LqFSL/nOwUJaLFi4s1Qbf8j/o6eQCj3Zro4+QmTY6HuQvfhYEIHAf8KAOqIvAhBCme9aKYfxcKHtBrIAH4bAXCgEHPKLPqDZHJiUFx+2mXCmshYTJDzbhQowsa1lz2cljAasJQo+Ah96nzJFUuEajWsxrCyzpIX9prWIYsu+qywDQx8SqXZIX7dlC8eed/+T27tvb/n97mCUI+QsJWORcGco8XMUfW4bkO5xAx2tck5jlvAGKX4HMk2Yab2Axh1oAQeRatogM3uhLyiwxC5OThhNLxsiqLOEFuXf405Q38MyILi7X0FAptriFLfR3ggiF0eyUV7uyRyhkWaEpwvJG5FnshjMN18LWmuPPvtQvwX62NIoLSZjtWhHWggB+VxMEwZrQV+kHOEmGJw22mEvjlxBi8N5y56pw8UEmTkg4iDM4B0P03c9uKCFlMFKJ8DVTpfEBdY4IAbxSCkMLu3ypUB4dLHzqpl/+Gj301sbfmYGETO9WwgNBzTOBPoYAHvWN56cMdoEgmihl3G2YjxHdaQFNIAZ6hxtU/ykGa0FxLMhKJi/KICC0AKntlICG7PkirtYEysHlJc8cqQUoVhnGCglHfj1qjWGnL7NtgFrQZKaVZNgNEUZEkNlDBZZriirMtnKDlh/NOa0kBbBYHirWbjwcoFyOS1qMhL4MPvlgBqlDgY5oE2vfAckPDr00mXUdE01pgX5FbuKpKYEGyVM+IVnJGgR26jujTT2zMyf39597eHuB3d3G1tp6yDIopQfY7/J4YNCaHjT/G9IVKtMWuEoyExbeucUFFG7E8ZBR78tHYYW0JCVlg5TuPPQGsAVMKOeijkLKMltuEAE7z7bPrNBVnl9diz2QeGwM/vh9jCysXUBizJYcRBdYLfpX02FFqHaMAibvoriWNWjBaNkjonBTevVsvBlEcYgHwxXxeD8ZJMJHWdnOfaZ7dtsBi7pZN171FQrMAwMThCGFgoUfdsEOVMqwUdSZMybUwSD+0R49UAFLUL+7ZZZ+PJHVffvmXm0++GdDdfuHIo3ZVa9Ua/rIqYVLMvYRtzIKSLV7mbJdIfUjEjxFBa72m68DS7cxnAT8SppV/BAJBcG+Z0l4+ViCIuiAqNiJEV8fLRB9CCjLbOALDtjbb2r6eM+PnWKddKjJF1NBwMjpDbtH6iHlrOAtdRHjfARpV7cbhWxv7XU2dy84WUzc0MwiMRgcOZoEzCkhSi53UQSKkfs4hFHg9gEWPK74C5ILMIgA5C3FsqI+UrNbCkpbusOBIOyIa05QrrjkjKkUZc27R1q9397a/Pr7lEvcg0OcjTNhRuKAFhLDM4048PuYQ/x4LGnjg8bnD1DNoNs3cexAHA+LlIdpeMsDocNcGeZGDgg6r6O/bBlqTNMhfC4sJ+feScW7iw7Htt3O0j/o7pd1er7lXVXCU3W+nnuqGBLn6RyKfNaWtSz46ccgzsZZb7BnZ2xeIPrYXWR1yfgzxqFp3bcEB2E2TopUojlk95vCoubbhpQ3goSBRZuWCq8MKp0G5Dm2uIBOuT1anRMgyjF0m693h2KdzMqroGX7vltHce6+zce7n7/7oafd8eCkg4duEi3UimWRu1VIYiZUclOgMlL74xKJUaR5W4wKtgnwEt9gQCNuAuy6Fz5+Y4wKnEAHULWHPsZXbizYyV72JN00uMs3Yk6ryWjyRdO31IJqSdV5Y/AqTxj2emiSjAmCzuVY8rOuOhojCoUWXSW1gZPJHJAJLbjWQKWVYdNZxFcQ9W2dMiSY9TrpkPfzEB/yj4RYeVFDjvVYBPn4cPIYTXhznZpBZEo/phFQjxr9hq4A6EO61pasOXGV7v/2e3dtx7u/vX0Pwm6nHQjRkaHYMWuMBIkMjDHI4Yo0dTUXs2n7lK8erRh7JAMj8zsijqbtivyg2LyTYiOQIBGnSX2UzSJVGRgV/aOTzUbPj+icJc5xHdG3Hu9o+wFzahW2ceoYr0r3GQ+uoO8OJ6GAPqQiErdkZiqBLY1bcB2evENTtKOPJMlwcVwpRgkKhREZyQKc5PnWq4j2bZ+ZxvggHeJVV7gRKkXJFZYRebsbZa8o4yQm8sgoGskZKV6fawnYtytlP2mayy/Vf3Tfe1+8b27l7Xr3FF2M9hYvI2giovVb26tE7Pt+/fve8yBww4K3r9IfhbFQYwwuVjsD7rGZ8JzKIFX1HKQIwISOZTyRsSGXk6XpdugI92EpFpr1KTnC3dz+EIBRqqf119/PUaHRZFivSsrW1zdN7ryXPvjKBEkGJpimUs0uF5VhIXrUSkiiA8eluMYtEVNLy63TyJFRA/7IuSuOIioGLDPlyHqAzzyRCRWqNeTzKaLm/bMRJg6tnYQt1K0gOFasrGHKKqIMS0lkzxqrJo2sARcavcf377c4ntmkjMmEjmI2IvQJYDKH1N8UIOlp97H5jUOwjht7eeIAUyQknkTQlMlu2igsPR9xcCYq1ySAcVedmU/NhsOFe6Gi0ra/Q3DjeJAYAIE7CavrK+C0YtJAwGujkFUOogS4bAQfIFVmwh0yxH0IdFG1M05oyBl7WdsqGqQFIl85qJ7OCMekSjerpVQKYKRrVeKLEQ0W0URPEqRvBKPk+v1ZOE8PRjZenDe/1bVe2Ye7n64kXdE8kQ8RmbsOqNg5RLTaoERK+5ihec9BB9lXzKbnANKUaSFm5AcaQfaIAJTGDl0N24YyFB23adiBuSPSyxtQJGB7scLd1gYDsH+Eq+7lijylk4tZhkQ9MWlOQig0pZ4ZNDdKnDOyC/61kRAfSDmBo9idwtZZJr6zC+0mNa9nV5Y4Fn2o0SKOs9eUFUTdEuEUeOUaowOYmx9KQ4aoUi1uqRbr5cNqmEYjVSKc8zs3Ru7v2i+dk88cuTDQjDUd4l1qQhFXQdzMJnfV8zxxLJ1VX9p1SM0o2qnphSZA4WQxU2idndgKLBYYsemNYhGnorJUTAUEbVQ4zinS79wZwqMEhZf/vKXWYOBysajUzLB2lynrr44PwEB9LklBay+1XicIGeFLkx6u2Vi4jGpsN16PXEtqohT10ARcVa0zIxd8kpal60TVxPgZw8ooiixX70oOSvqcAMqMK0Jd1PDw3av8kQ3OUKrcmE5HhlVTGSK7uybO35HCrq9+/rD3ffae89M8MjmmU0UfwPwaiBkxd1UC26iSCXShKX3UISb0Dozdg3AsuIliqjRw99R6aMKdosaBSNUoEtOSLn55ptv6iNAiHRsUey2+Uh5m0t1WDEpcatNt6hScjPoFp1iYHCzoz9KdmKEZ04Ddl1FBnQce4k9R5Yd23HF9mTmwyiIEoR3BJXXhpRro8hZ66JpUMmcEo8rmtapqUUJcq4Yu04JNvY8FfLvpvIHD5eMUq8Ojxjh/puLXYeQfnyx++jG7h8+2v1lf6nwsG2NM1BlISm0Mpj8QjysCzX5XZZTiU1SxLcEClbO62P+XBQJY55WHuRPtGhLXzhAABfUAcKmdQmjwiPEBqwLy/uywG8d8EdtrWO/KNBHB5fbrsETlqHaAOJHdZ9/UmQHYDit8u4aLGTOx6Q7QkSo7plmj/kjKonHihQHPVvycVRoblZNgiGFpsy1ZSFzZEOTdYeeIvvY2vk7O5sIbtKG7XqQEiujRWAPKsVYxnDqOYocO5nWhnWlumTaCI308szMn9/effvh7rvrrbujMkpVwWeybQjC+sZS41r1Uo9TS+/ksfTOVGy9qwMf2TN/12VzNYNE6Vl/Xql8jR+eIpci/KURUgZgH7gkAuOClVLwsABIHW/++q//OubonE6tcrBF0zkKFCQhXsF0olhHMPpIom6wmbdX6h0VMvOkkSsokinMnGZIaVmXoNJ9NstBX4SeU8hrrI1tDiAt9BWhqMxoWxBmvgx4oRFL45JBJQZRSUHn549fZ4Qg5ZTt1ZGh1Cx8BBeTdQkqI4PuY+t6DxVQgUVdOXFF+9cAACAASURBVP22vf6Ti92HV+vuf32r6t9mivgfXlnKJdmDAY3ciHeTRBFFKqvOjNbHTD8iP9PSd7KnZE5UqhlRvcxerlSyu4Pq4k99ukcoLjXdKuMkUo56PXJv/uZv/mZX81WkNKn7xc3d8x3FitEs58xGVoDaWK1jnhbpZCEGTbQVZz7KRfdka7rsfe/q1/0RZOEc9fpZKnmyjmebdXVv9vgaRNiEbfwoCjWJSkylqxs6YFoqkmvg+OEpkB9FBMfkkramqETHhkqrAWtXu398Y/cPPt399GaN2j1irD0bsLpc1qqNZuSmgpg0x3mtQNPX8QAR3Usa00XHsc7SHWTpYzhbYo/H+il46rsvinBhumw9RSIl7kMiEHXhpWAutd1uxY9RUtajikuYP2BxN44Kz7AO2KKNyebLM6cl05nTvam+0GtBHjxGrCcMmz8VfQZEZQCikip/oM1WLtFChVTNnovDgspgM7ySv1wDXugCqOJxrDj4OQMyLUtCNDobyiLMYtOwHLPBVSQiqd3tz+qSg8yKbX7hmaNbu0Xf745NZSs2p8XYfHAiggnI7QQxunsZIDt58OCB57/DpM9qxKh0oYju6sWz7Ws2kCN4sRl9mZAjG9K5PGfBjuOaohafi10JxaFLROaYoonCvSm7nwk9lIskb/5m40U8ivEZc5V4HQLMxKSF7ox+RV1QKZEQgBhCz0wLYQkr6lKQzVCk4IB1hoqaAAU9x5RdZjJbR/6zs0jhVrakybMt229AF2lv4IHmYJMi2NS4WY04iyw+rEuzwvcEU7tf3N597dHuB3d2ZR8jw6YNVjyxjjOaxYx4acp4yKOssjIt9fDlHv6nPjIwurRTj5Fc1S5jUqRbtp6SP51nAFw+vjdr554qiTfqgOJMCyl6JV2evlVm1EDFG0t4BIJ18ZHrD8hPJt/kKQXQsw88V6GHvdqMthYydBnlLfXRzp/RDZXGNZHEpsBhK86mkW01dcnHeWxLimxlcReV4Z5x98U3e96BEVz3To4FpJH2dKHv5FDWiBYhxlFdBLdg09WrQNtnsykVkiKkJfY14OWji93Di90/eLT7qxKLh2JIYlMtUTlpcnm8qJUZUjtmI7TChGCqXnEpMuBZ8ZIuK9oYUX1XoGpHpQfZ3RrR4qzkhw1a0OVQqmlngo4g1AglnGaaIJ1eLOx6ZDs60aWj2flD7aUQm6asc7KNnp9pfAvyEG+az4yfbdke7B7IFYKRoKPiCWWwaSuuGC2uR/KGjFTnvr2phNfli/1jM3LeWTbxovESjHdFqnNMl0j21yAy04XvU4eZoZLvwNDJppZIM2klNpukQlp7y+zYYLN7N3aXt3bferj71yPeg/KcHsk9Wem6a38EYE5qTQu9TWVMpiIo+TtN7D9z6T100VEoq+z+RPUgu0nV60z9ObInfUi6GK3ZLJOpGRVsEb5aWXHfYgw9Cjf3AO7RS+mkcCORsFGNHbAtHnJFypmOaYQ6B1GvNBWG5iieQ8208RFqcB6FTcfYFCMQuhx0jGfrYShBzcwqp4c09akDJEbCQyicg9AcNimCmrMR4NS8TZ2nS2uLiJPxwZ01vAhoKjyctmZy+aqRnCKROPJ7tdnSD1X99w8/3f3VfuVqxEZ9xpncsxGPIwapGhEmoSmakcrHUUvvtBDK9K3gKWZxU8FD+al7jOL3YHRhKtcjMod1rb/iDtDWDD1ZfNmDiLb2rJMzqAZySoGyMowajXg8alSXZhsvATXTtQWhSoGB52iLwxLJu0JILS754YAcQZXcwq07Kt2A2QN2Wm0XvBzquMUzXAYIsh2Ctig/mVGZig8FATPbri5dCmgRZes1SJ37591v7b6e97x7IrRyvO2CP3DMZcQx1Cg9WwvOHEFws5JNfqvaOZmdFnrxGgcDWs+5JIGGMH5Rs+iPargM44mnslujZiyA5F+/cN90YjiLOFuJ2k5LcAs3S1SQZ8WY1oCo5J/Wt8FeOaHqrNgG2VfrV482IVQ4WIVQhkSGrQegQBuARag5y92pBpCU0shQhFBJbtFUd0qLJc4zs3iQaRUjn6yRqJVuwNCRhI96t4VbxMmqpY5RIFLtGgQBz8zsbg69Z4ZvBqH07RKa0GjngIRWcNTufKe12yowWtXmBf5UkxVupddZ3DQWHuNWJPnR2V45DYxpY71jf3iaM/jRNuYKaiLOH22zlZPrF+7pLnArkJ2Vk3uwdZuWTIT3sv6zvV40WBoBcQc106JPtxRA6Oq5n13lhN2lIS01PgcRB6S6UgPmjGNGTsokEFqwGkCNMW3TLC1H8pptmHoj34ec1RrmnIKrooAt2fe6IBrj9S2tJ0apj6hxW2V/DSzt3s2d73a/+nD3w7vPvWcm+IIYQjeURsNrGGTlmJZjWnzEplwmpL9kdLYLr+FK8W3VoU+d7d5rwD3dM6BVyHX/UN90aS0CuGcoGPN7Olb4uH7hHnG2gqpLT8EWbVyCLZYtBZaW/NT4FBF6Tl3d3HlBZ2w5lWo7fY+WApsDoU2B+Uudn6gyAJyKOZFTF4rdKgxxwOBtoj1KKkGA8YtsOBrVsVpjVEJ7oF7vSoIUlkYjW/f8Ro9VIUoQ+/oFUHHE3rbSdbFfd/++57GfcSrqLuSkxeXvDUhsZmlr837D0juPvn//vidVCNkTvveRdYWloWNyHBB449exHolZ934mUnnU7huNA+sX7j0T2dZH2YI5yhzEZtBc1PH1SNjbIiJHWi6ac5eY6gDRCqfNpo1MdXKQaaHNoup0ORW1l06ldDGj7RqUU2wDXDLuok+gTrNAq4A8GsgkhHnmIL4ub1OdTPm7zVJFVfMHNl0Byh6/fWN3++Xbv3V58Se3d49WegqxoEbMUnkQd1YFhy01lLvx119/3VPvivKcpXfNlPh6ja1wuFss2PudSSMhUbjgMqSK6FEK0mrjrFy4M4L8gFsNlOGJ5GNi2zQL1ru22D0eHufF1coIsLSBpQWECrJHOa0sZ+Z0ClBxRzDNbN94M0muuDqqOoxHAT2qtpuPldxWXJ35Uk0eIdbbGjG2qB7oAuRp6aMpdSaTEh0jBzVbHWZqF66q8c/399Wf/dYnu9+f+o7IzBnrNBOlEdQyO1bcY+k9yuthWERRiTLWqs+WOjg1uPbu+S3tD4+8ylUBJO6sHJxVZxUJT026cuEONaZwSrh2zsv9rJAJRhHA0I/mMG22Rf9ZhOl7ts1WGqCmp46PLBBrVFABHOW0We2umaUVVAehQStO14rIoU6Ei2ZNKF8wSNJIABT68nsVbAlJs6OVGPO/M6GOzQ3AJrLPWRiRAh83iqz9bOOmGhA7bq3RkcJvPDPzjYe7793dTfm7O01pePVVPKNt+dcIPKu79I6UAQgZG7LiViSi3GFjnLoNcLXU69gPpyh1RjChEXWEgrWC2wRd1i/cW/6OLyoAdswEGevZsBgtJ9DwoksdBCIkiVM25OKUr16P5F0HwEVnEUNVZvbTZsGp7vqiFaenksq0wSf0Ei6u06I7deo/YcJhg1ZsctWCyYKFyNYRBCaQ21oXXqNasm0imgWtmAUjgTlsD0/PzFzczn1HZK9vgx8pmL7kWT0uncInlt79YJRf2E41c54KPFGbqHpTS7S6PxH0cLrKD0+TJKMOQh1iM8izNd6okZdrvPIfYJIJCsbiIjBFsS4CRlhhmmGdh8HlcDpdNGvWMw8FPntGJbSh29ABdcQUnHJOB5olWjdNFuERlGOZA8i0c4k6OBplb9iEgF72uget7QByndhhJxMImmxdQauIalLZlFUUp9WYUXxMFrKpjvABWtDUlGBdYRAaFRJplXcowG+3QTr+8GL3+GL39z7d/fTmdVh3p1fYG8VPqZx0X+tA8cMj8KIqS7nylDDUEXjj1hetfu2qatfXKrv9qV7NnqcO2WhdPM4UV3mf9YoPOmrARiw40r+9DW0onGZ5ujei0SgWrnFjAUUyS7SqANR27XtmJiMUEWim2WrmFJWbcZ+cRVCEolVL4iE0fcleWdqz07E3BDUr3ln5ew0ExoB9OQ9KDotW6C0dTk2hUrk2BIU6WIsqpEffih85rCIv4nA+rT/n37d2X8v720wrapc5NWNWEEd12xpBXRUsp3JwPyRFGWm7l3rHqLRO/+qrrzrwIHvLSvUkP/qR/JFSBZ/GdVmzcBejl47LR+lJJyNJ+EgMVNnb0tUJBwLThF4vupRFoFvV8cNunnAppy4sK8+io8202EVlmzA4N1QaouxoX/RF+qe1lu3fsQQ7xL42NKFGpVv8a9JUr8uXXYc9agYFT4oPZmRU7dtSptZuQhRb9GrE5LizjTAQJlWmFqlZ1O7+rur37lyTdfdYzKZgy6WhLKkcJ+qDBw+8cCaWSBIpDsQ0JTtm2dt77723iYXqrvynjhkqjQQEEelUGjrVt+b5lQt32bemtuZigqyN2TmunCQqa/rLNh1OhY/Q+lRVF0n6OiEjF16nW5FQp0cQBUVSJyP9N1KR9IQ89TEKqeKV7qnplj4PfM5VqtINZnkuxwTU0sIfHZ86Kl0CTCgrjw64+klIxtcIK3pKYla9PtP497X7zd03H+2++zS6rw7wXAFUhDwIRI3fLvILhuRpdYk1Lb2raD0VAwKPxPgrToFFmBy9rocT4YWaVKbXik40YGdrFu6sAfcDwpW6JDEo1vmJARkWVpYjY7mRS6FxncbBLAezUQryZ9fqtNHlOiEQVVTLawNj0aYRb7VHa9xj89mzzI6dpWZ78gs+1yOlwQ010jk/mhzrUlXXCLMqy/i11WSNaprT2bloEYXUzIr57ESHDSLV8lzMkuGwwbQzP796zF3tfm3W3RUhanclUOOhG6GvvfYaKpXvX/nKV9Sy3P/wh6dMjrEJC26ANZjGclO9KGJrVqM1C/dFkxmDM74IEgmGMVW4uzVXU8Z3XYXBbNyJiRe8a1SGuGYcQeA6aRTe6ktY+ayRqm6+E9FFwqtfRc2X/NQINJLSxmqU3LZBZteqdE8hPPO8sBAcjYqNcyaN22wjyLNLpNq3bvqzqtfnPTMBlCzWfu3Obd944w38emzGUzFucU/ZCWPbhEan5D88TyP3V2JdNT86lOHomTULd8HlqExzTjIvW6R/JZ3gNWe0F33bQQCniVyJ352Y/QTxlrC6CWKU7RIGX3bMmqMlck0abiv3T+O3ptj5c1GK9drnd2m5JSeiiySds7rWrdcnu+3SaERIKfUI0NLS5owfGi1dFwa5/Jc9LF3cWHdXu3/10e6Hd3ZTQn8OanXbcAdhodm7esZDNs6eXseObuW7R2WIfRQqGmmj0lV6XY9UKxOFRk2lpNUKd1gcJX7CSUMxoxhQ+IDv9bCYCVBcvy5oVR9IDFRD7vxwwDa2Xub2WI7ob9873/5HRCiVgo5u4nfs/HW668ZOfOvaPimZEgqz1t4GrC7I5b9RQbZ/GxYcXaf7K34E/yXuRpArLBtcOK2ZcH92VS5ep9odRzBUH7vtaaRuQS55wmx6T8UIYqp5j7aj3veiR2MFTxe6NWuq0j0qauZJGmEnAvhAxMscrUiz1Qp3bo/XyTqkwGEEsLL+OaNNFuNFx4UQYB5xT49crmJfaiLBUdBhMKUGXH0c4IiSjQSUHDTgH1n/FLnBUc5QW2lDIyZ3nYpCyMdTrb111m5k3lzmpouShVk2UkLNN28UqMAKGp6hOC/BBJxVcq7a3SqOd0R+/7q8Z4a9RV1ov67hIVc5zoW929F21Pxc9Z4Z1Ft6jxfLHDajRWgk6G0oMR0q0j1DI5bfyJcJqxXuSo0Jv0xN9RzLWCtwdLnsHa/rdT1hNvdRRJBj7EnO4ecvrh9FwMisyP7o1S2e3IrVqdeDX4lK3B8Wm3ej6doEfXZFZUXhNTM8JKaiMApEZ2hq26IrkTkKjrGP77esLC5UGyScY3vCspRtzyVBtK6+8SeZvCPy2tTuvCYMT9YTHuvDy0IiOllNzxGAs4vPSjhix0tmDmV2SRtmc22+O2X8wInaPQelQ0xKnVmtcJfFMzWP9bmo51qIGqegDwlPXX1x/hQCUc+5KniFY5xqWeQ8qzNjkaHaGSSUynSoymLHEh3vUDfk34yxBInEvrK0i04HgWt2N4JQq270wu9Vub7Vej3xHlHoOlUbVFNCTf4mIe7HxJZ8501gLncQz8x84xq9Z4bhxVdYgt6cW6xRmHPbWDVnIffv3x/bV8lOWh0diGyH3V0V/xtZpT4Ub8IZjhA0OVhxeeII1hOUmdCFmQ70UlrhOwqsAGi4/cBQ1S7xgfaFHIvGQhrBCr828uBX1Bgr2Jz2Cyk1R6SZfaMibGdhowi/DOOa+ZRAf23uRuTpSNVyM5qu0xI1jZQaolO1+mmm++d0F2MpZZ8Z/aif+F2xQBlQLWr367TuTlkEpW82BnSffwm5fqPCc62ap9exTxjWOK+//roRSB6P2fQG4UQiOaVYkYPe1Y1+pCzFw6FWUWG1wp3F9BR2Zl/KPSvm8N1mvOiJnT6S/9rYZVLKAb0yY32319Hj4DcGxC/rP9rsxcmxCESNO7ZX8faYFc7ifns+v9xfuL9mRqIoBJF9cfDrDMiFyW8uJpeKdTQ5j/E6MlSYhdVdpze7Q0wMp5RSI7F2FEYunJ5na9/10vPuP7guz7sjxfoLh8LCEvUPftmA8XnrBx98UCqzW3EnbTz1TvKeaSXbM+l2Q19PKYpQx0IMvuoXfusU7qiN1UFmFIvrDrALiPaDRY+/9DFUSB+vxwFSZuqle9TrAGHf3LtUsLgeCBfUYiZTkyXhwoq5mJ0Ll8o3YXuTpWqzo3C/xUX3VK/jV+juubAzlHKpTcynSRVfiA+XudNGXqsX1jB1apmQC2NZGyZavxCZjInn3S0Bxntmhr7EnzzBGh3RhA7ltYNS84vS7kVRrMRSYZcaNo2jqIuldzIfXXo3rzZllUqzr3LAWcQHGjkolfUyFbn55ptv1vdS92eifDdSxO0L/TPlbrAZl4DkplU4RDVC+QSlhAleimIHUa9jvL6lHWrkDJHsJyh1dLRGTqqbo3SuJg/bQLHNjMhdwoVxFG5VTakKE1GKXzTiC8P6Al9aIq1mMtNASUcplnDNavfrpxSNUInTxFSiOEXpaDNsGE1dff/qJ0u/+XiniL82W8SHLlOTVeOY3gPDi1WZtkT95AEHOhJYevXkjGTEtHotCyrVG3nFj/CkLMWrhXTT1Vtx30/27Gt0BwW/plmRs+7UFNxcyOvKf/SYRvQ6eunoScyGu0YaaBMQ9YdYVnAx4ygUlU8KH0Wi/Fmx8YtlzSpQTKktrk8PY0gp5ld5hWZYpN7VuOvm+ETlJjlerCWruGYxkFKs3eagB9F2P9IFv2IFFfDFizMpblnlv+68333Di3/PQ4wpricAWqvO8cHne+8/WfmOUNN7Hfthy4JnxIHXXnvN2yRN6oahVz9QisnNUaqgqKWGCqV8lyWqOy417MA4ixfuqZJjeQqmSFc0nGaIA5q0cOn6KUWjnuMd4qyB6BDNUNz+w045Sh2q2fiZpW0vObLAVJNiUV6dYd84/qPEEwYbrN25sHrOfloxp/4T2C3pjYKi8caUUmQAZGn/qoZDODKfYoS0qzbv0hNF7R7vd196rmrjMzzBNp5uyrdA5HqogSOrnie8dLuIdh7LIbxncrgP+btjUkSUcFK1UKfM7c6+0HEoJaq7z6/gVosU7uxGdBio5PJNcCGUXww7EwHkBsvGwaYcsC1OwzhngtBadxTQqyARRuPIiKbpWrdkUeNWCIU12ZSu2incsUwYLLMcOE+2Hx0ZSTt6lSI0Kqet35DgFzW4xlHoonK6Zl8mqN0tt6vdf3iNfqvKrcIC+aZSeNiq3XhHxZz5Ovbh0WZeZXKeemdsR5feKRVhR4SfOVE73enCp5b+PkFSLla4I4nE9kCUlgYqObZ1nahKRiMmpuNrcyBqBKehUY9lvndtNL0GivA7Hj3fuXr1+vwB52DLAsljczBnnNb6SsMi4Yo3JCBlLYI2YFF8tibIAVBRKGmtazA5co5qE/isS9YogbuNg2Vxmxa9u7IoB50vQn130hWP47eqv/1o96PrVbvHEvUpP8Wyej2C/9jXsS9NloDAd9TubhRt3emoQ2Ynr1MhIQtzKCo7EA+7+s48Tr5s5Fk/To2B3DZB33EIGnIPiMjCWKF4MdBmi5eAQPctSj4gM1qldg2CZQkgk+WBMVu4RKnrRxafEi8mBwvkHrLcgp8yQrJdp/KCCwAW2vWNEJjmZSdcgKnInWQoyLLRpOrJRthCcDiUAT4RBgsCdThL2TPyEaKDZXQctTQnlU1HL5UVpuZofqtqCe1vP97Fi95rTr3oXGhCqCm6RohlT8VwZ3dlHoxp0+/EHGZGNhJSwccElKhOnetnhMjieqVqQoih2GgwtEFs9Iq7IQgkjzrQn3xj75Z0vGY5OFnhdTpAE0PBtQ1fEnw3XmxdUxpdv1oQKRM4wi+i9YWJoNCgb5Iq4v7Wra4nv1wrjdVZdE/uzELgyZ17wpT6GEkhckypMVsYB01uSHjHBBerKX/cfpsxMzXHuns8PFNTzkXn8syM2vC3Hu3+4Bqtu0OMESrgUMx/YyUbyy08FZPDZghMWkHPcerCp8IIYxEhnd/6AXUw9f+3d289thtVHsB3SBAPTJ+Ty1MyhEi8cQnw/T/FQIB5H0DRZEgUDkFCHKLw616jkrUv3na5XK6q036w3N51WWv91+Vftb3dNIVadlWVt2OpY7TpIEuJ+xN/e9sorBl5Pzt/xSC9o3Ipf7ZBLoc68E4CmjqAdvAb5wNF2mPqWBBPI2GPWeqPSSMI0u7u1LEq00xjmaVx7yXeeAstYEmGjl2NL365RISz7H/XMbY3MItyxa921Wu7nGtHwG59XWzXcG3HCu15kYgWI2y+KpyjvYXx2g24CkptmeLPT++ZGY+7q8Xe2QJuvzqt+a6YLVikvvzz4eGBp/nRqhfO+DM+4oSCK61JUvveL2R4ytJLMlxLokCsIwucUfawyRxx1zPIvqYm1n97Ijbm9kEahJNeDUq1UCTCR9K/BLprvWbUF1F8e6ZBpx/JDpEmbslP60iXXfD1pIXkQ3KMJN0Z42I/vUS0g5VM4ahsLkjxw8G4IBvSqDWOa1W2EWjJUN5oTa/tHjsYd4ey1zuiT/i6uhyLtO1Wqj8CT5McvBCc69IoCSBp0NFHI+V5eEkaitdyvYCrsY7scIstXyHubMegrKmPeBbVybLbLyhQv4psF/vuCGx9y8R3+x7VQOSDw+wB9JC4HGXbo+YF5eVayx1AR6IX0T0CfVWvo4xccF7ZFS4Fs4fUHUEN6AOff4iqQbWy5aOg5fOGoo5DiWTevBFK9eIzqruzFfj2BZJBIkv0mBxmTBrc3W9VvWem0wMu8ZAJaKb/8VSk97vWopStd8TUT2ltvQfhBJCwkjToNcNZe8QRdvS6+9iMNrK3YndX/Ufizog6sJ2LsN32RHDLuMQ6sJzckmr7faZj7u3j7D0COQHtMJEKdNc/RiVM1A9v39vg9ccPyJwFNYbh7FriAHd9YQrOiFu0QJgKahRDSbYY2MaUK6IZh0uzUiMJlkYKVSPCFERNKCEWj/z9iICKBA5rs9/N3qu0xplU5/FCDHdXmD0z81+9Pe8uqD2aJYELost3xYh0ib3rEJP3uNzZ1ns4dnD3Q0JsVdQsb0wXOEbqkEPOOkYC12ZhIXh8F49coIOx+MHZcMX/5IUjgTG1TwXrTadbdR0cDtCEFPALnWPVFN01bhmvLcYUX9J9pHXFeJhwo4sq5bzFOA32DT+M2Fwr3pSvNxjUaorajF+u1avx9oesSQJrlhECOy2HYk0SNaJxCFaJF79V/XU/3F3UyHVStx9UyOS3lNUgXNG503Im79l653i+TPB6HE5OWbrwcEag4GAJH1LWxrEsCcjWUvZwhncMxGq3PKP4/bz6VFyM4gM2qBeR+ISDshEAzsUV73dAYdMgatn2lA4sz2gE5VHXZoNBlrBGbVUpqTjdmb8AtJ0qWAO67Lbr/LxrPyVeuOV4mYfZVd/lkK01XWqf9lzYsALWNKKXQBsMsnhm5lf/PP2m4X13EW3PRcigZAt/eAqpgIxv9AtZfNXjhTO8PT31TiP3x1v5W4pASpQ5Rw7PSCOb3uOeksvCC1JCAh4L23fUDD8Ggyg6XOYo6qleSgHK5xbZDGWEw/XaQwC2Ypk9Rq42JnQi6/E9GSGSgjsuqslQbSJgyeNDeiMc77pihDZwGVxcB9bVjJ83EaWUqPG8UbiBw3EXtTy76QVohylYz7HfRGcSii+QjRdl3u9uE+sn/zr97+NeVluH8N/yOnbuwVWo1AIDybMscii364u+Y1Pc3nX4vPvVnD9P+LW9wE3fII0Z++b6Xvlx6lohlrfnWzxsefuOWjLlsTHDFZ4311c5jFwQyW5VrxYaP8bt0/46YQTU5Xpd0d2VUhxlhGNDbFetgXjrcVVwK13OPFaW78sIpA1WkVGfdjX49sFpFNtmZVmFyI28xG5HGS02cXd6IGe75bNHaHDfXdTHynbj69jth0aW6Jpi8XylzZsu+Z4frQJaLRNfVBMLZQMt24u2dKRgLMUjtHE2DiDc1mb1qjvuHHQM618iB4D6WxRR0Tm6I4p6ePlaJ7hUZ3rH4PVVmwqw33VfqiW4OZvs7IDLVazdlCBGRY36A2TwS6+mF+Ac8VHAHYle2gy4L3u1fyfq7pDeSCl1FzTbUUhwGwoJuxXa2ydaOALUglAubN9Ls7Tvfuz/VQW3LXaPhXAelF2Ap8DPtiSfkUMcXafHCATs1sNCCrQLBqFa2rzIts+xHclPHXA7EtYuqCbQXDgWSshEVXfcuRQpFwrXVzOmrCYwM/IA00G6SMBXk/x5ogwLpB04fVX0JeGtTU2HzFAqu0ukObkve4RmO8amu3OQ9YjurmtwMvWoO7gUDNS2bE5LGwOF5QAAEBRJREFU5o7W4OZ4sg1KQcGE4xgXse8ev1WtrxGs4zFufL04HcIHjI8jwq6+agVnpMXLly+ZSGRZ4ShnkRi7U43k8rkzaK4mc4FPR3rhdctRq0rcSU/Kgui+OUMxHVeO1Sf4x0umbw6UCzWdLs8W8vWzkUcNNzlEIFxNgmcW6OjPCHAx7kJ0D6YdyHAU2o234oIUpdRmhXmVv6UFOcts4f2rJl3VmGpky1Bt1SyHNMbd7bTh7tV+qyqurYIcvGXhD0/zLGN8tcNEA5AElJ0HMpfvtVBbIRaq5RXEPHtm94I4mXVfIq02VFv+2Ew94k6NZ9a+1glg6QiaIiDX1oa1071p7VujgGIkEAeEqrkk4G9BFunb+VaDfu9TCgVknH5VSJJDPC3I6WWHScUdjLWHsnJXqadKkvUauQAc1BYuJgNxYY6RbAnwOrpTDXEfkrv/yU9Uvzt9+vr02fcfX/S+3wHreB07Jn35OvY95uVaStsY3J31Xrx44bWYXvcePF56pJrYaTZPxpocBKsCHGqhmlTpet4x6hH3IbdbknELrkmmtRx+UCw4eBL4+YIFxMnCcruruc4QL7JTQjXLfXV3V8kPGZxqLHbI1AUnRYb4ngFhNF2QC/lgdQXnamQoZWwMMnFpTzF7d7dMBYSsvuCeIn45WlN3iAq1IX3yT+jPt6dP/3n6bJ93RMrAvEK+mn8d+x5wmzTCbRV33EOSImPGfyZJW+++oeKTMuddgltk9uWDCBNhvpayp/F1pJrSYBzApfuXF/WIO2nmRbkU7o26AyoHRgK84OuNqD/wskF2E2ZH2XnK18/Y23aRBkaNcQRIpxsBib3dQtx9Nam1grTdIY0g3LilLDekdrg7ona56rY8A7pgp/Xlp0UMu/cgxOaTsIPg3nNVHv9x3/30uO/+28e3KZY5YB2vY0d4dn0qZl5ceBEAcM4DACdvxNY7k3rhDJ+MvY9G1sDEE+bsvD3GaWS0+Y2AeqyFKGrSvKt1+qlAzZY8bbyBHGBt8i0KtilYttmjI5tvwS5vdjMq8GFSiDvyxrnbi3YtfJ9wV86MBjJJX89dRFoHOsnnM7tA63dZchdKHAJwQxL3AE4+TxEtzCMA3ek9fwZwtgN7V+TSRYO7x/9muvx01R1h7nEOXWyxe6fhqr57NAaWbMMPeaBysMcUlce0IqII7m5BYmoGdzGfUfeWEF93lF2WG42agHNxlTbXI+71GdLegKXxg4SlP+9epD0YLaGSEv3djs8NilugmlsCXVE3nYBUBSvUPzEvoZiruNFaGJAZ21+WqCtAZ667fH1qUgmhr2XJVPi717RTkIZ0SyijEWkvZhi2BFP5Kp5PcL4LcXcNgrv/csO+O9xjRXrgFvtVswd3Jx7nVBGutunrprRv650fxta7QsDyuHuFknpmqCfG/viD+z2CIoAzxdWVSSXivpbanhmo/T/vOg0LqOIOumgsp9/t0ojW5BwYvr1RgLjYC9Dr8PXkNjIa4NKfg11Il80uSwJ0xgdBXk4P7ZwHQ406sZ5sf9G11vLgjkWaZYnd1r0Ty1rxtrenEXqEJOW59HYBdh3h/5+ZeXrefflE3DieimGZOj88XS7btCXxuCVpEY/p/X6v+SFG6xergo5nVl6ZxOJ8J8o+BcUUsTJBHlykjyoRd5VsjNVeMtz0YoYeCRX0IhrAQPxMOz5fH24BMQ+dslXWgBzeQTtufyzoxbU7HLIQoMFliWAP6ka2jYs0uUJlcm7E2mXFEBHojscJyg57yGgR7JI80GM7xhmNGJLdypOh3bE5bSegcXf7HPFb1btTiHQRqhk39jKou+0PbyAjkdkxDHcXeg8PD1zx1atXnlCSUtzZNWcaX6TL82apZkaJRTKRUiLDhCNV+s+pXPxsxXC4HxcUAEWT0RwxZqArQoKyo25gdk4NCk5dYShuapZOhb9rH2A5imhnnKfE+PiTcAMKbKALtrsy7NfgSbky2u0nZPbItOOcx1qY8MHXQU8eoAv2UiJxpFJDZRt5v469a0d+u5ihRQR72CqSSe/a3cKddvx8VO1efe/k0ekff3v64ulHq5dGoDuC6D8oufC9CtbYUYQSVbJCSxSmS9U6vUMdYWjtJAax2520A7dZDC69O4oQhlUGB1kUmvC3SsR9mP2Vq7YOX4mQUL8lNbgGwKxcH+OrQmbf5LL69q7FjPobi1DibS7A3QJfT8pCbadElqY48IK1JVOBdogMCXcBQoYpdSsiz7HaFVFhZpDQrkcCESVcnqcd0K9WcdqJOw2GTJu0kzM3ps0Z3zj2o1vcXbzb1rUFCXSU3flYOfNmh52O0maPoXdLZSGJsos1T72DCUah5q32q+5HvB9I2ZO0lKIj7B7Lerr7fJFnAYjGZhuzCoZO43led74736DfT6EWNXitCsHbWMYIirfz2hEqtBfhA2PHgKGgcwVjxhTsyWGgXwF3ycRcQ6YUxrRb2dcjJVI9tsrZlsS7b5iDQNR0zmpRwCcRCNYYif8l6/3xabv9F69Pv3t6RyRN7TzStP7r2JNIBS8owieF3sbH+QqKVGQoMMmWtt4llnfffZeLbizKUn2wZEM1EsU0irRZg7iPRx1oJIkr3hxOGMjjrFnE+Z4HqW+BVf6pVgXRh3sXiU/GoWAjeac4uCCQW+tEn4mEPEtuLwkL7cDHBibuLEnB2MpaaJBDmsUS3dRr83yU2CEfdmcNoRfbnBvp0SGY3p30ibu/9ek7P/ifHz4SXE9Rj5RCQUYpC0vOOZJeKp1XvFPtiy++eP/997NzS4Q8yxiqQfuQqgZxZwUp726otN8AaXMEDaKR+h0yu9m+8NkSclwIZndvv+OSyJQCHHRBNfqqxMGNkq+2D8cqCWG3at21anCNDQ73CHDpsj5HAZwFg6nXSt5Fe9phD1x0SQxW1gj0Vk1SH/HyVoaUGhs+ZrEVPRj542bi3VPsr/CWv/39R3/5x+/L/W+myj48Mx3n9AWC6Dskrc0Itv0jCdPhJT/OweOXj5koe17IL59oY8saxD2+lNko6FHdpW9h7CCAsi0RN1hjjjLOMPNC+RLWgB5v81F3fD1BQ/KBd22pKSRhVHZrYAr9dImerFrtIuCrNl39ifAG9bWpMpmWalxr41INfFE+XNS3bYUZsfa+nneatwldHED3xIXzX0+nd946pWdm5vv2+GnAJ8WN558ou/j96quvXr58CcrL+n6GlzgV+Fo2lYvOhEx/1iDuzNHCfxFLOi+5gKK1lyNIWxdYLtErrw0ek9exi15idUrcXfPYBH1f++tXDX43Z13t1ctNJUetLUXcE2kzbCPQB7UdddM9aqp8ezh1EPLpKbiCCR9wAz/sLrewVTx00UvGuJQT9L464ITA8lTMtMEfUaR/nX7++jTkvjtNwWflrOqVSqFT6x17LZwdfrHKP4OUX5Uncn4vlD1UqEHcrxqrwZt8V+g6yAZFfjw241kIwfBGwBgC9ODrAb3F+kL7tN+MRiqTc/uiZks4XXplDDLl6wVJW4Ykl10Cvo0KXg7bzp2gtkcRd4ZNgU+SPdIdj7K2bGQdWBx3FgsEe1QQ9Cg7m3hu5BZzxd0//nZk7g6+4LgjVb3k5556klscqvxZEYy076PWcn4S/tbFM3F/fA0z/KIuZkCo4y3jDnN/YB0FM/SlLaErcZ8F9hgI0qu1pxHKGjYKj/PaYQN9vQS+7nuQtrUiXW2freDV0Rq8SUHUtnL5jLLNGvjKrpSFXxlfDGa4aINgXYokbXanIL4OEWLHUzGXSk3v+K3qx9+NzN3ZQTiM6qKxMQdQF1K9CxUf8VMZK+ecqVNtud6duCdLbZFyj74pa0fS2VKzt/TdQ7WyYw6p3RR91dTKROYqa7d2RhsSwal5KSgLT+/MX/fC15MWchQFY3Mh3RzpIhbMdIyLXVVjRgTFFBnbNNmCmYt2zVbDbL1Sx1AQH2o8kULB69idbbH7/WKS/+7F8PvuWKyyWH/9fNfypRpEFhX7LhyNO+q81rsT96a+IpSyZRZnRul3sTWP6POnMxZIfP2sZg9PbWdsMsZHsWMkqGfUUa2FvwayttVaX6DbGVJyOt0fmgElfUQ1m6DoVLpT9kLaj/A/Cn0uOvDD7sCiIBdtdnEi9j01AX2/uJP/M7zrcd/9dPrZ69MfRnzPDIPIn+wzJHdP3I+Xhg/INkEFMzzh8C67E3e58thfpsJGwSYGWyvVYCtbsI1fdsDDfWIwAQAUBRtMM6u1sXHk9u1vhm1xPJVYvblK3IOvw1eb7vh6skkkmbG9FDp7eGn6gkUUOJJJ619YnDS1k1XcAhCkoGHzmHFxeQwoZFC08KtVW+xXhRmeu8ey1gqTr45BbDgA9J2nyd+rZr7++mv348u3q1i3fHN34n6U8qq1fO1MAElkv50qU4zh30chtdO84QB8QCbiAPMFG4JjUyJGkKR2MnUjwwIR6DQNeVxbsAWs05TdiLQZYtBi7E13cZogy7DPWZfkAFxiv/x/Nun8n1xUIhpyRzMpztSNfLEg+dtilwH8arbg7mFw95++Pv33oAmVlwIxvNR1Qra7CxlA1YugO1NEGL733nu8wjtnvDiSk/Sl3WjEXaw6wLCErhWBylzt7C4U0ajrQaJaO3MA+6/zfD1pCsHoku6Md9FdbloLAbjRPmcZAJoytevE49eO1mD7qD1wPCtCDYqaLVJs2W55Pwn7cINwAOHfmq0i1YSjZlup8Y6H0z6k0+KBqZf88DTDmPFb1eG5u20CEdRjChX+Qdlntmwi1fg25ssvv+QwjgxPOKrL25988sl+qe2JRb+9kDxlm0CmBpLDdAZRraNg76fXVFQpeHji3r6OJAwfSA4AlFUOoPvYOAoTxyqbTP288Wuq8QHJl4JwlAHWOkDjCoZ4iigdadeFtNlC5q2i5f/0jLWi02w4A1G26ZEPLQeU8VHnyo7KbWyxO8xuM3WGtC1X5FbLV987eYvkj749/d+4/zadGeNJko6qRiQB5WBJBgi9Xrx4Yanp1zWiUsdbiLdzX+p4+4MPPghs9hDLakbw7JShAiHVWrjysCjVO801YxwCVE5PM8Ls9BEdWXinwbcMSzDB5iwCoeDIdgDj6L5FmPb7NotjtunkWemLXrIB+HhpEPfsAdvvSFM6OtoXNU9CIQzT5QmHD1jMiF/TqdbcoH3j0HH4BdhaHPO8JXrJAB544Da+q5l5I/uWKS774u7/8d3pP0fn7kFnswvrpd12uhOE0OCrOKdcIWN40RAX+uabb/zpYicJSw3rpxpvffjhhx999JE1B7VLjZvGeXh4CFukO9svmBVTl6yDrG8fcOMI8q/l2sZBGu8uIQqGdoSUSvgAeYKrFRHsTcBxJB3lgUhZuNq0qIyk4y3Htpe55WGSW8O2cz/WY3fzKh9wqLW77q3uZBYZjPBN5dXimopQx646incHMmCL/ZAF28d2e946eXhm4COtitvUkY8JpY2EEMlRRzxbJe0grlJQg8oiPFj7559//m/GSv2XwGIMmQAAAABJRU5ErkJggg=="},"cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; 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"error": null}], "response": {"text": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "name": "execute_blender_code", "arguments": {"user_prompt": "Align viewport to the camera view for next screenshot inspection", "code": "import bpy\n\n# Set all 3D viewports to camera view\nfor area in bpy.context.screen.areas:\n    if area.type == 'VIEW_3D':\n        for space in area.spaces:\n            if space.type == 'VIEW_3D':\n                space.region_3d.view_perspective = 'CAMERA'\nprint(\"Aligned all viewport spaces to active camera!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}", "parse_error": null}], "tokens_in": 42911, "tokens_out": 192, "cost_usd": 0.0389169, "cached_tokens": 21456}, "error": null}
{"ts": "2026-07-15T04:46:59.297209+00:00", "type": "assistant", "text": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "name": "execute_blender_code", "arguments": {"user_prompt": "Align viewport to the camera view for next screenshot inspection", "code": "import bpy\n\n# Set all 3D viewports to camera view\nfor area in bpy.context.screen.areas:\n    if area.type == 'VIEW_3D':\n        for space in area.spaces:\n            if space.type == 'VIEW_3D':\n                space.region_3d.view_perspective = 'CAMERA'\nprint(\"Aligned all viewport spaces to active camera!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:46:59.297209+00:00", "type": "tool_call", "id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "name": "execute_blender_code", "arguments": {"user_prompt": "Align viewport to the camera view for next screenshot inspection", "code": "import bpy\n\n# Set all 3D viewports to camera view\nfor area in bpy.context.screen.areas:\n    if area.type == 'VIEW_3D':\n        for space in area.spaces:\n            if space.type == 'VIEW_3D':\n                space.region_3d.view_perspective = 'CAMERA'\nprint(\"Aligned all viewport spaces to active camera!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}
{"ts": "2026-07-15T04:46:59.330122+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "name": "execute_blender_code", "text": "Code executed successfully: Aligned all viewport spaces to active camera!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:47:04.825728+00:00", "type": "llm_call", "call_index": 27, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:46:59.330122+00:00", "finished_at": "2026-07-15T04:47:04.825226+00:00", "duration_s": 5.48399999999674, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:46:59.330122+00:00", "finished_at": "2026-07-15T04:47:04.822720+00:00", "duration_s": 5.48399999999674, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "348467", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":[{"type":"text","text":"Code executed successfully: Aligned all viewport spaces to active camera!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; 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"}
{"ts": "2026-07-15T04:47:06.013689+00:00", "type": "image", "tool": "get_viewport_screenshot"}
{"ts": "2026-07-15T04:47:14.488272+00:00", "type": "llm_call", "call_index": 28, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:47:06.014193+00:00", "finished_at": "2026-07-15T04:47:14.488272+00:00", "duration_s": 8.46799999999348, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:47:06.014193+00:00", "finished_at": "2026-07-15T04:47:14.485736+00:00", "duration_s": 8.46799999999348, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "476099", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect camera composition framing\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"},\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":"Code executed successfully: Aligned all viewport spaces to active camera!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"},"cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. 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area\n    cam_obj.data.dof.focus_object = focus_empty\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\n\nprint(\"Camera composition refined for perfect product-to-hero prop framing!\")\n", "user_prompt": "Reposition camera to cleanly frame the entire lantern and background floor pool"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}", "parse_error": null}], "tokens_in": 45403, "tokens_out": 609, "cost_usd": 0.044829633333333334, "cached_tokens": 22702}, "error": null}
{"ts": "2026-07-15T04:47:14.497846+00:00", "type": "assistant", "text": "0", "tool_calls": [{"id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \n# including the handle arching over the top, is perfectly positioned in the portrait frame.\n# Currently, the height of the lantern goes up to Z = 0.50.\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\n\ncam_obj = bpy.data.objects.get(\"Camera\")\nif cam_obj:\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\n    cam_obj.location = (0.75, -1.15, 0.40)\n    \n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\n    cam_obj.data.lens = 60\n    \n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\n    # Alternatively, let's create a Focus_Target empty!\n    \n    focus_empty = bpy.data.objects.get(\"Focus_Target\")\n    if not focus_empty:\n        focus_empty = bpy.data.objects.new(\"Focus_Target\", None)\n        bpy.context.collection.objects.link(focus_empty)\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\n    \n    # Update camera track constraint target\n    for c in cam_obj.constraints:\n        if c.type == 'TRACK_TO':\n            c.target = focus_empty\n            \n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\n    cam_obj.data.dof.focus_object = focus_empty\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\n\nprint(\"Camera composition refined for perfect product-to-hero prop framing!\")\n", "user_prompt": "Reposition camera to cleanly frame the entire lantern and background floor pool"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:47:14.497846+00:00", "type": "tool_call", "id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "name": "execute_blender_code", "arguments": {"code": "import bpy\n\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \n# including the handle arching over the top, is perfectly positioned in the portrait frame.\n# Currently, the height of the lantern goes up to Z = 0.50.\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\n\ncam_obj = bpy.data.objects.get(\"Camera\")\nif cam_obj:\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\n    cam_obj.location = (0.75, -1.15, 0.40)\n    \n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\n    cam_obj.data.lens = 60\n    \n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\n    # Alternatively, let's create a Focus_Target empty!\n    \n    focus_empty = bpy.data.objects.get(\"Focus_Target\")\n    if not focus_empty:\n        focus_empty = bpy.data.objects.new(\"Focus_Target\", None)\n        bpy.context.collection.objects.link(focus_empty)\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\n    \n    # Update camera track constraint target\n    for c in cam_obj.constraints:\n        if c.type == 'TRACK_TO':\n            c.target = focus_empty\n            \n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\n    cam_obj.data.dof.focus_object = focus_empty\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\n\nprint(\"Camera composition refined for perfect product-to-hero prop framing!\")\n", "user_prompt": "Reposition camera to cleanly frame the entire lantern and background floor pool"}, "raw_arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}
{"ts": "2026-07-15T04:47:14.533669+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "name": "execute_blender_code", "text": "Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:47:18.946195+00:00", "type": "llm_call", "call_index": 29, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:47:14.535212+00:00", "finished_at": "2026-07-15T04:47:18.945187+00:00", "duration_s": 4.405999999988126, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "content": "Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:47:14.535212+00:00", "finished_at": "2026-07-15T04:47:18.943085+00:00", "duration_s": 4.405999999988126, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "478753", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect camera composition framing\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvWmUZEd175vzUJk1Tz2qZyGpNbTmuVtqSSAhzCgMzxjwgH3BNn6L5bf8wevZXnzwB9sX1vV6y49nDMZgX8sCg7AtgRBoQoDUmro1dLfU86Qea8yqzKqc329H5Dl5cqrKrDGrOqKrT8aJE7Fjx47MHf/YZ0eEe8WKFX19/a2trR6Px2WCkcASkoB7CfFqWF0ICeTz+UAg4PV4c/lcJpNJp1K5XK6lpcXt9rjsbwuZXC655PM5PqyQy2bJTMik0y63hLAUtItJWigYJEMqlZqYmPD7A16fdyFaVauOfN7n82WzWVoKS+FwGBZr5dXpPA6Fwh6PWzVh0uNF6y+a2s/ncv5AAJYQfCqV9HiFm1r801X0oE/lkU5RHWNHKCV9Kt1JPiI6pjuYjs2m02m/3+/z+yUncvN6vT6fVJ3LZTMIkG9Kiu6uVbtJNxIwElhCEuAnn3cHfJ7sEuK5HlZpV2x09MKFC77+flB7G2VQZ1xRoIxMU5NA6TFo6TyUQuWhIKcussBPGacZv5VuX+CaTXXzIIFaaES+sCYYCRQlIIiNP4CdwuVKp7nV1wSYbmVTqXLhP0FhPC6C2YFxhFxWgKHHw0OrjOT1+3wkJJNJULsMDNyk7OdzHBEdGyjVsdXUMioONghwDjbVwBRWQMDMMSpxPBB1dHSEDKqtorRrKUmmBNNTy2bj4+P1tFzmB97ySQ6g2UejZLIhcs6kM/6AAOuqQXorm8t7PPRUVXQPCdWj1lV3rwLzurFMbwDrEOfW5fWK1CjCTV46XW4d3V2VB5NoJGAkUI8E+EExFZ46J5oHFWfP1b2RlZ7IisoiqcGDyfELlenoEzBeId3t9XVe6vaF7GyeXA7KqIzEqZfl914RmMZrbcATDAbBCm1JqeTkpB4A0FBkqNRglE35+93BjgryKJa0b/yQ3TpnBih7lOnBmajjSj+PVqYjKwG0kPN4Oru60Jm+9vZ2VJfOOjk5MV6qiLHEb9y48ejRo7FYzEmOUhq7JxJxhjHQf0dHx4kTJ4hTx4YNG8h87NixqiJz0pm/OGa2lpbI/NE3lBdOAoWvZ4MV1oL7DZIx2ZeWBIpfFjSRwO+a3wMrp/pUoK2A3khwu9CthGLb8y4UPabZwcFB1FokEkG/nD9/vphhrmOiY9XIlIiLjrXJM4S85z3vGRsbQ8E6sebk5GRnZ6ceXTra2/WohhkeHc4j8pOZyLvvvmuTIkK2W2+9dWho6M0333Sm0/aOzk7mDyS2t7VRKSmhUIiRA0sNkwTSmcMMDw05S/G0ls6HEzrDmRnhkpkqoMw1m51qpCePzLCkZwTmy2yM2Zm6ddIsPFZJuvO4UlaKZ4vjN7e6FBSYEcCGnVJCzdwYCRgJNCgBfqajIyNaD0Sj0c2bN4+MjBw/ftyprDRJlE9beztxT6gzdNknXZ7CvD0U8N5wWR+q++X9586+cmF8/GhVFtBswZCAdf+KGwJrdug8fZ3hld0tJ86ND48l89nk4MGfa2VVRkEQcGen1khEMFOgoNasWWNnO3fu3KlTpxJK8fKorU2s26Bc1DK6dHh4WDdnpPUab+elDBZr+iI97eFzQ4nTg3H0VD41Fj55mukBFXV3d0OBIhSECPyg7kjn2tPTgxBQv/F4nEeV+tnmx5YV9aKQKSxGCB2YJ1lR+URNf+xjH3vwwQe/8pWvwIHzkZ1TRwD3AwMDmDR0HliEIZnEWIHWbtiwgbpJpC9Jhu8upg61A0IkD89XrlyJWJ0ZUbLI2plSGbeZsR9BhJ7mlkFXd5X9aNoIcl+7do2zRbSFxGkLOjPwRezt7bVTkImOw09ZA+08VIEcqBdRaOZ5xLfHzmAiNSXAmDzLv5qkzYNmlYC2QMOdADH+i5GigNGqsqzxuuORPbq4PSUWYiEF+FOBX+vv/u7v/smf/Aka2VG0GKXStWvX4oVIkjMPP/myXzrjQZlqtamU6Vidjtr5jd/4DeLr1q27//777cxE4FwPlsR1LZdccgnag0Q0xqWXXqrVqbMI8b6+vj//8z//3Oc+V5Yu1KzhAGoEdBdX0uG5qgqideRhPlNGSt/avNlP6RuM7vxGpaPU+w3l52I/rxKR3lFdpn7bKoOUlyAA3YpYN3aCVIBlXZeVVBK4KmpyEVN+yaRCUTIXIwEjgYYlYCsidM5f/MVfXHbZZWDIL3zhC2iPMlq2inMH2lf0tN9wWW9PuwDxTDY/mcp+ZPuG1T2CFWsFu7gn1KPz3LJ1xf/7J9v/+gu3fu3/2n75+qkgoijygiYpaEtgWCKRAEPrsGXLFpthIiiM66+/Hq2LVkfL3XjjjbbeDvq9X/jI1v/7szf8jw9t/cvfufG333+ZPeYAod///vf/+q//OqU2bNhw1VVXacUDt1SHxQQTDMQRFKFWM3W63VhuQe01tRVGdBQx5O666y6qrKqp7Zqw4oDCv/Od7/z7v//7ww8//IEPfODkyZM2dGbognXwN0MOA8ndd9/NJAxFCdNwf/nllwNMkQIRavn85z/PWELf//Ef/7F+9Jd/+ZfgbIozPaDZUOOrcN9999llbTamjtBT99yzkzw77tqxctXKUChIGyHLWMg8CrLwwHyjGpH8Aw/cz4B02223wgDDIfz09/dt2bIZEa1fv462gLBhr6297ROf+HUYq0ZExkj9lK4i8857dtLMdesuufXWW9rbZT5XFsLh0AMPPBBtjf7ar/1aJBq58847169fDwNcYRUKVErVfJmQ20c++pGVK6u8aSqjaW4bkEAt3N8ACZN1oSUAeqVKBc6kam1TKWdCITaZ1+kgeFCAbwEUKlBnK1/JkhdDA2hPZRQ78WOPPfbTn/6U95AosU996lPogQIp9fHe974X3XXHHXegqRm00G9XXHEFBgudgtqBGqqVAeCWW26Z1gbhpIyaffLJJ994441nn30WjcTA4HzqjKMl0MAEakFRYPSiXmcGHacJ3//+959++unKR84U6uVVAxYjxjYiPIKmMwNxFClmKm2XKXtU9RZVhiSROZ1FHJkItp4i0C96rFVXq/esT1VQ+t2O6BuZwMkfnWgP1fqJdLrqd+rF/mUVNZ9GAkYCcyCBj3/844DgHTt28GYSNQiMnILoaDw1Mp664fK+X9+5KRL2re6NfO/pwzjHTVGk7JHP69mypi2g1h11RIOfvGdzWYZatyjGj370o+hqdPLVV1/9vve9T5vGnfnR82g81BQaVY8C11xzjc7wgdvXXbmxm5lGMp3letMV/Xdft1o/Qr3TajQbtmyUNv4sGHT0I4oDFIGdmLx3795dG3w6uSjGqwB3mNu5c+cnP/nJTZs2nT59mre0jz/+OMoa4AgHN9xwQyUwxUmGIYT2g00J4MgzZ87YbygYtJ566qlf/epXcI+1hskHAoJv2oC8EMcHP/jBD33oQ7BOIq8P9HvhAwcOMK2husOHD9O8e++9l1Lbtm3j28DYQHV2Wbs1PGUEhWzVrwj+q8lkilfcaO1oJEK2q6668sortwLWb7vttnvuuYfba7YVOsOmSYRhAjls3LTpnXcOUKCnp/vO7XcGArxPcDMTYIUAUzHYozvXrF7NFy2Vrv7Ol7LPPvfcFVcwot/u9Xp6unu2br2it7eP1unxyFkpceT5zjvvvL3/7aNHj/T29HZ3d4Hvr71226rVq+6++y4yUOn111/X1ibuTLx6GR+Xty0mFOxtdPM8/RkRN6sE0F0W/haoJnhwKlYdo4KFBbVSlqIOEyyJPjUe6KfATd5pKlScQRcdOlTuy8gvFyh89uxZslHkoYceQnNyZcaOgkLXYb9YvXr1b/7mb9rVaTZB9rV0LBV9+tOf3rBhA3SgQMsAylxvv/12rtu3b3c2VL+EpQhDxf79+0HbuLXoV3bQ14MHxVHFAPd/+Zd/ef755ylOqarUKIK9h4AR50//9E+hw8BWZllncoILDURokS11nYerneJkUse1BIDWCId1opUZnClkdvSZwt3Ox1bc0emFrwH9KdMyy75vZVTKXVjO8z6lmGhiRgJGArOQACAQc/Lf//3fowBfe+01bIt/9Vd/dfDgQUiiwTB9okOuvfZaZw0TycyhU6NPvHjiZ6+c+vAdG5565dSufeeOnxXfEh2AT5gPUFlgaCtNPpUzff769/Ree2nP1g1d+44N7T44QHooUGLg16qVK1rOWZw4mnDfvn3gzNdff/2ZZ54B7FXqK/T2yy+/PDo6euTIEXwU9+zZAx0UI/ad6y7tTWeKFodUOnfTZX1gDwDt+vXrnXVhzsaMQgrwFZ0D8vyv//ovPUmAEGKxM2v5oBJh2E50RqoAdyi+8MILe/fu3bVrF6L/0pe+9Hd/93eMVdSKFRzkrd1xnFSmjjPfAlkiHRhlCIEOXQhaRTqMXoxhDB4od0AqQsEypFvCSEOvY7hi4OEpRBgg6TkogOlhUpfVmTUDpDPfYIaHc1I1ltyv7X7tQx/68Ntvv40eD4db+vtXYOhJp1P4C50/f27r1iuPHjlSWRCZxmJjP3r88e3b78Q2v2LFSniDAcTK10ixBGLO0y6YHB8bs2csTlJ086pVqzo7OpgOhULhU6fepaXRaOupUydZI+zMacepFCM6BWkp3xjmTidPnpJZU95F969evYq2c0u97757ihGa9QZ22cWPMA4u1t/iN95wsGgS4IepsB2bkAgUq1TBTs5KUKB+YCXxq3fkzIvvuJC2HiuYy4wdVfPSSy+hwRyZJSOaF/XFGAMDgFqlc8RUjxLjh79161YUqa7CySHUaulYjCk//vGP0czPPfccyhnszmCDjQNdhIrGsuVkAJCN2sSWg25BgTDqUJc2iBBhHGWQwLyCYkEdEUEzU5xSaFeolTXH5hCNhxovlUyhWlQ0ZblhdLAtOxBEz9Nqp9ycfGrvI2RKX6GKQdbOp2Vx2BCTuaMLKpC71jiFctL9Kkiq0Ke4MvCrG3miSGnePOw7ZIKRgJHAXEgAVYMa+drXvoatFmz61a9+FbutJoyzO/N/nC80ji+rrbcj/N4b1z72wvH33rQWc7v9ioxsgMMPf/jD+CCgQnUpVHwqlVFr6N2Xru14ef/5Z3efvnJjV0vQh+X70Z+XYDk04Re/+EU0EqGsUn0LgsL0gLbk1tZ4zpxYalBlmoKTiBpnnBlF22gisMpkAJwGQcwoGHQYC/QjtOUjjzyi6YDjy5QkRZj84F/E+tIS0tZNyaREJ6LTmS2h6/XL0A0bNjDD+MlPfsL1H//xH6uidkYC2syApBuMCACpSEpDWBA5ABehM5shD44uTJ6wSPFC+dFHH8UeD3M0D2cgHSEDow6lqBHKjH9EGF3ICRsMltj+GboYL0mhO622uJDFt771LRQx9TLA2Ol25Mzp0wD0w4ePpNOZwcEBxiEcZkZGRvfufQuUDNsgYzuzHUGm6HjM2y+//Aoj68033xSPJzCnAeIZRHt6epmijY2Nw9W+fXvxGuqW18ol4yikWlrCzz//C7hlLGdjh1tuuRlOmIxifc9ksnpMtWvUEY3U8Sx66623AO5I/pprrqbVd911909+8iTvCk6ffhd+eC9BdQgQr6y3336njIjc2iNYlWcmyUhgWUlAkLVCggLXiJepVXlcEuwE9AahgAVRZI6CQFVunbqVN6rYFPgxop2A4Cg3FBpTaE36hz/8IcoNRf+jH/0IvQFq56Xiiy++yKiAnYnpPfoN2zaGBkYvFDQeJrrgFDoW5Y4SIBvaANSOf4su8sorr0CtUi2TQqBG1ClKCYxONl2EGpn5MzagVGkaOpl69SNe2sIVBfWtvjK6QIecP1OBREYHDdN1Bh5hXtLaHlUGcSggTCYVJCr96aRXjAPWcXNnhxe6SfkmuRmraxm/6QLtByXda9HQ3Ve41R/iemP1qkT5L3/Sg2ABnyw7tkoXXGXktphmPzQRIwEjgZlIAN3y85//HNWHDgSJOTUAcV7xgS1RIBol6wo6W4M7r1vdHg387ycP4nPCZD8S8sUnC8smyUN+3LDBmTZoxnMu4DCr87N+YteJI6dH/T5vLJ46eb5kwyvwJGoQjFerPVDGCAtjwELMDUwwnDlpEUAL5UYe4uh8GqJC/vVDgzu2rcIvX+dnE8pX37mAZoEgowPmFUSBHw7jBYoX4Eo26GDdQEMiAQ3ocZhE8TprBOv+8pe/rMWw++67d2qFS5l4fFwjyJtvvhkqzJZIvOmmm5g3lFl0qAyzMU9jsVH4w5UH/lDWWqY8ZbSARU1NcwOXqE7ianR04fdCQb3W2E7XEZ2/7KpLleW0aZZl5pavS1tbieOpzqOL2FcSFU0ZC6agBtd8kxifyE82XUpHbApq9NFEJI3/FUHVokYSoaGMQDa1iswqARsU25856mVQxM/nGGu09U4IMpjJDsf8wxg1hQCr0zepRgLLSwL8HIJBsQrzw0klkwBKp9OLQnTym+JnzFWC2HWUBkZ5ZWVPdOQBSg1HIoVfE+pVWZp5hiLWmhDnOrA7Jm1Kkh+PPszh2uFEixPCRKBAxL6S4kx3ZtClnFfRscqPnI17UZVoVOwUwHSsKlx5sevMrOMAcb2HI8BcKw3gPgVpFPnRxtoIgkfiq6++ysixfv16tLSolGgUo0kZQdC53tamt6cHOoxYaHhIaYMOowO4nLGQUjxigIFJTQHrO2zAtpMgpiZ7/zVnOjIPKp8fJAkF5GNvQ+nMpuN0gd5CDpGiYeUiMTWuIFslXihYXSsxehm8rjpWepZtKPhKaMRAjRCkUnaiDEeqr6mt5MGkGAkYCUwhAX5WQ4ODALyPfOQjf/3Xfz1FTn6Gsrkh+rZt/Yc/8yf8oCdTGazmpNx0ed/5kYljZ8ZG3/y3kZO7qxJBz7So9YTBDe+/7uYdG1a27T544Yr1XWcGE68fEm8ZdpU58/SXbXxbRoSqtR7AIRvLsq1RQbOsX6IUU4WY2gemrbUVDYnJGBWKHsOAwitKVCh5RlpvjfRf/gcfufKydZ3oGowOr71z4Rv/vS8zGQuffAR8j6UGv3FewKJscSnHwIrCoV60K2geltCi6G0Mwahi9DNgu4xPfWvLilvReE7gnk6neEmKtmPCwfhEhEzod3QrFG1yDAnU6lXb0bCDJNqcmQScYf0lGzSx2ZMHUK5HNbvggkXgAUEHgzVXbk3PSVXgPX2x+c+hOoWum/+aTA1GAktPAkA19q4CeaO+kqmUAHfnj0X9fIrAXcA7WqoQBLZn2cRdlu3rIYH28xzYSko6lcZ9D4RaORLgNY720wpzTkRW0LFqH4bJCdGxEGd0wecQzY7NvrIWDEWoZT1LYaRhUNN5iNA8rcCnGBjKCMrQouzxpDNqMQogRiA76RDRErCBe1nZylvKMgkp6QgrE4MLG+RDlq4AQHOQiN7izXpe8inAXe2wVrDP07MQtegWgbv0Kj2tcTv7v8sbbnhmZKUJzOu0lAD0mQx9Kn6Peo5UUpm5MRIwEmhcAvyaxmIxVCkwEreLKQjwY4yoPQbd3nDHNZ++euulbxweTKXFlSUa9kMHi3st4I6GBBbrSb6vY0tw0wf6e9o3rmw9cX783QuFF4ZTAHfUCDtRas3B0kxsGZYWEYdGMDR6iVeRjCAwgxWYbSCpkRYBsrG42yYbgLtsB+lxb1zVhp/PmcE4fvmiemQ7yEe0ssJDhpaCqCEINRQR6g5qaGztPAM17Z04hX62ZQUF0XhO4E4S4kbbEZki8FaTKq0MeVVEIH7zBNolHDoH7OZhznBiJGAkMG8SALFxRCbGXfQ+KhI9YGtkqZNU2y5LXAyyBeSOpgYXchHt4fcXkVyeHVRkLRGWkgncuMWpY979oamjqGNBtNZWBrbYaAV8MseAMZ5yg91aHPF1sF5v2vl1hDGJ/GSjzZQiFGspzUoegp0mVak5j51CBFpIDEaciVXjDGxF3kpzwA8JAb+fD6hNTiYDAOsaqpveEYzvla3fJY/+kJiEkp6FLqHYvXJAFYtf6dZQOCS+9arfkQDnaXm8PipVNMzFSMBIYLYSkJmytTn4FLRQs9ZvF28Bv8tXvlEVZbOTY+BvIjgWQJWfvyaIEna+wcv7Ii53UV8VKkU3JmQLrMqADrE1EnpCVHqFfhPFZIVaOi7rDrl91VRHPudOi2u7RaD4SXWiuIoJxRjqGVNC8b4YY0gqDmSUtvF3IUstPV6kUB4DIvvrUN3lxcy9kYCRgJHAfEhANGM9oSKXJCiN6nSDFt9r5V3NaEQIhEIo/XrIz1keNZEoo0YbMdqgwWWqkc0y8NjjkOSE4bIChVss3yEMSJm0WJoZJ7QzTPW8jtSqIpXxo/R8D0eJeqMQwRwOfSIyJfLIWoJaEma0K+dE2ikDIR/2WKjbLpnlf+EJ5DGE6bQCc3xPFLb3zf9MrFCj+TASuAgkIL/jaosMp2i6O5/GFl2ZQXbz8gYyWU4b9Qc91deVUsqdKVmWY9OpR7+JZphuyNAqxSZrRzz5SVe66I1ip0uklhJWyqoqQTxt6mEY2pilHQqvpNpmueEdQbOwYvgwEjASaG4JoIIZNSItkQw2H5ZGOXzcRVeCVbnI2idxnxCbsQ7KPgScJRk9jrONVjuASLxE8GvEuxGIjC7WTiMOGdhw0ZE2/1EY470CSJf3AGyIhsGFbbKmrZYmcy4EzjMUYv8rXjBgf9aod9qy85cB/sMtsiYMizgWNTbtrdUW3onAbUi5QgnGl0mVbNxJlxGA5XrBgvjGKFu79LD0s3wV8CbFFYdtfFvZz0cmPLlMOoXXP29RVO0lJwzOX2MNZSMBI4H6JYC+nphkF2/ekk3jCVI/zbnKiWLBtW+uqDVEp8ziLl4wVfS47KJVc67TUH3TZC6d+GB36evrr2V9mYaUeWwkYCRw8UkAFQKGY9UN/uipdBr8jWVa6zQeCYzjDxynwBxIDg8KlG9yEo+JxGRiApTn9no7OrtYJIPwyIj/Oj7YAwOD+JozH2AXqUaEOl+wHsY4fwkrO/gbF3hOk+jsqn6Mq5Nb2sphGz29vay7Onf2HEb3LtmWd949f5w8VMaRPtAZT3pwNt6x586d7+6uehAe3ZGm33AzxS4FxKf54iuqAvBdgDt9q3pWPlSE/iXQUgTFBsM9vT0rV63iHQrTnUQ8zhLb8ViM6uS9sQlGAkYCzSSBeCKRCq1b6z4v0/KmDCjSoSFZCFsMpSC2mD67WJkESoB7cNXNrRvuSmbyHOLKEgG/z8MeN16PO3HhnY7YL6mXwrxmJYJBgyuaUS+zJY7yxBOfiIyOyu0SR3u9aSXaVXvgVD4iHVJkgBQUKIILP7YS6BDY5tygdi0KczUSMBKoRwLoKJQJPhBapagiRaWvNYtcxU1CPgoaS3wmxO1ZZ2W5pK4LnYaOAt4D7hPxBMZanV73VVdYd/ZCxiLDU5QUqKqCqFB1PtQUmfUjMLq8clA+lggKhTxtkQXIAGgGUrMAFtO5+N64GVkyevODstqB6sw9ZICwWJf+U3+WyBjh5c/5JplHNJbxhdFE1sgKUXGRQW7KEs+jhfV9KmuVuTUSMBIolQCvymLpllDQ3+lheav14y7N0wx3vL0cHw/w2lMzg42A3bQs5TRnDKK+qChs7TcA3RLg3rPhpgfv2vzYr45du6Vn37Fh1sm+OxC/4T19//Ffhd3B2Z39/vvvhwr8UZhtyNmYTHO3efNmfXof4yUKkZ3R7G2D2TeNnXTIRjpP2Z/S3iOTxbyHDx9mNzH2SWD/oEOHDrH/ji0FVK0mbq5GAkYCRgJ1SgA9QwCTYZetovIV0FOIVRSsYFf5U+sYuQHhgRmVDQIi2N3xO9Qm23QqqfdAqJONWWRT3E1TXreggGAVUJ2qgJYDVxpFo9HhBF2gioimotTgszqo80KDo11hTJl4vOzhgz2Ibcqq1OSRyYZqgnSZ+lPdR9ZSmVnwXTVTNZbRhG4Vw5OasWg6vHJROz/YwqhSp0laZhIo/abMonFzRmgWPCzHoolUftLd3uEfXhIzaidMxQAx56idHoYmRu2awH0skf7P54+OT6T1VprnhycoM54QjI5iQ5/i3wnw51Y7gNoGdRQu6s/pjK4N8xirSNSWeEpBQalmmS1oGzy74kOZPYN448BO8Mr+wUMTjASMBIwEZigBjcmwHwQt82uRkIy1/AfzCepTEbKroNyiScKsq87RlFWSuHCQE3v7RGJC4XleKtYBRYv1zVcMjnHUthjPoaCnrqmAMaxjpGigQrOKQtWiKrHqk6krmvFTlD9vZhFyOBTiMLyqwF3jawHuBNWRVm8y4yLBMrRbfPOpmylX3glj22fNA7MXhf6hg6cUI5EmO2POTcGlJQHr2zFrrueM0Kw5aXoCBf0zHZ/8JEeznUHXUFeAs6jn4D1Y/V1UJ4fTtaD4nAM0OBAJWM/ZsRymqR9s3LiRzR85gdQ+SrZYoJFYicW9tcW/ZX0nJ8c6Kdx4ed+PT+7lYKePfexjzrmFnYfdhUHknB6KlR0NeMcdd9iPtmzZsmPHDnaq/9d//VcS7777bgzz+innSJ06dUofQEgKZw2iQPXRgHZxEzESMBJY+hKoX3nOWVvxYscwWw7IRDeXAD4x2iqzLThQgUFBwxgXgLVkpXgwEOT0HpZysoQx1NIC+pszFmdHSCAqYL3QABwa6x3keOtJm2kaQcSB/b1qm9Q41uhgNt30oWabkTlbJmMJYojhyobH9IjmsKwMXcDorjuNKxzaf/p7ZtnaFYxXDKm2ykxFb/AvBBVw5/Uv71KK+36W1WRujQSMBOZIAvWMARMsNMq3twdGvP56tdkccSdk6uGwanUUrFqWk/K+/e1vY+x+//vfj37jLA4UETvBg3sxf/OU8454tYiuW7lyJQaFZ555hrNmOUX7iSee0BVxLgcZ0FHAb7CxXQuREp09NpF+54RsEe8Mr7wtOB59CjrXdnTnU+J4uXBiNqt89uzZw7lQzqewKxYOtxs7PwFFaT/lrG8HOKtgAAAgAElEQVSK8FQH6Gu7u53BRIwEjARmIQGtT5rhOotGzKgo5mSUHUBcEFsNChYkFeSuwLtAOcnrZtcxWcYDJkTdYQLGEszi1cREQi9XrUFvwZOVcbmA23NydHY9HCAP2kWbkQtxtK4lh3pKT59HSM/oj1GA9cF0mXZzhzds4VXrg2ccLm2wXshTaIb6UF/5AnyX/pSvgKTJDnVypC63YnGnv9UBTPq9cdW6TKKRgJHAAkgAnTSc62UlUVd4ErPJAtQ431WIllWDiLxI9HrRPI8++iggHtT+xhtvEMFy/fDDD8s6e7//ueeeY8DSQRfU7FHqU5/61Ic//GGM42UMlwB3Tqvasqa9LMe1l1bZRYFF+kwU8G8hsz421S7FSwEQOTDdTmHAo2LOiKIUj7QL+6ZNm8gAEWYYBBK3bt2q/XDsgiZiJLCUJaAQhMIMGjks7HUpS252vOPqgvlWqX/HGKAVKZSJqD+lWhVoV5ZbsT2rwOlN9BTQji3PwX3qwFR2EsQ0O/1+i7NjvIHSot8FkvIpUFm/Ipi2PCX0iwUaqAPNnLbUAmRgEsEIhymdCAttMRJNTOAtWj4NoLdgGwOQarTqL8kEgyqrxaj0uu5+jdp1U5WvphKaklqusBNonRsnW7TNp5GAkcBcSmAyHx4Yz7e5B6t6x81lTQtIC5WDFftzn/vcb//2b2NK1xCc+s+cOcOCTxQd6zk//elPY1CX94dqoxdwMl4na9asEeWmArbsxx9/HBs3buQ6xb6WuMokJtNHTuNaVBLeOjJUcq9u3n777Z///Oc6Heq6bm6J4PSiAb1dCkRO4JZlqcwtdPrRo0eJsLaVwJyDOEtd9SNzNRKYBwmokXwe6BqSzSYBwB9oOxDQB8NV9jtqsfCn8F8B/WKCpSHk5ow6IqiylnCYDOwbyE6ROEdjfZ/Dllay1RBxBgbyawUPk9zUR9At8xP9LkLBWV22oapnmHk6/mAH7M7QhdsP7iujIyNt7VX28MF8xZ4zhdcj0n8iBsvDHSEgF0sYqkYtKD1P4V0Kt9L3dHYewz2bu7v06DPDRpliRgJGAjOVAD/fkXjO6471toIAm8KCMNOmVCmHi/iuXbtQOOg0DNb/9m//xus+AkCXRCLaGE9Jbr/73e9yZc0n6c7Xp/izECqplwD3oCt+93XXPPXqqW1bevbrXWUuxC9b13H6iAtjOUZ+SGsSbNpo02JPGAz+pLAOFVZ4C3DgwAH9NBaLHTlyhNmGLjg+Pm6XWrduHY3BxI7vDpvVMBHBZ5+OtOcAdk4TWe4SmG5IX+7tv5jbNx99j7ZJZdKhUFAEW6xAo1wLs1tCFxhbgHIychANBEMUQoFyWA/G3VSSDb8TLZGIIMIiNav8Yn0q27MwrH3BNV6tgxlULG1ERARpt4DeOorNfxYANO9pOVyJFyYMGUiesYBeKKuZlFRKLO7CvHomzbEihcyqq+z+Um2VJ3jFSE4RgfjJpNMZMfDX52VUoGw+jASMBOZCAql8KDY61B7hdy922+URnMoEtWM37ZFHHrHj4HjdWDuFWzJzdaaUCaRME5YA9/NvPPrwuwdcHu+TavvHk29J2UO7XfmJwdPJ09/5znfKaHELUseSX5muH+1VofIppV566SU7HRd5HcfLh0d2um6MfWsiiySB5hjYF6nxS6va5u2qheUMRYLloqxOwXka7qkr0E+hP/lUTs/ynIBiJQXsKC43k5Oyg3sivmLV6iYBuJpJWlJonToptn5VqV1lNLIVCiKCpgiInZOYAOQsUeClB0Mgw0GlAzothWVaIZzrP2mF1QSapKdXSjpELVuT3Mv2/NrLX5A7G4amGSntHBYJ82kkYCQwjxLAVBCb4GeX7umIOH6681jjvJJ2KhCM12JTQC/NdcB70EmyBLjn02PpMy84H+s4fGCaqEwnhUdOvp15ZvDISSoYhNUSXp3EL8p49S64KEUxv41uIkE3ESvzK/O5pY59gmWOoLQKsqhU+QP0SQQlpe7kUy9OVcBd/GRa5KQesKMsmszlmk4X6aYJ3xKwHFe0tEoCClbmKLTZ7WKXFeY2tL5JArxhBcfMDoInIHCOqq0K3Ok1ekQ1QyF2aY4C8cXuJqbwu/Q/hCUwDWO7SRIoqnC7bCkjO/0XSzWJJAwbRgLLVgIpVzAWG29vjfrdyeXRyGhrG0s09c4rKJN5WqhZpqZ8K1asdBq5m0eUMvAuVZVaCReaR65LiZPFl+Pic7CU+qt5eMVeiwKpyo8C7Aq9a+SuwLugQOUz4vX6BM7m89hOwIZsBIm5PRgKO9+BViW70Il8M5V61Mi7fk0J6KXFqFYCWJZ9Mxea89r1MZNgEbAsJhA399DI8GjVvKzEBbnbUw7doVz1Tu52kcK8zfoJY8gnqMwC/Blo2Z6/rb3Dzm8iRgJGAvMpAXcsnnQFW7vbOEthmaB2xIVxoX/FShk/FiqgvuSN5EJWuVBNa6geS7U3VOhizbyYwlrMui/W/l6a7QbIysxf/CYcQZnZtamd1IKmVfoWHSiIVsAs+I4Plqj6sxl2cMdPJjFPRhQHZ4WoVsWKmcIwICkypZB2qKdE5BHWlsmJSZw9eBhuiWCB1hmE9WIo3GmyKoPAW02aZ0xusBVheJZJgLOcdU+q/cCOFMnPdQzHlclkMhQK0wUccprJyj4zlRMwOknaqyVTaI1mhZapZnApNIdmqTaoWYomhTQoDnBHhjhEzXUjDD0jASOBcgmks7KFYHtXX8A9Vv5s6d9X6qh5bRN7b5W4ysxrZXNNvHScmWvqS5Teogll0Speoh1l2J5vCRQRp65JoVUNWe0EAfKCi3VQyRgyKMmONOhilqUSkhMTPb19s2fXqkZQI9S45SqwUs0vNMCkUgJJhU+JqP/SGklTH/zY+CwA0v4V8oygOdRxuVFJcrGeaVTLLgJUrTLI1uYrVq7s7esDymoXGs2kLAtVa0OJCLsq6NWiRMmjL0JH1cvVDpqNmV3ZiHMyNUmPwBjeMswrqJRWl1GjLmFMzWBEiIWY4kbubdBeUk5zKNm1o0wmS8TsBVkiI3NjJDAPEhgbi2f8nb3dbDW+fAzt8yCnBkiWA3f0tVJ99ZJAG1Yq1tqFC6NL7QzL/8niiGBxal3+vWla2LQSQDXZEK7IZBHoSZqF3IkJgCcFVxnwKEZf4ulMmt20MHfPwC4LEY10iUhNCkijKqGvvHG04tRgssCnutEZC7Bb+Fe/XMU1cDkLChfYr9omV3ku0F/Bc0WAC1hfnnjEjV0FQfz6H28SWKApLEkR0nnP29baSq2WKMDlejIjMikG6zmfyrtcgD4gHqM4/7DZY71GXPzTrdbN0LMg4vUGt0uKZ3NKSHJqCfvMVO60AFkZp4RR4VV4l7+CjlMfSjA8KchFRCWLlUlWcxThPJMmVaRhgpGAkcD8SIDZ8Wg8E420t3pT81PDRUq1RG3Fsm3p9qtd7ka2K87EW8ZeC7MNw7ILhXFgIdu1CFUuZPNMXUYCCyQBgWgWeLOr1EpKozy5qpiCfgqgyjnSbjxkQHWyOUAuDx7FHSUQLBy3adOZIgIhICx2egWu4UAhZIWBBQcLkkxZCFPI2BnUTx+eCykq3SqsM6kcUkZQqH1VJdQjlarbzb3Ooz6IOgKnwLZEOElKjOWA+XNnz+LprvCtFCqZAmgiVmVCo8Cf4kLvmOkNuAKusIu9FBQkpo3qYCM2v0d0BERBufo3b4EBpgIC3D3ecEvLWGys8lgW5hvpdEr1H5zoblTIXclXhKoFAv86pq6Kf4Xx1ZSDuQaoHVKFxpgPIwEjgTmVANt/Z/x9Xa1xftNzStgQcwHcRcsjCbGa9N7sCfc0KpXJ9HA4c6RqKbwIu7q7UN8cEIXBo2qeWolo7va2NpT/+HgcE4vOphRyrRLFdLECZWVfCAaPSoNNMd/UsTorm5qIeWokYCSwsBIAPQpKU4hTKzdH/Rqog/dIE7TJRdCm9pn2uNWiVrc/4Mf0yylOE+Lgzp5ldQUgOwEsmMlkKYhGhYh2K6EKXaVFCGcVC1WSxFOnthH0q/OrVHhUu6ZIWq2gW2tfhTbTEMltp+kozennAG1SxQjteffUqbHxmDLNo6H5JI1PBMFVFvgSJK3wQKWouMoo6ZqQ/uQqVebFrs+BHsFgABd6xm/8czjrw1bjkqdG8Pn8vOXgPQB14rCEDBFdgaxVhEf0F6FMpraEhQn+K150BArSzSJp6WsCq2DZdLKMslWD+TQSqCoBvlJT/AirFrkYE1GDsVQo7A9Hgwnrd3gxysFuc97tjUfeMxFa5c5no/HDocmTs/wiOS3ueZensBO+x527t++1iG/ymfPbtve+MZKOnkr0Xt954KWhyzZH3w15Uz85d2MmVwDT+RqO8nTeB3/twcHBwTVr1jz19DO852VE4JSlFf39QHlsWmhzxqe+vr6zZ8+GQ6Foa+v5c+dWrlp57uw5hob33nffmbNnLt2y5bHHHuf0k7gK/f19nCPV37+Cw5tQwR3t7YmJiZHhkRUr+lHxJLa3t7Mdgd/nf8973nPu3Nl33jkAiGdt2fjY+Oo1q6mRodSWpokYCRgJLEsJCNSsNmKoUVfjvQKCB83Jn7VJCX4sTPXBo4BuzLrsLN7R2VmniNB4PT096KVQaCKbSWOvSE5OABAprgGtsKTQpILgZJRPsKOCj/pTDgQC9QqiBBoLkpYIeBm2iEluKeIBf7a2tcFfbHQ0FhtbtXqNelLgtGRWoNCq1QQ1YZAGCyXB5FKbqH14EXNHNqV8ZYQ1OaBUhKNmNWKhp1K8z6UBgt+lIP/ZA0YCoNyO6IfkYwLDH0Ry2fyF8+fxp7fYqPkJHTZ7oSKIQJzWArLLEL9uqchO8VegRVwLVxhUkpJP+af+pL0kSHPkzFTpXBb1FsqaDyOBuiSg9Mf0OeWbdtGG+EQy5W5rDyZQCRetEJwNz3kCp1d+YiyySScOdN3ZPbyrd+CnaCNntor4VE+rSzbgyVzdceTrRz6QyASfvbAtm/cA0wdS7ZPZwGCqze/Jbms//MrwpXZNVb+nIHW2Yn/hxV257Av4Ld537z2A6SeeeOK6667r6up+/vnnb7vtVtT/gQPvXHfdtSh+jnsFiK9bd8mlWy79xS9/icYFcGMf2nrl1mAgeMkla59+5pk77rjj9T17rr322r379kWjURT6qpUrX3nlFSi3tbX97Kmn7rvvvqNHjjJkUnLz5s1wmEhM3HTTjWfOnB0biz1w//u+/o/faLotmW05moiRgJHArCUg+NJWSXZEcJ6Qtj6JCO6TBAXmdLWgT9EPAHfl4A7CCwTVCazTcUWlwFdwLcoKX5Tu7m4Aonh9p1ITEwlMFZzlhF7CdYSXgayJJJvAXr+fK7ZtcVLRQYFNatNAs1a1wNm2tlbeZEIQB/PWaFQAah1B2bBVPrJ7PNhEurtrvmWlUZJVxCQRvWAVmVA7wBegz7tUpis8QmicvIFzi9p53ZNNytsG6qLVbMszFosxDejr7weOC8HagQzJZAZwzWQFwSAfDPAo87ISMCadplM1fMemXpZJJGLJxBKOKqeBe6bdbClTLjFzPycSqPgmTk/V+qJOn7N5c/DjGpoMh3LxzgiLUAuG3eZld6E4G+zeCWrPxAfTY+eoM9i9caDr1vDkqej4vilZmOIr4ZYDKfRzZy5g+i8GroqlRV3qK5FkTgwzqZwv6E1f01EC3G316OSDXmxR50iBucPh0Lp168bGxoHaJ0+ewqr0+htvgKcZ3sbHx1DNnDP+1lt7f+d3f2f/vv0dHaw+ltHixImTL/zqhe3bt2NoZ7DAPHTk8JFTp97lreu2a67Z//bbR48exVS/YcMGXv7ybrqzoxNr/Z7X96xYsYLh8JK1a197bfcf/uEfHDp0eNWqlYlEOwOJDNYmGAkYCSxrCQhAtLBaSUOtX7+tBgT1iR3WeuBy6dPv8JNRjnaBMnNvCTXHDTgbPQaQRZGK8VlZ7kHn7tZop6tLZ6QW/DuAsIJ9s6zslCAoUi21BA0LH1agCFFHDRLVaF5l0eVUWaYI0oxyl5KyspqawruQlcyAaF4IQELnrFodibpSrrSKC7IFEGO6RyGTSKA42aCDsQYTezIpbkK8rmC2glpmukKzW6IRnbOMq6q3iMjrZWmBlynQREKsMGXZIIW4nAKiSc4hrHhDqn4gC1PVFA0uFat1TsnKqja3RgLzIIHyX/p0VZR82afLvBDPJ9KexGS6M4JTtHmR5RS4e6TtKrmXw+9ysYPP9t26npvRtqunA+5OIuVxjoC29JrjEVVEvJM6oact0B7xHz4Tt58ns/6HT+y0b2tFML38938/9oEH3x+PJ55++ukNGzbiP4pXDG7rnGmC2t2z5/VYLHb55ZcdOngQCxTf3D179vT19h08eBCaBw4cAJezscAvfvGLu+++C0+ZgcFBGRlyuY6OzjffeisYCNxyyy3nzp17/ufPv+/+9/HgwMEDvb29AP3nnntuy+bNb775Jm9Pobl37z7okHnfvv1GWdfqL5NuJLA8JAA8A/OptpQNb2p05KJHyUKE7EUEyLJIzOHKopzGUo4JuX64iSY9ceJEV1cX0B89E7Cs6SBdFZRbuM/jx7Uatav+FYjDJsyoq7AmAFMxJYjcdl0hSWN7laYwPUbu1tY2YCgzBV1GFRYSNbrSPTE5IVSkNvGVQUt34gukwLcMBRKxuFMkFFll25b9ZPgnpQsXmXTIaiI9FeH1Alu18IYhmUzxfgHgzkPoiUOO39vb21enJHnDzmsENDZTJsaF0ZEqxzBpvx1hUOZdBeFZH0qSPKIpqjG6PfmsAH3NO2wiv5kvfxLCJhgJLKIEav3Ap2ZJ/SSmztL4U7TBaLolkBnobo02XnqZl8h7Ajm3mLx94c7xY7s6rrjfExAzRNZTboyYVhAyJqhApLqrDCfT9YZGdKZgwPtHH9r0tceOHDg1rlMC3sxH1zz/r8fv1be1rqjpsfHxJ37yJBkYC596+mmd8/x53B0v4NmCyZyUd0+f1um8bAVh73UVXh8cUaidRwxcuMhbZc8TwSWGa39f3+49rzNo4ZDz7LPPkcKZHS+9/LLO+YxKiUYje15/gxTGFM0AI6jOYK5GAkYCy1ICDCSFn/mU45QCfFoVKvSnZAGQBS+CPkGgmM+7e3rrFxHLMVF6w0PD51JnwZ14oaDTiICq8YmBLGZ4QbGF/4Kb9R0xClr2a5LxIVf32qItcFpaoq8WP6LGS/4piM0FbFp4plMEy9tpOd+4deKeIHc3LwfgUEShg1j9JbN8KjqC0SuC2NVVECd2Ma4X/ut0aMEvxFksQNPDLdGOru5Kq7nVkPJPigD9I/j+yDFMfuEDzxnlW29nRRwwpTpP96AG78hDUDqh0POFD5mL0CBYhhamH2YYdIc1u7OpmoiRwPKWgPVjmb6V1i9nupy4T8fHhztaPd6gQe1VhOXOJQOZ2GSgZ3LgcHLgiL+1P5dKgN2DqYEquWsn8d6SoJ/LUIIlW3kllpRIZf3/ffpWOykS8n7xQ5v+n/88rLF7Luc+mWhgPLPpzG3k3HkB8SYYCRgJGAk4JYCKw8WiYuTRgxZX5x8gUxJAh4qCOxKJUpAdtjh9CXAaCsuG7vUH4CkBXMiKGnZTYbk8YBH4DRjFq10820Hw/IEZrUAMAK8hOpgdvMt/lcCnwuwkqcBNMcnBk7Cu/msorxG4vmo8boFwScPRngWllNA0maLw2lMEIJk0Ztfg1oHWxbFEwC4BaK6DcjfJgoV5pKuHIGRxp5FxxR9gL3xWfzJ7oe0OZqePMplB/DBBRAuJmUHZIMUDKhaOocd//uREWDiQmOp6fVUQ3rpnOoYE4ZwInaEmA+rZ9EyZHEYCRgIlEuDHNzrp8eZi3e3GN6ZEMmU33UPPv7viw/62/s6rP8QjjCXeXKpz5MWybFPfYhpxZhCHxYGRZNifYUSxHwS9qYfW/Pzbx95rpwR87t+5f/3/99iRY2cTOZfnZGIOjhK0ic8ywheI0bHR4WGWlZriRgJGAk0oAWAlVmQBuBVBA7xicuFe4B+BdDQq7+iIoSUF4AJAGwSdmjilOju78ECBLLgWamKWlpWayfh4nPWqWKTZ2gQdDnYEwwN50cOA1MLVjlh4XoNswdoCSwuh2BANnEvudYOkXTroqQlxVCVvBohoKqwcPXP6XZ1H+X7zXALo1oHccTKROynFilZ1uCzFifC2kyvt9QeCcmEHR3FxkRcLPHdy1ECcnWTgM5fzSwd48TvC1IRTkJMC1OEIflSi7kirx3WaPEBcSmJicMdjx4MXD+zROqYrcE4GJ00TNxIwEqhTAsl8aGzwVEfPSp+r5IdZZ/GLKltb7PWcJ3She0emYw0ND6UG+s//KJCaid2ZMYJtGNlZ0Yda9PsziUR2OJG1lwFn8t43RzfYwj09OAlkH5/MpjMYV1x+T+be/te+fuRBO0OtCOYZvBXxE9UZWGbEBskzGwtrVUE6Y8/OnTt37do1RR7zyEjASOBikABQORJpAU2WwLIimCvYZ4F8Cu4J9tOwFXAH7sRMrKG2gMVQeOboU8kafAkIB3661FGsWv4KbspFoHBhoaS4iWPNJhW/cHQmEZ4CMXURDZpJ01SJQJkGEtF5UOg6p31VmFY1UmN26wo3q9esofHams8C0onEuABbdY9ylsmEGM6JyASCW+Jgc9UOrjK54J8AcwXPBR8LM3MZoIw88JMhQqeMx4srrHQ1pCMu2qSbZSN3IgVWihxJTDXOg0sPDRHgnk61173L51w2zNAyEljiEkBzjI4n+YGy9S0mjiXemgViv2NkV1vsjcGu28OT70bih9z5mcgNJX/VlVuPnzgJ04WXmLze9ftSMbsVog8Lmi+WSP+vRw+NjBdrSmV9j757h513igibxmzZsuXo0aPbtl3z4yd+ctttt7x7+gzTBbZfuOP22+n4t956Ez7QwhBhMxnSV69efejQYSdNxrDW1ijvc3nEro7ORzpOhq4u2WsZmxbLWDnBGxA/MDhUz3CCgQ0b0dDwMMV5lX3X3XdhjHn8Rz/u7u5iH0lnXZwGlUwlV69affjIEWe6iRsJGAlMKYEihpoy29w8BE8qIClI1BkK0F1DvOINWdAfEojhJ4NyVCBa9jFsa1fbWzmpzEVc6SW5aL1XfNFZnbgwprjTj4VToKfP7wmHwqz7P3f2DA/6+lfobLohOqvzqtC4JPBGQrxi1K4yMIEry8pVq9XTgrwUCC+VnZPQgsSZG+A2zwoBRIStJzPC6IMcilzRy9riXuhJzVVJFpVdl7DKocD10Vqso618S7EgLTOVGAksYQmk8qHYWKyjtcVnDkNtsBs9uQlvZsyXHpkZaqc29vxFMY6OjqK3q3sf+jzZazsP7h7ZTO6xhGzT6ww83dm3+5GTdzkTq8a3XbvtlVdexQmHiQEZOA7pxhtvYB+Y9evWYcH5j+9//9ZbbhkYGLzzzjuHhjipifOYchs3bmRU2bRx4+uvv8772uuuvfbIkSMf+chHHn743zE7XX3VVStXrtj10kvs0R6NRAD9PLWrvubqq5kVPPnkk7feemt/fz+7JRw4cPDaa7cdPnyYoerw4SPbrrn6+PET8JBMpZ566qkHHniA9w67X9utgftVV1/F/jbww96Rv/97v/fd73733PkLq1etPHjw0H333cuOk88+8yzuoRs3bmDLmn379p84eVKNwXb9JmIk0LQSsMBL0zI4F4wBW/lJ4uVR3QRcgvIUclMmaTFTqL9oWyuon43WlW+L9qaYC7ZmRUM6ztGcgsrBCo5ritjCZeNLOauozkqY0GCnFxgsZJnlgI1lqlNn8YXJxvQJ6zg8wh7Wfu1/72SSOH1d1p8WuJdk2lNokszg1D/KuD1jY+NMXbC484JjYdpiajESWBYScI8msm5fnm0Gmf4vixbNTyNsVWqrJzuFoyaKimm62u3iVsaR0dGjx45xJunBQ4equyGy4eN/nb7tg6teuLr9CCcxfXD1r9r98QdXvnhHz5uboqc/sfZZbjW10JTjBZB9fGxs0DqvlMVeAGX0LRuxnzlzBjS/e/funTvv/tnPfrZq1SrUKTQZQ957370s6tq+/c5777nnB4/+EKi9f/9+EDlnM23evOl7//H922+7bc3q1T9+4okbrr8ONG+1y9Xa2so+7tvvvPPabds2b9ny5E9/BuD+j+//4Morr4Q+L16vuOIKzPz7336HDeZvuP6G8+fOv/jCi3bxF1/cBWMPPHA/R7Hu3bs3EmUcb920aRPnPZ08eQorPsPJxg0bbrzhBnhgTsKrbLusiRgJNC4B/SNemGvj3C3BEgLmBOXlnFBXtcPWgkTsP2Lqn+B2yYUCoSBOGgA7gB+7VDWrDGR+opCpNBid2QCfejtzGqwLNVK0gVpml5XWiQsQ0zCP7HvDFbTtJEkGaXlhwlXoPslQ6OfCb0rLiGQdQYFL36qt6+llJ0ETNxIwEqglgXQ+cGE0iVt1W4CTlUyYUgLF4cXKZqdIRCmrkhSXx5fxd8b87eNuT66guVBZ6CzHn6YFMH77nXfIU93ijlIfSbW+MHgFe/1Q1ZmJ7ngmtGvocnzfSXni7I2JjKxI2NLHKiLXIXlVWz3Irl6RSF9/3yWXXMIBTKMjIxcuDKBzDxw8+Bv/xyd7envB38wh2GGdIRM7GWby9vZ2TPJ4qR47dpyyd955Bx4sUF+5ciXLxRhK777rLrgHf+M56kTt5OHs1fe9770UpBbEg5om547t28mGe/3NN924Zs2al19+RY0KuQsXLnB0KwLc+9Ze/c4acz6bIuOug6keBJ85dBjIjn399dffuOWWm8H9u3a9BJO40OzYsZ3NirHfVG+2SV3yEmhKOLPkpTrvDeCnjYM4P3wiWgOWVsnPXaCdfMi1+KnUhQ+FQxoYESUghugm3jrW/oICXoG2qkE1L3ZmnUPUpuB2EZLGvkpaNV3KSdkAACAASURBVIsv/AP4gUmCrHP1evCZYckBPo2lnMj6VCtFOlNtBUmfljRXQ3Yrm0uWECSTanGBjJEmzEwC+pczs7Km1BKSAPqBDbLykfW97dgyDGqv0nWevKs7lWlN5yZ9nvNBb6YhM4rLFegfCK8/4vaJ2TqXDCcObsmMtlZWU6ai3Z/+zGe1MUP2Bev/kDtYr1tn0Oda0+kairuGTrzWnTlQWRMp2NQ5NvX4seMsMAJ2r127dmBgAIM/3wYGRVx2RkZH0KGgZHalhA0wOi9/WYrEjgxkQ3ETwbtdhhjIqWEGiD88PMQmA6yq4mBCNh+g7Mc//tCPf/wE+amIU1pHRkaJMPpSDuKx2Cj2G+C4nDs+MSFrrjjmMJWCFFHGA4A4GR577PFoaytssI8bQ0UCNrq72DiMs13ZdR6G8S5ijzMMNswuiDfzuF61O5Zgohlbl2CnzRHLM+t7XDBRFszz9dJGsVoQ1OFB4rwui0ALAY3Hno+ohPjYeEx+2qFb77idDdeHh4ZOnjhBVpy/56gpsyBTTQq4eYTDKMsgmur0qZMtkWhnV+Fw1mlrQtFFopEV/f24A/Ea89jRoytWrmSqM23BBc6AekfN0ilYfwYHBhITExyu5+SBU/xQ4Cwm1v5CaOPCalkZJmThr3UwFC3GzE6n8x6FQ7Umzpw5jR0H08+aSy5xEjRxI4HFlEDtyVDtJ/PLbzrrisVTkUCOn+H81rRkqQdy+ctGJ4bd+YTXFci5erOuY23hmK+6SXe4/cbw5MlQsrhQ09caj175xuCZyZY2b2oyxwZj0daW2O5r8+lyWwwKjeHKklOpj/vEiefz3jKrhpWz4hNfmSGV6E8PuWqgfcaVN998ixGU01LJi5Ef9SoaVkHw2NiYbFDgcZGNp/IeU42sKGL1SHIS0UOvfHfRx/jcj43h0IkyxvrOS3FIUfDVV1/lEflRz3gxkkIG6Oj8mMbZII50brV1CkIa2ZMSzyce/eF/EoGU5oSC0OHsw/Fx8QhigATxE6FGyFILPHAlxQRLAtXwhfXMfDatBJZft/HDRMHxWy6VecXYR4JOU7ZablqisuM44B6Mx76NEU4BLKNRSnFR75SrjOJAlGJ5Y6diTeteVUo0HmGq3Iv3DL0O2mYTSDoUXT2K+aY0wLl+XUByoXeV0b2Qq6RZ2uwu7xiw2vBaFXcqNHwpPXNnJLCoEij5xpZwUvtJSba5vRmLp7PB3u624VmqiMJvcwrmps8xReFFfrR5fPJ4wMXxEPAx4XXFfK4tsck9XYV9FCuYK2+qv++Cy5MLtXiOvpHIJPOX397q9qf8XcOpcyVGCuigBrEaa4KA+BJcP3TkFwDTisqmScAa7WrfUjUTlRHsRxpJ27dzFcH1iqNYZ0xNxq5mHb1m3Kg6Ci6KNqiDr4smy9LugGblXtxdCkCuwCIfoi9tnUlExdWnvgiOjUajmGzVulQ2sU32hJvoqIravwnhv9GuAO8W/EqU6pO75gssu1UrDWTEYhUuEyqGK+doIm3QnKsrbagiByvJdpghgWEIOpCj+EWp+Zuvsw1HzSQBQOBwuj0ajLf6Rqr+qhpi1voJ1i40fY7aZWf9ZJa6z5vOpn3F15Xo4lFPviOdHfEXE0t5LKnQE8AByRXpxBbtaevx+IMiC09A3GamDiXAvWpW1BwuLljFzw8NJTgyumomk7hAEljU7/gCtXFxqllikl1i7C5on3LIjow3JSLixsJ3RV4KykxDQPwuyMRLP1xogHRYeYsZmy9mI84ZoE/Bu0oYtBc6ctt8AWzN9Elax/pUzmFSb1OdnUIGoDysO7gnWtLr0iw7QUe0jUYtQW6+RhuOjAQWWQLxpCeZznZHxmwNs8gMzXP1tnqYQT2UzVSUT7tdoRwWgar0xMhSeOTJB3pGvC3iikJoafPgLaOi7szYdC973TUWp6ryLp/bfWP/igf+x++fPH1677PPrsvm1l+5dX8y+dJLLzWnrtdsN9+1eh82H5/Ny9ESkOASYLF5+3duOcPZ2dKb0isOYOesRyUrBMuFI4WibW0k4UEBXlQufMVXhc5iTRMvfOEEq1qtrZc3GqyzckapMjzXW3AB89EoYQ0rO46PXi+OiyxJcAJ3yWDNQKz2KP4KbRPEXpCR+mAmp/bBzLMeoLOr++zZszhGMltbwDaZqowEmlcCGNpH8v2h7KmuqPFor6ub0DSBih1K2rP5g8Fa5nZRSd5oMrTqvDeaSJ7riu+9vOWy/d7I2JrLxA2mYzKw/nh3+mT4dDB3Iuixl96XcYM+q2lxD/r9d3d1HxmL/eWXvyz8BQLozdbduz//+c/3dHQ8/pOf2LTo76Fhlm9Gfd6CnrQfLYvIsmzU3PdM84qpeTmb+14wFGtIwEJzVR4LeOdcHhZggWJZu8qCllA41DAarkJ5HpMEq+svNuC1EeCu8C67yohNqPBWYgrZzGMLpicNqwwurEpiJZQG7s4yWNzlDKZCknzyn42SLbROgsiINoqc1CaYvJDu7evn9CU2PGBt79nTZ8KbZG1rgYb5MBK4WCWQyAQnU5nO0ICnxaD2Br4EZ1sCqxPJ00G2lnG5fbnIpuFceyLiCyTPd0+cbS0xKLhcG8fPsI1irPXs4Om+2IG1upr4viuCq0/7O4ZvO9Vy76sr/GkMRimovdHme7gnMCnHWFcJNa1Kn/nsZ0dXrTwQj0OCsHPnTq4syvzqV7/qO3So1/EeGXNIR0dbPJkbHok12YofUdyz/lPtX0aX2UukKoVZSagqxblKnBVnpvDSk4AgNYXWqrGOPtN/8ln4l3eFW1oAcABBtTJ10l4GVI3C4qfJC0/dQGU7t98v1MkZxZVWBxhLUDTqLLqg2YDmdAdV8grF7/exK4yzegTAREt8fnRr9EBld64zq4Xl2ZdGO9io9yqpiYlJdhAqyWhujAQuMgnwGxpOd+RTsa4WNt64yBo/6+aeD/mGo6GNKde6YKr9rgMDngt7jmXOHYnnOg503nCE852dNaQzp937/Zf+YtX7Dvo+OJi8fTS1cTLbOulNHrtk7QtXvm/XqmwyP57NpPK58Wz6mljmwaESjeckVd3YwDqtjZs2ffOf/snOys4wOo4J5PkTJ65ubXt2aNB+6nHn21u8uVA0Fhtz+SKtYdnpxX5aX4TB9uINzdX45uLm4v1WmJbPQAIKxVnfYOtT0SkBqIL3rH/gWHYawYuaLWJBh2wj21268+AM2JjvIgBuqpApiNpat6HqBLjrdZliji6RUUN05jszIFtbgogwrWLocdbIDjH5vKy50n/qs9gWifHfSmBvSPZ/FILJJDSll2WjszxbCfeYJapOsZr4xSSBiVxLIh7raB33+oMXU7vnsq2DAe9gMNx18/Fz+/3ZlEx98ln36PFwujcWvfTc2Nsr7MriHtdbUf9bLj9qqSWX707n1iRz2+KZcC6/bjIPyE/nc//7/PFuf/C6SGdr2H/TWObxLn9Vo3v1GRZ7Aj/5zX/CniEGGaXZX375ZarX8aF0evtnP1PxhpEtGTC9d7SGPWOxMU4+yoqHjtad9Vzt1i2lSD0NqyfPbNtcTx3155ktN6a8kcAiSkCWM9aq3oHzrCwkufLhFlwM3bIXZDqNJzQbv1qPm/xT2ZsbBd9q1oJuFtyOw4m2WDdfQ7H+4NYiXj2yPtULn2JitwKDkcw/pmVefRdwheJPdnaXjfyZm03yh9i4nZaAVaH5NBJYPhLIuzxDk8Fscry7lV1RzNaos+pZfxQ7QFyjdptQ4kLA23Heth3Y6UQYc+Ie94mg91dt/h90B7/bE9Km+Vavf10ociGd3BSOki2YzYdleKoSqlvcQ/n80LlzwWDwpptuQmPu3buXpTxXXHFFX1/f0NDQG2+8Ee7rE61a3Aq3OFKS3tHZiUIcS6TzqfGOzg4Ghyo1L2pSkd1FZKMpmFjE9puqjQTmXgIgOSeO5UdWQ/UVqlZP3RwVJ4aSbA6dBiLkXLa552xOKWobCiTBnXa8nhoU3kUmahNJN14obocar4fAwuWBVRYdqA51s3pYNbbYmboh0n4FvXmg+7pSrZJDb0rDoVpMzBjCOMN7cmIyEAxwNJP6glQWWrhmmpqMBBZYApNp3J5HWaTtdTW7oltgycysOk8wnRytokPwenFjcKjceqa0GjaiGfS7e1KueC5zfDLe7vMdnRzfEIri7j5ZhaoUBrjzRD8sZon7/Vdt2/b6s88+//zzuopt27bt2bNn37593NLVp195ZWp1L/A96s3lOoYGB73hrrawnGqnSc3yWuRyloRmXHzxOZgx66agkcDylwCQjt/odD9TDQHlSnbOggAgYm7gcE1ss4pAUwvK0ToguOOuLq4L8JeCqOXpjdZ10ZyXTJjYBZHLwUniLcMGCfSUo6ZCQwopklXmabY4dEReHXvZFV4CJ6didpftJeX4bA37S4k4qJuokcAykwDf9ZF43pdP9naxn5IxtM9N92ZT/mB7Ln6unBojybSoXZd5od2/JZGdyGY/3rM27PUOpOXEz1dbfRM13h5XB9MXEolbHnzQec6tbOVuhdWh8EtPPcUgZyXU/GRU6O7pbQu5h4YGR8YmRIEqrTqba83K6nkwm4rtsvVUZPIYCRgJLJoE+K0WQ8mNTi6Banm9aQl2W1wpOPTHZ63nKZJo2phuSJUWTsWxMlIrM7X4yij0OlX2xX3mxj9GTTAA2853vMJVsSGqNQ5GSzsYm1c6w/b8KfmfpM1qLiD4ni3/58qi5KjdRI0EmlECyax/cHC4tSXQajZ8nNP+ycSCoXDUGyg68kG+pTeZHe6vs549Ld4nuwIdgSAO7i0e39pg5J2I97+7a3psVneVQb397Mkn773mmsd37dIq8Pz585qD9vb2+666+pEXfuVkaOqBg/fOvT296axreHgA5dvW1j5zXTl1TU6eTNxIwEjg4pNAmatMFQHYoA4XCh7nXfgEope0xT2VTAXDS2FDNEGfKtiRwn09H9JuyivcLjsoNnPAQiQ7Qno8gGyAt80qU46KpisPIFswkpUsbp/XNzY+lssHeZ2CLyqkVEnJFwj4K6nYVZiIkcDykAD+dKNjEx6/p6e70+WquVfJ8mjsorQi9samFdfuHz6RnhwO4B4TXZlsiXSOvNbA8dtPdPrfjHivTGR9ufyxsPftsHcK07hPqe8qLX3ypz+9sb1je3fPG+nUcCy2a9cucl5+6aW//Xu/98///M/xOsztxReWirzf52JdPy8rR8bTnnyyvb3VaMwqcjdJRgJGArOTAP4f9RMAtorZ1evFAZo/jLJtHR31F1+snChPZhp64tEoD1JQioJpXaBYFW+UxgLlhz18MoPBEBHehCQSiZKKxb9d/pckVtzIQJN3TU5Osi41lU4HlCuUCNCVb/J9PyuaYhKMBBqWQCrjGh2Ls2e33218YxqWXp0FMnH/0ItXRdYNRtfH8OxLXVg3vL/hw93eDXj4q6fG6hZ3SqILXx4dWdvR8Xu///st0ejRZ587+9ZbOa/vb/7mby5cuFBO2jJylKdX3LOtZFe7K532sEKI4zDa2tuVU2JFPpNgJGAkYCTQuARAcrW1EVCtlKLc5nGbBsPiJ5ORHUcyzuM5S3M34x07rgBqG+RMQD9FtMWdCUuDxRcuu70FAs2UHSHVOa9Fi4+avcCN6kZpj0Tz7spvAH2KuyYF2RfSzRIGMcUTPK1tbQvXGFOTkcCCS2BkZMTdsqKngyX7BrXPr/RxZx8/3ONy8TfvwSfKTg90+uqoEW14YmTkb/7n/+RVMpoeW0VuoAKyO/LXHwW+d3d3pzO5oZExvzuL9kSJ1l/c5DQSMBIwEqglAVEm0+kTMdTq8nmXLObhNB9B7jK2VWx0W6uexUsXW3khOKJW0nSfbKtIFi0AZNX8FneYxbsFEC9HpZZuu85toR+lP22paBEUb+lTenlw4AJ7FvOMVvOMU7cY2qaTlnluJLAkJSDeDSMj+DYHAhxZUPwtLMnGGKZLJVC0uKMWMUuw6L40g9xxIkllop1SuszfTp4+4vd5ujui+NMPjaWpunUpeJZO3yqTw0jASKApJFAyVlnwTnOm7+TKf9FgbCkDbk/LXpAK1DVFA2oyYYNVa/ZRM2f1B9ZrTmVyBw1Xz9UEqfQFe3QKWAdxe730KDscON8wFHG76krV5XSp1fXWJwktLZF0G07yScHsGNuFXnH4W+y22owuNiOm/hIJaEVRkrQkbmJj4/lQf08PSziWBL/Ln0ne9E0G+jz5bDB93p2f7UvOouZCjW3YuHFwcLAhPY57KJ7rs5E6kJ21s6lkcnBgLBDpjIb0yqHZkDRljQSMBC5eCTSIvPO4UDM+Y3HHRoVCa7D4IsgZ3A6TGr3PAFkoK7UY3MVa38DAvjgQoLAjpEs212eVajqdwcdSC10JYToB2Fy7XZFoxJ8KMNZQUNotErQfL0I/miqbXgJL7+vBmpCRVCTqT4b8LAhZevzX95WY7ldfH5UFyxULXzqea/GmhvJuXy50bWf2VDh1dja1F4E7VFips3r1mtmQm3HZQDDYHQxOJjPMHELBYCQaVZp1xvRMQSMBI4GLWwKOMau2mmcvSC9naoJl2b2Et39+CxTOj+wcPM2iAtGN8qeblVcW6BlShowyWs+w+Cwa0UBRbElgdmUj52yQorEKOQj2VjMYPmu2oThBEbheGFnEll843akBVkxWI4EmlsDYWCzjiXS3cCrw8nZgqPlbb8LOiQfXJpKZQPJwgbfk4HB0k98b92XHZ8xtCXCX0SuTqT3IValFXA99JUSqZKo7KRT0hYI9uOsMjiZD3nS0teFluXVXZTIaCRgJLFMJCDwrCeX31kN0HRtBEvK5AnBviXCEaq3sVrGm+mxIX9uca3O9NHUJNBZfGbxkANzK4l6ymZ3219fYncZNBd8dLYUUzcaWb8vDRIwElrQEMLSPZlpbvAn+L+mGLD/m495u3/ghZ7v88ePjbes6EnudiY3E3UXMDWo/c/rdgYEBZYCplwiLftauvYTNYeotUEc+XE5xOp2YyF8YmWgJeCItZv1QHVIzWYwEFloCzYj5UF8NsSV7QXo8GCzYDBIP6EAH+xwvgdBQG8vag4jA7QVfmeZenArn9A5nnbr8ftlYhjOYHHvg8AikPvXMpVxQ9j0NL5OLuTUSWJoSGE95U5OTndGEx4PdwYTmkgDKq2zbL85TzbhnBWuLwJ2N0EDtzNsaajRvGy9cOD+3wF0zUIDvaTf0W1hYFIk0xJjJbCRwEUvAhicXpQwqLO5TSAGHaYy52HRZBJnL53ycN7FEgqOPHdH6mBfkrs3t7JDY7IZndupUh6eKxd1btn1C5YosGjaNOJTxXuQkQpg+e30SNbmMBBZBAvx42ec0EmntalvevjGLINu5qtLnLnlJCNm8JxDIlx5J0WBlxZmA2GCUNq+kAG5+6KGHKtN1Sq1StfI3lB7253t7ez2+4MDwePnpGw0RMpmNBJpLAqCL+ftrrqY2Mzc+n7xZxlVG8CsuGd4l8qLZmpyI4bzxYJeyyDROYqFKFCzu8luRrWBKhxsF0UuTKvlSmSRZRQp34ixDwZkIr7IGk2IksAgSSOQiwwlXV1d3eEkc9rwIEmqKKltTp1MtJWtH09ENrZPHZsPc9OYlFNwf/uEf3nfffUePHn311VdnU9mMy4aDvnAwmojHeScQ7egL+Yxv4oxlaQo2KgF73G+0oMm/OBJoqMM0UsdVGn8MUBwwcXGYnmmttvm4fgIKs6rsFGaJa7OiV92PLKPivS4gG7bpHYlbNnUylDBP/9XV94XZivK6acyxqk4hm+lAnYIy2WYmgVzePZKOBDOD3dFZeVzMrHZTqiEJhNLnO4KB0bZLXZl4nhEmEOlJHvLk2Fx/5mF64I4X+9NPP338+HHOOp15PXNREncZ/uKJxMDIeDQalWNTTDASKJdAXUN3eSFzf1FKANjn9wfE4K4s7goE1/v9qTffPAq2yEIxVl91sqZT43VVsqR4yU195OYzF67tdJNgcgXcVVS2w6ROHsmJTJW1V0lSmeymQUtOUVUb1NiJlXRmmjIPJGfKyvIuV9rRpXfLtuUTyXR8fLyrGwhoUPvS6OWW5KkW17sZXxs7uHvjM99Mxm7t9OYl9jY+cuTIjTfeiMXdLraIkUhLiJ3j8cUfHGU9xqxmLYvYClN1qQQY6ebqr5SwuVs6EpiTb4A0t07/DzXOF1xl8qC4LEYKwe5TfxdVjukyTU1itk8FdZZ16zRMl9bITEX96aYoiTkylFFe7FuYxItJ2qeBu0D1AkJTYhAXT+FRLoV0m+WilLR8rFbqDOW57WImslQkUPq151ex1P+mHgax1w5PeHMuX09Pp8dV3Bd1qXTXxc1n3pcZ9c5iC0in9Gpa3LFn79ix42c/+xnjWSwW+8pXvqKLrVixoq+v780337S1p5PcgsWxuEfy+fHx8XjaG/FnjfV9niVfHAHnuSJDfi4l0FzdtlDc1F8Pru2AdYAf0JBdZbDg8jeXHTCvtGbDKaBV49bCFGReGZ0VcTGrp4VXDdy5OoceB/gmWlUiJNrphYiCd+LjPivOTGEjgbmWgP1NrSQ8mQuNx0Y626Ne+c06vtSVWRc7ZV5+V/NCdLElNdP6qwP31tbWL3/5y6wK7e7u5mTTb3/722fPnm1vb/+jP/qjAwcO/NZv/dY3v/nNRx991KlAZ8rAzMuhweETHsbGJkDwxnmmtiin0Aa1C5kn8yOBpuiMpmBifuTbIFWAOxgWMy67ynA0Z4OlFzH7rLpQLU5VR6dCRq3SVNbrRWzOVFUzrVKoHB932HTTWy57DbEehJwQXGWdbpQX6VFUl56qbvPMSGCxJcAPdXh4yBdq6+nAPbixff8WhfdZ6aZaHDdCdLqff606Gkyfh2rQdQSUk9frm0InVwfu4OAf/OAHH//4x5988klbu5H4jW98Y9WqVXv37t21a1eTqDza1tbWBjNjSff4wABxZhoNir/5szfynW3+1jQ9h4sp7sWsu+k7Zq4ZxJqrNioBw4nFnd0G57qGeaOHV8BsaBeRLmQU1J0NtQUoqxan0lsY3tkdUo9bAuJpSLEtdlPsEbVCSFaClJUJm9nnYAE6z1Qxcwkks96xRKajrXMJKaeZt3aOSlq/8jkiV4tMaTW20qmVfep0zu1m6UImndaQm4X4gWAwEo0q01JpUberOnCn5C9+8Yvdu3ezAyNxjNmA+G9961vnzp0bHh7+sz/7s0a3ey+tdu7vBL6HXPlgN149Y4lUe2u0yb7lpT089wK4GCkumkwXreKLsZfntc2gX4+XQ3zE4s4RTEttzj/zLyJjjD3MZDNLwFmWYUhmWSh6x8bztScvSEbeJ9iBaPFOeULLI8H8thjsvCZiJNAkEnAPjcZ9rat6WkeahCHDxhQSKGqYKTLVeDQ2NsauiaLllDFCa7nk5CR/7R0dZa7gVFQduGvi8XhcR9gxjZKaItOCGlUvfjIc4s+Dih4dHWFNbUdXn88zhwaV2fTL4gunGThYBAkuQpXNIGnDgy0BvgE1wZnCgh7UpVjcMxlvy5I7d3Am328Bq0sMsAq7CrV7sD/RU4XeVW7qdu8SscRhpzm/Bvph4cpgYT8zESOBZpNAKp2LZSLtbV6/26D2ZuucOeYH+7gG25w6etNNN3Z0dB4+fHj//v36feDo6KhY30sdSaYC7po7IPuXvvQlnN0ffPDBxx57bI5ZnhdyeeA7Q3EsPsnOzG1h0fWOeoy+dgijjuiCymtBK6uj8SbLcpFAlW+WG09pr2A9tRek3lVmqTRXmqOaJHubN7j3vJhgKK7AbRWxNKMIZGMZOouJFrsA6a3cLTYLTeFDtcX6tB5X+1wija7Guklb9hLgVVEsnnEFO3rCY8u+saaBaDM8ZJBDazT6B3/whf7+fvxZtm+/84UXXmQdqd7tFjf1rq4up6ym3w6SLRfZxx34/swzzzhLNkEc/Vvzj+0h2qPBjog/Ph4fHsdGg5FGZ24CxheEhZqisQRRZ4a6mK2T1rTZ6qrMZDISmBsJCBbE9QJ7u+wxmF9Si1NFAvyeZhiU1V2XlbWezR1kpsFLZGkv0dIxq4rhXKC8BOtT31W9Gh/3qmIxiYslATwFhtKd4YCr3WdQ+2J1woLWm0wmWbRDlffccw+oHRz/2muvoeZuueXmjRs3alZwfCc42XKaop3pJfHnn38eu73tOVPybCY3Mx9uGq0NWxQeQmjnsVgsm8u2tbPCo1TvN0pxrvMvnCwWrqa5lpGhZyQwKwnw1dcgrvw3oG3VbArJbpCgd6+vuZTDlI1WiLW8QVOWsB5SUqYp+pabKtjXyto8n8Ivw5n811aoAmskW01pHmYNJ0YCM5DA6FgiH+joCgy63UtnlfwM2mmKOCSgnc9Ra5dcshZP95dffuX4iROotBtvvKG3r+/Q4cOi491uTM9+v98uV9dAhZK/cOGCXaZaBOL1/1UjMJ9pGr63t3eMJ3ODgwOlb1pnW3H9za6as97qqxZuKLHemkw+I4GLRQJsKUNTlaeMGN317dJoPL/9WRjcpY0CeZdGWzWzdnOl6TMKxYIqJrMXA/pnJElTaG4lwMr4gUxP0O/pCKZkemrCRSMBrYK4nj17jv3Nd+zY/p5LL7355pv4GgBWEQMRQtl70RKLO/7vM3h1yH6TS0LIvBbvaHHlQp2jIyM5f2tnxGf7hi7ED2Uh6lgS/WCYNBJoFgnYpy2hN7V+bBbO5pkPhdjlYqkl4lZ0nqueHXnppdnvaUZTZSyU7YSWztxldoIzpZtWAmMTLjZ26o4Oun1s027CxSUBjUJRR5x2esUVl7Oh+fXXX48IsMSPjoyiqURDyZ5aJUZ2MLcoMSUqN1lnYI0OBoPNIGndhmk5wY21q6ubl61DgwMeb7Cjo80evKctuzSGtumbYXIYCVxsErC1nG64pS20cUupRh6gPZeSXGbDbLmpeTa039mYsgAAIABJREFUFkRmtmlc+bs7q6yE3pUphfzFVqoYl2KKk6SJGwkshASAWyzAiwZzrVEge82v7UKwYupYJAmAnycmJtDH7LT+1a/+r+3b72D79ePHT7DDzG/91me/8c1/GhwcREuV7rAy5XaQi9SQ+delbtlZrKe3N5v3DA1e8Ho97EJjW98Xq9WmXiMBI4EFloCF1DG9LtFhU6YaKH0QAAE3/XQ6w3YCtEs2zHG5dGIoFPb7OYfPQ6JWdEWMsBSQKzwXJhrKId/G8LP9tojJvSiJ2VIz5Y0EGpFAPB2cjA92t0csLdRIYZN3uUgAPxfUsihqlys2Fnv8Rz+mZag7vhUkPvTQx77+9X8kj9PBnQzTe7kwIVi7di3ON5y+dPbs2fp9aRZiRJhdHV53rqenmxevwyMxXyDS2lIY1ZbLV8K0w0jgYpTAVFisXGPIPflBcKzYXCrC4tXo0OAgap0tftH1BFQ/mhn1zvZfkUgJFNCwPpVKplKTgoBzuclksq2t1WpuuUSaWQjwCnRnnlLCZIPg29ngpTpfK2m/uVliEsjm8iOTvrB7tLsjusRYN+zOtQTQya2tbRw9pG0TGrJLJXnXz59//pVXXkHjYYMvq3Yq4A7kv+WWWy677LJDhw4NDQ1dc801999//49+9CPgexkVpyose1TX7WzL11VJrUy8g+ju6mC3nZGREZB7a3tHA84ztYiadCMBI4EmloCoHDZM1ppHI/fmxu0odN6oYk3Xmn3Fin40Pqtp2QN46heGqHFUHME+XSqSyai2FhqM6hseGgzxajYcblrjn9U5yuTufCm7qGNHE3/BDWtNKgF25+OH3NXT73EZj/Ym7aMFZisUCubzbWwpg/EFDVzA7krlYWTp6OxAe5ex5EPvadVXqQBvu+22np6e73znO5h2KLZ3715GCHzny0jIbWXhKpmaOok3EWxxL/B9LOnJTdDSph3DmlqOhjkjgUWXQD0mWKWyrN+4+EyAghed8UoGUOWM9Ol0imkG2LqjowM+UVOoctHyUmBGypfBwaoM1ceeuWwnPDw0BDG/P4DNvumkofkFtzPhslmXuPPOalLtT1tYuusbK1ybrHliJDCtBHIuz/DoRMjnAlbhxTZtfpPh4pEARhP8YVD1vE1FsaPVfH5fMBhqaQlXVcXlQN6WFK9cr7zyym984xugdt7Drl69+sSJE7yZJVxyySXE7ZzLKSLw3e9Kpz1DsRReoa1hOWZ7OTXQtMVI4GKXQMkPWm4EBzafzwQOMAB09A/vSQkVigjOBcaWtKburrWhL8UZJzxuT4sKDBhivBiR97Yow+pmmrprmduMqsFqczSLLjIpCKASgNsttDKbTyOBRZTARMabGBvu7Oj0uA1kX8R+aN6qgdnoW22I4FoVr9vce0Tx239WMn7tW7ZswUOGAQPTzic+8YnPf/7z4Hier1+//m//9m+5WnmX4ScjVnd7MOzPDwwMjI4ntSiXYTtNk4wElqMEqmM2reWc7QX0AfxUbnA7etP5cBHj42Nj58+fw3TS3d3Na0AsMRWofVbcFVqtxYRYHIFHVEelVA0D58+fhxnH80WLWn0qQH2uJlmWYrfHPxMxEph7CTCpZGOQXN7b3ckWdga1L5oOafKKUUdY3NlbBtjJFwbrCS5VtXiubnEH7H/pS1/ihc4Xv/hFXs5u3boVLLtjx463334br/djx47deuutx48ftxRfLeJLOx2Le29vbyoj8J3BjMnQ3A6fS1s6hnsjgaaUAFC8xJdCMymJEtPDss24ZNY40AKG9qNFiaCp4/HxSCTa19c/PQPCvPyvO9iZidBg9Yejf6G89WmRCwSCsDE5MYECxHmGl7kNVmcRmoNPJ2+a+UJKWXfrbnTmrl759DmqlzOpRgINSWAylRlLZLo6Or1ejqw3X7uqwmsO5VuVtYVKZO+AkdERXn7qCoGa+C6yogko39nJl6fcqFQduDN+fO1rX9u8eTPoHEIPP/wwqB1ndzZg+d73vsf1+9///vJG7XZ/BXxu4DsSHIylAu4ku+sY+G4Lx0SMBJpUAhqXTjtQKuQOhFVGXMpMW6Ce5s6ECFoaL0TwcU9PXz11CKclKLXhSrWENI0pasSxnj+mE1jfWfmD2+UUmefpEb2khhsxtxPHBXS2Fc2awGwZMOWXuwSYEbPm29+2trcjvtzbOsv2Nay7Zlnf/BSfuU7Br91G7SILt3vlihVnz50jiqc6NvhK7F5zPRbb0DzyyCO6hW+99dY//MM/sOiV2zvuuOP06dMXCWrXzefKW+Oe9iCvHXiFMTY+PkcDvE3eRIwEjATmSgJyIqaFahXNknFBK0bSC6nktTQuU3ISZ//XWEPSqdTgwEAymcK8ja29scJzlbsgjJrk9EsAzEKwCsM1883fAzpJcLv8NTL6WH1rMVZyP12rrULm00igMQlM8ktJ4LLc3uozqL0x0S3Z3Fo5NXxltNL7ydBwBiA2bu/v6/vMZz+zor//oY99FFs72H28gDlt4q6awB3lSPjoRz+6Zs0aKGJlx39m+/bt7Om+a9euJSvcWTHOy2Lch/yB0ODwqBLlrKiZwkYCy0sCtlpZzIgC3xqelYA0RA1bKlifSlFKCvZ2yVueX2WexwsamfObE+wN1909g2WgImVM0bIDQcOcz6y5rW1tsArD2C9gfh5FU0FaplQqwPnUy7YcRZ1iIV64tVMtko4SJmokMDsJ8GMciU2kXZGelozPV+7hMDvapvQylAAuMewHQMPUyOXq7we396HiuK5cuVLrdvKAwJ2Nr+4qo3NQ5qc//ek999yzc+dOjPnA1n379uE2s8Aq28luM8RDAV8o0IY3Ec4zIW+at9vNwJXhwUigDglcFFjFRmbTCkTEIbkVcq+/2LR068iARh0bi3WI8+uMR/dZ92bjTWZ0wWEGX8zh4SHODWHxTx1tnW0WNTEp9JUayepteFn7nBOcuVrhOtu2mfLLSALyo861t4XTfr9AMROMBKaVAKCcPBq1o9xYmApkJ2XVqlUjI6Mff+hj3/sP8UvPZGQLYJtaMWYnOSPY8H/4wx8C/6Grd5d0Pr2Y40xjWKw1MZFl5VZLa1dLsOa7i4tZSqbts5NAvQBldrUss9LiED1dk7RgNRaU3NMWmI5gY88TCQ5hmWTnFjR2YyXnMvfMG82IAPODg0M4wLe0LITlwq1PxVNdxXA0jRikZSWtU+VKC8nz6eiUljB3RgJTSGAkzrkKnq6WBDszTZHNPDIScEogn5cFqUDzq6+6amh4aGBwYOvWK8Kh0OWXX4aafeedAzzV8NtZqi64CWTHyq5MHc6yJu4CvMthCrnshQsXpti7x0jqopEAUGAO/y4asS14QzVkA7wVekvfLwgbnELN8so5Qu0z5XsODM5umkBDhoYGF0BsVjsZhcqmZdaTBpgowfQNlDNZjQSqSSCdSQ9k+iKBbHvEp02n1XKZNCOBmhLga7Ni5Yr/84//uK21dd26dfhxcHTSihUrdu/ZYym8EkU3jcW9Zj3mgUMCLWF/S7iXjXsGRpKRYE5M8SYsMQmU/CqWGO+G3VIJgOymh2ZqpaNlm8eDWu1XUkpnzu+wgIwMD7dEIix2nwPi+js7q2/u9HKamk8O+pic9OHyzr4H2Iemzjyrp8rJ/f9n701jo0nS+04WWcW6eN98r367p1szmtFIGMlzaCxLsrQzgqzDlmBhsV5AX1eADHigLwLsb/5uGzBgwLswICxsLFaWJc8srB2PbK0OG7LOGR1zdqu7p/u9X943i0Wy9vd/IjMr62QWWcWqIiNYzIyMjHjieZ7MjPzHk09EwC6vsYQSh7KF+6D6usMrMeULew3slXOn5dR8/kUqlfHa8BroVAOsVM2khTRr/+W//NfJiQlmf4koPH785NmzZw64x/1kyFAD3HFY5NUSFUsYGRurIZKw1M3LRieJb8YMWl3bPp7MVphD7ebJOJASJXyPDyTvnqmeaKAJOLO7xKH1apWhMeM6nCYYXbS1uTk7N1fXBFe56ShWZ3fuqKwyq3NjamqiqyTEoqeOr7qZNNh9fW62S6I1qT4E6/BcOcc6FdXeJG8nSdDpFqlOqu047yUvUsf1+AIdaIAxhbvnUxMjO5MFfGOG4j7qQDqf9Xo0gBGHyXadI/rnv/D/8A3z+7//U7wjMAT/p9/8TTeglHFQdaOJajA3QysuMfCUZVavR8KhqEWrk49ofp+9l2uTU5PdMa0NheSXZ9I3eZfXnS/ZqIGkKCc+O7gbHQQaTowJO7pr3cpKC4uLjBZqZPjSKUZLm46IwkIj7L8KX2nWqltY3NjcwHbRi++NsrIHju2aREem/Y4EbvIBRvdIcJ90RurS1+pKBYeBxysJeEHhhke6IeECAl0/vX9WLB9vzk14j/auq/Z2EQSUM0xof1+TrQPfP/+FL/zxn/zJ4uLCe++9z+KpJGKmMFBZo5Ya4F5zxh9cQQOs04Su949PD7Z2Jwp0bW5S3+a2v0SucF/4olUN9Og2EgA1WMqrvXkVpAYnmPFdAEBeMgy+P9PHRp1pXqzKeacxN00vqL3Tgu3zO1ZDUdrnbXq2m+CHwaOM9mENKT4s0Po1re8qiVwj2OVygdyvMA/PVVjwZfungYZHsiHh+ng7q4xtHaeL6f3JqS6/1jt4IDvIen2a8TVdTgPYOxhGenh4KAtFKvXkyRPWSopITUxMNi5754F7pJ8uR7gAk/lMJZfGeYYwObs0PnqtMx+3laeP7V5bvvzJPmlggG6Iq7HCc1d1gGmnTIfwgxegSmGBpeqr1d5YIbZ2vDuYQrHxVDdSLs+uJDfpL+Ee2YpzxNzd3UXkrtvdow8C7t3WioFE6ZGPUON3h0Tlfabbq4G9o7NS6XBuZqLTjz5JVNbBw9xB1iQ1K89V+wJXLZ+Uz5uXj7cPa3XhDANQxPDhXmFsSWHluzonGSd+O+D+2c9+ljJ1zTqdgz//8z9/6623bp76eiER2sf+xMtmb39vt3TIi43lV7taUQ+e4K7y54l1RQN9vsx9rr4DFarVa55dMvDvfsriXGXcqFTi5y3KNaeWKJVWmG+gi4ual7cHwa6KbTonHgob7jun0LwEK0mtrb2kleuOK39UiSF3g9wYpS4cBZsAkndb8IhTH7mRGjgfGd3cPS2kTxZmCjg13DwZL9mQRIrovHyXH8Euk4sEu6ZILsdwoTyvDKzvtF80oW2G+7cD7qB2FmBy88PD+xtvvPHxj38cyO79tju9koCJqcnCyGRBn5JT+al8qhl87/zG75QPn7/3GujbVexbxb3Xaac1yKOiWSteryKzzSsjqFB/3Q2YPJj5cbHbHjLGZEy6WPQS/F+tdPMKFxYWmR4Xz5k2L57mJVumhgZ3XVj7MNIyZ4sTyOlEDbfad/uKt6jbJw+9Bg7PskeHh3NTmdHWi80PvZDXLkCXn78W5NwT3yvhukqdVZbA227udba4WLdyC2wH3OOiAj2///u//5Of/CTm9la04vlvd7zFHTQyMj09w4tnd3fnbLQwlUulu2x9v91av6z0La/WZQleXK4PVV7M1I3JIWxXG9B3mETU/bSzjM5DhiMDhbUFL30ED0xwPj83R8t5aSKtC+qrQjfohlppXdMlziAyU7zTaWHbJfGjQcPoVW6gyblCwlZCWm+tA1LJK/U5b4wGtFLB1mZh9uH8JCtc3kBD+425Uq0E6ekTHk531arypOmslISTIfNCUoD2jVaO+N7eSL5QmJyY1PJzta1YO+D+jW98AxLOwwZzO0aUX/7lX2Yh1tsH3Lt56VGpg+8729vnlXOWDW9mfU96vW9zvm5elQv1eK2VXciNz9BOA7VNXPOcIfYL8nLYERxsTjSWCmzl0R6LLVIdO9mlqHi+NCmVdMJ3V3DHEO8IXAQddr80i40F6Wg1Ae528RozK6X+VogfN3TvmpPwqbdXA4cnI0fH5dmZmbFRTfrhg9dAnQYu3wDHCGFcp6nESYY0WmOaODAhvoZHx8d85wHTz8zM1LT1qdp53GOktCbo5z73OZbVcIm/8iu/8sUvfjGeYXjiXdFtl8Xl8szMzvIxffe4cra7ju97Ot1d3/cuM3xpcten/eur6dLK8AV7rgHB0BpvdbBaizuDjEwuqNNkEPrrFnNMwauZpK5nLqkrgHeTN45lu6UA0UF8JrpGFba+xZUo8ybTNJq6VhXazKQeOM0kEwnBeUi6q961i34lCX3hAdMAHu1bu0fZ4vz81P6AsebZuWkawNbuUDtNJZHVlZW//3N/f6JY/PJXvvKbv/n/4jxDKxqfEZI2q6XFnfff0tISfopOSYPk135zmlreQAx0OcvO4jxznhqfmSqO8U1kwMI1MXRN1QyYcj07vdFAgNnYxe6rWDSqNchosFCg8OqfE/nESVM7NzcX1dHLSDOZellfR7R52WBJQqVXfH2oU2X9E9A2fa04cA+uX0dsCbZbAfXVBlqBHYrls3dHA0yLdDi6PD1xnh71qL07KvVUWmmA1ZN4X9AMffSjH/17f+/vlplVBlyeyTx6/PiHfvAHv/XNb7351lvMFFkoFOLt3oXD81tV1910WE3+627V/afGi212dm52enJ3Z5sFCCvdHv6SXLNNcyZVUNPCyROTVuPzeQ1coIEAltVhOm5FgtsGsXBwqhLtRFBSpy8XgP77e3vXhdrFY1WgS3CMiuq0dAkibYugChRyiXX9aqkipQSVp8y5Vk6NzgYnouPGSFxGCRsIbHb3K6qvsTKfMvQa2NjYOC2X57O76QEBR0OvUS9AOw2U8Gu3987f/JufZumld995B3M5NviXL17wWRAfd5o7IuD7OJWWFvd4JuIUZmQqjjdMkpBsLshq21pHyh821cBoqoJjEp/ut3b2Rs6OZ2Zm4x0sXYKmxbqbeB11dJdjT81roEYDauYCcBbHpNzZ4SHR6D63NLPnahPmqCHY0QFv/d5MI9MRFwkyRxqo6iVBqUtlmV9YYHwU328vVToopE8iROXhEljfk1GruaTBgRGCHNNK1rWxyWj6XDdTA6XSye7Z1KzcVpPiopupCC/VNWrg9EzL+/DawrL+qU9+Kp/Pcfjg/v379+49fvzkzTff5BQpmITiTLW8QQH4//pf/+vITfNrX/saUxk6Etjt4yQsHnsPNJzzCXUaaKOsMSZkmC6ened2trdGszNMHHmZV0ubCupY8YdeAzdSAzWALZKw5sHg4FxoUM7OtI3WuDUvFpVvH8FJxjW77bNd/SzwVYbn0VEHZGnT65r1NlUgpqHfALCbwFeSuk1d7hQ1Fgp5lHMlhxlD7u6LiHsNOeKBLBcy4S402Xor68V8+BwDqAHuou398mg6u1hgWo+WoGgAOfcs3RgN/M7/9ztv//Xb3/XR7yrkWShg5L333mMKR3nOGHCva+i4R3l/ufdZzVsNH/nf+Z3faa8UJpzBSIxDWPtsN/hsjcq6J+fY6Njc3Pzp2en2zg6jsqamZ0YHz/e9e+J6Sl4DXdWA8J0DaGxrnlEO3M/Vx6JLLh+NIw8aC19chY+9PdZaWuyIQoShmWCKgsYNaVU2YIzAKdvRvVCEhvfouDSaGs2Mjz987VXGtcO8nbPKXQFXxhLYiKioSmI8AaJkIhTEOkAh0H+QiR0DfGs0ZwyQyZhRqTASkmq3Z9VujO6XBu4RI+5bSlQ1bEbxZtVX9YjoVZ2aniGlO6ETKZpV4dOGXgN4GGOUBMlkMvF7ZOjl8gIMhQb4vMM8o67h/eAHP/jJT3zCjebf3tnmKyV+Lp///Odpl+u+Al2+cwmhH/3RH3333XdvDHCPXg89ud6dU0fDeIgyJ//2QXn09HBqeuoy1veeCOOJeg0MrgaqeK09j5qnBPwWADhe2g49ty/U6iyrVTN+qOlZ1yg7WEzcHRpkTI0xGp2nenQ0PZqh68CfUgxIk+gANegSsgHKjJCmK89WZ93GoXqD9m5DeXdS56t/5ZPy2vpaCGUr49nxh68+pFJxZtjd9gLxZzLlK5xhyMFRXav6nWME0jBeC6Y/dTnEbRiaKgHloKL43AhNszVPNP7P9YFErOmoNtQf155tfmQg7TIFm5PzqcOnAW6m3VKmUi7R3268qYZPHs/xEGoAc8bhwQG3Iq04ru3RcCDg+z/4X/9BLpsFuNO61k0afnngjop+//d/H48aJo4cBHX1sAnuIemLNYc5bW5ypFxObW1tjWUnp4sZ38RcrDWf41ZrQNC9hfWMh1k/h/0E21EUR5YgM/NlA/YLZ24XtDT4e35mRvSRCqPPaXnHxtJsoxBUWVudqhdPlbMzZu+V72O7ELZLtq/icpMldoh81Gr12XeFlCjrRRGoCG6ZtxEzgXoMYzjrhWWtbtjRXjuVkSk+Du+NTwjyIgDbQ8chfPIQKIfsrmbeQxjdLwfcdbH4GQeQFYdR0KnYYZReEzERlOKkdqJb4sVlawj5gxujAe7VnZPCVOZwfDJ7Y4TyggydBkDkeKTjSUjj9Id/9Ec0om+88R20VG9+6018Xj72sY/R4hVrp5RBxhrgzhdYGvHkktNG80bghZS8SPKcFzbGyUnV5+wh6fqqunXM1WUZwvJZZX19PT2en5meGEIhuqUMT8droKUGDNW5Ob8b8tQ/M/FjwUthw84DDSuWEtf4ylgOIWsVM+bA4pChgVgw7/n56Rn5CdTjIm5rR8EmQMcGkQMTuMsdlVFRyxXvaYQYNNxXJRFDCuLMiTk7Nx+crozgKvDtd98VcK8NAdxHFtKt7+E+AQCU9W3AqgGXj6XHQPr5gplv4Mr4wlSPeb4EnGfEX+kErz/APbYlfBKmpqZc2Sp/CWK6VAFwr81tH0xqk+xInLhgezbhMammveC0391CDeyyrE1pf352PDVyM5dPuYXXdHhFnpqepnmkJ0nD9Gd/9uU//dM/c7LQTv7e7/5eNpcrTkzUSVeDuWlmZYzpMEQDWC8sF39PXpi5gwy9otsBC9eTNTOWwqpXPh99+eIZatd6Wj54DXgN1GrAweLaNFlsa4NBQSE4Z4HHLB2O2qzN1+ooMi1jXsfcvrCwCL6lanMhwV4urxJCaH53R5irLQ0nFLmh0NwG/icuXVvSjQqcBTYR4VzXIQi6BJwvFjWt78lJeW93d3Jqyhm2zS4d8FsLjl2rrp4Df9TJpFXKZ+gWQZ48fgxNirBFHeByiwieQxmrAfMKa5tOG1J3O0biAOcF3LU3fG9YX5gejjPjeOCPj0xMQIoAci+fnDx69Ig4LxoqhyClAnbb7kQuwO26WLWlggsb7NrSMexeRfTt8/qzN1IDZ5XRzd3yVP48W+ARiHXmbqS0Xqhh0AANGmMad/d2WSeV5pHmji2ME8HDsBG1c6oGuLeREWOJjPktQqJGs0XZ5sndp9i8nmFMzYyeLy8v0z97ub6Vy09MFtLuxTaMsnievQauVQOxhkVRoTg1lARaTwzDFzID3MZHnJwgVkOQmNs3wahutnKHxG0rJxKD4eZNwrhXQXcaZIzk+huThZtNBhfzTGacGPCXCBAZsmb/Bg2LL0OtasQjXM7n1Pn5OYrsbO+8/+1vL62sCCInDrBHXVH2XDb3yiuvmsMJ7OmFoZ0QPvGK9U9OEfno8Ih6OYs7jMvieILhrIkADwbvMzCmQB22QRxk4J+XyIMHD/g6waAr7PAH+wcY+1GKyxvx0ywSyB7wV9cLcwqyYrHLWyXTAp0F172az8duugb2SqmT/Y2F+Tk3uehNF9fLNzQaoJmfnp7Gk5Amkc+TvCFccxpvqOPCXADc+az5Qz/0Q8+ePfvJn/zJ//yf/zMt/le+8hWa8jiJpPGmbWrSwj5fvQZ4Ry4tzHKZmToa6zvzNsTeX/WZ/bHXwG3SQAuo5lQQAWCLABytcRREdl7pTRUFZHQO3HaWCVgqpeMDLMe4gmxtb+VzOdpGYDroXGNcDf2qwUulHCgvZCec0Xp8PAuc5eGlUqqMcHnTShMlXrpdNSbVdxGfhtuFxENyIbBv2iVAQEF7fSA453WAOwx6ODw4LJ+WSeIkNvtxWqUsn3kZXpXF9i5zPTieKRRKx+hK58az9A74aLC7s8Oa3u5F1VRkcQVfgu1wC+Bumqt5ookXnAok1pFRal7Cp95ADZxVxja3diYLmcl5b2i/gdf3ZoiEicN9h6R50lxhrT9IpmkDXTPY2BhiHfn0pz/9W7/1Wz/90z+NiZfBkTSgP/dzP/cbv/Eb4MUaTTUWrjntD3qlAb3/bGTD5m4pO3rCV5Xqq7dXdXq6XgODrgGHywBqsZYpwHukKDE6YciOpwabRyupMIGwCDpwG9zIKhbH/ErHQFUA6PHRIYjz5PiIz5uyoOfGaXDBq8DWXC4PYAWtmsW5JfFWlV6YHlIM9xcW6F4GAemUvFzofvCmic9PgOZB82imXD45Pi5tbW4yXtWZ1WmpGKKKQh49frS0uEhZAmCeubNOyie7OwD4XWZ8h2CcUwi6Ns31FigRP0u8nfxx2B4rZvhfZNu8GmPZfXS4NbB/PFI6OZyfmxplYiIfvAYGTwO0SPjJYL/AdZJ2Se8vjD7pNIgO20cjv9YIupavtv0j99/6W3/r137t1zAq/Z8WKAw5SP/Ij/zIF7/4xUZaPqVfGuBicXGPjyubuyfZsfLlpm7oF/O+Xq+BbmvA1kAVOpM9WQ0b/zpy2J2DMGKW49SIjN+BY3cDKzSAshDncsxmCLI82N3DY4Ty4Pyx0RFwJ/O05hmbCeQsFLCvQ4rQQKY3CWaJvizpUEGXLd+0HLI7RE5/pljUmCpBeXo4pRI+7kx1z1jV/f093lETk5PgeOzueMmjfL7u4krP192FxQXAfZw4bjYBnfhgXMsRaLq9wgP4zk4ic83d3cBdYFfqui5WXCQfvxYN8ORubW0WZ+/N57jKXPdeXGvI+uA1cHkN0EJitDgyX3RaJN2mNlyKcUGM5ucepp2so55udSd/13d9F14xWEp+/ud//seTaFR0AAAgAElEQVR//MddsX/6T//pO++8g+EEl/lm66fWEfeH16oBB9+Pjs6ZeSZfnC7mawxX18qKr8xroL8aMFDajIXqm9ve5LSZ9joHiI+O0kTWFdHpyjk94YODw53t3Z3tTdy1M+kUpvTixGRWM+GmWLqlT4Zbg6vtMWudPHYokcNSCG9AFmVUNdOs0OXTeBUxHKCYzozY24faNzc3sMdvrK2/fPFyamoS585cPg92B8Ez4H5zc3NpaTlWX2hxVxdAICmOtl2KyxwJoEz1IZam3ppoWZaoUH0BfzzsGtg/PD4+3ON7zuhorYNAlwW7PbdQ7CHqsg5vNTlG+zjUjhbu3L37Pd/9Udq4t95886/ffocUDEaYQmSajYUaw0YsfQQ70y/8wi/8i3/xLyYnJxlL5E7xEfNnf/Znmdik7mtmvKCP91cDfLYmHJXOmTWZiLe+9/dy+Nr7oYEAlLWtWsZqIKAc0i0K+KbRqyuCXzvWdJo7nLSBuJh+cWefX1qanJzCKry5sTHTGWrv2js+fIXqo6ph0I4oxzMbbq8Tu5eH6Hx2dm57a2tl5Q7+8RhE33/vveWV1cnJCTd7/d7u3vT0DOYhx4Wuj3NjgtNzt/pSlX8D8U3ZDeUKNRVmqh7TeakSCk/7/Q3QAPfM+uZ2YXp5oVADd26AaH0VYYgel+pj3leNXVw5bSA+MuRDuTjGfN/3fuxLv/Vf+ET5gz/0g3v7+y9evOTUwf4+7WG8rWsO3MmBTR3YF8/qWAD76/tm2KpezJfP0Q8N5LOj+cVF/JrWto8LmSafWvrBlK/Ta+B6NNBiHvdmlfNRkWQaOvljCPDXv5xwhmFmw8mxyVdfe/XR++8zb1ch/HBJdvzam1G9ljTjNMDtl6vQ3m6SvF7oy5FLWoo+knU2cDRKLy4u4SSDVYkOEh2nQwYTHPCe2oteMeQMhx9oqpu6jxt2xRz3TWUI3t/slDM40gG4nrksG652UhF8voHVAL4Ah6m5+dnJHhvaB1YBnjE00LQ16KNmoqanngfNrKW2SY3Tg/v3/+qrX8WZkMPf//3/FnkM4jND2xg1iZxtDtxp1ugH/LN/9s8ePXrE7F1Pnz51tVH4P/yH//CZz3yGdtal+O0ga4AuFl+nuVhrL1/SmaMzNsjcet68Bmo1cPnGl0awvqWEmKMXRQTXU9g2qFSx0THXgNbxgHM27tpM2kjgCcJUzMS4NKkgyFGWHupvuLrNuF5N1yQPqzq5Ket5b9E1Yl554BZTau5sb6NTPmjE+NAcNbqglRF6WVyo2CkuXHgURcKExr1ktX9HzTpdCYo1EvIpA6kB+tFbG2s8qgsTTF3dvx71QCrHM9VXDbRsZ5hWC8Zcs3b/wQPWrcO5SykjKYb1f/WrX3WfGc3AFBFJNQfuFPva176GV8zbb7/9b//tv/13/+7fQRc0T1hdXYWEd3Dv603QWeV4yxAE323JcT6kdFbe5/YauEADUYNyQb7rOS0wZzaMJtUZ0hW7xjJHocUd23mq0ccdJ5n19TUmVJ9goq60pk8huI+b2zu7zBtzWi7bqqKQM4pNquxNkgkYCdJpHbW8CsxeW7ApZ87oBfESwYaE7yb+SJiHQO2M0GLG+MXlZc5G/DC9pD6G6JJqvhq0HZ1yMLxWluBkrUih24ydDKB7JSAbo+ajQ6wBpjA6GFuZnjnTSr4+eA0MlQZo3OCXuXP/+3//U7d+B1b2T33qk9/65jdpHsH1fG2MC9QSuLMW4B/8wR/8zM/8zJe+9CWmgPzEJz7xL//lv8TU9MlPfvLXf/3X4yR8fCg0EMH39Z3SRLZSN9ZhKETwTHZPAzf83VbTyDXTGvLrh8UdI64yaFYZg/sUrSqHDDw4jx89Wlld5ZsVgNL83cezuSze7UwTyUfI4+MjrCZucSKZ4TX5LuWqRJrV3600qrlKRfa6uFBZV2BWxh4D3NZBUk1ohu8VTJpJ/4d5Zo5L+8dHRzjH4CKDMEzPs7S8UlshCFtTT4rOmZt0P36+nfgmWY14gvBVGH9tlynOsI93WQPcGNulTHo0M5/Zij+8Xa7Gk/Ma6IEGWHUaqq4Z523Csjzf9ZGPMGT/f/zhHx0f8eEomCGt1mDRwlXGsffuu+9ub2//8A//8Je//OXXX38dT0TCr/7qr9Lg9oB/T/I6NAAKKTKn5/7+xv7+xPR8NtPutXcdDPk6mmvAX5fmekmUWsE+0XzCZlMrG/vZJmZxD/wx6iA3vtdLy6svX65ltraY88RG+OTwegdNpjNpoDzPFFAQwzCmERw/AKO0kAQhVs1BLhdyt0nEfIeZDIfWYFNHAGbgkCFJbeg14to2mS88hbCga6dPtnaI3PogIXXpmy2+LnwKPisds2IT8+AzCb5C+aRE5kwmPT07N7+wBKivqwsqpJAHsi7uMkBQF6tlJylQS6gdsRdQNubqavGHw6gBus3MMTozM8f9M4z8e55vuQb46EgbSDMemWBevHwZ+KLTuKnZk69g3XwwF9zrLLr0hS98gdLf+ta3GOl4y1V8M8TnNacZlJnh7qi8t7PLrEHxQQ83Q8Z+S6EHzod+aQDjuYCZfgbRQz7sqsQvjQC1AU3aTKLOVaa2jJWl0VxZWWXs//r6Bv5m2C8mp6ZwemdJJlCmm7ac75vpfI4h/SoBQgVkntk6RDjTsLzQSZmVNXDFUTNswdAmG0F6w55xxkKOW+9DBNoyBz0HQO3YWFO/uOp0kBGUbUkodgLG7UgCgMzdS8WhZ8lhn3iZf4fXjAQTplY2pEb00ikfJdAF2gCys2LVAfYk1l0lGyqcmp6ZX1zC9ShWWxClLjd41io9Z0WrKA8pUbxVpD6Hjq1YuK5Tq4I+ffA1sF0ujlbOFxdy1oEbfH49h14D9RpwX3FpF3ldFQtFVklSuzlS+cAHPjA/N+eGXTFso84EU20E6+nVHrdD7XUWqtqC/mgwNcD7cqIwXszPY67YO6pMF7UC4mCy2g+uOkNR/eDQ19lSAwDHcJgpMK3hUpJgaW5vOWXtAHqqwWwROM1yQvwAoTSG777zDs8L1ndmmMHrLALxGJiDYA4h5MmnqmBU0FNW4wDTC8gblmWvNGC9oC65tIcRItTr2LVIYFwmXZmFncM/5TepQv7pDxid8DjYi2xVTDty1Kw4UUdHJ8LirmtB1QE0Zy9obvCcJCKOpORy0jm7+tk5SD2QEKN66aSEG/JJCe8iskEd5aTGcwtLy5MTU8B9EWkWYIMaxY1JXfcCQy01YjejUJfmZCOx86J1lPxh3zRQPh/bKY1PZ3Yz+XbflPrGn6/YayCZBmiFpqammRWX5vM/fv7zdY0S7Ty2odrB+qJbBe40o6wkwuIXtI/JalQumtH5+fnk+X3OgdKA3TSawIEFusojuelC/ReZgeK2c2Y6fad3XoMvMYAa0LyO9Y0Yt4KbsD3gl2P7YRcnK+jPWky5c7RHgmNjadpZ2tOJiUkWZFp/uQY8xdis+XMLBRA8GJSmVnZ4NpzgD2cRh3Sxyo+mU2l9FKU67SwE6FtM8287MDA4VUZ74WGCg/ZELIN6GiflMuNl77/yiiHpAKarsAU+oxENzflKCkULxuO6bOSGT7x9QOPwZLZt48y0IMTsEL2Vt6rFIshbcXU2Ts/LtkcjUaA3AmTHiHSK79AxmB1LO8Z1XYDUKKtWFQrFyanpQnFid3eHOd0dJ6234go+XYWOpdaZq2ekCKcMi5ly7azUrcB1sYtuB34zNBqo7OyVKmelhZlpP3XM0Fw0z2hrDRj2ntvZ2eZVQq54o8QpFrVQ41wbqsCd3A9eebiwuET7WJun3RFvKD/MsZ2ChuQcixdy2bl1eNviyMvtMkiMB/hmkFjyvAyyBoRiW7ViIWKHf0WBn8JxNH82cRbIuLGVbBQV/MwzMr+wyCmqosFl9vHdnV38QMDaUBCCBzvzHTSblT9NJgNMxJdEHt+x4FKE7nUKmC27tvESwHriVrvkqftTijEfCKudhSBjWI40x6USdIDlm7kHQv1U4ItPB0Eey1xF5qenQuiuu8C2GgDpwQEfDQi0G4FLEHD9tEwiqJ7aEAcNsMoso05zuQIrpKK3CHyjxrDzYtw220gB/EtYKYBuRrWIdOP006xks7RIakWcbjsj0Izo7UkL1dcvicuV9M7B6WQ+l037lZX6dRF8vd3XgN4m8wu0y/omaR6V9urQtFtNK6sCd07z7sD00jSfT7zxGuAtBmTn7bh9cHK+vzM1oQUjuy21f0l2W6OeXoMGBPNscCowI3bDYaR2uIM099MOYAoBzpn/BwkXYxPwI6A1qhZszZMyPTPjUswMjT+IfEM0vyEQ1pC0WeFH0wBP84zXoSUJ0AvNy+cEzE4jHGzsWIDeRdzpGnOMTPbiOGRZwJa/eLCU0EBuJ0YqjKCtilkZcc4/dg6x2IdbRZnIxa1nKuO6TOqY0g2pWzTwhCGBvIhPt4T3jTorU9PssK0TRUqEiNQVj1CBwL0D0PETsTgnlcF4gTN1a8KzRMIDlxadCXOwN+WEGtKxaUh7KIXFY/l9NKaBmN4stZmCY9l7G90t58+Ot+cnc+rG9S70knbvuPaUh10DNEZ8i+SXRJAa4I7NhG+XrglOUpg8vHympqdbtcsJifhsg6MB7p7Ziez5eQbnGV5xUzPz6VGHUfraZg+OgjwnA68BgK9DhI2cxm9igbbUyDk2ZUb0p2S24BA8emF/FUcQzMetMJ8D4gwnmhiZdAwANwm0rtilscczoQrTCFARiWpsNaG4LPFkNsP76BguNs4yX2ueF64XUA/gZs3OuDHp2BhVB+ENuHPsAlUQAWfTRVeco5ERcPzz58+Mx8Azx4zpxIXV7U+QnRpkILfq6XsYy2O5fCE3m0NjmfEs/RHcg1C+uDR+yNw+YISHmYsULrGCDkXt9xBk0SVUfe3rkagWbMdGBSPQf1HZW3z+Qr1ej27KlczW+lMGMGcns9G17G7VwQ0C0QGR+YriVeW5IiFffBA1UAXuNNxvv/3XrILREZs00Kt3CHc7KuUzD4AG2rVP4AZ8T3lP7+7vn58e49SL2WwAePYs3CgNJEN3HYsMWXNcb1rQQd/oFEZ4g680ZBqcWr/ORZQvHsEfBuQaT2kfFy7Hu50nqHbtswBN08mQlV6GbFJk0uZQ07Aw6Yps5SBoMKZ88bFPC4GmSMW7hkpJIacD+TzPjFfVUy2RFJxoGOzN5K/alMi2MoJrnGVRArO7vHj+jP6G0DauK/CKLtR3SGdzefx8iLjAGZdN2rIQErnknvEAdGPaA/eoInVz7LNwTWUhaJfg8SbNqSDShMpYktOBKQdZakj5g4HUwF4pzYCJxYXFHjUXTuj4vXP9agju1i5W3C95ui9JF5Vyc0hV0RivDWbV7VQyXgOdYv1Oq/D5k2mg+08qL/CZqeL5eQ7nXcDE7CyDJPyrLtnVGP5c1fupGhsaqcDCIUILeUaK+EtFQgkJCBwb5AMgAlYNHIdFWuyZhByn8BYnO0iOICkIsg14rQLukDZtNTPcYK5mRqj3v/3e5PQ0Q2PJ5qTmLMu74h1Jb2D/gFWNUhFCVY1aSbusPWLbj6ktX3nloVmunVK0vZ4AGwD39so0C776G67fIs7DQJLjOEyo3ccvtztjXRrBf+u9yF3ehwHWwFkltbV7NDE5O5m94UvHVO/p3l+Oxseim3VemyS9FaObKukFraQtFxaXn/iJn/jt3/7tYGb4XvDiadZr4NoegvqK48eAdRaJBN9sb2+NZKZmivqeH8/g433XQM2NUnPQd9b6wECA7QTQDJuGCtE+NLgHaZbEvS0rM39jYzi0XMgxODiCwhdmvnoGJ04csDrLN+4pwH0WgWKLQ3lUESJgUKfFln/L6Ojh4REgPjqriNB7qBTXaZHneDWlJnMvDy7UJNCcyyIWrFfSqPb2bLs7INgGHyJ0BFm2AWVR92HgNLB3wPyh+/Nz86nU0cAxN8wM9fo5t8et9wq6IWJcUlGJgDvvgJ/6qZ/6xV/8xXv37v2rf/WvLlmVLyYN9Pp265WWwQpzc/NAHNbkAuQ0naKoV3XfULr1t0L98Q0V+zrE0qyOYSBWp1kO9TOkqlPOcAt2BRdyh4cFW+4d0G95+npOBAI6WWqqRDBabA22rVSItP6GIBLSTj8gu+OYVuVChdOhgkeZyDUHZS23cO9mka8qQBfUPq0ECgp2UQbBdy54fXJ03kf6roHzkdGNtZfF+QcLRbqj/kr1/YJ0xoA9gZ0VSZL7uu+DHomBqN2Q5GLgji3nc5/73Ec+8pFf//Vf/7Vf+7UkKr5leXp3hQdOkbxombYfS97O4dlIeRvf90Yb2MAx3QOG6i95/XEPqvQkE2sAICqQ16SFrLtOhljJenbGLIPgV27mJBZ3JozpI9h1ahD+NGnghOcx0g04GLDunkqG3PIN4ejwSIb55m4hUpJlrtNMRK+3EZhHmW3qAGEjDJLaVTqvUztK0OTzLcuHb0gpywXbayPsTmvWsqg/0ScNsLrZUWqKtwzfivrEgq92EDXQ+jG/JLdhm3DJ4pcv1g1JLmi5QO3/6B/9o89+9rPHx8f//t//+/X19cuzO2Ql0W7C35AJdnV2edPPTmQmJyd38J7ZPbjQZnb1GrtLocl1Nd8BIEDCX/2t0V3+PLWrasAs7s2sqmozo8sfxs2QK8szTmBxENyKi4HwjXbSOZ+XmKR8EKPRBpJqdhftRgvFQh3eBbZKD65j07fXV6Dd9sqM4DURhuyaXI534x/B2+B2J6ET0MC6JVjMytGrCZjwuwHQAINNNjY2uNALBS509SoPAGuehRuogeg90JXINSuoncXdofYf+7Efe+edd/7xP/7HPFTXzFw3qvPPfze02IwG81HPzc+Xz0e3tl5i2ZudnWe+uGYZr5rW/BI2T71qXb78DdCAYbkYmI1E0j0T65qFh/IFl89FilnWz09OouytIoNjqQW3W0ekyine7SxyBIcAILMpM8f8+Mb62uLSUgN8F3KvGqOrNK411l6ZGAX0QaCimSiZP4fZh+uYMw1IC3V6AJ47xG75g6juCQRWUGeNflodNX/YLw0wQepRataNzegXD75er4FLa8CaoEuXTlQw1qCN1LeDEQG+rmJrB7W//fbb/+Sf/JPBQ+3XoKhIGT7SUgOZ0XNW/Cqfnm1u7YylTvF9b4IPrHTLC9byRMtK/QmvgfYaAJoJuGlnyy41v8d0q2KYlqsMgF5TII6BDttTHoyzwVJSMAPfJqX4IrK7u7u8soKtnQNOqDOSTheKxfqnklJOkvjbwKUM2NaQPZJV6F8xw30dd+qkWUfNgffwbI1U7kC3ggsiZjFXMEz2+75ogIuxeTCSTefnczhNtQQkfeHNV+o1MDgaiF5iRJo/J7Tyn/nMZxYWFrC1g9qdh0w6m3njB7577sHKs69/+90/+QZfLnsjUsReb8h7qlfWQOMVGk+PLcxOMs3c5uZGJj87XZBbqg9eA/3SAIC1uS2Z2zL6iTkdsLwQoNeQ+5hbZLQe5taJUcWAdSeu6VCAM4CeBtxj1c7OzeLiztcDWmekIGCxZsrFWJb6aARo609cz3FbZYLqnEMLNvJmwP0iVxnrvZkcTkpt+YcsOkQ51yOir6WVBjC0M08dHu020KJVLp/uNeA1UKOB5t8KHz58+A//4T988803f+mXfsmh9qnluc987n/efrL+x//3f6XV+6H/7e+OZZqD/hryNQfRC7N9pKaMP+i1BlpeDL3Ymv+q0Ke2MF9p6OwVxs/X1tew/LV9I/daLE//lmtALhFtVKA7l3/zkGFtIyIc4llr1vc25XTqJMGUkReQ6MLpUDqTwtGjZc5lc5io5d9uqF2b0frZcsgW1i8QaxnDhGvft1cmrCKIuHQW91pXGeSA+SQsxwSObgyGNNTb75OQ8nm6ogEu6FYpx/etpaUlj9q7olJP5PZooDn4ZgmPX/mP/9d/+tXfONrXyO6Fhyvf+7N/+/f/jy8cbGld1Xf/+Ou0ow8+9h2KKOglaBG/6b8GWl6Jlie6zDPwfXFxkXXUN3dLmVSJMawJX65d5sOTu90aABmEcI19Fd+53qhrtZSqWVlseRcg7uhYczt9rSbHM+PQ7u9d7UQLZAkFhatcLic5sLjzVw2jNOmMWYrkiJi3oqGeotPXFYFhlNm+NnP7kY87g1MZVxPPrKvgjquXN3berqVk49/tLCLkbpcP16hYbh+9Pg2UypXd49RMbi8zWbi+Wn1NXgM3RQPNgfujR49Kv/u7H/9f/qfD7X3cCmk6f+9//4+lg+NIalxlMOREhz7SOw201HLLE73jpQPKrPnCqi/Hx5oogNjExESEFTqg4rN6DVxSA1XP7ziBhodGiM9NiwTYdWM6gwGR8WK1cZrEC/PUlujykR6lEKzHbSbCwdlxJwhV0iPRdkQWdzrSceDuGHI0Ikpd5jIBOamxwW09Xs51PQxqk7eCD1D8rOLNmhWh9GYhSkdRnG9WtFkxn9Y9DaD57e1tHrTF6ZmRkZpuWPcq8ZS8Bm64BgDuNIWuNaxpE1++9Xjt7afZYo7RWuWjJlPtMi6fhbVxjdjZ2b3hSrqaeDVqrSPV7lxd1qE8xP5HYC7R9bX1XHFispgfSjE800OoAc0b4n5Vc6w9b2zCn/YYp8806TsROZngmMFRWx8KnK0p0t/v+wEGBZUHrbeuENOkjI2lkULWlrNU+fTUnYTVo6NDvn01XkZ01Ef8ChhHmY1cxVLk9SNru2VtnMCRCxbL3CwaaMphdbshiJrFvY+CN2P05qednI/t7B5MF4vWhwwuzM0X20voNdBtDbT7Vkhrebx32BS1wwZmVAawMn9Tt1kaAnrhez8CALGI+3od28bOVRFDkDgEsnaBRbD74tJiZjz/4sWLvT15W/ngNdBTDch23nz0fIDz7BEOntJoQCq4HZ+Ks+YFq/yynBFFqsd9iZnNmJrV07A42wwzuHM8qplkhN0Dw7wGYpb4+BUWUSn+BZzA7Wz6BqFA4y1WhoI3BVnY9XXBIffGzlJrziPpRCaWjSjyCrgzpKHd60/lfOieBraPRg/3dxdm8o1ffrpXiafkNXArNNDcVSaJ6Cwx+KUvfQnXyWJxIkn+wc8TvNKbMtruXNMCPrFeA7nMSG55+ejo6OXaNovCTBQu8G2tL++PvQYSawD8CigUSIsXMRTo0LrDrpwEwJFTucC5srkzA/oFoHx8PEvrJ1ew/gUnF7JELCDF+HiGBOzuQsMM3BRGVSB+ytQ5Z/iIBw1+pBY7HdG47givjzZqhDcuiCQ04I65XV2RWFCPg5TaxNj5IEouk9d6KSLGoYa18mWiMbNP6boGyqfnW5vr03Or2XwxuBRdr8MT9Bq4TRqoAe50hYN3WGIVONNO4ux9yFjT0tfV3+5cXVZ/2B0N4F5FYBawtZfAd4Xu0PVUvAbiGjDgHk+oj/PsB5BPrQAeG8LscjJJM3ki34jq88eOGX7Nh6MBuXUNy5oI5+dY3MWm5BKkFyh3YUQW66PDo8mpwFvGSgn3Ehw2tuh1b3CiKxTaDU/kiiAMXk94J4H56oC7XUP1T5qGAKwbSo+QO5DdBXD7RYC/KVWf2JkG9sr50+NtpitgEHhnJX1urwGvgRYaqAHuvLEu8jhsQiabbfeSa1KgG0mtGmvRbneuG3V7GlfWAG9rwsHBwfr6WgHze9uX95Vr8wRunQYAfLJBGDQFwNU0CRwI1gYb0BtIDmgoJ5NUKpNOYwZury+ydWrgaE/wMmcd6jYx3BSWSIEvu1CpLTLK2CSYdEGnRkcPDvYj4E6NwrXu3xDuZXi4chnYQ5mtyJhEsrhLpnNgny5QTWaJX5tSPR3IFwkXdFPM3A5BTE711KplfawLGjirjG3tHkxO5icn+/ltqguSeBJeAwOmgRrgHucN6/tP/MRPsGWGmT/4gz+In+ppvFUzrErbnespU554TzQge3uxCHzf2Ckx+zuW+J5U44nePg2AyfANMewWITdpQUhPP7d3qA/oJyCuJKY8T6fLJ+ULFRb5nFyYs1cZQsCKpDBPLTJOV+T2c8rsluURnHmwwnBIAKeSnZVuGpkJ4GzjiWtJaa9G2MY9huunroh9EmlgyrRgAJzLFwQViLolYaISglSJXPEO7pFmehLZ2z8sn6fmpwupkaOeVOCJeg3cYg20HJ3jTO8/8AM/sLq62hX9uFdl8y1vzvAXvVqbRLrChycyYBoAu89PZ7GosdTXUekCY+eA8e7ZGVANCMs1x6SuBQohvIy4isvKLucJzcci4HtRCKZ+vyhbD8+bdFEPBHQO5yBTIqfl05PSSfnkBKE0/Y1NgY50paMj4rUsCcPy36/Q/gOv7OL4tYtFAXc8lOJ8kkjQm6N9kHiBiOiHElZMXjd27dsX9mc71sDZSHp9t5zOTc9N5XkIOy7vC3gNeA1cpIF2wP3Zs2dzc3PtjSKOvnsZBtsQgkdY3EWaAPGo2EVc+vM3XgMMcV6Yn8eRdWN9/fjYG2lu/AXvuYAamyqcVg2CeGGb41opHQq7p87MPQZvb2ZjqdhCqtVizWKsBHShR02zcl1LiySTBCMV7OtwDi4H4MIYTo+sSEqEYxIJ5CcPs7lXOUA9BtuTtPDVUt2LwV77quHZWdzhn8z6pFAbHPQ2DehEFAmRukloB4bXXTJU3SeIWPZasv7o0hrYPzzZKeXmJjP59MWfrS5diy/oNXDLNVDfFNapA1eZRP7HtIHRr46EP/QaSKiBVGoin56bnwdyrG8zbq264FdCAj6b14DTACAPgEZoqpAAssleLaurgLuZom2ikdEkFnemwd3d7dv6FWI6DMSB5eB1eh1C6Abcy1jdyzaPjHA709QH7v4HNd4ywrWtVBSS7+EeBbafTRjeAv+fioB7E5Svl05VFS14DeC7dVJMXPVXRvo7DX8LVoc4+byS4pNpajQ9l923mYCGWBbPutfAgGsgrabPeHXZnt4AACAASURBVGzaANK2bu5tf/TvfJo3w3t/9s299Z0Bl8ezN+waAIiwUgwzjO5x89mrvc2EcR0Je1ouv1x7OTU1zdJ9c7OzW1tb0zMzVMGqrsxyw0QimCkZm4h3gbDcKFZYfZ0vnZTyufzB4cHkxOTO7u7MzMzW1ubs7Oz29s7U1BR+w/Rs6WPQxT09LY+Nyl0Bix5+C5oxcDx7dHxULBT39vd8vdes52fPnzGbOSMouExMQpq364sLu+zQMrmeY5QG7B4eHJJtd0ct2+b6BniObz4Li4s7Oxdc342NdUZ8Xv/1he3xTOa4dEz/Fh6A5U+fPr0/Nra7u8NyeNyQ6BmpuSGRmqeJm5MbmwWY8J5ZX187LpWy2fGD/b3Jqam1l2tg2PX1DQatXv/9/OzZUzhpUy9sq9dh/5sbG/MLC0+fPomeo82tTR69/b29menpo+NjRsjos0M6Y0N1weapk1O+QowdHh1lx8f3dvfy+dzG5uZEsbixuUEKDz66Ivjn9+rtFY/Vi6PJlcLY7tbzSp/ayYWFxTpnqo7eDj6z18AQaSAdWMpbsIwh55XXHh6f5R7/1duv/I3vPC2Vv/m7XwaZtMjuk70GuqMBOpPgLQIgWzh7bu7qjfLa+tqnvv/T+DHbJ/MLDXVtBBEsaHO69alLF3QkL1380gWHuN579++vrK7Q68NSq+nACUz7KJM05lsw3ile4PIELwFqj8jzysNX8CR58fwFWb/zwx+mD9b6OurM5ubmzOyM3U5RxivqWXQggaM6vQsB7lIJnA2L2hE5OznaPwDKHu4fYE7f331bHZBKJZfNLy8vf+D1DwDWnSEZMaEg4STd4f7e/vraWm58HGE31zfn5+d+9Cf/Dl4odFfee/fbW5vbTx4/pZN5sH8wnqFTMD45OUU/dnp6KjNu2jPVRUJ2JQLnq6t3eK7bUMNljovIpcOUm01n7ty/PxFbM4R3U+nkeH5+Hqm5fNjmjU09mFppldPu44M8hsoMOEaBx+ji8LCQL2ysra2+fu9DH/5wm9r9qSQa4E57cTi5PHGokdH9C9xOX/vqX3HrtmWhnxy2Zcyf9BroTAMtZ5VxZHjtHZSO3vjh7/ngD3/v06+9+43f+bNXP/Hhd//oa51V4nN7DVxWA1i4KYpZ7nQ0N50fvQp8x0zO251wWV58uWHSgJA6H07GQOBYxgPgri8pqTODu4C79BhjOsfOALvgD5vDPc0NRn7Q3+hYq/sk6LPxuWZzY3NpefnqSqF2QDbGe5Al2BrAioc6iQ4LOY8WGMdODLB2zIciCLrj2xPN8xilU9xgvW1kaqnAN+VL5VMEJCCvqUXsy6+mfAqolWzuX/7lmtUeE3U+X5icngLHA+jHs1m0enWRN16+mOF5HGvXO4JJFqlFIqpDGuYdjucn1eQINu4SG/eVFJNMUoY5dkaZB1OX1p11/TfX6c7msnFqV5foFlLgoyhWldU7GhnSZ/F5gsoXetV34b7ts5gXV+87Jxfr6AbkuOB5oynf3trKjTxE1DsfefVwZ3/j288e/8Vb5eOTGyC8F2FYNDAzM837G+8FrGhTM/OZdq/7ljJRvOU5f+LGaQCsZtjXXJx5nVXf2g55CtspzbB8WTPJaKYRYCBfFPEKt2+R7ZQCxMdq3y7HRedgDyswbi3YgW2aGuoXR4BjFmeVzxWYNfhTXEfVjc5omKWNTMVODjWeDrZkYYudGV8v+blrQvczkhAQuM6qCbl8deUNyhuoZUiAYzfYwQkpAPqdbZ6b3efPn1MckfFUWVxews7NZFBXAb7ArIvso5qdkyCZuB7A9NouN1LqClpo1LT0YqnhNthLgSLmp4Ns1FkHKdxga+XFTKp0/8GDDor1MqueXB9izdxAKsM9hgPJ2lAxdQFwp3WenZvTy8TCK9/7wUd/8db8K6vPv/VemOb3XgPXoQFe0FjfeWHsHZbOSvuT0zOZdGctNa7z18Gor+MCDUTNyQX5rnga6CYUKyps+KlegLF+wnsh6CM6OuomXzEDPVZmpnI/STKyAjrgQKPVGbOUwk3FzZ4EbnYrGAhqOna1FcJ0u9g2FjVkapkq4H4T4RROCCQ64A52jwIpFKau3NgYTvBksypUDbVjzDa8D6YlY2CkF3GLhsyk6AvgIESg+NT01OtvvIHfuZHqDC9A2C4FHLUM5AGKmTjqhBBRByOWHd5I0uVsEkyPwXW32lBtGCQgfRgcqJoUHLgkJ8lAsXV4mtk6HF3Mr40P0spKdpkHSk+emUYNDMUzB9sD+NjVKPMC4I6hZWtt4+SPDmnAsdbQ3h6u745n0rzVcBmsoeQPvAZ6rwFe3tMT2fNCZu/oDGMgs1IAsxJWi2kzYU6frYUGhqXZFfvpjK2BavA3Jg4iONwOFAwBvOFvbNNjY2msyFiCAe6xIi2j9CR3trdx+WiZo9kJPK53dncYEQuAjp+PgDuJwXsjZF6HUdwdhFvANR7coBb6HpinLaPQt2aFlMkdmzuDcW1iGXnLME9LCv91KUFBYBbgPjk1bcBdqD2IcCIWVT53ShH9M6j3L77yFwuL8x/97u/u1Ni5s7PtXOCMh+Yb6uBhh0+q5dKQyQ6rmV2G1nekqZCNU1S0E+/qwzAMvUprgGOtBewD0yjvxfPn6Vzx7ux0aqSVL1kHjNlF6iB/m6zw1uasP+U10IkGrvmx6/jWbdd40VB+6EMfYuQW7eY3v/nNN9544/DocOV7Ps13Umbk+Df/5t+49rQTdfi8XgNd0IDge3H0PD/LV/zKWHG6CHK42Pru7JpdqH5YSVxzY9RnNYHKaaB4nQdvdNrGUAHac9oSDL6LVQCijWIdZXwmA0PDvO2kKBTyYFDGTrTL1HAOLxGWLAhgqBmMq3U5ziJO4+Zk4RJOGzxRO2//Fa2Qur29hYwIK1kMjAN15SQTCw6tcpYsehBclWaHZnqZ+YV5UDoaiEJ4YHsZ4t1fiOUNu3Pu8PCISWmWl5capGyXQKmVlem6HIiAMQgR3EhTDoWt6WqcV+iDMAjYSReVglUykGjpyONEis5LU+goUJVJav0USURm5p+pZvWxBBo4Oi+sb20vzc3Zxyjp9eqh/ppdliJ0krwCLkvel/Ma6KkGOn0OUi2BO437pz71qT/5kz/Z29tjwqzXX38dxs3HcYonhDdC8DrsqTieuNdAaw0AfcBMfPbe3trEjDY7OxeAoRZFhv8bUaePdwtFDGpyd8VzzVSdrMBWgVuhdos53EcSQzzLpyASHKlp5ZiHpK5g00O5x5tJuP2NV1eWAZdgZYObBjbFkPP4sEh4oH0sTrQmGCo1lC50ynSlzJEDwCUPpcDVNNFIQQqO+ARYLU7gXza7cucuDvSi7HBtpYI/5PS0nNACP5laBK90C3HsTkKA6M+ZVbOzT6+UReI6jWnKF4bkahocYiXy0PFYWJhDUU4Q/P5rxNdBUyclA+qC6SahQXc7cse8tZBDBTPj7WcgaajtFiegtLXDQupk8/58XvfNQAZu8oHkyzPlNdB9DdQAd/cucZVgaP/n//yfJ6kwXipJfp/Ha6CLGgA6Makcb/qt/ZOR06OZaXUsm9IHkDVNv/ZEoaahDkMhANCQJUSF1BRAxgCOiHGhduBj8C//6TEgLoeUAtIxO3jCC4ST98uXL1dWVhLmJxt1RPM2BqBdfNl/GNGxnWtP1sFT5kxEQh4BsAtbm/7cTOLaVLBU2/wxPBZ6LkhhxndFRJqNXGXA7hQMwbhh8vDQ9tUUwXcLrkbqSsKnqgrD2suX8/ML4VGwB53fv/+A2TnxZ0P5bLHKP33y7J233+HSoIlCQRzG4T6ycxjUHl1YCRijLfmCn3QhCC9Nkb1VKxEr7KPSAB0pPGaXVu7ni3hYxZU7WPpJMihlsDj23HgNXFYDVeAOrGHRShYfUfOWOPBZk8VKEmf3Gb0GeqIBXuHzU7mzs8zm5nY6NzU1MT4moFYTAAc1x104iOGFLlDrLYlh4rUbmgDSmWOHuw3YRgqIoKagsX4WsE5TLTZ02jRnuk7CBRAcH2wQcwfdQmtgAY4OdzpuHIJXjXQnqqxaQgOCNwLB7R3FiYBd2FoDrg3/bmcA3h1pG07eRw45ohSKRbzeAeQBKDdkXh+PHUOBR4mAKR87OWeSKMrlQVEscIbS4kXw0S9OsExZoVDh426BQeQAd5ZMYm0kYDyT0OMR9/zZi5cv1x688mB1dRVuURrsAtsdHe2CqEuAJZMuhtuF6GHdeh1ccg/c45egaRxtbeyPnB5sP3jwirtLm2YbkERMjRMTfvqBAbkano3eaqAK3HkysXksLi7xuCavk6Xpshrq5IPXQP81wMt4cXH+9IyJI3dTlVMcA+LzUFz07ql58/dfmJCDAWUrZG9g95iZwYgNbRl3AWlsFcK9bNLuUztgmjYN4K5MyWRbXFxcX19jOaFk2V0u8QUDVCersbB6UJu4iof6Y50LW2gTLgCoLtE13mxrIswqEA8RZqU8pRkmi1u/dXKEapUzQPERWq8mGGCXnyQRMrMld5zf9nGWUuIVU5eHJaaWlpacazuc0P2IwffdncJuvlDYK/KdYPerf/lX3/rGN1bv3OEjAzlRn0jVaSykHmhHRuIQxEsyiUcJqgsz+n0TDZQq2bUn78zfeT0/2cHXpCaEriuJr0bXVZWvx2ugzxqoabx4h9Bi9pkjX73XwNU0kB5Lzc1M4tu7vXecOjti/goHfmwLKqoFRlerq650D0nX1eQPL9IAWNgmUxFUq8sLbgt/3BQKNH2BgzgYPp0WHsWhonbi8Doi0SGz0ODOgdNInSE5ylAXEZCs48gxIZ7szzB9TakwPUoM4boSnIBGU7C0JqIjSwt3HATTKhofIFmYB8XKFF0Rwq/F7joSQAeij5xp0hqbZJMUJcqNnilrkgJ3VISe6z5NQIFp67H+cIqAJuCGPCiThZ+woeJ/j8WdhX5AZkTok7//3vuPHz2+e+/O3/jEx6U5U0rs0XMySzH6C35OCYF8iExdVs5vmmhgfWPjbGzmzt27o6mjJqcHMskG43nsPpDXxjPVbQ3UAHdGBm0xQUEnnz5pYvEw9o1gt6+Lp3dVDTAb4JwmBEwz7fRYdmq6KDSWkGgMBCQs4bMNnAYcogN11oFk67kJ7wm9BxtFnJWdpszZYsGjyZu1hYUF/IDv3r2bVAsGIy0z3FVvtwCDGl8here0CLhHkRCvQ0RA1bbVfQBTdUoDb6uHArImd4p1WYlyhicFSUMSoHDgu028cp5iFCdAPSwuc7Vs8QqG223OSQ4SSr22ttY4GIDVYpcWF53jEGy4KwKsJoULwVKvOMYA2Zn4Fcy+tblVLG4B4vGDp1ulzG1DwJnjUIxKAHgfN2ebtkVv6cmT05EXa+ss3jKRHbKODXfILb1mXuzbp4EqcAfW/PVfv9XpXNc0nSzTzYfL26c6L/HAaqD6Osd4x0g4LRyzsYETMyeq5waWfc9YlzRA64Q/h8ApCDW89IbVqcDhdrIoAmSUxd2GcgIZhUsTG5KhRRGMxEyIknCEnEzBwa9mzGxMbiF0B7GDXXCoo1g2J1x1i6y1gVPx7DqUyArCsQR32u1MU0qzM8HGOc44ndiWdwXmdrO4l8sJuzcoBxWhKFed26JkCEbOljAWYHebtV3gXZPro9s0s8rgi88kM9MzM5s8zOsbztM9Ti0Wl3pNMklCuiSRvNYLMbUmZDtG8+ZHmeX5KL18d3koLXEwv7rKpDc+eA3cfA3UAHdmL44kXnjtzvz95W/93leilKYR2sNOsX5TOj7Ra6CtBmrAStucTU7ixTu/IEfkF9vl9Nku9tEQuzTJ7JNukAaCqdyF3sBvgugOyDm4LgBLzAXQKK0Zg1OFF83NPaHri1MXXtrf/va7Dx++eqH2DPsakDRUWY/cwzvddSzEc5VHaIvZ2ipCTCpqCvaNocZ9hcR4kXifxGnFZdDWYmzqgvnLmJ1dPRoCsF2rOzEwgJxx4q3iz549e/jwYd3Zw8PDXC6LRGYJp0sh8QSpEZFJY0i1Q2d/F3zPZJgDZ3JyAvMq00dKF2jD/ULSNezYZbeN3QLwinYqTNivNVnDEn4/Qjfs6dMnMyuv3cmw9NiQ2drd9ePLv7+QXgO3RANV4B4XeOkD91771IeXXr93sLn75Gvv8HkxftbHvQaurIE+vDUx0S3PZE5KE0/XD7OpI9YR8y/vK1/HgSYwlrZhpgGUM4wqft1lt63buCSBOoCigCNeGlo8tcMBP5iDsWIkGCQHTg2C8KlBSuFzRWpnrYQxcVsfTAZtoCKBDG6HJEkBm45G8N2yuMfNZbZ6XDEVFANGwbaWEpEigk70H+wsylcMebfLwd2N6DVi7TaoZWqqfsYPiO/t764sr0CMXlM6LSccqCAtHSe2rnZO8eHEwDlpwvByoclmsbprWK9UpF88QMYo2QbpdGj/gTgVXOihFS9ym+Pb2zuH6VUcvUZHh3gqdCzuK6urHV9H3XE+eA0MmQaaA/fl77i/8sFXJuan73/PGxvvP1941SZMoKEL73Laz0d//qYH9EN2tXvO7kC/C5kvDAXk8vm7+ZHDw5EnT55kc3kcaUZl8/ThBmpgbFRLxSGYQ4RcZm5QB/bCbRUYg+xAp4BCgDtbXDs61Qg2v0eP3k8A3IVI+Q/xJADc0KWwahWEwmH444S6E9qFIc6bSWfkBK91M1sKB0ZPnurK7mCsGvHo0GSGdAhrKRcLEAhRe4DdMbS7gaqoVfZ2gfc4J63ijDO53+BOyaJRFIcF0L950bAMqha0soGjugSITG1lrSJ1itwjI/okQhXSxEhqV+uqmiT1tZJHP6cJaVZRbQy3ywpFI1Bf6FYeo9i1w1whnbszcTikhvbous1MT0d3g+6ShCEqkzB/j7J1wHGPOPBkh0kDzYH7X33xf2w/W7/7kVe/8vn/9tqnPvLoK2/SYEd3+N2Pvv7wb3zoyV/+NZ9Lh0lWz2tSDUSXOmmBocgXR1Ss58Jv7+D4/cdPJwvjQC5DBkMhh2cyqQbk8WK+HMFrEfgG4jPsLlRsIYDDuGdU5FcjBGnA3SH+pDWF+ebm5tfWXjbOeBie1x4UaZBYlmyN/qzwk0uI0Gb1yXMY3phlnVFBWoF3efKIbTZQCnI7TCpYSqo11A6+q66zcycoZ13VhmGJhkcuXYDfofQQojsW41uBdZtHRkBaqJ3510kAXjtyrbaMSeXrVuNZZorBfA5qL7PwUlq+8iYUKknR4+JCgN1Z4XQsfUoGEo1jpOA3ZgNqpYgW2N1yGXYXZA/AuxuYqjEP3BiN/Ny2lF2FneXllUymcVXa4VMGs8owbajjO3gwBkwIPYGtwqBx3I7XVjL49OvTQHPgTv2P//Lt9XeefvTHP/XOH32dL5fMbeyYmnuwfHJw+PTr71wfj76m7mhg0NqG7kiVnAote13myWJusrjKd/zHjx5NzN2dnfCv8zoNDfchWPDkxMy0hlsN+jm463C7oT7BPwUy47KBwMRAjZewuFOWziG+19vbWzMzs610B44URjaju0F34pqH0fIHD6ls7+pkkEiKwVPHpNgUs4ZjiQiWqi8g3B28bHFvZ+kpyx5liHghj0FZl2AlVDgIjqvIN8bAfCN2N/QObj85KVOz6mgdmAEGhpvOMsxsjweH+0xlJoTOzDZj6gDAAYGIOimjKS5EJp2hAhJDRkwycjgJa6sWSHc/FbH/UGIjDBGm9q90NHqhtoabcIQmn28e50bL9+7dnFkl4naZwbxI7Z6TbnBsT3M3CEGjp7x2k9EuyTtsZFoaS2jhplfnN95/QXs6Xsxlizm2/KZX5t778puyevgwKBrgIUvyGxR2+8VHq/nCaPHvP3gwPlrGz2H70H9E6tf16X69wuJnmmhcWFBNVtBsOUOtgF+A/syMPZrC84MUSjHLLbbkyzGE89VJ6cT5ZTWnUMWgDig7SFm3NXwd4xlSesjDIFhrQROS1weXKVa5gdmoguCEqQRegM0G1QX97RciZFRgUZs/hoMwyNyOmRw7+Ulb0D7ChGPgchQSY6Ua5XmEznGpxI8VWPkRFNHR0b4WTd1n9Cq10MehpyJJTTJIuI+9HFXJ1cfQnUF3h+TtDoAOInGJsUTVZ781x/snY0+3ThdnsnwduklCt3vibpKcrWVJAgI6zdO6tiuc6ZSJhPmvwNHQFb2g/dp89HLn2XpcqoWHw7GOWpznoY1zw/rQNQ0weqkNrWIhVyw82D8sPXr0dIphhn5W4DbKGpJTADuDbw6vs3UPVLAV7DMg6DYMewSZkgV0CJa9nMXdKWZxaen999+7e/cedBpVBZykIoeOrWqsJ/idV8yIInM55mbw9Mj5qDPEO8DtYKj1PQyuindoszO7vDC9fNkt0ep0QhMVZo3QqxHDmG+a0Smd4xMAkD00tAe4PQDtQGRbfilA7TYk1eH2E3bQaRTQpQDvX7x88eDBK60y4K+CxffFi+dE+KILI2HPQHv1Dk5OgPGY3DlPPdKZsc+Gs5QK62YfRtU3cyK5veudmKAqi2zqj91OH3fkf/782Xg2f2++5eegVhdr8NMTzsQ6+IIMFIfRc9UVrngyexiGiderqqHJe+WqJH35izXQ3Vvs4vp8DjSQZL6wiUJ2onB/b2/3ydrhVO50cnLKq254NSBYaQ7lwmy1YhjgNNgcbjDnYkcWALbZBw331ZZJfARJxmI+fvLk/r374qE2CD8aRBX0JIxqDKmAujnA4Os+Kh94zMvgVHxHwPOa2zFkWFGA/ahD+I62xCNE8FQRw7ja2L9QubIYdBWyDYOSDLcL0yoEQN0daDAqCUDmAEs713Y5pWNKh5jcWerlE2moPnv2FFzeKH5Ys/bM7nLnzj3QJPECVcUCfR704xZbJeKs7da5ANMzYtX6DFKfui6NARGrP+udmOxoXsps2qFqJHKTUg7PchtrT1cWFtD5TZIrksUe3ujIRwZRA82e1MvwGWvALlM8UZlu8UplPWDXA/dEF7GTTF284J1U6/NepIH2Fvd4afD65OQIKzU+fvyIFdcnOfBhODUAsMM6a+hU8E1AHgysZ1SYz0IQA66DVoUDbTpCzMkcghcvJzfUWBCU5VRXm0xRV9H0hwrmiW5Q0hjCTSeVkisHAUZTWs5U/+7nXgBOFBiN3EYQhsSYl0tozhfsjv4DJG47aYIgyC5y9AwcXLfs1ajj0VA7hnbZwC0A2vEFMugcqM6o1WwQfGFB66HWpDY7QMOrq3dYsZtJCYsTRbzPXUVga04ZZHfeQOjEXQuBb5xpOCU16ZrWB5Mp1JtJaKLa2ALW1UrRYcjUl7m5xyhg7WQ+dfj+/RV8Y9ylv4HSJrnZbqDYt1Kkxkf+Emq4viehK+wiYYzjywB3WlO19+FYqEuobGiLdOsKDK0ChpnxTr1fyE/Y3tl+9OgRYw1Z9mWYpb+lvDPpN2jTwJs1fOETLGxsjiUOJnOA7RikCsIDEQMVwaRXVFkuly8Wy42TzMAMLAm2y8Qs7EtF/IsfQ8LBRkfk1bhVLM3M2Ug3Yux8NKV/h9qjomRRMIO0URU+txoMkhtar26E2KtBpnaB9WBnEcraMR4rcloxKG3TMgLXge1yYdE3gsjcXquqtZcvgeD5QtIpFxGDr2F0Bra2NvFQYiQr1ywG3IXfTWA2xnulQrZcPufklyi1DASvOPVL7GVnyuHAfYWgP8BY2IYiVY3cpBie3+tr64v38rkczVf8ug+PlMm45iIPj0ie0/5roL7N6Jyj677hIo5TIx0Dd9rQ1z754Xf+8Os07J1LOmglIk0MGmOenw40cPFVtBz7DbPKJKljZnpmemp6Z7/0+Pn6dDHrre9JlDY4eYDggE1DbjSz/LgVtJVHuAFkYi6A54Sj8f82S6/h4EtZ3GO349T01Ppaaf9gf2Ki2uuDMnMpErARGthgrCQsYT8GpIsXNsB0dnZEQ0saPBFSMJTSBIzKZlK4ygRaTEbBbRdCs3tkQHdu7PLScWhW18igrRC+zOqUs0J2ZLg/MrJrNKqNSDXcXioB5cWTmIhJa1edIaUkM0qk03tgPDu+tLysGQq3t7O5HEZx9EMwwdkQqdaFzZ8JXVWF9FANpgcdKuLEM724jWmGKWWyTDdZLXNDY3yxWT8Yr+y/uHf/XipVXRN9+MStucIt2FePjofIB6+B69NAkhuzI26S38HtgHvp8HjlO+7P3lukbmfqYFuYnfzaH3/9eI/1GjT32eLi4uZmuzF/HfHd7cxdV2y3GfT0GjSQ6JolylRP+tLzhQFEZiZz0xNZ1pHB+j638rCYueSUI/U8+eMea4BxjQxydLiNqoTZ2fEfx+0Cwpqm5azMeu9cbcHkAOBe6k6Ly7SwuMhko0BGBs8B1ons7+0XinkH3MWLqhCOxDPGJleUPd3FxEoQKCec7CC7SzNWVZUhFuEW2dxDfE4sMMBbTAfBWSF0FVNBlXGI3UzswvAOxwvCcxAZ2zVru4LZ20uUMlbEPBO/7O7uYRLn4wYeLEy6eq9hrSVX3YVb5OIb10RxYn19jRlmMLoD3x1mx2BkF860ldJoBDLrWkHUNhJHFbCRWIFw7lgpmNttThm3xpZNPansNzSUznNrG5sL87lccfmGilgvFn28+iR/7DUwVBpI/rZpB9y3Hr3kVyf4u3/8DZeSz+c/+9nPvvXWW8ldh+tIXe0wuYxXq8eXvpoGEl2nRJmuxsfIyBXnCwMksI4M3/Q3tze3DnaZTK3p7NRX5dKXdxro0i0B+Ds6PgwGZmruFnBegN25oNVg3ifUDMAjUb4ZGhx6mY+K4EOZ07Ws6JmhxXOGTGxubEJ7aWk5zdpBGfoSygFvjoFAYtCljmV3t4g76eApcXOIj45q75MQqhpClbQG1KvbCLczH2Kw/mhQqVxwDKuTRbb2AMZbtOrYDmI31/YSY1KxwxtnBps1HiCN1k7PKs+fPSYJ5L2+vo6YKBCH9cy4kDf5q2UMXwAAIABJREFUa/ltd8QUM0zqtLS0tLm58ezZM7TEMFWIhDBdZVGsyBoZtgF1Q+6WhgaUC8bcT3qxLgrCcgE64scIDtMG/Z8VHtxdLKZGtFb0LQk8ULdEUi+m10AVuGPbmJ2d3djYSKgULCJf+MIXaD3vt57wKyGpZtk6aOibFfdpvdXAxZfn4hy95bCRelfmC+OVPz87XZmZ2gCLbW4wAi+XyzXWdUtTBu+iY73GCQS4ZqCuiuycuVoAjv8gBONQSRPSZFRruZzkngERAnrBtbiTOLyM8Q+0CWiFiLsTMHMwVRGIaml5iRRwsJxPam2ErJoEGgWg25hY48Cx5xCr0QoIRnTV03A1wAVxDiSr7Mu2cTuX4rYgc1fEtmZxdyA9BO7uyLbqfZil3eZsx9heEnBHJKcf4w5Hd4WXa/i1T0y4YdyOdKXCO+Lg8AD8DL/kAZGjBzB9CLRbPiW4Zd+7d5dxvWT+yz//88JkkQjoPYToFRZvCnVQc89Rs/tJMcRtJ1UQZHDXhzLKtqx4yE+UzzNP33+LRqlY0CfxWxVu1YDjW3VlvbCNGqgCd9rBVx6+ymLdNHGN+Vql8H661Jy4Na1tK+I+/To1kOiSJMp0nVx3UBef1zvI3TYrD8vC/AJPysbGOn1dLPFDD9+H+cq2uVYO3gmxBe0ajRuikqxITTAHFXJFiW1uGLJxFrAuHDxiywOl06B8yrZihrUB8K5++uRpsVgA4/MD3SuzIU0IAm1DzA62dlZq0XM0bduEvLXWarFdBDpBCKC7Hbn+hPoX/MuP3zHpWnqUg0eMBqgqhBPeaK/OhdnajV1D7RSHTzkWwYvYUT/j6dOnCwsL0SOABqmCrwbkzIxU52/BJsrkMVStXg0hO04PRzRiAWovX7xYXFxAbNjjLFq6f+8VVmba3to6PNyjRkQqTkyGbkOxwgbUTTqymEa0D/6te6WvDXwEqKs0TmJo46nN7b2TwusPXsGJaGiFuALjjFf2k39dQX++6DBpoArc4Zp2NjCZdEGEW9l4dEFvXSaR6DIkytRlxq6fXNedIHn9Y9wCCmysr2N9n59fzGb7N0fy7biIl7htaNbw7nDwjeIC7EbF8A3XsBqAo+4MRYgcHR7NzNYsVQMRjNCs7MlMKzhwZACf2bGq/TjAw0aj2QZ78927d996602Mx8Bc8Cu5hDDtR6WwEt86zshDJNoaYQ6Dygx8Ky4BbUukJsi5Wyg+ZnEPyormmCbBRD/OqR0EH0J3OfO4zon82oFFjEk9LRtoB7XbzJUp5uE55dPTd374I/iyGGPaGHPkEDdRIhFsos4sCjNQxh4PYzyVOcai2hQxpFD78spq6fjIiAh2kwFr+0x+hgAPG+vM0/MS8zsZ2gbVbahdohtqDyzudBnCW6AtgeE5Sb/q5ebh9PzyXPrFDRMt8UVI8XAlzuwzeg0MtwZqgHsCUS5qLROQ8Fkup4FEqk+U6XL1D30pXuC9kAFQxQBE0M/mwfnGxlOs70n8KwJO/PXqxSWppQnww7zNBeIG4BZwbu7gGwJIOUCiwsuCnmTDp4MToOrt7a3Vu3dIpFjpuAR8BT/j/p7N5vIFYdfaehxkCu6xJreaJUEWD5A33/wWFaaX5DYjnsSYLO4uiDED8UQCzF63Cyu2O1p0XcRtEdQRhKaT2UztVgcY3fTgCFi+c+A4BMzcrmlkDLibvZ0T9mlAHjIn9FVOpCOztTv2jjW9zNnrOLSoBwKxkK0IPFp/Q+xFZ4xF5DL0rFQUfmRDWiECi3Pz805qlRJ/EkUppm00f+fu/bm5BY0ECKbsDK6Cq4KtyRlsIuSuvov6J7K43zBXma2t7aOjw5Xl5bGxUqTmWxg5ODjAWesWCu5FvoUaqAPuDa+iW6iSaxc5kdITZbp21oeqQt7ZveOX2eoWpkbPist4zpxWxhfmphiV17vqPOXkGmAwIphbUE5lBO34gQUBhOBhh+A5lBmZE0y+KP8NzR3OzIYUOgZXlo6xCjM3OUA/AqUNDIg8/QKXHuyiTJysJqUKhSLGY6zO1fMAdxuCKUYsiCUDvo3beCnFhZmtduHckQCsS2CCHYVJgq+s+hSM6jQyeH4bnDWLu6zscpFxW9nbge6l8kkJ2ztsqJ9T3UmeqYmJTNp5nnAoHi4ISFT7GNKtKuTzlVyOB4evEHy8wvcydGc/1yhUhgvHdCf68p/B4u60o2NXqQhHLAT6MOAfaELQnTLZXNblH/Ytl+lFaW4yX7kzOzPsslydfxbNuzoRT8FrYCg0AHCn1QsavqHgeIiYTKTWRJmGSOjBZRWIkJS5y14UqmA2jLNKav3lCxapwV5rxsik1fp8vdAAyPLgQBBcxthYBWA4UKiC0KgCcBCc6FLBjsxyuLW5ydxBzOXP2VjR+mhEtnkmTsdOQArXE2ZfwUXeoVgHLPFwd5DdwLGirtLGbVR9JJCLGB1IhjhdIkto5ypjOwF3prUJKBjKB6mTQRZ34XaAu23ZlE+dkwx8wj5acqidrSKpkWIhL8EVj4kXMdc0YuqtO0MtzPjEzMKiZkNaT0osTKVcTiIH0aNS8JrOZlyGhoqliVBuJ70jAmqnnMYo494UkRreCOs6759kVua2rZMzvHJ0jXMUcsO+pXRNNZ7QjdOAWnDaPrWYPnSigYYXRrPCiTI1K+jTeqABGTi5Ir2/KGOpyvLyEkDh+bNnwKE7d+4EwxB7IJQneaEGsGRr8KVzERGuU3Nnd4FBTm2EkfVjbkTZ4QUUgamvfeA1eW4kuGPMjNzsxnINa+0Zlu2UJfvkhE8Bjnns4NQboPZwFzBHDou5fb2whsxJNLGCHfH6ECB56YAxqLZuaMATORmAirzmIeNcZQTcBduNy3K5RCViythwW/Jz5LTo+KwqtZ7F2mNK6QpUA3MzscAC/utBUkrOyjw1UCejmwxIVVRLyLuGByqsNzwhqo4yW+lF5fmzgAIoRWBUgj4RhIWGcY8UzzaP86Nn9xanE/Lv9JIw85BmY068IeXcs+010KkGBNzjT7Way05p3Lj8iZr1RJlunGqGWaAOXM+7ISZQ587du7xlge+p7PziDMb30NLZDfq3hsZVnzSBvsqI5icXhgvwHGicdH5VQGro3XLYGa4fxsxguGpzZcebyiZccrpJqmoGEiuYxd04EmfOVcZBZOHkgDORcMA12BovQdUGTZVgfAcREWsSnALAxPilRHxBAD5gifTA5I7RXdPM24Qy5mLkejXU7jgKsLvkEDZ2arQFrYyzhk1VDdK+hImyoAOeyrxNqFrNxkScJS3kRLbjo6NqelTMOhJOG0oL6Im4sLr7ac+/BcF2WzL2/DzDaOLkX96qNQ5KbO/obKc0tjqXHxvtYFLLqsYHRY52fOhCdh5YHW91VSNSfPAauPEaqEcS0TPDo+7iUeRm6CJRE5Yo083Qxy2S4ooLMF1OUw6+g4OYCgPgs7C4lL7pqzbWKmogniUwOPZjAGvYqtG2wRjYLwoGRw3qwb9SmR3y3A2LrBEoaiGVrWrliIkZ5Yil1ZBIjZgxu3ySPnF5HbysusoIvAew3fHoWLIaxVxAjWJhDEaCuDnKhIjVEZax2cWIAM+DQuxIZbmoMivXyNNdYB3ULtDOPJBENKI36j6EtnZUY1DZ9AcFpZMQMlUlHurazjjuajLRlWKW99kZGUo5XT1n40e3t3dNihHGA7DGH2O+I8rIinrskEIq56jblo1+AWw3Eg634wMEemcCm7BsRG84Ily4teOp8dKje3NzcW0NB/edcFm9ExKXooi3uCfWls849BqoB+6RQK4p5JAIT0W0jTIMYCTRA58o0wAK51m6qgb4In9VEpctj6199c4d4ND6dil1urswPz9UzjN9fmauXj361/jUAL/Gr6IgugsAULCdEB/1CbeToEiYO2gRo2NLrz1yWWrTwuLah+dTYGNWIk2Xrfl1mLpSGTvTFOlxuzuMxYG7CBs/RAJuRDSE77aXuTlA6RzH4g69O+AelnClZfgfGZG5XXDdwfYyR5SWDsKAWqjXdKJdJKWzw0OhLsBhmAdGLBoek/PwiAkfK3Ozc6FOSLOoiikfEz6yxToOZH/y5Onk5KSbiIa+hC5OEKp1kDkgILHtP1SstCDBNSWoJpuPsaFSwxAOTkY3X7y/vDIyXgS1+9BEA/i4M6tokxM+yWvgxmmgJXCPS+raVrd16eE30tj7I16ge/FEbWyiTN3jyVMaTg24j+995J1JrFfmM+WTsbW1NRDa/MJCL7/a9/yp6HkF3btU+GMwOUxlRlDOQV32eMEgAggQGMgPAzAQVrjRoCMpxNmGXAQRIGGY0rAnS+uT5HbnGRvKCFF6cYSIBIwxpbmAu4PvilF5ANxVt6AqFIwNcRUwogoNiOvYDkLruoQNA8jV8LwBdxJdveyoFlcZItjXzeAuDxmhdi3roSotCEurTvs3OYIN3YzsePNlp4xFV4/xHFPN7u4Oy1HFzxG3DpORNSmCs0wcmc0ykPf5s+cPXnngEuEL5TgOHGmJbqVCeYXdFUxu+mvAfXokZMPiHujQ0Rr4LUK8OMimT9bu3w/EH3iW+8MgXbv+VOxr9Rq4dg0kAu6NXLlGVk2lhfqGOUy/cB80u+3zJcrUnoQ/6zUwwut/ELTAqj0rq6sYgFkhcmy8uDA/HZ+drwWHXX4GukyuBdODk4zFff8gmsqddks/ABztGME2KeaBnmaJn+NjXK41Q6Iz+jZoqjnsq2sKGyQPz8vyXCxOfN/HP/71r33VmbpdXoFMN6uM4LuAqUz+ws4G1sWl2HQHQRHtIvgeoHbwq+BqFCzqYLsDsfqq4MpbcTLSf6CImduF3XHJoB5VHOH0QAnBrlp6dPSND36kbvLsGHGYDdiz7ka1OJZRE8g4CMmpn6TrUgvITRD6t6xwtrS8hL1cWmLogYJjMCwvrO7iVkZI3iLSAIWcj1Clj5/dIkaTRw4PD1lvauXea+MTA9F2Jef8+nPy/OJVdf31+hq9Bq5fA5cE7nWM1jbWan2rjXRd1sbDDrI2FvYpXgNJNYCnbNKsvc2nO57ZLVbv3C2fjT598j4m2KWl5QjKJKncPzRJtBTlAQaDxQXfDN0J0xlIFBI2c7tasIpQHeePjo5JvXf/AV9I6i9KvKWLqBNpuB5hxmAfng/2d+/em5qefvNb39zb3Q3QqkCmTQcZTQoZWrwhbmyGqN32kiCgbSKJGRcBpYpWFb6HsN3hVwzPDpQ79snH8kqUcaNRyYPIUYUuT7it1jc6Nj4zN3/n7oPJqanwbMBOKKljT5TqAlQwe0eJdt4yK1afW5fMMDfzyb/37W9/8EMfol+hZVPNxz2W2/GGNALspgqpIAhmcecG4ARrZ0VVD3KELuWL7ZPRk/37Dx7wNWiQWR0Q3vzKqQNyITwb16CBqwL3WNNZ5bZpYkObXM3vY14D16CBOQ3q6kVofr8nqWl87Pz+vXtY3x8/fpYtTi/O5M0HIElRn6czDQBHwXwgVEN2hhRBeABLwqhOYdYFEeL74XJih3ZQr6aaFpfawcYAgluBMGNs/GpEyM5NTU593/d9/Pj46Pnz56w9xMzuY+eMW8bHPZzNXWbl0LRsbMrGrEhAyBAqcdCpthbTxoB7AN6DA5cUgmAyuUAZCrO+khzAT7G7a9CnVRLmCOvi2MmYL0wsLq8urazifeRQuUsng/KGPJlmQyLh3uWsIRkUrsH3EUF1JxhRyrWR+/3Ji+d7H3j9dY4yOOfEFRHStz2lHVwPdhwIt9NrY1RualQ+7gMfjs/SL569v3Ln1exoMGHowLPcfwZZ0WxiwnvL9P9CeA6uQQMXAPdYI9uWmaT52hLxJ70GeqmBTizuHd/QHReIScqouwf374DhHj9+hN1ofmEpgfNMrLyPJtAAk6bzage3geQsu9vquoGPWTQeX2rgPEgZTwwXyqPC7mF+FQoxZbg3QqIQRFrC9DCj9lZYGzB4vlB49bXXHj58lQ/9OzvbLBSFLz4gVTxZIE+I16NIQKwOr0PX/cnC7kIE4Q2ykyaA7lxlAo5ZPrZyUjomI3xRoanDWAsqYVGqTKFYnJqenZ6ZnZyaRoH0bYKTtlORCK9HvQo7VaemoM4os7pNcUqBel0aLPNx4KTE2kzH9GzpRx0dHu3u7o6PZ3B8gtWakqZVCNtParCYw+7SB3Af5E5ZQl3BgTrkcjAAppJffeUeMxuWBoq3AWdmwK/sgGvPszdcGkjTStY2ns34vzhHs1Jt0yBJ64pfo5tvoG1ef9JroAsaMB/3ft3vF/OP4++DB6/g1fpsbX88dbSwsOCQ2sUlfY4EGsjn8uh2amoqgHcqohbIQU0z102QoAYJ6C7LOxP/pMGOGrJpE5JQIGwIbV8HSx0PYQ5H3aVZRWE0IFJDAcM6Q+vc6DpgJjXC6t7+Lg73AFZYwOQMpAtqgGNXi3GrRBfReeWSSwx722gngzMJBt7Nfk0OEdC/QfnTU7SAtIg8nhnPF4uMNwWj49CCJ0yxqOlcnGsKBWJBtQYhhtejNE4FQsaTlOq4D4rGTzo3d1LgFcG5KEzhenhwcHh4gMmdAjh8r95ZhU8rjB4gFVITIf5NA2wViQIGd4B7ZTw3zjUNKh68XalcwdDOg18oeN+Yji8PfbOOy/gCXgPDqYGwFQtbv2uTwrXXEWqnfpdybQz4im6bBphVJvaeH1DpCwojh4ejT58+Acrj3uPhe1cuFQY5oLBBWME7QbsgpAQKK0yiollmXDI659Bh9/NxmX7xsRZ8dyt5uoKxNtNoRQRDumEFLY0TIYWwpPaMSB3PMgJifGZ2RmzCkS13CpMEJpEsHZesM8FasM6CLkwPagHcB5Z2wXSrW8XVslo9Gus6Np7GYE6M067yfKH48LXXstkcZnWs6ZnMuHotyG+T6kQSBOTYhbyKgCMRTwvJxtUbZaspHZIWjVDp5IR3iVI+LSHo0fERPZiDA7bISMaXL18urSzpy4BCSDhgSVksm4soHnRXMLar53POGAYVG7zAZdpYXz8rPGDqmMHkcPB0Vs+R11u9RvzxzdVAWo3fADRl1ddBTNfw1TQ9lsVHvQaSamCIJpQw+F4Aszx98iQ/vTA3OQSOuUkvQ5/yCZezfqqtwgOwVdvC1oy2+wf7MioHgXTgfeCpQiks0ZhpySDruwVoOIQYiWIuMg3NaNR4NZyhYHSSaHg+3EenhU1TI2OazhxbuKvOCgalhWYRgw2AF5JCrupbSKxahOoKUAGy6UNnGADrr33gDSmnNhiReJIlNEslU4xvjhzx2rQ4pfo4qFW1wxhmcRQsW/tJiX9wO5AduzsSOnEO9/dxnnHAPSBDPQLoBBJchK26MO5Lg1HFfg9wH5GfT4Ok9exc+7EmmNobm52YKeYO6nV57cwMb4V1N/zwCuI59xq4UAOhxf3CjP3I4N4A/ajZ13kDNdCXlVOvokdmN+N3cHz2+PHjYrEwO9ujwbVX4XGYygLaAIWMqgTgCeOJd+2w6ebzBXvxAwOFOEl1gUSM72l5FFaw2QtcapQjBlxZvV03wJCqStWHMK22HQuOgpM6CvPFy9d7y1dpWO7gtMC5A+i1eLSaO4DRIWnXdTnXNO1RkOBVlN/InssYVMtBjLhj3RKqqc1zVqmgcatO+bDrE+yTAt73zEYpd3Z9VlBgi5oD9YQc8i3CgW+xXcONu2LBtQt2+ioRXDAYnxi8qb4Zl4ykd1ZWBrBH4S7ZsGx5GIeFVc+n18AVNTDQwL2NbN45vo1y/KmmGhBiG8JQzI0V793b399//PJgYvxkxpaIH0I5+s8yVvPj42N8QgTxYMd25nZyZt4jMQQrTOiAINvqUp1Yvq2oDNwhIARbCslzaAWcmFUYy7ERc+nBUXTAuZqswQmlGSy149qjIEuTglVKsRqrZFTQTPGGm2PpDrcHpWPplr9KNKjYvg/UpFoR29QkByJYmm2cfkPALUU6HaI+TSDPn3A7sF0brgs6FsNGx7HFuANm2YdCQMQ4RIOQ0hVVLSJrPzO5s4GKDPm4yKdy+QGaC5K75tl+cWZ8nKVhA9X63RU04AenXkF5vuiQaWBYgXvd12V7LwyZ6j2716wBrK3XXGMXq5tQGNndO3v06BEjLAlV7NLFam40KTzHDw/3K7OzDtxZo1FhpXSMuCEUdCjRgWbDjEKCcrzGNIzpHfXYzCuBedSQujYAUMOgmrUFHM+OQ3c20GhrnO0gqbJVW7FqmtKblFXuaqagYDWh9rRoWLB88VxBArbrUNgwa7hvB9PJU2VZBWoyu/tTSgtjlkXI2oaK4ncOYmejobcagcvz6UbiynRqhAPqAW8UT2fSTP5Tc+cHedgFcN2pPdgG10VXBH8n5vARDwMQtvfPDtOL96Y2Uik/g2F3rgdfaCYn4zd3d8j2g0pwT/ejal/ncGhgWIF7pN34Pc5TGz+M8viI1wAaGOQJJRJeICb/5gfWfPTofZb5nA7XjU9Y/JZnwyZn1nGHqoX0wHoH+/tayFNW6AgQNr7+aVfk1IEC5SJuWcmNnd6BSndK9CwYXHQeNZqF0B3K5xoTMvZg+zM7cs0FiSHxeLoqrTJUbeBiadVoVDBGrLaIYzjK56SQSAo1yNtSLN02VTKW086at3xQuygFitHgVzuyTKFWrHsjCO2Cfadw1yNMIofBb4fBpaaAdtivYJgKF9FqUlVOMXZZwiJwGQPwKNw5ynANGHebSfd/Lkh6KM+fP2PG8TsTzI0Tyue06bdX0MANWoBpAO+K2qf/CpfJF+2KBoYeuMe1UHdzcfvXpcQz+/ht0wDw4GaIzLyWhO3t7fde7s/lK24awZshWk+lAPABIkFOgpLCdyPEgY7ykwlA4IX1q0UxpMgmQO3szAxf87qN0GoUcfDdgKTwpFmdhSux1zssL/e/qMWqtlxVskqrHkWsRug23t5V88VOWxF3Jjrv0HZAzFKbGPipOYTJUpWB8gA+6wxBrNnWUXJSSzqJbRv3GSL4JKFpXnQmDLogVfaJu5+ohiIrUpyYwFspqM7VpK3KBkVEqKpyN0BVV/z8PF/I973rvlvK7Jdzq6vMaHmj3rym/z5vWAmhWJzoMxM3tvqovbhOCd1zfZ01Dk1dN7n5aHrZuQGbpg/NFfOMXlYDcWBxWRoDVG6GMDKyvb31/vvvzy3dm8jVTwwyQLwODCt4yzCdIsY5kBxwe39/j+VSuTFciMHEOMc0GCB+g67WegQvMbUjwpt2mrPaR0HlY22Nw+QRoowiAFfFgbbhTtHwkJMOhXJW9ejPhSBmSbF3qkXDPEHWujQ4hH7Y+QDV892A4IhwUqUCsCxhdBROvqhjS3CZldMYkBpgMOBa4kiqAJPTXXaxcO+OVCIMYib81TAfiketqrIyMjk1IV51HPGgEiEl24eqDBiwhZzIUyioLJG+BNTwfKtczOzfmQZc3uTXbl/US6XeftEvzfes3ugZ71kNiQjXtEmJSvQ+061rQaKL4G6K6LD3qvY19FkDDnX0mYluV89Y1enpmZ3948fr67Ozs97m1F7BDFA+PDqcmmYZJoXdnV05uBssbFKQ1sHguPY6bXtL1EySZpnWRujXndOBYvybczezsitYae2rgcpr4KbKVEPtUW16eM4KBBW6HO4MWxfEh2O8rq07HGNJoyOgJI5X3/Oxj4F4lCFkz+IC0lGAem0cUG7ck8tQ8v/P3ptuN7IrXWKa56E0V6mmc86dvr69Vnev9bX922/i5/Nz2E/Qttv3u/eMNQ8qVZWqNE/ee0cAiUwmKVIiJZICRAKBiEAgEJkkd0JIJBQMqRtmF2IGXIcac2rHRMMKEG26WaNCRQpimaZ3rX9r4D4Pgu9KKBkJGvNOrGTvvAJCwup5iDDYOIpbJr4dXXzdefPw4aOJibyva69ijwWE2BuqV9az3fsbgfR7u+tR8K+9Tu3eO+AeA5QGDEcmrUadTAxTBIZmqUzloADGPFicXV548hnp4GB1HpOL+QesEiSvYmOZ/f3vNguMFRQnJ8czszOGq1OMK218JYSvbJJ6h3l3VxBkT2iAx3AVINTp7XljK8GmFsWLJA2hlGWT0JOIU29RBkOBecGy3DjSc2VXtFqwkH6ZySzFsRWIsBo4MsFDYu+lXIzIJYG3CkFzUAGRCyJTJKRMwmRhap92+eIfX/YmbXXnQsAkPamagnGxmf0E1rjH/w44l/pmCH26g+odvmlBEhbnnPLRsHfyocC/dd4fLkwfv33y5GnhcKZ6EAFMW/TAajaZI9DTCISfmM46Gb2/wD0NFL7404RYVjipNNMDGoHh3i8M0ASPWcVO79gZenf309raesBnA3q4euI2ogTQicm579/3sTUkHq5kAJqoWjgdH/z0q9S+B4wTRUY0ywUkw/Q1B+HtVDitTtCzVqHEpTrE8XKHrexLyO6IJQ5WorUUnjo7LRKMb9qmr4bUC0xvo26MdtRrBc2AIiK3ZKVYzkERraoTV+GAI0Jnd2gUMlTJkRckjFYDcMlOU+AwLljrMj9nW8owRmmAZaRwytywyXaupcddDGd4hha2AU1t3wKNVdc7n/ceP342MXvbXd/C6Pqti93d3UePtvvNq+xPjkAvIpCBe01U098P+/2sUcqsQYsAHnQyaC5fx19tC722s7MDBL++vo6nRV7HypC2AeZbX98AyMPE7fkEtgjkoucyCAQDCDLw7LtA0+2EmSQgB3AEFq3Ng5ZpQhcQXI3wTaJrArNYVMhHMnsiYRf9K0c9LssW+qUmcbC9A1qNM95k4Mok8IsqlvRbiqKKWuSDoDPsQmSpN/rJ5IOQjOHim00oSEpnu74JpKM20YrkNMkOLcmOSHIUO/xvxLaUcQa55iEYaomsSPqvgK2TwZ2p8wu3+RgHHPR3H3Ynxs6eP3k0MjLAu9DqCAxGlmfcB+M4ZS+7EYEM3K+IYvghKdT4c1HUMjUwEbhXM9CA7MAwn74efvryce3BAnY8HJgPBpYZAAAgAElEQVTj1GNHAYmJirGx4+XF2NiE0UTJ+lwbRA8u4INuH/cy2zTrcvLiahlUCNsNd9JkhKXq379FsFyeMnogziUW1pA2xO9M9SXjUqIi3noRqlpLMdkHEXJIAvHcZbFViu3pibcPJD2JHZBmYh5csBamY50Hz2gWr0qVHL0ppaXiRYkl8VQlpbT8YHlszP89YRzlsiJv3JD+T0DYrq0nsU4GFnH5emt3ph5ezO3sfNxa35gaKz2hNvE5k92PAP6Nlr/luh/WbLEvIxCnc/rSu750yn9G5Jt+cvvSy+xUQwTwn+sG3jAzAEnXH8xtb61//br3+vXro/P8YS8ON1Y+f/u2xztIgZk1uy2IHRSKDzmp+AYhGAmIKHZDHkAmcW2kZYBNZV1tSmDVRLTJVirUlnzxxDQJuxXTlKktT0IrbyOmILCJpeU6YqsDypRojMlsO09199oUrDNv476pHcWybqWTCUdi70qdUNHe6oEVvszPpAdrA+XREdyEPTauLWWC3Era5kupWCejFe7cUoYT3gu38sAyePD+/bv9s6mnm/MZtZePUs9reVeZnoc4d9A3Ecgz7jc6FOXfGf70Vzg3sp4bdzUC9/ObHah0a2sL848fdj+dHe3nxTM4p3Bz6sHBAR6CA8Jm3Hmi4aPLC/GiQIWfaJ81Z6mJdxFic16c952mOdrjj0yzxQn4IplxijTpTgHMefKvD8y300ZwxLmsi80+wxtkkShmslxmC6FLQlfsNkmBTVdCe/JISxNUIvKWZNrLilBlGzVMFcyOzNESde1tLM/VSWgmOfsCF5Adn99x28+RsfOoJoPwFfm+uF0rZfBUVnte8sJiz7eUOTod+/ju942NzZmpL3A4p1uOAOZl5ubyvQS3HPXc3d1EIAP3bsadvzAhxd/bwMjlHUcA/0u9Yw/urnusE3i4vnJ+voR/4u/snAHKD/etuq0jjR0hsQk+wPXLVy+fzj7V03BsMQw+wYYIgTNJsC4ITj4rUjAtqQTsLiH1baULwCb1aULN0YKkcssER40MPZqGqVZoKsI2/kgI5aaQVTBZjLLQlM1iqk8LaUplgU8e+7Muade7p0Jkpj0bbW28oTyVT2piXFqSCZbhJZZ1gn708m6oezk7Mzs9M41DxkQPkMwOCdFsHBIXymDjePxfBXen4plNeOCwmvQoG33/aW906acnT5LrwB51lc02icC9WgnZJAaZfV8ikIF7r440f4jKCb83jcyySq71MAL57iUgmK2th5x9//DhbHzp0eo0OD2MeF+aPjk5mZyYmJ6Zwdg3N7c+fviw/fhx6ilQo7Bh/LA69E7m24EPgR+BLm1tPFob7Xmcm6dAFwDCmjScdhTo2BHFRSWIQwnjkqJzw6wBpRYl+A5nVVKA1iYPZmiDr5jYKEmpNq2wV2ZIpJicINNeLgzVYI9losCGeoELgiK8nWssa0BzTOqIytpSJt6ZakLTkD+e2XJ+25LSJtxPT/F81vPZ2fnpadv0M2naJRKn07uj1c0HYzMTH8uXZl3qYIDM8IDeWcImUfgf2p11nzvOEbjFCGTgfnvBTr/W7Be65e/07Tl2T3rCfmH3ZKSth8nZ94cPAXbevX0LzY2trYn7BN+Pjg7X1teB2hGH1ZVVTMx+2vm0uraKLd4D8MIntUDYrMS3ILmAPeJH7F6fA2yqDaCnTdjzs0+Uz1KAlWVNYkcVtpqIRwDr+Bb9kiqnlCNE7OKUDxaqfMUE0hStML5zVKGCZMFzbxwseTdFleb5kr7T6lGWaAp2xTcVdyX26WpkyxB0FxYXcMg43R4n3CnUSzpq7Cvcido53X5xenqCbhawxmaiFxeoo/j/1enJybNtnDk5NZ66txiT0ZHb3DXoFgeWu8oRqIlAvl+tJii3wOIvl3517Ge6+mN9Cx7ciy4Q1+K1soJdznPyCAAAPdre3tza2tk7e/fuHXa7vg+hOT4+sl0FgdqJ3MfHNjc2gfnevHmDNbIA8cKUCRxEUIQyWQZCnMCIMFRS0yEcRZUA1d7W2AGmLJpYaDPJ4pwxeHrmaCEr7fFIPQephUYDxc6V2KP5EjiV0g49RyCvIXWONY/VaFHWNMY4mhrChm+hoGG9ZazogSbdbOzTTYHvrJERPCEYhytF7RSZG95cvipuRO1C7kDV0FpcXq42NLs3yE9OTl99HpudW8KH6AZmctOuRcBuZuiauWwoR6CPI5Bn3O/44PiPZfDCELz/XgVmLhsicJ0rnfu8xr0hgM7ALObDlfGzs3XMHY5OzK2tLAzx7DuQHfbyX9/AdDsgIK7pRoHdL8cunzx9+urly99+/XV9YwPPsMJi2XHsNhNnx3muYWrd5s6d0EQ78aUWy0hI+Ah86Io2425T9fw4c8EMSsubHA2Tu54aQZ+lJXEsQ17gXQesNUWBaGlGOJjtPcHTkKytqRvPOapYb+CYFVeQQbBavOS8WtNff7sj7MxImZVpdQrzbhM83GmKp9sChGPGHcG1ZGGkEg3QUJF0a+o5Z9yxuP307PQUTRaXls3nbuWfPu+fTqxsLx+NjeW59m4F9aZ28FV2UxO5fY7AgEQgA/f+OlD4Haokwgb9PlX4w1W1UfZ2TPdzV5l2YjoxMfHw4aPTi7GPH97gSGxsbY+Phgf2tNP+1nRudpocHhzOL8xjqfSYnlgKSMc53JGxy/Hx5z/8gH8+vH716ueff97e3l5aWpqcmsI1DHSILwnyUfJOVDRIl8cAOfrKGahAx+RNETqiyjHAJDC9Fn2Q5MIatAaLhXpT3vC5h322Drnh1TJyJQ/o1SfkTSNMz5f1ZSgooGsbqFtDYT2FNqhxrNKLjUgYTzKj5XWhSIOyZqVy8UxgDV2FgaECSoD1o8ODA/wb5OJsdW0dCyEw427RoytM3jUacJ5dkN0y3ZnKZ6biXyjAcwsLC9ZA+Y3OIUzrYoEZ7mxeW8QukxkpJnG9Y5KfPX22bsUPfjhyyhG4swhk4H5noW+z48H/irjRL2WbUWpH7b7t495OTFKdybGLRw8fnpyevf+wM3pxjJ1nBJVSlZvRd3oi2CTs3NwDrrjgmgsmAEGCd0AwrJpZWPjLX/6Kk+TFiz/ev3+PfTNxpQe8CKDPyTy0ITLQHDrBJVoLKtCIBAXTRBBAC8KCUPhqPtBgQZmz8tBncvtW8RxyF5mCtOgC3pZA+VqbwIklRPEVmU5QAGPBHrix6m0opbKqdELaIgIJWWCbnurSk2W2l660pMsm9ic1SKGCw3R2eoLNOr9/28Me7HPzc1uPflpcXsIh4AHz+CRd0y9LcZ0RbHBPGYBsEPMLi1PT02XE7807Lb7sX+4fXeBW5jy522nobkEfJ0H57GirT56J10jX6OkavViTa7p47f5ywwGIQAbuA3CQal3EVwc+0ZbXKvSeeZvfXl0YTd4vrJ0gTk1ObG+tYq+MV69fTc1vba1MleDSgB3zYsQH+wcA4vjfAqC6IKBlVDAK06fA9EvLS//p73/f29vD7DsWvuOJmxsbG5iyNQQ/hkU2+NwxIsQJVrB9UYsVoFDjqwH0k8+q5tbRMz/B0qMb+jjDED7UvI21AYfwBxxSZiEHZYk8JBW2+N0YzXLTV59CzdJzJ6wD60MaZhh66Na6YO5dsqVqbFYmaCK+zXnrR22tN5BYD3MJnI1TDjPsmGI/PjrEFcjyg5XHT5+vrq9D4ezsxIC7vvD8FDTL3qfsIhNmJ2zHCwvccYCWV7g4np3dIMG9t59PlqbOHq/lfUtuEMdeNr3e1VSvv8/4Sb1h6qmLXfDvhsPLza8TgQzcrxO1fmhjn7j4ubNPd6x2w8OefmF0w8EObWB9c4cthlf9qmM7NT317NkzzFm+fPmC4HVzqzLZOVihAfAChsRtqZxuT5KAIIeC0dlTVEEDAaytrWHzUAz/65cvuHMXIH5ubg570cwvLExNYjPJCSB4TNID6jMsAI8oCGAT1G4T7SZQsKgY3vic2hGwpjTBywDLiY/xRlXtPIOmEruRvmWokmNFmzlb0lbR0BhsLkuSiJJFqvLlclUIuOPLpVEtSNBCJEvapg0ZU47FMIgwbhcGXD/c/w68jnXps7Nzj58+fbj92B6ZBJ39g++LiwvhwNFv2DFr7i5rSloipNl2n3GH6s03gd3Dk4f39h5tY6J9Vr3nrB8jgHOpD92yj3l3HeOnp1up3/3r1jiHzU4G7kNyRNMPM+GBkEHKbBhnLz6yDZ30E2M4HznUy8OIiD17/vzk+OT1x/2ZsUPcuHnzycs7OSMwj7u8vKxZ2xS4E1YbjDcgHWj6CCQIgI6JdjwLEzc5fvn85f0HIPjX09NT2F5wfn4e1zOID3Tw/CbBeLRGK8ffXMMe6GLiXR9MWudH1D6j1pW3CtidH9yaGXfDvXTNWpFSxUqSkaolINbITC02FwJXAyl41WjryUyT5ktWmPsrlAXDOOoMpHXK6XBcQeGWUdw2enJ8jMdgHXz/DuCOtUqcX3/ydG1zCw9awjmGJtCk8vkZZuJxGcng+sUOvGCKZsMCdy7stxl3zLcDxuHOVJiCZdO/Rn5+cfl+b3T28gK3L1+jeW5ymxHAR/U2u7vDvrr4lY9PZvdTv/vX/RHfvsUM3G8/5j3vUZ/GYr7OPkc9+Yj2fCjd7AA4oJvmemSri996XfIQsOnJ5tTh4djbt29npmdWVruw9qBLrrVlBhhufGICowiztgCBTAKCdeHWUhUpcPYdAHFyakL/dtgAGtzDBOyXr+/fvSfixI2P85iFX5idm8M28IbjsWgek/FcO8/F83X24TU+jVzVHjIfB7pCt0KkbOhrdpNPromAj4WeKQCHTL6dFiMwY4UKydv44KQmzC/TinRt1Zm0ybEoS3PKsQCGOB3gGzgdCbvDHB+dHB1jH30QwPAzM7O4IXhlbX15+cHE5KR7ckHIDvzNR56y5RlWNuHSSAucoOLJe0Y/fhcue/MrA8zSa50MOHMLS9fGc9++7X3+/GX76U8To9eH/sHfXPY8AoeHh3n7gU6j3OTrqTMz/BLpUeqKf/Cthy72aORXmM3A/YoA9au4gzM6PWmtWcrp1wF23y+DYt2329piBweqtaE7luIOgcePH2Njlrdv3s4sLK8uz99NPKthuDq+8Hll9YGm2+FymmzGvZTDvFkksOd0PHN8XrAuBhPrQOezszObm5tElqdnQKF7X/e+fPmMJ9ECN05OTQLFz83NToLyyXh0yxtfheW5Qj6ssNcwYJeT9OgGANT6IqGO+RlNP6dQszYioEY5clFOOA0QSzZrVlge9U3AqlisprTX2Yg9Gi6XLWnJK2NKaNBZc93IiLoJ1gHUDaoDpx8cAKkDxU9MTgGsr29sLixha/UVPrx2bNx9NNQNzC0TsADcjgzbpc/PzypuGr0OTnCLHjIJtBtu9+ZYFH/KHdxxSYALKGvZfg4j797xGhWrxUZG7sXzDdoPTt9q4p9gfevbcDtmX5jXHqM+ztdu3V7DAXCxvYEErQzcQyT6orzh+XX1GNIPCTpD1bpM+VdbGUwNQ0bX8b3nh+U6Tt1Jm9m52cdzjw9PRl+/eoFl31g8Q9jbWepUvzPrFW1ARixumZrEdDu2JeEsuK1y14kPTxpf0YD5yVxT51b1OXjARYD4mdlZrMB58uQJMCag6nfshLL3befjJ6BWwE50NDE5ga4RKIBUm5IHjucSeSYAeiT5pAzOoIocEW08Vw1FE2Hzs0rgLMhrDDIlcAIFKfGMZjWwAp2WFIZ6pNWF1bDbIswB0io5vgZEJ7rW4Ln8hVBdE+vHemTpJbZzmZud237yBFu74F8Tk1PTQOoMuTuvWfJkfYts0bbxME7AeESPYVJSWw3EMhiArjK0Qks5o6n6E654XllbQ95R2j8Z+/z+JbZUwn6gHTXMyncbgW/fvmXsfreH4Hq9+xfr9RrXteo+mOm6i3D7Zl5m4F535HvF68Xxv76vduak549+Uu2H9fpm+7YlftRLvvXX0Si51ueV2Sk+tAj3FL58s4M55tVl3LTXj9EEEsUdyatY2+ObCRr8CyCQ+Di8EHEbAXOulWEqclFkSwx4rY+NPfIFvUziPTONvQs3NjcBJA1BAsofYlnI0bEA/Uet++Cqa6yIx8IbblODreKVwNTEPMpqEpaHwymUN7fkoWeE3EwqkbFM3mKIGTG5WI7Jg5iIGUnQnLPYIQXKUTEWnXPVC/+OQKJbKOI/DPg/A9LS0oP5baxZWMStwBNY4iLMjY7kHR+o5L0Z4EZLuwiIfdFz6HLAINA6rnECN0nBaTrMF+G7AXdtBImLibHxiaVOHr2E/3y8e/dhevnx4ydPGPGcBioCuIQeKH+zs72KwLU/uvYl2iu3Knav7aXsZOBeCecNqzc7Gjfs/MbN/dc/2LHB3OrZHLruRQlkQdw12IeoF4Exmx3HZQ5AdW7++/fvL1+8wErv1c5nN3s3GLOMZRqzvIUU0+0FZNcZYOdBkhskD2eHxaISEa9SU9rexNkAkOhUb8OUAKsXi0uLEWASVp6d45ZJIN5Dbnx4gEf58u7LM243DlAMTfMSc/GA9UTBWFyvXeQL7wMFTwOaJ6HkBB2hN+5GKJOqs8jR7DZWt2iumsvKOX2O4kQ3d3JtOv57cH6O6wuoYsSA0RNY8jI1tba2gXUvOAW4Lkjb7CDGRNtpohOA1cjZpaF24msl9e480IidImprhbg+6WKEq4/QdRihXy/FsaEVUoHb/YIJc/8n6AG7v+MfRKk7LWgckY8fdx8+fjY1jkuR8ihaNMuivonA58+f84a/fXM0BtKR633s7wAgjY5k4N7pGXa9g9tpL32hn56RNuyU0xcuduLEPdsO8pZOVNyaiYT98l6+/7YwdfbgwUoFvHVyiLqpC+gGALe0vo5JbCC/NKEbA5l01eNUUHKiiF4hD6qJlxRqvL7KhZAZLH1OCEWNMIAJttabXC5Zvcg1000wC/gOpAw0b1P1h4f72CkRs9oAzegcUgBraNH5kVHOXZOPrjlAyNQfXHLLWIYDXAwRJvSLzlLYa6PHw2NHx7CsBS2xjoeLeSanlpYfgJ6cmMQlBP8bMI5/FOAPGJodxmHHUJh9Dt0C4P1hTKKsDDUXSpNBxQUL2tGo0yxHOKJkI0hH9bLPLIwj4nYEAxFSDPV/gJWVNYxLRltlnGj/8Hns4uj5syf450Er1cGR4VG84d7mwXH6Zp5y2V5OOQK3HgH73uqo25vjqAzcGwN+jQPRaGTYOJVTzX9k+2+ULQ7e4M/HtBjcHR+JJaYRTCG/+XSwMHmKCvDi3fqEOdT5hXnM16a4nTCb0BO++YtF8FREcJxMgUVqFilWIiFTUqYprVInXFR7KRlABUugk7mSMUDWEOK6Wkq7DbIoNcSPCuAqUTGYyNyzMBAOV87QqUCTw44F+uVukIAZkyvFOpv4rjfyykZET9whI0NuQF1KUijslCl1432xwJv94Ari6OgIHhZHSO1oi0ZlnktkSAm169IG/8LAnamXI+ubG+VuampHJ+cfD2c21zenx7jmZ0jSKHC7YjgI4+FZ2I2EGfdHjx51w1K2kSPQ2whc48NZ+ZjcW+B+jdD19lgOlvX0NLJQppyuj6Wzo9VEe/9gv+uO3dhgE19vbPdODGCZKdaZYr9EPLFoeXVzcf7OdlY2IIdLtbrp9hBzL1EYsi3FLCiVmEUlio1IcyoR8esCgQJdOZAZuhHypBqSEC0yglFWQkHSkviuCZWiWqEkCTyUMcUmxkmrpJ2bkmUabkgHpVA76/6uLeKg0ArjJ4bU23sKhcXMapxzh7KNXgJMGWOan5tHHh5h3j00Qsf2Qs9IBO2WR+jOf1ngXxPn59h6f3W16c2paPzx48fLycUnD/j/imh/GIjBQe086N2IOIzkNe7dCGS20acRSD8moIcPuKcD7NNjMGRuFb/yYWB2DBr5Ju/gCHWgGvpuXvZ4l9+u+tp8FP0vWVpexoz714PzVy9fLj940OOwl+IRj8H+dzxuczFMtwOZxWl3zt4SQSMJrwfMRqAdUISZsbpoCp0IOq4cVK2kM2awaOES60/ysNREmuTLJj4upgpISTvEpUmKHCNiFSqNNDhiMouEKNUIfvXHXowDtwNJjjOlxR7US+yqqDrfWijHGNyAjYp159nwoq7UxEOwzDZiwX8YyCcFEgt13rx5gwfZrm+sc/WHJNDVvxpsot1y3j+ApflcnY8HJEvz119+wUNYca9A7DESx8cnH/fOVrV3Z2QOJHH/lsQ0O0zYVQYbEDWTZn6OwDBFYECBe+UnYJiOyGCPJT0wKV0zqivENS1uyMJCjmtZuHVHr+VlfzUaHV2en1iae4KY46mry7OXWAffwsPuhhg3VwKnA/Ol0+1E6cTI9oq+sCro7KjZaK9ErQYicbggHdyHxkHg9q3rsn1TEc9WY9NE7bLsAjQTbEckjelm1cGKKcLqyIl4FxzAaM2bg6Ai+pZUI7QGJKmI3BAwiSYJA6iTkZ2+0do1vb8QKmtNExSg0GSxSEBS1XGH7u6nXTwOaXt7G8fUHePWlOauz7nH22txYwPNjI3jfoDffv313/7t32gnSR/3sAnO6vb6N1zPJewBJDNqTw5a3gsyCUYmhzwCtT8SfTJmfKs2e/WJh/fIjWZHosL3iFS4SZW/lfa69eCtrFSegBhdaU3cuqOD1mGz8AEYrTx48Hh9HouOMft+sL/fTLO7I97f53Q77qUkVC8l9QPQyESoCH8aEnnOD+JQBt1Yj0TRhjoBELIXpUhQmHJcQQWy1CPoJeZN0eyY85RXkv6zgBsyK2zqh5T2EHilslAtsZtVqH51E9cIg6hpAQ1JWcpkJMDAf07Ozy5++/X3X3/9DQuxtIMkUDtWxyDjn92TyhUy2AwHu/RcjvDpTuPjL37/A7c6RN+xiub333+fmzjamv0+8KgdoxqoJTHxKPSIwJNTe2Q5m80R6LcI3PmMO7+lc7rbCHR2DDrTro6sbn6uqtOj+u7urlDBzQbQI+f6wGyP4gI8hkumlZUHmDf99OnTxsZGT+8SPj45xkNLsY1gOt1uSJbYsACuGK6QModtQw8BKDg6KoEd9SLXm6lI6WCQiqG1ujZNZ4UWQSPqkhGYLDnxDY7NjYOBGvPwhoxJ1VTH2GkOH2wqPTLNWqySqLAq1ZJqtSLd2IBErDTYDW0TDejYtDt8sP8FkPDKKOE7dqp8/foNmuLxtNjyBtveoA+ubkfSlDue/4TVMmPj2LAS91ecYOfNf/7HP//rf/tvaLJ7Mn/46ffnz5/zSAxoylPszQ8c/hvTXJglOQJDFYHbAe4D+0U5FMe6s+h3pj1IAWqYcR8k5zvytS+P4Sg2esdrZ+fjx2/nG4vjvYDvgKVHh0e4SMBsK/BZSJrM9aCgAFs4kjENoVJZQXRBFtRYT3hFc2OWRV7DtUG8ViBLbDkAOuoUtDFdwB6QuLZFaF2wHK2wXAQaWuYCsVC9V2uui9G9IX/Zqs9orV4ijxtkFX1V5VGdnSCoNPJQwjYEMfkksi3hiS0dv0OT8B0o/WD/EGvaww6YIyD12NZj3Am8sr6F+PLu1PNz/Nflzes3208en8z8sDTycfXR1fvMRE/6iIh4PU+xNz8q+HdKc2GW5AgMVQS6uFQG37LNXkMVsn4bTLOgR37hcGS1IArtO6bgY3fTly9fumvwdqy1OFbNRLfj2PV6WV/feLoxjzvJXvzxx9FpAyS8ntHQCrsH4slFmIvldDsBepLw9USWI2OGLjnDEjLgSLJSdugjclNhQaMDaiKL9gsh2JErLaspp1YkKCyS2ww9hw6CQmwZGOXS1BmJlF+qpAKj2xC3UmmQOcMK5IlCqeZ8FRRU3/BOx3RsVPvNT2LLSN55jDn2yanZ2fnFxaXp6RlUuXc9ZJCOjP7j//2fWzNfFxcG9s7FjNcbT9AGDg53Ay8zcgSGMwKdAnd+jzZ5DWeA7nxUzcId+YWHkdWMKFQHgKpgOozphqkf9gtrdmRa8G846j5sDuS1ubn55OnTb/uHr1696tbiVEy3n5wc4x618nS7IdZKgBEVchqAMpjVFFlOsAhvmqB+Igp0YZ/yFDRbp4FjTWVQGc3JIGomM4bRSXcuZJHoSZkZeBy5dVarkTZMjMlCvFRQ7cqs5IAqKSfQVoYa/dMr+g8JOSokldvUD2+SoqXn7Z3WYInhuZc+XzgNkLCn5P73/c+fsUwup2GOQGUR2DAPNY/t3kegslSGX4o53UIEOgh0B6q34PiddZHiePzrGL/lKacdtzDL247alTr5gFwZonYUALE2HsxdLM3s7Ox8ORxZmR+dmZ5pp2EznYODAzziHkspBN6E3RzCEQw2vqIdx4J2XJmHd0rWHPWiQamJYU+3bsiZhuRAqYn6NSRqufotd8Sar3G3ZShgYKEMl8uYIgu9rUOJJecnJHxEjOsuxUJWYk1EDauskNZqrJZYsULCKyiYVET3yHA5P9WktUGkSg5Xbwq0aSUyklhOkyQgdTKI2XF7ridOuZ9PnJxfYKU79nQHjmfnOQ1jBDJwH8ajmsdUHwHMuOPbMb7qlTK30wjEgDYjSgabKRm/pJorjABQQIAkHQSk9X5hrQ9CKu2gy6x6VQSAsDD7vvVgBruFvNk5ONHD6q9qVCPHrYnYToRPXBoHdksTlNOjF2lyJYrWyBDTCiejuMQ1YTXH9YGpR4ImBUTdttEpJyiwZcJHNbzcmNsJOkEeurSOvZsGZpQaYaYDk123TvUa5NZIUlZKSxeMEq9iIhFzoHxRnW9LqNqfZAwuOdITbbid8N0SLuQw7b73de/tW97VmtOwRiBflQ3rkc3jaoxAp0tlGi3cUw5/KZq/SkFpoWeiknau3DQCCGptwpKMFoeitklm3k4EMEkK9P5wbQ7PLX/9+hU2Bum0X+w1ia3iOd3OSVdhORUEfgJ2wnYlBOhoMD1dSKf1ciXxyZRKqpJGDgrHC+4AACAASURBVAj26Ik+hGSUcxJ+1C9UiybO87bgi0LmAtNkBRZLvGCjVNbrpM6U1FtavLo3syXnUrNg2CvKo6mg6ww6prfHiO7z6CLJhGqBIzZAu7C71sogg8Yv//o537+Yxn/IaOwEOmQjysPJEWgWgQzcm0XGf1T4k1H3qjarVTJmVTXXexuBymQ8DoKlvF9YiESflrifdGtr69H29s7Xkzdv37cP36GJ5ST+xKXKhDsPv38Oie+EAFmEJFL4L3BQJnLnOodFKizqgUswqYQi0mxFkClRouB8Sq2RjLNleDk7KAQ1iYPMSopcXGiVVa5dq3ykynZCb6E0aaiFstwmeOrcOPyoJQ7asqQJI5WzirrCCbFeQuukULUrN868ixrnbaoA8gf7B7//9lvsIBNDFoH89T5kBzQPp0UE7jVw589A81c1ai1U9VNS1c/1/ohABB2YkrHHuveHX9mL+ghgyhxT7w+3Nj58+PD27buzi6s/XfuabseCZk6322caqC1+uN2AFcj5gliE+xBlrEdRiXDNUFiLoBw1gziU1pHVoGStvK18gEhVZskbpF5kpUn1IgsimtI7MFqWZtTyVorRecWypSaF0WAkGpqUeybM1ivRKzg+HhVoVzR1t8QDzaQDaiQqmm331TLC7Zhz5/agf/z+x/ExtozMaQgj0K173IcwNHlIQxeB4Qfu9pVfm1ePZq1SZFa1c33AIoCJN24PrQQE79SADeK+uIspUjzifmNz6+POLlYnn57H669qBLD+ATOqU1NTWh4hCCckRywXYKET9llODXRwEhSqdhKFU6kwFzjsN0nmjTPKbV1RBdskb5CygiFY06DLmrOcjM1NMeaJVspzQ5FVQ9S1rFFLWKFFyXhgpv6KhsBe0QLa2StySLgB6pK0wEYKVUWHYmmQoaT/uviUuzaOxEUdpuBPT05/+fnnUg+5MiwRaH0L07CMMo8jR4ARGGDg7l/X9qXdPPfj3I52PiWGOgLptgNA8BUkaCfIUAdg8AYHuPVoc2Vtbf3Dp29v3ryuXTyzv/99YXEB24cQuGuFO0tBt8ZvBQN4MRCoUodFyNUmKlQI6Zd4xgl8M0JrjkJdAD45SkaEamxh/VInGKM2aGoYyywodz0vZLeSScRekUyE0l4Vzbar0VJbLYJv3nsxruBOtBI1Shx5GzyWMelxCIqHERpfminSZGjaHeeEEmbcuaf76MirFy+/f+/O7lLR20z0QwS6tWlYP4wl+5Aj0DoC/Q7c8V3d7FUdWDM941e1c/3eRSAF7o2DB46PUB6nDJLljZqZc8sRwGz6480lPLnpw4f3b3cO8ODM6ACeuDQ1PTU5MQl4Zrg0AjcDqWlueA8HFsz08JYOdxQZPFRPfiZ4Edum7RpU9L0FbtHI+42+ywvIqRE9UkWt0obBSsIzLwr1aLaGCM1CWVZp4JpjZaVr1Brtwogzi8KomINwmXr0Kllix5gFAnwedx7SQKhmma7gCuQONI+TB1tDXmM0uUmfR6AfHtPR5yHK7g1NBPoCuNu3c21eDXStkjGrqrmeI1CKAGbdSvXmFUPwKY7Pi+ObR+uWJIDv29uP15en32Ll+7u3uAxDOjo6nJ+b10KIEnITzovfC/6twQLvmFI6MhsJqkVVw9mBYWzlwTIhZJKon3C8gXNSTdLJW6Kq3LsNaoVXSYeRTBsL1EZJlXDNUoOqzpX12JpErLRqViiJKqpqlFZBM2IKbYyvEeIzxvwzPQiU9E8XX+mOyzquc8diucuRD+/ef/r0qZVnWTaAEcCGVAPodXY5R+A6Ebg94M4v1SavquPN9MDPKUfguhG49mZwnIx3qIVzmHg+L5G/7kG4abvJyfHHjx9j8cybncMXL15gKwlsAcl1EYR1ngLEs77Sb42U5tcRv5BYGL+grGXge61QCoKUE3hUDieLNYQWHAtGAqG6sU1KQXibkPWgHko3Y6reIvIiUdVGvcqKuj0n2HOp94QBkjEoicnxeEnkdKGkeLFJElbRrOsFwpKWTmkBFefdefsyzpSRkV/+9a+L9PFPPY9B7qDnEVhdXe15H7mDHIH+iECXgbu+aPkN2/iqjrdRI3KqqrmeI9CFCHRlvzA+xlE4HvidVDjVRebsliIwOTn5aG1mbm4W02yfvnzD1DuBG1GbJ4I6vQOUU53exYPmrlq9gX3lQLxdqscumVhYv6paRveUjAjVxIyauY6MJDKwraZcpspSa1fKr1QI2v6vJS8C1zuM1Y6ItvqmUlC0eKVRIx3kHLC0CwJ153hALeRV5M5rOi6YUcKMO+9Qvxz59Gn3/bt3HY0oK/d5BHZ3d/vcw+xejkC3InAd4G5forV5jVu1evzKzSlH4FYj0PX9wgzpIDciT8Pf5uE8ONhffrD8/Ifny6vrb9+8/vhx55LbAEaADl8qXz0uIvYzoUrLki8kJyOnWnfE6EhSzQMKL8yGRoYuQ0fedagmbaUfGiVytne2g9hYS7QS8gpxolmQHbZprt5cUgwidEvdFvquBg1/saC+ZRSrZnoWJorw1ouHBG9LEblzS3fuCwnFX//1c+29zmYw5wMXgZWVlYHzOTucI3C9CLQC7vwarHvV9FSrZ8wa7czKEbiDCPR6v7DanWruYJz3oMtzpenpGcygzk5cPn32bHHpwYedzw7UHLwZtgt5GSbiyykIRFaCFnmRKPRrDIXWRIQhgdRLzogZhaYm/Bi1XRh1ggAlLLBWJ0q0Ikm9dnVjozoiGAllnU6ZV9ZETYwSl5USo1yFyF4t1Bg+hRCZHWoWIq3KvJRsxQwn3jXpPoZ/0WATktevXpXdz7UBjsDXr18H2Pvseo5AJxHgir9mr8JOM43IL1QzlSPQpxG4/f3C0sUH+KwgVTeh7NNQ9btbeOLS/MI8VrcDigmhjc3PTW9vrWFhRPp9xu+nBNBJVBqa4J+xpVcWhhoPnR8+KyRwjiuxubFpM1FTBT4mUpBlhVBjiXd8mUFryVx6RRYEUZ8iiYOko7LSMqkm5BUWo2YgQql2pQo5YBjPiFSe0tRSWBlHhZL6FlO1FJ+cNOnU0KaQWi8TV8vopgjeq/Lrz7/k5zFdcUAHR7y4uDg4zmZPcwRuFIFkxl3fgP5VWqFv1EVunCPQFxG42/3CfF1NAHj4hOV0vQjwJuPRS2wygzsNA1gjnKu8ZNy+yEB6vB31laNfrrXjlB3FUrukEjoJx9o8EZeeSDPk1ox58gYpvjKJAkVTgbbSaygCnzrlijjNslKzZkol/lUtauW1TDfbWoZQScFDhrEZFYhwMKhE3fCiHiG73gTwvtSdk+5j45cjl0DtL1+8KA0tVwY2AriYH1jfs+M5Ap1FAL98+pJv9dXZmcWsnSPQnxHok/3CHMEnMbKPYMLIZKsI8IlL83zikkAZcJkhNKK08F3GrzMDcPh+I7ucxDBuRebVyE3qgRdKmiSNDsx6JMgseIm+c40T+KGUEa+oKAm8i2YCE7snNQ1d3rJI/z/UUrEjYVNngoBBUehApC/0wqp3VinDsYYKdZI/kP5ioTcKTxG7T2BnWJr847ffDg4OOhpQVu7PCMzOzvanY9mrHIGuRyCZce+67WwwR6CfItC3+4UBMUXQZAAlwJR+Cl9/+IJZUix1wK4yYZEM0ZklAj1hNnmKEPLl8WRhZDIMlyUcIxsUEw03mCpG2+V2qNkrONHggPThMYyFN+2KHZStYt2luTXwZiUl27A01b1luuSN+i5x3OeqUyUdCgMDZSAVyXAEeKyjEdL2x5OAiWWgxYhZutL99PTsl59/jmYyMbgRyKueBvfYZc87jUAG7p1GLOsPagT6ZMa9dfhq5+NbN7lXUmwmMzc/x71BbHG74BignZfCckJsMSoF8AM/AYEJ7LNWsQUJYcKgr0raNG1LZVcQWKzISs1cE86qi9DM+zMmKp6IREMKXRScICnKIAtlIamh2lJSu/Y1QzehRSgDv1w2k4IvEbNGHUoZDEnssFtsLFpBBp4JvWAFi955uRcXu5s7b16/3tvbK7uWa4MXAVzMD57T2eMcgWtFIAP3a4UtNxrACNztGvdrByxPxsfQHR4eTE1P8eZCvyeVcExAzpCcwBwhHQlBNwd40QIJCSPH2hgOjMxawjVdNdSqqkKREiYactM0E26pKfmNskZOaGT6VXm1HrQby3haNYqMc6VCs4Y1fHO2ZoDyNzoNItKwktIyWjAMl8eePL5W2IEPhx88S+FCT4vebXsZ3COBpe4Xl//xj3+gZH/51T8RiEe3PeL8/OK2jl97DmWtHIGeRWCiZ5az4RyB/orA7e8q093xN5uM7ybC6q7HXbWGLfyOjo4erDzQZjIBjvk8KqqO1B2wBQhGHII3qsyZvDSmscp5UAzcaj2YkAUZl9VCjTy9PZcheugEC6uluUkbcjhOUFlKsFTmNDAS9VYy+dHwCNGy7cSUyNb2ojaGdnnZoNvAcH3wkdKOE8147CwQqsppxBRnRczRoVXQFkwmoHSyRI1dkL4MM+7A7hMTpycnMPfp487n3d3VtTX3ppuFjaybFu+LrY4ip2N8W5HpyLPbcupG/aSfvBsZyo1vJwJ5xv124px7ufsI9Hof99sfIb5u7RuXSEXdD99PSowqdo2YnZsVag+Tp/i1FkLT6C0GHgkiYo9FWjgNmwk3MkXEWuyYhHEdQYZKjUZgRRWD5s5221GYGA5dBMeCoaL0xgWjIwqt7YVWItxc3S+2izqy3wVl80+H1K9r6GsRc7oVqlGBpwDZ8jlUrJ7YgyBJcbWMFs0A7cPCP/7nPy4uQHij7hGwndNtRAAH+Da6Gc4+un7aX2lwOON4a6PKwP3WQp07uuMIdP3JqXc8nqR7IA5DYJabZJh+x84vzk/PTmZmZvzJlwbDALP4a138ZIffbjEpSGLUaVAcMQYLMtVojwgvcMvdBW7iXtQMRqsl29iYqpIr67G7qzXTk6ROu17edgd1zauNFSobbcmDRA9kCGjkkhdfZamfBCqkzzhaoqGi5kyCdpxM49hehmlv7+u7t2+MzvkgRgDPZBtEt++rz8XnOH6gu0oMeVwzcB/yA5yHFyMwPT0d6ftARAAlHHMlaOzrkBzgiUtzvCcVs6WOvAosRs9VcyIM1cZNIQV1qYmgxLZKieWmnBdELAkmQ72hw0IgirpsErNQScoySc3GVJhtlIETTwOT+qYz3sh9qG1YYVbsVKRtVIOfoWxoUghEebXgNjYw9wnLmbxEjS8FVrRn4bzRIncusTLszlZY33458vM//3WGRwTkNJgRyDenDuZx65HX+EroxatH3nZsNgP3jkOWGwxoBPjgnnuZDHKlwKsFGOrDCJ2dnV5cXExNT2OO1NCXfSkbIGv8guYXtn1pJ1jOxqWBO6YrRtpBOEJbMxQbOsECb/YecvXikDLwXNugpXLpS/VmWXqQ6yzJQXNP4qv0ExvB6YTVFpn0VqdfMYtq5KR0XVPyPLIhwHZ4yOefnQUoiqQLP3+mKhC8YXesnUcgsKH7H7//TqM5DWAE8naQA3jQBs5l+07qVn794Wfgfv3Y5ZaDFYH4b/HBcrsX3ka8ZiCpHYTUNTc6/9LD6vY5TrdP+KNSichiIkq2Fz208ahwsvC7YDRqRVkk1C7iQJkuywrDsVuygm2VNaDV5LWmUmZKpz3V0HYw4yGt0QAriG1EhfXAr2/VIfcKY+y16Llsu4FfZRTHOBpxFT9IOCNk304HdKQquf7mKRP/XWPYXbhdJCfdR0Ze/PHH8dFR2bNcG4wI5AcwDcZxyl4WEcBXzjVfGbgXUczUcEfA7kIb7jF2OjqDWsjxItBRikRgNC+v8bXT3Fit5OT4BIAL/wd31G5AzNAZoVp8Cb7DhDGK0SRWIWo7JbqGAd2i8QspqfB241FY4bfqO7apUWqUNXKqza7A0FG9lSW30a6p5AyK5tsjghOhTCwZyzE5+CkMJyLnsfHjQynV8TaOVeUCedVEwI4X11+NjXHS/XIEs7a//fpre05nrf6KAC7v+8uh7E2OQM8ikIF7z0KbDfdZBDJwrx4QQRwDOsi5eZ9eRggTRQzkoqjsRNVi9+s23W57t8f50gSDEcvRmeAZMBsZ4ig3mo6RZ1loQEaapEFGIKwMtVTV6CgRfIw1trdKBVRG2+ZaxUij/Y44hrAbNo8MNiQ2qWPxwt+gc7MyQHyWgQbZVjeJkg4gPSHPjmeMZoMxa6cTQk1CY3DYupQcqhtgZ+77ywC7Ux2bSV6OvHr5KkNARnLQ0uLi4qC5nP3NEbhmBDJwv2bgcrOBi8C9WCoDtNL+q+UhBPIigg85CBpO8FjL1t0RYiMgTLVjuh1zogkEczwmj9gRHYujdkZ0QGNowHuNjNDA9EOtpgwGVVa1q/VS+1ILVhLthLQ2BaMx5lVOqIey1GulkvTbTF0bJKbQu2LiqmrSRVAtRgNOlAduKF2mRgGBpzwJqhlPDG8hAplxnK8qlUxgVLFoxu9S5aQ7h3x+dvbrzz+XLjyqHeZ6P0bg69ev/ehW9ilHoAcRyMC9B0HNJvsyAgN8cyqASZuvXkaeyCZALEPw5tS1+rx6PICPeFTq7Cw3kxHOIjyP4ItQnc5EO4Eisy5FfiTU3jIDk3XNUl5oqTJUogIZeMMvS42E9xUUTN+1G4pmqLpBsY7BxnUGGniG0asmEg8LUS2z0ovZr+udrRssNLhT9GYUWjQ0quqwzvNCSSVp49gJQ4ksBQxvV4GacKcGl7rjKapICse7t2+/f8/rLhiPAUorKysD5G12NUfgJhHIwP0m0cttBykCfbcdpLCEIYor8v4Ls0/GCx4aZgKUF9HmqK4e0snJydTU1MTkBJc0eCIeQyIuMyxGKCaqiKA45ktdJ1CvpMiJhBTSml0keLtUQE+KulPBJdMvxJV+26sGcBtKtErIxIZx62Wu1ihMnHfYStVGvaSfMmmjCw2uHis0opI6D7VQ0rwppRznxr4bNcJxYGl04LCq88RLFlwow9ISlsxwoTsn3YHdL84vfvn5X7GrTAxEBHZ3dwfCz+xkjsDNI5CB+81jmC0MRgRu7wFMEVW0JgYgbK0H4FIhdj1OXpgMj6G3kUF8k4RNtXGtpXsHA8BCiS5Yg+Hom6iiM6eCjrtQyMWoVJv7yQ6LlNLghqr6Mi1nBUnRtNBOec3ogISr8hI/LGcHU3wTllSq7Q2du0qdk40NAqfebplbroWWNyvhJV8qAu04PJwK3oGdF1bxQ8JzBY3Y3EgvVRF811WhJt/1MCYbwfu3775/+3Yzv3PrW41AnnG/1XDnzu40Ahm432n4c+e3GIH5+fmb9ua4wZBE8/ym3fS6fZvDIOC5Rrp0zEQwaSDe+uvU1MXFOVa3l2ZGDX4ZjhN2E3QO5o1T7sZQW+C1HFEidFKF0YkwGIuwPTKcKHRDJCqqhUK1aYd1osyAla0MtdaG3AEot7xjOxhTGSqtLd9MSr9izGTKPZWADBBlhVAPipTiRDBVj7uwuykYYC/nBt1tEn6Mq2UQlIvLX3/5RR7kbDAikNe4D8Zxyl52IwIZuHcjitnGIETg+/fvVTf1Iy8wYJDgqrzavh/qnY7BAMwteW4gngAxdOhQPszKB3a1BHY6Oj7GZjLFMpkCbRl248DDG80Nzxlmq1pzAOdKTXRio+YRChK3EKpsWYaT9EsvM1r0aE2KhgUVu68SFjrlFjUjXS2RFg3JDO+CK6rUuCwzU2Ve27UrG4eRhrJkuZYpDUgoNAWvlJpSiKRMWqStFvhSKVhg20oZbiqjdVhYJaOF7qPjvHfDQvRld/f8/KzaVa73awTyrjL9emSyX92PQAbu3Y9pttifEeA3u/3yx7w/HXWvopetib4eQ6NzDuWFczEwpLi0JlXGncSQAk4RfIXkEM2OojUWoiOZvonRUmMSlhjNKqVmpUpsIW5ZhFoNN7a4BlHAYKcKhllL6ykd+yIzvCPTiWb8ql6i3kRUsMsRKfi1VJvKIahtqqMrnil+MNix0yWmcH0JxbORQXgCeMhx1Xg5csGp91rvM7MfI5A38ezHo5J96k0EMnDvTVyz1f6LQD/9L9UQReu8/yLYbY8MF8WlNRHBIy4A7tMz04JUnhkqI+yyF+uG0Vjg3QDPOfOapFIl4de0LNtq0bCkCD35UbZdqrU0FTULvFhQUVigyTphodacUrvWnlRMV6p1pttQqWkmL9yVOo/qeMEMZLVinR+U2MEgEhddnEeUhFOLc+/2Xx3NwoPWLao8A0++Nf6PLnSey36LQH5yar8dkexP7yKQgXvvYpst91cEbuvuJQMUrfP+ikyfeBMRPEDgxfm5PRZHuIvBFNhKPRUeC/DMS/KKVK7V8ZtpFLqRMlUDgwWz0UCFU6nGllUiAt9IVDXCGg7yqdRC0ZoGJS7aNk6Rp5xEmLILZadML9FuotHAjow0epFOI5TQJEMVpb2MZ7A7wO/CeqDUVPZ19ujcKWw5ZI/QnXVhdyF3+x8PIjY3t/Dqxcua0IVectlXEcBTb/vKn+xMjkDvIpCBe+9imy33VwS6sV9YRBAtiP4a9YB6g+fQHx0eXVxeFHOjRGLJC2REc6RZVdkw0e5SqUf8FoiS0MxYboLG3HUqvbD3KIkkOYHtUisamITDeGt1RoDGznOuA/UgLJlrWkm0E7IC+kvOlNVKtaa9dF9QcsnMgxW5kaAIR91epF1XZYLaA9tYqqUnFrE7kq1452oZHAf8w2fv65fziwtrm/M+jwBuZO9zD7N7OQLdikAG7t2KZLbTbxHAj3fptbKyepWLJf1K84gKrjKS5V2IwNj4+Pr6Olc3EVEZMouEqjgegmdeOs2uE7LkSTN+SanU3Htos2FsmugnpHVjjAiII+G4vexLIoWgqJEqakkbY5ZFtlS7zEuawFILWUnxxpWrO2qIV9EnRTwg4bCXJOTbaWFinivSt1Y8iUI7oz23WXZWtF4mVDEDPz4+cZFRexHkfqfOz8/73cXsX45AlyKQgXuXApnN3EEE9GtdRucCT8avOqQ17p01qZrI9duJwOUlkBNuJr64uMRSY8NYyZElGFM1grgAygr3AOO8EspC1opqWzsqsu9YaWW6mawWzhJuR0EkjFVUWU9fsYtCxSjlBTPqOdFcUtW8dj120XasXJGFH1+QV7Wmium4Ms6e4HM4kbwuVfE4yQ7obm8UI1PTU7xBNafBiQAO3uA4mz3NEbhRBDJwv1H4cuMeRMB+cNvJO+s87xfWWbzuTpsbenAe+HJzY+PLly9whLjK592Fy/xHmj/VJFmGXOQVtaDTUWmdyJe0nXwoGEGr4Djlgo7RYMDvaljOJDKDyO2FwjgNDrQCoo1eu5FgK5QNVq9k1Lds7LAwRBnehUpBFUqiSlqJzK7adMawrc4enkAJbecTcptk1yoZrnQfueTamK1H23j+F+oE88GbISAYgSFNfnyHdHR5WDkCaQQycE+jkenbiUDrX8Be+ZD3C+tVZHtg9/Tk9OzsbGp6enZmdn//QD3YaUOSlNdQGMQjQdJKVVxidORHwnXqi7a06pXqubUAto4JHsF3FIkw1B15ksZaAPBkWEOjGkZWO4tcqxttl2wYt15WUpQjVU61Xh+nqlZRT/VB81ATmxcKBUVJFBREYKKUBksl4nMkLLfA3u3Pf/xxaWmZG5IWNgrTA00pbAxZf75uEtu8VOYm0cttBysCGbgP1vEaFG9b/y7czSjyfmF3E/fOe8XZc35xjm0i8ATL1bW179+/acsIA4x2asGoUBVqAbqR9IqRRV1UyyxpEQ22bNAgLFlokJYZJejLCt7xlaom+N20oOf6RQMyQjI6qARuWtZPvKc2TLvEKVVSa+3SFQMNwRKjgVtnHUqu7OrC4ILwQvEF2DbcbXVTkj2H46EzVJXGsJHR8dHhk2fPH6ysftvbOzk+xh3SENU5kXk9iQBife1Xvjm1J4ckG+3LCGTg3peHZTCcavEd248DyPuF9eNRaebTJbdyPz09A3haW1t78ccfePAtbhYkjsKrDNZrbFCnlMRo4DaqlDitK9fAdIZfI4oNRCjZH+gErruERTLpnuq38jHqRYLmW6cqsE8aGBkYoWxtrl5atE0OSUKWWiV8JwnDExXQ4VU5Joa7lbOBEHooVaElii8uznGCbT9+sr6x+fXLl52PH+3miqSXTPZ1BPLXe18fnuxcVyOQgXtXwzmExsIvYvxpLIgBG22ekhmgAwbsCph+cnKCfGFh8emz569fvd75tAMsj1EIbrHkiJR5zjqT8YxumrdQKkQFVbJbYrNSYjTtskbgGBaFvVRKL+L3AuZae9WrzIppGqNKnVoVm1ealqrRCXHrrJUEdV02bYSWClsau5QOcvZAPt7hyJPVLEEnORyE5d6OBEkrVVGGkwo7Pz7a3n705Mm3b3ufdnYOD/ahhCXuhVKm+jsCMzMz/e1g9i5HoGsRyF9MXQvlwBrCL1SL18AOq8HxvAiyISR9zABgvLzEguOTk1MsmFlaWvpPf//70cHhy5cvjw4Pfa02AZihMFJMobRaYDRwo5hESVqqFGo2w9tEGNSuEEutALFOlZAuKkEh8gMhQcTigQC38kI3UjWvKLRoFcxAhdIbxGokwsBY1jILhXbGXmhfRaXWUvqqdkGuNsi8raA6aRAO40VpUfve169fdne2Hj7afvJ0/9v33U+fvu195Xg1Fx8s5rLfI3B4eNjvLmb/cgS6FIGJLtnJZvozAv7L1Z/O3bJX9pt9y53m7hCBjs9CPAFHCdPtWLGA1TJTU1MTE+N/+vOfPn3a/f3337e3txcXlyYni68vdVGZSk26Tci2j0hoE8qkYSNLnEY28W4NF6YSAckEF4tMJroNozvXWgpGk9/4kii6qlasyUhkC8xHmbErVbYp9MtUU0FQM4Umak3YoS1KROyS74RlXOQlpq6nKkNTI7vQorpFlg/lxecfJ1UocWp9x+z61y/j46M//vmvYE6cRwAAIABJREFU6xsb3799//x598vuZ/yvx84kbOYuazkbgAjML8xXPv9dcbp0vrVv8ZrN2u8ga97rCBS/fPc6DIM9+HpkMNhj6oH3Gbh3Jai3c7Zpwp3gHQBLd6mOTE8Du0/gqUwLCwt//PE7ViRvbW0B0GNDv/K4ImhrhpkL9SvG0iCOjEaiMFqmACureAI/6oST2Hkwetjsdz7B7wCh0mKWvEGmL69IwxyhrlMFGVhF2UpWaAXKtJO8rjkGWGVX68Fex6UOAB9wijCOEuUXqRQzSqVkVwPA7qN4HO/hwcGXz7v4f86jx4+xrh2nELacwsajmG4/03MDcGBgE9eKhdlM9XcEvn37Nj8/33UfS6dW+9abN+vCR6ALJtofSdbsxwhk4N6PR6WJT82/DJo0yOw0AnmpTBqNSPfhWWWXWJpz9wzz7qCmpy8nJ6fm5mb/+te/vXv79tdfftl6+BC/1oDv4+MFxmpjRE1UAjuUDFJKx6B1SAA7xssJ/Oq6SVJFLTHp0JO/z3xzfjyQYohJgcuNDPWgizJRDKYSXbebclK61DgVBDpVwJDSqlQaGKFhLNkqtAxlFBpRxxYvZoEIDYnV0ysGRR4M6F1cnpwcf97dPfj+bX1z88nzH3HmHB8d7X//+o2LZHaOsdwCangxjY5P5N9HC8UA5MvLywPgZTy5buKrzs+rP10dddFlcx31nZU7jkD+Yuo4ZD1u4D8aPe7lPpq/PzenDsE5BKBF9IV3SJgf5f6QlyNTU5OYCn385MnygwcvX7x4ffJqY3MT9DSX00zU/l+lhwHpwLTDd/xEqhHL8A6fR/18VjKXGZe53oEILUslhAhUYxKzTlBRdZWgGUpqpXTSqgk70agjr2iFMJlGJIIRTaSjcoU4aiD0Z6dnWBjz9fPn+cX5//Lv/31+fgG3Pn/b+7aP3Ub39iA62N+H2pgdHPQ4OjKmh6mGPnPZ1xH4/PnzvdrwV98hNzoipU/fzc2ZLyWjN3IvN24RgQzcWwSnp6JufVB66uRQGR/0/cLu1xkT8DqhesCbtuQdy2empqaB0e2OVRzWN69f/+uf/1xeWlpb38CPN9a+234g7UfsSs0rFVp/VPBzBguWU7OgROrXTlliBuOWqo2+UCHltRAYVOPLLZxeTCF0k6PHQeR8NXGaRVqXVfLMfqIlzZg1UUj1g6mU10hbTBr5kRMVhNTbM2qNdcAwOF7vHR0DnQOe498yf/3731fX1gHi979jdcx3Q+3IMROPcOmqr7qyKTqTiX6OwOrqaj+714e+6SNyTb+afhRvYtR8aWr6mq4OZbMM3Ht9WG9+Ivfaw/tiv2/nY/IpUnsKGnSHCN/kBFVAYViifHGJJ6qSeXkJ7I5H0mPlzJ/+/Gfs6Pfx48cXf/w+MTm5ubmJdfBYVAOg1ojCbhrtq9vD39ZKNhKbcMdQOO+eJNUCai9BaEoEtCNhdVTjy6mRw/Mp6E5OALg3JNkwLi2FVI/XC2mgilJ+FNVWVNpR1EuYiJjXCirqdU6cn1/gCUoHB/vf9/aOjg6wJObp8+fYOgZDPzw4xBp3oHak/e/fTo5Pzs/OLi4SX8Lxw4R75z3nFncTAcy4P3r06G76vn+9XvuDUfqY1cbt2qbN2tUd1PY6YMwM3Lt1wG54unXLjWynaQQww9ZU1htBPieuHVfMfQq4E6CXEu815KPpLy9P8HTVCWDzcc6vA5k9fvz44cOHX79+fffu3Zs3b/DYppWVlanp6fGxcSg0IvgmvhUHraCaqF7Fxm9Ig40aXmSF35yIoAN8N7ROsVQCgdJJp8wh8S4PXjN2S0toa82jt5QHlCxSDImlKSrJgjuFWiIEWeaXaw3ictNKDdFqaF5RQURxg2lwSQFOmuB/Mpg7x7ahmGAHaMd+RA9W1/60+bfFhUVoAcpj00CgdkB2IPfj4yNcBKIJble1GIW+eNTwzvu4h4AMQDkoa9wHIJS9dLHhC7GtzpKP+FX61+vArHbQzVVu9FiegXubAb7J6dBmF1mttxFYXFy8SQf5DLhJ9DpuK+AORFVC7apIAqB1cX6G9/n4OF4T+APMwhy84XXAM8D3f/3rX7h1FfBd62e4oSQ2+KtZBN/JoW1HF/DXrhOMUA7XdTGSSux3ghbDL4ZKzwJqN6EgtTLVqYO3Fx5dMtTK9NKYW/OCA7WgRBtFKtcKfqSaKzSXxMZdJsICGpwnZyenR0eHnEb/tocY46A///HHhcVlXLGdnp0eH2NBO25DPTpA2v+OxTNnZ6eE7CHRMRyI+FIV51WXHc7mehYB7CozNzfXM/PZ8F1GoJ1v3db+tfXldPNuzIm2Omvt7xXS/MVUCVC3Dl3FbK7efQQwF9vMiXzUm0XmzvjAlRecB7UU3SAwxdvAu7iYfQf6Oj8f4+Q78DvTGJbK/OlPfwJY+/Tp0/v377EcYmp66sGDB7h4w/p4LIKHpu487N2Rx5e3GY9EGIQzOAyw0oUy4QufZaCNUE1s4u34dj2ykhYjuA97bW119B//gX1R9mdnrWM2ClZJtk6moTatFYP0apNBs3WJmFhgorP1+jgNcNyBv3GTA6bPcXcpth7C7v4//eWvq6tro2Nj52fnWEB1cgrEfoyXEqD7ERa443zhJaG9aIjOo2N7sT8emZG8VIZRGJDUi70gB2To2c2rI6AP9NVqqcb1v9F639m9Be7XCG16TDM9eBHAJByOej7wg3LkDLJXckPstt4dA4lglDOnfNLqhYC7wPvYGJ6Cjkc1PXr4CCsi9g/2d3Z2dnf/AG7Ds1pwKxvm5wBwJyYmMVWPVKylue4pAmcKI02jLC38JrAXFg0/D2QEwIzRy1DKKwNaSKhjLz5v6PLZu3cn//W//P7ixeny8rNve48XFn4fHwdgTczKJjPaZQr9WY0MUUEc2QnRSuZq/j+DpFEwnLBa2UGMCOJ9bQz2hb+4xFXa+RnwOBP+r8JF6icnAG3PfvgJxxR3OOBEAF7H9DsA+6km2gHZoYNzAFD+4jz9H46QO64POdziqItCNop/0CSOZrKvI4CTAZfrfe1idm6gIlB8I7ThdquvsTaaJ18/bWiPjtwH4N5R/NuIWlYZzAjs7u4OpuP30utRPCjHE8YfSJZh+Tu/KgWUhX3BJ/7CFCzgO6bfx7h6BnPvSBNj0xOYZ+eMO1AdMBx2/9v5+BHTsDCFyznsTjM9PT2J3SSxXH4cNySC3faXRgny0dMIAQGGoxnRwLGwzaNJJVdEUU4FA2M1kUrSxjGKVb2oYxRsPv3llw8Pt/bfvQeOwa2pH1dXdy4vfnzy7MUfL4BkpcrmloJ9Nm9IgRfKioKzrWiiU25Sq1TLLNrxeHM91AWei3Ryisn1I9xairUuR4eHE5MTCwuLT549X11bn5wgXgcwx6gN03OeHcDd8Lr+J6MrO1rjS39c2y6Ch4zHpTjooPiKx6/wKFN9GgF8hPvUs+zWPYhA8d3RxmCv+NZrw8IwAfeOQtdGbLLKcEUAEG24BjTUo9GSGOG2IsOArQJExe++8OYnn1PxDIiVwHpYwnx2NurYnfenohEXwSPNzs1i5xnge9yniMs5gFrgt9mZGWwGPzc3z4U0UALoZ2Pd2qoOaL2NBDcSvAd/zVmagIP6kmIZ3olF+a9Muk7BAquBb5SzTDR6enpxcnoxPbW4uLD/7/++//kz9DEQxgoPHTq/+J//9/8z+ep/nP/nv40dLclONJYaDp4UXQWO3GHF2iWtE40oLvM6r8FrTIwj2cQ6wDpWwmBlOv5Vggl1XGU9//GnpaVl3HYM20DeUDw6PtKiGC6NAWDH8hi0BVK3xDh48otBwXWwhNw13nCEedVmLmOUWG/Tufu5xd1EAMf+bjrOveYIdBgB/4ppo1Wz79pBBO7tj7qNwGSVexOBFmvc700MBmagwNnY8APYyqC6+Y2qoWASEAlt8atNb0NcVEEbZZBggQSajI6eAYRp+p1T8EyC5diLZml5GRsF4lcfT+HBMzXfv3uPLgnuPc3heU9IBuW1hp6NeYHQ6nuIqK821vSaEiqY56ZH2pNIOMGqFzZAIUw1UmbqFobTr19fHR6sr61iTf/8wiL+t3By+A1ROjw5x5XJ/Nz8f79ceXG8HK16V6VCHQZwHiXWJOTepwpzwluBU1CxsRNBEspUDicJn/kmXD/VonWugdG0OtjT0zM4Rk+ePscjkzDLzo6oyvUwBPdKp5hcRzphazBp7uoE2/zTlRAPgr0DxX7wb5rU1Uz3cwRwld3P7mXfcgSuEQH7dag0BLNvgXutwxX/czVH4IoIpKfR0s12lbmipyzubgS0VEbLGi7HyiAMkB0IUBm75CEWVDfoWMB2w+7KiW41jSsQD+RNGK91NNxkhuvcgdRnZjY2NgwLYlU0cDw2IPny+fPp6Rlg68z0zOzszNz8PNbN28p4Lo/nvDwTLGLzQCFyjwJ7BDLX2hijw0WHrjjotMNYu/6IVQqixPEyGWLaoCm2qrW6mJiYerj148jl9pcvL548/br3FbPUGy/+D0g//vi/4/8MWPt7+Ze/jO7tmWFZYxacEMHsyhSblDTNzZRVa8wOI2fBufolJGBtrW7hni9Hh1iOjqgi2rjB9MHK6uzsHFY8wXIE61yqjsScpAF3TM6DJ7heB9ntNDIx+mb/SkLuMB4PnH1d2BkF9sLCfDqoTPdzBOIXQj87mX3LEehKBO4cuNtXZVfGko3cxwi0fwLd/j7u9/F4dGvMAINCWob2KjlBcYC31iHxuU1yp1PuQOXQ4+R4UWiVMx6oyYU0QvA2/W7z8ZxYn56ewn2rmLRGG+A7QENM5nIO+Pv3nY87hI1nZwDr0zPTAMSYl+f6eE3JaxKfMN7+cGnACwQu0cFJqvMUeNbWyJivfu4WKDcZE8GwBMYzEmO1cbMKgWSgWeJ9+H3/686nnfOzBw9WTh78b+AsTE9hnckff/wBqPzy1es/z8yNPVg+1Ly1WWf0ZJtEJcl6hXdlNXoNghPjwuiG0w1k4x4DYHTspm4L0GFwArcXTE3iHwV48O0i7zeYQdQwTiTEH2pE6oi6UDoKkTiAmll3CC5txKBlMugOFaJ7Q/CYcbdDE8E7R8gDg+Obl00zGAOScEgHxNPsZo7ATSNwm8Ddf6Zu6nJuf58i0MWTBhjrPkVu4MfaOHcagVmctcYg+XOt32w7VYqVMobuDbuDNqhPEI2feCxlJuw9Px8B+hsdOQfXJs61oAaT1EzQBR/oDQ0WlxYvNzaAEgUbzwk99RwfLEQBHkXXgPywgPl4zBYDhmI1tubjdYvsmAkhtxl62NayHeugBBntqEUQYoSB4QKYiHKYQlohOLr8+v+d7/3l0fNlLv6+PD3Fo5cgGF1ZXX06P7ewuoYl25fHJ7yHV8nbW2vjxB6izHWrBSxXEwG0gWHGSPPhXLvC20kRqYMDyBA6gHKEBTeV4naC2bk5VBEmXj/Z4RGkhiFiegaamRJirHXv3MaxBL/lBk8AxcGc4ulgVEOuc4qOmrMcp4WfUbHIKEeGmwS0Jqo69lzvzwjg09WfjmWvcgS6HoEeAXf7Cuy6t9ngEEagh+dK2TR2mRjC8A3pkLAu5evXz4DBRGQRfxm4ClUbOg8yYB9y4U6vEptjoUo6407wTlMC8QC7nPUmgGczvs7OZP4Uu3cLsxNdC18zI6yf8FsV6dDCArCfJS3YwPaDuD/ylHueYIHNF20+qHlmdIr2QOwzM5ybRwKmB1q15fIG7lFjZ3alQCBJB+imDcZyDpND5Lg0WOZGqpwZXZp+ujSxsrI1MY6V+phoh5/Y13xuf3/9zZvf9HzZ05lpixKborEPnYQlYxqN0ZGwwoaqmkjOdzuUBrLGPyW44IXLzLFjOuA2ho6LlanJqenZmdW1jadP57HSCONGJNWzd27mYYCdaOW6o35Nqit+FFFaSRoG3Cfb3A+DUEwq2mheWHCKNjHdXiSdDQyvpYXFRTsAgZHLvo4ATr6+9i87lyPQvQjcHLjHL7ruOZUtDVEEent+dGIduGGI4jrkQ8FcLIBqitgwYIdjhr+JYYVkFQmeCMK5xKIRsUeKKJjNqZRMvQP32kIaEkLxsMO5YdIXo+cjwAJooMSVL0LwwNzE8UDf4EMOs+ZnzI0QHOV8MVbJA9EC0GPpPGboAXAJegVGYQvT+0Dzk1O+nbx1IhjP7uwawv4F4H6gU6NYOsI3N+E7drpcm5zc+uc/p/b3z/7X/2V9a+v4l1/+x8b6xbdvArgeQ2iGpLBYhVzCZzhHv4XHbX0LkDmWuGAUCA56hQxRRhjgNrzgvxImJ3HzKPbcxFhwicK1Q9pYE4E0ZM24aqZbA0fmyaKUVoIEwFq+6eKEB1nV4LZK2vahULWdlDgBdYwFBnQc2QMrKiDB9jVWy/lARAAn3kD4mZ3MEbh5BDoC7v61dvNes4Uhi0APz4zumTa0MWSRH+LhbG5uffr0Eb/HmlouATNHWgF4EYGFRIAutAgdyontRBpopxkBtVAaA3psxz9SKW0dc1vFcxOd2NS4zcLb6hdMmWti3ebMzRUCeiU05CQv8CjOQKFS0oaNMU2IyerDo0ObrObaErbBtuUA91jDw83sQYMDjI42kBHMj5FGxVE+OPyPACezrSeoYYHKyP/5fwFY0556V4HRs2LBgDKW8gj52iipiA8cpJgsRzf6hwDX/0xOLWMdOpcAcat7dqZrBgtI/IiiucKpDBVcAqhn+UqPhZp5YWBxMCDPTvH2hDGIUhDhSOCj1OEBI74KWUmvYDel5Ai6iK6rO2M4ll9YzMA9RGUQStyFguciD4Kn2cccgZtGoBlwb/hCu2lHuf1gR6CHJ0QPTZdiHufVStxc6dcI4J4EbPeC5dKAj7UILDpuRxaITpiTaKxY6U6YTqRHHVhpKDXfapjQYGhB0wxrOkEpNBFubKV5oHD9cx6GbZE258eJqYmiUQjeEmSP48lOwvqE9XRBfgjRpmg+Aa+1o2UDCPjmlYBAMP85cHGOuXCDxBwqwSj/9ucX0B1unzVXwKdrEoW4URs64FE6OhYJOA1OEBPHQ8XCq1wDYMYEV8B00gohdHrIxIsVpyXlMGDd6EigCjqtmkIlRzM0ja1Dx8GXwnDRLsbV3FDcjWcdKmY2WGMwxzoZPI21qGeq7yOQj1ffH6LsYNciAODOb++u2cuGBjACPTz8PTQ9gIHOLncYgbW19Z2dj5joBVLmq5wqxoTADBkLaHom2G60wCihX0DQZYqQkNAPyuhNlFA7MyH4ICJwtM5xfgs8cuUI3n66ByyIbjwRNPu0eIT14I1hsTvmsSFDUxiiORrGOK0gJlaNcLcxAReXmWxlaW/vKwhb7yFrtOh2SahmrEJicq+b5UoHzkz6jT5EAjpOQ83d9w7Z3LwQJSEpJPfFKuWcLdLU4FNksF97Qd8JCsFHwWsMlqwit8MUjhprPA64TXli4k9/+SuODlzKaVAi8O3bt4zdB+VgZT9vGIFmM+43NJub910EHFN03a9e2e26owVq6b7pbLE3EcBS6cnJKTxWB2gXSMsSuopEY7dAypCCDwQmbMaMJGCxi1SVhvGp6xQBNMgSgmd/+NOJLkBP67HqUuMI4tM9asiN5ONBy5bYnToFwdl6wXrSloJIbikzt8xPy8kZ422v6so6Y4d4s8Aei7NrILCmRlVmeMeECgTmYsGUGVYN2EYKMFeqxm9sW3QfLcIDs2tEqWrqgYVSydR9OODIPUogQPh5DEIyWahJ7pVCh+OLSTRGQdzOhEJ9It4sLRdnBGfa3/7+n9fW162a80GJwPLy8qC4mv3MEbhhBDJwv2EA+6t5QAbd9qpXdrvtZ0t7WLvQUp6F/RiBlZWVd+/eTqzqTtAAPx1wBX+J9iAKCVJANtQExwmXUQlMK2WASgaJpU4eIaIMScGrBu60jyKllSr7ES8qmB/2mTFjJjfLkJp3plbK6WdgsKVaB0Z9mYBOu/gw45cnZ9wKfe8Eq8y9P1SRZIWlaqiK5wLnJ8xEajpN8tiy6Mzcj/rWNaoJR2M0lrPdLemYc8rBVjASXZcWnDCkhGOkptcTruohbnbsLO64ihr9y9/+7eHDR3b46yOeuX0Zgc+fP+cNf/vyyGSnuh+BDNy7H9NeW4w/7V3uqFd2u+zmtc3l/cKuHbo7bIjLLTxO6N3bd1sPt7D9SgrXolc8cwHWhb4A0FQDdkfJ2XZwLCd4F65U4VrAgw7fraWUHLmaisFnUyMnBej6zBTraijGW8ln30kb6DT2VTmngul5UiRVQ7+mYQOkXlBwwgfCXk9lzjTLaqbkqrQhIyVuWmmgaxhyhWaCLBKBwU5coWCByZe1A7t1ki5bFKlUCex0wl08rZIBl7AdfcUjZYcQOc6Kp89/fPzkaUbtis+AZaurqwPmcXY3R+C6EcjA/bqR6327CAG62VVPjHbTwd7ZyvuF9S62PbWMpavb29tv3rxZW1vF3ZaA4YbOKp3i1AYic4wuHEbsnKJ2NCsUgqLQPFo6fCcKhGGbiQ8Y1OB4BbLzo9QA4mVG7emNIcTEz2Yfv0KRVGPNObQYhJE2h72ZnC84hlFDvVJatWLHddIipS084iRsIxtyjQXcUNJHdKckOsgUK2eHoUrT9GMeGqudmWVEWieD8Fwno6UyhO423Y48IUafPHv+p7/8pSlqb3boMJCcLALh1LyTeGDG/dGjR3fSde40R+CWI5CB+y0HvNpdr34OemW36v8A1bFf2AB5m11NI4CtwZ8//wFrZo5PTtbW1gx08xzXRDuBXZICJCfcDgIgNClZKbDOelQVJVhskD3C2VCFffKQAkcV55BNki4VfFQsxU+jZIHbUBbSZEBkhmpTOio0IQBtrTcj4ujqq6bsMla8eSyck2i4SsERxcx8lklUlehKmYh8NggikqboPAhozhSSvJEThcLrBOzGIXiHAfQfDwlOAdxjsPXoERbJ5NV0dpZcM48xvWb7GzQbHclr3G8Qvtx0wCKQgfstHbCefKf1xOgtBeT2u8l7Dtx+zLvYI0DV48dPsMnMu3fvtra2cNcqpt4B2fgh0MQpoBkJgr0CkBsyL8P1OiViPzUDqhM6JLTjX6zSag2HbJ93B8m+mSqfzCvY1ka5a2oQxq5wWA3SEh2ZZSLqaIA06USoWxmGWRK6SAYb6AaGGxafTVyBpTnhhCQWY3KQoKLIWS3Jva3Enrl+UJJG4MWKS4nXidgDaucqGe1FaYcSOSA79uDf2n7857/+DU+3tYjnfBAjgF1l5ubmBtHz7HOOQKcRyMC904jV61d+qOuVOuX2xGinTgyPPr7Zh2cw93Uk6+sb+/v7b9+83djcaPyd9ptSFZxiMp0wmtDRk9cCfI9gvyCgCIxum7hbI0J2GAmbQgYm19DTdmLdRE3yK/QKMVBqSKTCO5AuBSY1LckDHZllgl5KJRDeujAi/TLXasy9VcEgSoZn1klCVzRY1V/kgyAdmLIiyF3F3aaoXNA7nTdPZAbME4AOGRE6YLr+WFqyEiHDUcPpgadJzS8u/vDTn9c3N/Pmj3YmDW6e52UG99hlzzuNQAbunUWMP99dTz0x2nUvB95g/l/qwB9CDQC/0Lhd4f37d4fzhzim2AodzxaCJEyuF+g9cIAIXSrMRqgYYHuE95GQoYBTUdEfUSaSMgPxYLNGW0zlz3BcNmPClnmwEJXcYsJXR5AHCck6OjKbEIaW1To0lyaQrvVuRBv51SqKGNX0lxLe1mG6jYV6ShwYvbFazMWmKelVpVENhCbVU4bxDMNrnQyO38T42PTM9OOnPzx+9gz76Nvgcz7QEcBKSNwAM9BDyM7nCLQZgQzc6wNV/h2u1+mY2xOjHXtxbxvg7qV7O/YhGziepbq9/XhnZ+e3X3/d2NzEDzZWzsRJU2JwDJj4zxF6ROWG172KghDQkXogBBo9XrIErBiqNSDeRLZaxtVggVRoFbmtCCoXDRKqQnqVhY2PzdzDEjNKy0Sh7HwFgMbMspUps5EWJ2V7a7KSv1g1VUjEcQ2TqmPy2RQe6G3mjBPyoOKaqpYyWU84NgOfzMPb/Dt2zZ+cnNh8uP3DT3+an1+oXHC1OkJZ1t8RyHsP9Pfxyd51MwL3Hbj3BEv3xGg3j/r9tJX3Cxum4w5MvbGxsbi0+Pb1m/fv36+vr2P2nU8htdl3YEDBcnwWDZFi7BGvCx8Ss0nUMPluqgwWgCALTqjzzzBnwWEnVqOkktr8Fqi2Cz3CmosCp6aaiiItgspNOHEUYXReWhGkXjNj1VzCRk3jKI/GSEQOCdZSJuViKdoUBQUrxQk6VrHBOV1XEK+niStlsE5mYmJ0aXnlpz//bXWd9zdXDlj/Vfvfw/6J2Wje7bd/Dkb2pNcRuC/AvSdfgT0x2usjfn/t5xn34Tv2M9MzP/7008np6Yf37z7t7DzA45pWVjD3hjtZ8el07J5u5U4MTvToIL6hdIwO1OfBqofsBuKhUqiZfvGdECTNgl4jL1ih+wrHq6m0lo7MMsHm4gAHm+c2zmo11ZGGqxk/zWXHmruiuggq5OkvJcq0eQTPqBqSqCIL7FLJFiGJLKF1m18HXj8/Ozs/P4PC7OzM0x9+3H7yDDPuzY6J+MUhbKmWhf0VAfwXrr8cyt7kCPQsAkN4rvfke7cnRnt2VLPhugjkNe51UelXXiefuKmpySdPn56dnmHPmd9++21xcRFbRs7MzOC33KFueVY9Tr37Upl0xbtDUYSlBrJrltawqMVNXtpkvDG8vw6iKjQd9ZP27KcwKixcVOUmqmrtepGOBBUMRIvy5sZxgyaPow7VQscNFAy3WebTdJmDmkWqQhRMmpQSNZhQs5dVVSuzqB9SCagb01mcXT8/Pz05QQa/Zman11Y3nvzw03xeA22nwDDm5+fnwzisPKYcgZoIDCRw7+Q3vWbM9ayeGK3vKnPvJAJ5V5nbDvvtfqYmJicePnp0cX7x+fPuiz/+AHC7NNNUAAAgAElEQVRf39jAU9AB37F+xsFtEgJ4Z8yA6n0y3lUAx4kxY6rWfe6+kR1bdEIAn4bkFIuEZC2tum+BFUohcOk257gdYF126UUgrNo6N116UPtHuzSAt+RelOsUJnpWVZ5kAZuHkreeMrEQGetYCgO0fomjfwrAfnx0dnY6MT7xYGVt++mzlZVV3oGqqy7cucwxi+bocxqWCAzC2qdhiXUex11HoN+Be/d/+rtv8a6PYe6/vQjk/cLai1MbWn38IRobH1tbX19ZXf3+7fvrV6/Hx8ewfmZudnZaE/BA8GGRjA8zViNBQbUClmFUa8U5dq6WcbYx07x1gNQwVRctRBm5rpMwRSZsqAqFskzpgkmXJQmtosjgq8aUKAAOF7WkYqTAuMchcGgh2GJZNFLfkpIqEdSSIjORqloNDPFUFtKoJoJ8lxl0v7jEkpjjo8PjoyMA+Ln5hSdbPz58tD0zOxtvWWYk4GAC2bn9kKF5i6Bp5HwwI4CP7GA6nr3OEeg4Av0C3HvymeuJ0Y5DnBv0SQTyk1OvPhDD8pEBXFtaXsKtqwf7Bx8/fnh3cgIEDzSP/WcwE19sQYMfe+JN4nAgQcbHfv8JDJVYjZVCLCxqGmQ6pVKZt44aIApWQZm8ru68UITSGyTV4ChZkY5E8DMOwYiozEHLmPPVMDDFs8zVSlqmW8mtrXIoUz/QkTC2oio5+ocONYsUaEhSkSrQ8ol2UOcX56fHx4cHB0Dtk9NTmw8fPn76fGFxqZ1noEYQrwjgDHAcb0HO+WBFIC+VGazjlb29SQTuALiHX7mbuN3QtidGG3rJjEGOwL3eL+xefkCAuucXsOnfj4Bkx0fHQPCfdj4BoAHRLy0vYxUNVlDwNlZuREN8WULpFQSvOfYKiOenIU69gzYI3OFnhI0MObJhQjkZikRiimJ4FmnhaNqJBEyaNHJEkFvLN2ljLn1r1TJnS/aOP9fzGrmWXEEV0dS0WqSsHrlBAQysX9d6mJNjzK9jVQxGu/Rg5ce//HVzc2ticpLBuVaKOB6tI4jH50ZRupbF3OgWI4Cr8VvsLXeVI3CXEegtcO8+Wui+xbuMfu77NiMwtPuF5Q/FVacRQPgMNhV59gwo7PTs9PPuLp69is1GgN2XHzyYn5ubmp62pfAG10sIPkzCC7laT9dYKgP4dwUIBGYNKaGcDEUoTVNNAiuWJPSu50SR4Woqux0H2t7Y9Io8VWtoIGBtlmTFBiOu14OGK0GBOgUyF0NMsl1gYm4Oc36BZevC6lgPc3h2eoorrrmFxa1H2ND/KZbE2IELAbxpGUE8h68UoXxg5LK/InB8fNxfDmVvcgR6FoGuAffug4fuW+xZFLPhQYhAO/8978dx5A9CF4/K6Ahm5ja3tvDC/9Zxv/Ln3c/v3r7DFoFLS0vYjsYQPKfhR0fHRsc4oe6racwJw4fAlUK3hWOE8kmKeC/hNbSRrEbTQC/RqyXvq76aKoumWsEMnpZFgUt4bF5Y6Qjela1f8oJ+IW/kCISbJturmbd1umAmclpnktOikHEzR9xoiksrrFxHOjw8OjrAhffY2Pjc3By2dFxZXVtcXJqcmuouXveA1xURykOYQXxdhO6Yh4vwO/Ygd58jcFsRuA5wL/1CdcXR7lvsilvZyFBFQOigz0aUz/y7OyBA5w+UgBIPDg++7X17+fIl1kxPT03PzeN21rnpqSksvcBMfDoZX/bXpt7BI5C9RmKjomFBBXsFh9CWKRShJKtKsx6ZIiKnIKoKNgL7kPhHxdC5s+SSc+pp64k5m+DNItAiyDBRqLoKuFy3zgclnQOgn5ycHOGWlMP90+MTXDlhG8dHj5/hJuP5hUVs/YlLKkbi7lIK4uFFxvF3dyiKnvf393EHS1HPVI7A8EagKXDvPqLovsXhPSx5ZD2IgDBCD+w2msynemNMboNzzbhjah0L4fECqgRmxP2sBwffv37d4/6ClyOTUxNzc/O2nAaz9QDxQPy4+dXn44FAbzg0gmFLoVSNoJcpMF2trpqI1Io6RXNKI6cgooI3CUrqwD4r1rIxF+qW1UaZI3EIlFyVfTgjFtptHevVMa+OmXWsYDo9OT2x20wBhoHRHz56/GBlFagdYb9zsK5jUZ+lOD6D+PoY9Z6Lf5f1vpPcQ45AX0TAgfs1f/GaDaHL5pp1k/k5Ah1EoGtLZfLp3UHUu6J6CxEnXsX8+dQU5tmnMbmLKiAmEOXR8dHB/v6HDx+xPTiYgO6AkoDyuNfZcHycj7/hso2IpNOQVZgOsk2DqBlJGb11OhIOrRtFoaFr1lYxeDNOaZt/bON/RpbAOtepB5SOWXVOq+PWUtxhenx8gk06MbOO1S8bWw8fPODMusD6LRx3DrKLKQXxXTSbTV0Zga9fv2IZ1ZVqWSFHYAgiMIGvxpt+O960/RCEMQ9hACIArNCul/mUbjdSN9S780A76q39FgQQx1IZzPcuLHAyjzj+/BxA89ve3scPHzFVjCl5zLzjH/RYWjM1OTUeJuNtibzlMIJUaz9i7jSIEX5HpiHoqFyqEibTNVOWSAailQoR9SPCVvNo0y2kUofg4cJAooJnvYWc/BFf9AKYrjXqnE0XSAdKx0XQ4fHJMf9fMT6Bf3HMzs0/3H4CvDUzM4vnZ4GrWNlohjC3+XicDQxYTl2NwIoutrtqMhvLEejTCDRdKlPv753/zta7lbk5AldHoLodZD6Zr47ZTTT6PL7Y/REAqgMngSm52n2Ci2osLliRDUyKxfGfd78A0GNlDS2OjgKy42TD3D2mjSex1EaAHkwkYlYlqFmqDXGE4FFa4kT8LXEqinSViE0MlMNRoW2fIYcd41fUCqYoAXZkwuYsdA8pV6Yr4bpGYP0UW6sjAaYf4WoZw8eGmwgatuBcevZ8Fnv4TGEPH2D0O16qHmN7a4TNx0fUntfVdDHyu7u729vbXTSYTeUI9G0EmgP3Dn7R+nZ02bEcgSICfAATzup8YhchuTY1uEEsUNPNpz2BwKenZ6Y3ZtY3NjghPzJC/Hp+foq9UHC2HR2eHO2fnX/FkhAgXJ56YyOYmJ/AW3+TE5OYpDdYH2A8l4zwD0mEna+xgi44fZ+mCLXFrOL1EkCHhrxU5nQE7BTyj4m7uvifQXKMSqvRBczjRLoWpgOtI2H7F2Tj45w1x8UKJtFX19YXFpawUSMuX8a0S0/qdaYZ73AseUgR9lDNwblGBPKM+zWClpsMaAQCcK/8GAzoaLLbOQLNIzA/P99cmCVpBIb064BPR+3Z0Ai1R2xaHdsUArlymbwlgWEs8EbCPDQ2I8c89Le971jizTlrwv3zcawSwe2uY5yShx1QSukcPcVYCw41e8dCMB81fzyUehMCN2xu9QDKhcwdoBtKdzkhP93RhQcuPbBr+inwONYDMT8/h9rExCSa0KfxcSxumZ2Zm1jEWLGF5gyvP7TWBVK6kjFoOPjtlAh9vCDz5TQId45hO7ELOljjnneEDMHI5ZBHYIITkD37LRvy4OXhDVQEsGn3QPnbXWfv64ccYB3J8HrvUHvrYyUgi7loaAHmjiS7XxhoBlbGVDawMqrcYuX89BI4eYRL6sU8A7IHjAbux3c1lAXqRtGES1ZGLoHngcQB6DlQMoi/UaVUcNzUEAhgf4gnJ6akyMhIgTgelwPQhadA3gDl0JmdnYe3SJhHx1p/wHVcMPDaAc0ypkQQepN8OU05wnlRzZXBzrvKXBmirDA0EQgz7kMzoDyQHIEmEVheXm4iGQ72fYXm9UdPS9ghuiuwXu9VlStITzg8MTI5PV2V1tYNahOdC5Tb5DkXsgDWM9EkCkzdk1RdfGUJHHQ9tSgUKvqpINN3FwFD8ziaugzNO8fXHAns457/p1oTl8waxghk4D6MRzWPqS4Cnz9/rmMPEC9D8/YOli2JMYzTXosB0jJcDlA+njgN3J/UMjmcEYhndLqKJk/G28HO62SG86TPo6qLwL27r78uCJl3LyKwurra9+PkDGnzV9+7f7cOclWMsE1/z7LfbZBy70MWgQjiudQJ3x3Kh2yM7QwH2xi1o5Z1cgSGIAJ5xn0IDmIeQlsR+P/bu7f2tpE0McCW7W512+N47Nvdn7dP8mezd9mLzV2SnVza43i8brfarRQJkYZIkMKhCqjDq9bTpkCgDu9XKH4qgWAeK+5WzUcFa+JOk+/tOLF8uxPIXaB/cXw3yxxX6HNveoz2hXsZ7Zc8YpSVsIymYpLQsfGiJe6ND4CGur/WNe5S87UG1e6SmFDX/m8UXhDXUldP/gInZ8Pxcppwqpw8lX9fRrYwvJN75J6b7lbQq0OtI2XT+EeqXOIeCVIx2QtEvatMQfNv9oGZ2sAuX9/lIKIw1c7+LQocL6cJuVitSfzDez9aDG+iPq8/u/pVYWwoJe5jpexXukD4aPpxXVh/whrXLnuF5cJjvm6SNx4ITBfoJ/Hd0V0qf0zopxeZxRES9yzCsKgRG77yFvZyInFfNNAcXJBAuF9Yr7UbzhG9Vng4RsAlMWOU7DNFoNnz/zxDqePi+EIulZkyRu27nkD0+eD8PIvZGYl7TE1l5Sywv19YOD+jn6I5d7r0tvWW2EvvSjPtd4LlHOox0Tnuc1yGD1vSZiJDZJNq3L85dagU2whsIHA8hxbWPXwSSNwXsjq8GAH3CysmVN0lMaG54Sr24YmrnK5k1tJYryeZdUtzkgj0r6s55u7HB0mq7BU6fqyGPU3vPTkPqxEYPAluJO7VBFhHnhCwJPMEUA5P7+7FLl9/FInBmfvRHn4gkF7g+Bv08UH6OifU4AOYJmDZtXABH8BUeAA1f7SAiyBHU22x44+UvapMNXRm4fcWwVAngckC2563j9/CNLnxDiBQkIAV94KCpamLBNx2YBFfooPzy9e3zT8SMSuWQGqB/kr88eL41JUey3/z5s3xsQcE6haw4l53fPXuh4DE/YdFDo9WSdlnrHbnYKMNBIoWOF4cH3qxzm/Cnz59KlpM4wmMF7DiPt7KnmULuFQmi/iFfH23NBfedTrnBX3OMVl0WyMINCrQX4nvE4Rz+dJT/d1GPn737t3IPe1GoHQBK+6lR1D7xwp4c+pYqUT7HZfYz9bgwkv4yO9ETVMsAQIrC0TM2kPLP3z4sHL7VUdgKwEr7lvJq3dtAfcLW1m8Wx2/vwn/hZpnLrGv3GbVESCwocDsi+OtuG8YNVWvLGDFfWVw1W0m4H5hUehHLo3vs/bw8Um7vH2ty1yj9E8hBAhsJtC/OD40ovvlf0xr/v73v4/ZzT4EKhCw4l5BEHVhlID7hT3JNP5l8lpR/Uti4v45/FqtniNAoDaBbv4I81J4cH0x/u3bt7V1Xn8IXBCw4n4BxubqBBq8X1i31j3+/0tivr8gZv86u/u40zi/Aixpj2MJEKhDoEvfj4vxg5PL58+f6+isXhB4UsCK+5NEdqhEoL77hQ2+gG0RrXBJzO6imC2qVicBAm0JdHl81+cw6XQ/vn71ui0FvW1YwIp7w8FvrOvFvXspvCZd/946gLtL2Ls/Ylti3zoW6ifQosAxif/629d+/60i9DU8rkzAintlAdWdiwK53S+s5JeW3RL7Llk/vmxeVPcEAQIEkgvc3t726+jPTGGe2k9Y/ec9JlCwgMS94OBp+iSB9VfcS07NB2kPL39S9kEeGwkQ2Ejg7u7uUs2HaWu/2nBpJ9sJlCMgcS8nVlq6TCDFNe7VpeaXicNVMfL1yzyeIUBgQ4EXL148WfvjZXh/L3wSzA6ZCkjcMw2MZkUXmHRXmYYy8ivQP27suL8w5sqeniJAgMB2AvfhcpgpX5f23l0DOKUc+xJYX0Divr65GrcROL+Pu+z8YiT6KfvFnTxBgACBLASmJu6XGt3P2iXxl5Rs31ZA4r6tv9rXEwifnBomYsn6NfGHfN36+jUkzxEgkJvAmEtlprb5mMR3Gbw8fiqg/RMJSNwTwSo2O4Fv375l16Z8GmSJPZ9YaAkBAhMFrrw5dWJJA7t3GXz3f+n7AJBN6wpI3Nf1Vtt2Aj/99NN2ledac5evh9b5+KRcQ6RdBAg8KXByO8gn95+9w3EZvivBvSZnSzpwtoAPYJpN58DCBL5//15Yi5M2N6Ts4Xt3o5j9d9K6FE6AAIGUAl+/PvoAppRVPSr7eK/JR1v9QCClgBX3lLrKzkLg4bL2m5uHB1k0aqtGWGLfSl69BAgkE3j9+nWyshVMIC8BK+55xUNrZgmEjPzK90ORbSfu+z/wWmKfNbwcRIBA5gKfP3/OvIXWjTIPUEHNs+JeULA0ddHU1+ilMje7+xvv4MI/LmR3DhEgUKPA27dvM+/W2cXxbhifecTybZ4V93xj03DLLi2fLyJp7M2p9/eHq9hvXMW+aOA4mACB3AU+fvyYexMft6+fx4e3GT1+0k8ErglYcb+m47n0AosW0Sc1r6HbQe4vZN/l674IECDQgMD79+/L7aV3uJYbu01absV9E/YGK02yiD7JMXwA06T9C9u5W1/fp+xuFFNY7DSXAIFlAsWtuI/vrgWY8VaN7GnFvZFAr9nNTOeZL1++rKmwYl379Zrd31ozlV+RQlUECLQokP817rOjcnIZTZjlT7bMLtmBhQpYcS80cJs3O8wel743b9twA968eTP8RLlbu1X2EIjdVTHh2xcBAgRaFMj/rjKxotLP2l0cH0u1rHKsuJcVr01aW0lG+OnTp0344lfa3SjGW07jyyqRAIEiBdq8j/vx4vjwIt1P6IsMoUaPFrDiPpqqiR27hduT/1fS87/+9a/F96RbYg+ztTeeFh9LHSBAIJrAVp+cGq0DywqStS/zK+xoK+6FBSxecytZRx8PUvC7l7q3nIauytfHx9ueBAg0I3D7y21GVwtmmUdbla/mbJC4VxPKKx1pLkcftCjxfmG7e7GHq9fl64MRtZEAAQJ7gbu7u4wk1nnJnfjrQX/3cHH88TKbjNw0ZZyAxH2cU0l7rTNnlCTStbWkFfeQr4dkPSTsUvbyBpoWNylg3t0w7DfPXrx4sWH921S9YMidZO3hbVP3N/vi+tn9Nr1S69MCEvenjTLeY8GJm3GvEjWtjPuFHd94GhRk7YmGgmJrFTAj1hrZp/oVMs+ndvH8RYGHrH1/b7I4LzuicRE7whMS9wiIiYvwWhQHOPf7hXWr7McZNE6nlUJgUwGz16b87VQucY8S68GsvXtpmlZ+ihPfLwOHGEjcDxK5/JtivOfSt23bken9wnaTYoDp7sW+rZDaCfQETEU9DA8zF2jxUpm1QtLP5sOssFn+PHtG2qzFqSIkcU8lO67c2SNxXPH26glkd7+wwxJ7r40eEogtYI6JLaq8DAXyenNqhkCRmnSSA89ZjI/UkgnFzJsDT7o6ob7ku0rckxP3Kpg3fHoFeLhA4Pb2dsHR8Q61xB7Psq2SzB9txVtvJwjkMr1PaHINux4X48vI4CeRz5hv18r1fQDTpEiO3znE/Px7/OH2jC+QwZLM/q38u6luxpQQH0SJWwqcTw9PbtmyueomkLXAb1+/np9AWbe4rsYdM/jQrZDEN/p1PgSf3DJLyor7LLbTg0JwfOUusNlFkN001uXrzc5ouY+OGO0zDcRQVAaBGQKDb2FKfUaazgcj1U/iww4VLsYPdnvexklj9DDgJO6TsCcZTyrZzskFVr7twMM92MMcdjKNJe+oChYLONEXEyqAwJoC//jHP16//suaNYa61pknDtnayp2LVt3gC6Bsfo5vN+Bunkncr+itc1ZeaYCnYgqsl7jvl9h9dlLM4MUqyzkdS1I5BHISePPmTU7NidmWhZNWnnn/YDYfU63qsiTux/AuPDuO5XiQqUDyS2W6S2JC781J6w8Bp+/65mokkI3Ap0+fXr16nU1zMmrIkqlx5aTfMvzIcdNy4r5kPI/ktVtGAgnfnNql7PL1RNF2piaCVSyBWgTevXtXS1cy6se8qXd2un/yEiqPvzQU2rmrTBiBJ9+XTGyvUyD+/cLCvNJ9h/nmZMqpkzBNr07Oy/Mf01SrVAIEqhH48OFDNX0pvSPnU/iTWwa77EV1kCVsrHXFPYwTXwQeCUT+ACarAY90L//gXLxs4xkCBKIIWHGPwrhVIVNfJbpF/XDU7NX9rXoapd46VtxD+E6+o+AopCqBwfuFTe5hyNe7L6sBncPJmXf+42RiBxAgQGCaQLjGfdoB9i5ZoHudCT3oHjzff0TK+YvPcbeS+zrQ9rJW3EMUfBGYKfD58+eZR4bDuny9qUtinG3zh4sjCbQssP7ccVPxXWVaHkkj+36yjHby5/DZw/GwSjeyFevtlnniPht8PUE1lSLw9u3byU2te33d6TV5QDiAQP4CLZ7YX758Wf8+7vkPhTZbeJLHz0YYfyKtnOJnlbiPV5odCAe2K/Dx48fxnf/x8Unjj8lwT6dUhkHRJAI7ASdnzHHw66+/xixOWQSmCIw8mWPl99sm7iM7O8XPvgQuCLx///7CM73NhyX2kj4+yWnUC6CHBFIKONlS6i4o+9u3b/V+BNMCF4fmJDBy+ngyv185cR/Z7JyktaUWgSdW3G/uw9mSb77u1KllHOrHdgLOou3sE9f8008/Ja5B8fULnFwcv1WHr89T4dmkifv12rcyUW+jAsPXuB+W2MON2Lcfr9u3oNGxodtFCThPigrXKo39/v37KvWopGaB/sXxmSTxg9yxEncz6SCvjRkJPLqrTC9fX6+JzpL1rNWUrYDTINvQFNywmwwWXgrm0/QzgX4Sf/bkxhvmJe5m3o3DpvoZAg/3ce8uiQnHJzovnRwzYuOQggWM+IKDV03TJe7VhFJHnhQYmbibmp+UtEPuAl+//ufuduz3z+OM5jil5I6mfU0KGNxNhr3kTrtUpuToFd/2la+ruZS4m7iLH0k68ON+a7uz6tkvv/yyW2WfOrSn7s+dQKYChnKmgdGs5QLenLrcUAmzBfp/v18hiT8m7ub02SFzYAYCg+M3nEDhK5xS+7Pq97u7aw0dLOHaAZ4jkImAsZtJIDRjM4FwO8jN6lYxgZ5AP4nvbY75MCTuYdI378c0VVZagadH6/1uRIek/fEJ9OLFi4eGPV1C2h4oncBoAYN1NJUdGxbwAUwNB7+5rj9vrsc6XIpAyFgGv6+0Pyyx71bZuyX204znPtyovSvwSgmeIrCewOD4Ptm4XmvURKBcgS9fvpTbeC1vTaC7GmB2r4+XyswuwYEEFgicZtezinp8ScylInaJuy8CaQWiDOi0TVQ6gfoE3vjc1PqCWm+PHl8N8NDPkMgMbj9nsOJ+bmJLGoGTlcTux0VVdevr+8E+NN5PKvxxqcyiSh3cssDJmDr/sWUcfSewmcCnT582q1vFBGIInGQxV1blrbjH8FbGiUDIZ5J+PSyx/7iKfUyF3//4I2mjFF6+wJhxVH4v9YBAdQLv3r2rrk861LTASR7ft7Di3tfweLrA+Zpj0uTnJgzm+/C/3fez3Wfldd9j2u1+YWOUat/nOGQGH9Tee/0jUKnAhw8fKu2ZbhH4IdAtWlpx/yHi0RMCIdVZ8etRbQ8Xf42+BGyone4XNqRS37ZHA6e+7ukRAQLnAlbcz01sqU+gW4a34l5fZCP16HxFMlLBJ8Wc19NtOex2HxbYD2/ZWJSTuV/YgbSCfy+NmkUjpAIXXSDQpoBr3NuMe5u9tuLeZtwPvV4rz5lcz83uFjD7vwqFq2IOrV32r/uFLfNb7ejJg2W1lqmIAIE8BdxVJs+4aFUKASvuKVRzLTNkRCffsVt6Uvzxxyn17O8Vs7+Kfd/cKYde3dfMfpVn5SePQ+P8wcotUR0BAsULWJcpPoQ6MFrAivtoqrJ2DOlQ+q+olew/HWl3AVd492mSpvtbahLWa4VGHSDXKvIcAQJNC7gSsunwN9Z5K+5VBHyf7j5aTY/drfMaoiVl4VKY3dUw3cedxm53rzzvXuphRHw4ODS6jRFrURQBAgQuCrj3wEUaT1QnYMW9wJBGS5mH+564+F6lIV8PGfuVu5X29l3+0P3ClhmuNy6WtdPRBAg0J+Buv82FvOEOS9yzD37KfCll2VdgD1fFrJWyd02x4n4lJL2nNhoUvRZ4SIAAgUkC379/n7S/nQmUKyBxzyx2KbOmlGWPYTzk68muYr/eCNe4P/bZejg8bo2fCBAgMFvgZvdxfL4INCEgcd8ozCknmZRlz+LqLonZKF8/trixu8pkNwqOgfCAAAECBAgQmCcgcZ/nNvGoZElUsoIndnB49/DZSeF9p8nfdTpc+dnWeu8XlvcoOAuEDQQIEIgrcL/75A9fBJoQkLgnCHOyPCpZwbERdkvsobHRPjspSvvKv19YMfGPEi+FECBAYKTAixcvRu5pNwKlC0jcY0QwTUKVptQY/b1UxsMlMevdKOZSQwa3F3W/sPKCP2huIwECBFYQuLu7W6EWVRDIQUDiPj0KaXKqNKVO7928I0LKHr7WvUvM1JZmeb+wssM+NQT2J0CAQAqB29vbFMUqk0CGAj6AaURQQnLV/x5xxJO79MvrHj95SJY77G7DvvvO5kL2K0pb3y/sPOZhiy8CBAgQWCrw9evXpUU4nkAhAlbczwKVIJtKUORZs9ff8HAh+/oVz6xxxfuF1Rnwme4OI0CAQGKB169fJ65B8QRyEWg4cU+QXCUoMpeB0rXjfnePmP0i++7ejoV1N3biXlj38xpJWkOgAQFzxKQgL7kvzOfPn1+//suk6uxMoFCBlhL32JNo7PJyHkL7z04KVwyVlqz3TZddKtNStPtqHhMoU8AZW1zcZocsHPj27dvi+qvBBOYJVJ24z54GhiyjFjZUQZ7bunedhraVnLJ3tKPfnNpoqPMcgFpVvYDzrfoQr9DBjx8//vrrqxUqUgWBzQUqStxjT/+xy9s81hMbcBM+0CJcGlPP25eHbgfZepAnjgm7E3gk4Px5xOGH7QTev3+/XeVqJrCqQMlpWXjR6H8vc+uX1D1eVr45mK4AABM5SURBVF7JR4dV9v0bT2vK2kM89h/AdBLnksOk7QRiCJycEpN+jFG/MghEEAgr7hFKUQSBEgSKWnEPLymRvuKVFKlBORSzW2LfX8Ve/lUxg5xfvnwZ3G4jgWoEzGzVhFJHJgm4xn0Sl52LFsg1cY/3+hOvpKIDfbnxvXy9bqs3b95cVvAMgSwE6j4HsyDWiBoFwl1lXr1yR8gaQ6tPZwJ5JO5RX6yiFnYGVtGGw70dbxoR+/TpU0XR05ViBBo5v4qJh4bWKOA+7jVGVZ+GBTa6xj28lPW/h9s2amu/mO7xqMNa3ml3CfufAWB3b8dKr4oZDO+7d+8Gt9tIYJ7A+eQzuGVe4Y4iQGC8gE9OHW9lz9IF1lpxDy9okb7ilRSpQeUUc1xi3//aVE67I7X0w4cPkUpSTOUCJpnKA6x71Qnc3t5W1ycdIjAskCxxj/TSF6mY4c63srW7F/vuVjFNc1pxb2XAX+1n0+fAVRlPEihX4O7urtzGazmBSQLxEvdIr4eRipmEUO3OvSX2avs4vmOucR9vVe6eJpByY6flBGYLvHjxYvaxDiRQlsCCxD3SK2SkYspiT9za7kYx3VXsiasqqHh3lSkoWJeaarq4JGM7gZYF7nd3M/ZFoAmBcYl7pFfLSMU0EZiZnXy4KqaVG8VMUnIf90lca+5sZlhTW10E6hOQuNcXUz26JHA5cY/xWhqjjEstt/2HwMMlMWFD21ex/xAZerT/5NShJ2xLL2AqSG+sBgLtCrhUpt3Yt9fzXuIe46U1RhntBWFJj/dL7I2/63Sk32+//fbn9++DCzNh3O4+NXb//5Gl9XebffjsA7vaZx8++8Ar9YYyfREgQGB9gTCre3Pq+uxq3Erg5cP91OfW79V6rtyy47pLYkIZlthHQ97+fPuv//rf//zzz5cvX/z++90vv9x+/c+v4WM7wkfuhY/L/vj3j+/fvQ//f/tf3oYtr//yOjx7+8tt2PPly5fhqFDP8+fP//jjj59//unbb99+ffXrl398CdfNf/p/n9799d2Hjx/C/8P7X8OWcE1OePa3376FPf/443s4KhyrXs7GlfPIvJFonnz16tXolwI7Eihb4OZf/ut/C7nI+E7I1MdbJdmze+OpfD0JrkIJECBAgAABApkKfP/+vXepzIVGytQvwKy9eXche7ie494bT9eWVx8BAgQIECBAIAeBR4m7HD2HkJy24XijGKvspzR+JkCAAAECBAg0JPAyJOvy9RwDfrgXu6vYc4yONhEgQIAAAQIEVhd4tOK+eu0qHBA4ftypX6gGdGwiQIAAAQIECLQqIHHPJvIPl8Q8c2/HbEKiIQQIECBAgACBjAQk7hkE43AVewZN0QQCBAgQIECAAIFMBXZ3mPa1mcAxZffG081ioGICBAgQIECAQBkCEvct4hTy9WPKvkX96iRAgAABAgQIEChOQOK+bsiO+bol9nXh1UaAAAECBAgQKF1A4r5KBLt8PVQlX1/FWyUECBAgQIAAgfoEvDk1ZUxDvh4+6zR8yddTMiubAAECBAhsIeC+zaupd+nUatXlW5HEPU1sjpfEpCleqQQIECBAoFUB6XKDkc8w6Nv8LiFxjzv6QxRvdm88tcQe11VpBAgQIFCYQIaZVmGCmpu3wDoj/PTXA4l7vFFxzNdl7fFQlUSAAAECKwqsk4us2CFVEShb4OSUvJG4L4rnffcxp12mLl9fZOlgAgQIEJgqcPKiPvVw+xMgUJiAxH12wMK92MNFMSbN2YAOJECAAIGjgFeTI4UHBAhcFJC4X6QZfuJ4o5iQtp9edzR8hK0ECBAg0J6ARLy9mOsxgfQCEvfRxjf3u3eeWmIfDWZHAgQIVCQgEa8omLpCoFgBifuI0B3u7WjaHoFlFwIECBQkYF4vKFiaSoDAM4n75UFwXGK3yn4ZyTMECBDIUkBGnmVYNIoAgWUCEvdzv/uQqO8uibm/MfGf69hCgACBTQVMzJvyq5wAgU0FJO4/+I/3duwujfnxhEcECBAgkFBALp4QV9EECNQkIHHfveG0e9epN57WNLL1hQCBPAQk5XnEQSsIEKhCoOnE/ccSexWx1AkCBAhsISA130JdnQQINCnQZOJ+uBTGEnuTY16nCRCYJCAvn8RlZwIECCQUaCtxPy6xJxRVNAECBAoTkJoXFjDNJUCgWYE2EndL7M0OcB0nQGAnIDU3DggQIFCDQN2J+/3Dq5UbsdcwVvWBAIFBAUn5IIuNBAgQqFCgzsT94ZKYEC8pe4WDVpcINCggO28w6LpMgACBU4HKEveHJXbvOj2Ns58JEChAQHZeQJA0kQABAhsK1JK4H65it8S+4WBSNQEC4wQk6OOc7EWAAAECjwXKT9wPH5/0uF9+IkCAwLYCsvNt/dVOgACBCgUKTtx3F7KHS9jvb7w8VjgwdYlAMQJmoGJCpaEECBAoXaDAxP2wxO5C9tIHn/YTKEpAgl5UuDSWAAECNQqUk7g/5Ou7G8V4/axxKOoTgc0FTC2bh0ADCBAgQOCaQAGJ+/HjTr2oXouk5wgQGCtgLhkrZT8CBAgQyEog48T9sMTukpisRozGEChKQI5eVLg0lgABAgSuCuSYuFtivxoyTxIgMCggRx9ksZEAAQIE6hHIKXHv7sUePkPJx53WM8D0hEAKATl6ClVlEiBAgEDuAnkk7g8puxfj3IeL9hFYXcC0sDq5CgkQIEAgV4FNE3cfd5rrsNAuAusKyM7X9VYbAQIECJQpsFHibom9zOGi1QQWC8jRFxMqgAABAgRaFVg3cbfE3uo40+8mBeToTYZdpwkQIEAgmcBaifvh406TdUTBBAhsKyBN39Zf7QQIECBQv0DixL23xO5Vvf7RpIetCDibW4m0fhIgQIBAVgJpEvd9vn7/zI0ds4q1xhCYJyBNn+fmKAIECBAgEFkgeuJ+H27C3t2I3at95FgpjsAaAk7cNZTVQYAAAQIEZgjES9wPN4o5Xh0zozUOIUBgLQEJ+lrS6iFAgAABApEEFifuxzzdx51GColiCCQQkKYnQFUkAQIECBBYV2B+4n7fXRAjX183YGojMEJAmj4CyS4ECBAgQKA0gemJ+26Jffe+0y5vL62/2kugPgFpen0x1SMCBAgQIDAgMD5xv392s0/Yd0vsEoUBSpsIrCLg7FuFWSUECBAgQCA/gRGJ+8O7TsM6+42UIb8IalH1Ak676kOsgwQIECBAYJTA1cT9IWWXN4yitBOBZQJOtGV+jiZAgAABArULDCXuN/e7a9hDz73xtPbw69+mAjL1TflVToAAAQIEShPoJe69GztKKEqLo/bmL+Csyj9GWkiAAAECBLIW2CfuvZQ968ZqHIGSBGTqJUVLWwkQIECAQP4CL+9v/nRJTP5x0sLsBaTp2YdIAwkQIECAQOECL2/unz979mfhvdB8AusLyNTXN1cjAQIECBBoWqB3jXvTDjpP4EkBmfqTRHYgQIAAAQIEEgpI3BPiKrpYATl6saHTcAIECBAgUK+AxL3e2OrZBAGZ+gQsuxIgQIAAAQKbCEjcN2FX6eYCMvXNQ6ABBAgQIECAwDQBifs0L3sXKyBTLzZ0Gk6AAAECBAjsBSTuBkKtAjL1WiOrXwQIECBAoFEBiXujga+x2zL1GqOqTwQIECBAgMBBQOJ+kPBvSQJy9JKipa0ECBAgQIBAFAGJexRGhawgIFlfAVkVBAgQIECAQL4CEvd8Y9N8y2TqzQ8BAAQIECBAgEBPQOLew/BwYwGZ+sYBUD0BAgQIECCQs4DEPefoVN82mXr1IdZBAgQIECBAIJqAxD0apYLGCUjWxznZiwABAgQIECDwWEDi/tjDT/EFZOrxTZVIgAABAgQINCggcW8w6Em7LE1PyqtwAgQIECBAoF0BiXu7sY/Uc5l6JEjFECBAgAABAgSuCkjcr/J4clhAsj7sYisBAgQIECBAIJ2AxD2dbU0ly9Rriqa+ECBAgAABAkUKSNyLDNsqjZasr8KsEgIECBAgQIDAOAGJ+zinVvaSrLcSaf0kQIAAAQIEihOQuBcXsogNlqZHxFQUAQIECBAgQCCtgMQ9rW9mpcvUMwuI5hAgQIAAAQIERgtI3EdTlbqjZL3UyGk3AQIECBAgQKAvIHHva1TzWLJeTSh1hAABAgQIECDwICBxr2YoSNarCaWOECBAgAABAgQGBCTuAyjlbJKslxMrLSVAgAABAgQILBOQuC/zW/tomfra4uojQIAAAQIECGQiIHHPJBCXmiFTvyRjOwECBAgQIECgLQGJe4bxlqxnGBRNIkCAAAECBAhsLCBx3zgAh+ol6wcJ/xIgQIAAAQIECAwJSNyHVFbaJllfCVo1BAgQIECAAIEKBCTuKwdRsr4yuOoIECBAgMBEAa/VE8F2u9/POMYhkwUk7pPJJh7g7J8IZncCBAgQINAJeAktaCRsHqw2fnOQuCc6JzYfv4n6pVgCBAgQIHBBwEvfBRib1xBIMfzy+2VA4h5lLKUYLFEaphACBAgQIDAk4IVrSMU2Ao8Ekp4ms34rkLg/CtCUH5IGc0pD7EuAAAECLQt4OWo5+vpersCMM/fmmcR9asBnME+twv4ECBAg0LCA15mGg6/rBK4LSNyv+3TPmkTHKNmHAAECBC4LeCW5bOMZAgRGCkjcL0GZYi/J2E6AAAECBwGvFQcJ/xIgsIKAxL2PbALua3hMgACBtgW8JrQdf70nkKFA44m7WTnDMalJBAgQSCNgyk/jqlQCBFYTaDBxN3OvNrpURIAAgRUFzO4rYquKAIFNBBpJ3E3nm4wulRIgQCCegIk8nqWSCBAoVKDixN0cX+iY1GwCBBoWMHM3HHxdJ0DgSYH6Enez/pNBtwMBAgS2EzBJb2evZgIESheoI3H3OlD6ONR+AgTqEjAr1xVPvSFAIBOBchN3LwuZDCHNIECgVQHTcKuRz7PfGY7H+zyltKpkgbIS9wzPypKDr+0ECBC4JGC6vSRT+3aRjxjhCjD97hFxPEQpKv/EvYJhHyVSCiFAgEACAVNsAtSVixTDlcGbqi7F6PLLwJIhlGfinmKcLFFyLAECBAoXMK1mHEDByTg4mhZfYPmAbzn1zypxXx7K+MNLiQQIEChJwDy6dbREYOsIqL9+gdlnWQUZ/+aJ+2z8+selHhIgQOCagOnzmk605zBHo1QQga0F5p3OWaX7myTu89y2jrb6CRAgsImAKTMBO9QEqIokUKfA1OkiaaK/WuI+tdd1xl6vCBAgMCBgghxAGbsJ3lgp+xEgsIrA7ElpTMafNHGf3fJVXFVCgACB9QXMi1PMaU3Rsi8BAmULPDnjhR1SJO5P1ls2q9YTIEBgrIDp8KoUnqs8niRAgMCpQKzE3fR7KutnAgTaEjALnsUbyRmJDQQIEFgksDBxNy0v0ncwAQJFCpj5DmEjcZDwLwECBNYQmJG4m6jXCIw6CBDIRaDtOa/t3ucyBrWDAAECncD4xN3sbcwQIFC7QKvzXKv9rn086x8BAtUJXEnczeTVRVuHCBDoBFqa3lrqq/FNgACBygVOEnczfOXx1j0CLQo0MLE10MUWR64+EyBA4ESgS9zN+ScsfiRAoFiBquezqjtX7JDTcAIECKwlEBJ3LwRrYauHAIHoApVOYJV2K3r4FUiAAIG2BE4ulWmr83pLgEBhAtXls9V1qLABpbkECBAoS0DiXla8tJZAYwIVJbYVdaWxQai7BAgQyEZA4p5NKDSEQMsCVWS1VXSi5VGo7wQIEMhdQOKee4S0j0CdAiUnuSW3vc7RpFcECBBoREDi3kigdZPApgLFprrFNnzTcKucAAECBNIISNzTuCqVQOMCZSa8Zba68aGm+wQIEGhIQOLeULB1lUAqgQIT3gKbnCp6yiVAgACBUgQk7qVESjsJ5CRQVNpbVGNzirK2ECBAgEBmAhL3zAKiOQTyFCgn+S2npXlGWqsIECBAIF8BiXu+sdEyAtsIlJD5ltDGbaKnVgIECBCoWEDiXnFwdY3AOIHss+DsGzjO2V4ECBAgQGCZgMR9mZ+jCZQokHcinHfrSoy3NhMgQIBAJQIS90oCqRsErglknAtn3LRrop4jQIAAAQLrC0jc1zdXI4FVBHLNiHNt1ypBUQkBAgQIEFggIHFfgOdQAlkJZJkRZ9morMKmMQQIECBAYKyAxH2slP0IZCSQXzqcX4syCpemECBAgACBKAIS9yiMCiGQXiCz1Diz5qT3VwMBAgQIENhaQOK+dQTUT+CSQE6pcU5tueRlOwECBAgQqFxA4l55gHWvJIGcsuOc2lJSDLWVAAECBAikE5C4p7NVMoERAtkkyNk0ZASaXQgQIECAQJMCEvcmw67TGwrkkSDn0YoNw6BqAgQIECBQnoDEvbyYaXF5AhmkyRk0oby4aTEBAgQIEMhK4OXvv//+/PnzrNqkMQTKFtg6R966/rKjp/UECBAgQCBPgbu7u+f/92//kWfjtIpASQIhWT5+b9HuY+Wy9i341UmAAAECBJIL/O1v//Hy3/7tf9w/e/ZP//TPP//8c/IKVUCgMoFN0+RNK68skLpDgAABAgTyFfj27dv/+d//63/++7//fxXt00MpjXrwAAAAAElFTkSuQmCC\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\\\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\\\n# Currently, the height of the lantern goes up to Z = 0.50.\\\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\\\n\\\\ncam_obj = bpy.data.objects.get(\\\\\\\"Camera\\\\\\\")\\\\nif cam_obj:\\\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\\\n    cam_obj.location = (0.75, -1.15, 0.40)\\\\n    \\\\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\\\n    cam_obj.data.lens = 60\\\\n    \\\\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\\\n    # Alternatively, let's create a Focus_Target empty!\\\\n    \\\\n    focus_empty = bpy.data.objects.get(\\\\\\\"Focus_Target\\\\\\\")\\\\n    if not focus_empty:\\\\n        focus_empty = bpy.data.objects.new(\\\\\\\"Focus_Target\\\\\\\", None)\\\\n        bpy.context.collection.objects.link(focus_empty)\\\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\\\n    \\\\n    # Update camera track constraint target\\\\n    for c in cam_obj.constraints:\\\\n        if c.type == 'TRACK_TO':\\\\n            c.target = focus_empty\\\\n            \\\\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\\\n    cam_obj.data.dof.focus_object = focus_empty\\\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\\\n\\\\nprint(\\\\\\\"Camera composition refined for perfect product-to-hero prop framing!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Reposition camera to cleanly frame the entire lantern and background floor pool\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":"Code executed successfully: Aligned all viewport spaces to active camera!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","content":[{"type":"text","text":"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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"}
{"ts": "2026-07-15T04:47:20.188253+00:00", "type": "image", "tool": "get_viewport_screenshot"}
{"ts": "2026-07-15T04:47:26.241603+00:00", "type": "llm_call", "call_index": 30, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:47:20.188253+00:00", "finished_at": "2026-07-15T04:47:26.240098+00:00", "duration_s": 6.046999999991385, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "content": "Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:47:20.188253+00:00", "finished_at": "2026-07-15T04:47:26.237092+00:00", "duration_s": 6.046999999991385, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "644196", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect camera composition framing\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvWmUZEd175vzUJk1Tz2qZyGpNbTmuVtqSSAhzCgMzxjwgH3BNn6L5bf8wevZXnzwB9sX1vV6y49nDMZgX8sCg7AtgRBoQoDUmro1dLfU86Qea8yqzKqc329H5Dl5cqrKrDGrOqKrT8aJE7Fjx47MHf/YZ0eEe8WKFX19/a2trR6Px2WCkcASkoB7CfFqWF0ICeTz+UAg4PV4c/lcJpNJp1K5XK6lpcXt9rjsbwuZXC655PM5PqyQy2bJTMik0y63hLAUtItJWigYJEMqlZqYmPD7A16fdyFaVauOfN7n82WzWVoKS+FwGBZr5dXpPA6Fwh6PWzVh0uNF6y+a2s/ncv5AAJYQfCqV9HiFm1r801X0oE/lkU5RHWNHKCV9Kt1JPiI6pjuYjs2m02m/3+/z+yUncvN6vT6fVJ3LZTMIkG9Kiu6uVbtJNxIwElhCEuAnn3cHfJ7sEuK5HlZpV2x09MKFC77+flB7G2VQZ1xRoIxMU5NA6TFo6TyUQuWhIKcussBPGacZv5VuX+CaTXXzIIFaaES+sCYYCRQlIIiNP4CdwuVKp7nV1wSYbmVTqXLhP0FhPC6C2YFxhFxWgKHHw0OrjOT1+3wkJJNJULsMDNyk7OdzHBEdGyjVsdXUMioONghwDjbVwBRWQMDMMSpxPBB1dHSEDKqtorRrKUmmBNNTy2bj4+P1tFzmB97ySQ6g2UejZLIhcs6kM/6AAOuqQXorm8t7PPRUVXQPCdWj1lV3rwLzurFMbwDrEOfW5fWK1CjCTV46XW4d3V2VB5NoJGAkUI8E+EExFZ46J5oHFWfP1b2RlZ7IisoiqcGDyfELlenoEzBeId3t9XVe6vaF7GyeXA7KqIzEqZfl914RmMZrbcATDAbBCm1JqeTkpB4A0FBkqNRglE35+93BjgryKJa0b/yQ3TpnBih7lOnBmajjSj+PVqYjKwG0kPN4Oru60Jm+9vZ2VJfOOjk5MV6qiLHEb9y48ejRo7FYzEmOUhq7JxJxhjHQf0dHx4kTJ4hTx4YNG8h87NixqiJz0pm/OGa2lpbI/NE3lBdOAoWvZ4MV1oL7DZIx2ZeWBIpfFjSRwO+a3wMrp/pUoK2A3khwu9CthGLb8y4UPabZwcFB1FokEkG/nD9/vphhrmOiY9XIlIiLjrXJM4S85z3vGRsbQ8E6sebk5GRnZ6ceXTra2/WohhkeHc4j8pOZyLvvvmuTIkK2W2+9dWho6M0333Sm0/aOzk7mDyS2t7VRKSmhUIiRA0sNkwTSmcMMDw05S/G0ls6HEzrDmRnhkpkqoMw1m51qpCePzLCkZwTmy2yM2Zm6ddIsPFZJuvO4UlaKZ4vjN7e6FBSYEcCGnVJCzdwYCRgJNCgBfqajIyNaD0Sj0c2bN4+MjBw/ftyprDRJlE9beztxT6gzdNknXZ7CvD0U8N5wWR+q++X9586+cmF8/GhVFtBswZCAdf+KGwJrdug8fZ3hld0tJ86ND48l89nk4MGfa2VVRkEQcGen1khEMFOgoNasWWNnO3fu3KlTpxJK8fKorU2s26Bc1DK6dHh4WDdnpPUab+elDBZr+iI97eFzQ4nTg3H0VD41Fj55mukBFXV3d0OBIhSECPyg7kjn2tPTgxBQv/F4nEeV+tnmx5YV9aKQKSxGCB2YJ1lR+URNf+xjH3vwwQe/8pWvwIHzkZ1TRwD3AwMDmDR0HliEIZnEWIHWbtiwgbpJpC9Jhu8upg61A0IkD89XrlyJWJ0ZUbLI2plSGbeZsR9BhJ7mlkFXd5X9aNoIcl+7do2zRbSFxGkLOjPwRezt7bVTkImOw09ZA+08VIEcqBdRaOZ5xLfHzmAiNSXAmDzLv5qkzYNmlYC2QMOdADH+i5GigNGqsqzxuuORPbq4PSUWYiEF+FOBX+vv/u7v/smf/Aka2VG0GKXStWvX4oVIkjMPP/myXzrjQZlqtamU6Vidjtr5jd/4DeLr1q27//777cxE4FwPlsR1LZdccgnag0Q0xqWXXqrVqbMI8b6+vj//8z//3Oc+V5Yu1KzhAGoEdBdX0uG5qgqideRhPlNGSt/avNlP6RuM7vxGpaPU+w3l52I/rxKR3lFdpn7bKoOUlyAA3YpYN3aCVIBlXZeVVBK4KmpyEVN+yaRCUTIXIwEjgYYlYCsidM5f/MVfXHbZZWDIL3zhC2iPMlq2inMH2lf0tN9wWW9PuwDxTDY/mcp+ZPuG1T2CFWsFu7gn1KPz3LJ1xf/7J9v/+gu3fu3/2n75+qkgoijygiYpaEtgWCKRAEPrsGXLFpthIiiM66+/Hq2LVkfL3XjjjbbeDvq9X/jI1v/7szf8jw9t/cvfufG333+ZPeYAod///vf/+q//OqU2bNhw1VVXacUDt1SHxQQTDMQRFKFWM3W63VhuQe01tRVGdBQx5O666y6qrKqp7Zqw4oDCv/Od7/z7v//7ww8//IEPfODkyZM2dGbognXwN0MOA8ndd9/NJAxFCdNwf/nllwNMkQIRavn85z/PWELf//Ef/7F+9Jd/+ZfgbIozPaDZUOOrcN9999llbTamjtBT99yzkzw77tqxctXKUChIGyHLWMg8CrLwwHyjGpH8Aw/cz4B02223wgDDIfz09/dt2bIZEa1fv462gLBhr6297ROf+HUYq0ZExkj9lK4i8857dtLMdesuufXWW9rbZT5XFsLh0AMPPBBtjf7ar/1aJBq58847169fDwNcYRUKVErVfJmQ20c++pGVK6u8aSqjaW4bkEAt3N8ACZN1oSUAeqVKBc6kam1TKWdCITaZ1+kgeFCAbwEUKlBnK1/JkhdDA2hPZRQ78WOPPfbTn/6U95AosU996lPogQIp9fHe974X3XXHHXegqRm00G9XXHEFBgudgtqBGqqVAeCWW26Z1gbhpIyaffLJJ994441nn30WjcTA4HzqjKMl0MAEakFRYPSiXmcGHacJ3//+959++unKR84U6uVVAxYjxjYiPIKmMwNxFClmKm2XKXtU9RZVhiSROZ1FHJkItp4i0C96rFVXq/esT1VQ+t2O6BuZwMkfnWgP1fqJdLrqd+rF/mUVNZ9GAkYCcyCBj3/844DgHTt28GYSNQiMnILoaDw1Mp664fK+X9+5KRL2re6NfO/pwzjHTVGk7JHP69mypi2g1h11RIOfvGdzWYZatyjGj370o+hqdPLVV1/9vve9T5vGnfnR82g81BQaVY8C11xzjc7wgdvXXbmxm5lGMp3letMV/Xdft1o/Qr3TajQbtmyUNv4sGHT0I4oDFIGdmLx3795dG3w6uSjGqwB3mNu5c+cnP/nJTZs2nT59mre0jz/+OMoa4AgHN9xwQyUwxUmGIYT2g00J4MgzZ87YbygYtJ566qlf/epXcI+1hskHAoJv2oC8EMcHP/jBD33oQ7BOIq8P9HvhAwcOMK2husOHD9O8e++9l1Lbtm3j28DYQHV2Wbs1PGUEhWzVrwj+q8lkilfcaO1oJEK2q6668sortwLWb7vttnvuuYfba7YVOsOmSYRhAjls3LTpnXcOUKCnp/vO7XcGArxPcDMTYIUAUzHYozvXrF7NFy2Vrv7Ol7LPPvfcFVcwot/u9Xp6unu2br2it7eP1unxyFkpceT5zjvvvL3/7aNHj/T29HZ3d4Hvr71226rVq+6++y4yUOn111/X1ibuTLx6GR+Xty0mFOxtdPM8/RkRN6sE0F0W/haoJnhwKlYdo4KFBbVSlqIOEyyJPjUe6KfATd5pKlScQRcdOlTuy8gvFyh89uxZslHkoYceQnNyZcaOgkLXYb9YvXr1b/7mb9rVaTZB9rV0LBV9+tOf3rBhA3SgQMsAylxvv/12rtu3b3c2VL+EpQhDxf79+0HbuLXoV3bQ14MHxVHFAPd/+Zd/ef755ylOqarUKIK9h4AR50//9E+hw8BWZllncoILDURokS11nYerneJkUse1BIDWCId1opUZnClkdvSZwt3Ox1bc0emFrwH9KdMyy75vZVTKXVjO8z6lmGhiRgJGArOQACAQc/Lf//3fowBfe+01bIt/9Vd/dfDgQUiiwTB9okOuvfZaZw0TycyhU6NPvHjiZ6+c+vAdG5565dSufeeOnxXfEh2AT5gPUFlgaCtNPpUzff769/Ree2nP1g1d+44N7T44QHooUGLg16qVK1rOWZw4mnDfvn3gzNdff/2ZZ54B7FXqK/T2yy+/PDo6euTIEXwU9+zZAx0UI/ad6y7tTWeKFodUOnfTZX1gDwDt+vXrnXVhzsaMQgrwFZ0D8vyv//ovPUmAEGKxM2v5oBJh2E50RqoAdyi+8MILe/fu3bVrF6L/0pe+9Hd/93eMVdSKFRzkrd1xnFSmjjPfAlkiHRhlCIEOXQhaRTqMXoxhDB4od0AqQsEypFvCSEOvY7hi4OEpRBgg6TkogOlhUpfVmTUDpDPfYIaHc1I1ltyv7X7tQx/68Ntvv40eD4db+vtXYOhJp1P4C50/f27r1iuPHjlSWRCZxmJjP3r88e3b78Q2v2LFSniDAcTK10ixBGLO0y6YHB8bs2csTlJ086pVqzo7OpgOhULhU6fepaXRaOupUydZI+zMacepFCM6BWkp3xjmTidPnpJZU95F969evYq2c0u97757ihGa9QZ22cWPMA4u1t/iN95wsGgS4IepsB2bkAgUq1TBTs5KUKB+YCXxq3fkzIvvuJC2HiuYy4wdVfPSSy+hwRyZJSOaF/XFGAMDgFqlc8RUjxLjh79161YUqa7CySHUaulYjCk//vGP0czPPfccyhnszmCDjQNdhIrGsuVkAJCN2sSWg25BgTDqUJc2iBBhHGWQwLyCYkEdEUEzU5xSaFeolTXH5hCNhxovlUyhWlQ0ZblhdLAtOxBEz9Nqp9ycfGrvI2RKX6GKQdbOp2Vx2BCTuaMLKpC71jiFctL9Kkiq0Ke4MvCrG3miSGnePOw7ZIKRgJHAXEgAVYMa+drXvoatFmz61a9+FbutJoyzO/N/nC80ji+rrbcj/N4b1z72wvH33rQWc7v9ioxsgMMPf/jD+CCgQnUpVHwqlVFr6N2Xru14ef/5Z3efvnJjV0vQh+X70Z+XYDk04Re/+EU0EqGsUn0LgsL0gLbk1tZ4zpxYalBlmoKTiBpnnBlF22gisMpkAJwGQcwoGHQYC/QjtOUjjzyi6YDjy5QkRZj84F/E+tIS0tZNyaREJ6LTmS2h6/XL0A0bNjDD+MlPfsL1H//xH6uidkYC2syApBuMCACpSEpDWBA5ABehM5shD44uTJ6wSPFC+dFHH8UeD3M0D2cgHSEDow6lqBHKjH9EGF3ICRsMltj+GboYL0mhO622uJDFt771LRQx9TLA2Ol25Mzp0wD0w4ePpNOZwcEBxiEcZkZGRvfufQuUDNsgYzuzHUGm6HjM2y+//Aoj68033xSPJzCnAeIZRHt6epmijY2Nw9W+fXvxGuqW18ol4yikWlrCzz//C7hlLGdjh1tuuRlOmIxifc9ksnpMtWvUEY3U8Sx66623AO5I/pprrqbVd911909+8iTvCk6ffhd+eC9BdQgQr6y3336njIjc2iNYlWcmyUhgWUlAkLVCggLXiJepVXlcEuwE9AahgAVRZI6CQFVunbqVN6rYFPgxop2A4Cg3FBpTaE36hz/8IcoNRf+jH/0IvQFq56Xiiy++yKiAnYnpPfoN2zaGBkYvFDQeJrrgFDoW5Y4SIBvaANSOf4su8sorr0CtUi2TQqBG1ClKCYxONl2EGpn5MzagVGkaOpl69SNe2sIVBfWtvjK6QIecP1OBREYHDdN1Bh5hXtLaHlUGcSggTCYVJCr96aRXjAPWcXNnhxe6SfkmuRmraxm/6QLtByXda9HQ3Ve41R/iemP1qkT5L3/Sg2ABnyw7tkoXXGXktphmPzQRIwEjgZlIAN3y85//HNWHDgSJOTUAcV7xgS1RIBol6wo6W4M7r1vdHg387ycP4nPCZD8S8sUnC8smyUN+3LDBmTZoxnMu4DCr87N+YteJI6dH/T5vLJ46eb5kwyvwJGoQjFerPVDGCAtjwELMDUwwnDlpEUAL5UYe4uh8GqJC/vVDgzu2rcIvX+dnE8pX37mAZoEgowPmFUSBHw7jBYoX4Eo26GDdQEMiAQ3ocZhE8TprBOv+8pe/rMWw++67d2qFS5l4fFwjyJtvvhkqzJZIvOmmm5g3lFl0qAyzMU9jsVH4w5UH/lDWWqY8ZbSARU1NcwOXqE7ianR04fdCQb3W2E7XEZ2/7KpLleW0aZZl5pavS1tbieOpzqOL2FcSFU0ZC6agBtd8kxifyE82XUpHbApq9NFEJI3/FUHVokYSoaGMQDa1iswqARsU25856mVQxM/nGGu09U4IMpjJDsf8wxg1hQCr0zepRgLLSwL8HIJBsQrzw0klkwBKp9OLQnTym+JnzFWC2HWUBkZ5ZWVPdOQBSg1HIoVfE+pVWZp5hiLWmhDnOrA7Jm1Kkh+PPszh2uFEixPCRKBAxL6S4kx3ZtClnFfRscqPnI17UZVoVOwUwHSsKlx5sevMrOMAcb2HI8BcKw3gPgVpFPnRxtoIgkfiq6++ysixfv16tLSolGgUo0kZQdC53tamt6cHOoxYaHhIaYMOowO4nLGQUjxigIFJTQHrO2zAtpMgpiZ7/zVnOjIPKp8fJAkF5GNvQ+nMpuN0gd5CDpGiYeUiMTWuIFslXihYXSsxehm8rjpWepZtKPhKaMRAjRCkUnaiDEeqr6mt5MGkGAkYCUwhAX5WQ4ODALyPfOQjf/3Xfz1FTn6Gsrkh+rZt/Yc/8yf8oCdTGazmpNx0ed/5kYljZ8ZG3/y3kZO7qxJBz7So9YTBDe+/7uYdG1a27T544Yr1XWcGE68fEm8ZdpU58/SXbXxbRoSqtR7AIRvLsq1RQbOsX6IUU4WY2gemrbUVDYnJGBWKHsOAwitKVCh5RlpvjfRf/gcfufKydZ3oGowOr71z4Rv/vS8zGQuffAR8j6UGv3FewKJscSnHwIrCoV60K2geltCi6G0Mwahi9DNgu4xPfWvLilvReE7gnk6neEmKtmPCwfhEhEzod3QrFG1yDAnU6lXb0bCDJNqcmQScYf0lGzSx2ZMHUK5HNbvggkXgAUEHgzVXbk3PSVXgPX2x+c+hOoWum/+aTA1GAktPAkA19q4CeaO+kqmUAHfnj0X9fIrAXcA7WqoQBLZn2cRdlu3rIYH28xzYSko6lcZ9D4RaORLgNY720wpzTkRW0LFqH4bJCdGxEGd0wecQzY7NvrIWDEWoZT1LYaRhUNN5iNA8rcCnGBjKCMrQouzxpDNqMQogRiA76RDRErCBe1nZylvKMgkp6QgrE4MLG+RDlq4AQHOQiN7izXpe8inAXe2wVrDP07MQtegWgbv0Kj2tcTv7v8sbbnhmZKUJzOu0lAD0mQx9Kn6Peo5UUpm5MRIwEmhcAvyaxmIxVCkwEreLKQjwY4yoPQbd3nDHNZ++euulbxweTKXFlSUa9kMHi3st4I6GBBbrSb6vY0tw0wf6e9o3rmw9cX783QuFF4ZTAHfUCDtRas3B0kxsGZYWEYdGMDR6iVeRjCAwgxWYbSCpkRYBsrG42yYbgLtsB+lxb1zVhp/PmcE4fvmiemQ7yEe0ssJDhpaCqCEINRQR6g5qaGztPAM17Z04hX62ZQUF0XhO4E4S4kbbEZki8FaTKq0MeVVEIH7zBNolHDoH7OZhznBiJGAkMG8SALFxRCbGXfQ+KhI9YGtkqZNU2y5LXAyyBeSOpgYXchHt4fcXkVyeHVRkLRGWkgncuMWpY979oamjqGNBtNZWBrbYaAV8MseAMZ5yg91aHPF1sF5v2vl1hDGJ/GSjzZQiFGspzUoegp0mVak5j51CBFpIDEaciVXjDGxF3kpzwA8JAb+fD6hNTiYDAOsaqpveEYzvla3fJY/+kJiEkp6FLqHYvXJAFYtf6dZQOCS+9arfkQDnaXm8PipVNMzFSMBIYLYSkJmytTn4FLRQs9ZvF28Bv8tXvlEVZbOTY+BvIjgWQJWfvyaIEna+wcv7Ii53UV8VKkU3JmQLrMqADrE1EnpCVHqFfhPFZIVaOi7rDrl91VRHPudOi2u7RaD4SXWiuIoJxRjqGVNC8b4YY0gqDmSUtvF3IUstPV6kUB4DIvvrUN3lxcy9kYCRgJHAfEhANGM9oSKXJCiN6nSDFt9r5V3NaEQIhEIo/XrIz1keNZEoo0YbMdqgwWWqkc0y8NjjkOSE4bIChVss3yEMSJm0WJoZJ7QzTPW8jtSqIpXxo/R8D0eJeqMQwRwOfSIyJfLIWoJaEma0K+dE2ikDIR/2WKjbLpnlf+EJ5DGE6bQCc3xPFLb3zf9MrFCj+TASuAgkIL/jaosMp2i6O5/GFl2ZQXbz8gYyWU4b9Qc91deVUsqdKVmWY9OpR7+JZphuyNAqxSZrRzz5SVe66I1ip0uklhJWyqoqQTxt6mEY2pilHQqvpNpmueEdQbOwYvgwEjASaG4JoIIZNSItkQw2H5ZGOXzcRVeCVbnI2idxnxCbsQ7KPgScJRk9jrONVjuASLxE8GvEuxGIjC7WTiMOGdhw0ZE2/1EY470CSJf3AGyIhsGFbbKmrZYmcy4EzjMUYv8rXjBgf9aod9qy85cB/sMtsiYMizgWNTbtrdUW3onAbUi5QgnGl0mVbNxJlxGA5XrBgvjGKFu79LD0s3wV8CbFFYdtfFvZz0cmPLlMOoXXP29RVO0lJwzOX2MNZSMBI4H6JYC+nphkF2/ekk3jCVI/zbnKiWLBtW+uqDVEp8ziLl4wVfS47KJVc67TUH3TZC6d+GB36evrr2V9mYaUeWwkYCRw8UkAFQKGY9UN/uipdBr8jWVa6zQeCYzjDxynwBxIDg8KlG9yEo+JxGRiApTn9no7OrtYJIPwyIj/Oj7YAwOD+JozH2AXqUaEOl+wHsY4fwkrO/gbF3hOk+jsqn6Mq5Nb2sphGz29vay7Onf2HEb3LtmWd949f5w8VMaRPtAZT3pwNt6x586d7+6uehAe3ZGm33AzxS4FxKf54iuqAvBdgDt9q3pWPlSE/iXQUgTFBsM9vT0rV63iHQrTnUQ8zhLb8ViM6uS9sQlGAkYCzSSBeCKRCq1b6z4v0/KmDCjSoSFZCFsMpSC2mD67WJkESoB7cNXNrRvuSmbyHOLKEgG/z8MeN16PO3HhnY7YL6mXwrxmJYJBgyuaUS+zJY7yxBOfiIyOyu0SR3u9aSXaVXvgVD4iHVJkgBQUKIILP7YS6BDY5tygdi0KczUSMBKoRwLoKJQJPhBapagiRaWvNYtcxU1CPgoaS3wmxO1ZZ2W5pK4LnYaOAt4D7hPxBMZanV73VVdYd/ZCxiLDU5QUqKqCqFB1PtQUmfUjMLq8clA+lggKhTxtkQXIAGgGUrMAFtO5+N64GVkyevODstqB6sw9ZICwWJf+U3+WyBjh5c/5JplHNJbxhdFE1sgKUXGRQW7KEs+jhfV9KmuVuTUSMBIolQCvymLpllDQ3+lheav14y7N0wx3vL0cHw/w2lMzg42A3bQs5TRnDKK+qChs7TcA3RLg3rPhpgfv2vzYr45du6Vn37Fh1sm+OxC/4T19//Ffhd3B2Z39/vvvhwr8UZhtyNmYTHO3efNmfXof4yUKkZ3R7G2D2TeNnXTIRjpP2Z/S3iOTxbyHDx9mNzH2SWD/oEOHDrH/ji0FVK0mbq5GAkYCRgJ1SgA9QwCTYZetovIV0FOIVRSsYFf5U+sYuQHhgRmVDQIi2N3xO9Qm23QqqfdAqJONWWRT3E1TXreggGAVUJ2qgJYDVxpFo9HhBF2gioimotTgszqo80KDo11hTJl4vOzhgz2Ibcqq1OSRyYZqgnSZ+lPdR9ZSmVnwXTVTNZbRhG4Vw5OasWg6vHJROz/YwqhSp0laZhIo/abMonFzRmgWPCzHoolUftLd3uEfXhIzaidMxQAx56idHoYmRu2awH0skf7P54+OT6T1VprnhycoM54QjI5iQ5/i3wnw51Y7gNoGdRQu6s/pjK4N8xirSNSWeEpBQalmmS1oGzy74kOZPYN448BO8Mr+wUMTjASMBIwEZigBjcmwHwQt82uRkIy1/AfzCepTEbKroNyiScKsq87RlFWSuHCQE3v7RGJC4XleKtYBRYv1zVcMjnHUthjPoaCnrqmAMaxjpGigQrOKQtWiKrHqk6krmvFTlD9vZhFyOBTiMLyqwF3jawHuBNWRVm8y4yLBMrRbfPOpmylX3glj22fNA7MXhf6hg6cUI5EmO2POTcGlJQHr2zFrrueM0Kw5aXoCBf0zHZ/8JEeznUHXUFeAs6jn4D1Y/V1UJ4fTtaD4nAM0OBAJWM/ZsRymqR9s3LiRzR85gdQ+SrZYoJFYicW9tcW/ZX0nJ8c6Kdx4ed+PT+7lYKePfexjzrmFnYfdhUHknB6KlR0NeMcdd9iPtmzZsmPHDnaq/9d//VcS7777bgzz+innSJ06dUofQEgKZw2iQPXRgHZxEzESMBJY+hKoX3nOWVvxYscwWw7IRDeXAD4x2iqzLThQgUFBwxgXgLVkpXgwEOT0HpZysoQx1NIC+pszFmdHSCAqYL3QABwa6x3keOtJm2kaQcSB/b1qm9Q41uhgNt30oWabkTlbJmMJYojhyobH9IjmsKwMXcDorjuNKxzaf/p7ZtnaFYxXDKm2ykxFb/AvBBVw5/Uv71KK+36W1WRujQSMBOZIAvWMARMsNMq3twdGvP56tdkccSdk6uGwanUUrFqWk/K+/e1vY+x+//vfj37jLA4UETvBg3sxf/OU8454tYiuW7lyJQaFZ555hrNmOUX7iSee0BVxLgcZ0FHAb7CxXQuREp09NpF+54RsEe8Mr7wtOB59CjrXdnTnU+J4uXBiNqt89uzZw7lQzqewKxYOtxs7PwFFaT/lrG8HOKtgAAAgAElEQVSK8FQH6Gu7u53BRIwEjARmIQGtT5rhOotGzKgo5mSUHUBcEFsNChYkFeSuwLtAOcnrZtcxWcYDJkTdYQLGEszi1cREQi9XrUFvwZOVcbmA23NydHY9HCAP2kWbkQtxtK4lh3pKT59HSM/oj1GA9cF0mXZzhzds4VXrg2ccLm2wXshTaIb6UF/5AnyX/pSvgKTJDnVypC63YnGnv9UBTPq9cdW6TKKRgJHAAkgAnTSc62UlUVd4ErPJAtQ431WIllWDiLxI9HrRPI8++iggHtT+xhtvEMFy/fDDD8s6e7//ueeeY8DSQRfU7FHqU5/61Ic//GGM42UMlwB3Tqvasqa9LMe1l1bZRYFF+kwU8G8hsz421S7FSwEQOTDdTmHAo2LOiKIUj7QL+6ZNm8gAEWYYBBK3bt2q/XDsgiZiJLCUJaAQhMIMGjks7HUpS252vOPqgvlWqX/HGKAVKZSJqD+lWhVoV5ZbsT2rwOlN9BTQji3PwX3qwFR2EsQ0O/1+i7NjvIHSot8FkvIpUFm/Ipi2PCX0iwUaqAPNnLbUAmRgEsEIhymdCAttMRJNTOAtWj4NoLdgGwOQarTqL8kEgyqrxaj0uu5+jdp1U5WvphKaklqusBNonRsnW7TNp5GAkcBcSmAyHx4Yz7e5B6t6x81lTQtIC5WDFftzn/vcb//2b2NK1xCc+s+cOcOCTxQd6zk//elPY1CX94dqoxdwMl4na9asEeWmArbsxx9/HBs3buQ6xb6WuMokJtNHTuNaVBLeOjJUcq9u3n777Z///Oc6Heq6bm6J4PSiAb1dCkRO4JZlqcwtdPrRo0eJsLaVwJyDOEtd9SNzNRKYBwmokXwe6BqSzSYBwB9oOxDQB8NV9jtqsfCn8F8B/WKCpSHk5ow6IqiylnCYDOwbyE6ROEdjfZ/Dllay1RBxBgbyawUPk9zUR9At8xP9LkLBWV22oapnmHk6/mAH7M7QhdsP7iujIyNt7VX28MF8xZ4zhdcj0n8iBsvDHSEgF0sYqkYtKD1P4V0Kt9L3dHYewz2bu7v06DPDRpliRgJGAjOVAD/fkXjO6471toIAm8KCMNOmVCmHi/iuXbtQOOg0DNb/9m//xus+AkCXRCLaGE9Jbr/73e9yZc0n6c7Xp/izECqplwD3oCt+93XXPPXqqW1bevbrXWUuxC9b13H6iAtjOUZ+SGsSbNpo02JPGAz+pLAOFVZ4C3DgwAH9NBaLHTlyhNmGLjg+Pm6XWrduHY3BxI7vDpvVMBHBZ5+OtOcAdk4TWe4SmG5IX+7tv5jbNx99j7ZJZdKhUFAEW6xAo1wLs1tCFxhbgHIychANBEMUQoFyWA/G3VSSDb8TLZGIIMIiNav8Yn0q27MwrH3BNV6tgxlULG1ERARpt4DeOorNfxYANO9pOVyJFyYMGUiesYBeKKuZlFRKLO7CvHomzbEihcyqq+z+Um2VJ3jFSE4RgfjJpNMZMfDX52VUoGw+jASMBOZCAql8KDY61B7hdy922+URnMoEtWM37ZFHHrHj4HjdWDuFWzJzdaaUCaRME5YA9/NvPPrwuwdcHu+TavvHk29J2UO7XfmJwdPJ09/5znfKaHELUseSX5muH+1VofIppV566SU7HRd5HcfLh0d2um6MfWsiiySB5hjYF6nxS6va5u2qheUMRYLloqxOwXka7qkr0E+hP/lUTs/ynIBiJQXsKC43k5Oyg3sivmLV6iYBuJpJWlJonToptn5VqV1lNLIVCiKCpgiInZOYAOQsUeClB0Mgw0GlAzothWVaIZzrP2mF1QSapKdXSjpELVuT3Mv2/NrLX5A7G4amGSntHBYJ82kkYCQwjxLAVBCb4GeX7umIOH6681jjvJJ2KhCM12JTQC/NdcB70EmyBLjn02PpMy84H+s4fGCaqEwnhUdOvp15ZvDISSoYhNUSXp3EL8p49S64KEUxv41uIkE3ESvzK/O5pY59gmWOoLQKsqhU+QP0SQQlpe7kUy9OVcBd/GRa5KQesKMsmszlmk4X6aYJ3xKwHFe0tEoCClbmKLTZ7WKXFeY2tL5JArxhBcfMDoInIHCOqq0K3Ok1ekQ1QyF2aY4C8cXuJqbwu/Q/hCUwDWO7SRIoqnC7bCkjO/0XSzWJJAwbRgLLVgIpVzAWG29vjfrdyeXRyGhrG0s09c4rKJN5WqhZpqZ8K1asdBq5m0eUMvAuVZVaCReaR65LiZPFl+Pic7CU+qt5eMVeiwKpyo8C7Aq9a+SuwLugQOUz4vX6BM7m89hOwIZsBIm5PRgKO9+BViW70Il8M5V61Mi7fk0J6KXFqFYCWJZ9Mxea89r1MZNgEbAsJhA399DI8GjVvKzEBbnbUw7doVz1Tu52kcK8zfoJY8gnqMwC/Blo2Z6/rb3Dzm8iRgJGAvMpAXcsnnQFW7vbOEthmaB2xIVxoX/FShk/FiqgvuSN5EJWuVBNa6geS7U3VOhizbyYwlrMui/W/l6a7QbIysxf/CYcQZnZtamd1IKmVfoWHSiIVsAs+I4Plqj6sxl2cMdPJjFPRhQHZ4WoVsWKmcIwICkypZB2qKdE5BHWlsmJSZw9eBhuiWCB1hmE9WIo3GmyKoPAW02aZ0xusBVheJZJgLOcdU+q/cCOFMnPdQzHlclkMhQK0wUccprJyj4zlRMwOknaqyVTaI1mhZapZnApNIdmqTaoWYomhTQoDnBHhjhEzXUjDD0jASOBcgmks7KFYHtXX8A9Vv5s6d9X6qh5bRN7b5W4ysxrZXNNvHScmWvqS5Teogll0Speoh1l2J5vCRQRp65JoVUNWe0EAfKCi3VQyRgyKMmONOhilqUSkhMTPb19s2fXqkZQI9S45SqwUs0vNMCkUgJJhU+JqP/SGklTH/zY+CwA0v4V8oygOdRxuVFJcrGeaVTLLgJUrTLI1uYrVq7s7esDymoXGs2kLAtVa0OJCLsq6NWiRMmjL0JH1cvVDpqNmV3ZiHMyNUmPwBjeMswrqJRWl1GjLmFMzWBEiIWY4kbubdBeUk5zKNm1o0wmS8TsBVkiI3NjJDAPEhgbi2f8nb3dbDW+fAzt8yCnBkiWA3f0tVJ99ZJAG1Yq1tqFC6NL7QzL/8niiGBxal3+vWla2LQSQDXZEK7IZBHoSZqF3IkJgCcFVxnwKEZf4ulMmt20MHfPwC4LEY10iUhNCkijKqGvvHG04tRgssCnutEZC7Bb+Fe/XMU1cDkLChfYr9omV3ku0F/Bc0WAC1hfnnjEjV0FQfz6H28SWKApLEkR0nnP29baSq2WKMDlejIjMikG6zmfyrtcgD4gHqM4/7DZY71GXPzTrdbN0LMg4vUGt0uKZ3NKSHJqCfvMVO60AFkZp4RR4VV4l7+CjlMfSjA8KchFRCWLlUlWcxThPJMmVaRhgpGAkcD8SIDZ8Wg8E420t3pT81PDRUq1RG3Fsm3p9qtd7ka2K87EW8ZeC7MNw7ILhXFgIdu1CFUuZPNMXUYCCyQBgWgWeLOr1EpKozy5qpiCfgqgyjnSbjxkQHWyOUAuDx7FHSUQLBy3adOZIgIhICx2egWu4UAhZIWBBQcLkkxZCFPI2BnUTx+eCykq3SqsM6kcUkZQqH1VJdQjlarbzb3Ooz6IOgKnwLZEOElKjOWA+XNnz+LprvCtFCqZAmgiVmVCo8Cf4kLvmOkNuAKusIu9FBQkpo3qYCM2v0d0BERBufo3b4EBpgIC3D3ecEvLWGys8lgW5hvpdEr1H5zoblTIXclXhKoFAv86pq6Kf4Xx1ZSDuQaoHVKFxpgPIwEjgTmVANt/Z/x9Xa1xftNzStgQcwHcRcsjCbGa9N7sCfc0KpXJ9HA4c6RqKbwIu7q7UN8cEIXBo2qeWolo7va2NpT/+HgcE4vOphRyrRLFdLECZWVfCAaPSoNNMd/UsTorm5qIeWokYCSwsBIAPQpKU4hTKzdH/Rqog/dIE7TJRdCm9pn2uNWiVrc/4Mf0yylOE+Lgzp5ldQUgOwEsmMlkKYhGhYh2K6EKXaVFCGcVC1WSxFOnthH0q/OrVHhUu6ZIWq2gW2tfhTbTEMltp+kozennAG1SxQjteffUqbHxmDLNo6H5JI1PBMFVFvgSJK3wQKWouMoo6ZqQ/uQqVebFrs+BHsFgABd6xm/8czjrw1bjkqdG8Pn8vOXgPQB14rCEDBFdgaxVhEf0F6FMpraEhQn+K150BArSzSJp6WsCq2DZdLKMslWD+TQSqCoBvlJT/AirFrkYE1GDsVQo7A9Hgwnrd3gxysFuc97tjUfeMxFa5c5no/HDocmTs/wiOS3ueZensBO+x527t++1iG/ymfPbtve+MZKOnkr0Xt954KWhyzZH3w15Uz85d2MmVwDT+RqO8nTeB3/twcHBwTVr1jz19DO852VE4JSlFf39QHlsWmhzxqe+vr6zZ8+GQ6Foa+v5c+dWrlp57uw5hob33nffmbNnLt2y5bHHHuf0k7gK/f19nCPV37+Cw5tQwR3t7YmJiZHhkRUr+lHxJLa3t7Mdgd/nf8973nPu3Nl33jkAiGdt2fjY+Oo1q6mRodSWpokYCRgJLEsJCNSsNmKoUVfjvQKCB83Jn7VJCX4sTPXBo4BuzLrsLN7R2VmniNB4PT096KVQaCKbSWOvSE5OABAprgGtsKTQpILgZJRPsKOCj/pTDgQC9QqiBBoLkpYIeBm2iEluKeIBf7a2tcFfbHQ0FhtbtXqNelLgtGRWoNCq1QQ1YZAGCyXB5FKbqH14EXNHNqV8ZYQ1OaBUhKNmNWKhp1K8z6UBgt+lIP/ZA0YCoNyO6IfkYwLDH0Ry2fyF8+fxp7fYqPkJHTZ7oSKIQJzWArLLEL9uqchO8VegRVwLVxhUkpJP+af+pL0kSHPkzFTpXBb1FsqaDyOBuiSg9Mf0OeWbdtGG+EQy5W5rDyZQCRetEJwNz3kCp1d+YiyySScOdN3ZPbyrd+CnaCNntor4VE+rSzbgyVzdceTrRz6QyASfvbAtm/cA0wdS7ZPZwGCqze/Jbms//MrwpXZNVb+nIHW2Yn/hxV257Av4Ld537z2A6SeeeOK6667r6up+/vnnb7vtVtT/gQPvXHfdtSh+jnsFiK9bd8mlWy79xS9/icYFcGMf2nrl1mAgeMkla59+5pk77rjj9T17rr322r379kWjURT6qpUrX3nlFSi3tbX97Kmn7rvvvqNHjjJkUnLz5s1wmEhM3HTTjWfOnB0biz1w//u+/o/faLotmW05moiRgJHArCUg+NJWSXZEcJ6Qtj6JCO6TBAXmdLWgT9EPAHfl4A7CCwTVCazTcUWlwFdwLcoKX5Tu7m4Aonh9p1ITEwlMFZzlhF7CdYSXgayJJJvAXr+fK7ZtcVLRQYFNatNAs1a1wNm2tlbeZEIQB/PWaFQAah1B2bBVPrJ7PNhEurtrvmWlUZJVxCQRvWAVmVA7wBegz7tUpis8QmicvIFzi9p53ZNNytsG6qLVbMszFosxDejr7weOC8HagQzJZAZwzWQFwSAfDPAo87ISMCadplM1fMemXpZJJGLJxBKOKqeBe6bdbClTLjFzPycSqPgmTk/V+qJOn7N5c/DjGpoMh3LxzgiLUAuG3eZld6E4G+zeCWrPxAfTY+eoM9i9caDr1vDkqej4vilZmOIr4ZYDKfRzZy5g+i8GroqlRV3qK5FkTgwzqZwv6E1f01EC3G316OSDXmxR50iBucPh0Lp168bGxoHaJ0+ewqr0+htvgKcZ3sbHx1DNnDP+1lt7f+d3f2f/vv0dHaw+ltHixImTL/zqhe3bt2NoZ7DAPHTk8JFTp97lreu2a67Z//bbR48exVS/YcMGXv7ybrqzoxNr/Z7X96xYsYLh8JK1a197bfcf/uEfHDp0eNWqlYlEOwOJDNYmGAkYCSxrCQhAtLBaSUOtX7+tBgT1iR3WeuBy6dPv8JNRjnaBMnNvCTXHDTgbPQaQRZGK8VlZ7kHn7tZop6tLZ6QW/DuAsIJ9s6zslCAoUi21BA0LH1agCFFHDRLVaF5l0eVUWaYI0oxyl5KyspqawruQlcyAaF4IQELnrFodibpSrrSKC7IFEGO6RyGTSKA42aCDsQYTezIpbkK8rmC2glpmukKzW6IRnbOMq6q3iMjrZWmBlynQREKsMGXZIIW4nAKiSc4hrHhDqn4gC1PVFA0uFat1TsnKqja3RgLzIIHyX/p0VZR82afLvBDPJ9KexGS6M4JTtHmR5RS4e6TtKrmXw+9ysYPP9t26npvRtqunA+5OIuVxjoC29JrjEVVEvJM6oact0B7xHz4Tt58ns/6HT+y0b2tFML38938/9oEH3x+PJ55++ukNGzbiP4pXDG7rnGmC2t2z5/VYLHb55ZcdOngQCxTf3D179vT19h08eBCaBw4cAJezscAvfvGLu+++C0+ZgcFBGRlyuY6OzjffeisYCNxyyy3nzp17/ufPv+/+9/HgwMEDvb29AP3nnntuy+bNb775Jm9Pobl37z7okHnfvv1GWdfqL5NuJLA8JAA8A/OptpQNb2p05KJHyUKE7EUEyLJIzOHKopzGUo4JuX64iSY9ceJEV1cX0B89E7Cs6SBdFZRbuM/jx7Uatav+FYjDJsyoq7AmAFMxJYjcdl0hSWN7laYwPUbu1tY2YCgzBV1GFRYSNbrSPTE5IVSkNvGVQUt34gukwLcMBRKxuFMkFFll25b9ZPgnpQsXmXTIaiI9FeH1Alu18IYhmUzxfgHgzkPoiUOO39vb21enJHnDzmsENDZTJsaF0ZEqxzBpvx1hUOZdBeFZH0qSPKIpqjG6PfmsAH3NO2wiv5kvfxLCJhgJLKIEav3Ap2ZJ/SSmztL4U7TBaLolkBnobo02XnqZl8h7Ajm3mLx94c7xY7s6rrjfExAzRNZTboyYVhAyJqhApLqrDCfT9YZGdKZgwPtHH9r0tceOHDg1rlMC3sxH1zz/r8fv1be1rqjpsfHxJ37yJBkYC596+mmd8/x53B0v4NmCyZyUd0+f1um8bAVh73UVXh8cUaidRwxcuMhbZc8TwSWGa39f3+49rzNo4ZDz7LPPkcKZHS+9/LLO+YxKiUYje15/gxTGFM0AI6jOYK5GAkYCy1ICDCSFn/mU45QCfFoVKvSnZAGQBS+CPkGgmM+7e3rrFxHLMVF6w0PD51JnwZ14oaDTiICq8YmBLGZ4QbGF/4Kb9R0xClr2a5LxIVf32qItcFpaoq8WP6LGS/4piM0FbFp4plMEy9tpOd+4deKeIHc3LwfgUEShg1j9JbN8KjqC0SuC2NVVECd2Ma4X/ut0aMEvxFksQNPDLdGOru5Kq7nVkPJPigD9I/j+yDFMfuEDzxnlW29nRRwwpTpP96AG78hDUDqh0POFD5mL0CBYhhamH2YYdIc1u7OpmoiRwPKWgPVjmb6V1i9nupy4T8fHhztaPd6gQe1VhOXOJQOZ2GSgZ3LgcHLgiL+1P5dKgN2DqYEquWsn8d6SoJ/LUIIlW3kllpRIZf3/ffpWOykS8n7xQ5v+n/88rLF7Luc+mWhgPLPpzG3k3HkB8SYYCRgJGAk4JYCKw8WiYuTRgxZX5x8gUxJAh4qCOxKJUpAdtjh9CXAaCsuG7vUH4CkBXMiKGnZTYbk8YBH4DRjFq10820Hw/IEZrUAMAK8hOpgdvMt/lcCnwuwkqcBNMcnBk7Cu/msorxG4vmo8boFwScPRngWllNA0maLw2lMEIJk0Ztfg1oHWxbFEwC4BaK6DcjfJgoV5pKuHIGRxp5FxxR9gL3xWfzJ7oe0OZqePMplB/DBBRAuJmUHZIMUDKhaOocd//uREWDiQmOp6fVUQ3rpnOoYE4ZwInaEmA+rZ9EyZHEYCRgIlEuDHNzrp8eZi3e3GN6ZEMmU33UPPv7viw/62/s6rP8QjjCXeXKpz5MWybFPfYhpxZhCHxYGRZNifYUSxHwS9qYfW/Pzbx95rpwR87t+5f/3/99iRY2cTOZfnZGIOjhK0ic8ywheI0bHR4WGWlZriRgJGAk0oAWAlVmQBuBVBA7xicuFe4B+BdDQq7+iIoSUF4AJAGwSdmjilOju78ECBLLgWamKWlpWayfh4nPWqWKTZ2gQdDnYEwwN50cOA1MLVjlh4XoNswdoCSwuh2BANnEvudYOkXTroqQlxVCVvBohoKqwcPXP6XZ1H+X7zXALo1oHccTKROynFilZ1uCzFifC2kyvt9QeCcmEHR3FxkRcLPHdy1ECcnWTgM5fzSwd48TvC1IRTkJMC1OEIflSi7kirx3WaPEBcSmJicMdjx4MXD+zROqYrcE4GJ00TNxIwEqhTAsl8aGzwVEfPSp+r5IdZZ/GLKltb7PWcJ3She0emYw0ND6UG+s//KJCaid2ZMYJtGNlZ0Yda9PsziUR2OJG1lwFn8t43RzfYwj09OAlkH5/MpjMYV1x+T+be/te+fuRBO0OtCOYZvBXxE9UZWGbEBskzGwtrVUE6Y8/OnTt37do1RR7zyEjASOBikABQORJpAU2WwLIimCvYZ4F8Cu4J9tOwFXAH7sRMrKG2gMVQeOboU8kafAkIB3661FGsWv4KbspFoHBhoaS4iWPNJhW/cHQmEZ4CMXURDZpJ01SJQJkGEtF5UOg6p31VmFY1UmN26wo3q9esofHams8C0onEuABbdY9ylsmEGM6JyASCW+Jgc9UOrjK54J8AcwXPBR8LM3MZoIw88JMhQqeMx4srrHQ1pCMu2qSbZSN3IgVWihxJTDXOg0sPDRHgnk61173L51w2zNAyEljiEkBzjI4n+YGy9S0mjiXemgViv2NkV1vsjcGu28OT70bih9z5mcgNJX/VlVuPnzgJ04WXmLze9ftSMbsVog8Lmi+WSP+vRw+NjBdrSmV9j757h513igibxmzZsuXo0aPbtl3z4yd+ctttt7x7+gzTBbZfuOP22+n4t956Ez7QwhBhMxnSV69efejQYSdNxrDW1ijvc3nEro7ORzpOhq4u2WsZmxbLWDnBGxA/MDhUz3CCgQ0b0dDwMMV5lX3X3XdhjHn8Rz/u7u5iH0lnXZwGlUwlV69affjIEWe6iRsJGAlMKYEihpoy29w8BE8qIClI1BkK0F1DvOINWdAfEojhJ4NyVCBa9jFsa1fbWzmpzEVc6SW5aL1XfNFZnbgwprjTj4VToKfP7wmHwqz7P3f2DA/6+lfobLohOqvzqtC4JPBGQrxi1K4yMIEry8pVq9XTgrwUCC+VnZPQgsSZG+A2zwoBRIStJzPC6IMcilzRy9riXuhJzVVJFpVdl7DKocD10Vqso618S7EgLTOVGAksYQmk8qHYWKyjtcVnDkNtsBs9uQlvZsyXHpkZaqc29vxFMY6OjqK3q3sf+jzZazsP7h7ZTO6xhGzT6ww83dm3+5GTdzkTq8a3XbvtlVdexQmHiQEZOA7pxhtvYB+Y9evWYcH5j+9//9ZbbhkYGLzzzjuHhjipifOYchs3bmRU2bRx4+uvv8772uuuvfbIkSMf+chHHn743zE7XX3VVStXrtj10kvs0R6NRAD9PLWrvubqq5kVPPnkk7feemt/fz+7JRw4cPDaa7cdPnyYoerw4SPbrrn6+PET8JBMpZ566qkHHniA9w67X9utgftVV1/F/jbww96Rv/97v/fd73733PkLq1etPHjw0H333cuOk88+8yzuoRs3bmDLmn379p84eVKNwXb9JmIk0LQSsMBL0zI4F4wBW/lJ4uVR3QRcgvIUclMmaTFTqL9oWyuon43WlW+L9qaYC7ZmRUM6ztGcgsrBCo5ritjCZeNLOauozkqY0GCnFxgsZJnlgI1lqlNn8YXJxvQJ6zg8wh7Wfu1/72SSOH1d1p8WuJdk2lNokszg1D/KuD1jY+NMXbC484JjYdpiajESWBYScI8msm5fnm0Gmf4vixbNTyNsVWqrJzuFoyaKimm62u3iVsaR0dGjx45xJunBQ4equyGy4eN/nb7tg6teuLr9CCcxfXD1r9r98QdXvnhHz5uboqc/sfZZbjW10JTjBZB9fGxs0DqvlMVeAGX0LRuxnzlzBjS/e/funTvv/tnPfrZq1SrUKTQZQ957370s6tq+/c5777nnB4/+EKi9f/9+EDlnM23evOl7//H922+7bc3q1T9+4okbrr8ONG+1y9Xa2so+7tvvvPPabds2b9ny5E9/BuD+j+//4Morr4Q+L16vuOIKzPz7336HDeZvuP6G8+fOv/jCi3bxF1/cBWMPPHA/R7Hu3bs3EmUcb920aRPnPZ08eQorPsPJxg0bbrzhBnhgTsKrbLusiRgJNC4B/SNemGvj3C3BEgLmBOXlnFBXtcPWgkTsP2Lqn+B2yYUCoSBOGgA7gB+7VDWrDGR+opCpNBid2QCfejtzGqwLNVK0gVpml5XWiQsQ0zCP7HvDFbTtJEkGaXlhwlXoPslQ6OfCb0rLiGQdQYFL36qt6+llJ0ETNxIwEqglgXQ+cGE0iVt1W4CTlUyYUgLF4cXKZqdIRCmrkhSXx5fxd8b87eNuT66guVBZ6CzHn6YFMH77nXfIU93ijlIfSbW+MHgFe/1Q1ZmJ7ngmtGvocnzfSXni7I2JjKxI2NLHKiLXIXlVWz3Irl6RSF9/3yWXXMIBTKMjIxcuDKBzDxw8+Bv/xyd7envB38wh2GGdIRM7GWby9vZ2TPJ4qR47dpyyd955Bx4sUF+5ciXLxRhK777rLrgHf+M56kTt5OHs1fe9770UpBbEg5om547t28mGe/3NN924Zs2al19+RY0KuQsXLnB0KwLc+9Ze/c4acz6bIuOug6keBJ85dBjIjn399dffuOWWm8H9u3a9BJO40OzYsZ3NirHfVG+2SV3yEmhKOLPkpTrvDeCnjYM4P3wiWgOWVsnPXaCdfMi1+KnUhQ+FQxoYESUghugm3jrW/oICXoG2qkE1L3ZmnUPUpuB2EZLGvkpaNV3KSdkAACAASURBVIsv/AP4gUmCrHP1evCZYckBPo2lnMj6VCtFOlNtBUmfljRXQ3Yrm0uWECSTanGBjJEmzEwC+pczs7Km1BKSAPqBDbLykfW97dgyDGqv0nWevKs7lWlN5yZ9nvNBb6YhM4rLFegfCK8/4vaJ2TqXDCcObsmMtlZWU6ai3Z/+zGe1MUP2Bev/kDtYr1tn0Oda0+kairuGTrzWnTlQWRMp2NQ5NvX4seMsMAJ2r127dmBgAIM/3wYGRVx2RkZH0KGgZHalhA0wOi9/WYrEjgxkQ3ETwbtdhhjIqWEGiD88PMQmA6yq4mBCNh+g7Mc//tCPf/wE+amIU1pHRkaJMPpSDuKx2Cj2G+C4nDs+MSFrrjjmMJWCFFHGA4A4GR577PFoaytssI8bQ0UCNrq72DiMs13ZdR6G8S5ijzMMNswuiDfzuF61O5Zgohlbl2CnzRHLM+t7XDBRFszz9dJGsVoQ1OFB4rwui0ALAY3Hno+ohPjYeEx+2qFb77idDdeHh4ZOnjhBVpy/56gpsyBTTQq4eYTDKMsgmur0qZMtkWhnV+Fw1mlrQtFFopEV/f24A/Ea89jRoytWrmSqM23BBc6AekfN0ilYfwYHBhITExyu5+SBU/xQ4Cwm1v5CaOPCalkZJmThr3UwFC3GzE6n8x6FQ7Umzpw5jR0H08+aSy5xEjRxI4HFlEDtyVDtJ/PLbzrrisVTkUCOn+H81rRkqQdy+ctGJ4bd+YTXFci5erOuY23hmK+6SXe4/cbw5MlQsrhQ09caj175xuCZyZY2b2oyxwZj0daW2O5r8+lyWwwKjeHKklOpj/vEiefz3jKrhpWz4hNfmSGV6E8PuWqgfcaVN998ixGU01LJi5Ef9SoaVkHw2NiYbFDgcZGNp/IeU42sKGL1SHIS0UOvfHfRx/jcj43h0IkyxvrOS3FIUfDVV1/lEflRz3gxkkIG6Oj8mMbZII50brV1CkIa2ZMSzyce/eF/EoGU5oSC0OHsw/Fx8QhigATxE6FGyFILPHAlxQRLAtXwhfXMfDatBJZft/HDRMHxWy6VecXYR4JOU7ZablqisuM44B6Mx76NEU4BLKNRSnFR75SrjOJAlGJ5Y6diTeteVUo0HmGq3Iv3DL0O2mYTSDoUXT2K+aY0wLl+XUByoXeV0b2Qq6RZ2uwu7xiw2vBaFXcqNHwpPXNnJLCoEij5xpZwUvtJSba5vRmLp7PB3u624VmqiMJvcwrmps8xReFFfrR5fPJ4wMXxEPAx4XXFfK4tsck9XYV9FCuYK2+qv++Cy5MLtXiOvpHIJPOX397q9qf8XcOpcyVGCuigBrEaa4KA+BJcP3TkFwDTisqmScAa7WrfUjUTlRHsRxpJ27dzFcH1iqNYZ0xNxq5mHb1m3Kg6Ci6KNqiDr4smy9LugGblXtxdCkCuwCIfoi9tnUlExdWnvgiOjUajmGzVulQ2sU32hJvoqIravwnhv9GuAO8W/EqU6pO75gssu1UrDWTEYhUuEyqGK+doIm3QnKsrbagiByvJdpghgWEIOpCj+EWp+Zuvsw1HzSQBQOBwuj0ajLf6Rqr+qhpi1voJ1i40fY7aZWf9ZJa6z5vOpn3F15Xo4lFPviOdHfEXE0t5LKnQE8AByRXpxBbtaevx+IMiC09A3GamDiXAvWpW1BwuLljFzw8NJTgyumomk7hAEljU7/gCtXFxqllikl1i7C5on3LIjow3JSLixsJ3RV4KykxDQPwuyMRLP1xogHRYeYsZmy9mI84ZoE/Bu0oYtBc6ctt8AWzN9Elax/pUzmFSb1OdnUIGoDysO7gnWtLr0iw7QUe0jUYtQW6+RhuOjAQWWQLxpCeZznZHxmwNs8gMzXP1tnqYQT2UzVSUT7tdoRwWgar0xMhSeOTJB3pGvC3iikJoafPgLaOi7szYdC973TUWp6ryLp/bfWP/igf+x++fPH1677PPrsvm1l+5dX8y+dJLLzWnrtdsN9+1eh82H5/Ny9ESkOASYLF5+3duOcPZ2dKb0isOYOesRyUrBMuFI4WibW0k4UEBXlQufMVXhc5iTRMvfOEEq1qtrZc3GqyzckapMjzXW3AB89EoYQ0rO46PXi+OiyxJcAJ3yWDNQKz2KP4KbRPEXpCR+mAmp/bBzLMeoLOr++zZszhGMltbwDaZqowEmlcCGNpH8v2h7KmuqPFor6ub0DSBih1K2rP5g8Fa5nZRSd5oMrTqvDeaSJ7riu+9vOWy/d7I2JrLxA2mYzKw/nh3+mT4dDB3Iuixl96XcYM+q2lxD/r9d3d1HxmL/eWXvyz8BQLozdbduz//+c/3dHQ8/pOf2LTo76Fhlm9Gfd6CnrQfLYvIsmzU3PdM84qpeTmb+14wFGtIwEJzVR4LeOdcHhZggWJZu8qCllA41DAarkJ5HpMEq+svNuC1EeCu8C67yohNqPBWYgrZzGMLpicNqwwurEpiJZQG7s4yWNzlDKZCknzyn42SLbROgsiINoqc1CaYvJDu7evn9CU2PGBt79nTZ8KbZG1rgYb5MBK4WCWQyAQnU5nO0ICnxaD2Br4EZ1sCqxPJ00G2lnG5fbnIpuFceyLiCyTPd0+cbS0xKLhcG8fPsI1irPXs4Om+2IG1upr4viuCq0/7O4ZvO9Vy76sr/GkMRimovdHme7gnMCnHWFcJNa1Kn/nsZ0dXrTwQj0OCsHPnTq4syvzqV7/qO3So1/EeGXNIR0dbPJkbHok12YofUdyz/lPtX0aX2UukKoVZSagqxblKnBVnpvDSk4AgNYXWqrGOPtN/8ln4l3eFW1oAcABBtTJ10l4GVI3C4qfJC0/dQGU7t98v1MkZxZVWBxhLUDTqLLqg2YDmdAdV8grF7/exK4yzegTAREt8fnRr9EBld64zq4Xl2ZdGO9io9yqpiYlJdhAqyWhujAQuMgnwGxpOd+RTsa4WNt64yBo/6+aeD/mGo6GNKde6YKr9rgMDngt7jmXOHYnnOg503nCE852dNaQzp937/Zf+YtX7Dvo+OJi8fTS1cTLbOulNHrtk7QtXvm/XqmwyP57NpPK58Wz6mljmwaESjeckVd3YwDqtjZs2ffOf/snOys4wOo4J5PkTJ65ubXt2aNB+6nHn21u8uVA0Fhtz+SKtYdnpxX5aX4TB9uINzdX45uLm4v1WmJbPQAIKxVnfYOtT0SkBqIL3rH/gWHYawYuaLWJBh2wj21268+AM2JjvIgBuqpApiNpat6HqBLjrdZliji6RUUN05jszIFtbgogwrWLocdbIDjH5vKy50n/qs9gWifHfSmBvSPZ/FILJJDSll2WjszxbCfeYJapOsZr4xSSBiVxLIh7raB33+oMXU7vnsq2DAe9gMNx18/Fz+/3ZlEx98ln36PFwujcWvfTc2Nsr7MriHtdbUf9bLj9qqSWX707n1iRz2+KZcC6/bjIPyE/nc//7/PFuf/C6SGdr2H/TWObxLn9Vo3v1GRZ7Aj/5zX/CniEGGaXZX375ZarX8aF0evtnP1PxhpEtGTC9d7SGPWOxMU4+yoqHjtad9Vzt1i2lSD0NqyfPbNtcTx3155ktN6a8kcAiSkCWM9aq3oHzrCwkufLhFlwM3bIXZDqNJzQbv1qPm/xT2ZsbBd9q1oJuFtyOw4m2WDdfQ7H+4NYiXj2yPtULn2JitwKDkcw/pmVefRdwheJPdnaXjfyZm03yh9i4nZaAVaH5NBJYPhLIuzxDk8Fscry7lV1RzNaos+pZfxQ7QFyjdptQ4kLA23Heth3Y6UQYc+Ie94mg91dt/h90B7/bE9Km+Vavf10ociGd3BSOki2YzYdleKoSqlvcQ/n80LlzwWDwpptuQmPu3buXpTxXXHFFX1/f0NDQG2+8Ee7rE61a3Aq3OFKS3tHZiUIcS6TzqfGOzg4Ghyo1L2pSkd1FZKMpmFjE9puqjQTmXgIgOSeO5UdWQ/UVqlZP3RwVJ4aSbA6dBiLkXLa552xOKWobCiTBnXa8nhoU3kUmahNJN14obocar4fAwuWBVRYdqA51s3pYNbbYmboh0n4FvXmg+7pSrZJDb0rDoVpMzBjCOMN7cmIyEAxwNJP6glQWWrhmmpqMBBZYApNp3J5HWaTtdTW7oltgycysOk8wnRytokPwenFjcKjceqa0GjaiGfS7e1KueC5zfDLe7vMdnRzfEIri7j5ZhaoUBrjzRD8sZon7/Vdt2/b6s88+//zzuopt27bt2bNn37593NLVp195ZWp1L/A96s3lOoYGB73hrrawnGqnSc3yWuRyloRmXHzxOZgx66agkcDylwCQjt/odD9TDQHlSnbOggAgYm7gcE1ss4pAUwvK0ToguOOuLq4L8JeCqOXpjdZ10ZyXTJjYBZHLwUniLcMGCfSUo6ZCQwopklXmabY4dEReHXvZFV4CJ6didpftJeX4bA37S4k4qJuokcAykwDf9ZF43pdP9naxn5IxtM9N92ZT/mB7Ln6unBojybSoXZd5od2/JZGdyGY/3rM27PUOpOXEz1dbfRM13h5XB9MXEolbHnzQec6tbOVuhdWh8EtPPcUgZyXU/GRU6O7pbQu5h4YGR8YmRIEqrTqba83K6nkwm4rtsvVUZPIYCRgJLJoE+K0WQ8mNTi6Banm9aQl2W1wpOPTHZ63nKZJo2phuSJUWTsWxMlIrM7X4yij0OlX2xX3mxj9GTTAA2853vMJVsSGqNQ5GSzsYm1c6w/b8KfmfpM1qLiD4ni3/58qi5KjdRI0EmlECyax/cHC4tSXQajZ8nNP+ycSCoXDUGyg68kG+pTeZHe6vs549Ld4nuwIdgSAO7i0e39pg5J2I97+7a3psVneVQb397Mkn773mmsd37dIq8Pz585qD9vb2+666+pEXfuVkaOqBg/fOvT296axreHgA5dvW1j5zXTl1TU6eTNxIwEjg4pNAmatMFQHYoA4XCh7nXfgEope0xT2VTAXDS2FDNEGfKtiRwn09H9JuyivcLjsoNnPAQiQ7Qno8gGyAt80qU46KpisPIFswkpUsbp/XNzY+lssHeZ2CLyqkVEnJFwj4K6nYVZiIkcDykAD+dKNjEx6/p6e70+WquVfJ8mjsorQi9samFdfuHz6RnhwO4B4TXZlsiXSOvNbA8dtPdPrfjHivTGR9ufyxsPftsHcK07hPqe8qLX3ypz+9sb1je3fPG+nUcCy2a9cucl5+6aW//Xu/98///M/xOsztxReWirzf52JdPy8rR8bTnnyyvb3VaMwqcjdJRgJGArOTAP4f9RMAtorZ1evFAZo/jLJtHR31F1+snChPZhp64tEoD1JQioJpXaBYFW+UxgLlhz18MoPBEBHehCQSiZKKxb9d/pckVtzIQJN3TU5Osi41lU4HlCuUCNCVb/J9PyuaYhKMBBqWQCrjGh2Ls2e33218YxqWXp0FMnH/0ItXRdYNRtfH8OxLXVg3vL/hw93eDXj4q6fG6hZ3SqILXx4dWdvR8Xu///st0ejRZ587+9ZbOa/vb/7mby5cuFBO2jJylKdX3LOtZFe7K532sEKI4zDa2tuVU2JFPpNgJGAkYCTQuARAcrW1EVCtlKLc5nGbBsPiJ5ORHUcyzuM5S3M34x07rgBqG+RMQD9FtMWdCUuDxRcuu70FAs2UHSHVOa9Fi4+avcCN6kZpj0Tz7spvAH2KuyYF2RfSzRIGMcUTPK1tbQvXGFOTkcCCS2BkZMTdsqKngyX7BrXPr/RxZx8/3ONy8TfvwSfKTg90+uqoEW14YmTkb/7n/+RVMpoeW0VuoAKyO/LXHwW+d3d3pzO5oZExvzuL9kSJ1l/c5DQSMBIwEqglAVEm0+kTMdTq8nmXLObhNB9B7jK2VWx0W6uexUsXW3khOKJW0nSfbKtIFi0AZNX8FneYxbsFEC9HpZZuu85toR+lP22paBEUb+lTenlw4AJ7FvOMVvOMU7cY2qaTlnluJLAkJSDeDSMj+DYHAhxZUPwtLMnGGKZLJVC0uKMWMUuw6L40g9xxIkllop1SuszfTp4+4vd5ujui+NMPjaWpunUpeJZO3yqTw0jASKApJFAyVlnwTnOm7+TKf9FgbCkDbk/LXpAK1DVFA2oyYYNVa/ZRM2f1B9ZrTmVyBw1Xz9UEqfQFe3QKWAdxe730KDscON8wFHG76krV5XSp1fXWJwktLZF0G07yScHsGNuFXnH4W+y22owuNiOm/hIJaEVRkrQkbmJj4/lQf08PSziWBL/Ln0ne9E0G+jz5bDB93p2f7UvOouZCjW3YuHFwcLAhPY57KJ7rs5E6kJ21s6lkcnBgLBDpjIb0yqHZkDRljQSMBC5eCTSIvPO4UDM+Y3HHRoVCa7D4IsgZ3A6TGr3PAFkoK7UY3MVa38DAvjgQoLAjpEs212eVajqdwcdSC10JYToB2Fy7XZFoxJ8KMNZQUNotErQfL0I/miqbXgJL7+vBmpCRVCTqT4b8LAhZevzX95WY7ldfH5UFyxULXzqea/GmhvJuXy50bWf2VDh1dja1F4E7VFips3r1mtmQm3HZQDDYHQxOJjPMHELBYCQaVZp1xvRMQSMBI4GLWwKOMau2mmcvSC9naoJl2b2Et39+CxTOj+wcPM2iAtGN8qeblVcW6BlShowyWs+w+Cwa0UBRbElgdmUj52yQorEKOQj2VjMYPmu2oThBEbheGFnEll843akBVkxWI4EmlsDYWCzjiXS3cCrw8nZgqPlbb8LOiQfXJpKZQPJwgbfk4HB0k98b92XHZ8xtCXCX0SuTqT3IValFXA99JUSqZKo7KRT0hYI9uOsMjiZD3nS0teFluXVXZTIaCRgJLFMJCDwrCeX31kN0HRtBEvK5AnBviXCEaq3sVrGm+mxIX9uca3O9NHUJNBZfGbxkANzK4l6ymZ3219fYncZNBd8dLYUUzcaWb8vDRIwElrQEMLSPZlpbvAn+L+mGLD/m495u3/ghZ7v88ePjbes6EnudiY3E3UXMDWo/c/rdgYEBZYCplwiLftauvYTNYeotUEc+XE5xOp2YyF8YmWgJeCItZv1QHVIzWYwEFloCzYj5UF8NsSV7QXo8GCzYDBIP6EAH+xwvgdBQG8vag4jA7QVfmeZenArn9A5nnbr8ftlYhjOYHHvg8AikPvXMpVxQ9j0NL5OLuTUSWJoSGE95U5OTndGEx4PdwYTmkgDKq2zbL85TzbhnBWuLwJ2N0EDtzNsaajRvGy9cOD+3wF0zUIDvaTf0W1hYFIk0xJjJbCRwEUvAhicXpQwqLO5TSAGHaYy52HRZBJnL53ycN7FEgqOPHdH6mBfkrs3t7JDY7IZndupUh6eKxd1btn1C5YosGjaNOJTxXuQkQpg+e30SNbmMBBZBAvx42ec0EmntalvevjGLINu5qtLnLnlJCNm8JxDIlx5J0WBlxZmA2GCUNq+kAG5+6KGHKtN1Sq1StfI3lB7253t7ez2+4MDwePnpGw0RMpmNBJpLAqCL+ftrrqY2Mzc+n7xZxlVG8CsuGd4l8qLZmpyI4bzxYJeyyDROYqFKFCzu8luRrWBKhxsF0UuTKvlSmSRZRQp34ixDwZkIr7IGk2IksAgSSOQiwwlXV1d3eEkc9rwIEmqKKltTp1MtJWtH09ENrZPHZsPc9OYlFNwf/uEf3nfffUePHn311VdnU9mMy4aDvnAwmojHeScQ7egL+Yxv4oxlaQo2KgF73G+0oMm/OBJoqMM0UsdVGn8MUBwwcXGYnmmttvm4fgIKs6rsFGaJa7OiV92PLKPivS4gG7bpHYlbNnUylDBP/9XV94XZivK6acyxqk4hm+lAnYIy2WYmgVzePZKOBDOD3dFZeVzMrHZTqiEJhNLnO4KB0bZLXZl4nhEmEOlJHvLk2Fx/5mF64I4X+9NPP338+HHOOp15PXNREncZ/uKJxMDIeDQalWNTTDASKJdAXUN3eSFzf1FKANjn9wfE4K4s7goE1/v9qTffPAq2yEIxVl91sqZT43VVsqR4yU195OYzF67tdJNgcgXcVVS2w6ROHsmJTJW1V0lSmeymQUtOUVUb1NiJlXRmmjIPJGfKyvIuV9rRpXfLtuUTyXR8fLyrGwhoUPvS6OWW5KkW17sZXxs7uHvjM99Mxm7t9OYl9jY+cuTIjTfeiMXdLraIkUhLiJ3j8cUfHGU9xqxmLYvYClN1qQQY6ebqr5SwuVs6EpiTb4A0t07/DzXOF1xl8qC4LEYKwe5TfxdVjukyTU1itk8FdZZ16zRMl9bITEX96aYoiTkylFFe7FuYxItJ2qeBu0D1AkJTYhAXT+FRLoV0m+WilLR8rFbqDOW57WImslQkUPq151ex1P+mHgax1w5PeHMuX09Pp8dV3Bd1qXTXxc1n3pcZ9c5iC0in9Gpa3LFn79ix42c/+xnjWSwW+8pXvqKLrVixoq+v780337S1p5PcgsWxuEfy+fHx8XjaG/FnjfV9niVfHAHnuSJDfi4l0FzdtlDc1F8Pru2AdYAf0JBdZbDg8jeXHTCvtGbDKaBV49bCFGReGZ0VcTGrp4VXDdy5OoceB/gmWlUiJNrphYiCd+LjPivOTGEjgbmWgP1NrSQ8mQuNx0Y626Ne+c06vtSVWRc7ZV5+V/NCdLElNdP6qwP31tbWL3/5y6wK7e7u5mTTb3/722fPnm1vb/+jP/qjAwcO/NZv/dY3v/nNRx991KlAZ8rAzMuhweETHsbGJkDwxnmmtiin0Aa1C5kn8yOBpuiMpmBifuTbIFWAOxgWMy67ynA0Z4OlFzH7rLpQLU5VR6dCRq3SVNbrRWzOVFUzrVKoHB932HTTWy57DbEehJwQXGWdbpQX6VFUl56qbvPMSGCxJcAPdXh4yBdq6+nAPbixff8WhfdZ6aZaHDdCdLqff606Gkyfh2rQdQSUk9frm0InVwfu4OAf/OAHH//4x5988klbu5H4jW98Y9WqVXv37t21a1eTqDza1tbWBjNjSff4wABxZhoNir/5szfynW3+1jQ9h4sp7sWsu+k7Zq4ZxJqrNioBw4nFnd0G57qGeaOHV8BsaBeRLmQU1J0NtQUoqxan0lsY3tkdUo9bAuJpSLEtdlPsEbVCSFaClJUJm9nnYAE6z1Qxcwkks96xRKajrXMJKaeZt3aOSlq/8jkiV4tMaTW20qmVfep0zu1m6UImndaQm4X4gWAwEo0q01JpUberOnCn5C9+8Yvdu3ezAyNxjNmA+G9961vnzp0bHh7+sz/7s0a3ey+tdu7vBL6HXPlgN149Y4lUe2u0yb7lpT089wK4GCkumkwXreKLsZfntc2gX4+XQ3zE4s4RTEttzj/zLyJjjD3MZDNLwFmWYUhmWSh6x8bztScvSEbeJ9iBaPFOeULLI8H8thjsvCZiJNAkEnAPjcZ9rat6WkeahCHDxhQSKGqYKTLVeDQ2NsauiaLllDFCa7nk5CR/7R0dZa7gVFQduGvi8XhcR9gxjZKaItOCGlUvfjIc4s+Dih4dHWFNbUdXn88zhwaV2fTL4gunGThYBAkuQpXNIGnDgy0BvgE1wZnCgh7UpVjcMxlvy5I7d3Am328Bq0sMsAq7CrV7sD/RU4XeVW7qdu8SscRhpzm/Bvph4cpgYT8zESOBZpNAKp2LZSLtbV6/26D2ZuucOeYH+7gG25w6etNNN3Z0dB4+fHj//v36feDo6KhY30sdSaYC7po7IPuXvvQlnN0ffPDBxx57bI5ZnhdyeeA7Q3EsPsnOzG1h0fWOeoy+dgijjuiCymtBK6uj8SbLcpFAlW+WG09pr2A9tRek3lVmqTRXmqOaJHubN7j3vJhgKK7AbRWxNKMIZGMZOouJFrsA6a3cLTYLTeFDtcX6tB5X+1wija7Guklb9hLgVVEsnnEFO3rCY8u+saaBaDM8ZJBDazT6B3/whf7+fvxZtm+/84UXXmQdqd7tFjf1rq4up6ym3w6SLRfZxx34/swzzzhLNkEc/Vvzj+0h2qPBjog/Ph4fHsdGg5FGZ24CxheEhZqisQRRZ4a6mK2T1rTZ6qrMZDISmBsJCBbE9QJ7u+wxmF9Si1NFAvyeZhiU1V2XlbWezR1kpsFLZGkv0dIxq4rhXKC8BOtT31W9Gh/3qmIxiYslATwFhtKd4YCr3WdQ+2J1woLWm0wmWbRDlffccw+oHRz/2muvoeZuueXmjRs3alZwfCc42XKaop3pJfHnn38eu73tOVPybCY3Mx9uGq0NWxQeQmjnsVgsm8u2tbPCo1TvN0pxrvMvnCwWrqa5lpGhZyQwKwnw1dcgrvw3oG3VbArJbpCgd6+vuZTDlI1WiLW8QVOWsB5SUqYp+pabKtjXyto8n8Ivw5n811aoAmskW01pHmYNJ0YCM5DA6FgiH+joCgy63UtnlfwM2mmKOCSgnc9Ra5dcshZP95dffuX4iROotBtvvKG3r+/Q4cOi491uTM9+v98uV9dAhZK/cOGCXaZaBOL1/1UjMJ9pGr63t3eMJ3ODgwOlb1pnW3H9za6as97qqxZuKLHemkw+I4GLRQJsKUNTlaeMGN317dJoPL/9WRjcpY0CeZdGWzWzdnOl6TMKxYIqJrMXA/pnJElTaG4lwMr4gUxP0O/pCKZkemrCRSMBrYK4nj17jv3Nd+zY/p5LL7355pv4GgBWEQMRQtl70RKLO/7vM3h1yH6TS0LIvBbvaHHlQp2jIyM5f2tnxGf7hi7ED2Uh6lgS/WCYNBJoFgnYpy2hN7V+bBbO5pkPhdjlYqkl4lZ0nqueHXnppdnvaUZTZSyU7YSWztxldoIzpZtWAmMTLjZ26o4Oun1s027CxSUBjUJRR5x2esUVl7Oh+fXXX48IsMSPjoyiqURDyZ5aJUZ2MLcoMSUqN1lnYI0OBoPNIGndhmk5wY21q6ubl61DgwMeb7Cjo80evKctuzSGtumbYXIYCVxsErC1nG64pS20cUupRh6gPZeSXGbDbLmpeTa039mYsgAAIABJREFUFkRmtmlc+bs7q6yE3pUphfzFVqoYl2KKk6SJGwkshASAWyzAiwZzrVEge82v7UKwYupYJAmAnycmJtDH7LT+1a/+r+3b72D79ePHT7DDzG/91me/8c1/GhwcREuV7rAy5XaQi9SQ+delbtlZrKe3N5v3DA1e8Ho97EJjW98Xq9WmXiMBI4EFloCF1DG9LtFhU6YaKH0QAAE3/XQ6w3YCtEs2zHG5dGIoFPb7OYfPQ6JWdEWMsBSQKzwXJhrKId/G8LP9tojJvSiJ2VIz5Y0EGpFAPB2cjA92t0csLdRIYZN3uUgAPxfUsihqlys2Fnv8Rz+mZag7vhUkPvTQx77+9X8kj9PBnQzTe7kwIVi7di3ON5y+dPbs2fp9aRZiRJhdHV53rqenmxevwyMxXyDS2lIY1ZbLV8K0w0jgYpTAVFisXGPIPflBcKzYXCrC4tXo0OAgap0tftH1BFQ/mhn1zvZfkUgJFNCwPpVKplKTgoBzuclksq2t1WpuuUSaWQjwCnRnnlLCZIPg29ngpTpfK2m/uVliEsjm8iOTvrB7tLsjusRYN+zOtQTQya2tbRw9pG0TGrJLJXnXz59//pVXXkHjYYMvq3Yq4A7kv+WWWy677LJDhw4NDQ1dc801999//49+9CPgexkVpyose1TX7WzL11VJrUy8g+ju6mC3nZGREZB7a3tHA84ztYiadCMBI4EmloCoHDZM1ppHI/fmxu0odN6oYk3Xmn3Fin40Pqtp2QN46heGqHFUHME+XSqSyai2FhqM6hseGgzxajYcblrjn9U5yuTufCm7qGNHE3/BDWtNKgF25+OH3NXT73EZj/Ym7aMFZisUCubzbWwpg/EFDVzA7krlYWTp6OxAe5ex5EPvadVXqQBvu+22np6e73znO5h2KLZ3715GCHzny0jIbWXhKpmaOok3EWxxL/B9LOnJTdDSph3DmlqOhjkjgUWXQD0mWKWyrN+4+EyAghed8UoGUOWM9Ol0imkG2LqjowM+UVOoctHyUmBGypfBwaoM1ceeuWwnPDw0BDG/P4DNvumkofkFtzPhslmXuPPOalLtT1tYuusbK1ybrHliJDCtBHIuz/DoRMjnAlbhxTZtfpPh4pEARhP8YVD1vE1FsaPVfH5fMBhqaQlXVcXlQN6WFK9cr7zyym984xugdt7Drl69+sSJE7yZJVxyySXE7ZzLKSLw3e9Kpz1DsRReoa1hOWZ7OTXQtMVI4GKXQMkPWm4EBzafzwQOMAB09A/vSQkVigjOBcaWtKburrWhL8UZJzxuT4sKDBhivBiR97Yow+pmmrprmduMqsFqczSLLjIpCKASgNsttDKbTyOBRZTARMabGBvu7Oj0uA1kX8R+aN6qgdnoW22I4FoVr9vce0Tx239WMn7tW7ZswUOGAQPTzic+8YnPf/7z4Hier1+//m//9m+5WnmX4ScjVnd7MOzPDwwMjI4ntSiXYTtNk4wElqMEqmM2reWc7QX0AfxUbnA7etP5cBHj42Nj58+fw3TS3d3Na0AsMRWofVbcFVqtxYRYHIFHVEelVA0D58+fhxnH80WLWn0qQH2uJlmWYrfHPxMxEph7CTCpZGOQXN7b3ckWdga1L5oOafKKUUdY3NlbBtjJFwbrCS5VtXiubnEH7H/pS1/ihc4Xv/hFXs5u3boVLLtjx463334br/djx47deuutx48ftxRfLeJLOx2Le29vbyoj8J3BjMnQ3A6fS1s6hnsjgaaUAFC8xJdCMymJEtPDss24ZNY40AKG9qNFiaCp4/HxSCTa19c/PQPCvPyvO9iZidBg9Yejf6G89WmRCwSCsDE5MYECxHmGl7kNVmcRmoNPJ2+a+UJKWXfrbnTmrl759DmqlzOpRgINSWAylRlLZLo6Or1ejqw3X7uqwmsO5VuVtYVKZO+AkdERXn7qCoGa+C6yogko39nJl6fcqFQduDN+fO1rX9u8eTPoHEIPP/wwqB1ndzZg+d73vsf1+9///vJG7XZ/BXxu4DsSHIylAu4ku+sY+G4Lx0SMBJpUAhqXTjtQKuQOhFVGXMpMW6Ce5s6ECFoaL0TwcU9PXz11CKclKLXhSrWENI0pasSxnj+mE1jfWfmD2+UUmefpEb2khhsxtxPHBXS2Fc2awGwZMOWXuwSYEbPm29+2trcjvtzbOsv2Nay7Zlnf/BSfuU7Br91G7SILt3vlihVnz50jiqc6NvhK7F5zPRbb0DzyyCO6hW+99dY//MM/sOiV2zvuuOP06dMXCWrXzefKW+Oe9iCvHXiFMTY+PkcDvE3eRIwEjATmSgJyIqaFahXNknFBK0bSC6nktTQuU3ISZ//XWEPSqdTgwEAymcK8ja29scJzlbsgjJrk9EsAzEKwCsM1883fAzpJcLv8NTL6WH1rMVZyP12rrULm00igMQlM8ktJ4LLc3uozqL0x0S3Z3Fo5NXxltNL7ydBwBiA2bu/v6/vMZz+zor//oY99FFs72H28gDlt4q6awB3lSPjoRz+6Zs0aKGJlx39m+/bt7Om+a9euJSvcWTHOy2Lch/yB0ODwqBLlrKiZwkYCy0sCtlpZzIgC3xqelYA0RA1bKlifSlFKCvZ2yVueX2WexwsamfObE+wN1909g2WgImVM0bIDQcOcz6y5rW1tsArD2C9gfh5FU0FaplQqwPnUy7YcRZ1iIV64tVMtko4SJmokMDsJ8GMciU2kXZGelozPV+7hMDvapvQylAAuMewHQMPUyOXq7we396HiuK5cuVLrdvKAwJ2Nr+4qo3NQ5qc//ek999yzc+dOjPnA1n379uE2s8Aq28luM8RDAV8o0IY3Ec4zIW+at9vNwJXhwUigDglcFFjFRmbTCkTEIbkVcq+/2LR068iARh0bi3WI8+uMR/dZ92bjTWZ0wWEGX8zh4SHODWHxTx1tnW0WNTEp9JUayepteFn7nBOcuVrhOtu2mfLLSALyo861t4XTfr9AMROMBKaVAKCcPBq1o9xYmApkJ2XVqlUjI6Mff+hj3/sP8UvPZGQLYJtaMWYnOSPY8H/4wx8C/6Grd5d0Pr2Y40xjWKw1MZFl5VZLa1dLsOa7i4tZSqbts5NAvQBldrUss9LiED1dk7RgNRaU3NMWmI5gY88TCQ5hmWTnFjR2YyXnMvfMG82IAPODg0M4wLe0LITlwq1PxVNdxXA0jRikZSWtU+VKC8nz6eiUljB3RgJTSGAkzrkKnq6WBDszTZHNPDIScEogn5cFqUDzq6+6amh4aGBwYOvWK8Kh0OWXX4aafeedAzzV8NtZqi64CWTHyq5MHc6yJu4CvMthCrnshQsXpti7x0jqopEAUGAO/y4asS14QzVkA7wVekvfLwgbnELN8so5Qu0z5XsODM5umkBDhoYGF0BsVjsZhcqmZdaTBpgowfQNlDNZjQSqSSCdSQ9k+iKBbHvEp02n1XKZNCOBmhLga7Ni5Yr/84//uK21dd26dfhxcHTSihUrdu/ZYym8EkU3jcW9Zj3mgUMCLWF/S7iXjXsGRpKRYE5M8SYsMQmU/CqWGO+G3VIJgOymh2ZqpaNlm8eDWu1XUkpnzu+wgIwMD7dEIix2nwPi+js7q2/u9HKamk8O+pic9OHyzr4H2Iemzjyrp8rJ/f9n701jo0nS+04WWcW6eN98r367p1szmtFIGMlzaCxLsrQzgqzDlmBhsV5AX1eADHigLwLsb/5uGzBgwLswICxsLFaWJc8srB2PbK0OG7LOGR1zdqu7p/u9X943i0Wy9vd/IjMr62QWWcWqIiNYzIyMjHjieZ7MjPzHk09EwC6vsYQSh7KF+6D6usMrMeULew3slXOn5dR8/kUqlfHa8BroVAOsVM2khTRr/+W//NfJiQlmf4koPH785NmzZw64x/1kyFAD3HFY5NUSFUsYGRurIZKw1M3LRieJb8YMWl3bPp7MVphD7ebJOJASJXyPDyTvnqmeaKAJOLO7xKH1apWhMeM6nCYYXbS1uTk7N1fXBFe56ShWZ3fuqKwyq3NjamqiqyTEoqeOr7qZNNh9fW62S6I1qT4E6/BcOcc6FdXeJG8nSdDpFqlOqu047yUvUsf1+AIdaIAxhbvnUxMjO5MFfGOG4j7qQDqf9Xo0gBGHyXadI/rnv/D/8A3z+7//U7wjMAT/p9/8TTeglHFQdaOJajA3QysuMfCUZVavR8KhqEWrk49ofp+9l2uTU5PdMa0NheSXZ9I3eZfXnS/ZqIGkKCc+O7gbHQQaTowJO7pr3cpKC4uLjBZqZPjSKUZLm46IwkIj7L8KX2nWqltY3NjcwHbRi++NsrIHju2aREem/Y4EbvIBRvdIcJ90RurS1+pKBYeBxysJeEHhhke6IeECAl0/vX9WLB9vzk14j/auq/Z2EQSUM0xof1+TrQPfP/+FL/zxn/zJ4uLCe++9z+KpJGKmMFBZo5Ya4F5zxh9cQQOs04Su949PD7Z2Jwp0bW5S3+a2v0SucF/4olUN9Og2EgA1WMqrvXkVpAYnmPFdAEBeMgy+P9PHRp1pXqzKeacxN00vqL3Tgu3zO1ZDUdrnbXq2m+CHwaOM9mENKT4s0Po1re8qiVwj2OVygdyvMA/PVVjwZfungYZHsiHh+ng7q4xtHaeL6f3JqS6/1jt4IDvIen2a8TVdTgPYOxhGenh4KAtFKvXkyRPWSopITUxMNi5754F7pJ8uR7gAk/lMJZfGeYYwObs0PnqtMx+3laeP7V5bvvzJPmlggG6Iq7HCc1d1gGmnTIfwgxegSmGBpeqr1d5YIbZ2vDuYQrHxVDdSLs+uJDfpL+Ee2YpzxNzd3UXkrtvdow8C7t3WioFE6ZGPUON3h0Tlfabbq4G9o7NS6XBuZqLTjz5JVNbBw9xB1iQ1K89V+wJXLZ+Uz5uXj7cPa3XhDANQxPDhXmFsSWHluzonGSd+O+D+2c9+ljJ1zTqdgz//8z9/6623bp76eiER2sf+xMtmb39vt3TIi43lV7taUQ+e4K7y54l1RQN9vsx9rr4DFarVa55dMvDvfsriXGXcqFTi5y3KNaeWKJVWmG+gi4ual7cHwa6KbTonHgob7jun0LwEK0mtrb2kleuOK39UiSF3g9wYpS4cBZsAkndb8IhTH7mRGjgfGd3cPS2kTxZmCjg13DwZL9mQRIrovHyXH8Euk4sEu6ZILsdwoTyvDKzvtF80oW2G+7cD7qB2FmBy88PD+xtvvPHxj38cyO79tju9koCJqcnCyGRBn5JT+al8qhl87/zG75QPn7/3GujbVexbxb3Xaac1yKOiWSteryKzzSsjqFB/3Q2YPJj5cbHbHjLGZEy6WPQS/F+tdPMKFxYWmR4Xz5k2L57mJVumhgZ3XVj7MNIyZ4sTyOlEDbfad/uKt6jbJw+9Bg7PskeHh3NTmdHWi80PvZDXLkCXn78W5NwT3yvhukqdVZbA227udba4WLdyC2wH3OOiAj2///u//5Of/CTm9la04vlvd7zFHTQyMj09w4tnd3fnbLQwlUulu2x9v91av6z0La/WZQleXK4PVV7M1I3JIWxXG9B3mETU/bSzjM5DhiMDhbUFL30ED0xwPj83R8t5aSKtC+qrQjfohlppXdMlziAyU7zTaWHbJfGjQcPoVW6gyblCwlZCWm+tA1LJK/U5b4wGtFLB1mZh9uH8JCtc3kBD+425Uq0E6ekTHk531arypOmslISTIfNCUoD2jVaO+N7eSL5QmJyY1PJzta1YO+D+jW98AxLOwwZzO0aUX/7lX2Yh1tsH3Lt56VGpg+8729vnlXOWDW9mfU96vW9zvm5elQv1eK2VXciNz9BOA7VNXPOcIfYL8nLYERxsTjSWCmzl0R6LLVIdO9mlqHi+NCmVdMJ3V3DHEO8IXAQddr80i40F6Wg1Ae528RozK6X+VogfN3TvmpPwqbdXA4cnI0fH5dmZmbFRTfrhg9dAnQYu3wDHCGFcp6nESYY0WmOaODAhvoZHx8d85wHTz8zM1LT1qdp53GOktCbo5z73OZbVcIm/8iu/8sUvfjGeYXjiXdFtl8Xl8szMzvIxffe4cra7ju97Ot1d3/cuM3xpcten/eur6dLK8AV7rgHB0BpvdbBaizuDjEwuqNNkEPrrFnNMwauZpK5nLqkrgHeTN45lu6UA0UF8JrpGFba+xZUo8ybTNJq6VhXazKQeOM0kEwnBeUi6q961i34lCX3hAdMAHu1bu0fZ4vz81P6AsebZuWkawNbuUDtNJZHVlZW//3N/f6JY/PJXvvKbv/n/4jxDKxqfEZI2q6XFnfff0tISfopOSYPk135zmlreQAx0OcvO4jxznhqfmSqO8U1kwMI1MXRN1QyYcj07vdFAgNnYxe6rWDSqNchosFCg8OqfE/nESVM7NzcX1dHLSDOZellfR7R52WBJQqVXfH2oU2X9E9A2fa04cA+uX0dsCbZbAfXVBlqBHYrls3dHA0yLdDi6PD1xnh71qL07KvVUWmmA1ZN4X9AMffSjH/17f+/vlplVBlyeyTx6/PiHfvAHv/XNb7351lvMFFkoFOLt3oXD81tV1910WE3+627V/afGi212dm52enJ3Z5sFCCvdHv6SXLNNcyZVUNPCyROTVuPzeQ1coIEAltVhOm5FgtsGsXBwqhLtRFBSpy8XgP77e3vXhdrFY1WgS3CMiuq0dAkibYugChRyiXX9aqkipQSVp8y5Vk6NzgYnouPGSFxGCRsIbHb3K6qvsTKfMvQa2NjYOC2X57O76QEBR0OvUS9AOw2U8Gu3987f/JufZumld995B3M5NviXL17wWRAfd5o7IuD7OJWWFvd4JuIUZmQqjjdMkpBsLshq21pHyh821cBoqoJjEp/ut3b2Rs6OZ2Zm4x0sXYKmxbqbeB11dJdjT81roEYDauYCcBbHpNzZ4SHR6D63NLPnahPmqCHY0QFv/d5MI9MRFwkyRxqo6iVBqUtlmV9YYHwU328vVToopE8iROXhEljfk1GruaTBgRGCHNNK1rWxyWj6XDdTA6XSye7Z1KzcVpPiopupCC/VNWrg9EzL+/DawrL+qU9+Kp/Pcfjg/v379+49fvzkzTff5BQpmITiTLW8QQH4//pf/+vITfNrX/saUxk6Etjt4yQsHnsPNJzzCXUaaKOsMSZkmC6ened2trdGszNMHHmZV0ubCupY8YdeAzdSAzWALZKw5sHg4FxoUM7OtI3WuDUvFpVvH8FJxjW77bNd/SzwVYbn0VEHZGnT65r1NlUgpqHfALCbwFeSuk1d7hQ1Fgp5lHMlhxlD7u6LiHsNOeKBLBcy4S402Xor68V8+BwDqAHuou398mg6u1hgWo+WoGgAOfcs3RgN/M7/9ztv//Xb3/XR7yrkWShg5L333mMKR3nOGHCva+i4R3l/ufdZzVsNH/nf+Z3faa8UJpzBSIxDWPtsN/hsjcq6J+fY6Njc3Pzp2en2zg6jsqamZ0YHz/e9e+J6Sl4DXdWA8J0DaGxrnlEO3M/Vx6JLLh+NIw8aC19chY+9PdZaWuyIQoShmWCKgsYNaVU2YIzAKdvRvVCEhvfouDSaGs2Mjz987VXGtcO8nbPKXQFXxhLYiKioSmI8AaJkIhTEOkAh0H+QiR0DfGs0ZwyQyZhRqTASkmq3Z9VujO6XBu4RI+5bSlQ1bEbxZtVX9YjoVZ2aniGlO6ETKZpV4dOGXgN4GGOUBMlkMvF7ZOjl8gIMhQb4vMM8o67h/eAHP/jJT3zCjebf3tnmKyV+Lp///Odpl+u+Al2+cwmhH/3RH3333XdvDHCPXg89ud6dU0fDeIgyJ//2QXn09HBqeuoy1veeCOOJeg0MrgaqeK09j5qnBPwWADhe2g49ty/U6iyrVTN+qOlZ1yg7WEzcHRpkTI0xGp2nenQ0PZqh68CfUgxIk+gANegSsgHKjJCmK89WZ93GoXqD9m5DeXdS56t/5ZPy2vpaCGUr49nxh68+pFJxZtjd9gLxZzLlK5xhyMFRXav6nWME0jBeC6Y/dTnEbRiaKgHloKL43AhNszVPNP7P9YFErOmoNtQf155tfmQg7TIFm5PzqcOnAW6m3VKmUi7R3268qYZPHs/xEGoAc8bhwQG3Iq04ru3RcCDg+z/4X/9BLpsFuNO61k0afnngjop+//d/H48aJo4cBHX1sAnuIemLNYc5bW5ypFxObW1tjWUnp4sZ38RcrDWf41ZrQNC9hfWMh1k/h/0E21EUR5YgM/NlA/YLZ24XtDT4e35mRvSRCqPPaXnHxtJsoxBUWVudqhdPlbMzZu+V72O7ELZLtq/icpMldoh81Gr12XeFlCjrRRGoCG6ZtxEzgXoMYzjrhWWtbtjRXjuVkSk+Du+NTwjyIgDbQ8chfPIQKIfsrmbeQxjdLwfcdbH4GQeQFYdR0KnYYZReEzERlOKkdqJb4sVlawj5gxujAe7VnZPCVOZwfDJ7Y4TyggydBkDkeKTjSUjj9Id/9Ec0om+88R20VG9+6018Xj72sY/R4hVrp5RBxhrgzhdYGvHkktNG80bghZS8SPKcFzbGyUnV5+wh6fqqunXM1WUZwvJZZX19PT2en5meGEIhuqUMT8droKUGDNW5Ob8b8tQ/M/FjwUthw84DDSuWEtf4ylgOIWsVM+bA4pChgVgw7/n56Rn5CdTjIm5rR8EmQMcGkQMTuMsdlVFRyxXvaYQYNNxXJRFDCuLMiTk7Nx+crozgKvDtd98VcK8NAdxHFtKt7+E+AQCU9W3AqgGXj6XHQPr5gplv4Mr4wlSPeb4EnGfEX+kErz/APbYlfBKmpqZc2Sp/CWK6VAFwr81tH0xqk+xInLhgezbhMammveC0391CDeyyrE1pf352PDVyM5dPuYXXdHhFnpqepnmkJ0nD9Gd/9uU//dM/c7LQTv7e7/5eNpcrTkzUSVeDuWlmZYzpMEQDWC8sF39PXpi5gwy9otsBC9eTNTOWwqpXPh99+eIZatd6Wj54DXgN1GrAweLaNFlsa4NBQSE4Z4HHLB2O2qzN1+ooMi1jXsfcvrCwCL6lanMhwV4urxJCaH53R5irLQ0nFLmh0NwG/icuXVvSjQqcBTYR4VzXIQi6BJwvFjWt78lJeW93d3Jqyhm2zS4d8FsLjl2rrp4Df9TJpFXKZ+gWQZ48fgxNirBFHeByiwieQxmrAfMKa5tOG1J3O0biAOcF3LU3fG9YX5gejjPjeOCPj0xMQIoAci+fnDx69Ig4LxoqhyClAnbb7kQuwO26WLWlggsb7NrSMexeRfTt8/qzN1IDZ5XRzd3yVP48W+ARiHXmbqS0Xqhh0AANGmMad/d2WSeV5pHmji2ME8HDsBG1c6oGuLeREWOJjPktQqJGs0XZ5sndp9i8nmFMzYyeLy8v0z97ub6Vy09MFtLuxTaMsnievQauVQOxhkVRoTg1lARaTwzDFzID3MZHnJwgVkOQmNs3wahutnKHxG0rJxKD4eZNwrhXQXcaZIzk+huThZtNBhfzTGacGPCXCBAZsmb/Bg2LL0OtasQjXM7n1Pn5OYrsbO+8/+1vL62sCCInDrBHXVH2XDb3yiuvmsMJ7OmFoZ0QPvGK9U9OEfno8Ih6OYs7jMvieILhrIkADwbvMzCmQB22QRxk4J+XyIMHD/g6waAr7PAH+wcY+1GKyxvx0ywSyB7wV9cLcwqyYrHLWyXTAp0F172az8duugb2SqmT/Y2F+Tk3uehNF9fLNzQaoJmfnp7Gk5Amkc+TvCFccxpvqOPCXADc+az5Qz/0Q8+ePfvJn/zJ//yf/zMt/le+8hWa8jiJpPGmbWrSwj5fvQZ4Ry4tzHKZmToa6zvzNsTeX/WZ/bHXwG3SQAuo5lQQAWCLABytcRREdl7pTRUFZHQO3HaWCVgqpeMDLMe4gmxtb+VzOdpGYDroXGNcDf2qwUulHCgvZCec0Xp8PAuc5eGlUqqMcHnTShMlXrpdNSbVdxGfhtuFxENyIbBv2iVAQEF7fSA453WAOwx6ODw4LJ+WSeIkNvtxWqUsn3kZXpXF9i5zPTieKRRKx+hK58az9A74aLC7s8Oa3u5F1VRkcQVfgu1wC+Bumqt5ookXnAok1pFRal7Cp95ADZxVxja3diYLmcl5b2i/gdf3ZoiEicN9h6R50lxhrT9IpmkDXTPY2BhiHfn0pz/9W7/1Wz/90z+NiZfBkTSgP/dzP/cbv/Eb4MUaTTUWrjntD3qlAb3/bGTD5m4pO3rCV5Xqq7dXdXq6XgODrgGHywBqsZYpwHukKDE6YciOpwabRyupMIGwCDpwG9zIKhbH/ErHQFUA6PHRIYjz5PiIz5uyoOfGaXDBq8DWXC4PYAWtmsW5JfFWlV6YHlIM9xcW6F4GAemUvFzofvCmic9PgOZB82imXD45Pi5tbW4yXtWZ1WmpGKKKQh49frS0uEhZAmCeubNOyie7OwD4XWZ8h2CcUwi6Ns31FigRP0u8nfxx2B4rZvhfZNu8GmPZfXS4NbB/PFI6OZyfmxplYiIfvAYGTwO0SPjJYL/AdZJ2Se8vjD7pNIgO20cjv9YIupavtv0j99/6W3/r137t1zAq/Z8WKAw5SP/Ij/zIF7/4xUZaPqVfGuBicXGPjyubuyfZsfLlpm7oF/O+Xq+BbmvA1kAVOpM9WQ0b/zpy2J2DMGKW49SIjN+BY3cDKzSAshDncsxmCLI82N3DY4Ty4Pyx0RFwJ/O05hmbCeQsFLCvQ4rQQKY3CWaJvizpUEGXLd+0HLI7RE5/pljUmCpBeXo4pRI+7kx1z1jV/f093lETk5PgeOzueMmjfL7u4krP192FxQXAfZw4bjYBnfhgXMsRaLq9wgP4zk4ic83d3cBdYFfqui5WXCQfvxYN8ORubW0WZ+/N57jKXPdeXGvI+uA1cHkN0EJitDgyX3RaJN2mNlyKcUGM5ucepp2so55udSd/13d9F14xWEp+/ud//seTaFR0AAAgAElEQVR//MddsX/6T//pO++8g+EEl/lm66fWEfeH16oBB9+Pjs6ZeSZfnC7mawxX18qKr8xroL8aMFDajIXqm9ve5LSZ9joHiI+O0kTWFdHpyjk94YODw53t3Z3tTdy1M+kUpvTixGRWM+GmWLqlT4Zbg6vtMWudPHYokcNSCG9AFmVUNdOs0OXTeBUxHKCYzozY24faNzc3sMdvrK2/fPFyamoS585cPg92B8Ez4H5zc3NpaTlWX2hxVxdAICmOtl2KyxwJoEz1IZam3ppoWZaoUH0BfzzsGtg/PD4+3ON7zuhorYNAlwW7PbdQ7CHqsg5vNTlG+zjUjhbu3L37Pd/9Udq4t95886/ffocUDEaYQmSajYUaw0YsfQQ70y/8wi/8i3/xLyYnJxlL5E7xEfNnf/Znmdik7mtmvKCP91cDfLYmHJXOmTWZiLe+9/dy+Nr7oYEAlLWtWsZqIKAc0i0K+KbRqyuCXzvWdJo7nLSBuJh+cWefX1qanJzCKry5sTHTGWrv2js+fIXqo6ph0I4oxzMbbq8Tu5eH6Hx2dm57a2tl5Q7+8RhE33/vveWV1cnJCTd7/d7u3vT0DOYhx4Wuj3NjgtNzt/pSlX8D8U3ZDeUKNRVmqh7TeakSCk/7/Q3QAPfM+uZ2YXp5oVADd26AaH0VYYgel+pj3leNXVw5bSA+MuRDuTjGfN/3fuxLv/Vf+ET5gz/0g3v7+y9evOTUwf4+7WG8rWsO3MmBTR3YF8/qWAD76/tm2KpezJfP0Q8N5LOj+cVF/JrWto8LmSafWvrBlK/Ta+B6NNBiHvdmlfNRkWQaOvljCPDXv5xwhmFmw8mxyVdfe/XR++8zb1ch/HBJdvzam1G9ljTjNMDtl6vQ3m6SvF7oy5FLWoo+knU2cDRKLy4u4SSDVYkOEh2nQwYTHPCe2oteMeQMhx9oqpu6jxt2xRz3TWUI3t/slDM40gG4nrksG652UhF8voHVAL4Ah6m5+dnJHhvaB1YBnjE00LQ16KNmoqanngfNrKW2SY3Tg/v3/+qrX8WZkMPf//3/FnkM4jND2xg1iZxtDtxp1ugH/LN/9s8ePXrE7F1Pnz51tVH4P/yH//CZz3yGdtal+O0ga4AuFl+nuVhrL1/SmaMzNsjcet68Bmo1cPnGl0awvqWEmKMXRQTXU9g2qFSx0THXgNbxgHM27tpM2kjgCcJUzMS4NKkgyFGWHupvuLrNuF5N1yQPqzq5Ket5b9E1Yl554BZTau5sb6NTPmjE+NAcNbqglRF6WVyo2CkuXHgURcKExr1ktX9HzTpdCYo1EvIpA6kB+tFbG2s8qgsTTF3dvx71QCrHM9VXDbRsZ5hWC8Zcs3b/wQPWrcO5SykjKYb1f/WrX3WfGc3AFBFJNQfuFPva176GV8zbb7/9b//tv/13/+7fQRc0T1hdXYWEd3Dv603QWeV4yxAE323JcT6kdFbe5/YauEADUYNyQb7rOS0wZzaMJtUZ0hW7xjJHocUd23mq0ccdJ5n19TUmVJ9goq60pk8huI+b2zu7zBtzWi7bqqKQM4pNquxNkgkYCdJpHbW8CsxeW7ApZ87oBfESwYaE7yb+SJiHQO2M0GLG+MXlZc5G/DC9pD6G6JJqvhq0HZ1yMLxWluBkrUih24ydDKB7JSAbo+ajQ6wBpjA6GFuZnjnTSr4+eA0MlQZo3OCXuXP/+3//U7d+B1b2T33qk9/65jdpHsH1fG2MC9QSuLMW4B/8wR/8zM/8zJe+9CWmgPzEJz7xL//lv8TU9MlPfvLXf/3X4yR8fCg0EMH39Z3SRLZSN9ZhKETwTHZPAzf83VbTyDXTGvLrh8UdI64yaFYZg/sUrSqHDDw4jx89Wlld5ZsVgNL83cezuSze7UwTyUfI4+MjrCZucSKZ4TX5LuWqRJrV3600qrlKRfa6uFBZV2BWxh4D3NZBUk1ohu8VTJpJ/4d5Zo5L+8dHRzjH4CKDMEzPs7S8UlshCFtTT4rOmZt0P36+nfgmWY14gvBVGH9tlynOsI93WQPcGNulTHo0M5/Zij+8Xa7Gk/Ma6IEGWHUaqq4Z523Csjzf9ZGPMGT/f/zhHx0f8eEomCGt1mDRwlXGsffuu+9ub2//8A//8Je//OXXX38dT0TCr/7qr9Lg9oB/T/I6NAAKKTKn5/7+xv7+xPR8NtPutXcdDPk6mmvAX5fmekmUWsE+0XzCZlMrG/vZJmZxD/wx6iA3vtdLy6svX65ltraY88RG+OTwegdNpjNpoDzPFFAQwzCmERw/AKO0kAQhVs1BLhdyt0nEfIeZDIfWYFNHAGbgkCFJbeg14to2mS88hbCga6dPtnaI3PogIXXpmy2+LnwKPisds2IT8+AzCb5C+aRE5kwmPT07N7+wBKivqwsqpJAHsi7uMkBQF6tlJylQS6gdsRdQNubqavGHw6gBus3MMTozM8f9M4z8e55vuQb46EgbSDMemWBevHwZ+KLTuKnZk69g3XwwF9zrLLr0hS98gdLf+ta3GOl4y1V8M8TnNacZlJnh7qi8t7PLrEHxQQ83Q8Z+S6EHzod+aQDjuYCZfgbRQz7sqsQvjQC1AU3aTKLOVaa2jJWl0VxZWWXs//r6Bv5m2C8mp6ZwemdJJlCmm7ac75vpfI4h/SoBQgVkntk6RDjTsLzQSZmVNXDFUTNswdAmG0F6w55xxkKOW+9DBNoyBz0HQO3YWFO/uOp0kBGUbUkodgLG7UgCgMzdS8WhZ8lhn3iZf4fXjAQTplY2pEb00ikfJdAF2gCys2LVAfYk1l0lGyqcmp6ZX1zC9ShWWxClLjd41io9Z0WrKA8pUbxVpD6Hjq1YuK5Tq4I+ffA1sF0ujlbOFxdy1oEbfH49h14D9RpwX3FpF3ldFQtFVklSuzlS+cAHPjA/N+eGXTFso84EU20E6+nVHrdD7XUWqtqC/mgwNcD7cqIwXszPY67YO6pMF7UC4mCy2g+uOkNR/eDQ19lSAwDHcJgpMK3hUpJgaW5vOWXtAHqqwWwROM1yQvwAoTSG777zDs8L1ndmmMHrLALxGJiDYA4h5MmnqmBU0FNW4wDTC8gblmWvNGC9oC65tIcRItTr2LVIYFwmXZmFncM/5TepQv7pDxid8DjYi2xVTDty1Kw4UUdHJ8LirmtB1QE0Zy9obvCcJCKOpORy0jm7+tk5SD2QEKN66aSEG/JJCe8iskEd5aTGcwtLy5MTU8B9EWkWYIMaxY1JXfcCQy01YjejUJfmZCOx86J1lPxh3zRQPh/bKY1PZ3Yz+XbflPrGn6/YayCZBmiFpqammRWX5vM/fv7zdY0S7Ty2odrB+qJbBe40o6wkwuIXtI/JalQumtH5+fnk+X3OgdKA3TSawIEFusojuelC/ReZgeK2c2Y6fad3XoMvMYAa0LyO9Y0Yt4KbsD3gl2P7YRcnK+jPWky5c7RHgmNjadpZ2tOJiUkWZFp/uQY8xdis+XMLBRA8GJSmVnZ4NpzgD2cRh3Sxyo+mU2l9FKU67SwE6FtM8287MDA4VUZ74WGCg/ZELIN6GiflMuNl77/yiiHpAKarsAU+oxENzflKCkULxuO6bOSGT7x9QOPwZLZt48y0IMTsEL2Vt6rFIshbcXU2Ts/LtkcjUaA3AmTHiHSK79AxmB1LO8Z1XYDUKKtWFQrFyanpQnFid3eHOd0dJ6234go+XYWOpdaZq2ekCKcMi5ly7azUrcB1sYtuB34zNBqo7OyVKmelhZlpP3XM0Fw0z2hrDRj2ntvZ2eZVQq54o8QpFrVQ41wbqsCd3A9eebiwuET7WJun3RFvKD/MsZ2ChuQcixdy2bl1eNviyMvtMkiMB/hmkFjyvAyyBoRiW7ViIWKHf0WBn8JxNH82cRbIuLGVbBQV/MwzMr+wyCmqosFl9vHdnV38QMDaUBCCBzvzHTSblT9NJgNMxJdEHt+x4FKE7nUKmC27tvESwHriVrvkqftTijEfCKudhSBjWI40x6USdIDlm7kHQv1U4ItPB0Eey1xF5qenQuiuu8C2GgDpwQEfDQi0G4FLEHD9tEwiqJ7aEAcNsMoso05zuQIrpKK3CHyjxrDzYtw220gB/EtYKYBuRrWIdOP006xks7RIakWcbjsj0Izo7UkL1dcvicuV9M7B6WQ+l037lZX6dRF8vd3XgN4m8wu0y/omaR6V9urQtFtNK6sCd07z7sD00jSfT7zxGuAtBmTn7bh9cHK+vzM1oQUjuy21f0l2W6OeXoMGBPNscCowI3bDYaR2uIM099MOYAoBzpn/BwkXYxPwI6A1qhZszZMyPTPjUswMjT+IfEM0vyEQ1pC0WeFH0wBP84zXoSUJ0AvNy+cEzE4jHGzsWIDeRdzpGnOMTPbiOGRZwJa/eLCU0EBuJ0YqjKCtilkZcc4/dg6x2IdbRZnIxa1nKuO6TOqY0g2pWzTwhCGBvIhPt4T3jTorU9PssK0TRUqEiNQVj1CBwL0D0PETsTgnlcF4gTN1a8KzRMIDlxadCXOwN+WEGtKxaUh7KIXFY/l9NKaBmN4stZmCY9l7G90t58+Ot+cnc+rG9S70knbvuPaUh10DNEZ8i+SXRJAa4I7NhG+XrglOUpg8vHympqdbtcsJifhsg6MB7p7Ziez5eQbnGV5xUzPz6VGHUfraZg+OgjwnA68BgK9DhI2cxm9igbbUyDk2ZUb0p2S24BA8emF/FUcQzMetMJ8D4gwnmhiZdAwANwm0rtilscczoQrTCFARiWpsNaG4LPFkNsP76BguNs4yX2ueF64XUA/gZs3OuDHp2BhVB+ENuHPsAlUQAWfTRVeco5ERcPzz58+Mx8Azx4zpxIXV7U+QnRpkILfq6XsYy2O5fCE3m0NjmfEs/RHcg1C+uDR+yNw+YISHmYsULrGCDkXt9xBk0SVUfe3rkagWbMdGBSPQf1HZW3z+Qr1ej27KlczW+lMGMGcns9G17G7VwQ0C0QGR+YriVeW5IiFffBA1UAXuNNxvv/3XrILREZs00Kt3CHc7KuUzD4AG2rVP4AZ8T3lP7+7vn58e49SL2WwAePYs3CgNJEN3HYsMWXNcb1rQQd/oFEZ4g680ZBqcWr/ORZQvHsEfBuQaT2kfFy7Hu50nqHbtswBN08mQlV6GbFJk0uZQ07Aw6Yps5SBoMKZ88bFPC4GmSMW7hkpJIacD+TzPjFfVUy2RFJxoGOzN5K/alMi2MoJrnGVRArO7vHj+jP6G0DauK/CKLtR3SGdzefx8iLjAGZdN2rIQErnknvEAdGPaA/eoInVz7LNwTWUhaJfg8SbNqSDShMpYktOBKQdZakj5g4HUwF4pzYCJxYXFHjUXTuj4vXP9agju1i5W3C95ui9JF5Vyc0hV0RivDWbV7VQyXgOdYv1Oq/D5k2mg+08qL/CZqeL5eQ7nXcDE7CyDJPyrLtnVGP5c1fupGhsaqcDCIUILeUaK+EtFQgkJCBwb5AMgAlYNHIdFWuyZhByn8BYnO0iOICkIsg14rQLukDZtNTPcYK5mRqj3v/3e5PQ0Q2PJ5qTmLMu74h1Jb2D/gFWNUhFCVY1aSbusPWLbj6ktX3nloVmunVK0vZ4AGwD39so0C776G67fIs7DQJLjOEyo3ccvtztjXRrBf+u9yF3ehwHWwFkltbV7NDE5O5m94UvHVO/p3l+Oxseim3VemyS9FaObKukFraQtFxaXn/iJn/jt3/7tYGb4XvDiadZr4NoegvqK48eAdRaJBN9sb2+NZKZmivqeH8/g433XQM2NUnPQd9b6wECA7QTQDJuGCtE+NLgHaZbEvS0rM39jYzi0XMgxODiCwhdmvnoGJ04csDrLN+4pwH0WgWKLQ3lUESJgUKfFln/L6Ojh4REgPjqriNB7qBTXaZHneDWlJnMvDy7UJNCcyyIWrFfSqPb2bLs7INgGHyJ0BFm2AWVR92HgNLB3wPyh+/Nz86nU0cAxN8wM9fo5t8et9wq6IWJcUlGJgDvvgJ/6qZ/6xV/8xXv37v2rf/WvLlmVLyYN9Pp265WWwQpzc/NAHNbkAuQ0naKoV3XfULr1t0L98Q0V+zrE0qyOYSBWp1kO9TOkqlPOcAt2BRdyh4cFW+4d0G95+npOBAI6WWqqRDBabA22rVSItP6GIBLSTj8gu+OYVuVChdOhgkeZyDUHZS23cO9mka8qQBfUPq0ECgp2UQbBdy54fXJ03kf6roHzkdGNtZfF+QcLRbqj/kr1/YJ0xoA9gZ0VSZL7uu+DHomBqN2Q5GLgji3nc5/73Ec+8pFf//Vf/7Vf+7UkKr5leXp3hQdOkbxombYfS97O4dlIeRvf90Yb2MAx3QOG6i95/XEPqvQkE2sAICqQ16SFrLtOhljJenbGLIPgV27mJBZ3JozpI9h1ahD+NGnghOcx0g04GLDunkqG3PIN4ejwSIb55m4hUpJlrtNMRK+3EZhHmW3qAGEjDJLaVTqvUztK0OTzLcuHb0gpywXbayPsTmvWsqg/0ScNsLrZUWqKtwzfivrEgq92EDXQ+jG/JLdhm3DJ4pcv1g1JLmi5QO3/6B/9o89+9rPHx8f//t//+/X19cuzO2Ql0W7C35AJdnV2edPPTmQmJyd38J7ZPbjQZnb1GrtLocl1Nd8BIEDCX/2t0V3+PLWrasAs7s2sqmozo8sfxs2QK8szTmBxENyKi4HwjXbSOZ+XmKR8EKPRBpJqdhftRgvFQh3eBbZKD65j07fXV6Dd9sqM4DURhuyaXI534x/B2+B2J6ET0MC6JVjMytGrCZjwuwHQAINNNjY2uNALBS509SoPAGuehRuogeg90JXINSuoncXdofYf+7Efe+edd/7xP/7HPFTXzFw3qvPPfze02IwG81HPzc+Xz0e3tl5i2ZudnWe+uGYZr5rW/BI2T71qXb78DdCAYbkYmI1E0j0T65qFh/IFl89FilnWz09OouytIoNjqQW3W0ekyine7SxyBIcAILMpM8f8+Mb62uLSUgN8F3KvGqOrNK411l6ZGAX0QaCimSiZP4fZh+uYMw1IC3V6AJ47xG75g6juCQRWUGeNflodNX/YLw0wQepRataNzegXD75er4FLa8CaoEuXTlQw1qCN1LeDEQG+rmJrB7W//fbb/+Sf/JPBQ+3XoKhIGT7SUgOZ0XNW/Cqfnm1u7YylTvF9b4IPrHTLC9byRMtK/QmvgfYaAJoJuGlnyy41v8d0q2KYlqsMgF5TII6BDttTHoyzwVJSMAPfJqX4IrK7u7u8soKtnQNOqDOSTheKxfqnklJOkvjbwKUM2NaQPZJV6F8xw30dd+qkWUfNgffwbI1U7kC3ggsiZjFXMEz2+75ogIuxeTCSTefnczhNtQQkfeHNV+o1MDgaiF5iRJo/J7Tyn/nMZxYWFrC1g9qdh0w6m3njB7577sHKs69/+90/+QZfLnsjUsReb8h7qlfWQOMVGk+PLcxOMs3c5uZGJj87XZBbqg9eA/3SAIC1uS2Z2zL6iTkdsLwQoNeQ+5hbZLQe5taJUcWAdSeu6VCAM4CeBtxj1c7OzeLiztcDWmekIGCxZsrFWJb6aARo609cz3FbZYLqnEMLNvJmwP0iVxnrvZkcTkpt+YcsOkQ51yOir6WVBjC0M08dHu020KJVLp/uNeA1UKOB5t8KHz58+A//4T988803f+mXfsmh9qnluc987n/efrL+x//3f6XV+6H/7e+OZZqD/hryNQfRC7N9pKaMP+i1BlpeDL3Ymv+q0Ke2MF9p6OwVxs/X1tew/LV9I/daLE//lmtALhFtVKA7l3/zkGFtIyIc4llr1vc25XTqJMGUkReQ6MLpUDqTwtGjZc5lc5io5d9uqF2b0frZcsgW1i8QaxnDhGvft1cmrCKIuHQW91pXGeSA+SQsxwSObgyGNNTb75OQ8nm6ogEu6FYpx/etpaUlj9q7olJP5PZooDn4ZgmPX/mP/9d/+tXfONrXyO6Fhyvf+7N/+/f/jy8cbGld1Xf/+Ou0ow8+9h2KKOglaBG/6b8GWl6Jlie6zDPwfXFxkXXUN3dLmVSJMawJX65d5sOTu90aABmEcI19Fd+53qhrtZSqWVlseRcg7uhYczt9rSbHM+PQ7u9d7UQLZAkFhatcLic5sLjzVw2jNOmMWYrkiJi3oqGeotPXFYFhlNm+NnP7kY87g1MZVxPPrKvgjquXN3berqVk49/tLCLkbpcP16hYbh+9Pg2UypXd49RMbi8zWbi+Wn1NXgM3RQPNgfujR49Kv/u7H/9f/qfD7X3cCmk6f+9//4+lg+NIalxlMOREhz7SOw201HLLE73jpQPKrPnCqi/Hx5oogNjExESEFTqg4rN6DVxSA1XP7ziBhodGiM9NiwTYdWM6gwGR8WK1cZrEC/PUlujykR6lEKzHbSbCwdlxJwhV0iPRdkQWdzrSceDuGHI0Ikpd5jIBOamxwW09Xs51PQxqk7eCD1D8rOLNmhWh9GYhSkdRnG9WtFkxn9Y9DaD57e1tHrTF6ZmRkZpuWPcq8ZS8Bm64BgDuNIWuNaxpE1++9Xjt7afZYo7RWuWjJlPtMi6fhbVxjdjZ2b3hSrqaeDVqrSPV7lxd1qE8xP5HYC7R9bX1XHFispgfSjE800OoAc0b4n5Vc6w9b2zCn/YYp8806TsROZngmMFRWx8KnK0p0t/v+wEGBZUHrbeuENOkjI2lkULWlrNU+fTUnYTVo6NDvn01XkZ01Ef8ChhHmY1cxVLk9SNru2VtnMCRCxbL3CwaaMphdbshiJrFvY+CN2P05qednI/t7B5MF4vWhwwuzM0X20voNdBtDbT7Vkhrebx32BS1wwZmVAawMn9Tt1kaAnrhez8CALGI+3od28bOVRFDkDgEsnaBRbD74tJiZjz/4sWLvT15W/ngNdBTDch23nz0fIDz7BEOntJoQCq4HZ+Ks+YFq/yynBFFqsd9iZnNmJrV07A42wwzuHM8qplkhN0Dw7wGYpb4+BUWUSn+BZzA7Wz6BqFA4y1WhoI3BVnY9XXBIffGzlJrziPpRCaWjSjyCrgzpKHd60/lfOieBraPRg/3dxdm8o1ffrpXiafkNXArNNDcVSaJ6Cwx+KUvfQnXyWJxIkn+wc8TvNKbMtruXNMCPrFeA7nMSG55+ejo6OXaNovCTBQu8G2tL++PvQYSawD8CigUSIsXMRTo0LrDrpwEwJFTucC5srkzA/oFoHx8PEvrJ1ew/gUnF7JELCDF+HiGBOzuQsMM3BRGVSB+ytQ5Z/iIBw1+pBY7HdG47givjzZqhDcuiCQ04I65XV2RWFCPg5TaxNj5IEouk9d6KSLGoYa18mWiMbNP6boGyqfnW5vr03Or2XwxuBRdr8MT9Bq4TRqoAe50hYN3WGIVONNO4ux9yFjT0tfV3+5cXVZ/2B0N4F5FYBawtZfAd4Xu0PVUvAbiGjDgHk+oj/PsB5BPrQAeG8LscjJJM3ki34jq88eOGX7Nh6MBuXUNy5oI5+dY3MWm5BKkFyh3YUQW66PDo8mpwFvGSgn3Ehw2tuh1b3CiKxTaDU/kiiAMXk94J4H56oC7XUP1T5qGAKwbSo+QO5DdBXD7RYC/KVWf2JkG9sr50+NtpitgEHhnJX1urwGvgRYaqAHuvLEu8jhsQiabbfeSa1KgG0mtGmvRbneuG3V7GlfWAG9rwsHBwfr6WgHze9uX95Vr8wRunQYAfLJBGDQFwNU0CRwI1gYb0BtIDmgoJ5NUKpNOYwZury+ydWrgaE/wMmcd6jYx3BSWSIEvu1CpLTLK2CSYdEGnRkcPDvYj4E6NwrXu3xDuZXi4chnYQ5mtyJhEsrhLpnNgny5QTWaJX5tSPR3IFwkXdFPM3A5BTE711KplfawLGjirjG3tHkxO5icn+/ltqguSeBJeAwOmgRrgHucN6/tP/MRPsGWGmT/4gz+In+ppvFUzrErbnespU554TzQge3uxCHzf2Ckx+zuW+J5U44nePg2AyfANMewWITdpQUhPP7d3qA/oJyCuJKY8T6fLJ+ULFRb5nFyYs1cZQsCKpDBPLTJOV+T2c8rsluURnHmwwnBIAKeSnZVuGpkJ4GzjiWtJaa9G2MY9huunroh9EmlgyrRgAJzLFwQViLolYaISglSJXPEO7pFmehLZ2z8sn6fmpwupkaOeVOCJeg3cYg20HJ3jTO8/8AM/sLq62hX9uFdl8y1vzvAXvVqbRLrChycyYBoAu89PZ7GosdTXUekCY+eA8e7ZGVANCMs1x6SuBQohvIy4isvKLucJzcci4HtRCKZ+vyhbD8+bdFEPBHQO5yBTIqfl05PSSfnkBKE0/Y1NgY50paMj4rUsCcPy36/Q/gOv7OL4tYtFAXc8lOJ8kkjQm6N9kHiBiOiHElZMXjd27dsX9mc71sDZSHp9t5zOTc9N5XkIOy7vC3gNeA1cpIF2wP3Zs2dzc3PtjSKOvnsZBtsQgkdY3EWaAPGo2EVc+vM3XgMMcV6Yn8eRdWN9/fjYG2lu/AXvuYAamyqcVg2CeGGb41opHQq7p87MPQZvb2ZjqdhCqtVizWKsBHShR02zcl1LiySTBCMV7OtwDi4H4MIYTo+sSEqEYxIJ5CcPs7lXOUA9BtuTtPDVUt2LwV77quHZWdzhn8z6pFAbHPQ2DehEFAmRukloB4bXXTJU3SeIWPZasv7o0hrYPzzZKeXmJjP59MWfrS5diy/oNXDLNVDfFNapA1eZRP7HtIHRr46EP/QaSKiBVGoin56bnwdyrG8zbq264FdCAj6b14DTACAPgEZoqpAAssleLaurgLuZom2ikdEkFnemwd3d7dv6FWI6DMSB5eB1eh1C6Abcy1jdyzaPjHA709QH7v4HNd4ywrWtVBSS7+EeBbafTRjeAv+fioB7E5Svl05VFS14DeC7dVJMXPVXRvo7DX8LVoc4+byS4pNpajQ9l923mYCGWBbPutfAgGsgrabPeHXZnt4AACAASURBVGzaANK2bu5tf/TvfJo3w3t/9s299Z0Bl8ezN+waAIiwUgwzjO5x89mrvc2EcR0Je1ouv1x7OTU1zdJ9c7OzW1tb0zMzVMGqrsxyw0QimCkZm4h3gbDcKFZYfZ0vnZTyufzB4cHkxOTO7u7MzMzW1ubs7Oz29s7U1BR+w/Rs6WPQxT09LY+Nyl0Bix5+C5oxcDx7dHxULBT39vd8vdes52fPnzGbOSMouExMQpq364sLu+zQMrmeY5QG7B4eHJJtd0ct2+b6BniObz4Li4s7Oxdc342NdUZ8Xv/1he3xTOa4dEz/Fh6A5U+fPr0/Nra7u8NyeNyQ6BmpuSGRmqeJm5MbmwWY8J5ZX187LpWy2fGD/b3Jqam1l2tg2PX1DQatXv/9/OzZUzhpUy9sq9dh/5sbG/MLC0+fPomeo82tTR69/b29menpo+NjRsjos0M6Y0N1weapk1O+QowdHh1lx8f3dvfy+dzG5uZEsbixuUEKDz66Ivjn9+rtFY/Vi6PJlcLY7tbzSp/ayYWFxTpnqo7eDj6z18AQaSAdWMpbsIwh55XXHh6f5R7/1duv/I3vPC2Vv/m7XwaZtMjuk70GuqMBOpPgLQIgWzh7bu7qjfLa+tqnvv/T+DHbJ/MLDXVtBBEsaHO69alLF3QkL1380gWHuN579++vrK7Q68NSq+nACUz7KJM05lsw3ile4PIELwFqj8jzysNX8CR58fwFWb/zwx+mD9b6OurM5ubmzOyM3U5RxivqWXQggaM6vQsB7lIJnA2L2hE5OznaPwDKHu4fYE7f331bHZBKJZfNLy8vf+D1DwDWnSEZMaEg4STd4f7e/vraWm58HGE31zfn5+d+9Cf/Dl4odFfee/fbW5vbTx4/pZN5sH8wnqFTMD45OUU/dnp6KjNu2jPVRUJ2JQLnq6t3eK7bUMNljovIpcOUm01n7ty/PxFbM4R3U+nkeH5+Hqm5fNjmjU09mFppldPu44M8hsoMOEaBx+ji8LCQL2ysra2+fu9DH/5wm9r9qSQa4E57cTi5PHGokdH9C9xOX/vqX3HrtmWhnxy2Zcyf9BroTAMtZ5VxZHjtHZSO3vjh7/ngD3/v06+9+43f+bNXP/Hhd//oa51V4nN7DVxWA1i4KYpZ7nQ0N50fvQp8x0zO251wWV58uWHSgJA6H07GQOBYxgPgri8pqTODu4C79BhjOsfOALvgD5vDPc0NRn7Q3+hYq/sk6LPxuWZzY3NpefnqSqF2QDbGe5Al2BrAioc6iQ4LOY8WGMdODLB2zIciCLrj2xPN8xilU9xgvW1kaqnAN+VL5VMEJCCvqUXsy6+mfAqolWzuX/7lmtUeE3U+X5icngLHA+jHs1m0enWRN16+mOF5HGvXO4JJFqlFIqpDGuYdjucn1eQINu4SG/eVFJNMUoY5dkaZB1OX1p11/TfX6c7msnFqV5foFlLgoyhWldU7GhnSZ/F5gsoXetV34b7ts5gXV+87Jxfr6AbkuOB5oynf3trKjTxE1DsfefVwZ3/j288e/8Vb5eOTGyC8F2FYNDAzM837G+8FrGhTM/OZdq/7ljJRvOU5f+LGaQCsZtjXXJx5nVXf2g55CtspzbB8WTPJaKYRYCBfFPEKt2+R7ZQCxMdq3y7HRedgDyswbi3YgW2aGuoXR4BjFmeVzxWYNfhTXEfVjc5omKWNTMVODjWeDrZkYYudGV8v+blrQvczkhAQuM6qCbl8deUNyhuoZUiAYzfYwQkpAPqdbZ6b3efPn1MckfFUWVxews7NZFBXAb7ArIvso5qdkyCZuB7A9NouN1LqClpo1LT0YqnhNthLgSLmp4Ns1FkHKdxga+XFTKp0/8GDDor1MqueXB9izdxAKsM9hgPJ2lAxdQFwp3WenZvTy8TCK9/7wUd/8db8K6vPv/VemOb3XgPXoQFe0FjfeWHsHZbOSvuT0zOZdGctNa7z18Gor+MCDUTNyQX5rnga6CYUKyps+KlegLF+wnsh6CM6OuomXzEDPVZmpnI/STKyAjrgQKPVGbOUwk3FzZ4EbnYrGAhqOna1FcJ0u9g2FjVkapkq4H4T4RROCCQ64A52jwIpFKau3NgYTvBksypUDbVjzDa8D6YlY2CkF3GLhsyk6AvgIESg+NT01OtvvIHfuZHqDC9A2C4FHLUM5AGKmTjqhBBRByOWHd5I0uVsEkyPwXW32lBtGCQgfRgcqJoUHLgkJ8lAsXV4mtk6HF3Mr40P0spKdpkHSk+emUYNDMUzB9sD+NjVKPMC4I6hZWtt4+SPDmnAsdbQ3h6u745n0rzVcBmsoeQPvAZ6rwFe3tMT2fNCZu/oDGMgs1IAsxJWi2kzYU6frYUGhqXZFfvpjK2BavA3Jg4iONwOFAwBvOFvbNNjY2msyFiCAe6xIi2j9CR3trdx+WiZo9kJPK53dncYEQuAjp+PgDuJwXsjZF6HUdwdhFvANR7coBb6HpinLaPQt2aFlMkdmzuDcW1iGXnLME9LCv91KUFBYBbgPjk1bcBdqD2IcCIWVT53ShH9M6j3L77yFwuL8x/97u/u1Ni5s7PtXOCMh+Yb6uBhh0+q5dKQyQ6rmV2G1nekqZCNU1S0E+/qwzAMvUprgGOtBewD0yjvxfPn6Vzx7ux0aqSVL1kHjNlF6iB/m6zw1uasP+U10IkGrvmx6/jWbdd40VB+6EMfYuQW7eY3v/nNN9544/DocOV7Ps13Umbk+Df/5t+49rQTdfi8XgNd0IDge3H0PD/LV/zKWHG6CHK42Pru7JpdqH5YSVxzY9RnNYHKaaB4nQdvdNrGUAHac9oSDL6LVQCijWIdZXwmA0PDvO2kKBTyYFDGTrTL1HAOLxGWLAhgqBmMq3U5ziJO4+Zk4RJOGzxRO2//Fa2Qur29hYwIK1kMjAN15SQTCw6tcpYsehBclWaHZnqZ+YV5UDoaiEJ4YHsZ4t1fiOUNu3Pu8PCISWmWl5capGyXQKmVlem6HIiAMQgR3EhTDoWt6WqcV+iDMAjYSReVglUykGjpyONEis5LU+goUJVJav0USURm5p+pZvWxBBo4Oi+sb20vzc3Zxyjp9eqh/ppdliJ0krwCLkvel/Ma6KkGOn0OUi2BO437pz71qT/5kz/Z29tjwqzXX38dxs3HcYonhDdC8DrsqTieuNdAaw0AfcBMfPbe3trEjDY7OxeAoRZFhv8bUaePdwtFDGpyd8VzzVSdrMBWgVuhdos53EcSQzzLpyASHKlp5ZiHpK5g00O5x5tJuP2NV1eWAZdgZYObBjbFkPP4sEh4oH0sTrQmGCo1lC50ynSlzJEDwCUPpcDVNNFIQQqO+ARYLU7gXza7cucuDvSi7HBtpYI/5PS0nNACP5laBK90C3HsTkKA6M+ZVbOzT6+UReI6jWnKF4bkahocYiXy0PFYWJhDUU4Q/P5rxNdBUyclA+qC6SahQXc7cse8tZBDBTPj7WcgaajtFiegtLXDQupk8/58XvfNQAZu8oHkyzPlNdB9DdQAd/cucZVgaP/n//yfJ6kwXipJfp/Ha6CLGgA6Makcb/qt/ZOR06OZaXUsm9IHkDVNv/ZEoaahDkMhANCQJUSF1BRAxgCOiHGhduBj8C//6TEgLoeUAtIxO3jCC4ST98uXL1dWVhLmJxt1RPM2BqBdfNl/GNGxnWtP1sFT5kxEQh4BsAtbm/7cTOLaVLBU2/wxPBZ6LkhhxndFRJqNXGXA7hQMwbhh8vDQ9tUUwXcLrkbqSsKnqgrD2suX8/ML4VGwB53fv/+A2TnxZ0P5bLHKP33y7J233+HSoIlCQRzG4T6ycxjUHl1YCRijLfmCn3QhCC9Nkb1VKxEr7KPSAB0pPGaXVu7ni3hYxZU7WPpJMihlsDj23HgNXFYDVeAOrGHRShYfUfOWOPBZk8VKEmf3Gb0GeqIBXuHzU7mzs8zm5nY6NzU1MT4moFYTAAc1x104iOGFLlDrLYlh4rUbmgDSmWOHuw3YRgqIoKagsX4WsE5TLTZ02jRnuk7CBRAcH2wQcwfdQmtgAY4OdzpuHIJXjXQnqqxaQgOCNwLB7R3FiYBd2FoDrg3/bmcA3h1pG07eRw45ohSKRbzeAeQBKDdkXh+PHUOBR4mAKR87OWeSKMrlQVEscIbS4kXw0S9OsExZoVDh426BQeQAd5ZMYm0kYDyT0OMR9/zZi5cv1x688mB1dRVuURrsAtsdHe2CqEuAJZMuhtuF6GHdeh1ccg/c45egaRxtbeyPnB5sP3jwirtLm2YbkERMjRMTfvqBAbkano3eaqAK3HkysXksLi7xuCavk6Xpshrq5IPXQP81wMt4cXH+9IyJI3dTlVMcA+LzUFz07ql58/dfmJCDAWUrZG9g95iZwYgNbRl3AWlsFcK9bNLuUztgmjYN4K5MyWRbXFxcX19jOaFk2V0u8QUDVCersbB6UJu4iof6Y50LW2gTLgCoLtE13mxrIswqEA8RZqU8pRkmi1u/dXKEapUzQPERWq8mGGCXnyQRMrMld5zf9nGWUuIVU5eHJaaWlpacazuc0P2IwffdncJuvlDYK/KdYPerf/lX3/rGN1bv3OEjAzlRn0jVaSykHmhHRuIQxEsyiUcJqgsz+n0TDZQq2bUn78zfeT0/2cHXpCaEriuJr0bXVZWvx2ugzxqoabx4h9Bi9pkjX73XwNU0kB5Lzc1M4tu7vXecOjti/goHfmwLKqoFRlerq650D0nX1eQPL9IAWNgmUxFUq8sLbgt/3BQKNH2BgzgYPp0WHsWhonbi8Doi0SGz0ODOgdNInSE5ylAXEZCs48gxIZ7szzB9TakwPUoM4boSnIBGU7C0JqIjSwt3HATTKhofIFmYB8XKFF0Rwq/F7joSQAeij5xp0hqbZJMUJcqNnilrkgJ3VISe6z5NQIFp67H+cIqAJuCGPCiThZ+woeJ/j8WdhX5AZkTok7//3vuPHz2+e+/O3/jEx6U5U0rs0XMySzH6C35OCYF8iExdVs5vmmhgfWPjbGzmzt27o6mjJqcHMskG43nsPpDXxjPVbQ3UAHdGBm0xQUEnnz5pYvEw9o1gt6+Lp3dVDTAb4JwmBEwz7fRYdmq6KDSWkGgMBCQs4bMNnAYcogN11oFk67kJ7wm9BxtFnJWdpszZYsGjyZu1hYUF/IDv3r2bVAsGIy0z3FVvtwCDGl8here0CLhHkRCvQ0RA1bbVfQBTdUoDb6uHArImd4p1WYlyhicFSUMSoHDgu028cp5iFCdAPSwuc7Vs8QqG223OSQ4SSr22ttY4GIDVYpcWF53jEGy4KwKsJoULwVKvOMYA2Zn4Fcy+tblVLG4B4vGDp1ulzG1DwJnjUIxKAHgfN2ebtkVv6cmT05EXa+ss3jKRHbKODXfILb1mXuzbp4EqcAfW/PVfv9XpXNc0nSzTzYfL26c6L/HAaqD6Osd4x0g4LRyzsYETMyeq5waWfc9YlzRA64Q/h8ApCDW89IbVqcDhdrIoAmSUxd2GcgIZhUsTG5KhRRGMxEyIknCEnEzBwa9mzGxMbiF0B7GDXXCoo1g2J1x1i6y1gVPx7DqUyArCsQR32u1MU0qzM8HGOc44ndiWdwXmdrO4l8sJuzcoBxWhKFed26JkCEbOljAWYHebtV3gXZPro9s0s8rgi88kM9MzM5s8zOsbztM9Ti0Wl3pNMklCuiSRvNYLMbUmZDtG8+ZHmeX5KL18d3koLXEwv7rKpDc+eA3cfA3UAHdmL44kXnjtzvz95W/93leilKYR2sNOsX5TOj7Ra6CtBmrAStucTU7ixTu/IEfkF9vl9Nku9tEQuzTJ7JNukAaCqdyF3sBvgugOyDm4LgBLzAXQKK0Zg1OFF83NPaHri1MXXtrf/va7Dx++eqH2DPsakDRUWY/cwzvddSzEc5VHaIvZ2ipCTCpqCvaNocZ9hcR4kXifxGnFZdDWYmzqgvnLmJ1dPRoCsF2rOzEwgJxx4q3iz549e/jwYd3Zw8PDXC6LRGYJp0sh8QSpEZFJY0i1Q2d/F3zPZJgDZ3JyAvMq00dKF2jD/ULSNezYZbeN3QLwinYqTNivNVnDEn4/Qjfs6dMnMyuv3cmw9NiQ2drd9ePLv7+QXgO3RANV4B4XeOkD91771IeXXr93sLn75Gvv8HkxftbHvQaurIE+vDUx0S3PZE5KE0/XD7OpI9YR8y/vK1/HgSYwlrZhpgGUM4wqft1lt63buCSBOoCigCNeGlo8tcMBP5iDsWIkGCQHTg2C8KlBSuFzRWpnrYQxcVsfTAZtoCKBDG6HJEkBm45G8N2yuMfNZbZ6XDEVFANGwbaWEpEigk70H+wsylcMebfLwd2N6DVi7TaoZWqqfsYPiO/t764sr0CMXlM6LSccqCAtHSe2rnZO8eHEwDlpwvByoclmsbprWK9UpF88QMYo2QbpdGj/gTgVXOihFS9ym+Pb2zuH6VUcvUZHh3gqdCzuK6urHV9H3XE+eA0MmQaaA/fl77i/8sFXJuan73/PGxvvP1941SZMoKEL73Laz0d//qYH9EN2tXvO7kC/C5kvDAXk8vm7+ZHDw5EnT55kc3kcaUZl8/ThBmpgbFRLxSGYQ4RcZm5QB/bCbRUYg+xAp4BCgDtbXDs61Qg2v0eP3k8A3IVI+Q/xJADc0KWwahWEwmH444S6E9qFIc6bSWfkBK91M1sKB0ZPnurK7mCsGvHo0GSGdAhrKRcLEAhRe4DdMbS7gaqoVfZ2gfc4J63ijDO53+BOyaJRFIcF0L950bAMqha0soGjugSITG1lrSJ1itwjI/okQhXSxEhqV+uqmiT1tZJHP6cJaVZRbQy3ywpFI1Bf6FYeo9i1w1whnbszcTikhvbous1MT0d3g+6ShCEqkzB/j7J1wHGPOPBkh0kDzYH7X33xf2w/W7/7kVe/8vn/9tqnPvLoK2/SYEd3+N2Pvv7wb3zoyV/+NZ9Lh0lWz2tSDUSXOmmBocgXR1Ss58Jv7+D4/cdPJwvjQC5DBkMhh2cyqQbk8WK+HMFrEfgG4jPsLlRsIYDDuGdU5FcjBGnA3SH+pDWF+ebm5tfWXjbOeBie1x4UaZBYlmyN/qzwk0uI0Gb1yXMY3phlnVFBWoF3efKIbTZQCnI7TCpYSqo11A6+q66zcycoZ13VhmGJhkcuXYDfofQQojsW41uBdZtHRkBaqJ3510kAXjtyrbaMSeXrVuNZZorBfA5qL7PwUlq+8iYUKknR4+JCgN1Z4XQsfUoGEo1jpOA3ZgNqpYgW2N1yGXYXZA/AuxuYqjEP3BiN/Ny2lF2FneXllUymcVXa4VMGs8owbajjO3gwBkwIPYGtwqBx3I7XVjL49OvTQHPgTv2P//Lt9XeefvTHP/XOH32dL5fMbeyYmnuwfHJw+PTr71wfj76m7mhg0NqG7kiVnAote13myWJusrjKd/zHjx5NzN2dnfCv8zoNDfchWPDkxMy0hlsN+jm463C7oT7BPwUy47KBwMRAjZewuFOWziG+19vbWzMzs610B44URjaju0F34pqH0fIHD6ls7+pkkEiKwVPHpNgUs4ZjiQiWqi8g3B28bHFvZ+kpyx5liHghj0FZl2AlVDgIjqvIN8bAfCN2N/QObj85KVOz6mgdmAEGhpvOMsxsjweH+0xlJoTOzDZj6gDAAYGIOimjKS5EJp2hAhJDRkwycjgJa6sWSHc/FbH/UGIjDBGm9q90NHqhtoabcIQmn28e50bL9+7dnFkl4naZwbxI7Z6TbnBsT3M3CEGjp7x2k9EuyTtsZFoaS2jhplfnN95/QXs6Xsxlizm2/KZX5t778puyevgwKBrgIUvyGxR2+8VHq/nCaPHvP3gwPlrGz2H70H9E6tf16X69wuJnmmhcWFBNVtBsOUOtgF+A/syMPZrC84MUSjHLLbbkyzGE89VJ6cT5ZTWnUMWgDig7SFm3NXwd4xlSesjDIFhrQROS1weXKVa5gdmoguCEqQRegM0G1QX97RciZFRgUZs/hoMwyNyOmRw7+Ulb0D7ChGPgchQSY6Ua5XmEznGpxI8VWPkRFNHR0b4WTd1n9Cq10MehpyJJTTJIuI+9HFXJ1cfQnUF3h+TtDoAOInGJsUTVZ781x/snY0+3ThdnsnwduklCt3vibpKcrWVJAgI6zdO6tiuc6ZSJhPmvwNHQFb2g/dp89HLn2XpcqoWHw7GOWpznoY1zw/rQNQ0weqkNrWIhVyw82D8sPXr0dIphhn5W4DbKGpJTADuDbw6vs3UPVLAV7DMg6DYMewSZkgV0CJa9nMXdKWZxaen999+7e/cedBpVBZykIoeOrWqsJ/idV8yIInM55mbw9Mj5qDPEO8DtYKj1PQyuindoszO7vDC9fNkt0ep0QhMVZo3QqxHDmG+a0Smd4xMAkD00tAe4PQDtQGRbfilA7TYk1eH2E3bQaRTQpQDvX7x88eDBK60y4K+CxffFi+dE+KILI2HPQHv1Dk5OgPGY3DlPPdKZsc+Gs5QK62YfRtU3cyK5veudmKAqi2zqj91OH3fkf/782Xg2f2++5eegVhdr8NMTzsQ6+IIMFIfRc9UVrngyexiGiderqqHJe+WqJH35izXQ3Vvs4vp8DjSQZL6wiUJ2onB/b2/3ydrhVO50cnLKq254NSBYaQ7lwmy1YhjgNNgcbjDnYkcWALbZBw331ZZJfARJxmI+fvLk/r374qE2CD8aRBX0JIxqDKmAujnA4Os+Kh94zMvgVHxHwPOa2zFkWFGA/ahD+I62xCNE8FQRw7ja2L9QubIYdBWyDYOSDLcL0yoEQN0daDAqCUDmAEs713Y5pWNKh5jcWerlE2moPnv2FFzeKH5Ys/bM7nLnzj3QJPECVcUCfR704xZbJeKs7da5ANMzYtX6DFKfui6NARGrP+udmOxoXsps2qFqJHKTUg7PchtrT1cWFtD5TZIrksUe3ujIRwZRA82e1MvwGWvALlM8UZlu8UplPWDXA/dEF7GTTF284J1U6/NepIH2Fvd4afD65OQIKzU+fvyIFdcnOfBhODUAsMM6a+hU8E1AHgysZ1SYz0IQA66DVoUDbTpCzMkcghcvJzfUWBCU5VRXm0xRV9H0hwrmiW5Q0hjCTSeVkisHAUZTWs5U/+7nXgBOFBiN3EYQhsSYl0tozhfsjv4DJG47aYIgyC5y9AwcXLfs1ajj0VA7hnbZwC0A2vEFMugcqM6o1WwQfGFB66HWpDY7QMOrq3dYsZtJCYsTRbzPXUVga04ZZHfeQOjEXQuBb5xpOCU16ZrWB5Mp1JtJaKLa2ALW1UrRYcjUl7m5xyhg7WQ+dfj+/RV8Y9ylv4HSJrnZbqDYt1Kkxkf+Emq4viehK+wiYYzjywB3WlO19+FYqEuobGiLdOsKDK0ChpnxTr1fyE/Y3tl+9OgRYw1Z9mWYpb+lvDPpN2jTwJs1fOETLGxsjiUOJnOA7RikCsIDEQMVwaRXVFkuly8Wy42TzMAMLAm2y8Qs7EtF/IsfQ8LBRkfk1bhVLM3M2Ug3Yux8NKV/h9qjomRRMIO0URU+txoMkhtar26E2KtBpnaB9WBnEcraMR4rcloxKG3TMgLXge1yYdE3gsjcXquqtZcvgeD5QtIpFxGDr2F0Bra2NvFQYiQr1ywG3IXfTWA2xnulQrZcPufklyi1DASvOPVL7GVnyuHAfYWgP8BY2IYiVY3cpBie3+tr64v38rkczVf8ug+PlMm45iIPj0ie0/5roL7N6Jyj677hIo5TIx0Dd9rQ1z754Xf+8Os07J1LOmglIk0MGmOenw40cPFVtBz7DbPKJKljZnpmemp6Z7/0+Pn6dDHrre9JlDY4eYDggE1DbjSz/LgVtJVHuAFkYi6A54Sj8f82S6/h4EtZ3GO349T01Ppaaf9gf2Ki2uuDMnMpErARGthgrCQsYT8GpIsXNsB0dnZEQ0saPBFSMJTSBIzKZlK4ygRaTEbBbRdCs3tkQHdu7PLScWhW18igrRC+zOqUs0J2ZLg/MrJrNKqNSDXcXioB5cWTmIhJa1edIaUkM0qk03tgPDu+tLysGQq3t7O5HEZx9EMwwdkQqdaFzZ8JXVWF9FANpgcdKuLEM724jWmGKWWyTDdZLXNDY3yxWT8Yr+y/uHf/XipVXRN9+MStucIt2FePjofIB6+B69NAkhuzI26S38HtgHvp8HjlO+7P3lukbmfqYFuYnfzaH3/9eI/1GjT32eLi4uZmuzF/HfHd7cxdV2y3GfT0GjSQ6JolylRP+tLzhQFEZiZz0xNZ1pHB+j638rCYueSUI/U8+eMea4BxjQxydLiNqoTZ2fEfx+0Cwpqm5azMeu9cbcHkAOBe6k6Ly7SwuMhko0BGBs8B1ons7+0XinkH3MWLqhCOxDPGJleUPd3FxEoQKCec7CC7SzNWVZUhFuEW2dxDfE4sMMBbTAfBWSF0FVNBlXGI3UzswvAOxwvCcxAZ2zVru4LZ20uUMlbEPBO/7O7uYRLn4wYeLEy6eq9hrSVX3YVb5OIb10RxYn19jRlmMLoD3x1mx2BkF860ldJoBDLrWkHUNhJHFbCRWIFw7lgpmNttThm3xpZNPansNzSUznNrG5sL87lccfmGilgvFn28+iR/7DUwVBpI/rZpB9y3Hr3kVyf4u3/8DZeSz+c/+9nPvvXWW8ldh+tIXe0wuYxXq8eXvpoGEl2nRJmuxsfIyBXnCwMksI4M3/Q3tze3DnaZTK3p7NRX5dKXdxro0i0B+Ds6PgwGZmruFnBegN25oNVg3ifUDMAjUb4ZGhx6mY+K4EOZ07Ws6JmhxXOGTGxubEJ7aWk5zdpBGfoSygFvjoFAYtCljmV3t4g76eApcXOIj45q75MQqhpClbQG1KvbCLczH2Kw/mhQqVxwDKuTRbb2AMZbtOrYDmI31/YSY1KxwxtnBps1HiCN1k7PKs+fPSYJ5L2+vo6YKBCH9cy4kDf5q2UMXwAAIABJREFUa/ltd8QUM0zqtLS0tLm58ezZM7TEMFWIhDBdZVGsyBoZtgF1Q+6WhgaUC8bcT3qxLgrCcgE64scIDtMG/Z8VHtxdLKZGtFb0LQk8ULdEUi+m10AVuGPbmJ2d3djYSKgULCJf+MIXaD3vt57wKyGpZtk6aOibFfdpvdXAxZfn4hy95bCRelfmC+OVPz87XZmZ2gCLbW4wAi+XyzXWdUtTBu+iY73GCQS4ZqCuiuycuVoAjv8gBONQSRPSZFRruZzkngERAnrBtbiTOLyM8Q+0CWiFiLsTMHMwVRGIaml5iRRwsJxPam2ErJoEGgWg25hY48Cx5xCr0QoIRnTV03A1wAVxDiSr7Mu2cTuX4rYgc1fEtmZxdyA9BO7uyLbqfZil3eZsx9heEnBHJKcf4w5Hd4WXa/i1T0y4YdyOdKXCO+Lg8AD8DL/kAZGjBzB9CLRbPiW4Zd+7d5dxvWT+yz//88JkkQjoPYToFRZvCnVQc89Rs/tJMcRtJ1UQZHDXhzLKtqx4yE+UzzNP33+LRqlY0CfxWxVu1YDjW3VlvbCNGqgCd9rBVx6+ymLdNHGN+Vql8H661Jy4Na1tK+I+/To1kOiSJMp0nVx3UBef1zvI3TYrD8vC/AJPysbGOn1dLPFDD9+H+cq2uVYO3gmxBe0ajRuikqxITTAHFXJFiW1uGLJxFrAuHDxiywOl06B8yrZihrUB8K5++uRpsVgA4/MD3SuzIU0IAm1DzA62dlZq0XM0bduEvLXWarFdBDpBCKC7Hbn+hPoX/MuP3zHpWnqUg0eMBqgqhBPeaK/OhdnajV1D7RSHTzkWwYvYUT/j6dOnCwsL0SOABqmCrwbkzIxU52/BJsrkMVStXg0hO04PRzRiAWovX7xYXFxAbNjjLFq6f+8VVmba3to6PNyjRkQqTkyGbkOxwgbUTTqymEa0D/6te6WvDXwEqKs0TmJo46nN7b2TwusPXsGJaGiFuALjjFf2k39dQX++6DBpoArc4Zp2NjCZdEGEW9l4dEFvXSaR6DIkytRlxq6fXNedIHn9Y9wCCmysr2N9n59fzGb7N0fy7biIl7htaNbw7nDwjeIC7EbF8A3XsBqAo+4MRYgcHR7NzNYsVQMRjNCs7MlMKzhwZACf2bGq/TjAw0aj2QZ78927d996602Mx8Bc8Cu5hDDtR6WwEt86zshDJNoaYQ6Dygx8Ky4BbUukJsi5Wyg+ZnEPyormmCbBRD/OqR0EH0J3OfO4zon82oFFjEk9LRtoB7XbzJUp5uE55dPTd374I/iyGGPaGHPkEDdRIhFsos4sCjNQxh4PYzyVOcai2hQxpFD78spq6fjIiAh2kwFr+0x+hgAPG+vM0/MS8zsZ2gbVbahdohtqDyzudBnCW6AtgeE5Sb/q5ebh9PzyXPrFDRMt8UVI8XAlzuwzeg0MtwZqgHsCUS5qLROQ8Fkup4FEqk+U6XL1D30pXuC9kAFQxQBE0M/mwfnGxlOs70n8KwJO/PXqxSWppQnww7zNBeIG4BZwbu7gGwJIOUCiwsuCnmTDp4MToOrt7a3Vu3dIpFjpuAR8BT/j/p7N5vIFYdfaehxkCu6xJreaJUEWD5A33/wWFaaX5DYjnsSYLO4uiDED8UQCzF63Cyu2O1p0XcRtEdQRhKaT2UztVgcY3fTgCFi+c+A4BMzcrmlkDLibvZ0T9mlAHjIn9FVOpCOztTv2jjW9zNnrOLSoBwKxkK0IPFp/Q+xFZ4xF5DL0rFQUfmRDWiECi3Pz805qlRJ/EkUppm00f+fu/bm5BY0ECKbsDK6Cq4KtyRlsIuSuvov6J7K43zBXma2t7aOjw5Xl5bGxUqTmWxg5ODjAWesWCu5FvoUaqAPuDa+iW6iSaxc5kdITZbp21oeqQt7ZveOX2eoWpkbPist4zpxWxhfmphiV17vqPOXkGmAwIphbUE5lBO34gQUBhOBhh+A5lBmZE0y+KP8NzR3OzIYUOgZXlo6xCjM3OUA/AqUNDIg8/QKXHuyiTJysJqUKhSLGY6zO1fMAdxuCKUYsiCUDvo3beCnFhZmtduHckQCsS2CCHYVJgq+s+hSM6jQyeH4bnDWLu6zscpFxW9nbge6l8kkJ2ztsqJ9T3UmeqYmJTNp5nnAoHi4ISFT7GNKtKuTzlVyOB4evEHy8wvcydGc/1yhUhgvHdCf68p/B4u60o2NXqQhHLAT6MOAfaELQnTLZXNblH/Ytl+lFaW4yX7kzOzPsslydfxbNuzoRT8FrYCg0AHCn1QsavqHgeIiYTKTWRJmGSOjBZRWIkJS5y14UqmA2jLNKav3lCxapwV5rxsik1fp8vdAAyPLgQBBcxthYBWA4UKiC0KgCcBCc6FLBjsxyuLW5ydxBzOXP2VjR+mhEtnkmTsdOQArXE2ZfwUXeoVgHLPFwd5DdwLGirtLGbVR9JJCLGB1IhjhdIkto5ypjOwF3prUJKBjKB6mTQRZ34XaAu23ZlE+dkwx8wj5acqidrSKpkWIhL8EVj4kXMdc0YuqtO0MtzPjEzMKiZkNaT0osTKVcTiIH0aNS8JrOZlyGhoqliVBuJ70jAmqnnMYo494UkRreCOs6759kVua2rZMzvHJ0jXMUcsO+pXRNNZ7QjdOAWnDaPrWYPnSigYYXRrPCiTI1K+jTeqABGTi5Ir2/KGOpyvLyEkDh+bNnwKE7d+4EwxB7IJQneaEGsGRr8KVzERGuU3Nnd4FBTm2EkfVjbkTZ4QUUgamvfeA1eW4kuGPMjNzsxnINa+0Zlu2UJfvkhE8Bjnns4NQboPZwFzBHDou5fb2whsxJNLGCHfH6ECB56YAxqLZuaMATORmAirzmIeNcZQTcBduNy3K5RCViythwW/Jz5LTo+KwqtZ7F2mNK6QpUA3MzscAC/utBUkrOyjw1UCejmwxIVVRLyLuGByqsNzwhqo4yW+lF5fmzgAIoRWBUgj4RhIWGcY8UzzaP86Nn9xanE/Lv9JIw85BmY068IeXcs+010KkGBNzjT7Way05p3Lj8iZr1RJlunGqGWaAOXM+7ISZQ587du7xlge+p7PziDMb30NLZDfq3hsZVnzSBvsqI5icXhgvwHGicdH5VQGro3XLYGa4fxsxguGpzZcebyiZccrpJqmoGEiuYxd04EmfOVcZBZOHkgDORcMA12BovQdUGTZVgfAcREWsSnALAxPilRHxBAD5gifTA5I7RXdPM24Qy5mLkejXU7jgKsLvkEDZ2arQFrYyzhk1VDdK+hImyoAOeyrxNqFrNxkScJS3kRLbjo6NqelTMOhJOG0oL6Im4sLr7ac+/BcF2WzL2/DzDaOLkX96qNQ5KbO/obKc0tjqXHxvtYFLLqsYHRY52fOhCdh5YHW91VSNSfPAauPEaqEcS0TPDo+7iUeRm6CJRE5Yo083Qxy2S4ooLMF1OUw6+g4OYCgPgs7C4lL7pqzbWKmogniUwOPZjAGvYqtG2wRjYLwoGRw3qwb9SmR3y3A2LrBEoaiGVrWrliIkZ5Yil1ZBIjZgxu3ySPnF5HbysusoIvAew3fHoWLIaxVxAjWJhDEaCuDnKhIjVEZax2cWIAM+DQuxIZbmoMivXyNNdYB3ULtDOPJBENKI36j6EtnZUY1DZ9AcFpZMQMlUlHurazjjuajLRlWKW99kZGUo5XT1n40e3t3dNihHGA7DGH2O+I8rIinrskEIq56jblo1+AWw3Eg634wMEemcCm7BsRG84Ily4teOp8dKje3NzcW0NB/edcFm9ExKXooi3uCfWls849BqoB+6RQK4p5JAIT0W0jTIMYCTRA58o0wAK51m6qgb4In9VEpctj6199c4d4ND6dil1urswPz9UzjN9fmauXj361/jUAL/Gr6IgugsAULCdEB/1CbeToEiYO2gRo2NLrz1yWWrTwuLah+dTYGNWIk2Xrfl1mLpSGTvTFOlxuzuMxYG7CBs/RAJuRDSE77aXuTlA6RzH4g69O+AelnClZfgfGZG5XXDdwfYyR5SWDsKAWqjXdKJdJKWzw0OhLsBhmAdGLBoek/PwiAkfK3Ozc6FOSLOoiikfEz6yxToOZH/y5Onk5KSbiIa+hC5OEKp1kDkgILHtP1SstCDBNSWoJpuPsaFSwxAOTkY3X7y/vDIyXgS1+9BEA/i4M6tokxM+yWvgxmmgJXCPS+raVrd16eE30tj7I16ge/FEbWyiTN3jyVMaTg24j+995J1JrFfmM+WTsbW1NRDa/MJCL7/a9/yp6HkF3btU+GMwOUxlRlDOQV32eMEgAggQGMgPAzAQVrjRoCMpxNmGXAQRIGGY0rAnS+uT5HbnGRvKCFF6cYSIBIwxpbmAu4PvilF5ANxVt6AqFIwNcRUwogoNiOvYDkLruoQNA8jV8LwBdxJdveyoFlcZItjXzeAuDxmhdi3roSotCEurTvs3OYIN3YzsePNlp4xFV4/xHFPN7u4Oy1HFzxG3DpORNSmCs0wcmc0ykPf5s+cPXnngEuEL5TgOHGmJbqVCeYXdFUxu+mvAfXokZMPiHujQ0Rr4LUK8OMimT9bu3w/EH3iW+8MgXbv+VOxr9Rq4dg0kAu6NXLlGVk2lhfqGOUy/cB80u+3zJcrUnoQ/6zUwwut/ELTAqj0rq6sYgFkhcmy8uDA/HZ+drwWHXX4GukyuBdODk4zFff8gmsqddks/ABztGME2KeaBnmaJn+NjXK41Q6Iz+jZoqjnsq2sKGyQPz8vyXCxOfN/HP/71r33VmbpdXoFMN6uM4LuAqUz+ws4G1sWl2HQHQRHtIvgeoHbwq+BqFCzqYLsDsfqq4MpbcTLSf6CImduF3XHJoB5VHOH0QAnBrlp6dPSND36kbvLsGHGYDdiz7ka1OJZRE8g4CMmpn6TrUgvITRD6t6xwtrS8hL1cWmLogYJjMCwvrO7iVkZI3iLSAIWcj1Clj5/dIkaTRw4PD1lvauXea+MTA9F2Jef8+nPy/OJVdf31+hq9Bq5fA5cE7nWM1jbWan2rjXRd1sbDDrI2FvYpXgNJNYCnbNKsvc2nO57ZLVbv3C2fjT598j4m2KWl5QjKJKncPzRJtBTlAQaDxQXfDN0J0xlIFBI2c7tasIpQHeePjo5JvXf/AV9I6i9KvKWLqBNpuB5hxmAfng/2d+/em5qefvNb39zb3Q3QqkCmTQcZTQoZWrwhbmyGqN32kiCgbSKJGRcBpYpWFb6HsN3hVwzPDpQ79snH8kqUcaNRyYPIUYUuT7it1jc6Nj4zN3/n7oPJqanwbMBOKKljT5TqAlQwe0eJdt4yK1afW5fMMDfzyb/37W9/8EMfol+hZVPNxz2W2/GGNALspgqpIAhmcecG4ARrZ0VVD3KELuWL7ZPRk/37Dx7wNWiQWR0Q3vzKqQNyITwb16CBqwL3WNNZ5bZpYkObXM3vY14D16CBOQ3q6kVofr8nqWl87Pz+vXtY3x8/fpYtTi/O5M0HIElRn6czDQBHwXwgVEN2hhRBeABLwqhOYdYFEeL74XJih3ZQr6aaFpfawcYAgluBMGNs/GpEyM5NTU593/d9/Pj46Pnz56w9xMzuY+eMW8bHPZzNXWbl0LRsbMrGrEhAyBAqcdCpthbTxoB7AN6DA5cUgmAyuUAZCrO+khzAT7G7a9CnVRLmCOvi2MmYL0wsLq8urazifeRQuUsng/KGPJlmQyLh3uWsIRkUrsH3EUF1JxhRyrWR+/3Ji+d7H3j9dY4yOOfEFRHStz2lHVwPdhwIt9NrY1RualQ+7gMfjs/SL569v3Ln1exoMGHowLPcfwZZ0WxiwnvL9P9CeA6uQQMXAPdYI9uWmaT52hLxJ70GeqmBTizuHd/QHReIScqouwf374DhHj9+hN1ofmEpgfNMrLyPJtAAk6bzage3geQsu9vquoGPWTQeX2rgPEgZTwwXyqPC7mF+FQoxZbg3QqIQRFrC9DCj9lZYGzB4vlB49bXXHj58lQ/9OzvbLBSFLz4gVTxZIE+I16NIQKwOr0PX/cnC7kIE4Q2ykyaA7lxlAo5ZPrZyUjomI3xRoanDWAsqYVGqTKFYnJqenZ6ZnZyaRoH0bYKTtlORCK9HvQo7VaemoM4os7pNcUqBel0aLPNx4KTE2kzH9GzpRx0dHu3u7o6PZ3B8gtWakqZVCNtParCYw+7SB3Af5E5ZQl3BgTrkcjAAppJffeUeMxuWBoq3AWdmwK/sgGvPszdcGkjTStY2ns34vzhHs1Jt0yBJ64pfo5tvoG1ef9JroAsaMB/3ft3vF/OP4++DB6/g1fpsbX88dbSwsOCQ2sUlfY4EGsjn8uh2amoqgHcqohbIQU0z102QoAYJ6C7LOxP/pMGOGrJpE5JQIGwIbV8HSx0PYQ5H3aVZRWE0IFJDAcM6Q+vc6DpgJjXC6t7+Lg73AFZYwOQMpAtqgGNXi3GrRBfReeWSSwx722gngzMJBt7Nfk0OEdC/QfnTU7SAtIg8nhnPF4uMNwWj49CCJ0yxqOlcnGsKBWJBtQYhhtejNE4FQsaTlOq4D4rGTzo3d1LgFcG5KEzhenhwcHh4gMmdAjh8r95ZhU8rjB4gFVITIf5NA2wViQIGd4B7ZTw3zjUNKh68XalcwdDOg18oeN+Yji8PfbOOy/gCXgPDqYGwFQtbv2uTwrXXEWqnfpdybQz4im6bBphVJvaeH1DpCwojh4ejT58+Acrj3uPhe1cuFQY5oLBBWME7QbsgpAQKK0yiollmXDI659Bh9/NxmX7xsRZ8dyt5uoKxNtNoRQRDumEFLY0TIYWwpPaMSB3PMgJifGZ2RmzCkS13CpMEJpEsHZesM8FasM6CLkwPagHcB5Z2wXSrW8XVslo9Gus6Np7GYE6M067yfKH48LXXstkcZnWs6ZnMuHotyG+T6kQSBOTYhbyKgCMRTwvJxtUbZaspHZIWjVDp5IR3iVI+LSHo0fERPZiDA7bISMaXL18urSzpy4BCSDhgSVksm4soHnRXMLar53POGAYVG7zAZdpYXz8rPGDqmMHkcPB0Vs+R11u9RvzxzdVAWo3fADRl1ddBTNfw1TQ9lsVHvQaSamCIJpQw+F4Aszx98iQ/vTA3OQSOuUkvQ5/yCZezfqqtwgOwVdvC1oy2+wf7MioHgXTgfeCpQiks0ZhpySDruwVoOIQYiWIuMg3NaNR4NZyhYHSSaHg+3EenhU1TI2OazhxbuKvOCgalhWYRgw2AF5JCrupbSKxahOoKUAGy6UNnGADrr33gDSmnNhiReJIlNEslU4xvjhzx2rQ4pfo4qFW1wxhmcRQsW/tJiX9wO5AduzsSOnEO9/dxnnHAPSBDPQLoBBJchK26MO5Lg1HFfg9wH5GfT4Ok9exc+7EmmNobm52YKeYO6nV57cwMb4V1N/zwCuI59xq4UAOhxf3CjP3I4N4A/ajZ13kDNdCXlVOvokdmN+N3cHz2+PHjYrEwO9ujwbVX4XGYygLaAIWMqgTgCeOJd+2w6ebzBXvxAwOFOEl1gUSM72l5FFaw2QtcapQjBlxZvV03wJCqStWHMK22HQuOgpM6CvPFy9d7y1dpWO7gtMC5A+i1eLSaO4DRIWnXdTnXNO1RkOBVlN/InssYVMtBjLhj3RKqqc1zVqmgcatO+bDrE+yTAt73zEYpd3Z9VlBgi5oD9YQc8i3CgW+xXcONu2LBtQt2+ioRXDAYnxi8qb4Zl4ykd1ZWBrBH4S7ZsGx5GIeFVc+n18AVNTDQwL2NbN45vo1y/KmmGhBiG8JQzI0V793b399//PJgYvxkxpaIH0I5+s8yVvPj42N8QgTxYMd25nZyZt4jMQQrTOiAINvqUp1Yvq2oDNwhIARbCslzaAWcmFUYy7ERc+nBUXTAuZqswQmlGSy149qjIEuTglVKsRqrZFTQTPGGm2PpDrcHpWPplr9KNKjYvg/UpFoR29QkByJYmm2cfkPALUU6HaI+TSDPn3A7sF0brgs6FsNGx7HFuANm2YdCQMQ4RIOQ0hVVLSJrPzO5s4GKDPm4yKdy+QGaC5K75tl+cWZ8nKVhA9X63RU04AenXkF5vuiQaWBYgXvd12V7LwyZ6j2716wBrK3XXGMXq5tQGNndO3v06BEjLAlV7NLFam40KTzHDw/3K7OzDtxZo1FhpXSMuCEUdCjRgWbDjEKCcrzGNIzpHfXYzCuBedSQujYAUMOgmrUFHM+OQ3c20GhrnO0gqbJVW7FqmtKblFXuaqagYDWh9rRoWLB88VxBArbrUNgwa7hvB9PJU2VZBWoyu/tTSgtjlkXI2oaK4ncOYmejobcagcvz6UbiynRqhAPqAW8UT2fSTP5Tc+cHedgFcN2pPdgG10VXBH8n5vARDwMQtvfPDtOL96Y2Uik/g2F3rgdfaCYn4zd3d8j2g0pwT/ejal/ncGhgWIF7pN34Pc5TGz+M8viI1wAaGOQJJRJeICb/5gfWfPTofZb5nA7XjU9Y/JZnwyZn1nGHqoX0wHoH+/tayFNW6AgQNr7+aVfk1IEC5SJuWcmNnd6BSndK9CwYXHQeNZqF0B3K5xoTMvZg+zM7cs0FiSHxeLoqrTJUbeBiadVoVDBGrLaIYzjK56SQSAo1yNtSLN02VTKW086at3xQuygFitHgVzuyTKFWrHsjCO2Cfadw1yNMIofBb4fBpaaAdtivYJgKF9FqUlVOMXZZwiJwGQPwKNw5ynANGHebSfd/Lkh6KM+fP2PG8TsTzI0Tyue06bdX0MANWoBpAO+K2qf/CpfJF+2KBoYeuMe1UHdzcfvXpcQz+/ht0wDw4GaIzLyWhO3t7fde7s/lK24awZshWk+lAPABIkFOgpLCdyPEgY7ykwlA4IX1q0UxpMgmQO3szAxf87qN0GoUcfDdgKTwpFmdhSux1zssL/e/qMWqtlxVskqrHkWsRug23t5V88VOWxF3Jjrv0HZAzFKbGPipOYTJUpWB8gA+6wxBrNnWUXJSSzqJbRv3GSL4JKFpXnQmDLogVfaJu5+ohiIrUpyYwFspqM7VpK3KBkVEqKpyN0BVV/z8PF/I973rvlvK7Jdzq6vMaHmj3rym/z5vWAmhWJzoMxM3tvqovbhOCd1zfZ01Dk1dN7n5aHrZuQGbpg/NFfOMXlYDcWBxWRoDVG6GMDKyvb31/vvvzy3dm8jVTwwyQLwODCt4yzCdIsY5kBxwe39/j+VSuTFciMHEOMc0GCB+g67WegQvMbUjwpt2mrPaR0HlY22Nw+QRoowiAFfFgbbhTtHwkJMOhXJW9ejPhSBmSbF3qkXDPEHWujQ4hH7Y+QDV892A4IhwUqUCsCxhdBROvqhjS3CZldMYkBpgMOBa4kiqAJPTXXaxcO+OVCIMYib81TAfiketqrIyMjk1IV51HPGgEiEl24eqDBiwhZzIUyioLJG+BNTwfKtczOzfmQZc3uTXbl/US6XeftEvzfes3ugZ71kNiQjXtEmJSvQ+061rQaKL4G6K6LD3qvY19FkDDnX0mYluV89Y1enpmZ3948fr67Ozs97m1F7BDFA+PDqcmmYZJoXdnV05uBssbFKQ1sHguPY6bXtL1EySZpnWRujXndOBYvybczezsitYae2rgcpr4KbKVEPtUW16eM4KBBW6HO4MWxfEh2O8rq07HGNJoyOgJI5X3/Oxj4F4lCFkz+IC0lGAem0cUG7ck8tQ8v/P3ptuN7IrXWKa56E0V6mmc86dvr69Vnev9bX922/i5/Nz2E/Qttv3u/eMNQ8qVZWqNE/ee0cAiUwmKVIiJZICRAKBiEAgEJkkd0JIJBQMqRtmF2IGXIcac2rHRMMKEG26WaNCRQpimaZ3rX9r4D4Pgu9KKBkJGvNOrGTvvAJCwup5iDDYOIpbJr4dXXzdefPw4aOJibyva69ijwWE2BuqV9az3fsbgfR7u+tR8K+9Tu3eO+AeA5QGDEcmrUadTAxTBIZmqUzloADGPFicXV548hnp4GB1HpOL+QesEiSvYmOZ/f3vNguMFRQnJ8czszOGq1OMK218JYSvbJJ6h3l3VxBkT2iAx3AVINTp7XljK8GmFsWLJA2hlGWT0JOIU29RBkOBecGy3DjSc2VXtFqwkH6ZySzFsRWIsBo4MsFDYu+lXIzIJYG3CkFzUAGRCyJTJKRMwmRhap92+eIfX/YmbXXnQsAkPamagnGxmf0E1rjH/w44l/pmCH26g+odvmlBEhbnnPLRsHfyocC/dd4fLkwfv33y5GnhcKZ6EAFMW/TAajaZI9DTCISfmM46Gb2/wD0NFL7404RYVjipNNMDGoHh3i8M0ASPWcVO79gZenf309raesBnA3q4euI2ogTQicm579/3sTUkHq5kAJqoWjgdH/z0q9S+B4wTRUY0ywUkw/Q1B+HtVDitTtCzVqHEpTrE8XKHrexLyO6IJQ5WorUUnjo7LRKMb9qmr4bUC0xvo26MdtRrBc2AIiK3ZKVYzkERraoTV+GAI0Jnd2gUMlTJkRckjFYDcMlOU+AwLljrMj9nW8owRmmAZaRwytywyXaupcddDGd4hha2AU1t3wKNVdc7n/ceP342MXvbXd/C6Pqti93d3UePtvvNq+xPjkAvIpCBe01U098P+/2sUcqsQYsAHnQyaC5fx19tC722s7MDBL++vo6nRV7HypC2AeZbX98AyMPE7fkEtgjkoucyCAQDCDLw7LtA0+2EmSQgB3AEFq3Ng5ZpQhcQXI3wTaJrArNYVMhHMnsiYRf9K0c9LssW+qUmcbC9A1qNM95k4Mok8IsqlvRbiqKKWuSDoDPsQmSpN/rJ5IOQjOHim00oSEpnu74JpKM20YrkNMkOLcmOSHIUO/xvxLaUcQa55iEYaomsSPqvgK2TwZ2p8wu3+RgHHPR3H3Ynxs6eP3k0MjLAu9DqCAxGlmfcB+M4ZS+7EYEM3K+IYvghKdT4c1HUMjUwEbhXM9CA7MAwn74efvryce3BAnY8HJgPBpYZAAAgAElEQVTj1GNHAYmJirGx4+XF2NiE0UTJ+lwbRA8u4INuH/cy2zTrcvLiahlUCNsNd9JkhKXq379FsFyeMnogziUW1pA2xO9M9SXjUqIi3noRqlpLMdkHEXJIAvHcZbFViu3pibcPJD2JHZBmYh5csBamY50Hz2gWr0qVHL0ppaXiRYkl8VQlpbT8YHlszP89YRzlsiJv3JD+T0DYrq0nsU4GFnH5emt3ph5ezO3sfNxa35gaKz2hNvE5k92PAP6Nlr/luh/WbLEvIxCnc/rSu750yn9G5Jt+cvvSy+xUQwTwn+sG3jAzAEnXH8xtb61//br3+vXro/P8YS8ON1Y+f/u2xztIgZk1uy2IHRSKDzmp+AYhGAmIKHZDHkAmcW2kZYBNZV1tSmDVRLTJVirUlnzxxDQJuxXTlKktT0IrbyOmILCJpeU6YqsDypRojMlsO09199oUrDNv476pHcWybqWTCUdi70qdUNHe6oEVvszPpAdrA+XREdyEPTauLWWC3Era5kupWCejFe7cUoYT3gu38sAyePD+/bv9s6mnm/MZtZePUs9reVeZnoc4d9A3Ecgz7jc6FOXfGf70Vzg3sp4bdzUC9/ObHah0a2sL848fdj+dHe3nxTM4p3Bz6sHBAR6CA8Jm3Hmi4aPLC/GiQIWfaJ81Z6mJdxFic16c952mOdrjj0yzxQn4IplxijTpTgHMefKvD8y300ZwxLmsi80+wxtkkShmslxmC6FLQlfsNkmBTVdCe/JISxNUIvKWZNrLilBlGzVMFcyOzNESde1tLM/VSWgmOfsCF5Adn99x28+RsfOoJoPwFfm+uF0rZfBUVnte8sJiz7eUOTod+/ju942NzZmpL3A4p1uOAOZl5ubyvQS3HPXc3d1EIAP3bsadvzAhxd/bwMjlHUcA/0u9Yw/urnusE3i4vnJ+voR/4u/snAHKD/etuq0jjR0hsQk+wPXLVy+fzj7V03BsMQw+wYYIgTNJsC4ITj4rUjAtqQTsLiH1baULwCb1aULN0YKkcssER40MPZqGqVZoKsI2/kgI5aaQVTBZjLLQlM1iqk8LaUplgU8e+7Muade7p0Jkpj0bbW28oTyVT2piXFqSCZbhJZZ1gn708m6oezk7Mzs9M41DxkQPkMwOCdFsHBIXymDjePxfBXen4plNeOCwmvQoG33/aW906acnT5LrwB51lc02icC9WgnZJAaZfV8ikIF7r440f4jKCb83jcyySq71MAL57iUgmK2th5x9//DhbHzp0eo0OD2MeF+aPjk5mZyYmJ6Zwdg3N7c+fviw/fhx6ilQo7Bh/LA69E7m24EPgR+BLm1tPFob7Xmcm6dAFwDCmjScdhTo2BHFRSWIQwnjkqJzw6wBpRYl+A5nVVKA1iYPZmiDr5jYKEmpNq2wV2ZIpJicINNeLgzVYI9losCGeoELgiK8nWssa0BzTOqIytpSJt6ZakLTkD+e2XJ+25LSJtxPT/F81vPZ2fnpadv0M2naJRKn07uj1c0HYzMTH8uXZl3qYIDM8IDeWcImUfgf2p11nzvOEbjFCGTgfnvBTr/W7Be65e/07Tl2T3rCfmH3ZKSth8nZ94cPAXbevX0LzY2trYn7BN+Pjg7X1teB2hGH1ZVVTMx+2vm0uraKLd4D8MIntUDYrMS3ILmAPeJH7F6fA2yqDaCnTdjzs0+Uz1KAlWVNYkcVtpqIRwDr+Bb9kiqnlCNE7OKUDxaqfMUE0hStML5zVKGCZMFzbxwseTdFleb5kr7T6lGWaAp2xTcVdyX26WpkyxB0FxYXcMg43R4n3CnUSzpq7Cvcido53X5xenqCbhawxmaiFxeoo/j/1enJybNtnDk5NZ66txiT0ZHb3DXoFgeWu8oRqIlAvl+tJii3wOIvl3517Ge6+mN9Cx7ciy4Q1+K1soJdznPyCAAAPdre3tza2tk7e/fuHXa7vg+hOT4+sl0FgdqJ3MfHNjc2gfnevHmDNbIA8cKUCRxEUIQyWQZCnMCIMFRS0yEcRZUA1d7W2AGmLJpYaDPJ4pwxeHrmaCEr7fFIPQephUYDxc6V2KP5EjiV0g49RyCvIXWONY/VaFHWNMY4mhrChm+hoGG9ZazogSbdbOzTTYHvrJERPCEYhytF7RSZG95cvipuRO1C7kDV0FpcXq42NLs3yE9OTl99HpudW8KH6AZmctOuRcBuZuiauWwoR6CPI5Bn3O/44PiPZfDCELz/XgVmLhsicJ0rnfu8xr0hgM7ALObDlfGzs3XMHY5OzK2tLAzx7DuQHfbyX9/AdDsgIK7pRoHdL8cunzx9+urly99+/XV9YwPPsMJi2XHsNhNnx3muYWrd5s6d0EQ78aUWy0hI+Ah86Io2425T9fw4c8EMSsubHA2Tu54aQZ+lJXEsQ17gXQesNUWBaGlGOJjtPcHTkKytqRvPOapYb+CYFVeQQbBavOS8WtNff7sj7MxImZVpdQrzbhM83GmKp9sChGPGHcG1ZGGkEg3QUJF0a+o5Z9yxuP307PQUTRaXls3nbuWfPu+fTqxsLx+NjeW59m4F9aZ28FV2UxO5fY7AgEQgA/f+OlD4Haokwgb9PlX4w1W1UfZ2TPdzV5l2YjoxMfHw4aPTi7GPH97gSGxsbY+Phgf2tNP+1nRudpocHhzOL8xjqfSYnlgKSMc53JGxy/Hx5z/8gH8+vH716ueff97e3l5aWpqcmsI1DHSILwnyUfJOVDRIl8cAOfrKGahAx+RNETqiyjHAJDC9Fn2Q5MIatAaLhXpT3vC5h322Drnh1TJyJQ/o1SfkTSNMz5f1ZSgooGsbqFtDYT2FNqhxrNKLjUgYTzKj5XWhSIOyZqVy8UxgDV2FgaECSoD1o8ODA/wb5OJsdW0dCyEw427RoytM3jUacJ5dkN0y3ZnKZ6biXyjAcwsLC9ZA+Y3OIUzrYoEZ7mxeW8QukxkpJnG9Y5KfPX22bsUPfjhyyhG4swhk4H5noW+z48H/irjRL2WbUWpH7b7t495OTFKdybGLRw8fnpyevf+wM3pxjJ1nBJVSlZvRd3oi2CTs3NwDrrjgmgsmAEGCd0AwrJpZWPjLX/6Kk+TFiz/ev3+PfTNxpQe8CKDPyTy0ITLQHDrBJVoLKtCIBAXTRBBAC8KCUPhqPtBgQZmz8tBncvtW8RxyF5mCtOgC3pZA+VqbwIklRPEVmU5QAGPBHrix6m0opbKqdELaIgIJWWCbnurSk2W2l660pMsm9ic1SKGCw3R2eoLNOr9/28Me7HPzc1uPflpcXsIh4AHz+CRd0y9LcZ0RbHBPGYBsEPMLi1PT02XE7807Lb7sX+4fXeBW5jy522nobkEfJ0H57GirT56J10jX6OkavViTa7p47f5ywwGIQAbuA3CQal3EVwc+0ZbXKvSeeZvfXl0YTd4vrJ0gTk1ObG+tYq+MV69fTc1vba1MleDSgB3zYsQH+wcA4vjfAqC6IKBlVDAK06fA9EvLS//p73/f29vD7DsWvuOJmxsbG5iyNQQ/hkU2+NwxIsQJVrB9UYsVoFDjqwH0k8+q5tbRMz/B0qMb+jjDED7UvI21AYfwBxxSZiEHZYk8JBW2+N0YzXLTV59CzdJzJ6wD60MaZhh66Na6YO5dsqVqbFYmaCK+zXnrR22tN5BYD3MJnI1TDjPsmGI/PjrEFcjyg5XHT5+vrq9D4ezsxIC7vvD8FDTL3qfsIhNmJ2zHCwvccYCWV7g4np3dIMG9t59PlqbOHq/lfUtuEMdeNr3e1VSvv8/4Sb1h6qmLXfDvhsPLza8TgQzcrxO1fmhjn7j4ubNPd6x2w8OefmF0w8EObWB9c4cthlf9qmM7NT317NkzzFm+fPmC4HVzqzLZOVihAfAChsRtqZxuT5KAIIeC0dlTVEEDAaytrWHzUAz/65cvuHMXIH5ubg570cwvLExNYjPJCSB4TNID6jMsAI8oCGAT1G4T7SZQsKgY3vic2hGwpjTBywDLiY/xRlXtPIOmEruRvmWokmNFmzlb0lbR0BhsLkuSiJJFqvLlclUIuOPLpVEtSNBCJEvapg0ZU47FMIgwbhcGXD/c/w68jnXps7Nzj58+fbj92B6ZBJ39g++LiwvhwNFv2DFr7i5rSloipNl2n3GH6s03gd3Dk4f39h5tY6J9Vr3nrB8jgHOpD92yj3l3HeOnp1up3/3r1jiHzU4G7kNyRNMPM+GBkEHKbBhnLz6yDZ30E2M4HznUy8OIiD17/vzk+OT1x/2ZsUPcuHnzycs7OSMwj7u8vKxZ2xS4E1YbjDcgHWj6CCQIgI6JdjwLEzc5fvn85f0HIPjX09NT2F5wfn4e1zOID3Tw/CbBeLRGK8ffXMMe6GLiXR9MWudH1D6j1pW3CtidH9yaGXfDvXTNWpFSxUqSkaolINbITC02FwJXAyl41WjryUyT5ktWmPsrlAXDOOoMpHXK6XBcQeGWUdw2enJ8jMdgHXz/DuCOtUqcX3/ydG1zCw9awjmGJtCk8vkZZuJxGcng+sUOvGCKZsMCdy7stxl3zLcDxuHOVJiCZdO/Rn5+cfl+b3T28gK3L1+jeW5ymxHAR/U2u7vDvrr4lY9PZvdTv/vX/RHfvsUM3G8/5j3vUZ/GYr7OPkc9+Yj2fCjd7AA4oJvmemSri996XfIQsOnJ5tTh4djbt29npmdWVruw9qBLrrVlBhhufGICowiztgCBTAKCdeHWUhUpcPYdAHFyakL/dtgAGtzDBOyXr+/fvSfixI2P85iFX5idm8M28IbjsWgek/FcO8/F83X24TU+jVzVHjIfB7pCt0KkbOhrdpNPromAj4WeKQCHTL6dFiMwY4UKydv44KQmzC/TinRt1Zm0ybEoS3PKsQCGOB3gGzgdCbvDHB+dHB1jH30QwPAzM7O4IXhlbX15+cHE5KR7ckHIDvzNR56y5RlWNuHSSAucoOLJe0Y/fhcue/MrA8zSa50MOHMLS9fGc9++7X3+/GX76U8To9eH/sHfXPY8AoeHh3n7gU6j3OTrqTMz/BLpUeqKf/Cthy72aORXmM3A/YoA9au4gzM6PWmtWcrp1wF23y+DYt2329piBweqtaE7luIOgcePH2Njlrdv3s4sLK8uz99NPKthuDq+8Hll9YGm2+FymmzGvZTDvFkksOd0PHN8XrAuBhPrQOezszObm5tElqdnQKF7X/e+fPmMJ9ECN05OTQLFz83NToLyyXh0yxtfheW5Qj6ssNcwYJeT9OgGANT6IqGO+RlNP6dQszYioEY5clFOOA0QSzZrVlge9U3AqlisprTX2Yg9Gi6XLWnJK2NKaNBZc93IiLoJ1gHUDaoDpx8cAKkDxU9MTgGsr29sLixha/UVPrx2bNx9NNQNzC0TsADcjgzbpc/PzypuGr0OTnCLHjIJtBtu9+ZYFH/KHdxxSYALKGvZfg4j797xGhWrxUZG7sXzDdoPTt9q4p9gfevbcDtmX5jXHqM+ztdu3V7DAXCxvYEErQzcQyT6orzh+XX1GNIPCTpD1bpM+VdbGUwNQ0bX8b3nh+U6Tt1Jm9m52cdzjw9PRl+/eoFl31g8Q9jbWepUvzPrFW1ARixumZrEdDu2JeEsuK1y14kPTxpf0YD5yVxT51b1OXjARYD4mdlZrMB58uQJMCag6nfshLL3befjJ6BWwE50NDE5ga4RKIBUm5IHjucSeSYAeiT5pAzOoIocEW08Vw1FE2Hzs0rgLMhrDDIlcAIFKfGMZjWwAp2WFIZ6pNWF1bDbIswB0io5vgZEJ7rW4Ln8hVBdE+vHemTpJbZzmZud237yBFu74F8Tk1PTQOoMuTuvWfJkfYts0bbxME7AeESPYVJSWw3EMhiArjK0Qks5o6n6E654XllbQ95R2j8Z+/z+JbZUwn6gHTXMyncbgW/fvmXsfreH4Hq9+xfr9RrXteo+mOm6i3D7Zl5m4F535HvF68Xxv76vduak549+Uu2H9fpm+7YlftRLvvXX0Si51ueV2Sk+tAj3FL58s4M55tVl3LTXj9EEEsUdyatY2+ObCRr8CyCQ+Di8EHEbAXOulWEqclFkSwx4rY+NPfIFvUziPTONvQs3NjcBJA1BAsofYlnI0bEA/Uet++Cqa6yIx8IbblODreKVwNTEPMpqEpaHwymUN7fkoWeE3EwqkbFM3mKIGTG5WI7Jg5iIGUnQnLPYIQXKUTEWnXPVC/+OQKJbKOI/DPg/A9LS0oP5baxZWMStwBNY4iLMjY7kHR+o5L0Z4EZLuwiIfdFz6HLAINA6rnECN0nBaTrMF+G7AXdtBImLibHxiaVOHr2E/3y8e/dhevnx4ydPGPGcBioCuIQeKH+zs72KwLU/uvYl2iu3Knav7aXsZOBeCecNqzc7Gjfs/MbN/dc/2LHB3OrZHLruRQlkQdw12IeoF4Exmx3HZQ5AdW7++/fvL1+8wErv1c5nN3s3GLOMZRqzvIUU0+0FZNcZYOdBkhskD2eHxaISEa9SU9rexNkAkOhUb8OUAKsXi0uLEWASVp6d45ZJIN5Dbnx4gEf58u7LM243DlAMTfMSc/GA9UTBWFyvXeQL7wMFTwOaJ6HkBB2hN+5GKJOqs8jR7DZWt2iumsvKOX2O4kQ3d3JtOv57cH6O6wuoYsSA0RNY8jI1tba2gXUvOAW4Lkjb7CDGRNtpohOA1cjZpaF24msl9e480IidImprhbg+6WKEq4/QdRihXy/FsaEVUoHb/YIJc/8n6AG7v+MfRKk7LWgckY8fdx8+fjY1jkuR8ihaNMuivonA58+f84a/fXM0BtKR633s7wAgjY5k4N7pGXa9g9tpL32hn56RNuyU0xcuduLEPdsO8pZOVNyaiYT98l6+/7YwdfbgwUoFvHVyiLqpC+gGALe0vo5JbCC/NKEbA5l01eNUUHKiiF4hD6qJlxRqvL7KhZAZLH1OCEWNMIAJttabXC5Zvcg1000wC/gOpAw0b1P1h4f72CkRs9oAzegcUgBraNH5kVHOXZOPrjlAyNQfXHLLWIYDXAwRJvSLzlLYa6PHw2NHx7CsBS2xjoeLeSanlpYfgJ6cmMQlBP8bMI5/FOAPGJodxmHHUJh9Dt0C4P1hTKKsDDUXSpNBxQUL2tGo0yxHOKJkI0hH9bLPLIwj4nYEAxFSDPV/gJWVNYxLRltlnGj/8Hns4uj5syf450Er1cGR4VG84d7mwXH6Zp5y2V5OOQK3HgH73uqo25vjqAzcGwN+jQPRaGTYOJVTzX9k+2+ULQ7e4M/HtBjcHR+JJaYRTCG/+XSwMHmKCvDi3fqEOdT5hXnM16a4nTCb0BO++YtF8FREcJxMgUVqFilWIiFTUqYprVInXFR7KRlABUugk7mSMUDWEOK6Wkq7DbIoNcSPCuAqUTGYyNyzMBAOV87QqUCTw44F+uVukIAZkyvFOpv4rjfyykZET9whI0NuQF1KUijslCl1432xwJv94Ari6OgIHhZHSO1oi0ZlnktkSAm169IG/8LAnamXI+ubG+VuampHJ+cfD2c21zenx7jmZ0jSKHC7YjgI4+FZ2I2EGfdHjx51w1K2kSPQ2whc48NZ+ZjcW+B+jdD19lgOlvX0NLJQppyuj6Wzo9VEe/9gv+uO3dhgE19vbPdODGCZKdaZYr9EPLFoeXVzcf7OdlY2IIdLtbrp9hBzL1EYsi3FLCiVmEUlio1IcyoR8esCgQJdOZAZuhHypBqSEC0yglFWQkHSkviuCZWiWqEkCTyUMcUmxkmrpJ2bkmUabkgHpVA76/6uLeKg0ArjJ4bU23sKhcXMapxzh7KNXgJMGWOan5tHHh5h3j00Qsf2Qs9IBO2WR+jOf1ngXxPn59h6f3W16c2paPzx48fLycUnD/j/imh/GIjBQe086N2IOIzkNe7dCGS20acRSD8moIcPuKcD7NNjMGRuFb/yYWB2DBr5Ju/gCHWgGvpuXvZ4l9+u+tp8FP0vWVpexoz714PzVy9fLj940OOwl+IRj8H+dzxuczFMtwOZxWl3zt4SQSMJrwfMRqAdUISZsbpoCp0IOq4cVK2kM2awaOES60/ysNREmuTLJj4upgpISTvEpUmKHCNiFSqNNDhiMouEKNUIfvXHXowDtwNJjjOlxR7US+yqqDrfWijHGNyAjYp159nwoq7UxEOwzDZiwX8YyCcFEgt13rx5gwfZrm+sc/WHJNDVvxpsot1y3j+ApflcnY8HJEvz119+wUNYca9A7DESx8cnH/fOVrV3Z2QOJHH/lsQ0O0zYVQYbEDWTZn6OwDBFYECBe+UnYJiOyGCPJT0wKV0zqivENS1uyMJCjmtZuHVHr+VlfzUaHV2en1iae4KY46mry7OXWAffwsPuhhg3VwKnA/Ol0+1E6cTI9oq+sCro7KjZaK9ErQYicbggHdyHxkHg9q3rsn1TEc9WY9NE7bLsAjQTbEckjelm1cGKKcLqyIl4FxzAaM2bg6Ai+pZUI7QGJKmI3BAwiSYJA6iTkZ2+0do1vb8QKmtNExSg0GSxSEBS1XGH7u6nXTwOaXt7G8fUHePWlOauz7nH22txYwPNjI3jfoDffv313/7t32gnSR/3sAnO6vb6N1zPJewBJDNqTw5a3gsyCUYmhzwCtT8SfTJmfKs2e/WJh/fIjWZHosL3iFS4SZW/lfa69eCtrFSegBhdaU3cuqOD1mGz8AEYrTx48Hh9HouOMft+sL/fTLO7I97f53Q77qUkVC8l9QPQyESoCH8aEnnOD+JQBt1Yj0TRhjoBELIXpUhQmHJcQQWy1CPoJeZN0eyY85RXkv6zgBsyK2zqh5T2EHilslAtsZtVqH51E9cIg6hpAQ1JWcpkJMDAf07Ozy5++/X3X3/9DQuxtIMkUDtWxyDjn92TyhUy2AwHu/RcjvDpTuPjL37/A7c6RN+xiub333+fmzjamv0+8KgdoxqoJTHxKPSIwJNTe2Q5m80R6LcI3PmMO7+lc7rbCHR2DDrTro6sbn6uqtOj+u7urlDBzQbQI+f6wGyP4gI8hkumlZUHmDf99OnTxsZGT+8SPj45xkNLsY1gOt1uSJbYsACuGK6QModtQw8BKDg6KoEd9SLXm6lI6WCQiqG1ujZNZ4UWQSPqkhGYLDnxDY7NjYOBGvPwhoxJ1VTH2GkOH2wqPTLNWqySqLAq1ZJqtSLd2IBErDTYDW0TDejYtDt8sP8FkPDKKOE7dqp8/foNmuLxtNjyBtveoA+ubkfSlDue/4TVMmPj2LAS91ecYOfNf/7HP//rf/tvaLJ7Mn/46ffnz5/zSAxoylPszQ8c/hvTXJglOQJDFYHbAe4D+0U5FMe6s+h3pj1IAWqYcR8k5zvytS+P4Sg2esdrZ+fjx2/nG4vjvYDvgKVHh0e4SMBsK/BZSJrM9aCgAFs4kjENoVJZQXRBFtRYT3hFc2OWRV7DtUG8ViBLbDkAOuoUtDFdwB6QuLZFaF2wHK2wXAQaWuYCsVC9V2uui9G9IX/Zqs9orV4ijxtkFX1V5VGdnSCoNPJQwjYEMfkksi3hiS0dv0OT8B0o/WD/EGvaww6YIyD12NZj3Am8sr6F+PLu1PNz/Nflzes3208en8z8sDTycfXR1fvMRE/6iIh4PU+xNz8q+HdKc2GW5AgMVQS6uFQG37LNXkMVsn4bTLOgR37hcGS1IArtO6bgY3fTly9fumvwdqy1OFbNRLfj2PV6WV/feLoxjzvJXvzxx9FpAyS8ntHQCrsH4slFmIvldDsBepLw9USWI2OGLjnDEjLgSLJSdugjclNhQaMDaiKL9gsh2JErLaspp1YkKCyS2ww9hw6CQmwZGOXS1BmJlF+qpAKj2xC3UmmQOcMK5IlCqeZ8FRRU3/BOx3RsVPvNT2LLSN55jDn2yanZ2fnFxaXp6RlUuXc9ZJCOjP7j//2fWzNfFxcG9s7FjNcbT9AGDg53Ay8zcgSGMwKdAnd+jzZ5DWeA7nxUzcId+YWHkdWMKFQHgKpgOozphqkf9gtrdmRa8G846j5sDuS1ubn55OnTb/uHr1696tbiVEy3n5wc4x618nS7IdZKgBEVchqAMpjVFFlOsAhvmqB+Igp0YZ/yFDRbp4FjTWVQGc3JIGomM4bRSXcuZJHoSZkZeBy5dVarkTZMjMlCvFRQ7cqs5IAqKSfQVoYa/dMr+g8JOSokldvUD2+SoqXn7Z3WYInhuZc+XzgNkLCn5P73/c+fsUwup2GOQGUR2DAPNY/t3kegslSGX4o53UIEOgh0B6q34PiddZHiePzrGL/lKacdtzDL247alTr5gFwZonYUALE2HsxdLM3s7Ox8ORxZmR+dmZ5pp2EznYODAzziHkspBN6E3RzCEQw2vqIdx4J2XJmHd0rWHPWiQamJYU+3bsiZhuRAqYn6NSRqufotd8Sar3G3ZShgYKEMl8uYIgu9rUOJJecnJHxEjOsuxUJWYk1EDauskNZqrJZYsULCKyiYVET3yHA5P9WktUGkSg5Xbwq0aSUyklhOkyQgdTKI2XF7ridOuZ9PnJxfYKU79nQHjmfnOQ1jBDJwH8ajmsdUHwHMuOPbMb7qlTK30wjEgDYjSgabKRm/pJorjABQQIAkHQSk9X5hrQ9CKu2gy6x6VQSAsDD7vvVgBruFvNk5ONHD6q9qVCPHrYnYToRPXBoHdksTlNOjF2lyJYrWyBDTCiejuMQ1YTXH9YGpR4ImBUTdttEpJyiwZcJHNbzcmNsJOkEeurSOvZsGZpQaYaYDk123TvUa5NZIUlZKSxeMEq9iIhFzoHxRnW9LqNqfZAwuOdITbbid8N0SLuQw7b73de/tW97VmtOwRiBflQ3rkc3jaoxAp0tlGi3cUw5/KZq/SkFpoWeiknau3DQCCGptwpKMFoeitklm3k4EMEkK9P5wbQ7PLX/9+hU2Bum0X+w1ia3iOd3OSVdhORUEfgJ2wnYlBOhoMD1dSKf1ciXxyZRKqpJGDgrHC+4AACAASURBVAj26Ik+hGSUcxJ+1C9UiybO87bgi0LmAtNkBRZLvGCjVNbrpM6U1FtavLo3syXnUrNg2CvKo6mg6ww6prfHiO7z6CLJhGqBIzZAu7C71sogg8Yv//o537+Yxn/IaOwEOmQjysPJEWgWgQzcm0XGf1T4k1H3qjarVTJmVTXXexuBymQ8DoKlvF9YiESflrifdGtr69H29s7Xkzdv37cP36GJ5ST+xKXKhDsPv38Oie+EAFmEJFL4L3BQJnLnOodFKizqgUswqYQi0mxFkClRouB8Sq2RjLNleDk7KAQ1iYPMSopcXGiVVa5dq3ykynZCb6E0aaiFstwmeOrcOPyoJQ7asqQJI5WzirrCCbFeQuukULUrN868ixrnbaoA8gf7B7//9lvsIBNDFoH89T5kBzQPp0UE7jVw589A81c1ai1U9VNS1c/1/ohABB2YkrHHuveHX9mL+ghgyhxT7w+3Nj58+PD27buzi6s/XfuabseCZk6322caqC1+uN2AFcj5gliE+xBlrEdRiXDNUFiLoBw1gziU1pHVoGStvK18gEhVZskbpF5kpUn1IgsimtI7MFqWZtTyVorRecWypSaF0WAkGpqUeybM1ivRKzg+HhVoVzR1t8QDzaQDaiQqmm331TLC7Zhz5/agf/z+x/ExtozMaQgj0K173IcwNHlIQxeB4Qfu9pVfm1ePZq1SZFa1c33AIoCJN24PrQQE79SADeK+uIspUjzifmNz6+POLlYnn57H669qBLD+ATOqU1NTWh4hCCckRywXYKET9llODXRwEhSqdhKFU6kwFzjsN0nmjTPKbV1RBdskb5CygiFY06DLmrOcjM1NMeaJVspzQ5FVQ9S1rFFLWKFFyXhgpv6KhsBe0QLa2StySLgB6pK0wEYKVUWHYmmQoaT/uviUuzaOxEUdpuBPT05/+fnnUg+5MiwRaH0L07CMMo8jR4ARGGDg7l/X9qXdPPfj3I52PiWGOgLptgNA8BUkaCfIUAdg8AYHuPVoc2Vtbf3Dp29v3ryuXTyzv/99YXEB24cQuGuFO0tBt8ZvBQN4MRCoUodFyNUmKlQI6Zd4xgl8M0JrjkJdAD45SkaEamxh/VInGKM2aGoYyywodz0vZLeSScRekUyE0l4Vzbar0VJbLYJv3nsxruBOtBI1Shx5GzyWMelxCIqHERpfminSZGjaHeeEEmbcuaf76MirFy+/f+/O7lLR20z0QwS6tWlYP4wl+5Aj0DoC/Q7c8V3d7FUdWDM941e1c/3eRSAF7o2DB46PUB6nDJLljZqZc8sRwGz6480lPLnpw4f3b3cO8ODM6ACeuDQ1PTU5MQl4Zrg0AjcDqWlueA8HFsz08JYOdxQZPFRPfiZ4Edum7RpU9L0FbtHI+42+ywvIqRE9UkWt0obBSsIzLwr1aLaGCM1CWVZp4JpjZaVr1Brtwogzi8KomINwmXr0Kllix5gFAnwedx7SQKhmma7gCuQONI+TB1tDXmM0uUmfR6AfHtPR5yHK7g1NBPoCuNu3c21eDXStkjGrqrmeI1CKAGbdSvXmFUPwKY7Pi+ObR+uWJIDv29uP15en32Ll+7u3uAxDOjo6nJ+b10KIEnITzovfC/6twQLvmFI6MhsJqkVVw9mBYWzlwTIhZJKon3C8gXNSTdLJW6Kq3LsNaoVXSYeRTBsL1EZJlXDNUoOqzpX12JpErLRqViiJKqpqlFZBM2IKbYyvEeIzxvwzPQiU9E8XX+mOyzquc8diucuRD+/ef/r0qZVnWTaAEcCGVAPodXY5R+A6Ebg94M4v1SavquPN9MDPKUfguhG49mZwnIx3qIVzmHg+L5G/7kG4abvJyfHHjx9j8cybncMXL15gKwlsAcl1EYR1ngLEs77Sb42U5tcRv5BYGL+grGXge61QCoKUE3hUDieLNYQWHAtGAqG6sU1KQXibkPWgHko3Y6reIvIiUdVGvcqKuj0n2HOp94QBkjEoicnxeEnkdKGkeLFJElbRrOsFwpKWTmkBFefdefsyzpSRkV/+9a+L9PFPPY9B7qDnEVhdXe15H7mDHIH+iECXgbu+aPkN2/iqjrdRI3KqqrmeI9CFCHRlvzA+xlE4HvidVDjVRebsliIwOTn5aG1mbm4W02yfvnzD1DuBG1GbJ4I6vQOUU53exYPmrlq9gX3lQLxdqscumVhYv6paRveUjAjVxIyauY6MJDKwraZcpspSa1fKr1QI2v6vJS8C1zuM1Y6ItvqmUlC0eKVRIx3kHLC0CwJ153hALeRV5M5rOi6YUcKMO+9Qvxz59Gn3/bt3HY0oK/d5BHZ3d/vcw+xejkC3InAd4G5forV5jVu1evzKzSlH4FYj0PX9wgzpIDciT8Pf5uE8ONhffrD8/Ifny6vrb9+8/vhx55LbAEaADl8qXz0uIvYzoUrLki8kJyOnWnfE6EhSzQMKL8yGRoYuQ0fedagmbaUfGiVytne2g9hYS7QS8gpxolmQHbZprt5cUgwidEvdFvquBg1/saC+ZRSrZnoWJorw1ouHBG9LEblzS3fuCwnFX//1c+29zmYw5wMXgZWVlYHzOTucI3C9CLQC7vwarHvV9FSrZ8wa7czKEbiDCPR6v7DanWruYJz3oMtzpenpGcygzk5cPn32bHHpwYedzw7UHLwZtgt5GSbiyykIRFaCFnmRKPRrDIXWRIQhgdRLzogZhaYm/Bi1XRh1ggAlLLBWJ0q0Ikm9dnVjozoiGAllnU6ZV9ZETYwSl5USo1yFyF4t1Bg+hRCZHWoWIq3KvJRsxQwn3jXpPoZ/0WATktevXpXdz7UBjsDXr18H2Pvseo5AJxHgir9mr8JOM43IL1QzlSPQpxG4/f3C0sUH+KwgVTeh7NNQ9btbeOLS/MI8VrcDigmhjc3PTW9vrWFhRPp9xu+nBNBJVBqa4J+xpVcWhhoPnR8+KyRwjiuxubFpM1FTBT4mUpBlhVBjiXd8mUFryVx6RRYEUZ8iiYOko7LSMqkm5BUWo2YgQql2pQo5YBjPiFSe0tRSWBlHhZL6FlO1FJ+cNOnU0KaQWi8TV8vopgjeq/Lrz7/k5zFdcUAHR7y4uDg4zmZPcwRuFIFkxl3fgP5VWqFv1EVunCPQFxG42/3CfF1NAHj4hOV0vQjwJuPRS2wygzsNA1gjnKu8ZNy+yEB6vB31laNfrrXjlB3FUrukEjoJx9o8EZeeSDPk1ox58gYpvjKJAkVTgbbSaygCnzrlijjNslKzZkol/lUtauW1TDfbWoZQScFDhrEZFYhwMKhE3fCiHiG73gTwvtSdk+5j45cjl0DtL1+8KA0tVwY2AriYH1jfs+M5Ap1FAL98+pJv9dXZmcWsnSPQnxHok/3CHMEnMbKPYMLIZKsI8IlL83zikkAZcJkhNKK08F3GrzMDcPh+I7ucxDBuRebVyE3qgRdKmiSNDsx6JMgseIm+c40T+KGUEa+oKAm8i2YCE7snNQ1d3rJI/z/UUrEjYVNngoBBUehApC/0wqp3VinDsYYKdZI/kP5ioTcKTxG7T2BnWJr847ffDg4OOhpQVu7PCMzOzvanY9mrHIGuRyCZce+67WwwR6CfItC3+4UBMUXQZAAlwJR+Cl9/+IJZUix1wK4yYZEM0ZklAj1hNnmKEPLl8WRhZDIMlyUcIxsUEw03mCpG2+V2qNkrONHggPThMYyFN+2KHZStYt2luTXwZiUl27A01b1luuSN+i5x3OeqUyUdCgMDZSAVyXAEeKyjEdL2x5OAiWWgxYhZutL99PTsl59/jmYyMbgRyKueBvfYZc87jUAG7p1GLOsPagT6ZMa9dfhq5+NbN7lXUmwmMzc/x71BbHG74BignZfCckJsMSoF8AM/AYEJ7LNWsQUJYcKgr0raNG1LZVcQWKzISs1cE86qi9DM+zMmKp6IREMKXRScICnKIAtlIamh2lJSu/Y1QzehRSgDv1w2k4IvEbNGHUoZDEnssFtsLFpBBp4JvWAFi955uRcXu5s7b16/3tvbK7uWa4MXAVzMD57T2eMcgWtFIAP3a4UtNxrACNztGvdrByxPxsfQHR4eTE1P8eZCvyeVcExAzpCcwBwhHQlBNwd40QIJCSPH2hgOjMxawjVdNdSqqkKREiYactM0E26pKfmNskZOaGT6VXm1HrQby3haNYqMc6VCs4Y1fHO2ZoDyNzoNItKwktIyWjAMl8eePL5W2IEPhx88S+FCT4vebXsZ3COBpe4Xl//xj3+gZH/51T8RiEe3PeL8/OK2jl97DmWtHIGeRWCiZ5az4RyB/orA7e8q093xN5uM7ybC6q7HXbWGLfyOjo4erDzQZjIBjvk8KqqO1B2wBQhGHII3qsyZvDSmscp5UAzcaj2YkAUZl9VCjTy9PZcheugEC6uluUkbcjhOUFlKsFTmNDAS9VYy+dHwCNGy7cSUyNb2ojaGdnnZoNvAcH3wkdKOE8147CwQqsppxBRnRczRoVXQFkwmoHSyRI1dkL4MM+7A7hMTpycnMPfp487n3d3VtTX3ppuFjaybFu+LrY4ip2N8W5HpyLPbcupG/aSfvBsZyo1vJwJ5xv124px7ufsI9Hof99sfIb5u7RuXSEXdD99PSowqdo2YnZsVag+Tp/i1FkLT6C0GHgkiYo9FWjgNmwk3MkXEWuyYhHEdQYZKjUZgRRWD5s5221GYGA5dBMeCoaL0xgWjIwqt7YVWItxc3S+2izqy3wVl80+H1K9r6GsRc7oVqlGBpwDZ8jlUrJ7YgyBJcbWMFs0A7cPCP/7nPy4uQHij7hGwndNtRAAH+Da6Gc4+un7aX2lwOON4a6PKwP3WQp07uuMIdP3JqXc8nqR7IA5DYJabZJh+x84vzk/PTmZmZvzJlwbDALP4a138ZIffbjEpSGLUaVAcMQYLMtVojwgvcMvdBW7iXtQMRqsl29iYqpIr67G7qzXTk6ROu17edgd1zauNFSobbcmDRA9kCGjkkhdfZamfBCqkzzhaoqGi5kyCdpxM49hehmlv7+u7t2+MzvkgRgDPZBtEt++rz8XnOH6gu0oMeVwzcB/yA5yHFyMwPT0d6ftARAAlHHMlaOzrkBzgiUtzvCcVs6WOvAosRs9VcyIM1cZNIQV1qYmgxLZKieWmnBdELAkmQ72hw0IgirpsErNQScoySc3GVJhtlIETTwOT+qYz3sh9qG1YYVbsVKRtVIOfoWxoUghEebXgNjYw9wnLmbxEjS8FVrRn4bzRIncusTLszlZY33458vM//3WGRwTkNJgRyDenDuZx65HX+EroxatH3nZsNgP3jkOWGwxoBPjgnnuZDHKlwKsFGOrDCJ2dnV5cXExNT2OO1NCXfSkbIGv8guYXtn1pJ1jOxqWBO6YrRtpBOEJbMxQbOsECb/YecvXikDLwXNugpXLpS/VmWXqQ6yzJQXNP4qv0ExvB6YTVFpn0VqdfMYtq5KR0XVPyPLIhwHZ4yOefnQUoiqQLP3+mKhC8YXesnUcgsKH7H7//TqM5DWAE8naQA3jQBs5l+07qVn794Wfgfv3Y5ZaDFYH4b/HBcrsX3ka8ZiCpHYTUNTc6/9LD6vY5TrdP+KNSichiIkq2Fz208ahwsvC7YDRqRVkk1C7iQJkuywrDsVuygm2VNaDV5LWmUmZKpz3V0HYw4yGt0QAriG1EhfXAr2/VIfcKY+y16Llsu4FfZRTHOBpxFT9IOCNk304HdKQquf7mKRP/XWPYXbhdJCfdR0Ze/PHH8dFR2bNcG4wI5AcwDcZxyl4WEcBXzjVfGbgXUczUcEfA7kIb7jF2OjqDWsjxItBRikRgNC+v8bXT3Fit5OT4BIAL/wd31G5AzNAZoVp8Cb7DhDGK0SRWIWo7JbqGAd2i8QspqfB241FY4bfqO7apUWqUNXKqza7A0FG9lSW30a6p5AyK5tsjghOhTCwZyzE5+CkMJyLnsfHjQynV8TaOVeUCedVEwI4X11+NjXHS/XIEs7a//fpre05nrf6KAC7v+8uh7E2OQM8ikIF7z0KbDfdZBDJwrx4QQRwDOsi5eZ9eRggTRQzkoqjsRNVi9+s23W57t8f50gSDEcvRmeAZMBsZ4ig3mo6RZ1loQEaapEFGIKwMtVTV6CgRfIw1trdKBVRG2+ZaxUij/Y44hrAbNo8MNiQ2qWPxwt+gc7MyQHyWgQbZVjeJkg4gPSHPjmeMZoMxa6cTQk1CY3DYupQcqhtgZ+77ywC7Ux2bSV6OvHr5KkNARnLQ0uLi4qC5nP3NEbhmBDJwv2bgcrOBi8C9WCoDtNL+q+UhBPIigg85CBpO8FjL1t0RYiMgTLVjuh1zogkEczwmj9gRHYujdkZ0QGNowHuNjNDA9EOtpgwGVVa1q/VS+1ILVhLthLQ2BaMx5lVOqIey1GulkvTbTF0bJKbQu2LiqmrSRVAtRgNOlAduKF2mRgGBpzwJqhlPDG8hAplxnK8qlUxgVLFoxu9S5aQ7h3x+dvbrzz+XLjyqHeZ6P0bg69ev/ehW9ilHoAcRyMC9B0HNJvsyAgN8cyqASZuvXkaeyCZALEPw5tS1+rx6PICPeFTq7Cw3kxHOIjyP4ItQnc5EO4Eisy5FfiTU3jIDk3XNUl5oqTJUogIZeMMvS42E9xUUTN+1G4pmqLpBsY7BxnUGGniG0asmEg8LUS2z0ovZr+udrRssNLhT9GYUWjQ0quqwzvNCSSVp49gJQ4ksBQxvV4GacKcGl7rjKapICse7t2+/f8/rLhiPAUorKysD5G12NUfgJhHIwP0m0cttBykCfbcdpLCEIYor8v4Ls0/GCx4aZgKUF9HmqK4e0snJydTU1MTkBJc0eCIeQyIuMyxGKCaqiKA45ktdJ1CvpMiJhBTSml0keLtUQE+KulPBJdMvxJV+26sGcBtKtErIxIZx62Wu1ihMnHfYStVGvaSfMmmjCw2uHis0opI6D7VQ0rwppRznxr4bNcJxYGl04LCq88RLFlwow9ISlsxwoTsn3YHdL84vfvn5X7GrTAxEBHZ3dwfCz+xkjsDNI5CB+81jmC0MRgRu7wFMEVW0JgYgbK0H4FIhdj1OXpgMj6G3kUF8k4RNtXGtpXsHA8BCiS5Yg+Hom6iiM6eCjrtQyMWoVJv7yQ6LlNLghqr6Mi1nBUnRtNBOec3ogISr8hI/LGcHU3wTllSq7Q2du0qdk40NAqfebplbroWWNyvhJV8qAu04PJwK3oGdF1bxQ8JzBY3Y3EgvVRF811WhJt/1MCYbwfu3775/+3Yzv3PrW41AnnG/1XDnzu40Ahm432n4c+e3GIH5+fmb9ua4wZBE8/ym3fS6fZvDIOC5Rrp0zEQwaSDe+uvU1MXFOVa3l2ZGDX4ZjhN2E3QO5o1T7sZQW+C1HFEidFKF0YkwGIuwPTKcKHRDJCqqhUK1aYd1osyAla0MtdaG3AEot7xjOxhTGSqtLd9MSr9izGTKPZWADBBlhVAPipTiRDBVj7uwuykYYC/nBt1tEn6Mq2UQlIvLX3/5RR7kbDAikNe4D8Zxyl52IwIZuHcjitnGIETg+/fvVTf1Iy8wYJDgqrzavh/qnY7BAMwteW4gngAxdOhQPszKB3a1BHY6Oj7GZjLFMpkCbRl248DDG80Nzxlmq1pzAOdKTXRio+YRChK3EKpsWYaT9EsvM1r0aE2KhgUVu68SFjrlFjUjXS2RFg3JDO+CK6rUuCwzU2Ve27UrG4eRhrJkuZYpDUgoNAWvlJpSiKRMWqStFvhSKVhg20oZbiqjdVhYJaOF7qPjvHfDQvRld/f8/KzaVa73awTyrjL9emSyX92PQAbu3Y9pttifEeA3u/3yx7w/HXWvopetib4eQ6NzDuWFczEwpLi0JlXGncSQAk4RfIXkEM2OojUWoiOZvonRUmMSlhjNKqVmpUpsIW5ZhFoNN7a4BlHAYKcKhllL6ykd+yIzvCPTiWb8ql6i3kRUsMsRKfi1VJvKIahtqqMrnil+MNix0yWmcH0JxbORQXgCeMhx1Xg5csGp91rvM7MfI5A38ezHo5J96k0EMnDvTVyz1f6LQD/9L9UQReu8/yLYbY8MF8WlNRHBIy4A7tMz04JUnhkqI+yyF+uG0Vjg3QDPOfOapFIl4de0LNtq0bCkCD35UbZdqrU0FTULvFhQUVigyTphodacUrvWnlRMV6p1pttQqWkmL9yVOo/qeMEMZLVinR+U2MEgEhddnEeUhFOLc+/2Xx3NwoPWLao8A0++Nf6PLnSey36LQH5yar8dkexP7yKQgXvvYpst91cEbuvuJQMUrfP+ikyfeBMRPEDgxfm5PRZHuIvBFNhKPRUeC/DMS/KKVK7V8ZtpFLqRMlUDgwWz0UCFU6nGllUiAt9IVDXCGg7yqdRC0ZoGJS7aNk6Rp5xEmLILZadML9FuotHAjow0epFOI5TQJEMVpb2MZ7A7wO/CeqDUVPZ19ujcKWw5ZI/QnXVhdyF3+x8PIjY3t/Dqxcua0IVectlXEcBTb/vKn+xMjkDvIpCBe+9imy33VwS6sV9YRBAtiP4a9YB6g+fQHx0eXVxeFHOjRGLJC2REc6RZVdkw0e5SqUf8FoiS0MxYboLG3HUqvbD3KIkkOYHtUisamITDeGt1RoDGznOuA/UgLJlrWkm0E7IC+kvOlNVKtaa9dF9QcsnMgxW5kaAIR91epF1XZYLaA9tYqqUnFrE7kq1452oZHAf8w2fv65fziwtrm/M+jwBuZO9zD7N7OQLdikAG7t2KZLbTbxHAj3fptbKyepWLJf1K84gKrjKS5V2IwNj4+Pr6Olc3EVEZMouEqjgegmdeOs2uE7LkSTN+SanU3Htos2FsmugnpHVjjAiII+G4vexLIoWgqJEqakkbY5ZFtlS7zEuawFILWUnxxpWrO2qIV9EnRTwg4bCXJOTbaWFinivSt1Y8iUI7oz23WXZWtF4mVDEDPz4+cZFRexHkfqfOz8/73cXsX45AlyKQgXuXApnN3EEE9GtdRucCT8avOqQ17p01qZrI9duJwOUlkBNuJr64uMRSY8NYyZElGFM1grgAygr3AOO8EspC1opqWzsqsu9YaWW6mawWzhJuR0EkjFVUWU9fsYtCxSjlBTPqOdFcUtW8dj120XasXJGFH1+QV7Wmium4Ms6e4HM4kbwuVfE4yQ7obm8UI1PTU7xBNafBiQAO3uA4mz3NEbhRBDJwv1H4cuMeRMB+cNvJO+s87xfWWbzuTpsbenAe+HJzY+PLly9whLjK592Fy/xHmj/VJFmGXOQVtaDTUWmdyJe0nXwoGEGr4Djlgo7RYMDvaljOJDKDyO2FwjgNDrQCoo1eu5FgK5QNVq9k1Lds7LAwRBnehUpBFUqiSlqJzK7adMawrc4enkAJbecTcptk1yoZrnQfueTamK1H23j+F+oE88GbISAYgSFNfnyHdHR5WDkCaQQycE+jkenbiUDrX8Be+ZD3C+tVZHtg9/Tk9OzsbGp6enZmdn//QD3YaUOSlNdQGMQjQdJKVVxidORHwnXqi7a06pXqubUAto4JHsF3FIkw1B15ksZaAPBkWEOjGkZWO4tcqxttl2wYt15WUpQjVU61Xh+nqlZRT/VB81ATmxcKBUVJFBREYKKUBksl4nMkLLfA3u3Pf/xxaWmZG5IWNgrTA00pbAxZf75uEtu8VOYm0cttBysCGbgP1vEaFG9b/y7czSjyfmF3E/fOe8XZc35xjm0i8ATL1bW179+/acsIA4x2asGoUBVqAbqR9IqRRV1UyyxpEQ22bNAgLFlokJYZJejLCt7xlaom+N20oOf6RQMyQjI6qARuWtZPvKc2TLvEKVVSa+3SFQMNwRKjgVtnHUqu7OrC4ILwQvEF2DbcbXVTkj2H46EzVJXGsJHR8dHhk2fPH6ysftvbOzk+xh3SENU5kXk9iQBife1Xvjm1J4ckG+3LCGTg3peHZTCcavEd248DyPuF9eNRaebTJbdyPz09A3haW1t78ccfePAtbhYkjsKrDNZrbFCnlMRo4DaqlDitK9fAdIZfI4oNRCjZH+gErruERTLpnuq38jHqRYLmW6cqsE8aGBkYoWxtrl5atE0OSUKWWiV8JwnDExXQ4VU5Joa7lbOBEHooVaElii8uznGCbT9+sr6x+fXLl52PH+3miqSXTPZ1BPLXe18fnuxcVyOQgXtXwzmExsIvYvxpLIgBG22ekhmgAwbsCph+cnKCfGFh8emz569fvd75tAMsj1EIbrHkiJR5zjqT8YxumrdQKkQFVbJbYrNSYjTtskbgGBaFvVRKL+L3AuZae9WrzIppGqNKnVoVm1ealqrRCXHrrJUEdV02bYSWClsau5QOcvZAPt7hyJPVLEEnORyE5d6OBEkrVVGGkwo7Pz7a3n705Mm3b3ufdnYOD/ahhCXuhVKm+jsCMzMz/e1g9i5HoGsRyF9MXQvlwBrCL1SL18AOq8HxvAiyISR9zABgvLzEguOTk1MsmFlaWvpPf//70cHhy5cvjw4Pfa02AZihMFJMobRaYDRwo5hESVqqFGo2w9tEGNSuEEutALFOlZAuKkEh8gMhQcTigQC38kI3UjWvKLRoFcxAhdIbxGokwsBY1jILhXbGXmhfRaXWUvqqdkGuNsi8raA6aRAO40VpUfve169fdne2Hj7afvJ0/9v33U+fvu195Xg1Fx8s5rLfI3B4eNjvLmb/cgS6FIGJLtnJZvozAv7L1Z/O3bJX9pt9y53m7hCBjs9CPAFHCdPtWLGA1TJTU1MTE+N/+vOfPn3a/f3337e3txcXlyYni68vdVGZSk26Tci2j0hoE8qkYSNLnEY28W4NF6YSAckEF4tMJroNozvXWgpGk9/4kii6qlasyUhkC8xHmbErVbYp9MtUU0FQM4Umak3YoS1KROyS74RlXOQlpq6nKkNTI7vQorpFlg/lxecfJ1UocWp9x+z61y/j46M//vmvYE6cRwAAIABJREFU6xsb3799//x598vuZ/yvx84kbOYuazkbgAjML8xXPv9dcbp0vrVv8ZrN2u8ga97rCBS/fPc6DIM9+HpkMNhj6oH3Gbh3Jai3c7Zpwp3gHQBLd6mOTE8Du0/gqUwLCwt//PE7ViRvbW0B0GNDv/K4ImhrhpkL9SvG0iCOjEaiMFqmACureAI/6oST2Hkwetjsdz7B7wCh0mKWvEGmL69IwxyhrlMFGVhF2UpWaAXKtJO8rjkGWGVX68Fex6UOAB9wijCOEuUXqRQzSqVkVwPA7qN4HO/hwcGXz7v4f86jx4+xrh2nELacwsajmG4/03MDcGBgE9eKhdlM9XcEvn37Nj8/33UfS6dW+9abN+vCR6ALJtofSdbsxwhk4N6PR6WJT82/DJo0yOw0AnmpTBqNSPfhWWWXWJpz9wzz7qCmpy8nJ6fm5mb/+te/vXv79tdfftl6+BC/1oDv4+MFxmpjRE1UAjuUDFJKx6B1SAA7xssJ/Oq6SVJFLTHp0JO/z3xzfjyQYohJgcuNDPWgizJRDKYSXbebclK61DgVBDpVwJDSqlQaGKFhLNkqtAxlFBpRxxYvZoEIDYnV0ysGRR4M6F1cnpwcf97dPfj+bX1z88nzH3HmHB8d7X//+o2LZHaOsdwCangxjY5P5N9HC8UA5MvLywPgZTy5buKrzs+rP10dddFlcx31nZU7jkD+Yuo4ZD1u4D8aPe7lPpq/PzenDsE5BKBF9IV3SJgf5f6QlyNTU5OYCn385MnygwcvX7x4ffJqY3MT9DSX00zU/l+lhwHpwLTDd/xEqhHL8A6fR/18VjKXGZe53oEILUslhAhUYxKzTlBRdZWgGUpqpXTSqgk70agjr2iFMJlGJIIRTaSjcoU4aiD0Z6dnWBjz9fPn+cX5//Lv/31+fgG3Pn/b+7aP3Ub39iA62N+H2pgdHPQ4OjKmh6mGPnPZ1xH4/PnzvdrwV98hNzoipU/fzc2ZLyWjN3IvN24RgQzcWwSnp6JufVB66uRQGR/0/cLu1xkT8DqhesCbtuQdy2empqaB0e2OVRzWN69f/+uf/1xeWlpb38CPN9a+234g7UfsSs0rFVp/VPBzBguWU7OgROrXTlliBuOWqo2+UCHltRAYVOPLLZxeTCF0k6PHQeR8NXGaRVqXVfLMfqIlzZg1UUj1g6mU10hbTBr5kRMVhNTbM2qNdcAwOF7vHR0DnQOe498yf/3731fX1gHi979jdcx3Q+3IMROPcOmqr7qyKTqTiX6OwOrqaj+714e+6SNyTb+afhRvYtR8aWr6mq4OZbMM3Ht9WG9+Ivfaw/tiv2/nY/IpUnsKGnSHCN/kBFVAYViifHGJJ6qSeXkJ7I5H0mPlzJ/+/Gfs6Pfx48cXf/w+MTm5ubmJdfBYVAOg1ojCbhrtq9vD39ZKNhKbcMdQOO+eJNUCai9BaEoEtCNhdVTjy6mRw/Mp6E5OALg3JNkwLi2FVI/XC2mgilJ+FNVWVNpR1EuYiJjXCirqdU6cn1/gCUoHB/vf9/aOjg6wJObp8+fYOgZDPzw4xBp3oHak/e/fTo5Pzs/OLi4SX8Lxw4R75z3nFncTAcy4P3r06G76vn+9XvuDUfqY1cbt2qbN2tUd1PY6YMwM3Lt1wG54unXLjWynaQQww9ZU1htBPieuHVfMfQq4E6CXEu815KPpLy9P8HTVCWDzcc6vA5k9fvz44cOHX79+fffu3Zs3b/DYppWVlanp6fGxcSg0IvgmvhUHraCaqF7Fxm9Ig40aXmSF35yIoAN8N7ROsVQCgdJJp8wh8S4PXjN2S0toa82jt5QHlCxSDImlKSrJgjuFWiIEWeaXaw3ictNKDdFqaF5RQURxg2lwSQFOmuB/Mpg7x7ahmGAHaMd+RA9W1/60+bfFhUVoAcpj00CgdkB2IPfj4yNcBKIJble1GIW+eNTwzvu4h4AMQDkoa9wHIJS9dLHhC7GtzpKP+FX61+vArHbQzVVu9FiegXubAb7J6dBmF1mttxFYXFy8SQf5DLhJ9DpuK+AORFVC7apIAqB1cX6G9/n4OF4T+APMwhy84XXAM8D3f/3rX7h1FfBd62e4oSQ2+KtZBN/JoW1HF/DXrhOMUA7XdTGSSux3ghbDL4ZKzwJqN6EgtTLVqYO3Fx5dMtTK9NKYW/OCA7WgRBtFKtcKfqSaKzSXxMZdJsICGpwnZyenR0eHnEb/tocY46A///HHhcVlXLGdnp0eH2NBO25DPTpA2v+OxTNnZ6eE7CHRMRyI+FIV51WXHc7mehYB7CozNzfXM/PZ8F1GoJ1v3db+tfXldPNuzIm2Omvt7xXS/MVUCVC3Dl3FbK7efQQwF9vMiXzUm0XmzvjAlRecB7UU3SAwxdvAu7iYfQf6Oj8f4+Q78DvTGJbK/OlPfwJY+/Tp0/v377EcYmp66sGDB7h4w/p4LIKHpu487N2Rx5e3GY9EGIQzOAyw0oUy4QufZaCNUE1s4u34dj2ykhYjuA97bW119B//gX1R9mdnrWM2ClZJtk6moTatFYP0apNBs3WJmFhgorP1+jgNcNyBv3GTA6bPcXcpth7C7v4//eWvq6tro2Nj52fnWEB1cgrEfoyXEqD7ERa443zhJaG9aIjOo2N7sT8emZG8VIZRGJDUi70gB2To2c2rI6AP9NVqqcb1v9F639m9Be7XCG16TDM9eBHAJByOej7wg3LkDLJXckPstt4dA4lglDOnfNLqhYC7wPvYGJ6Cjkc1PXr4CCsi9g/2d3Z2dnf/AG7Ds1pwKxvm5wBwJyYmMVWPVKylue4pAmcKI02jLC38JrAXFg0/D2QEwIzRy1DKKwNaSKhjLz5v6PLZu3cn//W//P7ixeny8rNve48XFn4fHwdgTczKJjPaZQr9WY0MUUEc2QnRSuZq/j+DpFEwnLBa2UGMCOJ9bQz2hb+4xFXa+RnwOBP+r8JF6icnAG3PfvgJxxR3OOBEAF7H9DsA+6km2gHZoYNzAFD+4jz9H46QO64POdziqItCNop/0CSOZrKvI4CTAZfrfe1idm6gIlB8I7ThdquvsTaaJ18/bWiPjtwH4N5R/NuIWlYZzAjs7u4OpuP30utRPCjHE8YfSJZh+Tu/KgWUhX3BJ/7CFCzgO6bfx7h6BnPvSBNj0xOYZ+eMO1AdMBx2/9v5+BHTsDCFyznsTjM9PT2J3SSxXH4cNySC3faXRgny0dMIAQGGoxnRwLGwzaNJJVdEUU4FA2M1kUrSxjGKVb2oYxRsPv3llw8Pt/bfvQeOwa2pH1dXdy4vfnzy7MUfL4BkpcrmloJ9Nm9IgRfKioKzrWiiU25Sq1TLLNrxeHM91AWei3Ryisn1I9xairUuR4eHE5MTCwuLT549X11bn5wgXgcwx6gN03OeHcDd8Lr+J6MrO1rjS39c2y6Ch4zHpTjooPiKx6/wKFN9GgF8hPvUs+zWPYhA8d3RxmCv+NZrw8IwAfeOQtdGbLLKcEUAEG24BjTUo9GSGOG2IsOArQJExe++8OYnn1PxDIiVwHpYwnx2NurYnfenohEXwSPNzs1i5xnge9yniMs5gFrgt9mZGWwGPzc3z4U0UALoZ2Pd2qoOaL2NBDcSvAd/zVmagIP6kmIZ3olF+a9Muk7BAquBb5SzTDR6enpxcnoxPbW4uLD/7/++//kz9DEQxgoPHTq/+J//9/8z+ep/nP/nv40dLclONJYaDp4UXQWO3GHF2iWtE40oLvM6r8FrTIwj2cQ6wDpWwmBlOv5Vggl1XGU9//GnpaVl3HYM20DeUDw6PtKiGC6NAWDH8hi0BVK3xDh48otBwXWwhNw13nCEedVmLmOUWG/Tufu5xd1EAMf+bjrOveYIdBgB/4ppo1Wz79pBBO7tj7qNwGSVexOBFmvc700MBmagwNnY8APYyqC6+Y2qoWASEAlt8atNb0NcVEEbZZBggQSajI6eAYRp+p1T8EyC5diLZml5GRsF4lcfT+HBMzXfv3uPLgnuPc3heU9IBuW1hp6NeYHQ6nuIqK821vSaEiqY56ZH2pNIOMGqFzZAIUw1UmbqFobTr19fHR6sr61iTf/8wiL+t3By+A1ROjw5x5XJ/Nz8f79ceXG8HK16V6VCHQZwHiXWJOTepwpzwluBU1CxsRNBEspUDicJn/kmXD/VonWugdG0OtjT0zM4Rk+ePscjkzDLzo6oyvUwBPdKp5hcRzphazBp7uoE2/zTlRAPgr0DxX7wb5rU1Uz3cwRwld3P7mXfcgSuEQH7dag0BLNvgXutwxX/czVH4IoIpKfR0s12lbmipyzubgS0VEbLGi7HyiAMkB0IUBm75CEWVDfoWMB2w+7KiW41jSsQD+RNGK91NNxkhuvcgdRnZjY2NgwLYlU0cDw2IPny+fPp6Rlg68z0zOzszNz8PNbN28p4Lo/nvDwTLGLzQCFyjwJ7BDLX2hijw0WHrjjotMNYu/6IVQqixPEyGWLaoCm2qrW6mJiYerj148jl9pcvL548/br3FbPUGy/+D0g//vi/4/8MWPt7+Ze/jO7tmWFZYxacEMHsyhSblDTNzZRVa8wOI2fBufolJGBtrW7hni9Hh1iOjqgi2rjB9MHK6uzsHFY8wXIE61yqjsScpAF3TM6DJ7heB9ntNDIx+mb/SkLuMB4PnH1d2BkF9sLCfDqoTPdzBOIXQj87mX3LEehKBO4cuNtXZVfGko3cxwi0fwLd/j7u9/F4dGvMAINCWob2KjlBcYC31iHxuU1yp1PuQOXQ4+R4UWiVMx6oyYU0QvA2/W7z8ZxYn56ewn2rmLRGG+A7QENM5nIO+Pv3nY87hI1nZwDr0zPTAMSYl+f6eE3JaxKfMN7+cGnACwQu0cFJqvMUeNbWyJivfu4WKDcZE8GwBMYzEmO1cbMKgWSgWeJ9+H3/686nnfOzBw9WTh78b+AsTE9hnckff/wBqPzy1es/z8yNPVg+1Ly1WWf0ZJtEJcl6hXdlNXoNghPjwuiG0w1k4x4DYHTspm4L0GFwArcXTE3iHwV48O0i7zeYQdQwTiTEH2pE6oi6UDoKkTiAmll3CC5txKBlMugOFaJ7Q/CYcbdDE8E7R8gDg+Obl00zGAOScEgHxNPsZo7ATSNwm8Ddf6Zu6nJuf58i0MWTBhjrPkVu4MfaOHcagVmctcYg+XOt32w7VYqVMobuDbuDNqhPEI2feCxlJuw9Px8B+hsdOQfXJs61oAaT1EzQBR/oDQ0WlxYvNzaAEgUbzwk99RwfLEQBHkXXgPywgPl4zBYDhmI1tubjdYvsmAkhtxl62NayHeugBBntqEUQYoSB4QKYiHKYQlohOLr8+v+d7/3l0fNlLv6+PD3Fo5cgGF1ZXX06P7ewuoYl25fHJ7yHV8nbW2vjxB6izHWrBSxXEwG0gWHGSPPhXLvC20kRqYMDyBA6gHKEBTeV4naC2bk5VBEmXj/Z4RGkhiFiegaamRJirHXv3MaxBL/lBk8AxcGc4ulgVEOuc4qOmrMcp4WfUbHIKEeGmwS0Jqo69lzvzwjg09WfjmWvcgS6HoEeAXf7Cuy6t9ngEEagh+dK2TR2mRjC8A3pkLAu5evXz4DBRGQRfxm4ClUbOg8yYB9y4U6vEptjoUo6407wTlMC8QC7nPUmgGczvs7OZP4Uu3cLsxNdC18zI6yf8FsV6dDCArCfJS3YwPaDuD/ylHueYIHNF20+qHlmdIr2QOwzM5ybRwKmB1q15fIG7lFjZ3alQCBJB+imDcZyDpND5Lg0WOZGqpwZXZp+ujSxsrI1MY6V+phoh5/Y13xuf3/9zZvf9HzZ05lpixKborEPnYQlYxqN0ZGwwoaqmkjOdzuUBrLGPyW44IXLzLFjOuA2ho6LlanJqenZmdW1jadP57HSCONGJNWzd27mYYCdaOW6o35Nqit+FFFaSRoG3Cfb3A+DUEwq2mheWHCKNjHdXiSdDQyvpYXFRTsAgZHLvo4ATr6+9i87lyPQvQjcHLjHL7ruOZUtDVEEent+dGIduGGI4jrkQ8FcLIBqitgwYIdjhr+JYYVkFQmeCMK5xKIRsUeKKJjNqZRMvQP32kIaEkLxsMO5YdIXo+cjwAJooMSVL0LwwNzE8UDf4EMOs+ZnzI0QHOV8MVbJA9EC0GPpPGboAXAJegVGYQvT+0Dzk1O+nbx1IhjP7uwawv4F4H6gU6NYOsI3N+E7drpcm5zc+uc/p/b3z/7X/2V9a+v4l1/+x8b6xbdvArgeQ2iGpLBYhVzCZzhHv4XHbX0LkDmWuGAUCA56hQxRRhjgNrzgvxImJ3HzKPbcxFhwicK1Q9pYE4E0ZM24aqZbA0fmyaKUVoIEwFq+6eKEB1nV4LZK2vahULWdlDgBdYwFBnQc2QMrKiDB9jVWy/lARAAn3kD4mZ3MEbh5BDoC7v61dvNes4Uhi0APz4zumTa0MWSRH+LhbG5uffr0Eb/HmlouATNHWgF4EYGFRIAutAgdyontRBpopxkBtVAaA3psxz9SKW0dc1vFcxOd2NS4zcLb6hdMmWti3ebMzRUCeiU05CQv8CjOQKFS0oaNMU2IyerDo0ObrObaErbBtuUA91jDw83sQYMDjI42kBHMj5FGxVE+OPyPACezrSeoYYHKyP/5fwFY0556V4HRs2LBgDKW8gj52iipiA8cpJgsRzf6hwDX/0xOLWMdOpcAcat7dqZrBgtI/IiiucKpDBVcAqhn+UqPhZp5YWBxMCDPTvH2hDGIUhDhSOCj1OEBI74KWUmvYDel5Ai6iK6rO2M4ll9YzMA9RGUQStyFguciD4Kn2cccgZtGoBlwb/hCu2lHuf1gR6CHJ0QPTZdiHufVStxc6dcI4J4EbPeC5dKAj7UILDpuRxaITpiTaKxY6U6YTqRHHVhpKDXfapjQYGhB0wxrOkEpNBFubKV5oHD9cx6GbZE258eJqYmiUQjeEmSP48lOwvqE9XRBfgjRpmg+Aa+1o2UDCPjmlYBAMP85cHGOuXCDxBwqwSj/9ucX0B1unzVXwKdrEoW4URs64FE6OhYJOA1OEBPHQ8XCq1wDYMYEV8B00gohdHrIxIsVpyXlMGDd6EigCjqtmkIlRzM0ja1Dx8GXwnDRLsbV3FDcjWcdKmY2WGMwxzoZPI21qGeq7yOQj1ffH6LsYNciAODOb++u2cuGBjACPTz8PTQ9gIHOLncYgbW19Z2dj5joBVLmq5wqxoTADBkLaHom2G60wCihX0DQZYqQkNAPyuhNlFA7MyH4ICJwtM5xfgs8cuUI3n66ByyIbjwRNPu0eIT14I1hsTvmsSFDUxiiORrGOK0gJlaNcLcxAReXmWxlaW/vKwhb7yFrtOh2SahmrEJicq+b5UoHzkz6jT5EAjpOQ83d9w7Z3LwQJSEpJPfFKuWcLdLU4FNksF97Qd8JCsFHwWsMlqwit8MUjhprPA64TXli4k9/+SuODlzKaVAi8O3bt4zdB+VgZT9vGIFmM+43NJub910EHFN03a9e2e26owVq6b7pbLE3EcBS6cnJKTxWB2gXSMsSuopEY7dAypCCDwQmbMaMJGCxi1SVhvGp6xQBNMgSgmd/+NOJLkBP67HqUuMI4tM9asiN5ONBy5bYnToFwdl6wXrSloJIbikzt8xPy8kZ422v6so6Y4d4s8Aei7NrILCmRlVmeMeECgTmYsGUGVYN2EYKMFeqxm9sW3QfLcIDs2tEqWrqgYVSydR9OODIPUogQPh5DEIyWahJ7pVCh+OLSTRGQdzOhEJ9It4sLRdnBGfa3/7+n9fW162a80GJwPLy8qC4mv3MEbhhBDJwv2EA+6t5QAbd9qpXdrvtZ0t7WLvQUp6F/RiBlZWVd+/eTqzqTtAAPx1wBX+J9iAKCVJANtQExwmXUQlMK2WASgaJpU4eIaIMScGrBu60jyKllSr7ES8qmB/2mTFjJjfLkJp3plbK6WdgsKVaB0Z9mYBOu/gw45cnZ9wKfe8Eq8y9P1SRZIWlaqiK5wLnJ8xEajpN8tiy6Mzcj/rWNaoJR2M0lrPdLemYc8rBVjASXZcWnDCkhGOkptcTruohbnbsLO64ihr9y9/+7eHDR3b46yOeuX0Zgc+fP+cNf/vyyGSnuh+BDNy7H9NeW4w/7V3uqFd2u+zmtc3l/cKuHbo7bIjLLTxO6N3bd1sPt7D9SgrXolc8cwHWhb4A0FQDdkfJ2XZwLCd4F65U4VrAgw7fraWUHLmaisFnUyMnBej6zBTraijGW8ln30kb6DT2VTmngul5UiRVQ7+mYQOkXlBwwgfCXk9lzjTLaqbkqrQhIyVuWmmgaxhyhWaCLBKBwU5coWCByZe1A7t1ki5bFKlUCex0wl08rZIBl7AdfcUjZYcQOc6Kp89/fPzkaUbtis+AZaurqwPmcXY3R+C6EcjA/bqR6327CAG62VVPjHbTwd7ZyvuF9S62PbWMpavb29tv3rxZW1vF3ZaA4YbOKp3i1AYic4wuHEbsnKJ2NCsUgqLQPFo6fCcKhGGbiQ8Y1OB4BbLzo9QA4mVG7emNIcTEz2Yfv0KRVGPNObQYhJE2h72ZnC84hlFDvVJatWLHddIipS084iRsIxtyjQXcUNJHdKckOsgUK2eHoUrT9GMeGqudmWVEWieD8Fwno6UyhO423Y48IUafPHv+p7/8pSlqb3boMJCcLALh1LyTeGDG/dGjR3fSde40R+CWI5CB+y0HvNpdr34OemW36v8A1bFf2AB5m11NI4CtwZ8//wFrZo5PTtbW1gx08xzXRDuBXZICJCfcDgIgNClZKbDOelQVJVhskD3C2VCFffKQAkcV55BNki4VfFQsxU+jZIHbUBbSZEBkhmpTOio0IQBtrTcj4ujqq6bsMla8eSyck2i4SsERxcx8lklUlehKmYh8NggikqboPAhozhSSvJEThcLrBOzGIXiHAfQfDwlOAdxjsPXoERbJ5NV0dpZcM48xvWb7GzQbHclr3G8Qvtx0wCKQgfstHbCefKf1xOgtBeT2u8l7Dtx+zLvYI0DV48dPsMnMu3fvtra2cNcqpt4B2fgh0MQpoBkJgr0CkBsyL8P1OiViPzUDqhM6JLTjX6zSag2HbJ93B8m+mSqfzCvY1ka5a2oQxq5wWA3SEh2ZZSLqaIA06USoWxmGWRK6SAYb6AaGGxafTVyBpTnhhCQWY3KQoKLIWS3Jva3Enrl+UJJG4MWKS4nXidgDaucqGe1FaYcSOSA79uDf2n7857/+DU+3tYjnfBAjgF1l5ubmBtHz7HOOQKcRyMC904jV61d+qOuVOuX2xGinTgyPPr7Zh2cw93Uk6+sb+/v7b9+83djcaPyd9ptSFZxiMp0wmtDRk9cCfI9gvyCgCIxum7hbI0J2GAmbQgYm19DTdmLdRE3yK/QKMVBqSKTCO5AuBSY1LckDHZllgl5KJRDeujAi/TLXasy9VcEgSoZn1klCVzRY1V/kgyAdmLIiyF3F3aaoXNA7nTdPZAbME4AOGRE6YLr+WFqyEiHDUcPpgadJzS8u/vDTn9c3N/Pmj3YmDW6e52UG99hlzzuNQAbunUWMP99dTz0x2nUvB95g/l/qwB9CDQC/0Lhd4f37d4fzhzim2AodzxaCJEyuF+g9cIAIXSrMRqgYYHuE95GQoYBTUdEfUSaSMgPxYLNGW0zlz3BcNmPClnmwEJXcYsJXR5AHCck6OjKbEIaW1To0lyaQrvVuRBv51SqKGNX0lxLe1mG6jYV6ShwYvbFazMWmKelVpVENhCbVU4bxDMNrnQyO38T42PTM9OOnPzx+9gz76Nvgcz7QEcBKSNwAM9BDyM7nCLQZgQzc6wNV/h2u1+mY2xOjHXtxbxvg7qV7O/YhGziepbq9/XhnZ+e3X3/d2NzEDzZWzsRJU2JwDJj4zxF6ROWG172KghDQkXogBBo9XrIErBiqNSDeRLZaxtVggVRoFbmtCCoXDRKqQnqVhY2PzdzDEjNKy0Sh7HwFgMbMspUps5EWJ2V7a7KSv1g1VUjEcQ2TqmPy2RQe6G3mjBPyoOKaqpYyWU84NgOfzMPb/Dt2zZ+cnNh8uP3DT3+an1+oXHC1OkJZ1t8RyHsP9Pfxyd51MwL3Hbj3BEv3xGg3j/r9tJX3Cxum4w5MvbGxsbi0+Pb1m/fv36+vr2P2nU8htdl3YEDBcnwWDZFi7BGvCx8Ss0nUMPluqgwWgCALTqjzzzBnwWEnVqOkktr8Fqi2Cz3CmosCp6aaiiItgspNOHEUYXReWhGkXjNj1VzCRk3jKI/GSEQOCdZSJuViKdoUBQUrxQk6VrHBOV1XEK+niStlsE5mYmJ0aXnlpz//bXWd9zdXDlj/Vfvfw/6J2Wje7bd/Dkb2pNcRuC/AvSdfgT0x2usjfn/t5xn34Tv2M9MzP/7008np6Yf37z7t7DzA45pWVjD3hjtZ8el07J5u5U4MTvToIL6hdIwO1OfBqofsBuKhUqiZfvGdECTNgl4jL1ih+wrHq6m0lo7MMsHm4gAHm+c2zmo11ZGGqxk/zWXHmruiuggq5OkvJcq0eQTPqBqSqCIL7FLJFiGJLKF1m18HXj8/Ozs/P4PC7OzM0x9+3H7yDDPuzY6J+MUhbKmWhf0VAfwXrr8cyt7kCPQsAkN4rvfke7cnRnt2VLPhugjkNe51UelXXiefuKmpySdPn56dnmHPmd9++21xcRFbRs7MzOC33KFueVY9Tr37Upl0xbtDUYSlBrJrltawqMVNXtpkvDG8vw6iKjQd9ZP27KcwKixcVOUmqmrtepGOBBUMRIvy5sZxgyaPow7VQscNFAy3WebTdJmDmkWqQhRMmpQSNZhQs5dVVSuzqB9SCagb01mcXT8/Pz05QQa/Zman11Y3nvzw03xeA22nwDDm5+fnwzisPKYcgZoIDCRw7+Q3vWbM9ayeGK3vKnPvJAJ5V5nbDvvtfqYmJicePnp0cX7x+fPuiz/+AHC7NNNUAAAgAElEQVRf39jAU9AB37F+xsFtEgJ4Z8yA6n0y3lUAx4kxY6rWfe6+kR1bdEIAn4bkFIuEZC2tum+BFUohcOk257gdYF126UUgrNo6N116UPtHuzSAt+RelOsUJnpWVZ5kAZuHkreeMrEQGetYCgO0fomjfwrAfnx0dnY6MT7xYGVt++mzlZVV3oGqqy7cucwxi+bocxqWCAzC2qdhiXUex11HoN+Be/d/+rtv8a6PYe6/vQjk/cLai1MbWn38IRobH1tbX19ZXf3+7fvrV6/Hx8ewfmZudnZaE/BA8GGRjA8zViNBQbUClmFUa8U5dq6WcbYx07x1gNQwVRctRBm5rpMwRSZsqAqFskzpgkmXJQmtosjgq8aUKAAOF7WkYqTAuMchcGgh2GJZNFLfkpIqEdSSIjORqloNDPFUFtKoJoJ8lxl0v7jEkpjjo8PjoyMA+Ln5hSdbPz58tD0zOxtvWWYk4GAC2bn9kKF5i6Bp5HwwI4CP7GA6nr3OEeg4Av0C3HvymeuJ0Y5DnBv0SQTyk1OvPhDD8pEBXFtaXsKtqwf7Bx8/fnh3cgIEDzSP/WcwE19sQYMfe+JN4nAgQcbHfv8JDJVYjZVCLCxqGmQ6pVKZt44aIApWQZm8ru68UITSGyTV4ChZkY5E8DMOwYiozEHLmPPVMDDFs8zVSlqmW8mtrXIoUz/QkTC2oio5+ocONYsUaEhSkSrQ8ol2UOcX56fHx4cHB0Dtk9NTmw8fPn76fGFxqZ1noEYQrwjgDHAcb0HO+WBFIC+VGazjlb29SQTuALiHX7mbuN3QtidGG3rJjEGOwL3eL+xefkCAuucXsOnfj4Bkx0fHQPCfdj4BoAHRLy0vYxUNVlDwNlZuREN8WULpFQSvOfYKiOenIU69gzYI3OFnhI0MObJhQjkZikRiimJ4FmnhaNqJBEyaNHJEkFvLN2ljLn1r1TJnS/aOP9fzGrmWXEEV0dS0WqSsHrlBAQysX9d6mJNjzK9jVQxGu/Rg5ce//HVzc2ticpLBuVaKOB6tI4jH50ZRupbF3OgWI4Cr8VvsLXeVI3CXEegtcO8+Wui+xbuMfu77NiMwtPuF5Q/FVacRQPgMNhV59gwo7PTs9PPuLp69is1GgN2XHzyYn5ubmp62pfAG10sIPkzCC7laT9dYKgP4dwUIBGYNKaGcDEUoTVNNAiuWJPSu50SR4Woqux0H2t7Y9Io8VWtoIGBtlmTFBiOu14OGK0GBOgUyF0NMsl1gYm4Oc36BZevC6lgPc3h2eoorrrmFxa1H2ND/KZbE2IELAbxpGUE8h68UoXxg5LK/InB8fNxfDmVvcgR6FoGuAffug4fuW+xZFLPhQYhAO/8978dx5A9CF4/K6Ahm5ja3tvDC/9Zxv/Ln3c/v3r7DFoFLS0vYjsYQPKfhR0fHRsc4oe6racwJw4fAlUK3hWOE8kmKeC/hNbSRrEbTQC/RqyXvq76aKoumWsEMnpZFgUt4bF5Y6Qjela1f8oJ+IW/kCISbJturmbd1umAmclpnktOikHEzR9xoiksrrFxHOjw8OjrAhffY2Pjc3By2dFxZXVtcXJqcmuouXveA1xURykOYQXxdhO6Yh4vwO/Ygd58jcFsRuA5wL/1CdcXR7lvsilvZyFBFQOigz0aUz/y7OyBA5w+UgBIPDg++7X17+fIl1kxPT03PzeN21rnpqSksvcBMfDoZX/bXpt7BI5C9RmKjomFBBXsFh9CWKRShJKtKsx6ZIiKnIKoKNgL7kPhHxdC5s+SSc+pp64k5m+DNItAiyDBRqLoKuFy3zgclnQOgn5ycHOGWlMP90+MTXDlhG8dHj5/hJuP5hUVs/YlLKkbi7lIK4uFFxvF3dyiKnvf393EHS1HPVI7A8EagKXDvPqLovsXhPSx5ZD2IgDBCD+w2msynemNMboNzzbhjah0L4fECqgRmxP2sBwffv37d4/6ClyOTUxNzc/O2nAaz9QDxQPy4+dXn44FAbzg0gmFLoVSNoJcpMF2trpqI1Io6RXNKI6cgooI3CUrqwD4r1rIxF+qW1UaZI3EIlFyVfTgjFtptHevVMa+OmXWsYDo9OT2x20wBhoHRHz56/GBlFagdYb9zsK5jUZ+lOD6D+PoY9Z6Lf5f1vpPcQ45AX0TAgfs1f/GaDaHL5pp1k/k5Ah1EoGtLZfLp3UHUu6J6CxEnXsX8+dQU5tmnMbmLKiAmEOXR8dHB/v6HDx+xPTiYgO6AkoDyuNfZcHycj7/hso2IpNOQVZgOsk2DqBlJGb11OhIOrRtFoaFr1lYxeDNOaZt/bON/RpbAOtepB5SOWXVOq+PWUtxhenx8gk06MbOO1S8bWw8fPODMusD6LRx3DrKLKQXxXTSbTV0Zga9fv2IZ1ZVqWSFHYAgiMIGvxpt+O960/RCEMQ9hACIArNCul/mUbjdSN9S780A76q39FgQQx1IZzPcuLHAyjzj+/BxA89ve3scPHzFVjCl5zLzjH/RYWjM1OTUeJuNtibzlMIJUaz9i7jSIEX5HpiHoqFyqEibTNVOWSAailQoR9SPCVvNo0y2kUofg4cJAooJnvYWc/BFf9AKYrjXqnE0XSAdKx0XQ4fHJMf9fMT6Bf3HMzs0/3H4CvDUzM4vnZ4GrWNlohjC3+XicDQxYTl2NwIoutrtqMhvLEejTCDRdKlPv753/zta7lbk5AldHoLodZD6Zr47ZTTT6PL7Y/REAqgMngSm52n2Ci2osLliRDUyKxfGfd78A0GNlDS2OjgKy42TD3D2mjSex1EaAHkwkYlYlqFmqDXGE4FFa4kT8LXEqinSViE0MlMNRoW2fIYcd41fUCqYoAXZkwuYsdA8pV6Yr4bpGYP0UW6sjAaYf4WoZw8eGmwgatuBcevZ8Fnv4TGEPH2D0O16qHmN7a4TNx0fUntfVdDHyu7u729vbXTSYTeUI9G0EmgP3Dn7R+nZ02bEcgSICfAATzup8YhchuTY1uEEsUNPNpz2BwKenZ6Y3ZtY3NjghPzJC/Hp+foq9UHC2HR2eHO2fnX/FkhAgXJ56YyOYmJ/AW3+TE5OYpDdYH2A8l4zwD0mEna+xgi44fZ+mCLXFrOL1EkCHhrxU5nQE7BTyj4m7uvifQXKMSqvRBczjRLoWpgOtI2H7F2Tj45w1x8UKJtFX19YXFpawUSMuX8a0S0/qdaYZ73AseUgR9lDNwblGBPKM+zWClpsMaAQCcK/8GAzoaLLbOQLNIzA/P99cmCVpBIb064BPR+3Z0Ai1R2xaHdsUArlymbwlgWEs8EbCPDQ2I8c89Le971jizTlrwv3zcawSwe2uY5yShx1QSukcPcVYCw41e8dCMB81fzyUehMCN2xu9QDKhcwdoBtKdzkhP93RhQcuPbBr+inwONYDMT8/h9rExCSa0KfxcSxumZ2Zm1jEWLGF5gyvP7TWBVK6kjFoOPjtlAh9vCDz5TQId45hO7ELOljjnneEDMHI5ZBHYIITkD37LRvy4OXhDVQEsGn3QPnbXWfv64ccYB3J8HrvUHvrYyUgi7loaAHmjiS7XxhoBlbGVDawMqrcYuX89BI4eYRL6sU8A7IHjAbux3c1lAXqRtGES1ZGLoHngcQB6DlQMoi/UaVUcNzUEAhgf4gnJ6akyMhIgTgelwPQhadA3gDl0JmdnYe3SJhHx1p/wHVcMPDaAc0ypkQQepN8OU05wnlRzZXBzrvKXBmirDA0EQgz7kMzoDyQHIEmEVheXm4iGQ72fYXm9UdPS9ghuiuwXu9VlStITzg8MTI5PV2V1tYNahOdC5Tb5DkXsgDWM9EkCkzdk1RdfGUJHHQ9tSgUKvqpINN3FwFD8ziaugzNO8fXHAns457/p1oTl8waxghk4D6MRzWPqS4Cnz9/rmMPEC9D8/YOli2JMYzTXosB0jJcDlA+njgN3J/UMjmcEYhndLqKJk/G28HO62SG86TPo6qLwL27r78uCJl3LyKwurra9+PkDGnzV9+7f7cOclWMsE1/z7LfbZBy70MWgQjiudQJ3x3Kh2yM7QwH2xi1o5Z1cgSGIAJ5xn0IDmIeQlsR+P/bu7f2tpE0McCW7W512+N47Nvdn7dP8mezd9mLzV2SnVza43i8brfarRQJkYZIkMKhCqjDq9bTpkCgDu9XKH4qgWAeK+5WzUcFa+JOk+/tOLF8uxPIXaB/cXw3yxxX6HNveoz2hXsZ7Zc8YpSVsIymYpLQsfGiJe6ND4CGur/WNe5S87UG1e6SmFDX/m8UXhDXUldP/gInZ8Pxcppwqpw8lX9fRrYwvJN75J6b7lbQq0OtI2XT+EeqXOIeCVIx2QtEvatMQfNv9oGZ2sAuX9/lIKIw1c7+LQocL6cJuVitSfzDez9aDG+iPq8/u/pVYWwoJe5jpexXukD4aPpxXVh/whrXLnuF5cJjvm6SNx4ITBfoJ/Hd0V0qf0zopxeZxRES9yzCsKgRG77yFvZyInFfNNAcXJBAuF9Yr7UbzhG9Vng4RsAlMWOU7DNFoNnz/zxDqePi+EIulZkyRu27nkD0+eD8PIvZGYl7TE1l5Sywv19YOD+jn6I5d7r0tvWW2EvvSjPtd4LlHOox0Tnuc1yGD1vSZiJDZJNq3L85dagU2whsIHA8hxbWPXwSSNwXsjq8GAH3CysmVN0lMaG54Sr24YmrnK5k1tJYryeZdUtzkgj0r6s55u7HB0mq7BU6fqyGPU3vPTkPqxEYPAluJO7VBFhHnhCwJPMEUA5P7+7FLl9/FInBmfvRHn4gkF7g+Bv08UH6OifU4AOYJmDZtXABH8BUeAA1f7SAiyBHU22x44+UvapMNXRm4fcWwVAngckC2563j9/CNLnxDiBQkIAV94KCpamLBNx2YBFfooPzy9e3zT8SMSuWQGqB/kr88eL41JUey3/z5s3xsQcE6haw4l53fPXuh4DE/YdFDo9WSdlnrHbnYKMNBIoWOF4cH3qxzm/Cnz59KlpM4wmMF7DiPt7KnmULuFQmi/iFfH23NBfedTrnBX3OMVl0WyMINCrQX4nvE4Rz+dJT/d1GPn737t3IPe1GoHQBK+6lR1D7xwp4c+pYqUT7HZfYz9bgwkv4yO9ETVMsAQIrC0TM2kPLP3z4sHL7VUdgKwEr7lvJq3dtAfcLW1m8Wx2/vwn/hZpnLrGv3GbVESCwocDsi+OtuG8YNVWvLGDFfWVw1W0m4H5hUehHLo3vs/bw8Um7vH2ty1yj9E8hBAhsJtC/OD40ovvlf0xr/v73v4/ZzT4EKhCw4l5BEHVhlID7hT3JNP5l8lpR/Uti4v45/FqtniNAoDaBbv4I81J4cH0x/u3bt7V1Xn8IXBCw4n4BxubqBBq8X1i31j3+/0tivr8gZv86u/u40zi/Aixpj2MJEKhDoEvfj4vxg5PL58+f6+isXhB4UsCK+5NEdqhEoL77hQ2+gG0RrXBJzO6imC2qVicBAm0JdHl81+cw6XQ/vn71ui0FvW1YwIp7w8FvrOvFvXspvCZd/946gLtL2Ls/Ylti3zoW6ifQosAxif/629d+/60i9DU8rkzAintlAdWdiwK53S+s5JeW3RL7Llk/vmxeVPcEAQIEkgvc3t726+jPTGGe2k9Y/ec9JlCwgMS94OBp+iSB9VfcS07NB2kPL39S9kEeGwkQ2Ejg7u7uUs2HaWu/2nBpJ9sJlCMgcS8nVlq6TCDFNe7VpeaXicNVMfL1yzyeIUBgQ4EXL148WfvjZXh/L3wSzA6ZCkjcMw2MZkUXmHRXmYYy8ivQP27suL8w5sqeniJAgMB2AvfhcpgpX5f23l0DOKUc+xJYX0Divr65GrcROL+Pu+z8YiT6KfvFnTxBgACBLASmJu6XGt3P2iXxl5Rs31ZA4r6tv9rXEwifnBomYsn6NfGHfN36+jUkzxEgkJvAmEtlprb5mMR3Gbw8fiqg/RMJSNwTwSo2O4Fv375l16Z8GmSJPZ9YaAkBAhMFrrw5dWJJA7t3GXz3f+n7AJBN6wpI3Nf1Vtt2Aj/99NN2ledac5evh9b5+KRcQ6RdBAg8KXByO8gn95+9w3EZvivBvSZnSzpwtoAPYJpN58DCBL5//15Yi5M2N6Ts4Xt3o5j9d9K6FE6AAIGUAl+/PvoAppRVPSr7eK/JR1v9QCClgBX3lLrKzkLg4bL2m5uHB1k0aqtGWGLfSl69BAgkE3j9+nWyshVMIC8BK+55xUNrZgmEjPzK90ORbSfu+z/wWmKfNbwcRIBA5gKfP3/OvIXWjTIPUEHNs+JeULA0ddHU1+ilMje7+xvv4MI/LmR3DhEgUKPA27dvM+/W2cXxbhifecTybZ4V93xj03DLLi2fLyJp7M2p9/eHq9hvXMW+aOA4mACB3AU+fvyYexMft6+fx4e3GT1+0k8ErglYcb+m47n0AosW0Sc1r6HbQe4vZN/l674IECDQgMD79+/L7aV3uJYbu01absV9E/YGK02yiD7JMXwA06T9C9u5W1/fp+xuFFNY7DSXAIFlAsWtuI/vrgWY8VaN7GnFvZFAr9nNTOeZL1++rKmwYl379Zrd31ozlV+RQlUECLQokP817rOjcnIZTZjlT7bMLtmBhQpYcS80cJs3O8wel743b9twA968eTP8RLlbu1X2EIjdVTHh2xcBAgRaFMj/rjKxotLP2l0cH0u1rHKsuJcVr01aW0lG+OnTp0344lfa3SjGW07jyyqRAIEiBdq8j/vx4vjwIt1P6IsMoUaPFrDiPpqqiR27hduT/1fS87/+9a/F96RbYg+ztTeeFh9LHSBAIJrAVp+cGq0DywqStS/zK+xoK+6FBSxecytZRx8PUvC7l7q3nIauytfHx9ueBAg0I3D7y21GVwtmmUdbla/mbJC4VxPKKx1pLkcftCjxfmG7e7GHq9fl64MRtZEAAQJ7gbu7u4wk1nnJnfjrQX/3cHH88TKbjNw0ZZyAxH2cU0l7rTNnlCTStbWkFfeQr4dkPSTsUvbyBpoWNylg3t0w7DfPXrx4sWH921S9YMidZO3hbVP3N/vi+tn9Nr1S69MCEvenjTLeY8GJm3GvEjWtjPuFHd94GhRk7YmGgmJrFTAj1hrZp/oVMs+ndvH8RYGHrH1/b7I4LzuicRE7whMS9wiIiYvwWhQHOPf7hXWr7McZNE6nlUJgUwGz16b87VQucY8S68GsvXtpmlZ+ihPfLwOHGEjcDxK5/JtivOfSt23bken9wnaTYoDp7sW+rZDaCfQETEU9DA8zF2jxUpm1QtLP5sOssFn+PHtG2qzFqSIkcU8lO67c2SNxXPH26glkd7+wwxJ7r40eEogtYI6JLaq8DAXyenNqhkCRmnSSA89ZjI/UkgnFzJsDT7o6ob7ku0rckxP3Kpg3fHoFeLhA4Pb2dsHR8Q61xB7Psq2SzB9txVtvJwjkMr1PaHINux4X48vI4CeRz5hv18r1fQDTpEiO3znE/Px7/OH2jC+QwZLM/q38u6luxpQQH0SJWwqcTw9PbtmyueomkLXAb1+/np9AWbe4rsYdM/jQrZDEN/p1PgSf3DJLyor7LLbTg0JwfOUusNlFkN001uXrzc5ouY+OGO0zDcRQVAaBGQKDb2FKfUaazgcj1U/iww4VLsYPdnvexklj9DDgJO6TsCcZTyrZzskFVr7twMM92MMcdjKNJe+oChYLONEXEyqAwJoC//jHP16//suaNYa61pknDtnayp2LVt3gC6Bsfo5vN+Bunkncr+itc1ZeaYCnYgqsl7jvl9h9dlLM4MUqyzkdS1I5BHISePPmTU7NidmWhZNWnnn/YDYfU63qsiTux/AuPDuO5XiQqUDyS2W6S2JC781J6w8Bp+/65mokkI3Ap0+fXr16nU1zMmrIkqlx5aTfMvzIcdNy4r5kPI/ktVtGAgnfnNql7PL1RNF2piaCVSyBWgTevXtXS1cy6se8qXd2un/yEiqPvzQU2rmrTBiBJ9+XTGyvUyD+/cLCvNJ9h/nmZMqpkzBNr07Oy/Mf01SrVAIEqhH48OFDNX0pvSPnU/iTWwa77EV1kCVsrHXFPYwTXwQeCUT+ACarAY90L//gXLxs4xkCBKIIWHGPwrhVIVNfJbpF/XDU7NX9rXoapd46VtxD+E6+o+AopCqBwfuFTe5hyNe7L6sBncPJmXf+42RiBxAgQGCaQLjGfdoB9i5ZoHudCT3oHjzff0TK+YvPcbeS+zrQ9rJW3EMUfBGYKfD58+eZR4bDuny9qUtinG3zh4sjCbQssP7ccVPxXWVaHkkj+36yjHby5/DZw/GwSjeyFevtlnniPht8PUE1lSLw9u3byU2te33d6TV5QDiAQP4CLZ7YX758Wf8+7vkPhTZbeJLHz0YYfyKtnOJnlbiPV5odCAe2K/Dx48fxnf/x8Unjj8lwT6dUhkHRJAI7ASdnzHHw66+/xixOWQSmCIw8mWPl99sm7iM7O8XPvgQuCLx///7CM73NhyX2kj4+yWnUC6CHBFIKONlS6i4o+9u3b/V+BNMCF4fmJDBy+ngyv185cR/Z7JyktaUWgSdW3G/uw9mSb77u1KllHOrHdgLOou3sE9f8008/Ja5B8fULnFwcv1WHr89T4dmkifv12rcyUW+jAsPXuB+W2MON2Lcfr9u3oNGxodtFCThPigrXKo39/v37KvWopGaB/sXxmSTxg9yxEncz6SCvjRkJPLqrTC9fX6+JzpL1rNWUrYDTINvQFNywmwwWXgrm0/QzgX4Sf/bkxhvmJe5m3o3DpvoZAg/3ce8uiQnHJzovnRwzYuOQggWM+IKDV03TJe7VhFJHnhQYmbibmp+UtEPuAl+//ufuduz3z+OM5jil5I6mfU0KGNxNhr3kTrtUpuToFd/2la+ruZS4m7iLH0k68ON+a7uz6tkvv/yyW2WfOrSn7s+dQKYChnKmgdGs5QLenLrcUAmzBfp/v18hiT8m7ub02SFzYAYCg+M3nEDhK5xS+7Pq97u7aw0dLOHaAZ4jkImAsZtJIDRjM4FwO8jN6lYxgZ5AP4nvbY75MCTuYdI378c0VVZagadH6/1uRIek/fEJ9OLFi4eGPV1C2h4oncBoAYN1NJUdGxbwAUwNB7+5rj9vrsc6XIpAyFgGv6+0Pyyx71bZuyX204znPtyovSvwSgmeIrCewOD4Ptm4XmvURKBcgS9fvpTbeC1vTaC7GmB2r4+XyswuwYEEFgicZtezinp8ScylInaJuy8CaQWiDOi0TVQ6gfoE3vjc1PqCWm+PHl8N8NDPkMgMbj9nsOJ+bmJLGoGTlcTux0VVdevr+8E+NN5PKvxxqcyiSh3cssDJmDr/sWUcfSewmcCnT582q1vFBGIInGQxV1blrbjH8FbGiUDIZ5J+PSyx/7iKfUyF3//4I2mjFF6+wJhxVH4v9YBAdQLv3r2rrk861LTASR7ft7Di3tfweLrA+Zpj0uTnJgzm+/C/3fez3Wfldd9j2u1+YWOUat/nOGQGH9Tee/0jUKnAhw8fKu2ZbhH4IdAtWlpx/yHi0RMCIdVZ8etRbQ8Xf42+BGyone4XNqRS37ZHA6e+7ukRAQLnAlbcz01sqU+gW4a34l5fZCP16HxFMlLBJ8Wc19NtOex2HxbYD2/ZWJSTuV/YgbSCfy+NmkUjpAIXXSDQpoBr3NuMe5u9tuLeZtwPvV4rz5lcz83uFjD7vwqFq2IOrV32r/uFLfNb7ejJg2W1lqmIAIE8BdxVJs+4aFUKASvuKVRzLTNkRCffsVt6Uvzxxyn17O8Vs7+Kfd/cKYde3dfMfpVn5SePQ+P8wcotUR0BAsULWJcpPoQ6MFrAivtoqrJ2DOlQ+q+olew/HWl3AVd492mSpvtbahLWa4VGHSDXKvIcAQJNC7gSsunwN9Z5K+5VBHyf7j5aTY/drfMaoiVl4VKY3dUw3cedxm53rzzvXuphRHw4ODS6jRFrURQBAgQuCrj3wEUaT1QnYMW9wJBGS5mH+564+F6lIV8PGfuVu5X29l3+0P3ClhmuNy6WtdPRBAg0J+Buv82FvOEOS9yzD37KfCll2VdgD1fFrJWyd02x4n4lJL2nNhoUvRZ4SIAAgUkC379/n7S/nQmUKyBxzyx2KbOmlGWPYTzk68muYr/eCNe4P/bZejg8bo2fCBAgMFvgZvdxfL4INCEgcd8ozCknmZRlz+LqLonZKF8/trixu8pkNwqOgfCAAAECBAgQmCcgcZ/nNvGoZElUsoIndnB49/DZSeF9p8nfdTpc+dnWeu8XlvcoOAuEDQQIEIgrcL/75A9fBJoQkLgnCHOyPCpZwbERdkvsobHRPjspSvvKv19YMfGPEi+FECBAYKTAixcvRu5pNwKlC0jcY0QwTUKVptQY/b1UxsMlMevdKOZSQwa3F3W/sPKCP2huIwECBFYQuLu7W6EWVRDIQUDiPj0KaXKqNKVO7928I0LKHr7WvUvM1JZmeb+wssM+NQT2J0CAQAqB29vbFMUqk0CGAj6AaURQQnLV/x5xxJO79MvrHj95SJY77G7DvvvO5kL2K0pb3y/sPOZhiy8CBAgQWCrw9evXpUU4nkAhAlbczwKVIJtKUORZs9ff8HAh+/oVz6xxxfuF1Rnwme4OI0CAQGKB169fJ65B8QRyEWg4cU+QXCUoMpeB0rXjfnePmP0i++7ejoV1N3biXlj38xpJWkOgAQFzxKQgL7kvzOfPn1+//suk6uxMoFCBlhL32JNo7PJyHkL7z04KVwyVlqz3TZddKtNStPtqHhMoU8AZW1zcZocsHPj27dvi+qvBBOYJVJ24z54GhiyjFjZUQZ7bunedhraVnLJ3tKPfnNpoqPMcgFpVvYDzrfoQr9DBjx8//vrrqxUqUgWBzQUqStxjT/+xy9s81hMbcBM+0CJcGlPP25eHbgfZepAnjgm7E3gk4Px5xOGH7QTev3+/XeVqJrCqQMlpWXjR6H8vc+uX1D1eVr45mK4AABM5SURBVF7JR4dV9v0bT2vK2kM89h/AdBLnksOk7QRiCJycEpN+jFG/MghEEAgr7hFKUQSBEgSKWnEPLymRvuKVFKlBORSzW2LfX8Ve/lUxg5xfvnwZ3G4jgWoEzGzVhFJHJgm4xn0Sl52LFsg1cY/3+hOvpKIDfbnxvXy9bqs3b95cVvAMgSwE6j4HsyDWiBoFwl1lXr1yR8gaQ6tPZwJ5JO5RX6yiFnYGVtGGw70dbxoR+/TpU0XR05ViBBo5v4qJh4bWKOA+7jVGVZ+GBTa6xj28lPW/h9s2amu/mO7xqMNa3ml3CfufAWB3b8dKr4oZDO+7d+8Gt9tIYJ7A+eQzuGVe4Y4iQGC8gE9OHW9lz9IF1lpxDy9okb7ilRSpQeUUc1xi3//aVE67I7X0w4cPkUpSTOUCJpnKA6x71Qnc3t5W1ycdIjAskCxxj/TSF6mY4c63srW7F/vuVjFNc1pxb2XAX+1n0+fAVRlPEihX4O7urtzGazmBSQLxEvdIr4eRipmEUO3OvSX2avs4vmOucR9vVe6eJpByY6flBGYLvHjxYvaxDiRQlsCCxD3SK2SkYspiT9za7kYx3VXsiasqqHh3lSkoWJeaarq4JGM7gZYF7nd3M/ZFoAmBcYl7pFfLSMU0EZiZnXy4KqaVG8VMUnIf90lca+5sZlhTW10E6hOQuNcXUz26JHA5cY/xWhqjjEstt/2HwMMlMWFD21ex/xAZerT/5NShJ2xLL2AqSG+sBgLtCrhUpt3Yt9fzXuIe46U1RhntBWFJj/dL7I2/63Sk32+//fbn9++DCzNh3O4+NXb//5Gl9XebffjsA7vaZx8++8Ar9YYyfREgQGB9gTCre3Pq+uxq3Erg5cP91OfW79V6rtyy47pLYkIZlthHQ97+fPuv//rf//zzz5cvX/z++90vv9x+/c+v4WM7wkfuhY/L/vj3j+/fvQ//f/tf3oYtr//yOjx7+8tt2PPly5fhqFDP8+fP//jjj59//unbb99+ffXrl398CdfNf/p/n9799d2Hjx/C/8P7X8OWcE1OePa3376FPf/443s4KhyrXs7GlfPIvJFonnz16tXolwI7Eihb4OZf/ut/C7nI+E7I1MdbJdmze+OpfD0JrkIJECBAgAABApkKfP/+vXepzIVGytQvwKy9eXche7ie494bT9eWVx8BAgQIECBAIAeBR4m7HD2HkJy24XijGKvspzR+JkCAAAECBAg0JPAyJOvy9RwDfrgXu6vYc4yONhEgQIAAAQIEVhd4tOK+eu0qHBA4ftypX6gGdGwiQIAAAQIECLQqIHHPJvIPl8Q8c2/HbEKiIQQIECBAgACBjAQk7hkE43AVewZN0QQCBAgQIECAAIFMBXZ3mPa1mcAxZffG081ioGICBAgQIECAQBkCEvct4hTy9WPKvkX96iRAgAABAgQIEChOQOK+bsiO+bol9nXh1UaAAAECBAgQKF1A4r5KBLt8PVQlX1/FWyUECBAgQIAAgfoEvDk1ZUxDvh4+6zR8yddTMiubAAECBAhsIeC+zaupd+nUatXlW5HEPU1sjpfEpCleqQQIECBAoFUB6XKDkc8w6Nv8LiFxjzv6QxRvdm88tcQe11VpBAgQIFCYQIaZVmGCmpu3wDoj/PTXA4l7vFFxzNdl7fFQlUSAAAECKwqsk4us2CFVEShb4OSUvJG4L4rnffcxp12mLl9fZOlgAgQIEJgqcPKiPvVw+xMgUJiAxH12wMK92MNFMSbN2YAOJECAAIGjgFeTI4UHBAhcFJC4X6QZfuJ4o5iQtp9edzR8hK0ECBAg0J6ARLy9mOsxgfQCEvfRxjf3u3eeWmIfDWZHAgQIVCQgEa8omLpCoFgBifuI0B3u7WjaHoFlFwIECBQkYF4vKFiaSoDAM4n75UFwXGK3yn4ZyTMECBDIUkBGnmVYNIoAgWUCEvdzv/uQqO8uibm/MfGf69hCgACBTQVMzJvyq5wAgU0FJO4/+I/3duwujfnxhEcECBAgkFBALp4QV9EECNQkIHHfveG0e9epN57WNLL1hQCBPAQk5XnEQSsIEKhCoOnE/ccSexWx1AkCBAhsISA130JdnQQINCnQZOJ+uBTGEnuTY16nCRCYJCAvn8RlZwIECCQUaCtxPy6xJxRVNAECBAoTkJoXFjDNJUCgWYE2EndL7M0OcB0nQGAnIDU3DggQIFCDQN2J+/3Dq5UbsdcwVvWBAIFBAUn5IIuNBAgQqFCgzsT94ZKYEC8pe4WDVpcINCggO28w6LpMgACBU4HKEveHJXbvOj2Ns58JEChAQHZeQJA0kQABAhsK1JK4H65it8S+4WBSNQEC4wQk6OOc7EWAAAECjwXKT9wPH5/0uF9+IkCAwLYCsvNt/dVOgACBCgUKTtx3F7KHS9jvb7w8VjgwdYlAMQJmoGJCpaEECBAoXaDAxP2wxO5C9tIHn/YTKEpAgl5UuDSWAAECNQqUk7g/5Ou7G8V4/axxKOoTgc0FTC2bh0ADCBAgQOCaQAGJ+/HjTr2oXouk5wgQGCtgLhkrZT8CBAgQyEog48T9sMTukpisRozGEChKQI5eVLg0lgABAgSuCuSYuFtivxoyTxIgMCggRx9ksZEAAQIE6hHIKXHv7sUePkPJx53WM8D0hEAKATl6ClVlEiBAgEDuAnkk7g8puxfj3IeL9hFYXcC0sDq5CgkQIEAgV4FNE3cfd5rrsNAuAusKyM7X9VYbAQIECJQpsFHibom9zOGi1QQWC8jRFxMqgAABAgRaFVg3cbfE3uo40+8mBeToTYZdpwkQIEAgmcBaifvh406TdUTBBAhsKyBN39Zf7QQIECBQv0DixL23xO5Vvf7RpIetCDibW4m0fhIgQIBAVgJpEvd9vn7/zI0ds4q1xhCYJyBNn+fmKAIECBAgEFkgeuJ+H27C3t2I3at95FgpjsAaAk7cNZTVQYAAAQIEZgjES9wPN4o5Xh0zozUOIUBgLQEJ+lrS6iFAgAABApEEFifuxzzdx51GColiCCQQkKYnQFUkAQIECBBYV2B+4n7fXRAjX183YGojMEJAmj4CyS4ECBAgQKA0gemJ+26Jffe+0y5vL62/2kugPgFpen0x1SMCBAgQIDAgMD5xv392s0/Yd0vsEoUBSpsIrCLg7FuFWSUECBAgQCA/gRGJ+8O7TsM6+42UIb8IalH1Ak676kOsgwQIECBAYJTA1cT9IWWXN4yitBOBZQJOtGV+jiZAgAABArULDCXuN/e7a9hDz73xtPbw69+mAjL1TflVToAAAQIEShPoJe69GztKKEqLo/bmL+Csyj9GWkiAAAECBLIW2CfuvZQ968ZqHIGSBGTqJUVLWwkQIECAQP4CL+9v/nRJTP5x0sLsBaTp2YdIAwkQIECAQOECL2/unz979mfhvdB8AusLyNTXN1cjAQIECBBoWqB3jXvTDjpP4EkBmfqTRHYgQIAAAQIEEgpI3BPiKrpYATl6saHTcAIECBAgUK+AxL3e2OrZBAGZ+gQsuxIgQIAAAQKbCEjcN2FX6eYCMvXNQ6ABBAgQIECAwDQBifs0L3sXKyBTLzZ0Gk6AAAECBAjsBSTuBkKtAjL1WiOrXwQIECBAoFEBiXujga+x2zL1GqOqTwQIECBAgMBBQOJ+kPBvSQJy9JKipa0ECBAgQIBAFAGJexRGhawgIFlfAVkVBAgQIECAQL4CEvd8Y9N8y2TqzQ8BAAQIECBAgEBPQOLew/BwYwGZ+sYBUD0BAgQIECCQs4DEPefoVN82mXr1IdZBAgQIECBAIJqAxD0apYLGCUjWxznZiwABAgQIECDwWEDi/tjDT/EFZOrxTZVIgAABAgQINCggcW8w6Em7LE1PyqtwAgQIECBAoF0BiXu7sY/Uc5l6JEjFECBAgAABAgSuCkjcr/J4clhAsj7sYisBAgQIECBAIJ2AxD2dbU0ly9Rriqa+ECBAgAABAkUKSNyLDNsqjZasr8KsEgIECBAgQIDAOAGJ+zinVvaSrLcSaf0kQIAAAQIEihOQuBcXsogNlqZHxFQUAQIECBAgQCCtgMQ9rW9mpcvUMwuI5hAgQIAAAQIERgtI3EdTlbqjZL3UyGk3AQIECBAgQKAvIHHva1TzWLJeTSh1hAABAgQIECDwICBxr2YoSNarCaWOECBAgAABAgQGBCTuAyjlbJKslxMrLSVAgAABAgQILBOQuC/zW/tomfra4uojQIAAAQIECGQiIHHPJBCXmiFTvyRjOwECBAgQIECgLQGJe4bxlqxnGBRNIkCAAAECBAhsLCBx3zgAh+ol6wcJ/xIgQIAAAQIECAwJSNyHVFbaJllfCVo1BAgQIECAAIEKBCTuKwdRsr4yuOoIECBAgMBEAa/VE8F2u9/POMYhkwUk7pPJJh7g7J8IZncCBAgQINAJeAktaCRsHqw2fnOQuCc6JzYfv4n6pVgCBAgQIHBBwEvfBRib1xBIMfzy+2VA4h5lLKUYLFEaphACBAgQIDAk4IVrSMU2Ao8Ekp4ms34rkLg/CtCUH5IGc0pD7EuAAAECLQt4OWo5+vpersCMM/fmmcR9asBnME+twv4ECBAg0LCA15mGg6/rBK4LSNyv+3TPmkTHKNmHAAECBC4LeCW5bOMZAgRGCkjcL0GZYi/J2E6AAAECBwGvFQcJ/xIgsIKAxL2PbALua3hMgACBtgW8JrQdf70nkKFA44m7WTnDMalJBAgQSCNgyk/jqlQCBFYTaDBxN3OvNrpURIAAgRUFzO4rYquKAIFNBBpJ3E3nm4wulRIgQCCegIk8nqWSCBAoVKDixN0cX+iY1GwCBBoWMHM3HHxdJ0DgSYH6Enez/pNBtwMBAgS2EzBJb2evZgIESheoI3H3OlD6ONR+AgTqEjAr1xVPvSFAIBOBchN3LwuZDCHNIECgVQHTcKuRz7PfGY7H+zyltKpkgbIS9wzPypKDr+0ECBC4JGC6vSRT+3aRjxjhCjD97hFxPEQpKv/EvYJhHyVSCiFAgEACAVNsAtSVixTDlcGbqi7F6PLLwJIhlGfinmKcLFFyLAECBAoXMK1mHEDByTg4mhZfYPmAbzn1zypxXx7K+MNLiQQIEChJwDy6dbREYOsIqL9+gdlnWQUZ/+aJ+2z8+selHhIgQOCagOnzmk605zBHo1QQga0F5p3OWaX7myTu89y2jrb6CRAgsImAKTMBO9QEqIokUKfA1OkiaaK/WuI+tdd1xl6vCBAgMCBgghxAGbsJ3lgp+xEgsIrA7ElpTMafNHGf3fJVXFVCgACB9QXMi1PMaU3Rsi8BAmULPDnjhR1SJO5P1ls2q9YTIEBgrIDp8KoUnqs8niRAgMCpQKzE3fR7KutnAgTaEjALnsUbyRmJDQQIEFgksDBxNy0v0ncwAQJFCpj5DmEjcZDwLwECBNYQmJG4m6jXCIw6CBDIRaDtOa/t3ucyBrWDAAECncD4xN3sbcwQIFC7QKvzXKv9rn086x8BAtUJXEnczeTVRVuHCBDoBFqa3lrqq/FNgACBygVOEnczfOXx1j0CLQo0MLE10MUWR64+EyBA4ESgS9zN+ScsfiRAoFiBquezqjtX7JDTcAIECKwlEBJ3LwRrYauHAIHoApVOYJV2K3r4FUiAAIG2BE4ulWmr83pLgEBhAtXls9V1qLABpbkECBAoS0DiXla8tJZAYwIVJbYVdaWxQai7BAgQyEZA4p5NKDSEQMsCVWS1VXSi5VGo7wQIEMhdQOKee4S0j0CdAiUnuSW3vc7RpFcECBBoREDi3kigdZPApgLFprrFNnzTcKucAAECBNIISNzTuCqVQOMCZSa8Zba68aGm+wQIEGhIQOLeULB1lUAqgQIT3gKbnCp6yiVAgACBUgQk7qVESjsJ5CRQVNpbVGNzirK2ECBAgEBmAhL3zAKiOQTyFCgn+S2npXlGWqsIECBAIF8BiXu+sdEyAtsIlJD5ltDGbaKnVgIECBCoWEDiXnFwdY3AOIHss+DsGzjO2V4ECBAgQGCZgMR9mZ+jCZQokHcinHfrSoy3NhMgQIBAJQIS90oCqRsErglknAtn3LRrop4jQIAAAQLrC0jc1zdXI4FVBHLNiHNt1ypBUQkBAgQIEFggIHFfgOdQAlkJZJkRZ9morMKmMQQIECBAYKyAxH2slP0IZCSQXzqcX4syCpemECBAgACBKAIS9yiMCiGQXiCz1Diz5qT3VwMBAgQIENhaQOK+dQTUT+CSQE6pcU5tueRlOwECBAgQqFxA4l55gHWvJIGcsuOc2lJSDLWVAAECBAikE5C4p7NVMoERAtkkyNk0ZASaXQgQIECAQJMCEvcmw67TGwrkkSDn0YoNw6BqAgQIECBQnoDEvbyYaXF5AhmkyRk0oby4aTEBAgQIEMhK4OXvv//+/PnzrNqkMQTKFtg6R966/rKjp/UECBAgQCBPgbu7u+f/92//kWfjtIpASQIhWT5+b9HuY+Wy9i341UmAAAECBJIL/O1v//Hy3/7tf9w/e/ZP//TPP//8c/IKVUCgMoFN0+RNK68skLpDgAABAgTyFfj27dv/+d//63/++7//fxXt00MpjXrwAAAAAElFTkSuQmCC\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\\\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\\\n# Currently, the height of the lantern goes up to Z = 0.50.\\\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\\\n\\\\ncam_obj = bpy.data.objects.get(\\\\\\\"Camera\\\\\\\")\\\\nif cam_obj:\\\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\\\n    cam_obj.location = (0.75, -1.15, 0.40)\\\\n    \\\\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\\\n    cam_obj.data.lens = 60\\\\n    \\\\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\\\n    # Alternatively, let's create a Focus_Target empty!\\\\n    \\\\n    focus_empty = bpy.data.objects.get(\\\\\\\"Focus_Target\\\\\\\")\\\\n    if not focus_empty:\\\\n        focus_empty = bpy.data.objects.new(\\\\\\\"Focus_Target\\\\\\\", None)\\\\n        bpy.context.collection.objects.link(focus_empty)\\\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\\\n    \\\\n    # Update camera track constraint target\\\\n    for c in cam_obj.constraints:\\\\n        if c.type == 'TRACK_TO':\\\\n            c.target = focus_empty\\\\n            \\\\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\\\n    cam_obj.data.dof.focus_object = focus_empty\\\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\\\n\\\\nprint(\\\\\\\"Camera composition refined for perfect product-to-hero prop framing!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Reposition camera to cleanly frame the entire lantern and background floor pool\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"content\":\"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Verify updated camera composition\\\",\\\"max_size\\\":1000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvXmUXFd1PtpVPXerJ6m7JVmSZcmWkSzPs8EYTxiDCRgwQ5gTSEIegRcWvyR/5CVZWetlZSX5kfXyB2HlLRIC4TGGGAgEggEbG+x4HrAs2bI1z0O31K2eh3pf1em7a59hn3vurapWd/vWkrr22fvb395nV/W5u07fuje3atWqnp6elpaWfD5flz2yCmQVeLVWoK99dqa5ty6Xe7UWIGbei6suMzMzmE99fX3MrDJzVoFKK6D9ZkxMTjQ3Nc9RapZKw9TafwEn603Na6x10eaBf6nPL0EJp6enT548eejQoYbly5c3NTUVCgUs9LnSA7KfCShgFAztvhL8LvNvRZKzs7PzHzeLmFVgkVagMINf/IX527yAKjoxMTE+Pl7rhBoaGtra2rCIIRBW5tHR0djVDMt4a2urSgyvYmNjI2TFQNli3T99+jQNPYLBhnwMKvgisbGwUuDzQ2NDgydcWlPcMT3OnjbuvPot+Elg3SjnWJ+vx9sMb5h5rVE1gmnTqAZh9Ti8qXmN1cshAROWyLGxMb8D1pO29na1TGG9mm3qrWvtt10mT+6cOHPc1mNJWbZsmdpuni3kZpedV1ffYsIKMyMHHneunNiqptUSALAZ61sxpdlZpYQMwbm1PdG4MtfcbcbFeHaqfninc98EyyaojHCKQVqfAW4v1Qq/VthqB7gB2YNIucGMI4SS1c+Ojo6NGzfu3r17aGiI63FQwWSgwdxwGFu5cmV3d/e+ffvwaoFkw4YNMO3Zs8dZMs5TOxkvTO3IM+asAkuvAvX5/Aw7AC+9CVY+IyysJ06cwLKGZRSPY8eOVc7pYUAIWE+dOsWX5ebm5te85jXDw8NYYNUirBiw8Pb19zeXdmGwPqPzhh7LO9ZwLNHAAwzBWMkBu+GGGwYGBn7961/zTIpsfX2IBS/FBo36w+zk5OTU1BTA+AxzSv8YgAOStOa3t7UtxmaO1+RsyQuvMfNVAq8y3mbF17rczPvwmS2sAt53gdcYxl81FBZJLIxqHUBvfcEFF2AF27t3L1+sVDAsa6tWr8bCMlvfUX/+u+ryxb0GPFqa6q/e3I/m9vHtR48MHjl9+mWlN36CsKurC8rZ7kvr19yorP09ratXtO07emZweKIwM3Fmx/1qsTJ8sR6iA8bqhzyxvuHtiqVy7dq1BDt69Ojhw4dVzljW1FKMLhcR4Ts4OKhMkx0X1/dciDZ8bX97b1fr0YHRQydH0BoXJodbpvbjYwl8V6xYAXK4wBH8yAcNPfT42dvbixyw/I6MjMCEXxxpYwW1Wl2qFeKCEO7lM2SMNRfL9Lve9a677rrrc5/7nPpgRLNSSRcrXtrVRnOP4xleMAVAikgIiz7hMVt080gRSryW0CMuNvsJYAsoosoN6RorPuLi9B7bhWuMucCEKXR2dkJAApi5MSPua8t4adevX88/DGAuUNpIjwbTwSccAuBNoGTkY0yQMBQXvip5mPx1I99MyCqQqAL5+vJSkMjxVQLGwRF7HFhY8Nv6sY997LOf/SyWEefc8du6bt06tTXCMVgGjd90/FJLCxEtpyQgFpad97///RCwHN155508Otbk2dIWDAQV5dxzz8UqioSx1Fx44YVqOeUukPv7+//sz/7s4x//uKEHidrQgYADDAjRx+MnhsiZ1i7u1dXZ2dfbK62K9oLMfdPKeE28jzi71zkzBlbA7NDxTiu+3Iuw+As7ZW92XmPgC1kVGF764qtfV4c158///M83b96MHvL3f//3jaUPAKwwWE+KQZs6VvV2Xb25r7eruN86PVMYn5x5x00b1vQWe0XpUe7IW+a6weu3rvrHz970N79/wxf+101bzvO1iJQkElCJoTVHc4weWj02bdpECyYE9JxXXXUVVl2s6lgJr7nmGlq3mxvrf/8dW/+vj1z9e2/f+he/fc1vvWUz4CpndIxvectb3vOe98ALDfAll1wCHmVCOOyYYAsG0VEoPKRpKn25Vthub2gQD9VoMXGYAd3NN9+MkM6VmiIdPHgQ3eRXvvKVb3zjG1//+tff+ta37t+/n443OHQhdfTfOOTgQHLLLbfgQxhqgaSR/ZYtW9DNowoQEOUTn/gEjiWo5qc//Wll+ou/+Au06XDHxwNMG2x4K7zxjW8kX0rDL+ClUoc6+OKwipqCc82aNYgOfvXRBy+Jk+Ttb387Nq5uuukmJHDeeedhsvBFGkgYWWEuOFzhlcbwQx/6kPpkYvPgxfvwhz8MHtQd4De/+c3AoLagdZYXn9IQF6/CPffcA6/bbrvt/PPPRwL4iXKBAUERGgkg//e9732Yix0002QVCKxAfbTcBOJftTCsJD/4wQ/uu+8+7F5jEfvABz6AX1JejTvuuANr14033oiVGgctrDAXXXQRlgWlwdKKX1ssrVhtrr/++tg9CM6MlecnP/nJc88998ADD+DXn28lcBhkLFNYgfFAFKwk2PTiuwYExhS+853v/PznPyeNU0BcnFuJHSMc2yAAA04DiQ8VR48dw7Js6LNh5RVYMF1Z0FQamxqnpiaDoAsPtLBL7c3Oa5z/Sr/73e9GE/yGN7wBG/BYBtHqeHI4PTJ56szk1Vv633Pr+e2tDWv62r/981dmZhNMqaE+v2ltZ1ND8Vs93cua33fbBZ5w3ISF8Z3vfCfWaqzJl1566Zve9Ca1Nc4xWOex4qH7woqqPmxcdtllCvDW162/eOMKfNKYmJrBz2svWnnLlXOdGJZ3zBqrPfaysWifOXMGnafygjuWSnSz2PJ++umn0dGhBeUR/bKjcUdyt956KxpBNIg4Cx5nv/zwhz/EYv36178eGVx99dX20oyTZHAIwfzRc+OBNhR/ZaDPQzho/exnP3v44YeRPXZr0L+iQMgbc0C9UI63ve1t6FCROpTY3VFnR7300kv4WINwr7zyCqZ3++23w+vyyy/HuwGHQIQjX5ohrDiCgtb5FsFHFvyRFx0w8JgCDmxXXnnlFVdcgYTx4QQfjCAjHLGRgNfpwIED+LCxfft2hEDLjmSQAAoFLxx38fJAwBAfyPATUciXC/BFHS6++GJ8dMFkERfvErBJL9g555yDiHi9d+7cidoChljXXnstPvzgvYXXAkERGtVWn9vUX1t4xEzOKhBYgdI+gGM1CHR/VcHQuONvmqWueBpr0csvv4xfRl4B/LaiFT5y5AhgWD3wwRsA/MQndixQWOvwK4yP2R/84AfVMYB80dlLaywCYVMAKxt4wIB1Bo0yfr7uda/DT3z4JxII6o+wcMGhAmsIum2c1qKWPvCrgwfcsRSjcf+3f/u3hx56SHk52eCCxRMPrJN//Md/DB4c2IyddZyigxNoijmUzgctClFzrw54SlODn3GH9jh7DVJ6tVGaJc7V4VtwpSKYloVeGSyDpZVwIefpranXOG+zQm+D7eTPf/7zWACfeuop9EV/9Vd/hTYGCWAFw18CsYag3eL5jE1Mv3zg9I//Z99Pnzhw940bfvbEgUdfOLr3SPHcEvVA44TtAyxZ6HYiXfn5qtf0XXFh79YNy1/YM/D0zhMwtDRp37JQSyt+Ym0su5UkrIQvvPAC+sxnn332/vvvR9OLFdXAYN1+/PHHcR7Lrl27cI7iM888Ax4sjNhcv/LCvqnp8tcpJ6dmr93cj7cRejw0mZwH29lqaxjdI1Z+dHTf//731YcEEKEsBFb1wfECCZOSC9rxRhnA+Mgjj2zbtu3RRx9F6T/zmc/8wz/8A45ViIpdcHTeSRtEfN7CrjCqg0RxCAEPXkL04qgOjl44huHggcPAiy++iKJgZ0jNRH0JDBtXOPDAChIcIPHKgQE9PZJUvvyzEfT4vIFPeOiz+SRJfuyxx7D3jxcJGvTuaJpRL/TZeA1wShM+NuDFIzAJwOAF+973voc9byQAL+SGBFBWyJgIUgIYyePjCvKhTyzEAAEhMFO44+MHpoPXHh9GUUwImBpHkoyg4IcjDrQoCz6ZAYyDMQDoDMCGWJgFlDiBDDIe5JsJWQUSVQB/eyvkzBUtEcOrDYxfTPzxFEsNVhWsYHz6WByw8uKghWMMVjnsuO/YsQMArKJYxPC7vHXrVvzuY2GBkh8kwCatsdhM+dGPfoSV+Re/+AUWZ/TuWMewx4G1CEs0FhOeAJpsLJtYMY4fP448cdRBLLUhAgHHURwk8IEfSyh2ZyCovQN4YSkDmzEdyhArHpZxlTYPB7m1rU0tgyOjo9TT49Qd7DWgGngY+GyYtAKLq4J4n0gbWEknPp/4RVJkb5pe4/wUE0sNlpEvfOEL2KtFb/r3f//32LdVodGr4PM/Tr5QfbyRT1936x3XrPvBI3vvuHYdtttn2bqB/uruu+/GeQpYQg0vDC9c1/349mMPPH3o4o3L25obsPN974O7OAwr4ac+9ani9kP0lU5uhYw9DtUKQqYVj2OwU4MFXDFwEqvJh3vRDyRIFR8G0JhBRueGtg3HAmXCavnNb35T8aCPBzNfJOGCDz84vwjfL+U5kFyPtRs+NEZyWNOxo4yWEQ90mddddx0o0B1i0x1fYEJzDDA+TOAnskEwxFY7zfhwgKMIHoChGcVxSzHDEa0nNrPx4uEAgyYeE/jlL38JAQchyODEQQhHJvii90V3i0MOJoO/IIAE7njN8EkLqeLzEPQ4RwUabEUrXwBU/uiYEfr555+HgMOVXX20wiDBwQ/5oyL4ifzxgQFx8X6CF452/CVRtMCgFNieBzkeCAoevCQoC958aJ1xVMYbFH8fwKwBBo86hil39RMHcrx18KENFcbHA4DxRvnVr36F8qJKSAZeHA8ZH5AwWdQNWWH3DhvwaNbxaQrHWuzcIyUc5nEIBwMmC05Ypc8ABnM2zCpgVKCxvtDW2jydm/t6kGHNhqoCWBzUtgV2prEDhE/72J7BWoztAKxs1O/i1xnn42Gl/vGPf4yF64knnsBuAlYzrBv48yPWCiwjWDOxIOBXG8sd/dqip4dJrbGgxa8/4iIi4mJbAds8WCuwNoLt3nvvRXRYkQBWQpysCBkds1rT4Ih1BodPrAxYLYHBuqcWGYTAUobVAy01fkKPpQlKtYpinQEb9lY4G5owfMwAGxaZBx98EKslhliZ8VDVQCuPyaqNg+mpqe6entHSd62QNo6FamXDVWWQDGhr8DC3x7QQXqOGXPCDhT0VLTscfPG645NesaiaZcFXedHk6y2r11jT1wC/9VhM0CZh2wLNGLoUrHsUEQ0SVhj0e1g6sG50dHYW28iGjhXnXn7XDesvvWDFt37+yvDo1GvO7d5/7Ax2sieO/Xp86Ajc8XZCo4X9e/ArNqxLao8Al5S59OLN23YPYM/+iR3Hntl58rsP7n5hz2BdYebM7l9QF4pmCX0jZYIlC+9P5INGDlRYlrEUI20sjMgNU8B6DivwyBP7s+hXgYeAoKrjxao71rS2f9Xajed00kk9TQ35nz918OV9x/ODz2GBReOOrg/NG2R0bkgARwpMAWdwoDJgxlqKRRhbJ+ixUSjagsFiiypRc6uQ1NPm0EHylhFrMRJFL4iDB2YCGedmoEklOjVt/PUTUwKLWppxwoZ6JVRNUREcLVAmtcejXBQesqoFPhJgTUe7zPWUlnLhP5WXAoS44JXAVDmDkskXQ86phv4EKDrAkJW7ksmd80NpPxSAQpNgI5WGCBUSfQAOtHhFDL3Kx5O/xJ/pswqgAq0NhRXdHeM588TlrDi8ApMTE6rJfu1rX4vz3NBDq6MCPl1jO1ydcKLw9Hutfk+dv61Aen5tsaBjpxwYNOhYKrGiounHZwD8DRo/sazzxJSM1R8rM1JSW+xQqm0OtVmA1RjLOw4P+Azw5JNPYscBf8nFKo2/3mLFRltvEKpDFNiQCXhwVMPxDFQIASSODujgcVI7ZHWoQ5KKAQc2HOHoY4xStra0YE1Wcg1+yn2KbKlBGjWnXMCzMVNDX4ETCerrF1/vbs6k5q9qugDeNL3GdPECvbAOHD50CA3eO97xjr/5m7/xeOGT/Dlr1qBPm2lZfddv/p9YDscnp7FrDpdrt/QfOzW25/Dw6V9/7dT+p50kWLXUnwqnV958xbU3bVjd+fTO4xedt/zwydFnXy6eLYOryhz++V/y/pbzoJXCsoaWFVswkGlFRTeL7y/BCwumamiRJ1ZL/H0VGuynoL1GXCyhwJzquKF95Zb/4x0Xb17fM4vLUuZzT714/Iv/+cL0+FDr/m9i5cQZQdiMRs+GxRanlKOPR31AiCUaZ9UjH6yi+JsndkywFGN9RmvHkyQZLujvqafVGndokRmOMViy0amrYw8yRvaqoVcsgGHtVlPCgQcmfGRBZvgAARka7NkDg6ZcHdUo9rwJyAEvicp/3oJmgbIKLN4KdDQVOrq6J+tq11ot3trMZY5DIRY9bJPbRwL8RRGrXxUXHKyfWISxiCE2Lrl4pnQxRxxdsOuDlZ126HlN0SujBVd7SLQXBQDaaKzDagHHT+nAwKkggwcHJ7V/r9iwqGLZx/EDJKoC1LgbvvYQvjiO4JxQ21SxJq5DibNXnMA8ESyGeZg54q2i3sPZpntt3iVmwYtRXLraRHezYpE8dvQoPuFjBcNq6QaVtFiaevv6sDjM1DW2vuY9l2698LlXTk5OFTfUl7UWz4YYGZ+WGneskNhKQO8O8FTruQ3r71zZ27Vxdce+Y2cOHi9eWhEPT+OOlQ2b61jl1MYEdmGQhvKCBj00ZoFzIpQSmWD5Umsymmz8aZG2bNC4Fy8Hmc9h0x3n+Rw+OYLz8rFNj8tBonFHFHghEFZOdNT4wyZCYPHEKgo9osOETwJgU9scnvUZ17zHNbsoSa1xBykZ1Bykn/wQFegiUdVIzzOsUYiMNqvAkqnA8tZC07Ll03XaF3qWzOyqMhG1rmPZxaMqhB4SrOxY0BUAJz/ihiaxCxoOEnjMuZTOxYRsLM44JuGA6olLJoMN0e0EoOEnoZKvLeAwhaOUra+SJq5VibNXKY2a0yyGeWg5YnOxobEhnyt9YNMsNa9V5QEWdr7e7LzGyisTy4Dz5Sbw9zd2hrrtgqUJ5wJioVOm6dncbK64T2E8ZiaGZqcnDCWGcMRnQrW+YSGaqmsu1Jln4mF9mh6dO4/aYMDqSqslenQsjAYAaydO7iP+6Wl8nHB8e2A615yvL1/3nEgQOjc9jKtIkoYErJnQGiuzsiKNKdf6DHBLczPVCmCzcSf2TMgqkFXg1VOB/rZCXXvvbF0t9kSXThUdy/BCnRyORkiNr/ULNdPK84p7WeLslWcwPwyLYR5ajuioJicn8GWNs74NnOIF0maSwr/mLt4EvcaapzaPAV41E9VqWrtdEC1MJQN7p6cStsw3q0BWAbsCK9py011dWeNuV4ZrKjhIBLsGA3litjxVOuO8sYanldsxa6UJKEkcJM5eq9SrzRs3D6/da6xeploYnOXVvqwd94tPwa8RpfCv2OWsJxA3A2+CXmMc80K313hyQfRTU5M4neasVEpr3HO5fEMT/lphJz2bKxT3b2r9wC6R2iiiQK4/YZAxE7IKZBWoTgUuWd00uHL9jP7XRnshSBisYgKKVz0mokwnpE0k2C8Y6M9fXdgKZ5H6YdwqRBbU3BNyGMpwChwGcMdB4uwhmcRxeO1eY0h0hQmgkSGyJTaBMFcHqvSV6LEe713SPdEdjGW0bJQtZe8wKYDJBXHpYgNW3ykVo5FnWo44vzi7kYY9rJjApuSaIHpc9eWVl4vXp6cHzkWsxV4zTjXkZ9eUG3douy+4uev82yamC7iJK74igJPTcO9Z3E9x9PiL3UO/QmbAqCsDqH4aTbb6mi1MONdenTOEpPFA9jjRXp2miT/XqhMcbRP0oAIAVGCAC66wQ6eQAk/lyISsAlkFaleB5uaGBb3djpUgaCGtXYXmi/nszTR95IX/0qSf29zrvkCmWPE80ryNg+fuyK70tbyTPT3La/D76wg3Nz3Zkmb+Ph+hNqkSqL5TKkZjutXgMCirs5jXJLFypkH0+ILskfZ2dVVcuGID/iQu6+I4Y77Mm0JC492+bNmK5cupd9ca9/7zb7jr5gt+8PCeKzb14iqY+J7swRMjV7+m/9+//6IKhqtR3nnnnXBWvTWuqYkLkykTLkup7t6HFhy9OK6MhksUKxO+sYsr6UCGHtb//u//xgVnlEnd8wiTx3UScP0gXPEAn2CocVeY7GdWgawCta4Avv5esA6tQUtXrTMTjoy1Dls9/kU/geqVIiVTwPswAJIyeLibNwevMTxGAFKOJFsCaNND2tra8feftvYEf/yhYOlTTu9Jwa0FsWyplVSNrKuf28LMCvNEYnjUbIWNp0czzL92jw6+6l17cZqFwtjoaKGnx9G4w4zr3n/vod1nxqbUpTSPDY5BeWa0eAkFOOA7vLgmjvrbKz5GQ0kb6tgyx3a7UkKPh9qYx5SgpG/vgoF239VscfMjMOOaQfjFxpXgA694oEJkP7MKZBWoSgVa6guFfJrDalz0BbvgxyV+du1no2zCwU9Qn936nI3oZ+M1cc8zfSZpX8wkER3Y7u4uXKa62DmkTcBdiKLWEU4G18IiJyBbqp0HIuEhFNdrDM+kyrOpKl1VyeySpKwg7ryBGyLhRBLcO5buo4Tr+eLij7hyPN1K1o4XoinvuAPd0da46bwe3DmWe16zpf9H+7fhFhvvete7kAQ3KVldxx4358MuO7pw3D+VMJs2bcJN/nCl+q9+9atQ3nLLLdiYV9YtW7bgysfYjIcAzXe/+1208vwOW0SSCVkFsgrUtAK4Zu5sveNSXDUNGkpe41U5NA3xwBhLsGAm4M1UyFJQG1RhKMMp0TAgghcidDWJcggAe3PwGgPIAyHzNNXAbADDd+fwwB/Si1t4ybOLK1ucPTxRCxlGLaNkixWqrEjlVHZ3S5WRJn/R3Flo2spS0qhqvvWOaInTxZ3yvvzlL2Oz+y1veQs2rLEZj/YY17ZH34sPsbDifkc41RwXd8dNVfGrcf/9999+++243R7ut61mh5s0AYBfHLTfRm+sNeLDY1Mv7iteIp4/cAtZDPGLh/1ytY/OrZBxlgvuxocb6eGO3LgvFLeqnXiki5PX8cB5MmTduXMnXJCueoBf7bsTIBOyCmQVmJ8KNNbXFfLaZ3iKi+Uq7QOrfU0W/LT5VOpXQSkqDZ35x1VgPt5sC+QNkP6XqoIJJHF1YHE2rLr7TNzr6LY7GN1Apk1fJkYSdYSaKtEgTequC4bHB42LFGf3REjlGuAESADKk5hhqiqZwY1hgnTV9zPxE2eRoA1G53zvvfeiiUfX/txzz0HAhvXXv/513M4JDfAvfvELdOfqwb/YCa8PfOADd999NzbHjWy0xh13q9q0tniTbf644sI+PlQy7pmH30N14QJ121TC4I8C6MjRppMGd7dCYNwjCl4wqTtjn3/++QCABJ8w8IBy69atia6BQPyZkFUgq0AlFZicrsvPVvemQlU6bFYyq0XtW+NDkLM2FcWsyNmZjqaMez/FhY+za8EqGngjeY0VhS07z0eMcjS3ZObQ2Ng0PT1VbEpMi9vf0HpffcGYKpARVw0DmLwQr9EZEcoqO6FIQp2kBAx92nwC/AIgRjKeYVXJnHEQIT4Idquxi/3xj3/8t37rt7CVrlpw0B0+fBhf+MS3PfF9zg996EPYUIcMPTa10SfjrJO1a9dS74697B/+8IfY48Zp5EYq2jbb6PjUrkNDBuL5XQOGBsMdO3Y8+OCDSg92FRtDCDjpRTX05IWOHA8M8bVUfLZQ+t27d0PAd1vxwGcOyPiqqzJlP7MKZBWYzwqcmcRdp4tfaFmgD6yTlR11zva8Fv0EznYBF0r8hfBCnq0cKoy7bFnH8PBQZ6e5Mxj70qb/1a8w49jMNED1gyVkTF8nbR7yIGE+RBTgFwAhulgBZHjUoBzJKHGK+KOPPooOHu04Nqy/9rWvFc8Yy+fR6EIJQW3GF1PN5b71rW/hJ77zCT126EszKP7A+Sx40JCEcuOONr+pMHLLlZf97MkDl2/q3a6uKnN8ZPP67kO76rBZjk1+UCvPgYFyN4+/gmHDHxp8DxWp4K8AL730koINDQ3t2rULnzaUI79Y/fr16zEZbLHj3B1crAYfRHDOPnLgp9NQlpmQVSCrQO0qMDVblytMGvzJVinDeYkOq3p8kWu0sEo/T5OWy7HoLHLF5umVlROQLdWushmpo6Pj4MEDnV2JG3eTyEw0zm7i04zDYsgo2ZImG7ePN4bX6OZzadPSBPgFQFwZibpq8yFQPGV9fbmjRtOrtqTh+c1vfpNkdV0WKEkDWXXIXAMlf6Chp/Yb+tw111yDc2sUoqltecvqK3L5cr+v9LNjJ1sm9nEWktFn888HpIeQwoSPB8PDw/SXAggkc+ZMziqQVaC6Ffj4jV172q7jnBX3GBUTVDUbTlaJXMGsgl2DgZ6JqD97Jj35UIgsqO3wwUDbNVYTwB0HibPH5qAAXpr0xsDoATkAIqchW0ISCPN2o/DH+a7uLmzkhQQyMG7GMki2y5ayd5gUwOSFeI1GCkmw3NXr5zVyFr+clibALwDiz82wVptP0WusO196EXvQyoCda+xEV333GS07PvfiW6Y0u/LnA6gmRwcmXv4p2bhgnkDDbdWT+UeKrGuvXl0zpqwCi7YC2iK5GGeRfALxOzu1qoMQWVDzLJLPknvPkxwwD38mAbP0xvAa/aETWuVIsiVhCA/cHaO3t/fwoUPnrF3j8ZRMbsYyWrbLlrJ31SQ5WMBbh2cBIjwSOsFDTiDOWIwX9vDG8FAE+KWdth01eelsDkmjZblq9TlnRkamJot/ssZuOs5AEW8cmjYn+PHeuBgIZ6XjISU4z/psf32eC56FyyowVwG1FrFyBKyyDO0QKyZwcJ59VfJZJfc4+7PkGYQdbWo/y4AIARA+s+RyzQMEp1RRJmmdw94K4hzQfOTyucJsAT9F0AI2BJRKSUtNAAAgAElEQVRtIczLm6bXGF77tDRp/cIzi5C1j4QIeBT3wi+99DLcXDSKXPNnXMpF23GvecAsQFaBrAILtAJqGVqgyS2QtBbCYbmmpVjyE6xp9SJyb8/gNUYMVXmufqQkjG5s74peXHeut683xQzdjGUi2S5byt4BUkW/HalySOWEmXj9vMaAMsxB0tJ4/SoqsZm7N5IJTjtGkDqcLi6dMe6jTT5Z5YENbu1ykL4YmS2rQFaBrAIJKpB8WUpAvtShxcPBAnksoFQCKuLNthpvSW+AgASrB0mfSTXqEDAPR4KNTU34JlvA9fQC6BNBHLkk8g8EVz9M9RkxlSqRgiYhE9553jdfQrrYVyV5hrGUTkCqvAOcVL3oJ8U2G/fiH7MSPogrE7IKZBVYtBVwnC8XsLA4pxu3OjudJGXaJCS+s6FfCnOIr1vtZxkXoapvvPgJexDeTL1GD2lCE6pR/UfluXd2Fq8LmS6zuOhx9nRRI68wdhklW6II7udUfnFOcXZ3KpY24ZssIGoAxMoiRlEDSjsigsTFUesT/2nRcKOnttqpMstWXdS2/qZczryqjEVeVsxODp3e8YPJ0bkv1ZYNmZRVIKtAVoGlVQEszJ7FtMpznddgc7kLMQV1lSccTxdX/Lg84+zxGZQQVaIJjCbC4qohOhYbjPTOMq3D4oiEC7ofOXKko6MzXQ4ORkdcXVW9yaaJznNJ65/KL84pzs4Td8qp6hoQFRA8UrE784SyBpQUSkhUVxfqclN1Lbm62ca6CfKEoKO4xSeXG3eco9O+8U35tmQnn+Xrzmlfd2zyxf92BsG1J9esWYO7Q+E6UNjHd2IkZWdnJ76fi4ua4b6qSX3xdVucBrRixQqcTpfm3CMpp0yfVWCpVkAtbKXZJftFdRQkYGl2eMmqavPJkWpnWQpziK/Oq2OW8XUoIry18BrD+GNRtYqRkNeE42je2NCAE2aam5tTtC3e1UkwminEls4HCCDzQrxGT+BUfl4noVqeHAwT2PFIThPm583dyCRwGBbYQybPVbaAbqDQv6Nw8XChE437ityJi/LPtdWd8YRxmXiAXD1ufqSuKoMmu339jbmG4qUi87nZN6588tLuXUfGl9+x6omVLYP1udnbVz41Mt16dc9LF3Xu3T26ejY6P37mzOGxE3N3XOLxQPvJT34S/fett96Kmz/hxkxoxEdGRjZs2IBbNa1atUrdOGrjxo24MVNPTw8ywS/z+eefjyFMH/7wh3Hdyje/+c24x+rKlStxnR18lxZW/AQD+nJcrH7dunX45ceFM6EHv7qXU39/P+7udOedd3Z1dZ06dQp4dPC4Vv2mTZtwfc3x8XGeZCZnFcgqgApcua7pVNM6KgVfJEiZRKiYwAhWbT6DPnxYQSJJXIFNArfzV3fn8NzRw3YhjRBZUJObEsJQhlPgMIw7DhVnr14yQiSoBUtgaILF0XjtXiOFkIQk3ia2qbn51KnB9vZlKepgcpn5yXbZYnLEjcOYvCiv0RM/uZ/sIVs8CThNaZnC/MJQzsRsZRIyYI1/EZ+htki5/XSh95HZG0fq2mfq8jN19cN1HUcLq9fl99fX+S9EwzmiACUd+tjyjnuUUfG5KT+Nrv3/3fXW0enmB45fPlPIT8/Wn5jsGp9pOjnZ2ZifubzrlScGL+QutoxPArj9x3e+853iJ+zGxg9+8IMXXXTRv//7v19//fXore+77763v/3twLzwwgu33XYbhOeeew4NN1rwyy+//Bvf+AYI0fQvW7bspptuwi0b0Kx/97vfveOOO3AX2de97nXPPPMMWnY80Ls/9thjF198Me7e+r3vfe9tb3vb9u3b1Q1cN2/ejP4e3Tw+ORw6dAi3cf3IRz7yx3/8x3TnKjvnTJNVIKvAwqpAtGQtkKxqsA20QGa2CNJYVMWX37jVm0Yck9fuNVb13WBGwiEY+3qzhdl8zvyWXWxckyvWgQDpPYliTghj8qK8RjNe+rH8JgTnPOXgyT4sgzCUJ4y3CoafC+vSGW4YSqgXC1vRr4+feGX00Av42X/9R0baevYULtiU28ZIBG9B7f61QZv+yxOXDE21TRfq8XNkumVithHC5GzDmenW0Znmy7pfYSHdouqesRF+xRVXoPlG540eGieuvPTSS9iAf/DBB9GXn3vuuTiXBpviAP/qV796wxvegJNb0I4rxp07d/71X/81enpg8GsO4de//jXcoccNX/GxA8MTJ0685jWvWb16NVxwf4f9+/eD+fDhw/v27YPm/vvvf8c73nHgwAEAEIvuEevOONNmFcgqsKAqICxbCyrHgGQwjYQzwbHq7D3SB084y9pMMS79OHtIVgtioiGJ1gwTXEX3m7+zs+v0qVM1yM6bl9eYKJkwJi/Ka7STcdfRxmmauBhxdo1MHoAmLVOYXxhKJagKxX/KidseJQ13hmw9DLsaWiilyA0WuiG19J7f0rexfd0VDW09GJaUnCby5jrIrgfU+qky616rTpWpz89uWnZo90ipG+5sWr28ZfDMFDHMFOpfHD53qjC3Wz8zfMB5qgz67F27dt1zzz04ZeWnP/0ptsZxsvuTTz6Js2VwMgxMaNDRrOOcmaeeempgYGDv3r3YWe/r63v++eexQQ756aefxpkt0GMn/uDBg2jTcS7N8PAwPgns2LED/FdffTUa/W9+85vo7HFWzI9//GNs2F922WWPP/44MNjLR5ePPXu079u2bQPJiy+++PLLLyc9Y54mnglZBZZqBa5Y13i66Vw+O2HR4BC/XDGBoq8SjT/XRNbkGSX3QEKpnGgilZwq4woelk0YipJMJwQEiYPE2WMTCyCIg8TZY3MgQByT1+41UghbSOLnwOKP8DjoY/POZg7ROBg1N9kuWzSCsEEAmQyRLVLw5B6KKc4vzi7lY+vTMrn8oOP/7GCRhqNcRBFu7tmARx5cbXiUhnF2l09Rl9tduGC6rmFm7PTQzge7Nt+azzfiPqjdudOrcwe1CaoAOg0FhbqAO6XipqyFAvasc1dddZVa4rEX3vfaz+aau4Borp96x5pffmPfLZDX9Lb+r3s2feEHu146MHc2fVP99HvWPvDVvberEBMH/2dg+38qeT5/4kwYdQLMfAbNYmUVWJIV+MCVrcd7XzdbV/4THJaMyh4VE6jwVaKpbC5z3hXkktw1uQefI77WjyFOVuTKEFkIK6gNxjCU4ZR0GBCkOhB/YpXGCPD3J0DWACYZIluI3ykk8XNjBwcHW1pb8IU3J79f6WYs+3jtXmOZI0AKYBIggjo2Zq380vLaCYcxuVAunc1PmmC4ABTUyfnJgwSNekfh0pdmN42f2D12dEeuvrHjvGsbWjqvq3+kP3eIHJSguek2bHZjz3oOhhsQ69a50eRM438euoFM7S31n3r7+ReuXaY0s7O5/aN9ZD1bAjbdcdr62Yqexc0qsJQqMDY+lU/9106tEFh81D9Nmw0SVyDJX4cTk9fIYV5yDgjihczf29ObhtdYo9fnLNG6p4qvpQ0ODKRLyc1Y5oqzl5HpJU+bFZHKkLQJ1sovLW8007lnecIAqF889TPyc+kim/bMgTqFBosGHF7ScYXgb0AiqpBny5UpLshv78udbOnd0LP1zd2bb29o6diYf6U/dxi8DFWUPY/ibcvYw924N9dP3rP2QQara2rI/fad5523qrh/g225/aP93Hp2ZXwDFWfAn90csuhZBRZ1BcamJnGlqoU4hSodUaoytQpyqcC1KqkHkwiJCupg2nkEqqOhHHARTUWeBLfETUhuCWQL53fKcUENJwcc56zifNeZaf+1NQye8tDBWDZCku2yRSOIGwTQeCFeoyd4rfyS86rfNP6Tpc3VSi4ZDTVz4KKBwtD7sOCWwnYPgIQ4lWi8XA11U9fVP3R1/om+3Ilz8/teW//w1vyzuboCnISHTTeHxe8Lrs0IL3fjju+k/vr0BiI9dHL8L76y/f/+/3YcPlm8lmJjfhqXhiSrR0A/zb8PCrkWHTZo3/3ud3vSyExZBbIKBFaA1oxAfAarYQWSH0prmEwgdY1zVu9Pby41ziCKHRbGi/Iaozjxz3NH9XighahSAhZvqKK3t+/kyROeHttPFJd+nN3PXh2rNwev0RO/Jn5hbyM6QAhwskfpk0IJkdp+5kDbqms4FnLpwXU6GiNuVLIFcSoMvxLG0GHoenAUrvy4Jr93Ve7ghtzO3twRdO26B8cyOq5Gs57Pn7d+PS7lAl934w7aQvR5YGh06v+59+UjgxNnxqYnpop7cpMzDfcevFEP7B7hq6Lvfe97t2zZ8vGPfxwtOy7wgus2AoqT69/5znfiKu/4din2y5Xzeechq/U33mgyA7B27Vp8x/SSSy5xhyldTwYmfIJ///vfD9oLL4y5VCXx4OLxuJC8GuJ7sZ/4xCd++7d/GwVCRMIoAV+uxXVpcCVKQ58NswpkFWAVMJYkZnm1iwkro1bts1E0IVFBPb8ZVieJ6rDM78zTR6vVbBPyOuC4LuTU1HT6mcV4sh6IIwU1hwTKjik5PL0or9FBFqmS+1kLilLQz4jZfiYIBOvBjZGd6ywHrkgFjKKASPDnaiXzoF7ZcI2wXB3p7Oc4lHrROErJEZNhidTqGd8GQYurvrzkvqoMvn6K+y49fWoTHCanZscntb+h4zrub1n92Lah8xSddFUZWH/jN37j4YcfxvfH0a8/8sgjOCX99a9/Pa7Xfumll+JiMl/84hfRuOMDxG/+5m+iJ0aDjuvPbN26FV+oestb3oLryeDGSbgoDe7N9OlPfxr3T8UlI/FV2ttvvx2Xlbz55ptvuOEGzATXf8THgOuuuw7Xq7n11ltx/v63v/1ttNf4GAAAvnF7991340ZOaMRx91bQggTb88gHp8h/7GMfwwUocUFJde1IMOPbrrgWDbr5P/3TP0W2yAHXncT1aj760Y8iIqzIDVewAQ9mh6GqQPYzq8Bir8CmFYW6no2zdfWYCFaPyh4VExjhq81n0CcdVpBOEtckWHsK6k+d6W7ABDYhuKC2wwcDbddYTQB3dSCxmQBQhUgBFLGZxHF47V5jxaE5gSMSjtG40By+pBpSSs4F2UGnIWS7bNEIwgZhZF6U1yhlEeAECP2LaCxFZNCeCQXBelhGrnA5EEMwEB4cWyLgCiUTrwPNbD7RII2ggjoyzz0nQg0Wlrflxlpy0T1ADWcM5cfY6CjaUazq/X19Q8PD7h33iZnG7x967dvOeeTSrl24E9Pb1jzc1Thy1+r/ubH31+cvO/TedQ9gqEK0NMqhcHWa5ma0xeqOSMDh/Hr0xGjQccF1XA4SG9vqNky4niMad0WEIw2adbTmb33rW9/znvd84QtfwFUgcbslXLsdO/do6z//+c/jrqjoy7/61a/i8vC0YQ93fNllz54973rXu9C4A/m1r30NXTvw1157LfC4viRu7YQPDLhYJDp+3MsJ/TquIEkTwDUr8ckBfyLApScRER9uMMS1a9Cy4zqSsGKJQQ633HLLP/7jP+IvA7U47YeSyYSsAou2Ask3g6SpqqVNsi4m/SKbifASCur5fSECkgiAzF/OcjLz96aQc5i/OiCSIw3c6QXXiS5m4TDWLLn5jFXVSai3jPeNY0G4ArL84EAdxS2MgtQ62h4FAAkSCdFzuYG3eDnEMkoK7gQ5egjqyFzOggPJygRuV7JgZOpAEdeW2X/gYK4u575zaqEud2qy45GTF+FWqfh9Ojy2AvdgenRgC859h+bHR64ZnW5BpE39dbOFutNyTPxCokdXp6PgwurYNT9y5AgaYrTFn/3sZ7ELfsEFF+BS62jEsa2ODxM4tQa77MBgY/uVV17B6TG4uyp+sREBJnwuR/ONc2zwYQD3XkXfbLTOuO47TpVBk43OW1mxy47zcyDD96677sKpOKDCDj323YHBFjv6ftyKFVvy0OOvAWj98ZcI5IxvAKBNxxY+GncAIOATxQMPPIAkUTtw4tMPJiJPPbNkFVhcFSieHldcnLJHXAVw0J+POs1TmLjZJrXPR2mS5uTCV6m8ATRyRQKcXak7dBUxVeRc7LflGTpStVUtzc3j42O4MKRt8mviQsfZ/ezB1rAwAiqgdmkgAT6YXxzKslsKZ5EqQsnOssWZBVe6XF067qPkilBhznZQaAzXfB5/DC/r3NdxdxIZyuaGurU9dQMjdUd2itdxR8uOnW+cG4OGG038RRddhDNbdu/ejXa5o6MD+9m4hSp6aNzWFOelAAAN2mXIaJ1hQocNE27TgBYZfTNOqoGAFh/nqMAdZ7CgzwYY+k996lP/9E//BFpY0eij9cdPbNsjLlp8NPpouEGFbht4TAR6tPKIgnAgQU+PWF/60pfwUQFIhEbjjgYdZ72DHN0/SPD3AdzsCVv1+LsB9MAAadQkG2YVWKQVePOm6brzbp/K4YN68VFeIdQ42c/KvHms6jFx1tRyBemkck3lpGaX+jruVBwruKUgqCEEAw2/RMOAINWBeLIKCABvL8pr9IS2TQFMXojXaIczNGHebhQOvjierj6neMPHFA83aZlItsuWsnewJJNZFkthBImzA65D9JHBxodeoGW0FJyK5IpQXmevkeLbgsvPpTM8AyDwEFCCmofYNbNxRf5kV07b6y7kZqbzg7O56cbZzvpZ+7Ybc7z4HRkdG1V/lZo1bsC0bO21hfq5IzeP55cLI0eGj77oxKCTxgMtr7Ji5xudNx5OcGolGmh8bxXnrKdjQIbKseqJpcsn88oqMP8VqEbjXuXf67ki1IY1dYUrSCeVayonzK7Cxl0IK6iNaoahDKekw4Ag1YH4E6tCjAAKfw5kDWCSIbKF+J1CQj83HH+M7+3rwz6aM4Rf6WYs+3jtXmOZQ5YEAkttKQzKNPY4HxXCi7KMlsLIM4CTPFxcLh05QIizcyzJgpOgJrewcAJLSZ2fLfQcOpEfQUtdKLQ2n17dN9XkPo0FsahxJ8ap+lODbQ+icceJLblCU8fEFcsmLuLpaXLkhr1jLcbArl+ir9egAQN1nokTWOrSo2i4hE3UwTvBqZXYjE/dtSNo1q+nrnzmmFWAVUD4WzBDZOKrogLz8kYICOKFlI9LtX5NvGlUNXhFkdI6p/XTZt7d3TM4MNjb16tpwwZxCXjtXqMzvvzGsSyWwiCMswNuQSyFwamGXpRu1EdONlceSYDeEF6jFEXIKI4rzq7CCSiXuu+V/YebGyY7i+da188Wznl53+CF66cbtM+f5KcEGhbqZgfbHhgZHxg4PNm7rvnYntNrLnysYba7deocvCvtV14lp35qjTs3kIwLNuFKiHVnzhwbGBidmSkyZo+sAlkFllYFjDVlaU1ugcwmeY9w9hJfTLm6q0THR7e5Cqdml4irUKgqUAhzDFfHVSucKQ7pnq26jkWcr2h3k5bhLnvyKQseltpSlBMpSYntcQ6KX0C51C6dkaSX08C66Fw6cvMaCWUILieXznDDMAAlQAS1CtE1MHS8ITfZONemz+Rzh/ube7uePL2mPjfRVjhyft2EfQILGOdIJxuOT+dPNbXmB49Onj4+1X9u85X7el6/7UD36WOTTfUvrVvxxAWrpurd14/xNe4Nudw1K1e9+fd+d/+hQ9seeGD9zOx5F2/dPjHx2GOP0ekldo0yTVaBrAKLsQI4uFX8cB0gKyStAWUlGc13OvMdz1+b4GyCgf54Hqv3kAq/2mcQJRcQKQASsdXyWa5ZZQkm9HbD8b21M0PDyzo7qloBa8qWwhNOwLrULh1nTmNP46NiWp6WgufG5fTAOM84O8+CZJeTS0cOEOLsCiugBDXnJ7l1eORoS7k1r181OLTy6ZdeaJreUV/fPNt/0XP9nTfUHV9PeAhF+uJNU6dnG8Ymmw4UR7m6c7e07Xp25O7hdbc/tap0Fvts6/jsZTuPrBga+9FVG/F5gBiUhJ/addxxngx15M2Njbf19h06M/yd++578qmnjp458wr+DQzgYovdy5btfOUV4sKWPHmRMhOyCmQVWEQV2LRitq5742xO+xtfqvzLq0wqd5dTDShdYUJ1FaST0BXwhB40B/XVeZxGSJqkghBZUNvswUDbNUQTQF8dSJWSAY03H68xJIe4AIpDDiNbqhedmBzBcMUI3EW1o6PTXyeioPmAS6dTCl1NOsPfNRSwlpoUEIQHQULt5CBweu1kjKJZishgPgcACaKEiIHUkYKeyaI7kF0SuF+Eceki29wzQQw9GxIEAnsIaoYwReXRNjw2VJLwI98yNbzu8YNPt8xOFffICzO5M0cacr17ltVvqJtqKr5Bc3WdQxPnju1sa92Wb9pTqBtumuyeyh+GZTZXaB6o/72dm+pncg8PnZiYnXlk+OQ5Ta29o1MD3e2nlrWUvMtvcjTqYuOOG4juPzP87L59KuU3vvGNL7/8Mi6xgmsjbp6dHRgZwWkzypQ17uarmo2zCiy2CizExt1YrhZMSZFX2kdC14RwnlXljTvYhPiCmodXcjDQdo3VhHHHoeLssWkQIIDJC/EaKUqsEEfjtXuNFYc2CMxg+LLZ2Ph4Y1Nj7FdU4Un/GKmlIwWEuIeAtdSWwiaOg+h2PoLsenCIbucW5kxqHW2MCMU8DQiGhIpMpFBCpKZnspMmTiAPnZGrLQpu1N041EBFJkEdmR3PTo+Z5qauoydHSzczqt9w6OCe0zMT2s7X2Kl87wWF3NAqxdiaH2+ezLcOLe85sWLV4bZ1JxouPLbigiP5i4503Ty9sn+geKJ8d0PTvx3bs665bXNbJ4ZTLQ17+7qMhNC4u0+VwVURN55//j//y7+QA+3cwOehffsu7eh8YOAkWTMhq0BWgUVdgYnpupbiXRmkXi18ctXgCI92NpCpZoiVP/kjVaTkYWrgkWq6ifKoTm2qwxKYuBys9uWKUpRzAMJrjBjE5zBva6pMgbu4nMRNWlautGMwFBktnaUgqC3IWMtiKTib1wigbtdHnMeQZaBlsRQGFQ0DgC6IS0ecEOLsHKtkl4dLxz3j7K5EAnx4CJJdfg7dREtTy4qe/oHBU23NU/mhqXGznZ6ZzM82jtBZ6hNNDceXF88EI65C3Tnjje1jja9ccHR2095i/MnCDM5RV98mBayAM2nYZRiVI34SJ+VcFJbX1f3kn/+FX70RdxuFXl2AZWBq6qaPfBgb7ZpPNsgqkFVg0VZgeCLXUje5sNJHI7DwHlg01eqZMLUFOZm4OSzwpAPSC4DEFSHQHhfJ+8aJcw7MAbDqMYXHnEPKvxdq7qwClkJRYK99Bm3H7KwaulBeXUDK5K9jSQ2h9LAUHO81AuiyKx1nsWSXmwK5LKSzeLiCUBDkB6EiCClcbtzoskck5WfBg6vL4LLktXOjkvXCQxf8MLgiP66OdOo5spzu7xnfsHZ5U0t3rru+2bwkY75hNj/dFmHpbVFW5HL51un1y8duHW+5aQK3Rqqre/bMqff3ndeUzw9OFw/H+/q6Sp276t9LjiXR3Xy3FAoDR4/ii97XXnstfpe2bduGWxrh9km4DxFuh4QbKrX290OPeyfps8lGWQWyCizKCgyO5S7MjZ6uK/55LtUDa0r2WFIVEF5RQW1MHf1jGNDwOwvDaqQaMNdqhKlOdbyZeI12fO/ELaOlsAlxP8RTg4PYetdNlqel0PHmyAXXdfrI9I95O7ucXTqbFhoBaKkthZNNJuRwgUtQK0+vkZPL8DgKr10wCmo7IUPj8nPp4CaolWWquen0qhV1k+19m3YdeloLsmLTZMMgzpXR/LVBBB9ranx8y9rXPbvn1u7in5vubCrejGzPuuX7+8yvfCh3d+M+0th4yeWXP/vAAw899JBivvzyy5955pkXXngBQ+zSH3riiaxrV5XJfmYVWAIVmJzF73VxwyDhgZumntaPCJa+kKpEqZyWfi2jGQaUJwASsaV+no8YwckFJCNAnD2FK64XqBv1kYtsTqeAba2taNxLKsvTUsSyWQCLwlIYLontcQ6KX0ZZFkthZEjDAKAL4tIl4SSsElx0Lh25pTF6fYjZEFxOls5SgMSl09VATLT2L7uxcPmDR7c1FKbzufrCigsn1nRcX3ekk7kzUSfA6IVze0dbGy97+cjyU6OjbU0vrut9fkM/mv6Sj+5Yl3M37sdHR6+/664f/s//jI+PgxEPXModjbuS17S0Pvazn+FkdzXMfmYVyCqwNCpgLA9nf1JCg3EWE1t4GdWqGMJMBbWdRTDQdp1Xzfy96b0V8RqTFkQmc83WpbMjelGW0VLYhNC4ULnW1rbR0bG2tuj27y5QMJsrSBxhYnucg8pWRrksLp0965Qor5vXGJBCnH9Ke5ybkZkFZwomkle8zkKYipPnrGq9e+Vrd05MDjQ1ddaf2FQ42uF8iyOo6VvKY29fN/5RSk4QHNHLu89xx9VjfvqTn9x+2WXEfuzYMUXX1dX1xquvfn54uMyeSVkFsgosgQoUcLjPHguvAtnLUtFrQgexilhinRfIq1Q8rpttAelYKVw65xwJaFnJ4qK10FxBnkxZ1vX09AwMDsxNg3EzcFkkNx3oUpOu7K1JXjsZIUQP0kUK57OAIrWLkOlsTpdnGIo8bXj0tvFGVm7EooSSkuu85AK/4C+oeQQOKcmlc8Dx7pl7JqE4w8hWfnORy1wBNC9dx0qkmHRzSTfemtt7acvhm/N7ryyMzH0P1ZlhmcwVcS7VMqicsCq3e8cdtp/cd981Xd03reh9bmpycGgIV4FE+C0XXvhbv/M7//qv/zqSbbfz904mZxXIKpD6LBt/6eTNQ79f7awVZJTcFcvu2XsI6Qrqs5FnFcozr7PxBvMapeoKFbDUliIhoYJbLJbCSWuhYhS49AX+pC9dF9JyFnKDWoBSkl67ZbQUxMMFGWVZLAXn4XIYUEfpo+Rs5OEicunIAUIyu4zWLWzERIobr7MQlsLK3YVQEV0WXaePKE8rBrfoRidDrk5s3LGGPH761Lru7t/53d9tW73hNIcAACAASURBVLZs9wO/OPL887P1DX/7t397/PhxI042zCqQVSCrQDUr4Fywqhkg46p9BVJ1otVOa/6SSB8p4btdgLvULp2zwl6gbtRHTjYoLVS8gqh6e3tPHD++ctXcBbCV3vIX1AKOyJPZ49BxtC5/l454SAhAWRBLQWwQvEYOVLIL7tKRp2BkaiaSlytYhIuevWAfATm6mHSdPiJHCJZFV+gj7ujyVXbLx1IQj22BRmzc4YZlaN+pU3/7v/83Li+TKxQmp6ZmT2QtO9UzE7IKLKUK4Ncd/xbMI30TtGCmYCZir8AmIhsnrUB13ibVYfHnbr36lsLvD6vg4VK7dDa/F6Ub9ZFNRRoLGK8gXxKw405Xv7D8gQrVEaHgQ3aL0FIQ1BAEoEvt0hlsGHpRLqNLR7ReI6GUIGBD1QzHRB5DV7MREwnv0sHI1EyUJxAE0nnlFCwyOa5sSUxiU7Evp+LiqcWrqLrOgZmYmKCp2MLc115tQ6bJKpBV4NVVgfnogBZCRedvnvMXyV1XIb6gNjiEQ5SBqv0wLNsK8hAm6lK7dJ7IAtyldumczALQpXbpbE4LZSng49LZVEoDLM50P33qFK4OyTAWhaVg4DlRhggWQc2ZBYhL7dJxKshpIF4fr9EI7oqv++sjHW/ZFLuuZiMm8jwsta7QR3oGAo3lIuBIbTlYCjmuYHExCFBKw2W3efipMoVCIZ/PY3Odc8TK6Nrpdgmx4AyQVSCrwFKsgL20LMVZluZUwVRr3j7OV9ErqMF8pZg4TvIXR6iCS+3S+TMUPFxql84mF1DJ1Aat5RyvMBiMIfdvb28/eOhQd3ePgSkOOc5h9kNczi6dTSygLLWlsKmgCUBZEEvBmb1GAkao6NkyWAoLaSGUguGYKIBdasvLUsBL1+kjIlWCy8h0TAxwdFHKDAZ6qtC4a7R3eHQCd1zq7civaz6Zr5u7y5gRem4Yx6ydKpOiBUe77w6cabMKZBVYXBVI3rvUdn4LLZ/azlZmP6t1iDuCyGnPlyWsPAIq4fQEeDK1VBiBBXCXxaUzmGWIy+LSGYSuVFxuLp1NpTQCtqjGNuLY2Fhra+ucrwAlZq/dZXTpiI0EC2UpAHXpiIGEAJQL4tIpzniLC6Hr2IiJlDMEXc1GTCS8S+fjUI6Wl67QRxSLBN2uj6zg5CVbEjDIJHNxJgtNjx3rKZx8IVco3rF0z4m2gVWbLu8+kqNzU61oc576E6EgaI07YNh018HxoxTtfjxphsgqkFVg0VRA6IoWTf4JEn0VTVWsSnANgoFiqCoY6HjHuFw6ZjZFAZ5MbZJG41AWARfRlJ9loMvi0pW5IsmFsnSWIvJ2PAtYTY2zZY4fO9baFjXuDpqiSvMxMZbRUpgeIqHL06UzCFNCLDdLoeIwNRNh00e6wrJRzrqFjZgogF1qy8tSKC+mZiIxkmAZ4xVWACKDYLkLOi8JJ3QAXx5ZUTi5LVeYu/FR/czo0NHdR9rXrG46aXuSxpUZGfXGHee4T01NJdpER6OvrtxUpsykrAJZBRZlBdBnLbDHguj8qliTtPNJ61fF1BcglXBsc6ldOs+MZLjL4tIlJHdRuHROWi/QMloKJyeUFjBeIVGR3qLwxSleDjKXc14X0sUjBPFClY8AcaldOgpMgoxiFiaGpqG76CNwMAUTKSsdoY+C8IpJh+ojF8Klc3m5cJZOdgRUNloWS6Ei8Z8BECs97g+5RDEwPJ2PunZlb5geOj62YXVTGR0ca86lvOOOXxLcd8n55dQyvSU5f6ksVKbIKpBVIKvAEqnAq6qLFiYrqO1XOBhouzo1whHOpXbpnJykFDySqYmNCy4Klw4+gpqzKdkLtIyWwiYUaC1PSyFRkd7lYeksBdx7ursHBwZ6+/qE3GS1i43ykd0ESxo25sNEBNBHlBRTMzE+IQvscmEgJrpikw6CDtVHrihBLgkCuCIKcYlV8BHU5BZHmwRoxMItlugRifh2aCSSzS9weC7oxBjcfPiee+7xs2bWrAJZBRZvBWbwVZmKvq+CBq2qDyxT6l9VWRcAWfJCLc06JHgp6L0AgT3i1AzqEeNYgmJK/ETOAF6dHo15lUWXv7KSJWKxFGUWXXIBSRdBLUVkcD8THEL08OoiEH9uaWnBWQBYmpQnN0ULRAy97lIcURKWSbfQiEVQLtxSkot3vywS63fBLI9Y0BIZI7BAEZfGaJPzaTC6chK6C8uAieTo0Nl5uECaTncpZhg9KBATLDSzzc1Odo8seDbcBAtTk2h4Yig/DKwO9BpXLGso5PG3o1KmJb+phq7+lnGdwh75SMs77raf0uCDwSc/+ck3vvGNu3fvfvLJJyVYps8qkFVg8VZgfDrXUJhavPnPc+Zp95GxFi++R5rJVjxRgSCZ2lPrICIB5KFVJsvPUgg4D7NAAQ/LYimS0FrOlsLD5srG0gUQcgguLzMyMrJs2bIoLjeWdJYiQmrPFoopmKj5mMWNcNEzgS2Fy2KBdAUbMZGISIiM0bMy6CMLTAoL7XKM17kQFMMyxivIlwTLhywQBKOgDvDkEJIFOkFNbiQo4KZlxwd6t86e3JGfnYRppqFj+aoNK5sOEywSwniB4peDjJzNZ5zC/vOf/3zv3r0DAwOmLRtnFcgqsCQqMDSRa5gdWxJTySZR0wrIRxfZEpiQQJBMLcVKwCJAJWaldzm5dEALapvfC7SMlsImFFJ1ebp0tSZ0xuzq6jpw4MCyZR1mdCc6AulGNmJihC0+W2qmYKILqWgYiInKZnnpCH3kcrEQlsJKIlRh5WYphFgqQCg6jkSxCShB7fWh9KwMuYXL3igVsDTmpq/vO7F72ebh8Zl8rq63vW5N0+HyJWXstx5Pissswfgdd/yhateuXe973/u+9a1vcZJMziqQVWDJVGByNpcv+O6zJs+ULScyKLElzTZv4iApHGozW28iZ6kUbKZMNDKVLQbQM3RxuHSgENQSuQBPppbIld7F5dIlSV7wFwJ60Tx5C2gpKk0yJaHLjRIvGpuamtCHNDY2FrUWWlewERM1OhrMCQzHRFco5cBATCRWXcdGTCQwCczIRJj1kQsv6CxHS6EcLbWloAAQBGMytRCYx5FDeS1hzBRISJvsVYrVkJve1Hakri0Jrze0eI47zi1705veVPxad13d0NDQ5z73ORVz1apVl156aeIT63nCmZxVIKtAVoHFWQF00UvvgcOX/s84/TSasQ6SjuER2v0sc3BL5Mt1kOMeMpxbGIugZghT5B6QowdXu3QMGpm1Z5e/AnBLicVSaET6gGNLFl3BR0rW3Y0RhzMTqSMdKSAIDwvCFMV3H/0rvjXx5dSTJ0/M6Zit+A2/MlK5zL2Vi2rtXe3wVkwage5SHlmgiE5F0c36qJxHmc7yElyKasGrrC776rryCCRBTHPBrJeM51AycoWStWDlpOOpOMKgjUyGGkPXg6NcdqXjKIvIMKqhwEVYwW6oCU6CAYiGZIcgP9w77h0dHX/5l3/Z19e3YsUKfNj98pe/fOTIEfzF6g/+4A9eeumlj370o//8z/987733JrpwpJxDZskqkFUgq4BVgbO002zlUUVF2iml9fOkrh8X9BF3ky0c5ZddHC6dYpEtUhTBI5laIpeTqgK/QIGYLotLZ6eto/SRPBmbhzShFC4cSCx1pIieKZAFnkPU5+tnil+fL7WxHF2SdRo2YqJyshTEFVmiZzK4HHWQPuKOzMJEIPQRubjUls5SJOOT0ZSGAHEFFqBxVGQXOANoFYXsTyHCgGFEYSgeOsyDoXCdRjzQWqPxLn4oFR7uxv3MmTP/8R//8e53v/snP/kJKPCAO5Rf/OIXzznnnG3btj366KNKKdBm6qwCWQVeJRWoQVO5sCs33xMWV+/EZdKZ9JFB5jUaWOdQIEimdjIrZTIiAe3hl6MIXILaGULGWhZL4SSEUgfqI3kyEptFqCtc9DpCH7nwuo6NmAgW9ejt68Wme29vb6RQzwzKRMvGnXScPgLOp7BsAq+O00eCC6l1tD4SQKSG4HJw6biPkl0ol05Gc07ZE6j0xrDglIgcSLaQMxeSwFNicZPgwcHB8TFcbabYcuMWSW3t7cuXL587Q4xngwpeddVVxcst1dXZ13HHt7lHR0dBgS90o4n/0pe+BBg+B+BWqdPTxXu3qgdOqslunhoVI3vOKrBYK/B7Ny3f1XJ1quyTLFWJAtSMOFEWNriyvOK84+x2PrYGSzeUuJIvI2Oi7QBNnN3pxJUCgaBOFdHF5dKlIqe5WIyWQoaSxRZcLKE6mw0ay9lSuEBOKgvIqJiofGMUllkkdyGlEAcPHlyzZk3RanlZCsWhQ10gXecbEaNOGjMiL536bKopNglWbpYCUJeOGLggAwWLoOacSg4DxqHi7BQ3GJjQw8WLS7/gfgWKCC037bVDWLlyZXv5kkpFyPj4uHvHXfnjGkxKQE+P7hwUYMRNmpQy+5lVIKtAVoGaVMC1tNUkUFrSJJvucZOJs6fIEZSK1X1uATFWI7TAIagRWrZQXoYgeCRTG5zOocVoKZSboA7jFCgq4nQ5u3SUITO6RUJCYIiYkc+ss3B+nxeztba1joyOYD9RYNLV+kiFs3RMwUQjN5aCLlojw1GmdFlCdRTE5RCXknJ2ebp06dnikpTzIE8ShMzIToIX6DUShRKSYOGREG4Ew5dIT59WXTu2xbds2YIz1Q8eOLB33z5siKPlPnbs2Kr6+tbWVsoN8epx6ovaL8fOvDq3xqBFy/5Hf/RH69evBwAnuBtWDHG9SLDb+kyTVSCrwCKqwNXrmwcbShtaaZKudPHSYlaVTGOueIDU1D8vE6FcM+FGl93LLBoZa1Gcmir+UbSxkd1WG2MGquRww2lYQoLaiMsc/CLR6bBkat3XMSI6CNGDdJECz6RjOGbWRBlLFoZXOqaQRMs5UkTPziSZkX2NkYllL32WJUf2pcY5Fw1kk8+ZbXA0K+aiIrMQUOijoiIK2NrScuL48c7OzhITqUuC5QWMDyGnpztaOJ00mpP5rFBMK7hxtQvNdCSST6QhhRIitfPZgpCzDhfUOmiuwoYyGlqhlIGYIXgfBAxDyXSKyMtCxiRYOCWBKyx+6g+cvXL06FG00C0tre++556Ltmzp7+/fvHkzGm/07go7NTkZve2LIeEiXlWGyLEtj+u4g+X+++8nZSZkFcgqkFWgVhVYYPsAtORaqy4VgENcKG4np4qFiNVoW4i3lEkEKi75FTxkGrLo7KROGJf8GB3pLC6yMHSI6PKzdKSAEPAguIV1WVw6wzGC6O3j3Ii1tCW3CKy6CQZi4lynAajgorfdLIQmgsZOKWpjVB5zEcoBo/SiZ4tDQZl5zpeYcrk8dgmnp2fs5h4Yy1FX6KOIs/wc2YvzYmQRgHSRwngmuxIiK6kjhXoW1DDq7gbaMloKPUxxRKEYlut0D7LoamMko8gCwXooo6W2FWHAYJQdwKUJo+Oe8KjCY3RkBI04GvdrrrkaF4PB7vn27dtxessll1yCXfXSC1iH81zQhyMYhfSdKkNJPfTQQ+CiM2dInwlZBbIKLJkK0KIQPKPkHsHUZwsYMKXqQBJNkIVkopOC7EqgoRMcp3R5u3TE4zUSyhBcTi6dcpMtBi0fupxcOvgIas7GZRnOLEzkvkqOjNEztEyU8TrO5RKvS4RwgSk93aiP9EyVi4UQ1Dpu+YoV+IoqrklNcd3F0r0YWBMjVPRMRktBFi7IKMtiKRSPoPYavT6Ungvl0sFBUBMVCV6gYBTUxMmFAGwARDFWH8gzhRwcIA47OjZWOqM9t2rlSvTYuPTLkaNH4XTx1q34ZuqhQ4dgxQOb7tg9pySCGnd8Gjh+/Dj5ZEJWgawCWQXw5fdk61eiktWSmycSsABXB8KDxsp6SH1kOHuNBjZ2KJAJakXnNUoRBSdBDRbZIoVwOcksssXJr8PZiImGY2SJnpVZHwkuUDMcE2W8zh7qwnBMjIsSn14omYAjdVNjozqhF7vvRla8QqaJjSOq6JmZQhhcbqAQ1LJFcBDUMg9PX8kWhaUQcDZVHFBgJqI4exw/EQULYRE5XfLjTJVjoLvGAwfTkydPYosdV4t5ftu2Sy6+GEmeOnWq1LTn8ROnu0NT/F96aI27umJMZAp9xldXs6vKhBYrw2UVWLgVoGVh4aZYeWZh624cKs6eIk+dUh8ZdF6jgY0dCmSCWtF5jZ6Igl8ytYdfyK5S/sg/ehbCUGIRLnqOw8POoEzUDcRvqXUXy0yODMdEGa8cfVDd5gpEOiGMwCCF7urqPn16qLu7uwjw+ioGncfl4NJxX28cl7NL5yXxGgU2nqEL4tKRj9cYhpIpZAsxkxCGDUMRabBQK97gBADEd0fRl+Ot/PgTT2zYsAGXcNyyeQv0OD0GG/Bo6QuFWUDy9eVPqjhOa407oPg4myRoEVuKmtQpw2cVyCqQVcBVgWqvpmF8cag4u2sm8TrGykSnX5zd6SQpZTLBIqglftLLfi6LS0dUfsHl6tKBRVAr/sgYPcfhmZ25MK2i5T9FnG4gF5da1+kj5ajrfCMK5HKELt5XRxh8FoMQRlZr9Ohs9u/f191TatztUEzD3JhIAJeOjCS4UC4dHAR1GotMRYmRoGP1EYGU4DWGYZNQGIz6sGpEyadGiaADrnIaRG0Iak/MFay9rW14aAh5DA8Pf/0b37j8sstwDd/DR440Nzffdddd3//+94eGhnB9SOyqc0qtceeGTM4qkFXgVVWB0t/iks64sqXPtZAlzcDAh1EGoAIgRuiQIWNlotMzzu508ihlvjQWTyCYXIwunWKRLf4orkCMi4k2T2SMnglhKVwWBmIiIbnA7G6Rg5XMcJbCspE7szARZn1EeIs6VEEMyYjhJjtYFkvB3NHZ4G41dIE8ykcJzJOJBHLpyGgxhFkETkGdMkhAKikDcmYvBQdacnpPi6qsCCZNdSAKZi8nVJHkSrK1rQ3X/pqamoRxZGT04UceoRCzM7O33Hzz9//zP1taW9DHkx5CfOMOh3Xr1uHSkrhmzZEjR7KzYnj5MjmrwJKpwMTEdN3ctWJrNqdqr5RhfAGoAEjqojBuJjrp4uxOJ49S5ktjOeuB9KTZiIlGkrrFN3I56nggLAX3YkYmer10nAW1zBSOWZiozJbC5UU6CC4Hl06ApqRSbiwOE72UPcuXoxWxG/fIP3omEktBFltwYV06eArqqlvikpTz8KZi0FaHxSB1Db2BDAdXq2tA1DAJKREEs5MHBDjhkSoeeZcoFAdONe/tXXHkCL6QWjzXXaUECWeyPPPsM9t37ICAq80oD/rpa9zhcP311+OKki+//DJu7HTZZZfdeeed//Vf/4XfGfLPhKwCWQWWRgXGxyZz3Vg6/EuS38oqEQxkPkFiHHGcHUECIEGpCCBGz0QBXPVkVEgrsKWgfGQLQSRBdhUsgtrgZygmAqSPDC/dzqBMtF2Yl47TR7YjszOR0XldlDGdY6gXJaA7CGoB5EqUGEiwnC0FQSFYRkvhAhFD8bTguhxO60XTEwFdDJGNHCUhsbPg4A2YxsdI2EXh0pGb10ioKghJAiVpl4N5k5Dy+c63nxG7NGxvX9bfXzhx4rjaFldduwLiSpGrVq82zpOByde4v/a1r+3t7f3KV76iTnzHdWq6urroOvA8gUzOKpBVYLFXYHxqIocP/XNH1eAVMxiYqD5hrGEoBA4GJkqSwIyeiWTmQpydYwNlFyV0+CcclVwOFcSCq8VoKQx+3e4byY4JvKIUdZdIa4TgQ+bARIWwFOSoW/RRLX0pAQihUV24uBwFelltWYSoglrlw38uX96Dm0329fVz5ZwcTAK8C+vSKerqWoTwc7NgT66wLh1zcU6M27lcKVecP49VQxmrHR7Jk1F+yV3JL7lrMVHtgVNacLXH06dPjY6OqusmoVnHTYI7u7rUp1MN7WncwXLxxRd/8YtfVJ9r16xZs2/fvtOlx7nnngvZIMqGWQWyCiz+CmDZ8658XmMl0w8jriaqetnGZRVnT5qJwCeoFbvX6E/ActUV+sigsoy6Qh/JvjpOHxleGEb26JkQloIsJESQ6NkykIIECwqLrtNH5MgFBmEiIVw6MpLgQoXqFIkLTfQQXHaXjnwso6UIC6yjcrjNJC6fV9pnYIRMpARsQUYJFkGtp2THgUbwFNROisTKapKHcQn7A4kzdzgkp1btdFjiRsAKOnFyTRW4lEdjY2Nvbx+22/GAovhXJfX+IW6VbilC+RIzfA44r33Tpk04Qwbf4Ma9yt773vd+4hOfQB8PzHnnnfd3f/d3+MnxmZxVIKvA0qyAWj3oZzUmSWRccBFzu5JdKOgMoICqUM2CMNF5bI6zp8iEUzJ3QU0IspNGFggbCbhKWfFfsb5zYllRrnlEGHlpDnOvTdldf7Ecvjp0buT2Ut5RXObo9AJOf0SO2vSiyeoRdV/mWPS1oJZOj0sj5slEMkMgNVdasoUiBQT2IDXTkaiMNNQFwTPAJ+LxMsAY8CihyiGxHzl85kzRT+B2UgqhyrSml+CgYLJRJjQDxIytEFVjjpsC7EbPGJNqbczIIVUaqZxoCipqcg7y4wKx+gScJ4MLtx86dBAXTdq7dy/OSMd1ZhwOJWL3qTI4h+wzn/kMzpP51Kc+hQumbt26FZ8G3vCGN+zYsQNnve/Zs+eGG24Atfpk4KDOVFkFsgosxgpYR4nKJxFMWX1ghcmzhJjoJI2zO538SplStihGZcfPghupa/URGCyFnSeDMJFHt30iDXNgYjLfxI6gZz5MTBY3mgMnI50eg6u5rMfWR4QT1AF2wVNQg1C2yMZkPgJaUNMcSSgBHeju7p79+/d3dHYQ0i84KOYcBIugVk4pjV43f/4la5x/nD0gxHxAVEOcJFl4JIRjHsnD2JNXHEqfJAPOxDmgd9DgDJljx47ijPaSW/ES67iI+5kzI4ODg6tXr0b7zekg1+NeTeqMeDTr6twaaOGPW63i8ctf/hJnxxw7dgztO052Hx8f3759O05z//a3v01dO7bkSTbYs2FWgawCi6UCm3sL090XROe4V5Q1Vib6JxMRRAlVAMoUQRaWUHHPNmYScfagkBaIWJmFdEqILIaahiX71NQUZoA7TUZqtiGtphawLR35ag7lyhhFUugoOzwzd2d0DVFE64/I3faNcXSGZj5MtHLmKUQJkE5WeHmUfwQhEgjsIagZYk7U3aAUPAW1YiGjzc8IdaPXh/KIfAS0oI68tOcSVjlo+uKgpB4fG8M5vWhdLLOm8FHYbzvlCh/hIbCRm+wJiNdoB7TglsLwibMbcDWUnWSLTZQEy70T+iWEVxCJu3rkChKaY8W1TY8cOUwXbETbvnz5ciix8M3OzuAakTjzhb/J0Z+7G3fwHT58GN9GVR05Gvenn34aXTv0N910E1p5fNKlmWSNO5UiE7IKLN4KpGvcsWzZ/1xFCEPBMxjoipJMx0IZTaiLh6GTHoBddGUdJ3bMn5u5HBFwXUkuzmV6ahoyrhBcJDQmpxwid/XMSIpozUcflV8gxsDc5+KBYW4uCaKXQ8+5B/g682dljLKM0inmJTw4pISyFMqR1AIPqSMgnwYZS0KE0LXaiCBKiGykjhT0rONIDUH28RoDCAGJHgJaUEde2nMJKzvAUnrggu44o8B/wYwIq/GXBoJFDqsoBDcYZUucp7IbPy06S2E4YBgASe6UkDQhPG3WFfjxEiTPlnt7ZBCH/JkA/TquBan22tGy48Ltp06fxq2XDhw4cN111+HbpNhPn5mZXrasnWIBLH5URcuOxzvf+c61a9fCoQjN59G145rujz76KFFkQlaBrAKvkgpgJbL/WXO3IUpjAaFwYl3ACnV6nHJ3yDKwIug+6Y6IipQxlUPPNcdYqk0dS0p5mgrLwVLMFRbhIwYSSwrTodgll3WRF+VdmgaNnAxFH4lBVUEjKUOVW+TrDs0IyiLLp8jGPLVIZUvZVZO8nhGSgyDLDwbkWTEHhijmJj8IyCCk0z25WrcoX7IzKhIFI6mDCAPQFFAWSixUNxdOxYks6ElUoxIptGcdSyZKlTRMgNH7iLPLzsk9jRMsojM/5BCwwMdy8zoUjXFOcXYjgIInSYM8uGCwuoYcniQe5zI41JAD0soqISc9V+IkmcnJCQQpHgjq6nDJ9uU9Pflcrqenp6+3F29vKEuYSZ6I+xx3hYDPfffdd9ttt916662Tk5O438ELL7zw9a9/Hf0/p8jkrAJZBZZeBYIPNNUHVl5MlhMT/bzBQCeN7q2P4GApak2CgDjrqdTRWqECkmEQJiomS2EFmFO4oyck4eQsMhM5ArJskSMLPoLaCKjHlH1kCyd0oVw6eTLEJrt5nRO7CQ6CmtJLJghsvSt6B06eXNHba7AJcEFtOAtDr7PXKBDGqtG11YTXFTguVrGDLD2CM0ruEYUoPif3Jg+iCU6VPJRgMxEgLSURGMLI6GhRU+ra0XJjux0tOxT4iim+nHrLzbc88IsHMJyYnGxswp9Piw9k52vcgYDnd7/7XXyuxacBbOmr9r/km/3IKpBVYElVACuS+uedVfC6FQz0hgs1RtGi5xC/JFjiY05MdJlJZwiWm6WAg0tn8FgowwdrdtAXFpgbEymYS0dGQyhhZQfZYvDQUPfQRwrk0pE7FyygpUhICDijYKI3KjcaskVhKchBtiiI1y4bZQtoLaOloPRcaG605RKXl9D2KWlwN/jjx48ZN5ZMQwS2lG4VeQrTktTo2QKyDEPZMahf9cYIQ3H25B5Ob6VEbsHzo8DE550ZoTyCTekEB8fBPnhxV71Q2Hj+xqGhYVxYZuPGjfjmxoYNG9B278WF10t/k52Z1rbLYxp3lRKdNe/MMFNmFcgqsBQrELz2qMknhFdYsSha9OynC0MRB4Mz0WUmnS1YnvEKm4Q0zJmJZIZgqDG0jjE6RB8pKpeOB7HlkofsJltsKisFr7PXaFEJlyScqwAAIABJREFUCkoigM1FIbvJFooJQUAlUwcQunIPduPASBYyhFm2RL7acwnu9fEawdXR2XnmzBl8gU/xynDZAk+vUcvYMcBvWpx/AMRB7GhSw4jodz8uLymo0nu9k8cgjwB2Z2JQKo60TIYfD+KdKweGyJ44unvxXMFi5By22N96113PPPssLhiDMc5wKZ7z8tOfFmZnc8Wtc80tqHHXPLJBVoGsAku2Alge9BXCM9NgoIcj3BRFi55tT9liY6HR4fpIObh0cVSCj6C22RiQiRwnqDmE5AgbPZOBBNlCEEOIPKJnw4yhbLGxpGFOTCSzEmQLB1ooS0Fo2UIQEhiWiWSGIKg5hGQX1qXzOoQZgaoic3oqyjaB4I2mePDlVHxFlRp3gTyOCJ1WHERgVuoA/wCIM4RqAll2lsLpRnlBYM4erG1SkZRe5iCUDLGpoSG/uABOb640mLgJclhafg6iDCMjeJzQ2tpy5gyu11547LHH21pbr7ziCvI4evQo7jKG/fiZmdniZQYowYJ+qgxusppicx13ZE3hRcllQlaBrAILvQJVXqxiphtFi5453KXjdltmHkwknEtHRi7oQH1EOEFNdi4wLBMJ4dKRkQsWsKjA/9KJMk4j9w6SSywWFbnKFoI4hcgvevaCnEau1Fn0EcdB9hplrNfNayROGeWyuHRERYKMki3KOZk9GZrS8wi+b0F43HQTTuWtz+fxNTw0MLqFj9D1xOXP4S65OhRgTpWIatuYq6Vw5TynU1gCMBbSxQoBHAYElEki2d4JCaQpOImd4IB0w8l4BJG4ra29vn6g1EIXfvHggzgf/dJLLm1oqMflIH/5y1+hZQdLc3MTTp7hdNqOO976Kb54qjb6OWkmZxXIKrBYKyCuMFWeUCmOK5hL54+te+gj8hTUZCdBB+ojAURqSYhYomcDJ6gNlDHUnfRREQpNdIyxjQaXNSx5xLnF2S3WokJ30kfkIKjJzgUdq49kHLdIcsQVPds42RKGTeJvMaZ3DvMMQ5XSSgAtT6MqXbuiw/f5cKualStXltlrI+E3Sp6r18jzCQZyJyVHv9KUhqWwfWxNKieDhjhI76qMgXJByN8pGAQ2JjmlzUGa2GhAUkCASSYGQRCJGxsbcJekgYEBOOLcmAcffBDXgOnu6jpy9ChOAEMEnCSDt7f68mpEXtAa90ibPWcVyCrwqqxA8Drkr06JxsXl0vmpYGVOTORugppDuMzgTBQQXO2RI6Lo2Qn1Gp0eUFpOloJ7ciNkPuQwS46A0bMFCKfirjqdPuI4yF4jYV0ol87rQEZbYFxMNHCyxQCqoQW3FIZbTe1x5IlzDiNMOEUDHjNsam7Gt/fQ96DNSdJO6bSquYqbDrVgLqDXyKMREEoXEcc6ZYuAK4JJDadgv9iUOIDNz47HgSo8MMzDsNvDWEruAuKE9NxbyTwgl21koKaru3tqenro9Gng8QY+fvz4iRMnIt/C8hW9be3tKhDVJWvco/pkz1kFXt0VaKxvHAtbMaPlI3o26iaoDZQxZE5M5CBBzSFcZnAmcgRk2WIA1VCH6yPbIc5ue5DGcrUUMnTOAg/vUaXEKNMqljg7ZWELzJWJBk62GEAMLaylIB/ZQhAu6HB9xHGQvcYwrEwhWwxmeRhHEWdXzDpKH8mxE1nw3qwub0dnB24Nic1LOY2wmEaL5KWDUZhFWCxFriLKXHIKcxYiUONSSqQTEpRIyQ+AhK4hlDZGD6LC8yQMDx1uGGOHsfQSQ2VhJValR7Pe19fX2tIyeOrU5MRE8cNn6SNoS0trz/IefEuV3Cl9X+N+xx132Ge9t7e3P/PMMzt37iSuTMgqkFVgCVSgra1liE2jtFIJy5WgZt4OUXfSRxwuWzhKyRbWUgDn0tlUTg1zZWIc1Gn3Ky12S2H4x9kBtyGRJno2OJ0+NkbQMFImOsFxdu5kYS2FF82NTjmii569IKfRqXTRuXROZ1lZEUWYs47SR3JiZ93S0dGBG7qrxl31N67UYXGp7exlCgMrA5UF8LCIAJJHEicjH4uGkypsqnx4mGAC7iTJdn4GUo8WCydv3Y/U6YTwsIH8yI44S/Kyjo72ZcumpqbU346am5vx9VHpveNr3NG14wZMExPFuzrhsWnTpmuuuQYtu3GavLJmP7MKZBVY1BVoasZqkC+vdmUpwbSYExNtAq8xDi47yxab06lhBEyMgzrtHqVFbSls5wCI7QRNyS/OOc7uZCYl82YimUnwGgmlBAtrKbiD18iBJDMPJpKZBK+RUFywPCwFRys5AAJgHCrObsd1adBOVIfIRc51hbpC/GnuSbLB3uTE+ERzS7OKIrgKap4ZycDiEVAOBRSwZBTsxRjWI5WTxVJuDpkJZecPjILnqfx4bpwJsk5tGNMNPdEMQj14uJ/KG3idwGCv7pBnp+RC3dTk1MjIyPT0FL6iig6+ra2tsbHREda4qowDEamwe3/DDTdcd9112G4vfg7IHlkFsgosrQrU1+cDFy59edNHVBNBTXZb0D30EUfLFo6KlRkNE/1uwUCbRnfVRzYamgCIxw/e4p1TlVtafj21AJYACE3EwloKgup5cLVHZnRMlBwCINw1IbzkGuzjBXqNKsUACJ/L/MhBvVIQqJgvbhd/6ODBNWvXUvLUHemzF9TkZghJ4IQFhx5UkZLdZTTi0pCcSJPEm5xI0PloVBQYsRKhZDqikAQikwCkT8JKTn4hPDjnQSKlz5DQFW+FVNW8StxzwUhWEVSyc9Gmp6dxXju69rnvaRQKZ3Jn0HXj70h4V+O6STxhyL4d9+3bt8NTXWIJ2+04Zf5P/uRPcHH4rHE3ipgNswosiQrMLSJqLmzARGOessUA8iFzYiJHkBxnJ2CgEPFFzx63AIjHGyaLwFIY/nF2A24M4zcvXTkZJLFDliMTbTevMQAe5x9nt0NAw5yYaEO9RhuuNC4nl477x9k5dt5kPamATiYAIiePVsm4t4wLqzU5LkBJh3YFz7iynt3oCARKDSd90lIEgod5eOHcGEbHs6rMmzMZMiMmcU5w1cilMxilIdFLgAq4JUqnXiVC6ZDgBCdUcrI5OVf6eBDxFJXo2g8fOoSLOkIuvYcLuBxkfX0DNENDp2dmpvtXrtTvwCRfVQZ/dfrDP/xDXIZGBfjSl770ox/9KAqWPWcVyCqwpCrQmC8U8s1a85d83bQ8LIVUs2CgREB6xsREMktCEqzNYXlbCtsHmjCU7BrnD3scxEluKCOO6Nkw0zDOTkASdA99RCAuBEA4nOTIL3omgyHE2Q24GrqcXDrDOQBCHkmw5JRSQCuROFwaH0qvuM1Z7FhIIQklXAkrIer6+/tPHD+ORseJkAnIovzic9HOQUkId8UwEuDpp2JPSMDhkuxK0aFDukrL8laipZYiKb2D2+8QZ+VZAMvyi/Ospt2YFrbYTxw/Ueracy0tzbgge++KFbfffjs68B07XvzVw7/CNvypwcGe5cspCTCIO+5o/PE7gLuwKnR2XjtVLROyCiy9CnQ2FWbybeFrmbXoWQpnjcJQTlePMmKNnj1QZQoGSkwWgaVweoahZNcw/zCUM4pSMgImhjl4UGSySC0FQUkIgBCWBObERDJzIc7OsVx2+bl03CehHEcXZ1fhcLQPA1rZhXmGoSxyUhS33jEIyhGxBGhjU9NEaeeyhBB/KAJldkUku8tosxKcTAF+thO8XX420IWi0LZgExAmIRP5eQSKRkLUySsnpi4pXCm4dJ6QgSYjsjEMJImFqdwNcpoQ15eU+FbG6OgI+u2NGzfecvPN6k5KDQ0NuBnwVVdduWfvnv37DwwNDXV2dfFTXcxTZ2KzygBZBbIKLL0KtDYWpuvd9x3E8qL/w96YrnAeblCjMFSiYjLKYhrqXxRJYGI+cykJQI9a59BH0vRBZwA9AQSTPkcBZASSUbEWlS/LW/bgU5NRhiXiV2qiMFD6UPfRbeKIOTFRggOS6uHyc+ls8jCU7efS8F7AZSddemCYJ1BhQMpIF4r+ONs4lEbhrIhdXV2nT53SmX0jonHF9Ro9rIafi9rp7fSDUn/YKGW3gLqbY2QzcQ0c1NDhWTUVDxjJ6m2Q4Cd750Uclb0X00/PWTFKivOWlCMjZ7Dpjsfll132/PPP48pIOM8Ft146WbolE3bMcZCDdXx8fO61KHmJO+6cHzI+EOCbqfiKK643mV0L0ihONswqsNgrgC+m1uXqWTvBxMC5JffwE+t8+kjyDENJ3lxvMVkKjiY5DEVwp1DiCCAKgDj5bWXEFD3bCNIEQAjLBebHRI4w5DCU4aSGkWv07ARVrBTYcVwVLBVHnE8CTAMPNpPgeVmeydMuUqgNeD0HgUlFVMZcXWdn58EDB3FTGwEdo+ZkgLIKYGQYLbufuwJv21XFhr6UIdlJoFRgJ6U+G4JIAvmRICGVPiG9nyyRtZSgnaWt+f/Ze7MgWbKzTDBjzYzc95t599pLtUpVIAkhCSEk0ULAAGMYTNsMNgNj04zBGEu/wRvv8IYZD8zDGDw00IM11g9gMAw03a0ZkJCGRaJKtdfd7819z8hY5jt+3H8/q/vxLSIyr0fdyvjPv3z/f373OP7HiePHE6G6KvMUe/NZzCP+9zKhp4PHI/Hb7TPmplL54MNbr7z8EnaBRGt9fX1tbe3Bw4cffPAhW/VeqXTOOkwteFkLd6y5+Z3f+R2OAuVvfetbeLoBgxgbOzo6CszL9zIDZQYuQgYWJ8ZOKzPqdUrvmTTm6OJMnAA7eHcES6huQ5Vh5JbNhvOT6FqQvIHVIlPZru6gF3XdCmCCd9WN1nZWVCxlO7mlqFLTTYvUMxE5+8oZDl0L6oBMvfSNE2LJhYbcig6H60InfT58CO/NR4kH87SbzQa2sR5vjmfwHiZM76gQRrZ+krXiQ3CgSMQmt7ZhcE1M1womIi2wQ9LNcagvU7HwsrrUyuZZghp4g/cbPxaRZ48Km8T3CBO/P/b1r3/99u1bTz31FJ7EBLV79+69+Z23sHIGVTc2afZ+iSJD+82puNH1r/7qr2R/agsbzmBWHwW9KijbZQbKDJyrDMxO9E/G2HghvQobTAPg4F3yGtlIbmGEM8GYeEZjMJPo2jGcUZwVbb4UvofnBuqmpeDzpmwqt4wGYLppxVk7ozgr2jwOho8rdmSk/IoeqUKBxmGRIhGyRRJfgODqINyiI6cy4aN4byFQSMnaCwuLDx8+uHz5Sug9awCSA+oTceX6mNicsIWpqAlN3YEgTJTJaCRCRYjQjOwF6YpE8q6J1iLtGKhoEk3zLkGHx8jx84s32rm7FFU0bj/ln5Pr16+//NJL2L4d5gcHh4sLCy+99OJf/6e/6XZ7jbo0yZ5+jTuWz//AD/wAbTvjHmipWWagzMCoZWCyWelUvee0YWijf/lFGUB6P/u5OwjM3C0iQhbBhE4S225KKkTYdaMltGbd+101Upd8gcjjFeCxQxBczSJx3bR0iMARl8gtXZs4ad2JAB4NILeXs6IbHLScEZOXKQ4WDiq8J1Dk/5w7RhaBD2Ikh3C2sCiS63BBfBCVb4HiBMcC+0JKCKFd0H3FTNJO1tCxBXtdSBxoJQ+CrI0Ed5wQlasb8SKZkUL/JNN1KESICnsRNvcv+tQjGh5namrKvyhWxlDB85tTEexEa+LLX/5XmIAHjUesTEy0xHRKVXzSFP7N3/wNVtSI97omRSj1ywyUGRiFDDRqjf5Y+q/xvAte2eJcu1C3k1uQqY2QIeWWzUbkJ7cQrQU6yRoYmOXmNwzBg3TGdVYMHQiUbC23BDUDmUTXYB5mLjOQEd3ExLU+kzPY45UEwsGCqzjjkrqzBRRFI7SEHggk9Gwv2Z5pudlZ8Hw4AZXBzc/Pb21vLS0tx2ALZhI+zCCKMZYslIYNWFZjWm7z3M6hkGMiZJdOreTenGB9JTEykfbFPPcEKMYCbbFJOhmIvPEShtIcb05NTR8c7KNnWJGOwv3G9ev4SvrB+x987Wtfe/6553Bn6uzsnPJoAqlwx6S9+iU1MggspwEcCvdEVpGQpbDMQJmBIWRgujW+W0n2RORgwAveE0WdyijCg4anMSKMuSi5hR1y+PU6rm+VPp9ct4cpSrJ1X7aWW6IXI51Q3YjhMXMDsruIlvAqJEkYySsRqnQi3aTFjQQ1dl7os1toOgrZQZQ8AA2PwbUmW5ubG2NLKIH4S8KVGpo9Y3CzwFhVibdXLSLaNieyia8V5zlOLoNaW24xGczhn2zTxkIAHD+6aYghGUuBT2ZM2tRXgqNMRIsgXVpeOjtr464MnKxveC8U60DG6va//8Y3UNbPzc3zZ7sG4PIad0yf00Q9BRRL8DtWY9VKhTIDZQZGNgMTE41t+4x7MPQE74m6kcoo1oOGqjGSQ8RaRCjEL31RjJPHqwAYmx5qcujkFqJ3wVogRY0IOrmFDpYHho4az+HX0azegZIKIs57cJVPhJ7KCJkiOy9r4jQ8GEm6JwMxtCTWnnf/z9T0NNYeYCmC15ZwhUaIHlIiipEW7FV5AhTV1KUd4dkzZ3IeAii3WNy0XIKDjhifSDuau6tRJ2GSaw/cQxA19b4ShwjSJw4nMPe9fvny5sbm/sE+SnaqqDEtPjM7y5ajex0UraQZd8LVCWwnyTaSLF9lBsoMXLgMTNb7teYsdSsYBoN3EjgSae2i4TVUjRFtT9K0dgQQEAwoGVgy7cCP27uHHToARQN9FEBoEaUVIRMABDLCQBQltxCtR4RGnuV+8MTLvOhY6VAlMeKQDs4cVPTwKCSIEBWaSWITrfmZKFvLLd27yJGxQkkcBlbL3L17NyjcQzuZCtFDytTVOG8BqoASsOR3VyDZKkmLQiAi0lrVShJgEt3IIBILxahFOjHQSBhUK1XstI4aHQ9jOmM7P/bHxyfw8FR/IbrWwZjCHVuift/3fR82pvnhH/7hP/uzP8N8/De/+U2skBmJvpZBlBkoM5BHBibq/X6jlXjOGK6LHLdlbLnl3uu0dnYPCZfBcKD8w5ACHNaxE7olkFJog2jgupbGfUozQ4/4hVWOIRW6CcjgT2NxO86WwyBVUQVMixapiwQ3FQGSWHMk0Zq+VHowybFMiKxD8CGAefvoVc7OzvD8GbE3DrQcrGcgswQ3nlRzbnciA9n1Aly8y/2KMcksThKgVZcnhGJR80WCBATlmDvNAzKB90GookxvNse9xeco3MeVde1iBFGFO/am+dSnPvXnf/7nP/qjP3rp0qXt7W18En7yJ3/yj//4j7EcR0Qp6TIDZQbObwaWWv2zGjZxj3sVP1QGHoL3uIgM8gymBjSfxUDTAKexsUehSdLU6yl7ovmWEpKqn6mMDHFkYeVaEmlgVNYk7GpaO54JN2s3LVtuyZorJOwfoXowIZYMI7fIxkZwmBCM6S0vr2xtbqF68Y0SQtpc0RcPUjA5NzszcwlIIag7RCgKejOZA90+H44Sr9JM5YNjEBIRVjBkwqjEM8RFXGfoOcMKmb29/d3dHXzPRJnNF8ygiF9YmJ/EtjNaF62FO9bGfOYzn/mjP/ojLHz/P7wXbAGHRWOf//zn//RP/1SDKhllBsoMnMsMzLfGTqps71j/pY8TgSSvd8GDQKZAz2Ztd+jjpoFPY2MPxCJJWbIDLafwcoKxdC85G5dhb3eO5HHRBTy5U91CLBSEUIgt8HRjnUN2JEoCoFsDRgMwapkUKQadsGFkgpFAedhgeYTc0uMJOJhr73TOwnXDEmSgpL9rOdJVHDhmZ2auBBfjPkbs4EDylq5BQcAd0emgCrOyZULkc1rkFBaOFRibu2w8enRwcAANqtoRUrt9+uDBA6yfmccyd+EFkbVwf+mll7AqBog/8zM/8+Uvf5lb/cZv/Ma7776LOXxsEV8+P1XIZEmWGTjHGVicrJ1WJgodf+WxXW4lzVw2a7s3HzclfEozezhmSaqFOhwqvwhlJLllDtvExfUnrakJjs+u4bbIVKD80p3K1BgMmBwShIBq4tnsjXyeNP7XqBDHpAi4ohCbYumsqNgpTQUmzqtiTU2C8Qi5RUphqsNuYUeOnZ2dZA+cIXgB2UCGTgzCDKwY93ZxTEBcTOYx2tEdIBSoiXS0Ve5S6gPFQJzcfRUGiPMTVTtKdnhYWV5+5plnUL9/+OGHt2/fBgdLXeqNxvT0tOjfunMzJtp//ud//tq1azMzM6vBC99ff+InfgJLaJIvGhOdlnSZgTIDI5SBucnxbiXpMtCo+DECCf/4poQCI8rUIstmbQEF28dFtUce7MomSUozE1QMj3nC/ylf6S2jHWbDpctttJMkUvHB40nsPF3EU0hIDFUG5gyZ5xguNyIAIhzNZTWyJkKWU4vkRJAoOUEYOpEcTLQI8LCdnv9vanoKywS8s4IJA0I0SksHzvyjG91M6ySJXXQEfkK8M9FPBWXJQjj1TPfKYwa/8Bf5Jk/EOSdE+7S9u7vLv3Jincvzzz//d1/7+t/+7d+ura0vLi6iWyjod7Z3+r3gzPX6ZZ5xhyrm1HFPK/8SQDkBgYXzuE0bk+4is6TLDJQZOKcZqFf6rcnJg4SbuCudFco3gVSUEjVzgjH59KEzechkbAoqhpehZAdy3tHi2pE3ZEz/E4m9S1yGjKF79Mq1nwQsoBIPLgU2BeBOiEjcKhWeM4yumMErdbMA1PHxJu7Ko6LF82Bzw+IQ0iaQFGE6IsphJCIPAeb5xRLpTxSmDJrMiBBBHWjqKgegpoPpoFQQU7reKYZYutLrskf84nvV+tr62++8g2XuaH7jG9+o12pg4gXO8clxq+UvZwXPXLhjTRi2jvnN3/zNW7du7e3tYU8lZs3W3LT//b//91/84hf5chzOLP+WGSgzcH4zMFEfa7Yc7kwVeqiNoxpDUE5A5gRj8eijZ3WS1d4SnZXN/A3apzUYXYCLSLbo+NUvG4Yelncd5OwMFTwAigmPo4phewlQ2JmTkhOeAqOFLTIoZQoTTXQHSGk7FREEubJgL6+s3Prw1rXr10gxmhBcCaTFRvApkBblNGwKgYg0KIENjxFQxQQbuMn4rnRVaWYEz8c8dUyKIdtmnS3vY8djbe1Sp9udm5sDjUlzFOFvv/0OqvFqtdJBNd8iU/kBTGKH8PBVrIp55513fu/3fu/3f//3gQJ7vNbX17HwvVzgLuaqpMsMnN8MLEz0Ow02UkS85EFebkWYxYryQ4p0xdxkdZXVPjJAqzCPyK3goyTAJamoDPNJq2yZpCsmz1kBoYoeAniRl4djHS8zqg0SwEEvuA/85bpGC02XjBIRRmwPgW2x1+2F++vl5JB65Tmxu9d6Qf77Xp6oqSnmzaAYicjbg4rH+8bdET24DqvhjF6bLVPyfiQcazSb//zNb+IsRb2Nvddfefnl99//oNvt8PJbjNy6xv34+PirX/3qj//4j6P8/7mf+7nf+Z3fefHFF59++mlsKfOXf/mXIkRJlxkoM3B+M7DQ6p/WpBtfeF8wtHr/MGEZkD6Rra8iWDakSGvfDS1ej1SOFHKkSJUChMxrPp6BUsALF+Lg0h+8Z/QiIGZEMpkzdHaFpKhNSq48Hir9dTVz1SNgkYCxF7zI47QrrFWvAEgvWpZr4z8tEqOWyOQW4KR8rawsb2w8Co1FbEc6NM6BIp88Q/zMTPuXn9M+pICcQ5w5QPCAOBDRFOVjT+ArJXKD0hwvLInBencsbb929SqWurTZmpk+JsrxqlZr4mfJvFSGJ/m9997D7a6f+9znsNoGJTsexoTXH/zBH5QPYMrhbC4hygyMRgbWZhrHFb5TbAElXgGQprRJbqSGSduVlxuQq8NAb2iOgwBc33HZxSvlPoxGJxzRAzXKszPl2j3bQhoeDcVMwRVwALkT3RUu7SZvJh6FF0uY3JiNUvlxhw+c+hbemRbwnN79+BqNJhb6OlnYlJIHbUPy+alSZ8JkkQnR+aTAMRkVyEPHmHP/zXdEvaXTtUIRkoyC4iKdzxUgtYkI4bwQk5NT2MGd94h3anNzi5a0eMtc+qjpcYeG2Ouowh09x040f/InfwLizTffxK3Z5yUXZZxlBsoMOGZgfrp2XGnmORIObkyVPEkNx87b1PLEsvmI4ecWgjjex/hMKYaHoHzMLeqwEskP0tQ9vpCGJHnU8SwZBJjnB0sAlUjRWyBgPEvmLOzAMtm7yTdD4E64ND+HNm8Us+jWiyK0wLQjHnDDFxCTvonIL1wTesgLQwt5mahBBR4XpN8xuX9hK6CCd/njIoKHGiLXoyNEmu5IMyZaE03v5mn0aKLV+vh3f5z3D5PuOGNZ4T42ht0j6g3U6v5ghamHmMKdelxW7ZSKkigzcGEy0KyNzc4uHGbbUsZWIBSWJfUCpbazOM4TK10cBUSAsb8AVKV7nhP8wStXZxySO8sVmEPKf8U6voAiXnRWeF+krw+CZz+hmn+NIdgkJsWjJtI6ELmFGtG6mhtHcSU2p2dm7t27N+vd+RcBlnxSn4NlDj0iJkeR2FtHk0LVKCU8MDSJKNTvuQLHbPry0jLOTJTof/mX/zfWt+NjABqjDwYjLJLB3ut8X0ixW2HhjrXwzSbbNckr8UWdKJqvo4/SKGVlBsoMjGoGphr96kTMnanW2GlctmrkK1D9qe0s3vLEyicOXOPyDCpnOHMf+XUZ12cv8jzD9/0FDlizAHi5V2IRzyWhy5CSbRK0xL6IZjlAi3BWWvMvMYQoOAmpwLOiJheIbkXaiEQhJA8HFRL21xP3hTR6iA3BaMVrUorOopOInStYIs95KSuppCYReTk65zhYBoNNXx4+eoitYzodtt6dOgQRnqJU4wvciduXt4PktTtJXQhU+bBy0Sx1ygyUGRi1DFyd7R3Xpccpx0QYDikxivmJDS4NrPz8DRUJ17TCOsevl4XBi3nzXLE/nrdiXPLuBD5E74XR5JJPHYb9CqnsvkMnAlaeDgRYOylEQSQRvpkpKBPP7iWxRAxBpCOAKKKfh4s0AAAgAElEQVT+2OLS0sbGxtraWoR6FpFjRG4urGDUITccd63CgN1DeFw1UaBfuXLl8ODw6OgQN5FizgCcySn2nzEl4Yw7F2Nm3qhXMssMlBm4eBm4Mlc/rMTNuA9nPDd7NXOzHJj8EbNEA1tcsP2Y+KU7NsB+v9fpdDF0Yw6l1/cHcDyQDzM3+BEVuxaEu+BxePyNBc3YicDc64Jfgng+C3DswwcuqXfgF+BNcBM6DikmZl5z9iw7EGIIyJz9BbCR76agTDxzNgYVsRBRvVbHEyilfSEjO2gQDipqg+uAJXQoYOXz7grMcyBqG7PCFYwiY7xMv9/HPcSYDsYzRI06F5iJPSBnZrCeS9nhTUwz9V5e447xXRriSc1OIMW4YCRaXWMHKyVlBsoMDC4D+EVueXZiu6o9Bdl9rC0kWIN7AysX10XhZgwOg3UYmeX618fPqvjdv9vrQrVaq6M8x59GtcktMWHT7XRP2+1up4MhGuU7OI1mo1rxtgCmy0HoJ2PM8eaeT3LM9PljR+Itk2qQEyI8ZwwGnMK7zLyKnplf/6X6VtuBXuJ3iz8zTm5ezfA61xSdiadbyhxT4CaebCW25ufnsOXG0tKSyExAp4k6AbxBlfdP8Zus0wbULCwlFkDpHMKPEJEOJzY3N/HNCl+sMEZVvFq0XldnlhWTx7YZ5gXTM6i/aRsax4zACt+NytUyjukq1coMjE4G6pX+3Pz8Ni9khnol8HJijsDMHZ0kDioSXP94KnBhw0M2ztrtaq3WRCVeaXRQoXe7Z+1TNu/OLnt4sSIe0majXm9N1Gp1KJycnh7u7OCSODk5iZl4P3DxujrwXMs7MxZWx/OuUk+J4Hzea8pvsQdU8a1UPOEBAFVgRGoUkX0Og4pUG4jQFLiJZw+m1Wptbm3hxAu+NHLzUeqkEryxf0amYjiAJk+bMRgxo4qCKOJB9se2d7Yx9cB+IazX8GshFo1gWFtZWfE7AQTdagAddHfB+xgdZGwe4E7R4QGIsFAQ17jjG06KjU5R6+Pl3rtSs8xAmYERycD1ud5RY3XYA6I4JqmJiZKpusnbxaInj0ey4IOqFCJ+2Tw+OsIojcvb+PjE6enJ1v7mwcHB7s4OBmFU6+DSHBU4vLgHZ2FhAT/BTrYmp6enUOTvHxxg5+CJ1jgWUMIqdEsDucALpcVTYh0f1FWD8Or7oO6Tz0HnIYyAU2HbD0kNSG1T5DkSWhAx2IOIKSaECDF6gy+umKDEX0EtaSfZqMlteHdBewS1gM1pwcnFIyPSlkSEVd0T4xNY64evVZiVmJ6exvDVbp+eHJ+Ea2YiAEcnsYmCdFfWNMMZ9+i+Y/T/yle+gmem4joRrVlKywyUGTgXGbg2Vz2oLQ411KhrW5Qsl6AxGhbuI2OgYYgnJyeo2jGFjtfBwT5+7t/c2MAGwPPzC08/8+zU9BR+ZWZT7UEhjg18sYzRq+9P8TS+O7duoe7HJtYLi4tzs7OYptnb38eNelNTU3IF4wVM14nh5Ucs4oUkBt0TWAWSlAfFx9DSogaktlmc5uDMXKVfuTRNMcUADy44Fsj8/Pyd27cNp31MlJKYeikT1IKySEu2WRpIFeHytPW9RywHmFw42IQGvlO/4/dADFxTU9OPHm1sHnRWZo+wlml/n5WaYeGeGv0iGjoV7qjaf+RHfuQXfuEXrl69+tu//dsXMQ9ln8oMPHYZuLbY3PSfmTr4vkddWqJkg490yB7ZTUQH+/uIAmX6wf7Bo4cPdvd2l5aWX3r5lZnZ2bBUl+P0KtwqK/MbDcxgYcsCTGttbm7c+vBDVO2rq5cWFuZnZ2d2d3YfPHiAUgYr4GUAr0U1ApcFlYKlODQA5M2y/bw7GgU99XaYZ7ByzPyYNG5UiFEy6mOOhBZcemyH0PGRQUmDjwP+pnc0JEsxVQGtfygCyZCCTOp2b38P62Qq9cnW3Gpt//2Z2UUMemzzcrapvtIXHGCFQ97EYx+rE6sAWJsOROQrQofUonUoflcCOaldvnwZOYIFRnN879HXveBn2V/5lV/57Gc/+xd/8Rd/+Id/qC+Cx9mvW7mGUOqVGSgzMIwMVCv9z764utO4MnDnGPJo1DM7jxGbjdJyB+oscZC4/XR/bx/FN1bI3L2D1y08QeYjL7ywtn55fGKC5tcJl3rTPjsDM1zL7o3wqOBR8bdaE5hrRxGPMX9+bh6raLa3dzCjj9pdByRkK0EurRrDErBqnl8zRzHGUYwp+kg5ReykFO2neGmrNbnx6NH09IzZFfqg1FrnolfmzpwDLn79qNQamzvHC9PVmWl2B86jhw93dnfAx13156ADgw0RhXrMN05k8Jd+6Ze+9KUvvfPOO6jacdvvYCMsvZUZKDNQSAZuzvUO6pcKgbaCjuTVb3QruzEsd0G9jhtQt7a2ULVjNH71Y6+h+PYSjLgN+VTqDV0JpTl+kn7yyemDw4OHDx5sbW5i7mZ9fQ2+Hj16NIOl8NLaX+uxDAWKS0NQoe5gqXAmUonRC4MFOsxgTTFZ8zPMQCkop4hNSogebNc+uOpRXMkJlIO4k9tqp/dB51iNixHwpIhhDCBNxXRFQcXtOafd6ubWdqvZx68gmBrGbMXU9PS892uholw2eQaiZtx51f6DP/iD77777q/92q/ZqvZyxr08mcoMnLsMvLbeq1x6tVNpDCryBNeZBKo5Rj8cr+YO4DfMvb09bDiNX0Xv3b1z797da9euPf3ssw4rPv1unLXPcJXHGK474BoQYX086nhM4+N5fbNY+T47u7Ozc3LKbghLM/Wue7JxRinVthg9vhqo2o40Hjnh+Y4+Np1K93D6KxwJAbU7vqxOtFrgKnq8PFaYknHZyC8DO8fNmWZndrqFYWdvd4+tA8FPgdXqxAQ7NOVLzwBm3K2FO77t/PIv/zKqdsy1//qv/7qtagdoWbjrmS05ZQZGPAOffaq1N/v8oIJMcBFMoFpQ9MOOgFXtu7vYyRjF9+1bH+5u77z86qurly7hYube4zNtqYzBlm3m25pfWMDdq1g50JqcXFpcglOM9tjeIZE7A3hS1rDTnjReQd8QuoElGJwb8oJ0w5Bv3OeNH7JmZi2rZVQLnghxxhsaeWancAdqj4bf3jvF/fRj080ODwWTBajdG/UGFjI1GoYZh+FHPBoRWAt3ZBAl+wsvvICFj6jasSASAdfHG8997mPPfe61xnhz594mnnHFe1EW7qNxNMsoygy4ZmC22X/9+Su7jaIe/S3HkfjylthA9pdPa0hBoGrf2d5G0Xx0fPz+e++iLx977XXs/ZK0U17h3kd1ohmqHYMvzLVjpgb3reLbwuLSIha7379/f6I1ga3iVW0NbnCMEQolS6cN3TCwsngYZVt0dWQmtLGhKk54nPmjnDC32PQziL5j6CI3yCK1UDxunTTnJ85qVYqT+cPohxeGnSKdn3tsFO7mKZybN2/+4i/+4ne+851f/dVf5VX77KXFL/7yT+3c2fi7f/d/IbPf92/+m1ojZn38uU9P2YEyAxc0A9jB/aA5sAXu0tDsklEYJLZxwU2kw4Ogv4ls0ypjaEXVjuclYaHn++++Mz7efPWjH8Pu7GnxjHbUJU5AB8QYbtR78qmnsMXkhx9+iHL/ytWrDx88RBiKNtkYoYtlGkPRmcUGkR1djxhljO0fOzbGf9njGA4COsNfxl7lwgw8xL4vLi1hW9VYtfOgoCeOotZFQ+bsnDSOO7XF1ql+auOD4MU95Aj1wEaMM2ZeKoNblDY6+//nv/ujo4ND5HH55tp3/9QX/sv//h+3bj/sdbo7dx5hSn5ufRkEpOWMO31ESqLMwLnIwGefqB0vvtyrDGxiI/2sT3rLgo5EkQFhyQqbaz88/OD99/B78cuvfjT1pgpOS2XkFME1ls3s7+2hmpmbncNjmzBrg90ozTtFyrZiq8gMiX4y0+cm0HQ9Rfd4GaT2k7fNsnSuzqcVyhgsc2erwirmGcwE3TLmFClWc58A8uKpdvuVreNxTLQ3a2wnQ/3Vx8MmxvoYiHRRyaEMYMa98vrrr/MhHlcI7GAAFhevPnP1+c+9drRzgCfQ4uekb/6H/3R6eEKWICrYTw63Tnk75PMNJUVpSZcZKDMwmhmoVsb+508v3pn/3sGGl+kKlsl4AP3MIz52N2qvd3pyghUyWHr+0iuvpK7a0eOjQ0yW91OssYEt1rvjQXtXr13D4wzv3buHNTOYytGymL7P6S21IAbEOH8R55IY3m3X8hParqq5RJcHCGqe3d2dlZXVPMAeYww6U8Qc8BMi4Oy3G/VqDyV7rcLqRukrjccAr9tj9Scb9wKOpBnB5AEwbe/FNUVmhG1g5IeUTpNAiidw0lqXuzx86/ajd+6OT03gEdlnx/hRQ32hasdX1eXlZWwipsrKdpmBMgOjmoGrs73e5GBWt4sp0IdSURpDi2MpVxXH5BjjAYj1+BJGiUUpnbMOHvp9+/YtzHC/8NJLWar2jD1eXlnB+pwP3n//+vUb6+vrqN1xHdVu47P1WXFuOFCOlgoQmsCCrQFRV82XkzpiYxjUjeF0xhiTkUndJsKo5jO5kpNqFAxkaY4wTyoBcwgEE42FvZVQBmGJGmbfybYkEmfAeNQDJt6PzzAB3G/VhS04A6noC1Ulm25XREqTGyhMpWnUIU9GZUh1vs6JRiYXBRPmpTK+036/0z7D2hhjDLi6YH/34+Nj3JqN896oUzLLDJQZGLUMfM+1sd7qy53KcG/bz/MymSfWYI6WHDF+8zw8OMBvmHgSe+fsDCtkjNs4JgotxVIZER/DO2b9sRNla7K1uLC4vbODvSmTrpkRASNpng5cROS8RNpECIHCL0j5wEV4Gq7ogndvUMmtVFC7e3eSiGUMT+6AziPRGbpNB3ZA7ovM9EmnVvem2CfEqt3ike2Xghviyy9RlvxwNstS7JNTbQgo1t97772HDx8i0WXhbstSyS8zMFIZwC4h3/+Rue3JZ4SrwxADpCtUbjHkj5hbaGYgDJ5YU46nLOFxSNubG69+9DW2WXvmbmQs3BErdttAJPfu3uW1OyZosJKnsNrdnJw4buY0mRwUAmpyNKK8x6z/+JKMtWHYWGlED0easHAI6UuIeDhtTFEH/qCmcNIEcXJ8fHLa7lUnmvWesnuMDa6DkrRuXQZis3rc+CjcpRzhDE66Wh13pqJwT2r1uCW67G+ZgRHJwJWZ3tj0lX4e43IePaILCQfL4WqhIFKQOUATVq4Eltiiat/d2cH09suvvOo/uNTWDTFPMZfXaAinPmAx5Nr6On4HuHbt+vrl9Vsf3qrX6pNTk07Gg1BK1EfXUyARaEQvXf1FQAxFlFf/o4NHdhRHQ8oX5ncx3Y6ndY7Yl9Lo9EVLxcyKNFkpTKUJNZ1Dtk5Et9t7785Go9l8+nqi4QJ+s7p2iu98K/Wlwh0/GNHNqe79Kn/XcM9VqVlmYLgZeOFSdas++AXujp1Whmx+bc/neq5AiwHl40BEdKax1BD7KLSPj/GgpRs3nsD+dE6m1BkijGaQcoUMPUTtvrq6euv2rZs3bl6+cgV7va/V15rjw11nZextLDM6WTZzyh3MibYpS/x0/kQI7o9wkrkXgUaQpl5RbDqHRAUTi4uLjx4+WluzDIx63hGqziw4yHMEj1vbd9pTzz15udwfpqCjJhXuog/Mvn/lK1/B31u3bn31q18VRSVdZqDMwHnMQKPav7Yyf6+a+Gk+Q+osv5Ir1/P8L5iKA7GzcFbcNRqzJIdHh7i2PcDOLeMTN27erBRUDkT0UOwtaDG7Qc+npqcXO51b+GpxE7X75bt37uIvfmtVTC9oU8ydSKforphcJ3PFn9J0gnBWShycM/LoK9aqbLkvFg6YC01j3o3M0e9qwREiKw+3Txu12tUFnFCVmNlzfs4pmVSaPGA6O41SvVPp9B3BKaRE+nqQqTn9Met+mXzq/dOf/jR2FUiNXxqWGSgzMDoZWJ/uV2Yvj8w6mXSJwWAZ8S8dptWKj8wR/kjBCmEXYP/HRr2OJy4d7O8995EXzEWD3bwQidhVOAiac3Pzk5NTWDMz3hzHTmIPHjxkewEHUp8oJKCLBKrkK8emmCXApnnlGE1GqDTRZ7aZm5/DJzEzzGMNcHJycnevNdfqL862nBLBTxRR1Xby0iklKkfQ6fQjAHWRLVRdswCOddYEhTt2AcNPSI/NzEoB2S0hywyMUga++1pjo3ZllCLKPZbY0RRTMaRD0zKZwuBwBOqIhTW1vX7v9PgM0+03n3gq3Ybrjr5yUUO9fvfuXexGcOnSJVyhsQswltBIyElTIBlrDX5wRMx8Dpfm6CIwxDShP0pzwD0UP2LkOsHBG0r0eN4Z7hEfimvKkQuh59EYs67GwRVlm5qoHK1DMW8eVrvtscsLx5WxCWJeeCJRPikbihXxQdiyLZqAthbuHAtLZfybpUTski4zUGbgvGVgvN5fW1l6v9IyXNZto8V566NDvMoAGGFRYFLwu/zB/j6ebffo4QM83ujS+roYVkRMoqjA+EQ3Ao1FwFg52WpNLK8s3751Z39/3/RgJsEgC6lnROckxUfKFJDBJzFpzOdPX0kx74CROVp9a020jo4O8cuSEBbCHq1TxDGPOarFQnV7lQd7/bnm0dQCK9lj9YX0qiRss5ircANvZw/eESGmcMePuVv7Oy//0Kf63e4Hf//G/sbuwFNROiwzkE8GsBQBpQa2/drZ2cHj3DG/Mj8/jzN8enoaj7/BznfsgWT1Ot8iCcqdTgc74oEJERSgBmWYwBDmAAEU7sLBN1tMQOIrLvT5agcgAAf78YEJERSgNnS/452ttw+e/vDBO9Tf0zaeZVfv9nrYNrdSqXa7rL+np21UZoeHRzPT07t7ewsL81tbrL/Y/GRmdhbbjaM7xycn483mWadTq1bZUNvrYWsp9BfbMkA0NTm5f3AwNzuLzb8XvTzPzc/vC3kO/eKqWK12vTyD2ZqYODwK/M7Pb3l5xnYrI+mXHV+vv8dTk1P7B/tef3eD/s7t7+0LeT5t1Bt4KKCX5goeaYf1tDglcLbgIUcvvvTye++9uzC/sLW9xfK8s6v194ytwR2DUV/IM/mdE/Ic+sUxbZ+2sTyd+61WK51Ot4nj2z5FgYK19dPTM3t7u/Pz+CAwvzs7u9r5bPB7cnL89ttv4/eB6anpf/zHf3z22WfxQZifn9sT+ttun9brDTxMivrLzqtsfo9P/P7Ozs7BI/KM02OE/DYaPTyH3Hvhx2rvc3SK+3oPDw+nZ2b2dnfn8UHY8vK8q+X57IxvqYyvc+HxPT5Gktl4NSf0d25ubz88r9qnp/XS76mfZzzoF+NtlkvFwuLCndt3tJlKx1Iqi+dRtuXfW6xJOGw3drYerl1aqlXG5W7YTGxfhJi+J9MNbSbkUDRxUeY6olXgnCCtRFIrRV/EjQ41wrBfef311/mOvxgylF1lPvnJT/7sz/7sP7/9L29279/+p3dwie2cnr3x19/AFUR0jrKG1zois6TLDIxaBlAqXbly5fHcBKlaGfufPjFbeeW/YwcFwwU+weJfI1NUiKZTmweGqDIxqODQoHZx/AsD1gmTCcfiUJz2/1K3PUstC9GdFKVB3FISRQUjDSvmGF/wHjy4j6r9W//0j8vLKx997TXvYECa5wtfNQGnVSE5uMBXuPv37r/w4gu7u3u3b99+7rnnBvSZ0pPKe2PjQ6qLXDiBIT8tdZDgdA1PLl9HNtTVIjg4NfhZ6X0QOHLwcRjr4ysQ5Oxv+AHhZzPje24DBU/N+dyGKX+55EXXga3OdOEEblOaw4X0wpel//Kf/2ZFWbslqTg1Hty/j8cGoxZy0n68lXAqPtxp4zvq4rT1VsmkGTprn2Fv3KRWj5s+m2GM7jOmlA5Pj5/53KvPfe61u99671/+6u+f+PgL7/3tt6KtSmmZgRHMAKYVUWEMqMgYsf4/s9R98fln3lQuSOrlb7SClqOjFitvRivQhNHgSUaop1Hynp6cvPDSy7VazCCcEN5XZ99rvBncdOYRVouLS/gOjDugbty4sbe7t7GxYd1HLwJlFETDO48Uz6z0HsRprbgdhWOQWwx8/jEjHKp23LxxXs/njJ1PYt7uVh/c+XD1yvXxWi+JXZQuG7LKl1sGYq4ZmLbBrdYTYzeBdvnFJ452Dzbfv3f7H946O2m74ZdaZQZGJQO7u7ujfwtgQcn6zPXq/fFnVXDHcXJIl3s5OrEl0mqf0BbiJRImRBtMBsbqdFBgtPET5e0P3n/hxZexmqIw1zFZyuL36tVrb775BpaNXbt+7dvf/hessSliaj9LhE62BWbI4h/noOc09CyclSHTYp2ZHe+Bh2PTE4LNHIsZQPHAA1GYZks2TW6L22xh4GJNGlbuYU3aOZ7foWzZsgEFiEhNSYNoZdTpj23v7re7javXrlYqQdVOVkYTxcX5bUbkLbZTESmKEHFYUgiaMb9xoNBZWFykY3HjtedQuy/dKDeIjD1KpcLIZaDAG+lGrq9SQNi+/fXnr+5WFySuewOjRl7/uFNCc48hiSbBszln/x96QHR6wi+7WDBwwv9ywms5/MENEliJ++jhQ9QZN5980sFiFFWwzufGjZvvv/c+6pvr169hpX45W+Z0nPSTRThZc/uUZcDknw1bJOk/OfrHTx9UmI73Cj+qPJB4twDDy+kQxCktLCxiMVic1gjL6ejbYuR5IjWFEK0UUX+s0+ndfbjVmJi5tDjBykJSICvipCNEzHQIhVplCS8iRREi3h1SCIiYGff79+9vP9ps/+0RHu9Xq1Xx0Tja2Gs26lhCg+3MApDyvczAOcgAbhR7PGv3zz/RfzT58kgcIQxD4ktpiqJYmqYTYjXzU/DipaB1IsYTVnDihfW4d259+PJHP4ZdZVCrBDZY/3yeXphixw2Xt2/fQgW/sbHJt4k8Tx0oYx1uBujE18OIEOnKnOMV/TZhIn5rku0LiZu2E1k9Dsr7B4cHndbq4mxBz17DV69z/EPHYM+AqMId0yrPP/88HsqNK80bb7zxzDPPHB0frb36qaWlJZzZv/u7vwv+YKMtvZUZSJ+BIpclpI9qAJZfemnurcaNATgaqIsUV/eI+FA1c0BePoMuoI7GLi64l+v+vXvYDOTK1atyODlNGAagvDfCF4NA4HUsl85hKfCbb7xxeHhw9eoVXCDw0A/0jtyURJmBgWUAZ3u1hq/B+bwmsB3Q0dG5XP2VTwIMKA+2TpoTM2vzWBvDdhIr4oXZYeyAUhB4EQEPC5Od7TbfqHKwq8zXvvY11OjYBuHpp5+GJsZlLGfEj7wo2fP6ZcoWQMkvM5BvBh7P34heWe32Fl7s2T/p+Sb5vKLR5QIEpzmR31+MotgNALtt3r1zG6vb/SW55C73xPFeGGCZSzZBmfkfvgJcu3H9gw8+GB9v4qlMH7z/gYbJf0snX7zp/zWEVrLKDKTNAH7LSmuq2vFtglXu49pGYm/tTi5MVxcmgxXtxaQC2wqXM+6OqbXOuGOi/bd+67ccUUq1MgOjn4HHcDoQyy9+7KXWB+MfGf2jc+EjxM7uOAPv3b2Lnl6+ctn/esC7bS2yM2bFNOEuQmaeeJ9sTbIl+95TVDcebWiPZFI6JjWlhhhVUTT1VvFMfMkxcUVtYkqqZWMEMoDlu8F37qzR4KEH2D8X95GPzGPj9fNOPCvF/uqaopRomLto9neOaicnnWuLh2NjmfbIJ8cRhLdUJkJeisIMhIU7JtExBGNOKNFUOpbTlF+SwnSW1Ahn4DFc2fXqanfq6sv3K4/RM6hH8wTENjKYCsFgee/uneeee76gLSAT99129U8CdPnylXfefgsPkLp69ertW7efez7vbd1RYChxupQchi4oKKRh5hu5RiYB5UfofRaxjf3noRlFom1Ip/DBjSkJCZyFboui8PnKEZo992p7G7tD5oiZAYpSHouRm2a3N/Zov9aq7K4tTnte3ZFjgzQrBNMMhTsyuz9P3H5YuCNsVDYpvmI+hvXQeTrEZaxBBh63b5iYNPrKC63bEy8FCSjfh5YB3BjdaNQ3NzfP2u1rN24MLY4CHONnhKXllXv37129cvXuvSqWVmKxe55+9Ou4zsnT37CxWDls6iHn2aR+1CZDS4eiVaOlHNJFx+I8I9vwlQEPMDamLZ2nifGJ7a3trPtCGsI0H9swSKNJKDaZx5qI5pymI2exPTyt4LHZq0tz9Sqv2nWIkjPMDNQxxS4WNImm24cZeOm7zEDCDIjneULTc6l+fba7eO0jD6vYKZyN03yI9kZsf7S2DNrnsrMjHjQKd+zK9eDevWs3bo6PX7QfQLBdwVtvfae92r529do7777Dn3Q24kdkdMOjosoYYrTUaHIBmXoW+limle83RuyFjQeNZdqITA8z9lgMxoTCMLl7uH1Sr5xdXp4ZG+vl+F2IfFoJBGOKx6r/2Ar67FbsGp6yhPIdScC6FzSTvh63euixPVvOe8cft5+GvvRM8/748zQUCqMiJ+mWwYII5ln4By/SS2lKsovV8EbX/tHhEXaVuXHz5sXqHOsNLhwrK6v37t+fnpmenJzC1pAXr49lj0Y6A/2xubm5fCPEPhz7+3v5Yo44Gp6Heuv27bnZqcV5VO3la3QzUEeZjm2PsOPBycnJxMQEr+Dd40XVDqvHrSRyz0+pOToZeKxuTl2f6r3wzNPf4g9dGk6N3Jduf0IMlWHV7sKPDVJM0rkp/v5ACROZknaSBh7Z22g07965g9piZnY2iWkW3WDJaBYMZ1vMsm9ubJycHF++vP7mm2+urKygmne2LhXLDGTKAD6w3ubr85lQNONGvYE9VR6TC8f29m57bAIL3iqVcptv7VQYMYY/tmJpO34YEuvv5ScvP/d9H4uNFktrytU1sVkqFUYhA4/PdpBY3f7fvtz4cOq7hpx2XE7pH0IhetAE36WQB8Bp4W8QFh/K+F9/p8TMGybCDTYnxvYUmOcg2JsAACAASURBVBDB01KfeOoZzHQIviN+60BYojSIMi6L/IgbExx7MsAq9Qv9Wr20igf2TWGt8dT01uZmaqjSsMxAigxgnYzxtM/CXFpZ3tjYiEaICDXaENKkLwUwqbmuzwF7/cqd+9v1ycVLC+Pn7DlwepceD450cyp1efWpq09+8oXVp68ebu3d+da7uPiQqCTKDJzTDOCnoXMaedKwX1rpPvHsx96o5Tz/lDSM86EfMbZFiJz7dnJ0hF81d3Z2up0ONjtnBbjTS9FTmlaIUI9Two8cjBHxC4KnH6NjdcsE+DHh0cNH2Kh+fW3tvffeXVxeDooA2y8ePMSImCL9pRLanPFQFEibsqJWNkchA5hxx1Yw+UZSqVQxm4kv8xHrgY1njmMYWWzhIqM5D/Lo+HT7ZGJtdbFW7eYC6Nh3XQ3ehxuAHtJocpAlc+F+6dlra8/dmF6au/bqM5sf3l9+4jLrAIaxIK94wNWt/+87ZUE/mse1jMqYAewXlvs6SKOj4TLxMcXe7e9NvDLcMErvPAN7+/vNZgPLSLAKfPi/uQcDeNTRcdEx2VfGKkvLyw8fPsC+kHg07O7ODh5k4ymKiCLNUXSOCT0nnsEZleeaTGWQpi0YMojVNH+FIntyEA9EqueRoO7pPbd1h0wUhbk5LEJzh1Gsrc2V1ZWtzQ2c2FaNnAXG/kX0y6jvFBNAN3dOK43WlYVin6zkFE3krIIjwuOjZi7c/+lP/5+dextXXnzim//hPz/5yRdvffM7XTzUKsjKlZefvvldz9/5x7e7vXItVJCU8n3kM4DFYCMfYw4BYrp95vpH71WwmUz5GnIGMF3X63VPTno7W1sf+65hr1wqPhn4Yrzx6BHumFpfW79z5zaaEVOVxYfj5iGiKFIA8tR0xHJUUwI9N80U3bOZYFeZ+YWF3HuOh9sMdo2lrX+2niXV93HQqc32/MLEcWtiVKo4LA4s4quXLXHnmd+33j90+x/f+Yf/+F9f/vInH3znVgXPC2k0at6/laeutg+P7n773fPc7TL2xzEDGNkvfLebtbEfe3nq/eaL4nDOh0ORc+HzMCIdxIZdzeb4/h5uTm0sL4/Iw1wKzA3K9IXFBdTus3OzWNyP7hforIQuMxBmoF/cvMzU9DT2hQxdnX9q57i2c1RZm0LV/rgsHz3/B03qgbVw7/d6c+tLmx8+qNaqzamJ8akJ/MW/ubXFD77xnQJ+kpLCKhtlBnLPwOOwTubzN7uN699zisdTC/da0l2OIY9YnAgF4g2RqWl/5oS+MCQi9OMumuvSUeagcsXTUvHcpbX1y1jpPvBQh/BlbWFhEY9uwfO0l5eWHz4o94Uc+DF/PB32x46OjjONO+IoI9OzM7P7e/sFgQ8YFo9wvrszVuseXJpvYg8DV++2k0pOlCuaaGVDjuaLCEY62jyLlLvLgpCHrXmpDCFv3Xq4e2+DmiCWb66JzZIuM3BeMoC7l4qblRmFJKxO9r7wyvqbzWfig8HoY3tFiGwmKj8TRLSxVUor+SgYUtVFpCMRRj1CgapRQYIQG7hG4sKImzV3t7dfePElEUhUI9qGrhvaNAlqiAR2gZxsTe4f7C8uLdx/cB/LZlI8jXuI8Zeuz2MG8BkZHx8vKHL8jlSr1y7AmXx8Vt3c2Fpbna/XEi6k1MegvHIdILM7gDHABs2s8Hnh2OIoGt/ml/P7lptTo61KaZmB85iBfJ+rN4IZ+NHnKw8WPyNvnz6CYRYTUsRIGiGSYonVi1WQ4E5PTjDLjk3csWiEfWMUNniR9IKGO7qLJi6EAGZ/1TJfbfuBaWwvLjM3CNn8jjv57t+/98QTT05PT29tba2srpr1BsJ17ICYUkeTgYRfOnHNADZcd1VNrrcwv7C5uXHp0nmdtcQ4sLnf6bf3r64vJu/9ICxY4R7sQjUIf+fcR8yM+znvXRl+mYEwA5hxx7PGwvbFop6a7z377Efeqi1drG6d495gnUytXt/Z3sZ+MsNYJxOkTqxJGU9t+3pmtpkbQJvfcT9fr9trt09XV1fff//9ZbYv5NCK4RQdkE3EyGWJuffEDQyDdxKURBEZKPQjhntUej32TAasASsi+EIxz7rVB/dvL1262pptFOooCziyWz6yzT2BaQr3aq2Gr0e0FyR+EcZGe9gke4ijs3uHS83HNgMXeI37RL3/r1+f+nDmu/X51cf2cA+34xgg8ds6qmRsBPnR114fbjCD944HqbK59hU21479K87zIxQSFetipgPD4F2UlXSqDEQVzawmSQXqaDQ7O4tfz4IdTh2Nhq+2e9g7Pjlav3y5Whnpx/Hge1GtjofTFfISz5uCXOhxi051aToODx5/rTen2nBRtT/5iRfe/X+/7a3gZFr4noTlZScnJ6djEwV/dmxBlfwyA/EZuMC7ynzmeq9+7ePHlUn5cZu4kjn+S3NbEYYPl3/xB+YiauCHewyMuGEO5Tuq2IvYxag+4WFMh4eHuDF3fm4ez56MUi1lZQZcMxAx3nirwlxx0ujhgcBHh4dpLIdkg+eh3t/Yq9TH15Ym2X2oo/1ipWNhv2aI583A0iA6zYum4KNm3E+PTtaevbZwle1ihpTyxE4uzHzr7759ss/2+cLCzZWVFTzmGpeoVquFmfeT0xPMu5/n+RXKTElctAxc1DtTl1u9r7x27VuNZ2yLIBwOJAaWol5O0GmuK2Rj9EDSnPvliIuJjEajvrmxjxK20B/xc+5eTnC4ImCZECZzsDvku++8e+XKlfL32JxSW8KYMtDv12qYhTQOBSb9VLyJVusEiwtaQ9lC0XHg8Tt2eHSy255YXZit1wpc+p8qi2ajZN0zYzxG3KjCffvWQ/xTkvHe3/0L56BS/9KXvvTWW2+RAuZXvPK9xxZ31mrF3eVNHkuizIB7BrCgCz93uuufC018o/7Z1xvvz3+ml/zXsxHqYJoLbrRNtDR91x1xT9unzUaDLXD3tm/3f4pMdnUi7WifpCZ1ittEW0oGeTTEUOZm51j32Z2p2FpHWi1jjEq0zSOWEuMxygDOKG9lWrFdnp+fe/jw4VprKLeoGj805v4+2j6uNWcuz2Op3rl5ed1L0MeEHaPRJV8XBJswnMzqYeGOh/zhpiJMFDliQvNP/uRPMLOiPMcbHNwCCLSyfHfMZKk2mAxcyG+Sn7/RnX3q0+9V5gaTw9KLSwawjLDX7WKma2937/kXXgxNkl01HLUtaviFFC/+N4ygWEoMBY+t2d7ewjzo4tLSgwcPrl+/Hu1btFU1zddHM1e1fSzbj2FqWB0SdQ7lcB5UK1Xc2tftdIv9DS3DweucdR4czy612hMT56lq945NoQevIPCCYGPP1X5YuEMX1XbS4gYzSbDS/eDMRvmOL8Fl+a4np+QMJQOF7hc2lB4tTvR+4hNX/r75QoahfiiBX3CnONPwsOnj4yM8ve6iLtCKPYTYf6PZYDNBuJ/vnbffGYup2yPxzNdHMzcS6HERWlKjjRMa4/wmCL/qDCD4paUl7E6GjZIK9GU5eLEe9w5PD9vVy/OH+OQV/R0mNpjECuh12o4n9pWXQV4fH/eOBx6lwh3doVtOc+kaHr2BFy5jKN9BYEY/F9gSpMxAigwUO02SIqBsJvXK2C99evyN6e8v/LYs9uShYMDA6BqSfgc0RraOnX9r1BD1eu1g/wCzzhfsrEt0cKamp/AU1aXFpW630263y/E/UfYKUNYKBI1RgNMBQWKZ7gA84RzGmYz5ypG6ZwNl26P96sTY8frC/ACSULrwMzD4jw/3OJgHMOE3LLx4+c7p8sCXGRh8Bvx1xoN3XIzHf/1K7/Tq9x9WpouBl1CxO03YFkjO1Bih7shTsV86qHO+ptlA5mKsazYb+3t7w93CfOjJn5yc2tnZqa2u4t4SENjWfeghlQFc1AwcHB4uLJqfYiF/OrMmYGZm5uBgf2ZmVG6XOj6rbO/sL89PNRtl1Z714J4Xe3XGvbi4eclelu/FZbhEjs7ARSrcP7bWe+HFV79TvxHd5VIalwGqy+MUg68uZgOBi9OMrXDvVg8PDp586qlhnXXcb+A9eekSaREpDDOJXxswMdntdOYXFh7cv18W7mFqSirfDPT7qKdtkMKn06aSgD89M3P37t3p0SjcH+20q73j9RV2j1O+3UyQkZxUbfE7jjY5RXE+YAZXuPN88PIdPzZh8Uw5+34+zpGLEuWFWbSw1Or/9HctvdN6zXTrYTDKBe8X5eidm3549/xUTk/bZ50zLJUZjbht10R7dJEWkUIJc2KihaF+enr6w9NT9m2mmvjJIRJcTKOYkz4JahLdmM6UYvcM4ITc3cHTkQb0wARsGIUpSBQw7hHmrtnpVR882llanJtoXPAVyO6jTe5JHk1AJGTQhTtPBBaK4YWVoEe4G3aM7R05mgkqo7pIGcBQewG606iO/eQr9Xsrnz8bM142glEueB9Sl80FjJmrhOikpNiMUBOnGRa47+4etFqTw720j0JSULJjb5nLl6/U642jo8Ppaeu0aB7RFnPSJ0FNoptHj10xhvyhUt2rbdduROh5jzkbUPpxi+q9+/cuX74cEU9akVNq9o/HDo4O11dnq0U9bDRt+GntBnTk0oY3anbDKdx5FrwdbFC9d/A/ntlU8GTMqGW+jGfQGUi6Y9Kg43Pz99Mv99c+8n0fVAc0t+QWlK5lHofNXMXaSUmxKarpXUXjLqWyHD8n4kb8w4PDmdmZckzDBA22z8OCmdnZmZ3t3YIL96JOg/OPO+QPlepebeeQ4K2trYE9pgPnM+7Xx9aQ7D3nlyk1ghPcFnv/4dbU/Nr6EjRNyjnHM0i42O4IiRhkXKPna5iFO88GL6dQu+POaNDlpW70TpILEhEewDQ3d773O//y050XXnz9O7UnL8ghGflueFeSuMuJLPdm3FuHB/vLK6vBEvPi+mm9kkVv4241s0Wa2MAHYiVOpdLpdrBq6NGjRzb4kl9mIGMGvBn3jBgJzBcXF/FVAVPvCWxSqwYjzEm7s3lQW11abNROL1jRzgaYoJthntRhR9cIdR8nSt7HfYg9R8mOixzKd/wty/chHojz5Ro1AdVGoBE8b3JaadKO2rGaiqHRRepEiTEnAnl6sftDH3/2mxOvUTyJzFMo+8OmNHpKjRSYF94ER6fb62L/8tlBfEuMuJJxkVnBzI04NokNQqzm+PjJ8Qke63F8dITZGfpshhoxlNMp56QkOkpsIBqX9MhlYHd3d5DzMqhSNjc3B5mFrb3Tbm3qyiKerJTh0zjIiJ19Ycw0DwsXraPOGYlWFLeDpMTxsoA3B1YiIE54xIIZeOTl+8TkTKV/7p7+FZ3vUppzBsTz00bDJRft72MPL7bENlaTohQ1FUPSSUoomI7mN+a6/9tnl/5h4tO9sUJv75PC8YdNafSUGpL2eWl4X/AKKtu8wnSsc3aGZ8/RF8XzkpiC4kQeDg8PlpaWMcTj5wgsnknoyOmUc1ISHSc2EI1HjS7odDZ0M95TvIYBNiMLg+r0zHRBh9TWIZzY2INyemoqY/Cx5vjUPDqozE+MTbYuZkWU6vt8bNouskK4VEapJ5TmwHIQlu+dfr99jFKevlEMLIbS0YXMwCDnY9wTKH5DFmkRYarZ/8VPz705/6/O+g3DVcTAEq1LWs6At4ikoGs86vVqrXZ6fFJvNLHSPdUoyg6nf0gvxJFFpb65eYIdCKYmp/a8ve3l41G2smegoNPZEFi8p3gNgs3z/D7EPu4LiwSdI2Hr0Mzs7IMHD4r+fr53cHx8Vl+drdRq47ZIcuxsIqi8jh/GyXKNdKLMh4V7IrOilVn5Xuv2JybwizN8DeahaEV3ajTxkWp8bPhfitCFCR2uz82JBqGYi01O6zqcg7+2YCDK+MKjqoseZFNEiP6SlUgTc6Le/6VPVm+vfOGwMsnSQwIiDCySZSfYUU45QKc0yx7z0BDY80Ebjd3tnfFmI+2liB1O/5BmOLL8XDKeUXlnx3qY+amDPFQq+JmoPzU1ib3ti31cfN59K/GKzECG81sLyysS8gTUPHBGeLbjWoZT++ys3ShmQ0Z8eB/s1yb77UsLg3gorKW/Uey80t1D4Y46JMrV+ZOFJ0oBsbPCXamlCvCSEhKB4dOI0/e43avWGs1qB5yUWIIZ9VfghSSXRuuE2uefMl7gXZhchydApMFRzMUmaYpMBYR0FD5vpv6L24lS2xZqyM9q6rXYbNXH/tePV8ae+MJOZVjBs+E05ZCa0syWbPWzr7ZtdiI/jY1oH0Njxh0/EmK6YXp6VB6sGBNxDmLrYaZTp16rdTrdiVYL9/PReZ6D50wQllPBwiZXcXJSLImBZuD0JKf7NWMOsHS2Ly4sYrfTlQIeCXzaqTx8tLO+MlOvYySRnA40raqzmOyo6o9xu9Bjxgp3GkmJGKlso45pNSu93tnJySkmb/DDK69sIoKk0ocIUTmim9DnUkXHiCNiXgCa+o6+ZOkv4RBRUHKS4mPGHXfIFRRMFljlZKNmq97/lU+Njd34wu3KVeNUu8UpG1sN46uBZQEYUbY6Eqptl7DT2Nhw1YSizZbKVCsnx8dsj4ukvlQ8m9/zx683GqenJ+PYGrLbxenNh5dhd8NyeCxsijZOTooFEcnPkuQWCD2VUUFdjofFSZXbMxOSHGAsh+t28aDkXtqf1wxdQ192TltnB/evrc0zcZJ4DHA2VsoDXFA0gC0I2db/CH7K1EQg5i4a0aUyej/xwcDsO8Z9zGaBxj3dug5xqPQhgkQiIVZ+nCZ9UcRNSCQiKDRdkKBMNHT0JpjcBTni+tyLaKu4oKYNk7sjNR1WcaE0yZDzqWkjKFTS1wmbbUY+OXLEGc017rbgedXev/GFO7XrNh0Ln42AhlHQwLIAZGJbfhQ7B4Nh0m6rCfXaOCv77bP2FLtVTlWIcZBQ3YIWJNpzb1pZJdkF2hIzbMSIQ8VoCsM1xm0sVMNtdvhuk1uNFe31YkqTnyXJLZC5VEaOGc98VpkAvCcWOwaQpxo2PMC2B3ldXPA81Efbh3gCxMKyV7XnGamMVeQBlj05tPr4olgp9JwLgzCdPKGUUSOVGjk0v2XfDhIFGRVGevHHOuddE5S6LWmTXOjRUQCid9zhhElTDP14jDa+7CbfoEDyI3oXaeodaStS4iuEqCbSOiBxSI0IEingelM04dJojigVad2jItVdE8ddk0yGRWB4xXMc+XmlnKi5hKRgiudtUnwUv7/6qX4Pc+3V6zSMqAOO2k7qpCB9S6046MEwzE5IKT22ChQ91yY+C3hhBq592sZjU13NctajRHOCmmY32cRmTJ3bbDSxsczs7Fyj3vAeUGV86K9vl9thyQ1I71DJyZKBmJMuHtoEwAb2eMv8NSanpu7dvZtx71d+qu6fjB0c7K8uTdeG05X8kzOKiEM5S/JNhLgdpIKMKxBxbDQURFHGJrnjBCETQXyxfG/OXamfbSu2ZbPMgJ4BfmcqP52Uk0pRZteA4PyPrr91KTfU+SKH03CqK0OEu1F/4eOo2r94u3pN/PYffiB5uGpb6URxTXNBJHGlRnGRRCCH2QkpRd0qUPRcm9jUrFqtYeYPPwlm2VJG8WfNpVXAAPLunBJUsma1VuVpwVTLwf5B9Iq13CLPDcils5EHwwVA1omBixHLWI9Hy/ulD4d8CKnBbS3Ywzp6FUD0QeiNjT3a3JuYXllbhmJw7Ym2uUBSXAdjP6xDOK4jnOFzs1RGzyEujXh1OnisxxF+ey1/ftVTNBQOSk+Hj6FraGKNmwhZN8STU8VnYkegifGLtB60KLXR3CpCKorGa71f/Z5e9/oXb/nr2tXxKq6tx5g7xzzGSlypkXsAHNCyJscXFuQ0BhYxYY07KvheL10KDFYGFo/CKvDE3qVQPLViQg/F6ilGEquANBQiMMBnDV9pIKzVa/0+qpSL94o+GIn7GwMXI452FxyVaK2E0pSgKc3U4HCydztd79tq2tRkiGR+Yf7evXuXL19Ww3Jrn5ycbR43VudnGzXsoZchDjd351Qr7XE9B91NccjPceHOD0i9f1KfnMTSyYtavkfUl4lOSaWQ5bYR13XRL9EiCBCIr9DU5PrwwjVFfR5A7F8xQpFOYajMiCRCi3WXiwLm2v/t9/Y71754h82185c6XsW1A7tk71FDh1lm5ibzmk07cpJGTVM2V5FX0zATWFaOxZoV7GwGXs4RpOhA2gisdlaBLTjBwKvX+9j3Dby0H70w0zaH8Rrc0lXP5udc84Wjkl8/UoJazZIeocw3p1ojic8R1mfjH05ufr2LNxA0NjY2xlprl+fPWIdZDJFxJM2K4CgPsij3kX3OI/DRxkjafeizwp2fbWkH05FICZ9xx+pJlO/4NRYz8RnDciwx6YPKs+doRTkHEWuY13ERcUTalihRh2giuJXY5DRlgDdJQWnanBbKx7e7QvEzguNu1H/7vZWzaz8g7yETM1aG4pBKEUjU0GGWmbkpXJNJ2IGQIiEnrAJFL/dmVG9Jxur2fg8fAbwQAZ38uUYTlQJRRlHl6j0LGPshrt/LMt0e36d4Dd4DV73o/or5jtb0pVYDq8AJ9iIqJT5CWKwyxDysrK5sbDxaWVl1jwGLxx7s9uanZr3nobqdAYmz4h6Oi2aR7tmch0sM6XQKhE4XUEYrVuAqFxjxqsNprkN0RpepzXmcFAaaRPMIMaWKqj2X8l3JiS1mRU1p2qx4tKLU3VC0GkF6lDuS/etccQm/Ptv77z9W71z7/tuVK7KXmLEyFIeUDODUihnXzGIz18mfSSnsQEgpelaBohfdjInbLDZzJUcYjqpsun2MLZSJ/D1AMkvWiEqBLGPbyhQTRlQuJJnUYJOJLJ6iMpMsj3loy/l2QLQaWAUOoPEqymEwGzgpmU0Fbj4oAqAbiXMKTwkoNouRkdRqdWyYhABcEgC1w6OT/Xbj0jzu/mBLyMoXy0CBx69AaOuxczkV0hr7M9NUAWNgxQtNzgENZN7ktNXRAAXGSHjA+EvlO9Y0g8adrGJovHecI9LgRDS5RzEn0EeT80X8kh6dDCgHCOUUxaaIiJ8v4ejl5lzvlz83/53pz+2MzUWWNeaRwMzlPYmSiX2NGdfMYjNXhI2g4yNjGvFaES5sopi4zWIzV3KB0QBlKQ46bk5ltFqhunbGoGdgSa4NDYRi4ObCigKWZHIDLS8n2YdN13TE68Vr5JKx4YJIh8EWipOSzZj4iVFyOAAeRB/bFi0sLgSB5AAbQLm+z8zO7O3tibdRGS3xEdjYr9a7x2sLE1BInDAjaAHMgWZQHSoL6M/gITMd2mjjYDtI5RoT3Rx8BkSPYmwiLeqA5jtF4uczvECL5btoJdKwim3iqkyOFGVRRDpJCaXUU5pJ0UR9JVpRdFFpdNmWwMFkw8XLDz199oVXrnyr9emDsUmvamcnWHiSSccm+DDL4oArqfqNKJlJ35UnRyBYWQXQkWTxkTGNeC3Bdw6kFKKOFy0OTjZWuZsOvWtnDHoGlh6dxGEWCCKfK2J0v5lfg4bMQnJ4QPhNQgo0TcM1HfF68Rpp4nOzcc1DjF6M2C2WoWnlcAA4BPZTF4aLHGCTpmRqavrB/fuzCMP+wvNQt3YOFmcnxpvz3mhvUlUPqNo22eTPG3AGB+wuab6GcwzsUWZdC25HHgkJZtxx6cLiGZTv2LMJM2G2sIKLrk3u8yMuhBB5Fye1+o9BlMUKvtIUdSlg7hQiHoBoQiJIRVrEEWmuQwhoijR3wfUVTREkBc29UITkNAWUYiJCKUef3JEJlIlJIXGmiAOdiCahxRLjtf5Pv9R55SMf+XrtE8L6PjaCxYxiMeJoz4YhyMAiDLPMGoFVEN8rcqkT5iC4nllm5urICicqfKjGivnZws4iUPFFc+IoJQOpoXSEDQcaKzUjHsqgobEQEctJAZmxZsIqSJ2KHA21BFmwY/RixBbQkJ0+RwZLAyv0VCi1u7s7N1fwQ4siO4CuYzXmabuNBbtGxe3TVvfo0dryTMyjhtQDqraN4MNk+gc95bFnY4IwHzrMjth9j9QxQDBDK9z1CglJi7jUKdUSZdjGFxV4+Y7aHYsl8PhVck060a5FtWg6Iv5ow3RS3ndySqnghNgEvq6mOyUdLhKbIi2i6SA6R4nEqMCZ0CQp0dw1gUBBF4EJNeLzJociQ+9x9HzHDy7x/1LXRHNOcxEpcIPopgRtb6xM9v7Hj41Vr3/+7yvXvJ0DU44M7IFzES+D0ODIwCLMKBkpGQnVt9rWjawaUUGYZWau7lLnWIOAaqwM5wZOYZw84ros3UfASRylZCA1AsjgnQuFczUq9sDI/C5ZSg2zvs710gJLFlTNaeeAyL5pDqzaVoEG4TNSdU8GM0OYubLlcFqJc0RhGiwNLFJ3JNJkCifY/MJCDs4dY7SoLSwsYJeY1UuXuJx60u1X728ezM+MTS1M80+BBeB8sv28p0w/blhnicpzosGSRuaGjolF55ywnQp3sYjJq1/CFYVBKk3di03BxlcQ0AXMuIN5fNquTq832xu8U4raOWrqHScOJ5QmdY34nEN54HyxyWlFn3DIHAqiFfhKk3OghpdirjRFBZGGmtgUaVGk8Dk4MbH1EMopik1xrfOJQ4RiIjbhxUWNm7x2qfNTr81+OPu9D8eWvGKGp0UaU6QGNzOwYB2Z0kihGL+N9nyaHJOBVaj6VtuEQES8BqkSYXBvYJE6J6I0ooKIlXnbt+NEqJx1OvixT3ErN6OCiAkz3tQDkAKIDkYOTW5JllJD1rO3eCAwZRdp9oOVXVWQWHtpFQjGKUm3yCLBzRBmbiRQtiLDnCQzNzqMAUvTZAohbm1uzUzPuOWsqCxUazVEj6sM/3WX9+SwXd3bP1hbmqq6nviFJ7yo/qcKPNGlM5WHwIgdj5RnVwCR8d058XGKToU7lT4UtTf4skpFFClNrmzUJJzBE63xZr+zc3JygsAwE4+/g48BHpVcURjRJ7Eor+kiXwAAIABJREFUFWnqgng4iBlNKCZiU6RtIFxH0YxuilC844q+qCDSStJEkQuNgz43N2fT1GMgDhE2W853UatXx37kmc6nX33mn2sfPeq3QkB2GkpjitTwHYTqqSjDqS6xpIbvwQvDEEsYQKQwVIui4j6EpsACPIN7AyvQ9t/jNcjA6tsiYKMiXg4z7vFBWDWsAgrb/6JrOSctsYfWWv0TbyEYa2Sv12W8PqtpkBzlVNfUfYa1l1aBgpQt6AAs7uwM9PJ/d+2n0bPZ2Mw1AojMTJmMN47XEIMx0PgqiNluJnDqoJOSwY0Da35ubndnhweDJSAbW7v1qUvrSxjqw60R4mAypyPOQYH9j3OtyzFGsZuCdIHHKTwXFr/FsG291LxFK/bTLpXh1wPlqqA0eSxGTS3MgTIq/S6euY1LCJXvfBeI2CC8S4409auYQIH3N1ZTyZXYFGkFH01RKtK6Jg+GQuIKRqZuS5xoc0VKVu5EdBcUnETKii2aWCWlM4vjUKrJxXSj928+UR+/8vGv95/Aj6fSRSb6g0oQKiENa1JD1PQFBh8SS2qI9o60wb+BBTCVGzcDm0dgqs/oPoXaVt9GAZ4PClOMJxheMpyuoXs9zCgZ147XMMYuuVI11LakbG+EoeDjgMELSYGy8XiHqjpclEzXJk7KoMmeE8ZoFR1TM2XQOlQIFFK6VqGcTJmMN1Y1UvSzv7O7G7ujS6E54uCYDdza3MTXiPZZ99HW/sryYrN27PZ1gqJT00GCrESKvPou01vGxozeRqAXlovYuFIqRPQlJaLJrI7xlA2pQkUoNkFnuPyYHGbmUUg8TjFyBVvRFDsCGlJUcri+YtdIXGhdZt9FBMUXb5ICEUa1wTB5DEokRmZEPNHmihQ4/KBwfuwBUvzajpeIqZgkah4eHuojOwVphIqWiiYUvMjkNES1Sh8T7Z96dvmN1qd2+962A67lgHkcCLjSsMYagUAKQ9KSJHENCU5qkGXINbgxsGBo5hKikQjdiGIzFxqSgPlL5jOZtuiM3WbsTwREgIgWYn98OsLSoSOetcMn3RqEVUChxmtwVb8jfjCscu+xje5NncjWZ4qMCNcQY/RixOROJ6I6pGtHcEKgkIpQV0TpO6AC5YakAOvNNP3ErjJpzHTnCTl6VqZmZu7fv1+bvnx5dQ6/vSXEK1I9fYLSW8b3B8UYU4p1oWc6HnvwGrHdyB4SXNQxpPJRleDEJmheu4hM0hTLGq6pqPFqhtRIyvmEk4ggECJs5qRAhK6JSyxm3/EMM5Tv9dZcs9ob63u/6uqqo8qhJKOblNhYZkG9EVMt0i7uSJ8IbsWb6JECwjmkTH2Hmq4MJqp2HG6I8CIrcsGZiojUiFAUlJD0Jva+uznb/R9ea7aXXv/a2BPdnjfRrnZFtyOOcRyogGvG4OpmGWG6E1Y/IYQYYG5+Aa9gMTcKCxzfuVUQhpkHpbvhqPzCw2hMKSNMbD7b63bZOWO1yCMcM4bvkn8x5H8tUQhHTtYQBGYfQd4tUo2NVPAPDiZK8OGkT1OgKLsPuMq7k1JoY09+qMOomM7GiGWsqFbC8C1QKora1s1y64DrVIMeQgwnvg8xADiI/f7hwQGWqcRq5qHgBRxEreQXtcRhp3XS3r85jZKdDdQj+wp6MBoBxmcqXmM4PbHm0SrIHmf8GndtkA2dKiKlCT3OsfFDIDuF4Z5wOM2bRCtN7ku0smOHElxo2eKZWvP4aK861sHseygbeUpMMqU6lplLtyjP4uEwIisKFCeUFRE4otSIJvaOFEQrkSaF/f39qakp3R1XMGKSLRFGZJKKBBzNNns/+FTvo88/+U7thZ0xvr+vN/okHoKkIYDvJmbAIC2DTAwtivYwCAiaIRajRIkOE+rqMoVjAJJZMpYnk1kCoFUg6BjI0GFI6WqhzOqGBHwpCDZyx/Wb3TdMAh02hhN6DSnFxCwgl5xgf4mlAITNeI1QV6ZYEOZARD1U0V69jvywR0XCnWTj5N5JSXYqtgJachwwDe956yUO3xCTfiidUF27YvRITAOKgUXq7oRTH2LhBrgS0gvYFPXB4dHB2cTKzNj2WeOs3cZ2CLFhp1WQUy+3HDFNPXA0zVONjQnyiZ2qN3mGlAzLmkerIBm+rt0PHsCki0aEI9ZJNhqhiiK9GdEXXsZx81rvaHKi3umMYfYdpTzKdxFW1ASg2OQ0+UVTNIzwPkgRBcmDFyPkAYsKJOVMNI1SHr8oAofbgkkgxOT6yl9RTREpTQVTkcY2/buXYvUChSzuMPR88vLZj73cujvzia/31saw4VXayRdvw0dpCAgbyggXCoI+OL37KPzNw5CBRC+yxAneV1J+MTEAhRPXOq5BXVcijhhx4J6EIeGBerpR8FGyEEuk8Iwh7D2qz1AawuJmBkHoNaREH6CtAqbHP1aWD5fBH0zMXAbmvcxiFkRkIMy218cvmQiJRYXkJPlKY/Zq5nphWv/4NrHB+gC56rnG66TnpCSmwbUroo1OG1AMLNEucaCisUgbgGQWTi3csRYTjoiYgZY9h0APDxuo1i/NT0JhfmF+e3t7ZWUlFOdMyX2VWzm7ssIJmRBIq7pNgIMnXxyG0xs5vCwdkpEKaflr3Gl850WYrVlICKlAY4uqWAXulnqKJqdRsuO7O667R91mrbPPn8BKUgpWNzSKiCkStiRzvqgJWuwIGYqaFIkuJZGCCb4o4rTIIX1iEkGiaCKpvoJm7DUwqeMcX1QjBNIhDie2trbwuwqXkjkXKU0yJHzC5JqkoBP4wF+f7f4vnxw/mnvl692nOl3MJuDlTb6mGQy8KseDMFjzEc4g8Azsf+Td3/1xUhouRUxJYAcNJaIxlYN6HauVihkcSS71SjLikSeJnaKfsreg4+DiPME1COuxul2UqgF0oB60AwN6twpIw0YE0J5canBMBdnXULg+uJlLnmPEpEdEGA5WyLB6nd2bylB4YkIxWXBCEpi9mrkKjtKMt5Eci9ZWgahEtEE73je3dtJzUgKeIQ6KUSTy1iNs10DJwEYYgFRWnz32iD5xNiAj39p/s0D1PDZ21q082q+sTB01ptgToKCAhxV0e12c7nThMHrOzjSHmB3XCUHIhEA6mQpKbExIc+AsXbewBYdOZIYOOeFnUUJs6hp3pS5RmuSMTkcoEA2p3gRTVCAEoyZJYwlbYGQYq0CaRgKPQKuPnZ5VKkdHR/jBi37zUvqCJjniIlu/SMrdKThg6hyOTPjQIZoIMXiRKdKiDncUIVWUEzWpCxzf2IwQwRcFRoTIVGi9CY6NCb6/RZcwRoheIgyjRdRNFIdPzPe+8FTliRtPfrP3ymmHPz9PGAEEEphuF1bfxjpjj3FKgWXQES82sHlfI9z8RyCpIobsDZtSQKzhcVV1tCVFg1xjSUBiNa4iSYopHHHPEorQkL0FAnD5GcVWg4Ck7snqWqcURgAXsNV2wPfeBWgxHazHTBTGwK0EdQlGalgdWgVkLmmEzpCWaqWKjHh3pkKbx0ZmMhHayfyoluRYV7SKJYHVsVWgewLHSVtyrMPEiEUDq6pTHK7xOulZQxHj5XQCVReD/tjZ2ZnuxYljzZNVIMLutidO9x6uL89VsEefcHGZnpreYw9ztS+7t6bAKhD9ctopRN0sMydBiLG+5On2WPVAwdJ1CzuwyuvdOQHOiokiM6xxRyGiFDQ6oqgg0tBUmkYOB9Q1yREVQ2QuckgtlqC+cHN45BwjGsVDVphux6vdbh/3W43uPpXv3C/XJyudEMMjqWgrKuRIK74UZC6lDIhN0NH50aVkAi8citxFNCNEZJ47gWdiY427DZYnhHeHdHiclCuFz5tc58n53o88X1tdv/nm2HNf7eLxeHiF+wl4H17tE8yHGI3NYZW/qMDkafJATuOUGw6VFdZvAgAGFMEGfuLefRvYGQIhNIMsDpgBkhkBMSu/QUIRSVIUBTqt2gttCYU1BFkIJGixmy+xHWTNW+MuXMVDZYES+iVwg24Ry+qXNDghhAEGPy3530DRGL0vFGQyUGCMvlsEpEGHROB4JNKCXzLZljLVGkIKohJ8ChZmLlewymJCs4qtAiEgibRGEBOgahfjOEYshuSkqroXAaJkop5Oq5ZOoXCYBKpWg9A9hkf+8CM9xmhOCBGtZ5JiS9+NnZOpybHV5Vn91Me15v79e1GFuzUFVoEpigw8qfNSIxY03xDToSWLOLZLyRScQ3ZWTODfuMY9GFUT4ESo6kWPrkw1ExdRkxM6AkVImjom57BLRFCMgiA1fn1FU0QgR1xKIm++/fis3+ez72wuPniJmAEvh3eKhONTJBxabCqa7r6VyKnJCWoqgEapTVmxTdSkfsEK+GKTcLhfo4jrkCHX1PeCJCjuhaxEvihS+Lz55HwXJfvK+s03es++02VfDPQKxzvzgtNPGW8CNoGb6zkGqqmSDQhBaEbgyoH3CLRK1GJzwWUA5bFC9yEl6LKPGppGmYQj2fCGJdTAzMMMGgZzc7UdKqoxBW3TRFAgY9YmlzjT8MIQcXBwACJ0olLMWO+XCdKzJCSrhuqAV+5yDEAx2HMWeZCARHWzhqRua3Q73WazgZXueOEDGyRGRYyPRLWwOUzBF7sammvcIAJNwG0CcYjgU1aBpmllWFxa9UOBOiLJSOlDS28pRxBGqlKReqF7nFHsVA9OLBXF3g4hrDrmEI46NUyoLy/O1qtsJynjq9EcPzk9zbjXhdm90V9SphS31EiKlEnf8cCxREjJGF7EaborhZ4GQLLxa1Bj9WNkStZeQ1STrxOqLpfyv97w7WdesaImESpQ0I5V4IqimkhDKjZFWhGhycr3RgOz7yftbnNyvtY94ODZ/yp+ddeKgtgU6eyRFIEgnh4cn2LmIt4kNd4kHcVEj1DRVBREKWqp6Wk+F65osaZ4QnJxRHhQmG6OfWS585WPjFdnb77ZfertM28u37sDVS/IGD4D9f7o4438mdbMmTjq1lbZHMoKglTHk3fNigXovRRzHrZBnUMZBAFQ+A4lchxyfYokTlBkDrOwtiYMEjOCA5plkqLcYGaodRLbYW0rNoLEvLL3Vzz3BHwfXOCEZOjRlopQI7SSKd+SR4+/MpLBPuprmkFd9qa2ZG+eFCykAjuxnp3hhl3vZyhKrawe5S1KpgYRHHidr2RDVPAcyPFAbHVrFYiYCq2hB3I3QRqX3INqqbaDONi7NRZRyUhbLQ2CqAhEcFc9thFr+AuniJCZNoTwaHO3MTG1toQxv2s/RcYWFxYw6b62tp4wBilfkntJkhDVWX0gTpyjIUWWCCkZJCmKCC8vOXjIMXRA1TGe4iXGxa83+MsJUZQjXSi4GKdYkFF1CAUEYKzMdBE4BIKVM2igej85OcQ3aVynRV/ZaXJEoVKcHJznjaSiRxJxQhQ50uQd+twFQYlN8k4eoU+aii8bXzGJUFMAUzej9wvTAxA5In11tvdDT3efurx8r3bzm931dpt/++UVCY/O8In3PrdqGeX3xSudzMMl4zJT/WMfluOKTANSCnHfULHyQzFeuKUAQr/cRMTRXAeoEoLHNKQo7KQVJ8Bj70CgAlDkCzTcOkEJJh4JI7FboVhEM2uwDwIbFvDmRyfaIBw9FSG8T1nda5oywzs0fljsoIMMggiRZRPWsnREV9SzKfVNx/LELAr8h0fR4GmyHmh4QiZwzcPRHOoxiWFo6jEOY8Q8BvzVcEkSJbN8/COOgGs85D4qsKi4Q7vELsm31dIsCF0SQixhs8FHja3FMpzKNotYVwYFYLXPOhs7R8uLC82a0/cE3NrBvsnLJZYBWmKZ88VU7BIJIFMjmCPKM3NRAdFACaVB+YyKJ5TFXV5CzfwpLxP2dNRxVuGl+40uzkSpSOs4iTiAogsNh0WT8AHFpS4i8kuAZJ5UpBr2+83qWXNyEsU7XigHE34syb+BoGiJUL17RqIUDDFFetOzcP1DuVUMRI8iDTWlqRiOTvP09BTBoIM8YOqpmD3qC0m5Ceqttane91ztvXa12Zm69u2zJ/6qOx08qovt9Sh3M/jEK2x4ZyOvxvXGY/PEJ7vW6/rMWz8YYGXXfHA3lcWBZ9vlzXejXBs057K57EixDTyqEbJ2kCJPpvZRxNECCNAkBHZgA4H0zqHMMknRa3AQBVlQo8AsgGzer4etyqvedpBcG3+ZNrcgAAE0kBHLrERiAhM5DF86NN6pTOczU/U0JBvXBuVWjUxq8x6KmIEYlxiqYLzkBgJR2c9QyNLx+MkdaoDi34QkltAw+hHkGqn6VNu+gYcbJdOAOcM1Hgt0gGoXW78bxPh3DQwwdufx4QUa7D0mVFGVaHuY/bP2mamutVsQqDOxs7vbri+vLbFdo9hpbMiEylpYWtra2lxaTrwvpArkHGRmxSBjwXtmQBuA0MXAV/AemAgqAavA98F6i+6Ilwk1HYGJcY07F0ojPrvMShhoUgEUgPnvYhmkiJSmDYGrkUciyJw4ROgi4hRHTExMABy1Oy5LoN3Ld+o4zxV6ISaNd0rkiF0gqdh3Gy0aJqVFTMWWYqNgoED61CkwozUVWPdmrAsOhZBETfZriRAnBUyEGABn1ipYEtN7bb376Zvjjfkr751d+euzhbET/gGnr7soa6RPB8PhKgKb16YeQ+KGTj22ZycNIH41JvE8I//CpwsgRc9D4JCyfg1gvi0mDMgA5rPkcSH0FJR6Ql+Z0ADE2ELFGRgytvdSEMAzg4iR2EHMtr4rljciPUL9RiFL9RZOG7xwc+oZ28edg8ElS62CLNmSosSNb4i9UfC5dyGx0RGQLxGSMb22mhbS9glupEQgKOFxVHhmMZshYkhBYpgCswxcqvZSO1ASUD1SUlKFQttmT965rgrH2jZTVVfwZiBtKEb4ANpiFIgNbiLPM1HfAu2pRMlsDgSbqPDEEGL0BETRykTjtGLPXTGJonmOPnDqPtztTI9Prkxi75rAyOBPZdVrNSyv1caV6KCYVAUiC995EAPx8yYKd+B1kY0FbLS2dNfCztDXqG6Zr4AZnCUyjYpMA2L7uBNTrHKIL46ypAmC+ERwqdIUTRTaXROGemxkHi0iNcV7iiYcERrlBzPuYKJ8B+D4+ATSKcZj9EIgOkH6JCKOSERLRc0iaMV7RJOLKFdiMEamqJCaFuMRaUyCOmLONPs357uvr49dW27VJhfvdS59o7N4fNDwyjiMJfJwEn6AfHhDtZdKh531xoglrosOQ+FRSZWcxw09eN3ysEUHjMv+F3k+i5kaOsvY1m8O1k55WMyQmesv5t4PQdRQovLtRBA5P6JtiKi7C4BY1wXXpEg4cgDYrbzX69aqbGO44NxjqqQe1X3fFfmIIOJVvRSwndODrEWgkQid8SOlbgmRe2okICMQqpIoA11BGJjXQGZwRvNjE8AwS6t1oMTgrEpMaHoxYwHAah9/tbaacreCE1scjG9BMXzpF1AsRoIGJ80xmLm+sQYtaGsyzaHOkGwELF1T4ETqSYjisRQAiOwfHx3Nz7Nt1J1fzLnkwxLN8fHpLnseag+7s0v6bp5mZuf29vait0ZwQ/K0/AhSBOLsxMtDooHDGVpVZFvdq7xC25F5ixQmDytZz5hzZwu2jzsFZKNJYYhERGy6SCwKRTpF/Bycg+iOCBAz7vhS3m6u9A/ujjelr0Okc94JJEHMhtIdSk5ErkgHtiKtQCVqKjhKUzz6KB3QxIvrgCBHeDjM4kT/ykzvyYXe9cXm7Oz8bmXp1tnS33Smxvb90rQibO9IHzC1bA0hoSI0yJPAMygIUs9CCJEQQEhqLjrMxlwhaFDecBCOCcwt1xF4YuThNwEJyr8kejxVwJAUHnMDE0tfIPLWBQkhePqeCRMGLend5woDXCj2ZYbVRgYs5jZwzcSSSiDg2BDh0UuYcQcbA4J4jgXOEY8PIOFwMcwM3MBUemeOA+eGvPGTnB2dQEmytjTIudWIBKRqgZLZ7LcvZKaJh8B7B4RgfDUjmqokQ0Zmip/tZgDFl1lJ8SU2VXulHap6ghj4GLEVO/TCThkzSrKayIzhFACCkfU0LFlMwWt6JNGIUNWAhbN9cmoKKgaZhhQwQkSfozP6/e3j5livszaD31fZjSuBbYJ3PPXv4f37MzMzfnRJQnR3kyeq38s0nXUP2Nf0hkNhkMqzH47BFOYyeQIdLSKWyjj2OV81us6JpZVIw11Ek4tIJ8fYCJlj8jiVSHBzTK2z0WvW+ew7ZuKV7uQYTyIoCoOslMjFJvUUVkRzQ2oSQYAiES0VNQdAK8FM1nqLk/2pJruiYVp9fbq/ONWfn2i0JsZ79Zmt/uy99uzXO5Nnu9WgNm17NRH7aPsEEwSfdJlmOlwk8j2u9AeNoB28B4CeJMAIhJwpt8IYXKQqvNxOgsy9GbzLIHJQmjuTsqdkKKR9KBOGd0zYUGce7iwmHqBf0hgMo6xwZCzuAitchrrdDkYCbIAYFO5MFsh5b3y35roqjCk0CikOQH99gVZQ9MdO26eY524FjwrWYiAIMTgNR9AKSOaS/R/GGUjoXQ0XgTDtXreLN3n3j+ArRwQawXIiBI+y8WShakgBRLSTBIon1ybhqWAkICRVgwQ2QoEw2IOlKPlYVoHNl8LnvszYomqUHpdBOx5GhBRpgiCmAQvn1/4+m9U2yMjOTOj4nl5lrN2tbB/W58ePxicbSWFFUNCNZhN3WPn7QibFMoetcpOgBtEF7ypWAW2bK3aBxhgQRh9SBURhhoxyGYxPZsvCuLZ0kcNwS3JiORJUvIp1Ia+TRA6hUQlFhhCBJj6aIq00I0SKJppFv8RgRBp+0SOU7LhmY9N3TivBUJdBQETmvKkouzSBoEMRrOhCQRN1dDVFqtiOVBOhTjfGZif6Uw1WIEzV+6jOZ7wVj5VKdbJRr2KlYa3yT/d6X359caJZP+o3cJyOO038APpup3HWqbf3sSMBXvzz0mWbfCkvqVb11OTq3DuYxPehiEkHlzhw5UMSwU6eIAYfKWyaGTxIRcaZgan37mkQXxAllkpIXrwirIbsZVUeGDlCyPNBVE3JEbvwB4zgnfn16kHZKWPzAxlRF4tXCm7gGTHoMLBQQO41oR9M+IUDv8v0sI97FccSg4B33wszkg01K9EVp+VQdHMfQhZwUywJOzk9qdfqj7Ye3b1z58mnn8KI5MUQ+FXcKSC+lqJETWiz7sZokToRlUqnyz5W8sDCrtuhClEKEaqoqVQUgyY/qcKOhVSg4b9zgYivKKhNqPpgupHBi6Jk0BAdKNqiiNHG0zaEVKxDgYqjtRVLqy/N0BIT6bnGYAiAYxi77MOLRv3+7Nyc0ZuoRXEJhNFobK/dOj3YWFmYrsrPQxUMo8gA1Hc+Ozu3tb21It6iGhdWFLosS44URBe8y3h5taS4QlcSm32WwAilmZ3L8JnhgitMZqBogCDq4D06IZCyRR140WAKWvfApaIadMiECDLUOSQSDRVa1DnXNBKFOhE/kPHyHRdvvg4eneLpFZNMWUXSiHbvPkzwgj7PuUiTOwXNqKnoDL6JPngnK/vTqo0tTfamx/GYdJ/bqo8ttlgtzrbV4id1pTpRb4yhImeT45XTXuOo2zzpsfq73W3sdxv3OqBhX+l3K1joC/5x/+i/7t1kC2YIA5T/4qD8b8CzvAcfkdDYrOgX5lxoUPZwfL5IMwOq5onwYEQ1fqxJMxSJJlG050b05blgOfNezFR8BS2VD51A5JFCg8wFnmouiMzmsgL7DBGsSBi4dk3pOJOaPEgrgMyENMmxD+QJGRMfLq9er7B7Mbv6dkPc0LMylyTMK/vfB+b6wt9AJsjDQEFhPhv3xa6uXnr7rbfef/etzln38PDgte/6bnxOCFSw5cg+gv+miP9/9t60PXIcSdB0SX7rPiIUV2ZlVnc/07O7H2bmn+x/3w9d1dVVldmZGZfuW/JrX8NFgATpdHe65JKcEXLisAtGEDAYjWBC3okhEBYqxNUgts4hkABu0O9z9+nxx6sCOsLDA0iYjQW0WO5SZi+ZBXFnmMcEdvUq4SQ0oGWRxlMO+YTMUnUJMSeOD5FU+6V+OoqGUiOYkaIodgTOcYwgRIoEPIdKVDJH3kNigD89Od1Yj3ymw4NymNmElQuOo9q304eN7uhgl23aXUfKokRKLBVXZZjL3ScjA69o2623y4nlCOUnEqdBLkxGrFzI/IrJaeS0MCxGKYyVE6pZSxmXKO6uyW/X7DVxOSaja5Viz+OwRybG3Rl8mVE1oVBQlQA985TTA+0whlHYIq0EVxXNOkVxl2K+s6MC3vf6zk+N+6+uKqRqcsW1ZVBSFFJZn4KuoiEuQS1p1zSd5TdVorOOckKBGMDVmhjFCpPZgCSB49vtYaehytT8vN2qbbZqlLguurZSbzbqK/U1NccK2sOgftlvXPfVA0pV+jCsf+s1/t7je+mKgeYiuztOcPCwkvFdDiOkP1w4cYRgbLbXOFJrx/I8FAvpBe75M5NUKxBLx8IL7diRmLq5kEZ1IbYrtM1J0F2VukyqXLjkJCDr6kzaQPo1iZhajBAlgVSpAERn3K8VM03Q4lhA2yybl7PFVanYGA5Act00BXsdHB1NxAOzhHMgNaKCx1iXHVRWVvkl6d87nmwKQcFn2iil8pc0RFOXIilTWK5IJQwhXXl3e/fph09H34/+8V9/w/UudvzDw9H3b5jyIZbKGS6GqEdbKsLKENvUUZjF1ZAeBYES1YlmIoZ7lkiIq+npX09EK59fnU5DyMdIV5MfD6GRHBkjm8tHaGqy/JYCMgSErkUYjzcGwtC0J0Vb0XcpW6VKXSZa7WpzmeahJQgOIinSZF2F4+In0tB+nUsLCfrX3t7eWPB8bgb1tlc//fbfh+8/rq3oHZAcl1KJfAFW+Pbf+fn5zs6uIZQvSh6nHIx8no5QCRAHm5MYtzzIES6HWlIsomGEJAVJahzJKJK7hxI6M6fGCGLkGAM1qxTKmCykAAAgAElEQVQp8hIqowZVsc+CaWZCRprIhEiVgWvJnQzRhrhmOq6U+IikHQVg/LRD0YlUVSqbAsb7jsd9cPf99u6WtN6UMAXzVFknuZ/gOX9jdcSvuwmaq7jAxeFdl5fuOFao3W6vdJt8wcvcdSs13rlvjNbqxLFijjBBA3bdb132Gvcj62aorVwNGl/7a/d3rkTRu1W/ZX5S/bcMioWRgAERyjXLVmTOGQMNCHN7ZmBNgYcSQGasS6WuAMRkTMvSDRSnkwW3Z8XTp6y4pzGzolqUcZC+ze1RUehWTEuLer9cZRWMEMlJCIqpEngNphglpByyKrdAIUFdpUgEUK7cq1JlntAORpHUOV8SV+9JR1mMghQn4HjcxYckId2k3J6hCsAD06SSa5sQUNTCS02lcgX6QCFBDT8imraBs+D333779OOP9XqdwF8GnOPj4wMe0wfcFTrjX4akruA3uu4x8IF4HomEnEBYKKXXUY2FBCnZ6CZ6xHE1qKlLQDzqAhFUaBT5tepNqpOUhvJlSdcldGwKiKRdcXBd6pO1yJmzBjJkgkwGVAocv1LEBcFiJAhJKsqCaosTrc8UamihWoxn+GaB4gJl4TKcE45CggHw5OSEnh+B84ri3JTsVJ2eX66sdd+/50On6qWMUlJ41BORgkKVGbF9BYY7bncz9OWIksV0JRmMfPnyaxy1sYmQRoZ5Cn9cfQrcZfG48+ksN1K4chJxkqFYPvyk6dKU4oKk2ZWDSmOl8zlChY+eYJXEuDvTLU1qkrzplOVQ4FgMT22xVK7WJTTnVNYXJ1XlZ/20j1JJmhU8hiNTl3jfGy18zHk9sxJ2EKELdOqjZh3b2rSMXckJONlqDdtmawdh1a2vrrdWWk3VNcxQ36it1gergCsyK7X+cO2q37zs14d2fH4Yrf02WLu5ty5wgZzg0N3TTqsTIM4Cen19PQv6dLieQa8J5N7fpkKfjOpBScFrzQmpKGU3V2tmqanEogQ0QxRF31yYAEwIpg3bRBjLzjsnTVCYXo3kDao9ydkgBEwkkymPFSqCIaTLKdom5/Mh7Q5VTS7OPQOWtMBVKWS5XAOeO0mwnHYw+/W6KbbE6sDmk7MVxpZIPl2m65JSoYaPf3Nzo9frEeO+v78PSKvdYtj55z/+ycjDXjeaTIKU6kRehZBXK3DNx/7aHkQ+BSwQcdcZvYlXTSDHkmatvua9nGpImFOmuymS8uPuAg9EkBIRvAoF79cl6F4qZK1xxhDxsBVnB56I4YNomkGdLsr9BdbRzAUqVaG5mos1XgQfokgCH07k8M2JUnhFQH7DNJywS7P0oBJiAI1G28S4u6ICNI+ChRr1+sPj08ud3b12ndcwLBV79jDGJPMxhBW2+83tbbfjrS6sBGPo2uoQPJ9bfo2lNP7s0QjZOtScYlefl0jhqazHLQ9Nl5cGzCGTMI9TSupzCExVXJpqXCjXJQ3zkWe4TyqPs6qLje8CsmMR8yzpvPICXgtShQ+Mo19rXt9eNuurDTZHK3Hg/95ojtp1NTGqK0t6P4z5RpkbzbVmQ0JOFAgWCInmQ61u3Wqsglav+82Lfv3ac4FfDNfue6u9B+sCL93FjOBR+Jzul2prYCTJeFkOLUWldFZtyFUa+mkBlSZi6oiVKVG1GZhMXUb+XHjXPg+Fa5kLL9c5maj1Vc8FFlgFoiCKyBoxgq6giTsBLYgUR6oUo6LyYJwRRgrYJRRVxz+h5gBcwoeMFipJh7xUQUTI6iqhWfyqMvuTiKnVM67c1rt2OzldjSQsWYLIGV5kgcoQqVSPrUAIwW///RurCAsWsvYJWTquLM5OV2eA7TV32CaBpc4lwIeJDIiH+W4hhAR/ud0oXqflF6Q4ohYsqDOMLN8ENYHSWB6EJE11LroGF6gouqEQ1AUZwbciJDeXlIaHQlKAGfQQkJxAWJqqMsh4giakQghDMak2BSmyaTYWrQRevrocK0kglnmc64ojxBUg3Z290tc3bIx7pkVRRA11cXH5sLrzZm+D7x17g6HjWTKR4RAUCCsmoG/fvsmDX6eAjJzFzCYE18QCORL6OcUJQJIybNMYU0kDVR9PBqrxSk9zNqLlFCeCR1I+8xiZSH2EyqRFHtVJhM6BhZp5OXVSOYB36naJKYjMghLMx+UIPZWoTrqtpuxF2K7fjnbq2jG21j74v/evOk19ZeVCNVbWOs3VtYZsOq4QmfPqD6P6vVllyWRKFPhVv3FsXeAA8vSiP1h96NlYFm+AdtxzE/DJ6SK5KMUVPjWvz45DMmgKozRaMVGvlieVKMoo1ZaPHzQs5IKfZ2mI1UmRzqUuqU9SSi3+9TZ68uQxwJaLr0iLGCzaLPHAbGTZGUjgUzHpED4CYIvsnWXz/llJlPzoKqFsRaXEZU2prVLlDfUZBzzu9wSEYbg7YPCsCtTZMDE0FFkBccK4+hwZVLEHL0HkQ/lMY+0BXWEuM9yxA9321jbCSADPwFBM6DleLhGyjxuTGkYuR8Bd5gRHRypNRhzt8jR8hNe/3e54oTL2ilpIH1vS1uUQltt3VMJSm4t6/RWjOBcplb9AdEvMq8vUh21PVQe8grrcXXQCKCVAQiSpS1JWvwmUltpBpCtS1bQ4gAgywDo6Gi9drUv9Xw0hfSAX1lWkiPtkpkrT1dfXu6kO6FOKMkTWo8thp9nZb/NtPv1434losTMFtkLOXmWGQ6YAeAYEVtfcDrFKn3BI26/xWPrFfjoEyWGVU+zTSaVzMEJuKZzCSmChmSWbQcqCKDY5xSkRxmYtmQxbjZlTPJZsFCA+okZBKbSSpevZx50ez5GueA75WcR2M6huKNky1Npro+ba6Oah1hsS4T066ErMt9adRIEThdJWoeEUMRuz7UmjweNpPqaoLv0K09D9oHk7rPfE4S3zNVPQL3fv/3bxjdhws8mrhNetDO9WBrcT9xdtAZRFKws3c1fwGZXraArDoAUW3Wyy7O7aF4M8OuH061Wo5PO8M9KtGJufvpn2KvkX2bJLl8W4FHaI4NonkN4lC1nkwCNPCmXMzW5M6ZC4bVX6bIB1sUKRG50HX/X7+wcczKrCIyXwXlZXS4EpdEOTkjm/UFFh60n2a2fPeFrEgWVMEC0uvfPzs/6gj8oa9fqXr1/xwePqw1zAbmBEQiqigRmYtNDmNxHK01Yil6QSkADTyR5pGoAY7o0GjnYRTzbbSTzuIZWAuuaV6TIaJtoXDDGLElBTdYKVbYKUGnpZFKnLoihqMnjrRPY3uniwYJ4TOWmHVBpyCdWw2gPRpHS1R86R8Ot1Wv8mpLMQ+XQisD5Rl0Yew8BI5mpcwm9RWhgH5BJAGAQH6xNwcCTo/Dc3t5ubW36hkycotLRub28va+/3u0f+91AjdlUeS0W0sFKztfxUjjno9ORkP/rCSSAlmZz+VYJlCRDHLBBPSjMFDlQnMvWF3AorhSD3q71lHSOLlGHlIPJrHEhxIkbAsk1h5hSnoAqzCbcIsaSykIZXCZEkxt0rf7FJVKQ2D6SrmMe1xHHzvUx2HnRt3mxglNd0iS5trPLG19qovsb2bngpeC9zMFq96TeuBnVmz4FM05jy9WvZgrBun0jLvDp6CEZDxyJM3DbWN5lZTy7OeJTGlxrC2vG5rB2QizN5F8klNV2FL0Ci8iJarnUKvBxODj3eXvrTTz/lVMaL8ybtOLQqNSLOJGkB+TlX+Rcon1U5qFlUUMTBG+fLskijuF4lbczwMguAhLhAJFBJSrAzq4V+X3Yrxz7u93rKSAU+ISUo0SMRKaSfAVZ9UmD40hNm+If37xk0wJYJcDTqPfTwtf+v//N/CMOTPIE7g+G//8//iySRKnj7WE7w6On3335n8xlWF7TL52zS0UJYhuXkjazq5NOxUgsAbFkvIArcQSEQ31CyQMHZUBSQJBlAiBj2yIEBIFG5gVYnryMIB0tGn5WllCpTloWqTleIjZcuM/QUl3hdgU2vJI5YjkLIEEsaZTiZdlpeXr2HFcK6nCWqCzzUMEDF0k7wPN06ybJENJTBLiISqfOpSbUvHNksBt271WwFVzeCJ2TlPuE91Ls2H/J9s37Kngq+4ZhiJAjxIytCTCzBNSQ1AjcCT9qlsASnEiDQz0iSKYi3wGBmmGQKUujj6nPY5xRDLUXQA0zVeILk13hARUlLwOOWAs+vSQGmsjE8yy0FqrOFlVEMeuwzNtyJ/G6sSbu4XfmPPX3QHfLyJducoAq+O857me82Ru0GmqSAIX6lP6q3W3X1khb9XTR8N6hf9Jo3Q0VIld31G38M1i7ujGbCyxDm4kq1HdFdjxJI6+q45Bhtb65elIl9T6bJHDGS4hICJMCPlvKlcrrK567ADY4CL4ETUot63EOQCnJGRL91FVB9bSQmvrgZBRVegGDeHM9LIBKoJOWZjQk7jHXKJZgbW1V2UDHwAmGgEuBE7EQkSz+4wxMUZZ3Ii55w+ennn7rdztpaHYOAwHoNpMZDsdk5ZE9KMdflwGJQgTQr3fUu8fe//POXj58+rq3qoS8RRFKKUMJfUlaAsWkPQOmHEL7+1uoWqkAexHCPFISPgohwlCJ1eNQkn8paGFVjf3RhOuc4uWqbMC3jJJa71b6t1eegQpMSwADY0LGIcVe9unqApIAFSZENKGpSilEEXtdOgWVo5XqVwgorUSJBkkIEW62lybSLagH3oQL0VB3ZompT56g5WLr7Q+8huYcUWVdLzh294crxXXtn7bzFnmiguYp4IkMjLUKCFqotKdcpx2hnd/fs7IxXadMQmZYHABlBdG1koec4BfiRTD5gjFmsLEVUgeRQzSk21ptHKA/QA4mJEivzUJJkCJjPLb8moRVLKbyQSQzMlJUG9GnAYiEM9+bqqFWXXcAH7FoiQ7O8iIkjvN0Y9eXrOStUbbVqOx32apAx4LYvz3ybddn8W7UHixxHztqNfHmH4B9RBnQehmt/ZS/CXvJEWCpK7TyoRkNfVY+SVm9P3l5e3F5dXZG2z9nTvEVDZY5yUGUozR3GF7XEDaPAExyFMR6N4VJNCwli2K7xFEL4ZW6RNTD2auZ1gwka5U37CTsVuKIM94FEsDhykjI5cwpHmYw8KTNEERIgxkcVFfMv//qvvHuqA2CwgKniB9J6LlecpZQ/ZdXjrwB2rd6og8XwghH/9cuXw3fv0uMJhJQvOdeh7JrkEkr25MeVK2lZMcBXlhFDCcFPe9yFvUFNJHGFipRqnE5ZYEU5hFdFCkrarBMZMFNgwDxqtkSf1a/QSOh4FV5pPkyCnA8TUDeZoMwxDUolYwu8HmaLLJIx6tLFrhemKyQvf6Z7WjLmrEoVSlhvqJhTWKdQE0RNyYIYDAqTFBlbrYFz6tLcWCRjE4gV6B1BRpVfPdRvrs4P9ohVledRHmwgglce3AQC5CN5cIU1PtxKs9k6OzvlW6p+qaRzDP90U0O0fHF8uFjrYmUWx1ANQEpwKgESkIQdF8Fch3SNlSU8K6gYn1hZiGpyOYATsI+SdYW5EjoIP5EjjQ+SSct9XaXhjsMHL7iY4DicxFqWL1/yFczd7pAvXzKUUs7NVV9ZZQtwnu7e9VZwmTPGjlYaGOd8H5xYFJrNRIMJftFrPNjNT3qjlT+G9b/frrrtCDONCW9wrzrnguQUe4jTJ2ejvbm1xZhyddsf3t6sd1iepK9RMKGbUS/DMlMwfXMeGdOXvFy3VhgGzcPwkqoJMSeH3zafsV/up9M0/bplepwGymh4HI2Fr1dvhRLV3cDVXSxsYDkUWAQeFel/oxquc4K+eBsPg5jRkiFUH1SiYtVHOVOGD0PXADMUy31AQC+mewPbndvh+7fvR9+/7x8cJBawoh/8aKL85g01VCiWyY8CRhS53iN5ZVaeP+BuVysZtrfX9elf2zvU2WZSQEIzXaWMdK/Qg6HxhoBXqEi4cguhCgxxPw1+iCuKdVJJysvq8gy60Zw7OXTBdhmTSgpcTQBmMhGwdJHk+VPXxadlmZoKg2bWez5glmBgx2pQQ0VOaXhTJMURIVSRQslUGkI5iJacgSKL8SdfTQhtcSl3B4++Tq5GrZXzN7u8w+o+JeBIWBFsgT0HklsgR7U4kdDw4ESDrVbn9vamrbaX0VUCmkM9p9gjaZIZdklBjEbkUqZpxtA0TEI6jTOmXkg6ZDUAUqL45DPzWYyDcrR9JI9lWOzlDOEcfAs4jj1wOSD5hPNrLNfkrGDhEAuVwbxmR0F83tR3GxKOgv3dVr/sC85XeAhBAYYIFBw5TXYfJNBS1ZIdjOqEo9wOmU7k4BL1BmwBLuHg6l6REjYF//VhtXefljedT6RNUvkw+TUJdtWpufFEdZvdBpsxXF9dMR7hHmOGzkpvpplsBSWp7jM3UaPMKytMiZ1qVIyNh+EllT4uL69iGBOVBTQnwlwCvxYNsKPLcFgvt9PrFDohyuXTp4+Y3VjDDAsSJKMOMdETcvLmjTx7lCr5AQypjO2O5T5otDvt3b3db1+/NhrNza3NBDU7zalnmACo+2/MTZgIMUpciIhJ8L1EDqkIHp+XpPVAJoSlBWW4KEDvR6jkHELcU4wPlVeVKveyok19eIWKgSqXHwfhXZAQWICcSOkqRcLwsCdbpnj4hTqdcDR1Bs6iWYzkHCMUlBnCCYZK5ROkmsp01zBFEcoant8IipRqlHQlNfpIV/PGMz1L+peFkLPTy/2wdX702+6b942VTmjcB/CCYwqSriuFHLYN9qxLi39TxC3qqLaxsc430VrtdjH+rLU+f8tc09Q5v35WXuXwU3z1tQivSDGhsBk+rKnJaZMqzkdOCOXgJwBhqpBkUJlLONPVQg5hTlNBY/X/93/0RgPelsDmXtto14cY6fCT5xdrvVH9blTv1VSoibh5V25lF3DvQ5gEqNyvsEeKL1QgbcjVz5UAKwHiU5xr+ilkYa7F+850K6Hvo9rWzv5qTd7uKiULQFyVUqBzVVylxP3m+H2uBBNQeY+gBOASZKmBmTTADYsBwXYuxJbPRCiGTKjJm7cHe/v7WO3OZBfjPBgV5D4RdztjuU5qCGW+i+0+XBPTvTnQu7x/+/oNJ3ynLdtL68PcW8ntFtxsSbGF98/eYwSDhUGFb2cwvNN2VWSeTp4hCoqhH7TI56DSinbyY2VKn4EV4kaSNBUFnfykqzN538jOVFq5OVspFEx4aXSRB+OqBclrs8/Lr/LLBcMnlZPOgAWMlERgqkNx0kn3m1SJPR1RpQYwFT4FU5SgGFKKtF0N+mWOvKAEFSZjTglFLaXY7CxgU11LuuJK7fTsarUxOJDHSj2vc7rmRRMKM1aTWxEDzi+TG5JXT8TlOdPh60gpxS9QlKMaU6BlGGfIGaSIuz4AzWGQKuZ6cUXCqxaQSUSMNiOpzosz8iBc3wrKXCaHL/X5NT7JCFSqtY5VmCiEilAFG6XV/7/ev/HCEMLhnhk+1FSQeUi4RC5OPkEcV59APnVq8SRlesa7xtU5vyO6/3RnZ9cf3uP60q1YvLbEpZ2uNNW6wu6vObD/13SsllhLDZTXAMYXL4USkuJMsvK4xZAM1Ftbm4eH75rscqX3dnTudrHF3C0hN4O1UcREICsndTCeYLpLwMyg0eSZ/aDP23J//Pfvf/r5JwoDAXLuqZziAFUyThz5jizbVw+UAT0e20DYBli6jpwt8M+WqjurNjuIHFwFnfxocGN3x1CiawAHKMaMZHLXCU4cy8ieHYkUhOjQiJOAZoB9mPy0dAFLJElTQq9wbPPQM2AhBVXtiLiE5ZfQz1SlC1xeJxJMSysFYLPEpz3wSMduFqeLidE6Pr/e2dlvrlKl7MNAmxY5fqZfJOxlveLl4hi5pRpTdTQLs7GxcXZ6ygpcFwR1FqbwHKEpEipCvqQTUvZR4W+xbbE9JzVOSAvqCqKJhADVdiXvQcb8z4Izjvq4ekgErA3LpCyXQEwiT2CbjOEn1C1U5lwIYl5SySChDiJb+FwYb3WYyjF0IiSeYVFhIxeqPW6cYXDdbQ+Hze3Ts9N6981mM9icoczFW6h2TSyM7Z8JYuoi6mwWLEEgsrDl5ZbJpQbmogH6IBa2ij5P9dGZ2BExj73OPjDNZlNsbzHZ1dedGBrUP22AyR2gTV6xxygzfndyYLAVjSDyT4z3NbZXHzRbPIliuxk2mfn5zz9TO5OUPrK9GZEOUQlwF2lsoQ9YkJZGmENjJnlbHj8rPbiqSbgauztBUTLk800AmUUDT6QbvZ0cQcIiJoZ+gmDZSZ2FC5C9jIJNUL2aaFL3k1SVX+inBUxIW3m8dAImld6FSiqCRUFAR2FYosLEclC0VIEu8VlruABLQ8rHT4bcHb7v9vrm4XZl+2AHGe6TXpe6QgUUpSrUvH1EkJQmwlk54meHkcjOwrnX7/NsztNWHHmCUnqf4uD4TYAroCCHqE5e26/D6pLkHRXgLQEjJ1dG/icQtt4nHSvz60knBFIVSTZjgAtOCdI5IOM4jqNu8HOoa7HzKlEZLhYojBMiaf1zSD3/1uQNCEzYe7t7g9ra2dkx8a2s2uW2f/7tHd+rXBtdX3aJ8cgGQm2gWxp6CbjUwFQa4Iakp7F9i5qUJNZ8KjIBkrZIfvzxRxafRLZjXnNAWW5+9c9yocTM3hLhLs9RNR1kEGB9SLDMqnzNlH3ceYmWlcDG5gZBOL/99tsPP/xgSQUCzJJBYHjLJu6T37NWfse/HImMyr0CL+moFiaUDOX4QscD1Da38DM8x7HWCCKMoqLAkx8pjx0K1qBaDgVYvnUbkDO4XKvSh2GTwdC9UpPx05R42aCneeU+TMk0FvD19U23K8GQo9rq6dWg1dncXbvW96Btj5YzuUK6qZk+ZsED3oqwq9EJUXrYdqOQFJzLJqwpQlq2j1tf3zDVlpI9O6zHScA2EE9aZgu85KTCZFAp4FCUCXBS6fwroGBzfjQhXanI+QXunnPIti35Ba5GJ0JyqUpfNV5VgpNh50FJMqc+IZCCN1lTH3s5NY6wIKXj2rUgYk4lRtkRc632sLuz0+v3+D4iz7w3NjbLYk4l1mIh+R0gp+/nCYylkle1LF9qoCoNYI4o73iT+JBKaGIesBX6T3/6U8ds2Z68kCqGuDXHLS+5QzDOlFVEWv6b8UGg1aF87pj+3BEY7xjuhOPzOs3x0dGXL1/ev39vSeWc/XswB8Qv1msMs4P7hLg+nVnS3lDhJcdRzAibKRhHgXrhZ3hOwFoIK3D3I7wN/3wxLAeHJXQUZj6OgMhhcJUV5UGLRZ13GDaWqwYDXAXD56PlkBOE2FRGn81i+IUsUIcj1rTypYJe72rth+367/WR+t6XbrwQdiSibHIKI433GWuaHm6gd8cxA6YKeMPk6Og73zA2cEaLgWc41nINHurckAifaKkmqx9TXXiCYAirOGgBoswKqTlSISrFtkCSapjKXUlGGDiy1FlCJHWxLQhzESo5RT5xj36q2HIzRCxTnQ1zKT4ZQql6yaZXghQFaJrBE3yAKZAiIvhrKsq/K/O04GkPVxnBqfjJzmsH9ZvfNzqt8ArnkXhB5Z42klbl3zqTDBAJvWVqcTUQ7QDzEbc8K+7qfq+/tqFXiRV43B/uH3CEb8iHHUxou7cDpDLE1Y8/e1GA/YUk+j+DvwHRZ37le00ScUNcOxvp4XZvtwe8S3P0/Ui9sbpboRbhAjWMKrHgK6T7BKTyB5cphZlAH8Lb8J9QDIU5FodOkTTCg04eAyTVNmUx1NlmFLtEWAsbOYtZbLEMQgnnq2HmIUq09PDi/PxhbXc4WNle/QNi8uaeWwYEsE4QKfUbrW0kAxug5ECGMArIEbe0E5gskRoL57u7O+4+C+2jS5nOh0I6mIS0BfMuG0UqFxYZQEciSciViMDGrkearxAJLiT5CCkBS4plfcJVYwySSHePpJcUjPBI8L3ytOAJUFoqDymSTPBspZIlVRxyK5TWkjHnDKFUPVnIhVCZAsHhlgzfRsoSKiiZROQCMq+sKuceLNZCvqrxljVrRw9rtaPjo06ns9w4xR8CUvfAazHc83tLcS8rUztP2mX4PwMYDCDiuZU7m2iZcBieXHyc9+sbG1vbW8Sji6mtI2Sc/e0n9NfcmVeUHwvT3VkldtAR20xjaFL84r1Xwe6y63yr3WKjut9/+w0LvqrHBbSYyBzUQETv8pFX5vq77vH0N9Y0fdWKr87yI82wf5nGZgpkTWBJKKxSWjDM1MkiYAKudfbq/fNWUzqbPUgqiKTE1LhSqbFELFb0LEAGcAy8ggxg1P2XlCQATNl8FnB3d0+IJwCGVbZkWjDwBNWcAp++LsMYjJSGAlhId8lcAZSTC2lKc3AdjiheG+7S8bwLZJIT2NxgBLqzEigLV2Vi1U6SaEJjeGI5KMXNEYxBONBoIoYalrkc+FH6KIwPI8VbHeW5LJxcA3IV/CtRlsIkOOwOcdBqscHzyWWvvdZLnr6VZVYKLnsrqV4V7VqlCM4dKNQhBgqjSXZA8Qb6uUv0GAzCC5Jt72PI8Lp58K1QsYzV50LHWathH83oDd/47c3N169fZR8YjHdziOudnCqRH8DEpLe7ojEXysHuNnwGgh919FlPED7Q7xNPcI8b/57Hdfc8HLi7vyPNL6+o3t3d6gj66QatjPiyiuC+YwIaKJ1URTbLaFmyCBqQscf+TSaPwtJDlxqyxtwXhrhCuBpu3px/e7j6vv32Lf3N4wuRJOtT9F6H9EE81HRS6KSWAT5BAy5FCjJhmyYU5uV9GAJ85BEYFQnFMJXNjS1JAAxdKUgKTSqcHRRIAuMkjQLrWl0lr9OERxi0Q50CSEHha09tBKSIKKjAmBf0FG7Izle3BfTK9OW2FSFqJAegh+wAnAwp2RxAUcIx9yh7ZS4JDQ8iThGtzeBxj9Nclsol8C/DZBqZHlN2TWHflLu7wcnJSbfDvszJxszlZXCdswyKEjZXYtX/xnbCMnyqgWGAjBLKDDoCFbVvnlwAACAASURBVIz/UbRnUjhpQ6LaWIy25va0xRDPSIFLW3ncV7CnlRkxq9h8HWl/f597G6OdCZ6+iTkOM67s/f3D9fW1GBRcNgmPUYd8u3WIFY7Njj2vLXcp4uuSfLtUfaoGeF6PaclvX/aGrN+DjvHOIoEMVIRgRQfrCwiySph51+qKBFqSWWwNqCGr1MQB5PebVmf0ZX+rc7W7mxnrDBHdlWOZsYpI3QVCwxUFBDUlU1Se7IgdJvhOy9bmlqBqdGGQEErKi6g6oQIgKQ1qTCY8GSDvjrf1GdwyMEIuQcwlhWD4FAiW8STWwNllAPoIHwgUmSmJ6ixlE92SVCg+iYwWzp4TQFuiYJ297mF6SQtb/gwfhc9PlmU+GVkULQ33fP2UrTHXbvpLOD1mRERCVDmYgE8uHzY2dporNxEgr6joFvDApkiqZiVts13TnqegOBuKLGtKH97Qk8YxDXiydqTlqTafmfzGkc/tQMmlH0fipdVjH6MVnOK9XgXfYMLqxfhWx2htbYRrHdtaPPri02djGP7JIU47sd3FOse5jmMd7srBrk6U6qM/UPnesCceeLzv+N3vbm/vbm740AFIEK/weiA8BHGt8dYgclZIeUnqlWvgvtY9+uNvhx9/rte63B24q968fRvVSTBUB5kouC7Uw1cC7YazwFsPbAIylpoDSGiwrL18uOA2kbo0TwcfTTiJqDVC+EVSSj4Qz2TCU4p4iobUJkWZlClIyg14rFyqpNwCM0ylSnRdOPlaShYLAgbMI+WIxnBVc/15Sgq8lYBfJcSzh1GY4eJyTrtlKQRtEDaKlBLHMM1ARIRhkK9yjM5yeIklotgSyh3X9ApIFLHodLv422/6D5dnR5ubm8TS+NDju5kPXVHatticXY+tiPx4MuoDTHC3ghgM7y4cT0MgDH6KTA5uQj1J5YA+02Lnh0jLbx4s+8U5Ossp9jGfVVr2s1hZIbYFj3gFgtNzULL0H7Oq4mT/M4aPhis44PGtA2EMdxUeY4x9POzKyY5tIId43vlODSlFw/6oOqoqEDdFYsRnWRGFRQIKSdUts0sNTKWBldPLu+Hq2sePH2q1e02BrRqmIpWHlL4VknySysN15W5kMzjRPB6l27tb3G2hke2IpBIJDZdyEMmCQBeVEjVFJi0qlKRIQyUETSo8OUF0IkXZ1JpStogd8DmaCEqmEBiBs38axeYSCvESQUxgbC4siRIXNoGpBE7S/oBmfBK0PBIiCboQNzpVSVsj5wReVWV+GKyXhntGK0GBUb29AkHdNJnKCJVi3q33uwcHbBZ7dX29ubEpL7IuzKE0YdThd9r5CZjz5m70kliJZpYmoZ6kZib60ghMpO1noEceAHOJ+CLM5aXaqHjW6yVGtrHaxcBmHpKslMiBIS72Og+dKce3rf/rOn6Vae5ympKUmXJDxABIYaUKRhjsdb2GePabysx6HZf4FWigX2sen11tbu91V659cufn5wcHB37JAqS5zYIjmsfLxuOC2APh6K2YGOeGmg+VZhBwDzMOTXBcxsCEe6dIYSnKKTIRAYWShpKRSQ7D0Zwkmy6TqhRlA52UJilT5VACC9vDE4wASxUEJQYipGAgsoVlSoRiwMJmfGNdDdJG0uBkgFHa0nC3ilMKCjKByqbKVExuUhlMD8Fk50pfXWO9XxNRx7tskxIS+OK2pO6+CRlY2vacjBKz0Q3FIJQwLCjIJZIUANmqKoW0NJfnPA2U1/ZEFzGP3TTlA3kEPJL90WVPupkO4l0aTQk655CJTm4Nk5QiMdgJCZXilRW+wkjYqLLhjfGeuNplMtQIOmWImDJdAxKySvxMrxczI6ZsCLH+0CRoRwXoT0lkibbUABq4uundrO0ebPfXQqudKh4sP18VEerGPcJv2IT4WJcuTedDGuVyKRpeHMm4qT+gr8mkB950qc2bASmgkJdxAgbELaXEYjD4AmVQYsuAKJeEsqSSnAbO5IMChRGUJCSylrpXYnF8w10YFhHk+eXrMdytgtRVCDKqpJqfedEtL5263hlwgl+N+S7u98vNrR0+v5iBUgXTNaEAy91xcX7xUktPzpaAPccxxpeyyd14oGkgrLDT4M7aqGl4VokzS9urlGPRaOFjZoWc6zopJy7G7vp6l0+bXl5ePTxgTDeJF2c9ICHt+susDPCy/6O8rkRSG+faWle/yi5XdxD1uqsZG13tNiMBLCoanrddKW80G9u7u911doT8/aeffyKGvpyYRVD0D2TFKGGFUeELr0Usl3UvUQM8CTq6Wml3375dPY227+b6usLVZpTF/ApZdfDEYLfiaJ+svGWH62BaCjJZmtmSBMHnJ6Vhfpzh7kM7LskDB1OUfThARSKCQ3SJKFnBcRWCzZ/LC2qaYv56IEAD0zPThZA6MjC2wmMqMPYvwMI385IM97QuTLOdRuaRiPCcB5uxNMPrmwMu5vum8r4PN3snv+zs7DzGPg9ZFaVvgRxxbbElIGcP1UtayOLz6enpzz8Xgzx+rW3c43NecpybBjCIleHeZLPFWZhApNlqvn37ttVqKzOamY6PRLJjTK+30sNHwz3ATU0fkpuBOulNmO/idDeWOSkxzyXnnPGkMKYB5u3WRrPVHawDwMKAb8FcnJ+dHh/jcQemEsOdyZDFBttNsjJQ4s2ijyXuK9XA/f3daev/OWj/Rz3Hakcv7In+fLUj91q+JVhduyaeNGOsJ5iz0vxSeXkKGONgypI6n2ViX2uwBCqPlI8NTIKQrkjn8wjq8hS0Axb6fp200c8LYCKDRkvySSpFRgPKL4P68zLc063PbVnSxqpTERGqZjEBPZFmUonYfGJz9Wq0t8enH5jixXyvwrU2odQeeNBTvfJY0mts8DQveydksff25DsXy2OpgflrYAVTmZcyZ7yzcK7/96+/nZ6c7u7tsuTGfCduXpzujZb43PmykWzlSAJG2oDn/pDNIjHE8dbzq08mbQvJYpqPBiPO9+zfzp4yNzfyIszVJUkm0w+ffoRuJVpCLJSAKFq+SmguibwqDZwNdgeDm3e1v7EELGg4nzGZbgfkApqPWcV31rgHiWV9TKZT8Zpkws5nEDNnc6EDlkEmF8WrMAie8WDMpjSldN6jIcmAQMrkSNel8ylSqWwK2tXmSb4oMe55cqdV5Rr0SIlcuR6Jfw6b2LOXHNCkOGwL8ygP5vCrnZ+dMbNubm7pL5Mn8I+W8gUbc+cEMoV4fg6wCCE87j/99HNAYplZamA+Guj1ZV9F7q8ZyXOTsnU7dytP0mu1c1ba2OIY7PIiao3dIdnZXd5M5R8JbGTYASN/Uqr2hhT7XQ7sdQ5c8VSzByQSShF7Qw76Q/aI7PXYExKTf2Nr6/Dd+xnFduh8zgmxYIQ0mZnPQS0Tz08DLP7YSlT2IN/anpO3m+55PHy7Ofjc7Qa7okWVNeW7W1FaT1HIKl2+yfCKDj1HR2bqEjpITfclMFI2wTRsDU7A22bS9NL5lIQWzRQn0ElFkgqtGYbv2nwNd49zSuwFHMKLhM1I/zQFk9vr4xqFyb67t8ekfnFxjjXAcn9GH+GsikkJnPTnMYQzeJmCWm1uMe5jZFtWvzYNYEJjqqrt1SsIE4fO1tYW8buYJnjXxYPNSAAPRlHZR0ZCUPQ/9Kzd7RIpw12tgmTEZifEHItfl5HR5WLG9/ku0sM93126xWjAqu/X5Juv/vVK3Uh+VZn0Cv6A1RVWB0roMhgLB1N6EFo4yasViKUdISus7jCB6EStpnTIN2/epjpMVUxvBp3Li5M3O8erzfFWO0zp1gtoVlhtlOpEzQYbyN4/30h929hHOJfS5zg5phzbAt5BZhzDpF7QEvZJyrPR8ynjmSl69qS4eCQTrklqTHUCuACp5ySrVpdIPJnUk0Gbi4I1wCa4zOIXV7drnb2N+p2yChbykuX3Zl/cjBqkAOcQp1RVOXo+7WV6qYGxGhhh5bTWW9r3PePdpLzn8mBZJ+QVVGO1KxNenO/6EKnE0S52uW+3D1lFiPGO43t1dYAFjf8bMO0HEKLiwtEO+mzDprtB5C5TdxrPBkjimxWRU/deltlCliyo1NNdmEk0zHJLLeeYGXr0j3a7w0MkPuLLUmyuzOF1fN+pPxwd7q2Xl1c62OIepWTrrm/wYI2IuMVth0hW2cV/6ktWWUNyrteYi56wT1I5lMJiRmwMd6gHDIJMiPA8cs+7AUb6yRoxGXT8MjIo72zV+/2bs7NzHsHj5IvDPW2p39IJu/vG+rrp6RCxuD491zJb6QqWiaUGJtIAtrEEtHBPYYXMMj+p10wVazHdTbeFJoO2xMgEZdKXKZKXUFOHQlSwykQHTQDUD7/6rLAwfsTw12+vTtTiENjJhRKoIaqC3dyVtCGczRUaXdF71GK+2nM1WpEL5Q4WdxcXF1x9XibmwQ4vVPBVoM3NDTw7DkYnKmAecE7I39fWj7/94+Dwx2a7rNWuKc1ylyXsnzSlmiBPM7IKf1K5UswruPhQlAEoY3ymOBVmczpQIc7jVlYiYVzbdYrjNY/bxMm4PT+Jx7ZPmjRxsyZGGCsG7/+s7e7u4S88OjpqdTY21xd49e83v8Q9or6carXs4zqtWCLRSqBsvUNYJpYaiGiACZgYcvzhxIlgSNdqQfBJBCGnCBMKq5pfHJ/QVA72FdYByuOuJj4ZM/xeKfa49p3rlLXOXU5b7HrWFERtt8NComuUvd7qdL58+fLhw4fZLSG48vSfXwIAED6nlVJcYNOHDUxoeLZ+3v2aAC9TUQ3Qv3DxqneV+ywygcFfoyOyovBVFsYu2ulDd9i7eX94uLLC91BjEDEJgKOvYu/GKp9Z2cbG5vX1Fa8NPDO5JxeXYaF4TBhHsmz3GEdnunp/1J2OQkmsKKP5xriXlCwG9rQXJSbRHMpMIydr62TQU0rN2M1X6HrDtePjbzhdcr45OiXxuaCl1BLr7Sp2EDgNGoNIEdGCeoCunjKX9pvjwfrFy/Sr04C4zVZXuY+UNTxl8zGm9/f39uV7kLjwiVxQu8EQKaNeTcWQV7Y18x8eOkmL+avNdjHE9SHmOJu1yy/hM4TMSFZFzkjgO5us19lqstFobu/svvv4iZiWs9PT4+/f2YMS4aeU20Nbq68xQxNHX6+CmkfYt/XH3HbJrSqpJOdTeyVpugFbl9zcXKtLz6UZ7uzs8lrk7Iu0GRU4qNW/Hl/t7m93pv26N911lks7pg/N2LzS6CyfuEW5ZZ78ipQWeWpAcUE82+OpRDf9dGyM+xwU+1RNnkNTJidpGj+xDiZGmFy0DEZjFbthH28ZH2Tu7HzsrN5mQBa1IKuuEc/re564WQgqY6N3DDApCzEopyCp9fhlkyFqtn5ZMi8NlLxAs7AnOAQ3IAY1ptLU7ED/5ZdfMNjZs7Xb7WL7shmkstLxVWGp6yTkDQfme9jxJ9a5OcRiF2tdDk66Ukx5vQ5Q76byxuEt+0FenhMocd57uN/e28OAmFpspzfkadRZurCJDd9/bc1O0FGeMiG3XOnbLhA3yEzJ/dHR0D+LPVZNlxeXLPM0f97RX7QwyOu7/tXq/rt9nkyxddKUB/MU98iUyO4Wmhp/EsTiLthpt9mhtdOZvi2TyPJksOoh27O8rZ5MZcLYaKxSw315FeKX1OhlGvVMgxMXYupSHNUc97W1k9PzTrs5p52/phavLOJKDeffOOA8deePtBmMTEGuqZCChEeqZJy0y/on1UD+1cJbhpHML77Mm5ubqa/r6soqj84///EZgxqfOp51Ik848S1VDnabUZY7TnO91YwY82gEQ9kZ8Mp6V552a6mL155t/Ihilu1k1C4hd7eUYLpTisyd9Y2ffv4XTWpG/bJM4GussnIYDlsYjvkam5HR/NHzR4A076dsJIFP6uI+XF9dEabFWo3OgTl7+O7dYnpwR7XV48thfePt4cpxWpET5p/RxFTcRWjI8clxdzbDvXx/nVDNS/CF0MA4w724iy1EExZTCKO4afQ3Dc7cldCqXbV2t/HMHR8fr3e7z/FTF5gz06qp4JKUoFmA7QlUAIXg5qliCW4eyUdPFrTh0WV5WoZ4OhGAFa/af316WXC68+mlt4d8PFU+uqTNLwwy6POL6U2PQOsq9oUS6R+U09XlEB87/+Ssne10JGXTAzLEBUuEjCwGWm1c7pABEnsdt77aCqaCa4m0+tE/iwVWFcuV6fT9IB+TBRifvOUzWlx7LjSRjVzWtxImXsEVzGdbQQ1rx7PG/9hp/bW1cjI7ueub63Yn80bWgg+YOc1mfc4Nrt86yAEZU7yY196/Gosp4Ri1Lkx1XUb9pQpnuh6J+pLUpASnx5yU00zwOAM4bq6vT++7ndEJk8RM5B4XGUtmDgxnvHKlREqm4Bm5zaH9cyD5EhqpbOchfvFZPjxEcBdmGVdfmeEjTG3ldk82gMQ48+0zZZcrg11CYjiU3S5muzl0CptARcv3xfGO6a8Cam1HZAf6AU8JmmqX7hkvLvIow12WFb6cM5J95ej0Ct5f1O8psAzrtDs8gdk4OFBLo2ejm3M+G7C2/bb2j5VmBa9S0GzWt5HGL/hYYu+6lOQ8Zzs9O93bfWmf+g6uxmNtD5uj45TKn1l2nMf9mTXn0cRNemCSmpT5OEz8KHjCCGyVx+LEttogxUn5VA7fXV/vjJjcB6cnJ3yoGfEqZzEPgjgs50F2NprjOsFs1EtjM7gtiCSlRV5sQO3jxvwN36woKzTGNHd/p9PG0c7bhL/+8gt2yebWFq+Jc7vRkxkN9IDgXzlruGs7n1/ZNEad5GzMf1WJU17Z9XwyVWKgGWd6D3dE0Lfa7cP3HxEbphiFbDA1o8HNLpCMYmWbvYSLaYBlFs9tuG4st7gcXB28J3QD3QFiGAtdxiruqLe/Pvptu12l+YGK3rx5s9AtzwqXM+gmN10OQJbS+JJFM2AfS54KVaiV/FiCF13SKu+cIj7Puy649EFm0nZNgsyczbtouO7YBeLq9pbAVGZBXknDf8WcShrmVOGFa7faWPfzMEyVvBGh8QJurG+MuuvXD6vXV73N1ii7B4Xq34vQyc1Fms6EmvQKP0/4yCV+ng1ZIKnxdLMTdjIHlxYNEw1H+A8/fCI6mfsHFzj3/t3tHd83PT05FU/6aEj4Oy9PEMHMDjPymqr44uWXKrHRiZqAynCkXO7y2VSc7/R/5Wtn/MBcZ+sKIYI5z7hx+P4DT8+ImWE3WHCBxDqEV6/37fDwsLTgaUBGCYjDFIGm0EOa3KvJ03P4mAZmOleNC4sOWbQtlO9m6ktxPexenvM91NO19sbURKKIu7u70fJnWsgLxBeXF1W+RvwIY/wCzfZzvOzVKnI6nS0N9+wFTl+XdD6LUVwyLT5THV9wYSLFJi6IKWceZ2ok+pwwR15TYzIWX9pgwEDPTC6z5mjEi+p47pkCZJJXR1bkKZ5cQWmjNaq16gwx/N/e2oapo6zaHWm86qnTdVdHe5oEC6Fp0JY4Sw1MrgGMZ14T5HYQS3qSw1jtP/6wsbEBOkYbjlVz07Lho/jQxXUu1rv88l/scDL6rtflAqH+9JvR+tYWB62khBgScQcqKEx8aMg/MeuVbY+ZDd+NzY2L8/OvX7+yNaRGmaQdAovJiewEdQiz5ZGjAa4CwzVb9fNogjSa57Lu7e1lXSE5BJ5HMU07uus0Hr6+24vFtMzcCLY+o6POTGZRCHDvc0MuijQl5YjM9vmYAE8E/0LHkIl04LSZmFmu6JUl0npL56dTRzVUSvFmPuaxKQczfRRBTwx40YhuYcrGLcdMTZZVAbgy3/cH4t1nH1zx3lEmx0RT9dbmFlzYS47tePl4RH2l6FsYSjeBguwtac/RZsxcaD7ANDOdJYGlBspoALc2sy+GMrdGybsJSAJXfvjxEyExzmrXNyO/MFV04sSUn938yAla8qdP2p6XjE7x646VgdyMAGMy4qTngLWcRiNx+11cfPv2bTrbXUtOoD7DShmNvRIYFMvwy0MVQtXlYQnXc2WFOCg2anypGujV2t/Pbw62h83OXKx29PbCPO60iEmZGLYCn91L7S3xds1pCJmv0RFvyuylr8pwj1/5eOkUqq2M0BS8i1CYFZiJOQp2mMLfxvqezz0ynTPd8stbcfopLXM5jjgi7QmBZZqBE35A8aUp771jDJft7R2297q4uR8+XGLKYwe42uKE1Zw521vJnouRS9c+g89IlW7LEnDBNcDtgHHW7a6TKCkq9tw9VvsnrHbxtXMbqgMCckBE/eqkJukoy50CiCwQ+HGVIw1AgcMySwjyycGdPJJ/HHLLjyTeRjLq2Njc5HHat29fDw/facLlf3leABd2nmQ39/JYLw8SRfIWMPFO19c3XFMuEc8/iVN/9+79y2tspkUrF/3u/e3V+x0WsQ+Z2soKiHHno4GVkVsAQp1u9+zsbGm4z/dSuEG0QjYVWy4RyV6e4R6/DvHSiEJKF1VPsTTr0oD6EXkZcLHEm2sEtuYBM/fwPJdn6fz0MO9lG+gH/HeE2uOroxwznemZeRqvHQH3vBTf2Py4sXZT3nx3rK1q7VlV2HvBnh106QTv28mzOZ/q9MRKc10CvkoNiMH68IAXlRUu9w7ZYjUAI1b7D2K182aLxLxJ2HoSJAMFQ8ScEnpsFqpiUcROVz0aCOFJTrO1tjwoGlnk4VBGJEDsV8OKXOSkZLRWq4v/3bjrKRxtbOJ3//Lly7t3E9ju4GGe8otrmdElEfd1pNgin4O2o2dGTr6nw0NRXhgg+zoUIK0c1BpHZ9cbOztvNmn1ZDFjk2opvqvMpFQWCV7foXSeKebQRWrH65Nl/rf48zXcc3WTWzFj//HpLrDBV95Yn0gfDCLKa5/bYZihTbDsQOJkup3O7fX3Y77rcnvLM2Ce0KMzonGwRNhTmpGIaUy7FUuKYdVvzt4V8JKFtHjBC2RLR0CNRzKFVZZeCm2ZXWog0QD3C++CYgdr4zWpiKWUdXv3ww8/6AgZFRqeNdql50bNPipMn5UUINATNsr9rrF8rtQKGflTKXG4S1asdg6qxfNeX1MJFVozHG1uid/9y+cv795PYLtzg3PfY7zysM6X4EWmWafhGsDM0irjYQuhDqzcopfsRWog1aibu8FlbZvwmLXaaapqHlleEnh5bzGxGuG+297ZmYfGljSfrwYWeTyVKafgGFNdgFmmqph6cW0Z+hXDzNONU66xzE9rsofEWqNmHot312nkFgH0vLeKLbK9xegjkxoH7jzehru6vuIxOu5FXH/yOu1gIHH2K6skoCZh9031Qm2scZ5QkvSMbS8ZKll9CsfDC414B7u05p0qlolZNMAu6djBhIvQnwt8ZtjHeGc//fADu6zy/ApIZT9LkIk7tBhkA3nE8qZA/iRpXezKagcWwgKuqiRhDyHj6vC0Y7RjuWsnPG5+6Ekt/yTifdgYySM1+b+5eXGO7f753ftSAR4QwV5nOS+v07xEjzvtOj8/Q6s8b0SHfP1Hv0/M5bOqfrXnlaOrUaP79nB11u+hltcgoUflgZ8LJH1JR62l7/3n0oAcORkcXliLcho6r+InN9zDqSjWzPEQMayJyx6JzcRyFSAwWxTUTlxVKTHHHdcdrw1hu5yuHq6d/5XX3SiR2lFNvt+UeVWJW1ofoOinzcQLYPfrUHssGx7rY+Vj/bdabdz2ElSgIu/tQCDN8Iz3JGnfXsq2M4EBN1stBKOlVEidPi1/lxpINEBv1OtPws8IL0sqwhRdHYf0J4lrX2fVqm12ZmvQ/QMksgY1SUlnVXEy0gWd7S6lavsZSajeKVVAqI6qwISaJoMAkoKhnOFMAT/EYcsqXO7EIZZ7g18O8bufX3z+/Pl9CdsdeFrEfUpoHW/IGOGf7YnmcEHPz85k6BkO0BTPE3Z2dtGS1uSzbVnFgj/0ese9nf32ZfMRrXbawH30Im139oRlV4nlC1oVd9NnTm7ehrudbArVVAqokMLElXpGewLGE0uaQXATeKZm0oJHbD7T217ta399/fj4GFc6X4aLNMMYFmJI0BRQsMsJ+Y02S8+jOL1ubq6ZSJlE8QqyfwVYyspni0x20W6q7e2xSphnV9kv7Oef/xyjlqcIJZBCKILIq3OcEjKuaJl4+Rqgc9KRccTS8aIP8enDYrVLXPs6ce10UdVP5ewfoindx/SNEWqOGkxu8YmbQxXY5aSq0SVmmQ+gur3EdkcATdKy08Y75Soun9B3bidls9fFd44RT9AM8e7nn2v43d+NdRxgr7PExiFNk6x4z+ZMc9XwcsPzENJsmY/oLP4Zap5NGx5d0LMrvvS79a4ru+U8MnNWiY/M8XHYMXQQgvXCDHduqNhg9jgafQlcZhyD9HwyXhFl4cZTmgoiyj5aOBX5R0HSs+1srJ66ycx5vPhPMOjZFV94aXSb4d2bFc9ZI5l2c9tDjaPAyyLePrbKuX/Q7852ux02xxAPYlPemUMMDAu8/hgnKJeJmUMbTZZbViBdk4iVBxHAFQNZZsE54RAULzOzamCKazEtS/2Mm3kXT62xvD1S9EDla//IlEzHUwtOeh8+d2tFqzMY6s7HSM4dAWgTNxK2u/oReGXGgwATzVJAPOYmKSQVCAkV6S73oyTku0yqhl/ZZgYgXjWVHxpF3C3vqgLw/sM7LVyWsi5hNULbOSCa1UAe1lOVsyyRV+7vH/AFoAc1LKzwhHBn9+kijCMX7anUM4YvV/l48KZb+2Vn/c4uNMegVFuNANUSXBxqTFJ0TPxQiyPSjJIwkjzHxfyMra4QvcBwn2CKmwC0QtmjpBZIlKh8ExUyr858VEBiZhlCAgxADEHsjnZ6eskn3QntDeu9XFb4SWYyhgbFy4x3R0ff3xJ16Tn/jGXPZH1POHKP0YTxEStff/CSNMDassfViHXFjph683tPxGwyETErfhbalSRoFE2E6UgsE4ukARXQNWq123zVKCUXPe3+Dl+7Z7XT1bTNLtcefGYAOgAAIABJREFUu1EOsPSvPWkyYgRTJx3GdBoV3q7KnNXuomWwzaXGwqqkYEMZMTRlEmS17Q4ta72bfWbE7S7edjm4BUgb2702ev/xQ55Jjs2Pn/72Vsw4IamF0C2Y5De4LyZBHAtLQ9gnWz+1020krok9+La2tya8A+cmo7paYxvyxACj2u2geX55fbB5VN/IxD4+lnDmTnksdo/Jh2iZs9PT5t7eYzKdKy894MyVxcsmjuHO2BAZHiJFC6KJxZVsdgWpuXpGMs9EP61mo7W3RxT7yfWIvdI6q7el2h1tXbl5M7tfGKaSb9lnBWB8wZGDKc/sjtXCV+cx8XGUYtmDSzmRAO12C289tThLm62Wqpom7NW2rFxjsrK+3hKruQXTAIs9Oo+2dH2zlU4lVvunj+td/21UbbjjbxdDV1sh/q8qDVoozVZNx+pWhrj43JV9Dp5ymUu1stqlT5mxRXUvfoS2pi9oZPRbqpzYtg86ZnsZHTWjvs2E3MqBDzIJ9nc/P78g/fHjByNHIF2N2wFrmFsHYMMoBCiZU00sCTsGDEnYopF9rliqIx1Xh8cdPBLBrT6LhNHmjxFlPtXq4s6HdA5VVHq68sPo6i/vdvP9L3HcSoV1nTnO63mX0jkZHeiu3FDPuyVL6SvSAN9EiJntFVGfnkyFo/X0QjwOpjR11ubOiv84LY1wwR/J1vG3D1dHF6f4FTgiQGOLUs3PmRHYL2wspRQAI6YE0NTr8h5tziF+SPlcvLy0xth6fo5hcAMK1jxZLH4sA4ZdajF4COzhnVpq8w2FVGNyuC6LF14DeLDVl8vYKCmRVduOYrVvbEh8jNlDRqLbxc8tkGJRq1Pya03shI6fAsGz3V0N6FjMuorCyF1BJ0QeTZwE2eRQe7tLrLu43eVA1OTDTNLnh2yicnbKtiqjDx8+gugY64QyNtZ6D72ZR7cU4cmyuAYuLy+15ErmTYx1QtWzAk9GdyGh09dgzkI+1DrHF7d7m99aO4nVHulncTGqFVbeBY/zeRGlDBfXV1dsrPwiWrNsxKwaKAiVmZV0Kfxqb95SLBcEKDPRTSHXS9Fep7nWOTjg3fmT42MiZwqs5FJKSqnFziQF0fClyOYAiadUtpjwX40Knmlqu0eZOmLZ00yCaCHG21SEDBNWy655ymfPp636PKlHTp4DQDOH4bL4mWhA3pbuqX3cxUWNpcgvdqRY7cS1Ywgnn0flamtLkl9z0EhS7te0WZdYBUjXVn8Ua9tdIQk3qVA/GlYPN8Dam8PEv8MCaGAcL8NenVhPDM03VYEa8V0o9aoqqXoTLBUzc3pyBquPnz5ZocyZJQk0iDejpamquWZ5Q523gVEoa2lajS+AbbALl8pzFefFEj/vbzzcXrzbTn8P1Xawx2i4dFx7vNSXU3X76MB4hmxbl+fXroHQcPcG+soU85j3cWVCz4mQ6GJWfcyKP6emVUBWOdy7N72106v79eZQAuErOazGCHGphN6kRLQl5Axx7PLtHMcJthHhN8jJxh3YfETcIjumD+5YtSU2H05/aLVbRBYxS0GQQxOfVKQl/CNoQMzW+4fNLd5+ljUYF5f9ST4qqx2T3fna3WVUxrO+ntJlSblfkVbnJZUcCsKa7GTEe05WAARb4mXkpJKuTKOb8BWk0oRJ6ArJqsADEhx0MQHBAa987+7DTOpVVTHj8bufnJyCTdOA10T4VYaUvIAre8PP7WA9zC3DJxoIwueTEbIaajQODt6QmBvP1054WFv7eny5tb+7vakCq55OH663kaCn8dGr0rL4Nn9ppCcF5MktLp8pH0o/qeRL5pVrIDTcp+vM7u6pXLqXQFC0U4GGKiDxPLTZbQy6jTobYHFs7hw0VitzMyy+Swa7ByE5MIbyrhY+e0x5wn6YqPQHKTHx+ZqVXucQgY9vRln28voK5iGBxtq+zyO4LJ+TBlA7j1N2JNZbXoHAE/zxo/jajdWurwsrL3ZckRFCrGR9IA8JkcrZwSYflxQosa3FYgeLQRxobYdLVirAc9Z7SEMIK1jNQWOKo31lSJpD3li10TKkZIsZvb07Ifz6UAmelbFMYEN6UIQboTXq26s8YaA/hzxnyqFJ+j/RaPyShjgyLndpnEmnkyDfPgzPBjtv91mNPcb3UMuLNuFzWnV/ladeJeR0ZpY8O+JR0tJwr/JSPFtaoeGebcYTdu+sMM+mRLRWgeYqIPFsVJYSFMuVuf+q37w8+31zY5Pt2FMAU2RfRhAkrkR89hx5GtA+SMwabCZsR6w4LHu+4ollj/uU12wxsJjkxJwa1TDrSWjvbx7BZfnUGpCPp+IDXlslOOqnn34iDExb7Urhxiz2vNtSAi//l3FE/rlD19msssjlR5nf1HHTAC6edjUCWUvewgsl5ZWnwHrYDT8wNZRQAUzEEkk4PNu9hg2P8ADbePfhqEnUjATNHB8dU/XhA++qytGoy9eX+BwPTdYl0/1ChDdK0STvjiAM2uTgQxDVrgemk+1VYTFaHB99X9v91/fNxTLZ9VWge+CweA5XxLudJxSXWDUCwHDNlMYzN3Vp+CXg89BAXYby6TvS82jknKU06qtAixWQmHNbH5E8k/Rm4260u8em0cPrK8x3HoLPwh+Cs6A/F1yMdWx0jjy3PVYWaxgCDLDtBz35Ij0Hm22wqz0mkarqYdkTtQ8kHmG2ymGqoOqVKLCqC426UDJWLwpEw6y15CtLYnbKMxAV5aQinfgxFrL0T61k/1fLE1W+RpDJ2bjcxWpXdjvgas420TICId2fMuWeV4z0pC6l4qtXd4f9lV2Wh1Ko/O7acld+d/Z1l17BSx18kEgzJi+bRY467TY7tmhpsbabrSa/GNztCc0pqBESIH3y9pZ97WkSCuTdD0LVNfHl7+Nr4OGhd/Kwsb2501lbRKsdhfAs6/HV8sgcN7e2jo+OGI35FAkLaoJnGEwKZZAR4qkPPc48tRQvi3/5pdvLavesrVEz5qxELP4i3FxWlkU7Y0kQEY4FwNYQtc679dXLSfwNQWsgpUwkKbTuxQDglWTQAjrkKHi4jMK1ca9/iUzAoSXmWp2oD9ntnm0v1dc0awC22/I2LXE7zCWvRIclm4m/GW1jsl9f3/zX3//+5s3B3t4eOpT9Y9QfpzyrHavZcAHCOlcE3DuwcclR5CJljLc9gZFKgVGRNAqWnJ5KNSnSQtWSEgLkJZtIRoEcdADKV4l31wkWIEPsBlkB3t3drG9037w9dJzls6n9Qa/f6457zRqadLC72ztxz6/J/kt8L5mQAG58mDqCy8RTaeDi+uF+be9t92KuryvM2DomiHYr2PtL9/IZyS4UOrfDwZs33C94BPC9MCYTMIlThZxsO1urMQ6zI/GCjcORW1gNaJHyybX98i5yKR0sDfdSarITnp0/SyIVgFXSaQvov6wqDAUx30d3lxfXOPfYFYuSSZuIDepQsvbAazblnVpcAvVy+NEIu7uuUhJMHhhYHGiVNA7705PbwZD5Q4KaiO3GFQS6mmN6TKgSnNMUj35A5aVnsFzRAK3ndm81WyfHp0ffjwn2WFdf+eFdOnZDlVeQJcxdmc/hryv0E77OtF0LC/BVCIwyt4UIZYJEse7qLuuj+2lDSsJkVMANeTDJqh8ostbQtLji2Nk31zjFJcocML6Q8ObtW7+3wJ6lnHzmYCAAPiOdpsNcXFyqfkRfYmfJTbyJsqSJAWfRlyWPowGu47eHve7w1zfrVzXZ439xj53MG/+RbleR+E9rLXKPcK/p2809VtVDMcPx+dm5uu8ZEHgyJuMta2ANXFHrF4rMnC7y017h8RpeGu55Oko6RJLKgy1ZXhmhkvxeGhj7Wxjve3991L/d7ohrsHwjiwevKKWlNZ+nXjSPFc6htYpTeWsrvccws74+MO951H55coJrFp8QRiA2H1Wk0TD+e4w84lP1k18oc+TxfV7lBIJjltIuGovkGKZafmxWAj/wLl9dXd/dHbENKBYuMDSbOHj8zaCgHNSrV1Di/JbZWFzlTjWKpJBFXYKrfOpac6pKW9zw1zztr8LnJ12ujGjoKAtAPHoEq/CxMUkNB8AjD050lhgqjGoNVx8fhH17eJh3sXieT8QLz2e0eFxlvv6Ir16ialRgw/7+Pq3LQ7fiLs9PpoHbYev0rn3YPl5rPdn3UMs3/vT0lA9jl4efBdLdg7MQ8XFT96hfVTJtBopGAzeBQ1G384Bb9fLygsGEe5khCG9Cm7A2vAnqoZ8DfszE7O2ds7SVX2HkrbLRS8PddYDkUiUpVzl1okpaUwvxohAZobabt8P6kE3oGIz46iElZVrI+KXMnuwlyb2jogakQOdilBHktcBgk2mzTNn3zYIgVIxFDDv2ESKsmamFyGbmGA7xRvNyrWyIScAJlj3b2xtbVtHOXsrF0i3+5p60ouk/7UFEGrK7t8t7AzSQEKPrq+uz87NvX778/usv3Y3NTz/+BAzNJ7SGUJPTk1Mc0uiQQCQWSG0CW9GR2gMeMPSATryOqpzt2N/KBNczN2BY4SwPyCJJnw8GIJaY5Q93txJNzhsPm9s7UFJWe+/9hw//+Zf/3Nza5JbZ3d358U9/YsHMwRVkpcHei5eXV2y7+Ne//AWC+hLDwj+Qhyf4UMYz/+XLZ17QRQl45Uveqj6pZfrxNcBlPV35VLv46wf5slKp0fXxhUxxJAgtVfKMshUOZP7UxL3Jrdft8j94AYD7/1Ked9XEsyBjCJdYPg4oL9Iv32WaV7+p8iK/WsM9UGKQqeSyVU+xErFmIVJVk/yBZXp5sADw2GHc4MZrdHY2uo0VPtVeeDAw5dRHm5Yrp0BHMbQ9n4uXw3xZjGmwuiouIHxFqYgcqxwsCSxNDM3b2yu8tPzD+sTWx9/Phpek+dfpdsQlLIdMV8xApCyBJzsjNjarv9TDcsaERTgaxbMI+jD2MSuWS17CHg4O33/UDzEQHq882yziocae5mBzFb6euH9woF340k51YOTr5imCAqn2fIGY/MN65sVuIsi/ffvGRwD4lCkLIc3x6ury8uyMzswyYGdvH+1Bh29GvXnz5svnL0z2/OeTAvDl3uEXvnI0W/fNB+oODg6Ojr5rNyfrAajyKgQrAsTF6McaoIQIGAR+Mu0vGU+ugV6tdXzZ21n/1va+hzo5mcfGwOP+5s3bx+a6ePxypqZAUEaYnfBVbwYN7tab6yvcADKu1mX7GuLlcSswlvKHdR+QmCWjHi3OQmCJ+0oM93SfS+cr6QhzIVqJZOWJPEIbClhMbPNicOxhvtea5zcPa4NLAv6i/j/dfvY+L6+IXNu80Nkubctvn2nexK2cROoXCstlxazkv4vpTDVU26wquOMewxWblokHW1Nth2O2UW+12pBgZgKXqYvOM2/3EmITIwQv5dMyIiPq3t4uVUyWWO0AyJSJa/ryAre3W14CRnDqL//8h7L7BRebGTN6S97gNDv8aNudKtftlfedvWCGehmr41x5jbgxUBuqQlRWAeJ21y538lDrrG/4n/5FJMQACBVhu3/+/JnHHYiBPsli/Tfv73giz+7pX798/eOP31k71ZvM8HVePgEFeVAtE/3D3T0PSET05fFMNHA12Ly+PHm7016t9Z6JyEbM7Z3tgrG3VFte8cjMSMIeA/z3FcUocXcnwXJXvZ6E1nFLj2rsFiU7ZZV7YSmqUQrdeOWzW6bLa+CFGe5poymdL6+YsZBzJD2W9+wARdIX1c3OOUPBu7eLOXuAIZF67WGni6ewfXZ21tz6tL4WN9B5hS7Emy5XLKSjGZHWYJYk4Cj53lpXGCHv6l5jgpkAAxQTNDX3+LrQFqtEhkisiOyAeXt7h3ZZD2C/aj+TWMliUou3iTRmKLPU1AYB0yFcdnd2sV9xg5PFF87ag/d0uYDacMdsv7u/x/eNG/zTT3/maYKWmbUHi4ubm1viZLQAtBEKXz5/JipAE6HBlLh5UMxyTHUxzfnjE+lioWOik2CNwqoGcKZhDu10J1iGKBuEeff+A+wMX7XrPPSPjo4QgMh29mMlRpb3aK3THcVIwDtbNCIJ3/zaP9j39UwavSEJ7RI7fvIOn6L2MrMLdguPamvfz247O83DHXbdTd7jfy7KZ1eZ/f3WTNI+VUddsJ7gdIivfX09MBEZX2T06PXUhja4RRpkZW/Kmrwvq216h04iqlE9EkarfNyC9KIqrEDkiquCq1Ix7bmTS1/6dL5aAeZLvVpZo9TiDYiXRgnMpzBPgMzNGQVMoBg4cAE+9K9OrvAu3mVds8TmzqcFUapRabOQifzZOlcSoRUpcuDTJErJMQ3hsjhVNyjCF7OV/5icri4VmMO0pI1dZiPsTvY4PLu9JTScCYk5CMMX0xmTlTTmMP2N2B6yYYi5oy0JrOGH+wcJSV+rY7KzBCCcfHdvj8UAvDCqiTXH4c4K4uriooU3e31Dv4QKrhj3I4lNxzR3hbzRAcrZ2fnR9yNpjOz8I88NZIEhbngWHRI/A3E5lEcfCsS8sJ4h6oYvIbBzIy1Ssq2+ff9hfZ04msDdjpJ42WB/b//3336HBsb6/d39r7/+yrqXxpLDGS92e7PBE4Dd/b3/+s//JGgNptJgdYCFcpieWQsxtTvhbf0rPadvsURhYxWSRh2LMCnA7d3g/PruYG+jvnIsuHNnOKmA4+ELXqEZj/y0EBP0hHKCzu3ycZtza3Mgh5thGTMZMPESXJyfa86MALJiHxE332XMSbWP2rhFX65xQKUIlsYzgHNTz6SCTA//XAz3yJWKFE2vhxzMx+CRw7qC4rj08dIK2FVMIitn7H5LQzXrNWaf+/vG96OzTqe1sZ48+8Osr1jECsil5Z+EZEwfk+D7sLPI4dN5onQ14jMtKeMeN7Nx3UW3ymGi0gc28dnp2e3dHe5n+0LtAHMWI5ZQcvH/d7GY29DEzAUFTzS29sbGOjMPdrzyhuP7fri5vuYl0bcfPir3uaiQuW1jfZ34GRK+TcycpeZNs4GPollTdAa927uDg/2//uWve/sSU86qA9MZIxsYooaYRzvd9e3tHVggGACOl/BTB7xwpBGNQxP4ACorinans7OzCzwedyLNyOJflyD3Fi1mZdEgmIfVBs5OVhSWjAgvX18aDHmGgBfOlb/yxAx9dAbUHKX7Y8fR9+9rO//2bu+UBakxiqph6DPJkSNVPDmGI0DXLXja5sBeRaKay6dUVeKKMJIodwb7TSWzrR4hGdx4XoffQg9BjFSMLQwd8vm2pztmUU8JfTxGw55SfbH2xVUaL43hz1r2eJxmlTQHP9KASFEO8oIXZxsSu4sEimjmN602Q/np/Xp7eKTjhk9OTn7+878seBsnES+rj0mwl7BTaUAZ95iyYvsyCTm3U4oYxitTl36mjBccyD9+/wNzls1kcI8LsP1+LZY7hjtbnm9v7zoznRmOXVz4SiI9OUXZz2rjW/8yd8o6odPRrkdt7mNXQ5OVA4Y7u+kjho+OhLylen5+BgXSVBGWc3j4jkKCbXa2d7DNdUt5FPDt61fx9zMVP8jrthxMwBAkWubzH3+kDXe1kQ6guNx8jsv0gmhAjx04Sr/fre/uvGk3zmb2Y2ZblgxQsYE6Cz+DCCsrqb4do74sm1wDyTWcDFd2/2LYadR53GcwieKrSaTN+dmZHgMZmtSzxyGrf4x5/bRwMjaPDj2tPkTQsndBiUY9leGe2/zcihKNmQbksflNI+M4nEgbIkXjqDzH+mwz/XsDe71Tw2tZw2Tvrm/t7qYjcZ9jk5cyPwsNOPseAx6B6YlstMIWK7t7O8SyUCLfJpL4c9lShs8Z8XonLm1c12wwo3zqjfWNjf/861+JosGqxnp2v9pMzyqBcghq4qlaeGG444CHHZY3XHD5A0yIDQ8EsNRDp75EqBMts727c8qNozaSQ4CLiyvuJcwjzHGJlOFtVL683mywdGECVoXq/VcVu49ZT+PYklK/CpySZ5ldBA3wPdTblZ1369epqz8P2bIDdYVc9JjPIrlCmktS1WtAYubkgeHO7i4jjKPP6MHjQRzzjHNcRPYDY2jiqR5PKRl2OIL+6U/wjsQzSUx9F2QbnahvPm3PFTW3Yj5yJFSfjHEiQhWpSDMiRVVwel40skqQp/nd7m2/8eXr75/+pdOusZv78lhq4JE0wCSEqwX7GJc2U5R4oNVERBp7GkuaeBJeYGXrdCLFnUyEtWOCY8rv7e8R1A4Iz5qZw8RHfneHNYyDCnpsR4MljVdemfX8EHyvPuRkI86hz3MnzGgC4YlABR0iB28OmCMdr2yCaZWAmYODN1/VppAagOfgvK6KmU6sf7/fAgYZxOnebvPRU7xo7HGpIWkf5bxWy9IkmHSznJYlT6EBusHR4G179fiwczODl/spRI/xZMznPx07VrksW3QNMETwnJD/vqCMYwxcuANI0F0Z2vCA6IGOsWWma521gn3GC5nOWjWzGO5ZakGjx1QHsHPIPDH7yluUbY98nLxyNtMQzJNiMe4QLV233nu7v3XXG52dfiZwGVN+mpYucZYamFAD6gXTPtYtMd8Y6+zNQockwWzEwbQk30Lq9wlAd4Sp3dzaxudNCeYx/3WslwPwExABHU8VpMRnf3V5dn7OnMeXz+EFX3CJqufZNBlsfELTy9hq7PtOIDsPuxFGG99Y/3y0hecD0CFaBuLMoNp8J07m119+ObCQDEvI3HuQN9WWhrt/sRYhfTeon/W399e+NjovytKloy6CepcyVKIBTPNUFCLXl1GOg+GOUYgoeT7eIUY8Dyvrazz7w7gvxTrPXCmFnA/0uL1vrOE+ppVjqvObWX3NAolSVeMiTfKKvGRVDH06s5OfiML8+z1mzVbjbvvwHZEzV6vvtpo37VXz5Rq/3cv0UgMVagATHbsaqxcLW95SbTSxZZmEjOmO0519Y1bk+1OOKZB8r/TzH58xkV1hQYIIFlYARMLc368wjb07fI+9jrMKFBaocBITnEO5JGWXGLaJub/nBVw89MyDzpWF6c8UCFMtLRSUf11/QEr4E4RKmA00mT5VqIxsaIPbnbUBjeJRgHabCa/VFV5jnckxVtDgZdW0GjitvR9e/f1we7BSe1FWO/qgn0+rlSXeM9AAIwzjEgeyOpueoZKxiF/GJfV8T97VIcZGPeTke3zJBmJzb+FE1k5Wmgk7L4Y7DMfwHFOdFWLeJQsnUIUNpm1cw3QL0/kKGUJqvtTLyZonw4QduoAZX9aUtq6sEJMwql2d8pX5u2terdNjQQHismqpgak1gL2MrYyVTMdTUZviFsL3zgQj/vaBeI/UO6CBD4UXTNm9kZdNs3wxUJiloAkuljHmytbWJusBdnXk+6sEnxMcD5Y23EnAl4fNmNL4yHnHVBFMdoDR9EUYJQ5ysQTgddWb6xtYsJz47bffJRinhZUvn1Rk0cHtI88IJHRHgu+JSeUOYipFKm2444eHLasDUDT95e+Ta4Cv1H2/6O90vne2GQarG1WfvGFWgOUq0WriFZ0ZYRiaaHDyTHJUU28PyRejZUMbHPP9Po8tGXAZZhnQFrSf5Nk/ORfTzBYTYuUQm1PxQgtXVZv9Rpq0X1QVG0NnjqQrljS+qJhq3sFCcsKt1IZ7O5vsuYf3fbD+5736sf+6jANbJpYamFEDTC2EnWCIi+Gu3O1CUAxk+afj11vtjm/jrq/j0pbY9HU+vaTe9cSnfnV1iTWsotjxx+9iKPsomN14v6/7fe1WT8ssQSxFQcCQ4nC3ANLu7e1jyf/44498o4qvo2t3Jlxu7265j5j/WDYQaS+GOyyx3VstPvKq33/94YcfaB92PwRBpOFpeZb5x9XA5XDz5vL0cLu1Wnuxzxgx1B5XqUtuC6kBNdAxLOFNcAGxjEIcuEmIP2RY48khQxkjHr+MdUA+uzGqzpi6QMPqAonyCJ0y0tpIUSWCzItuJcJNTiTbnBKmvDw4A1HjKnhuV/G+j86OL1eGo/7euuynMbk0S4ylBnI1QB9TQSN19oLks656hmDC4MC3jbXBy6n7b8xrnVBhdlnfWOeDRyenp8ORfF2VQoLID9+9K36nRTaS7/Xju8oIEd52TQLWc8X1KghkZ/s2ZaPLe7HU8Is3/frqml0j9cbMTH7QpRyPF++XvX17yATJXIjYbJXDpjQQYZlCeI58g6rOZvaDh16vQ7R9u839KNiybxxSZ29pT5RlcloN8Kmbb0cX3c3Vw21Gtuf3PdTy7dae1/LwS8jXowGGFw7GGfbY9VvNYIUb4vT0BN8DjnnGMQYo3CsMegBzgOXDL0766cyUBVXIvC9NvNnx0hllmQvRGWWaG3q0saE1j0kElAFUJ13PzXmwLft1fP/2bWW1vndwWJcPkSyPpQYq0AC9i7mBOQAfeWe9q2cCcf8oy11eK33o4bPmBQy8QdSSJ8D9j99++9OffprEFhnBQj0UNnsy+qKzRCCLhewXjk2ziCX6BWH4SqsOvwEF4/vm9obpTf0Xphw43dnUnbtJT34Y5XpN8uHjR2bBLCPuRJp5e3tD7I3yyktgDzohbp7IH7bKwaZn7oSXcGQ65buwCzyJZhu4ICV3vdrx2dXhm/16TV54eNkHj4aWHw142Ze48tYx3jLOcPiUGZ142smYqUdpRh7SdC2GOQYrRiMf+KnS8zfcoxbVUzX3afjGVRAvnUXC6inOIs0C4IYK0TaTL5Zfj9/v3bt3/WHt28N2/eIvbMNnbtHQ+vfRl+mlBspoAEc4fY8Pkei3KZgPCCMhRouFIl81Or+4OBQrfvjmzRsmCaaGT58+/eMf/5jozSpogss2C0TdREUCQJnIEwSuaMOdXd4/f/7saMKFdYiSVwL1aRclMqXJDjNrVLHYIJgeC575jwqH6Ce4s/DR+99Z9GtJow0oYM1jxEME4WV9I2/EykNtHRQBOhwx9KGFrpAWQVJ0XnF2hQ422vzp4xt08PKtdhrJcx7nlpnzdV9OCXNW8JNYYuGUAAAgAElEQVSSZzxxL79qQdT4I49G9QNSAIhyxK2g7Xj15PCxR56ZDXff9nlSdS8S87hS4qWzyF09xVmkeRa4qMzXWnoIrq/WPrRPHlblo5Xcn7y66lsDaWjd4njps9DGUsjH0ABGLAbo1eXV97VvI8LAsaHFbTMidpx4ktud7YODA+YARGGGICrm/PyctN/xxkoJTeAh7Van9HLXMdnxHQc5jmsmnonI8oIpgqkQFwkJRQyxmx9YIPRlqSFxohInwwEQTik+AYvhjnlNbIwy3Kecz+AFEQ42kIi2HUXBneZwkGAjfGJy2NaG138x9NWyoa8te9qOzEjF/IqcTj9Rsi+jkOYf3Xa21re7DelIr+Tg6VDJXZhmVog/g8xMbDIC7p6eDG2BoJ9hCxg07HAkO9poZTLs4FdglFP7BOCkkO/f6Wek/DIeznWoKW24P2FfXaBOVyBKXEHx0gIyY6uqpziW5QsBYLLPtCSuTe7Sw8NDbsvv3743D//3Tu13fRNGodlOP/fIMswFXVa8KA0wiLOnAeYyBiVD/N7eDpHrPNLhsSxZzHXsDPaN4TumzVay0QFYPOr55z/+qb9XOoFGRmLo08OjswWBmiwesFzp0hPQFDN9lYAW1hK8HeseKOsZC4OYA6b6EMO92zk/PYM+hn6vd5knzEQC5AHTTLVeWHNSZSGRE31ykCB1wfPv21v1rJtvV8mmE9zmrDioZBHAfjjauI8qMEt8YUuuru9u6odvuoTtxh93LKzkMwqWCl+ekdqiohdMNlOL/KizFK/NPPdbTCuacS87+DDasGxmbLm8vOAxJFle3Aee4RHgCm/J0HCfR6+Yujs9A8RcfeVWTN2o6ilOLcpzRZzkthF1s4UfbwQ+DI+/fP1Cen9/L9rygitTZNNrWo86ZkbFXxZWowHsPyx1POsYivQ0bGW+80UEBw7gXr+3u7fPtjCYzjATg1f8xWJTAo/56GYyxnfMx+Pjo42N9I6NxVLyhijLUgg6UiG8fFEVYeAblo/JIQ8fXaLnn5wcu1mKFuFkogEcGL5mU8g12QD+ofcgAe9rhNOQeGLDUa8otM5pZ/KBK9todSHkSqhDAltpJma8C7WX92nrdQx/5mJ5+7bTUZvfT/kYwbKd4/l776DVOH/bxNH+xMqfYyNzSJ+envJudE7lsrhAAwUzWAFWQVXRrMZNlzNGFRB8NlWM/ByI629ow/DC8MtbTNj0DEqkGVUo6s6woU092Wfj2SjnCQUt6uJFddOJXD3F6eR4IVisg6doSXP14f37D1gqn69arf7J7rbs65ehEx+qsnApxPGWvUOIc3DVy8SjakCb3vihGY5hTMQIJTs7u7j9st2DrVOw2mVAl54jkMrJy9Ddx+O+vbvvRMcrjzOevSD16O/KyySgnddH4CiGO5u6RB46FdGmLbRRNrTx+jymOUEpau0hDMU+VnvDYO8O+vLImEJ2lMe+LyK9AHU0ikMLjDiE1kS9tmiP1ZbEO93fH18csThRu/LLC7Wg8/YtqiBNobLs8eJLQI4+Hq2V98P68f3228YXIm8fjelCMSKscaHkecXCFM173EzcGmHAaoGq8oa0ApTFqqKxDAgcOGh8yQhrZLex05NTqgg95Bf7nqj5dqeL44NhyRtxfTyTfqU3eUQTuUVFvbCoLpdgYUX1FAvZvaZKHX82XYtxN76vPdxcr3z+/Md6d30rbZ+NvWzxAWgsmpZWkEuCjm1eXJCxaC8OYEJ9Yr3xjilD7dXlJbaeYK+sEEPybvPd2EvDIIx9Lza90iJTl7Z6MXMlYKPV1BSwrfFtf//+TXzbE4pnLg/eLCWaZDUF9UshPuNGCxOTadPAljyxxkBIPgWFePqxL5burXz+SfzUEJQ5hkh3+fB4Q3nZKRuxxQeh7pPyKinSI4NpO6PRXCWAfn0zHnPPmkl25X/oXd9cs6rhVV2w0BuXWJ6oqO9hoT0se54AoDHmadY/orji+blUU0dno3e9i39+2GHvoNc7oeNxf+Ntq1pKc0ugp9DAJH1+wtEqtzkLN+1x6zPOy1DvHYyxPPfDMc/Ov4wnDBKU8O4QHnoZMbyX71/vfe6pK5Uc01fGVKeIlczOhWhJ3q8FjFtixqbybIv/bNv35cuXje29zS53XckRocwFziVVBjmvaWmis9DK47Gw5TRet9clyos6GuFbfbh/wOZmxGQAxQIjEpo4deU4L0+ohnVGFA1fDNDSYNeKxx2K/T5+F7Y0h5aIORptbm7+5T/+QszJpFcJE1DWA7a5WjiIaDpiRD702jwxYn+b+K4zuc1hymCjStyZn//4zEIFOExO9njh8S/toC0oR5uhzC6iomYLm57IeFy/xRvP57K0Femua8srOldKXn/2pbPW7gQzsS8q2mJKZu13d3eD0lAOq6l7cdU3uXxUsRBiM3zZQHNFnmNg4qdma5+aTrML1tF1c6tzsrM71fdQK9VBVrzHLNmxC+Opmb4gZUytg0dAfBI1TzqgRvUwd8m536Mb2mC6MIbzWA8ARgnG5KXhrq9Q0XUtqote35KF86Jbkv2rA1P7hVXQam4tjqt+68vRt412+k6bgcF0HWLMaDId0RlasUiorvEuEZNOPb2VCkZGhkj2NNS2tbKiGrymjIs0hle6TMbcHnat8q4KbeVxH/aw1dhhBhesMrDxvojJe3Ozt39gTe6yLKAMTXwyHqJus/xiFxJn3+104e0BlCJOFDsr1Q8fP/z6y68OQS06+lCDqXqqKx53XELsKINPmgUJMe5bWxMuERx1myi8aBZo+vOcyWcEw8eGjc531/l8VaZSFajd/fHTE5ODq55VFmmC9Og5TNXonCzbffKugoJeofT09Oz9xx/X5LsTJZsTDhclkeLiTl4aMp8cvwiDt73391tFENk6mu+JVJUyPJJZlsuS56uB2TvIxF0De51Bg8PXGkPB6zTcx1yAMdW+CidKz4vuREK8XmBG9gobv1G/3zjYuri4/HJ8vbX3vrtyUSHxSUgte9Uk2gphsYR4/R8fBv5OjFAeSjJEbm5u4e/UgLIHQlmTKCQd5ogbwbDVz4jF2sVfrZhSgvkFLIVb21tnZ6cM7RosJJDkgOSAgkIy5cRd3N7csoO6SI6/lg+JYd6NanhtBRj/fn/ACoERn0PwqVPBpuL6VUfCIJNSH5AiRlNIWdlw4eN0l0M9woUMljs7QvKNqXUWJNjutDdDaVlQ2JnoDOptX7a4z9MUCucKEoPz5fhio3W3sd759vlXLop+gUHedW40udCgA0lwIAcdTHqFOcYMFxNbFpZuqfMY5nk0Sgi1ovdxz6OQU16CcA5mUfGUrSwiKXXzEXYc18rrTYx75XSfBcFquga38ysx3Mfoa0z1LF1ijqRnEes14kbfPJtNERLlvFlbOb+9+H56fLDdrsqpP5tUS+xcDWCgn5+fYcnizcRVjJnOxov8epZNgDvOamcy1Xe4SwToLuOMJzGZOZTPHYeq2ndcjCoE29vb/6+//W19PRJFjRHGGgObDXaQIpwcGx1LWm97gsmM+Y2hzMbw8pqoBEOu8VIpid3dXb1gIFYHI/7HH3/413/7N2X5Qa93J1EbdzxkuGUf9PsH7Hs+gJp9vEDJ/f0dT5lA0b4fLEViPJBKlgDq/VQJcxePe7vZbl1fXlGVp1Knk2ViCg2g1fve6Ozm/odPnxorsrnn7m5ARjqXOojEopuxwz0Hznt5lUI9U6JvmM/civ+efsRWObKq1FTmPV9NZX2WEWqFZtKWQBcvK1NGCwUtnkrzBfSWVU+pgRdpuJfq4aWAprs0cyQ9nUBLLNEAby/NQxEEKex0atvt/dPTE4zC/f0DvFzzYLSkOYUGMFK5KOzTgh2DyYvFub29o14QrOQudURcIi6jtp5VnbXbJcS9j+Wtfdi4zLGfTk9OtrZ3fBLYymxdwzf7sPdljSA2mYRRAKMRsaqlXatsb9K4OL/gHQwJn67V+AQSTjrCYwRsdYVvPP37v//Pv//979+/f8f4AxiXPME5+Mj3D/a0OX5zfXN0fHx2eqbdtE4MoXxxube//4+//0ND0hzc8LL6MIa77M0iFiH+XVWlZXMUlomqNHB8fDza/PP7/TPCr6I00TxXh4OrBgCWLNuSpiClC1r7nsAcBi4CcriyOqSKHqavMms5iNCj6A8QhDJHitSk2VnxM/ycPVo06sLVwWUovJKCqTVfreaqpfZKrl22mS/DcC/VJ0sBZTVUsmS+1EsKsQQr0sBc9wtjRtP0Ty7uzlbf7I5+15NfkUDLuqo1gDmCNcybkdfXN8zVxB1gn+zuGsO0am5l6SEVbnBt9BiPO7EroyEed3znuny92yWyGeGxfx1dVh2Y98dHJ2cnfMd3lec57Di4vr652eY7oMm2g8ATH9FqtkbD33btpnh43Cl32wmrTUxkbeC8knwA9e72/Oj7MWsDtISEnXaLHS2J6f+P//iPDx8+OEmQkMj7NwcHCKNlo0reBJB3bJmIJZqIg0IeApDECoSaa8UyUYkGBqOVb1dr21tv1PdQZ5pv5Gqp64VgdInoQx4tM0Y8L37wkgMdQJ4a8bF33qjt8+ClRYgVPYfFJAFm2rKnA3Bo4pU0uQwRrQh+tTC5KAUKW9qSuVqTigLNFeIt10rF6pmp9jka7mU7Ulm4qRU4dwZTSzZvxDm1fL4j6Jw87ild7221R6NvJ8cXuEX3Dg4ba3PSVYrtK81iE8vOLw+Eql/Kq5nKHCF8KetlfEIFqa8siUUr/2vipMbeJcidpwBss4hgmEfEcZ2cnmDH+3Jiixy+O8RZ/vtvvzWIdfAC5X0w0tjlEEkV+lm9JMClil2ly5VbVnYXRoeUsEQgHuaf//wFH/yZ7Kz3xndhIgmGOfHTcNH2GeYbaXDB1v52Hhpg29E01gysInzuy/SMGuB7qNfdfz9c/zvKn5HUROiwky3r2m1irqKIXH66Fqs4bkD6Fn1AdaR7tbBclWdD/T6f1NUfrlIBXemN7aJkpyik702BJSgVjtDzncGmbN9ToUX1SmG03Am5VKFTRUFi2r5eQLLiquKrHDCbADTAmyTzGDwmkWdesE/SzrFMZ7qp5xDjHtc+ttH+wQFmzelgb3D5bXer2VidSfI4m1dZiqGAlYBzmg2zMT0xfwnPwNZ8//69M0kXTTH4KfmyEp2b/9bjrt5PJVRGWRt0FV6WwM+N89IXHtP5119//fHHH//X//nfIKpY9Ns74mbUBuFgAYxCaDivumIwkdCFupxflx2y+4uSAENKs9CI9EuseRXzQkENxX75/beNrS0Meg2mfzHJiIff2NzE/6oNethBXHDUN8yhrhjAccDzDvZO8dGX6ak1gIaPenv11fPDlX+yQpqazpwQueiE03DwCkSUBfLTJeix93f3bE1NPBXPbYjjYplNsBb3L5uuyqsR8p6JfP2XBDeFjsyJEswrpEPmVT1e+dgZrKQor3i6mE6Fr01hi2O4T3a9JoMuebdEwR6PU5T9XAufXdvKCJx7C1e7q8zYC4Ozan/1dLDVOD49Hfbu8JtO7xMay+z/Z+/Ntltnknw/zvOkedr7+6q6uqpP2+3uZZ+1bF+f5Ufwlb38ln6IPsOVe6geTtU37EnDliiREimRIulfZAIgSIIkAAIkSCK3NgjkEBkZSGT+EYiM3OkMaIJR4qJYByUwPWN0i0nGeS26SH3qbgBdbEBEgAyAlwNtEafdQGd01dlM6+mp3pjQa9LeWrX+9cu3L5+/YCeD2xm+JJycngJuUJ3Lp2jlQBBCEES3eXp2RiTafAI4m0joi6PIUQIZ8uLAWsTT01NqpCilBE69vbGra7vVemm3eR/iC8CPv/1tpVrTKNxqCOxhL9Fo1HmR0MAdovCvG8MrgVbDk5/lBHxhqE2+gVh04hNPEngbpHn5P0h8yxejM1N7agEfmbC5F2c5lo3WbHn9LOCnCH9E2iUr1lb0YvX6xye1HktB+OAEKZ4cOj+BsXTq48NUj52tZZti3Ex0Ptozd270QStaRQIR2BaJZ1PDgTc5e8u9Yo9aa2Ur8uq1+C63zZTFvDaOKuWymWd9v+lU4vSogZrz4eE+Wfl4VBqmEobKc31MbFtNTNV4QVHQUDxjgNRRRTfq4jJl25oi/LLoU2nbpWeCobVfGYWu2UpDRmDc/7FBEp1kCouQBBzRropAyd/vvl9/u0FhKVYumTTLSwm8w+TyBfzLqADCocelRyOMbsR4BprvfQHoOJBBnpx8+fIZYA2sJ4UYkBBkAPTnVx/AQxCh0tmAVh59f73RgKaVqilABH08fBJIxRia2zePjlU2Plkqgcd+pd+5P6uzhGBT0/RSHoPJoN/66MzzyKmOKp1ZvSviKofQBeer3XnF1J7+h+Ye1QwZeBR4KtjHl2Uh8wjuafy8udGlOLYI2Lps0WQ2r+LZoDzWMCJ4lYbI0k+ZyXvg7Wrd9XnjboXcwTcseIqqeeE/A/gLQ+m4mQA+Oz09GyRG35vtZL99fHICytkMK5GslSkZ1xawxgkosFDIo1tmIt8NKaGiBkarQY2bDgiR/2BcjmiyOVaqladWa8rAfepGaWBu+SuiFAH1NnZDg8cnKKA1p4dTl8bWqNKhICtWE2xumgbZUDVGO4i0VMKuQXTqOkxVNO+SirgjIHgzAxt/qj2YRH0vLxjAL5hsPjZt2N7MG/96kQArfm+ec7X0baOO6dT4TckLjZ3KS+/S/d9qFeODsjwzIujzNzfXvOLqxwr4/vLyrFX1vFRyiU0OC7j5MoTVPdSw2qczQ5Nzi2Z8skQCq4gq/Al+CfMhJPuQR1BiCBa4+2jI2jG6vn9+OA3hzodCMoC2BUDCY9OW1rh6j5dP/FQzW9PqpN01Np3onx4U39+zN9+bufrHg+zzfk4bIE4wHzp1jhiMMH2C+fD9sqvzqFL+GYuUNWrX6nbkwKJSUAVuPb5++YLa210/klz0HN15NKABiWNKhLW5Xu1KhnR6wqsMeIXqlPHwtI2B60rhesCKAhjWtwxgBE35iGDyQ0tvb27YRuri8tI12TjjhAReRtWnb/96fvkxlZhY8DCRKb5AApPjNt+LtOaeFEA50NxRSPRYAiOPUtu/qj0P+EI1wsUTn554prThGZOF/gClnjPjWXMkGEe6koCadpGz8xS8gMTkXV6QcSuSZtHHArYXNN03cPfEwJg3n8XGBFY422TdK7C9vOhKDVup8HLeAsuxgM8F/dtePV9UITJLh0/RzsElXefCc2PRvp+fHPZ6zZunZHbwdHB4rPxuz82/AwmowRC+rK187TJ0a/sQdgtift2B1rloAjpvraimS40tZZjGlDvIBF8YMHBniZ4LUs5ZUIQjZDTezsnEikm6mNrL3OkrYJ3z2pVtmF5eZFkwLcKxDNQ0QQPfsL4QM/ees4txX9XuUSFAzd1DO1dM4osTNz971HKXTWWgnt95+cSETd1SSrqjAsoJCxbU8nbKkMXSGrq3fmro1TwCfLviaxm4Hz85ls5evzBAeWntcQbPUvIn1Pn9ZItuwYKmLwXuC8oul8BKhZeTX5Zjw9UvY2+ldP9t819yJYZDLOzYotknl/HXkYm5xR0TIDFL2pHuwkimjfMcfkLKX67vy/kk8D25Q9/EmdtwHMHeLoA8baeB8PFJAlhfKJXdTAQqK+BOfxK/GUpHLcbltJZpTOv50P8VS8thh6OAwBbYA0BRb73kmAdLFvTlvDJh8TLvQXAsaEXSBF68yuUSblVRZ8I5yIY26BcBQAsxvD8IIMoXSOLXKhufLJVAb5D8fv90fHKaS27Mom8pkxvOsHDgxbVUUOzJU6nCApq8JwPf+egkH6BSKeze6PMwQDmeR1aW8PVJkH0qzfNOZAzuEY6MVOt5vZk3dy/uIgs72OKia061A3d/bTUYXqlwUI2OBBNBNWaWjs/m+Sw2W/9Wxcy2mlFDKdxnU2jY9CO7KJNjmgON5fJiYvhYLHa7b3edfL73rVavr2lcW86atxzIFm06oJAjUxSSLqBALhYi5U/dW5OCy81UpYC7QVGwu/yJgpzbjaB4yQHHr3LroQ90sBmgO3BPFerWTHd1h6xOUbDHolY2eAKykM4lhsMyFcuzI8wTMMhh/Ubnpfvl06ff/u53TmTiuFkJJB9fMNQ4vDjBZ2eM2mfl4yoGHQH7ALvKGkQmHiV5R53/dsqTAaYH3OPMnkeGS5A9MYB4eVL677zm5jDOkdFSbNgA+qLj2PnPr0EIPywa82b2xfX5HFAXE12SCnCHWbf8us23pNIgkiPEShDNcabhp5F+yjjXvmuxgjHmhqVik7JLMqnkBXWMK5/MVCzmi4luJ5FjfVWpdlorOX8ZGBePxhmmL+xRyvpIgCkzEyvDUMTiLJ9pKRoMRoYLwbRsDgU/9EHB7AQkJrN0MgHawPE507lvdqEGfY4LKFCdXk4w+466oNRUEsgD01/4J54maUSisLuKSGDzI2v+WGf8JjZRK72KTFW9q5eDUequNapWDxqp1tIBZleF4NAuHpZF3dmhRLVadYjdXBQPCA/1gueaB4SgwT2PFG/FjKV8pbRM7cH7vBjIO7na9YwnC2r+PpdtTgwTNdNemjMRtQMXq093Hrs6Mps7W6zOTMB3JHIMBdw+k5zndnouYNa0b7+rDXkLxDzx2LnK55SpxK73pVJnWLq++aVy8ruKTOTRCliRtlpParoRpI6FaKVSZTphiooWoxHjBukgIUtG0l0UcOcXTVyxWLj/fr/ijkXqFUBqmRcEZ6tFpaugQ4CFKAXFzFdAORp33RnAWFRNuzg+PDx0Oy9xl5h3I+zxKIlfSn9zXP73TKptj4/PvaJ2JPb09MQWB1skOp4RAkCWIXQe2zxf8satFPYcsblnEGYi02gefT4onwsgPkNKvoDZvXh0hew8gnF8FCXg9XaxTowiXkuto+VR5Cm8dvtprZ8yIbVgRVYmcG9ILApZbYMYQgWL2z9u3uJ8MMYy2VK6Uzo/aXcfvz58K9eO6lUXCxbHNQTcONQ/4DAMMPCfzGSAYc8O+34JWHY2csyytqnU0LgzIYtNeDKB/4pO52XB5G2jNPd0qcYdBuQzfTbL9D+XiosE1PawChF5DVZNUapRAe3AiRZeLVstjNzlA0OMHhbK865/mM3kT5M/YRa9MGOc6EoCBwcTm5e5KhP5TDxEPGjyrKnAF63ZDwvq5VkOPODAelbLMNFgeUMMUx5KegPlDwZY5eEVSsdEvukxg4skEI0hYymiWdSELU3z02Y/ZVYUzxqqdF/FaghV3EFuILhpntEwK2utmKpdXd0/Pt/0TuuJmyLbX5pBsgpMMq/5tZ/bor2egsb4UttqtQFnWjvLEH9ycsq475XUnuZfdCOMNLGUUdJhWlU3Tr6ndzvdIg4xFhVfIlFAsvF6YBHRJ7ZLAe6ZDMvm2EF1Cbk5ycAITHb5fC+NUPZR9BlpkdK40xF5xFAisydUqVxZpTlz6l9jtPFEhlIjKxrv30+OMtfsBhpKBXtJFBWD7By8f4GnkkC7wfc8m3q/NgcxjBJ8IqPvobbXQwF5QPaMGxTUKJ/iBEYJCOrxRFN2oOYvSoYNa0jyRyIuJRLYBHDf3xvnp+V+yvjr2+uryR9/CxGqi4kWi2y/FYddzln0R43qaHT39NR6vO+gT9J7hotJgnP2RUw6igfUhWElYhGH3MORGL2kko2DBgP3IlpxmkcJyLw6ecuYJgG8GJ2oDZhk26kV346oQAjOZ4z5EswtM7TyZjM/46IUugczPUq7l2eM8rMo1elClsYdHrDXPzs76zw/y2uJj266qPLV0xwfgjlkJ+/XnEx+op96pbfnl/PDx2RyNdTupTV+GA27DBIOtAl8DAyiywXKU9gy9EQ/Kcier6aEeeVEd/POnm5Y2/fJTDYuGatYOsvDriNB9rjF1JoC8sjYNjW6zaMuN9zP5DWf3v6mhD9DhzYCbslN89N+P2W8imMddXjlaYX8C5pjDsVzVRErVBtCUdgdN4YhEReKrPhkU5unxFkj3cpnx6nua9dlGJeB6RjAoOIFhDH+Evj2uqKdxhQbprynoqN+uVCsCxOXtQx7cJXFIiISAkCrW5CW7xssPhvvSLqMnFO6OXdSk1GLOsH2yrwUkC1+6yhtRTpRWhRHQSZydqZpDVpCdyRLHWiMoHQV1CSOBo/P9EOrtUba2n9MpqyKN8wRd/l7t1ga3Z8eVSye/J8sac1M6/3XpEoGTW9FdmaLY+OOR6PZeI8xS8TqkRrZIy84W5NQHyid+9xXSl7I0daD4Pu9vvaHoy7fWTfLs48THYYCxge9oQTUmF84EmyVxKcBSCA44B54hw+gdRsh4UcQfsq4b1y41N3zsaGcZvPbz1FbAcaYbjI3ls1sjJgLo08ajZ4eB4eP918albRLsx/GWeynUahj+Ih+l0rQiZZK5UYjRHtQhwaMW7ePZ2LJbgbuCHObOoqtOX4iWPEZxOSOh/hFXlzoQoBs0ZytEJiAWQ+XzWXB5ZChWUzbGrbLG4P8ScDYXbZx3XQYC33TnFB/d1R9uGY/1B/T3vdD9QX9gm59APRmhjvZrWtZcJFFk5g1/l5Gej3pAQhuGaOuZbSM0NJ0nm5R9vBJdp7anpd5tUEVb/gMOHzR5aSHq5y0fM7FWTDaIpxgamSPaNAZoT5iYIHy0trjDJYEfI3jsYQt+RknfiTip8x0vfOvw6U+v94Ip6C3jhh33m4SQ9tBpjk8KDzc3z8ks8fHZ9nE21SLQG9doDpWjMr6AiVftVoplysg9XhknJLV2i4Fqs+ouWWyTSaZt3it0l+lV+FHQPOy8syjqZy4lluWcW46XQhdW61UF7wu7As1hRp4F6F+YYE8TMOqyXPp7FUCxgH3T91Uengl+6HKHj1ew9I765WgY/7w0d9sO2ZjZlhzkUXKJJMsrsgXfC7emKl1uyJcymhxowK6/yy20buwOXm459pekSEAACAASURBVKM3oJ/BgXGDMUSOw+FbpwO+Z9RgGCSJdVbiKkedk48vAHjc0jY5ixuwV6nLgHsgXWI3JepTND6LLZVhWHSXVrw1GdjxcWt4nc8oqIgvwthZPLxl+8+tiyO27+k+Pj6JwkKZLlRrNaxrGB9jpD5fimtNQdU0g9vFvoQZDht33ECsDNwX6dqtpjJRMjnKYtIVAjOr6NvMIK8BJkg3Ne6yTBatm5llr3/7icJds3XUaOQjv7NSGBPISl3NY8dZYLrtkdJ+Zl/9/ru920xMjHgTg96kD35GFB1ELzAaYXMPsudMRh5c5bz3+XpMYCRjdyuQPcY5YH2Gnf25cxl0I0ZrV79xuy82nzLyWWypPMOiu7TircxweMjqpa0PjGUYGDabj3xcZCXRTz/JQqLf/vYvgrVT33oxRakByopkgiHGXPkbDblruFDUH44ncni5YAgXd5DLiigHlKxtXppxESEQuXohlKGHEwHuBEiqCB3JDMr3cYnf79Bsv77nDi4O3pKJPd0PNcAJammvBd5hBbjfPW6zrXdzt8kzL9v4DjOwEGiMBvdAc8f1aWB68qC0B8q3223mQa2u0oMP52TgnBMsSxlpGZc0WX3crLBWrD2D2icxTGhjM20mqQfhsRRXrGHri8/rZ0sa5rPYEqrzu/3SgnufYUs17mr1Tw/32AxTrP7Bmwfh9HTspZEM35vPmYM/NBJfJ9QYe3/HIyIAh7FUKZTU2mBs3A0HDqtwy2hDN8BaAD/O2N4w1T0/P6OXKpZkhQOXuCQqV0qrD0qyptac9/hl1lSzp540gPIKzosJjUOjV2ngdpUdJlK3LazUzg6Su/CVz7vwjf7gveDcEou7LqmMinMLxwlRkcCCYWHxHXZogB6H0hkWw6fxcO+QQ0WhXAC+s8qr12PvKrWIFgdbwyHrixiwSIIOyB58r4A98F7wPWEewSjEZ7QgxUARvYnJK2p40atow0XF5gJ5R6EZQfPg8575LLaU+7DoLq14pzJEz8bdWbwgMEYZLCiAXCg42TODvSqPjo7ngXLiz47r/feb79/v0N0eHR0x9DiTjmM3IQFzWJ1cg6rWkjLKMoX4niT0d+TmwwOGoE+PTw2WMB8e6X7Cwgbayl68HOlR4uKthwf3ZKfbKb2WF8xziyXEREDvsixhxL+b2SzVTA60ZlW9/mIeIp7a6b4+5f/6tPzv6eRTxFkNnj0DNGxgxqKTB9+cmGLAEgipYywii30gRvIY1CQS1ZnWCLBlFKXz4CTg7fVNxi5U2YNBr9/nVZBLbYyvkb0a3cRMR9D9RifZsbWivUlskyjtMaWhd7jmW6x4F1PHXcTxZmvtgnB37r/kAvqhEF1Q3+4n8TUtmo1kaGA5Ka/++GrkZR+vHXj/qNXr6ADcM5zNJM7Oz8FnX19K+ddPxydn8iktDlGSwOQzLR75QbncfZktvAd06nRpIDiu93/48Qc6Tz5fcJxOmGlA8OjdqYQZiJzN5kO1WqtUPPslZJKDW8vcRpvKSDuEf+OAxsp7a3ahBMK5b/VT+dpF6qfN7JGyKSnKDdfIYGO33t9DtCmB7WO9DB2Ra7ZwRM9B286n7LnLJFBki6sc8YOprG/YSXzQ7/WA8Iy3mCBiap/NysZVun0gfg3uw+uTrpABGiEY0o55NXZniGZpABOORvNbiONX6kIrFZ7Xd0MhOq+yfYz3AVNCEhNwB6sGTNV5xeczH7WUiiXYW33XbmDZB1ar5hrX3x/zqT4a2PDGjpCEsz9kFd4VM3Gv94i9Dx+bzVK5fH5+TlnZDNVAzYuEh1ELmZld6GajUQO/11++fOFca+UXlbSlgU31h2TimLRgXkC8agkxjGFK4y6fbDXEtxXd8VO+j90PThq562Jh864w1ydrbrnuAPq1bX0VT9dE3/Y6hcJ4HNYmAZG251u0Nu4WVgTQTWq1/RIP9wB65S9HdqjVLnGZ3xkJ0cqpXVOY69F6yNImDe6991mDT1fAfapN+r1Ja+VB82qwTjBUWzhev3tPlYrApdfneoLllQpPULJdhELURj8+tUkArGy7Wuspjy61d7uo1dm9UuAO+k7+Dg9Dscss5LMX+Sz2NtfX18X6WaPEeBFPUmu9464qG+HmRYA7H3Nd5ecujkY3Nzfozi8uL6cH/WWTImWpBUTNkbJ6Yy8WfqC2Z8nENLUFDKkxXxHR1GxdCx4EuQufBA80F1S3DUmtfqnbeTurPaZSc7el3IZ2eOfRdvO9Fw6yhA9TmVWm38i0O0gZhkpLDQih1rBJ4ox16EQIYyYmP2eqEVH8YILsGYkF2L/1WLSWlS2oZYdaBk6cjFEckACsZ5BnW6t5U4OtmnGF3s50D7ZwPIUZuLGxJd60lReLeW9Eg8m9aqWrlndsRShEHWuKIyckMPdD2ESuYC54SpWXRiZyHgUBZ/U6MN2wPw6mjmVUMKK4uLjojkrfrn8qFfP1emNZiTh9bRIwRgGGe3H24m5fJKDJl8+fgewM63ZGLcMVe6TDuQbutlc4asfVEt9/P3369erqgzaOdyhoi5oavZSNO8kyCYyTxJZmX4ANLb15KZYTd2eNsk1Ou3tqX/oWpVZOPRRhszbu7R5r2pcHw1ksvsXmTG6LYhlsCQoPaNSdZyfEKf41uFdHsczBGBJ8z8sABRn80bnIiwHb3r0PAgDuU3VzSdfUvVOjeVHvqEwGjsfqZrZMYDGr9oxVyzs2JBSijjXFkXMlgP333LSVE3iueMYwPxAd6gA/TQlMX87PL8y+v3IFfgkUk53i+Rn6/m/NQaVWr6af/VKKywUqAXNMYKh000kwj0E7/vGHH+ZnNinOYVOD7NlMIJ6PH3/4+vXrwUFjdi6ZIkbtYHITryszfSOHAdWhz9+e4PbuqPz9679dXrEf6vQcPCW33bmMoKGyEi7Du6PTwKhJfvYBdMPhfsN9NxLahTyMrgTVElyOZee5EEDXEwpwnxKh1ecMHK9Gdhn+mQM4ZxJQrFrZpoovu/T3IExQDYDEBD3zIiy6Jv3414sEglXJ8G0LpM47MSgFwza+avF1y+6l0QtroedlSiuXE62Xh6/vhVq+Vyms48EPvVXbW4HHEY/lpISrq6v5LZYBf4mem5FWALXDqERhiN/ff0cltOzD1ATr8wbtzXxhnS+dwFOYvR565WS//fHqAx+3A6cfIYLc8Hm3OUJcJpb12yjx6p0Xh4d2GZHI3TRs5zbqiWWZwLYpfQPzt+5PlutJ5hFi9CJXcWK3HMf76MMOtyQYKrOEw6I7W1Mc400CYgS5wt0BqWP9gl7nvf/OyjuWgWJagO9FNwYG3hgNLXetnKslhq1W5+vDc6NxWCrN9X0bGgt7SXg8hQq8pg+qP+mLIGmiFgNu3g9xAnN5uQC1G1JdTMdUk8+9Bbgc/fr1Szp9ov3POOdTXI+fI8cqdx229xP5u++Ph8flQg5xjO+us8S2N1Y3bkva99J5KZZmFhhsCfNh9BF5Uj2GUKUFcR8seWzBvmTfAHCfEq3uK9pljZjQqHurb3CaLdw5UzccDcdUQU+XKxVeXFOIpBdXHKd6kwAIiXtlBxVzxymVANB/eX7G8xOb2lA0h5vGXA5L8YkFKN5YiETuWo11sdX2W+qmW60lbnZbUxUFiRua7klW6IditrhsBSerI+7u7i4vLydLO1yp3u0Qb0Up85Ul4+jFxSU2MyyNULaYVtHxCWzbzWD0y4D1HMlYqB6yHZ6jW93Ba6pwflxOJXbU6kzPaNxU676O7390zxjTHJgLb3beKuE4SMYpakVpLRPJHq1Wd5JukHGbB+6TrTF6ju4BA30lOiqJwK2OWuTKudjYLA4rdsHFxFd7iVhCO04OSQKzyGaqkwCSWu32O24aez3th5U9tEuVciZzOGZpac8bZ43uGaKoFUbVETb5r4+PjwcnV4VMmAtPoiuJ9XOmvK6AblG0Dy1NxVw28At0djbX5YvWd9Nv8QjJawAmWxYh/BWAsPlMZMawsCnNOMrCJl1q9nEgJ5FUR6XzXxXk+4D6bGARNk92/XeUSN8+9UvVw9PUju6sxIDI+LadQxxjGZaK6+uDU5OHp4q3U8JLm7hEJJNKgx2VwVIhBZNh48B9yb3WrdR33DSRN1U6CscrNE8uZS4fjEzmUHHF6ZyycXQEJACumeICCIIvPKyHgT4gdYAU2x5VK5UF1i+LlmZt21AESms0Dur10VPi6OHhy2GtWMhMi2hKYvGlHwlIx5joHIwlejixkLcjWbAI2/2ySolsdFE6MCfownXgyw82LWoTkGS/P/jhx98QQxL+RvnX6bxgUcrXFe4yAfqYsMPE4dHhxeUFr6a4GSaA7HUgj0k2S6VPT4+ObojIAw+ztqoTzXNszJZHsjj4aXR8Uu5ldhK100G4hdt8F1ffBGN9PVQ//D7q2+YbNNVcTzLYoXZPicHn5fqBu6f75dwqfRf1dxftaBKiKXwKJ5KmVj6gISgAZp2bEMeuXwIsTmW7mG7npdVuKTCDl8YR3qwrlara65GbvXx8WNAjFmF6Wruc9vpFIjUiikbirn6Y/97N3t/8cnZ2vu22QJuR4/xaNTqfTlddUINgu/GJlQ2YjhsZlIj44we7oy/HyQC+BgSEmzienQHQrMva6FyWzbxUJxO6nFCKe1uulIHaxHCkOGZR6jV1UKmUc4eHeAomhdTBcIBXYbDpC5bCzy+8D3y/u2OfAUpZ/OgTVTlcT/Zm82r6d6rw1l7et3qp/NFFvsN3361txBzG5Y6qvznp2xL98PBwdna2Ldz65JOb5SOYj6WPohEp4r7d299WVyIPG7i7F7grdsnkSFGNPJKitfKcyYV80sVKXs1jbsnPqcB98ThnlCQA+kGhiDNEIAg7xDO4411lvpdG6TXzw5IxYUFhKbkgWVe5hPx8voJIwRrtpPg6urwCsY3qvz/MtjOpsd1FEDXsMQ2HW8+opFzzKu01W2Zb0kGzzgJozLXoq7xSciK2W8S898VAJZkAxFOWj0K5XB7tOm9ZaOM/fPz46y+/EM0lqVB7f5fbx4Z/wDJeUFHX91Ps150AnX/69Lnf45wgeg9APUg9n8uztg9D4WPWW2cyl1cXf/7Tn/EOiZd31mFrmhAE5zujvOneO31tNXC7Tmj+9/eTg9y3Qr67XZy75XZHblRimzTubu9NQPkcxh8XlLezY3hq63Y2Ue5dsMDdk9BcdB2VxQdR434od5PQ4BIiygElLuRFK29a3Zg8+KjDLBr/zkjALk0te32cyeghwrilS0sA05lrUTcCNfCnjqYSRTtjOpfgj+Pj41kl4lKaZgZ7u8y48e8iDl2VXJxJV7SokjErvs+Q0snp6TDxcv/QHL2/sTtPrH33LUyroFZP62cACQtWVhicM5LQeFuwHQ16hw12+Xl5vr29BkPnC4VSuVKt1XL5vIbsUn4y0Oe1jyMGOcvKS2ez93Zt35JJp8Ho/E3SYKXs8Onx6e72OyY5lEI3/z54//DxCouaL1+/8iix3IMiaOh5DZhYoDpFaEoZP5W6VZftXq7z/HrWeNid/VDpOyGPIZu6w6zVWauN+6baubZ6p4cZFxVvW9dy2cQINmtF4O6y4S5uuS1LGEQRveWA0rSu0UusRCuPm0COEbw9NqlE5NTrzdH5vZaabexcCmjU33poKPuoKlE9gkkw8MU8V1m/TNBB7z5xHfDFXA7Neub2r6UloWAUdpPVrE9+59ZpzzR9nkq8nxzWgYPNl0EinT0sDjCbns4UX3uRwOR9kLsoqmvlbkZha0lXivb3629f0bBj+vI//E9/W6/VLSC+rDZlOThZjVORubAaNtC8sxeBLsWnKl4u/vPf/+erq8uLi/Nff/mEqh7szmsGD9rsy4NTXVscRxu/v5Xz/e9nh7uysxKdbnn32OJbxsKMLeZ+N1hfPD2ROpVhSzrkFNcL7tXaGuQSuLvnfEGjnJNCJG2v0FaNFi4LXtWJmcA1M5Wa15iX8CuvPVTaaezBuSmNCDcVSIFGkpkVf+oMBPhoRK3uaI871Qhnf2FTmUK89CHb8Tjgo7A0Rftj8tUopHrSyIjNxuA08XJ7VE0riLmQ1pjfhdn2LFEswuUPsG60HOBLULh9iFTZy5o7xQldlE8cf/mHP+C4/fDgcHqemyc37rIMX0JwXET3mMl+o4a3menTiWw6k2bhMl+uMLn55edf0ejjauaHH3/gfUMwPbbvUNa1TVVhpzaZZE8J8XzlTviaqDy02ye192xxJ1C7ulMGal9ZOCHeuNVI42aAXroajbj0eiWw4vgQvc7sr0E+2jEL3P1V7fZ+h0t9igsvlemVhZZWnklJPmgz5aoP05xoPf1UDVt+6UVA002ls61SfJrcgmsw+svLsyD193fADQp17AcwgFmOIyeJ4qNjMiL6V54k7Pz4eyIxKxF0sCeJZj85ur25zmSLR8dHSczN5oUVK5tHdtvjDbwszRDtAFLSwF0+EQlwZz8vkujeHz5c8XHD6KjehOl89+2SM+gtz2gWkveBEd8BuP70y6/lipjKCLcAdzUqqnx2Lk3S8qayuWDnyDMXyeZbZfjWvGhkk4m+59K+C4Qqr1CJ+25y0AVla+g4RFoCKz2ZDi3zRC/CT4HLdthbAHCnlMuCDqJbGhUiace6A6pPw/QxjlcTlbG9q0xLye3Ux/uTDh1mtuBsjOP98BOJgHFvgb86Tlh+h7hxgpFJZ7SRrh+Kqsyur14K5I7YBwdD0tBl8SJb8/QGqdunUfb9nv1ifd+FPSyITG1QFnFq6C7DCbbjQGFSwfP4ePnzn/70+7/6w9WHq4eHZrvVZr0GL6rWetN5olNfCsVURtPR2TAh43khaVxKqhVeJiLHycYZbxG8QrCoG95qtTpK97vra14xfjz9DQXhZyCmMtA1FPy2CqAg/Yf/upGLK5qpeW6EEA0/DBLZW3YUPqwU81lV22TLgmfA1qywq5pi3lbzVMr2XrIyJN5LLrK3zzYAbohHT49YJB8QqwVwN6txX1WsFvVVCbksv5b69H3UYN1a2KpdT8r0pXiI9r12KU3HbCGLmLXCwyEwvdvpMuErld6Q1XjlSsWRG9+RuOn43e98l96TgovudS49Oq9jil39+vVb6eBDvWDXvO6JfPw0E726OTiIqhoR8yOgGnu8kQB3TZS1p8VS6o///MdKpXJ5xQ5IslsqluWtVgtXj9rnOjHiv1EAtJTSmm8DsgsuHwf8zWgYPY7iTKxzrAjhSwcU/zpWk2UBKzk7z0/Nh/u319ejk+OT03Os7cmGvVSv34d9nd9WpZqaNXHqkBNbolWnr5PACM2v/em595quXxxi2b/Sfqhj6c6vy0wJqVmwsIyyc7oX3s02TP8GQWOaprvr2E7GnZw2lmtyfNoYG64qdn5AnIpuqMOvCtzdN9Cp0X7jNlPrNLcawWvUTpodx4PmN6SSX6to/FXGrI46sN9/B5Rg+IKBLyCmWCx50uP6eF52XeM+3T9DumaCBFN2R/nr20+lwx9rmWcNH0OqbifIKiN0BWYFVAnwlgDUxV4cQKwwrzSUcyzLb26u3956WOehQWwcsNNAnRNswyjLImwQPM4cWZCtoTwUuOSlV98FDcGx99We3VOprgVo9PsDT59s0SQ8JLA6Q5+vC5IKbRZwt1stHKfigDKfLwDYqWbsHns0Ij9LTGwrvyfGAHkqDQDv4wHdzK0eJlK3j/1y7ews1VydgwlxrE5uGYVJKXNF/b5Z8F3QxqUrGpNc20pPnLrLZRXh0bDO45NYAmuSgKsOb/LisUubxRx+PQN3T3w6VOg7amMVu+V4AsfLtChBI3hmZktP75ach3whiiYo0oyqhNe3V3F+NxyCQlhUirdBDRo8tNXM6oaxqcdkC23czdZG77eY7BbPjp87za83t5VqNXbpsOAWiZrbpuim68ofSuskdu0DngXsUqaKa4NdUPj3u+/X365B1elshkemWFS7KBUKlXK5cHyczaIcRwnOH68B+GsXFTpH0Lw+kZihfNEiDjQPrK7VqgcHDfEN3+t1X7tvT6+dbgeYjnEMOVmTWsgXzs4vxEm8UurzFjHmTT4UpHATaWncx0nmmTx0sjLIzQNqltnc7+vrW/O9cVzpZ4NA7etvhyFl47NvYDKfGjmDbpc7Pt3lMnhTOxsEzWdMLzAJ2Ma/wGhuHyH3XXrZEzgXuLuvInjxbbLuYFpjiV3r47EJ1W3CmhVH8pyjj+doZfNSa/DSCZ5iIsF+MXzfx1hWAYkhy0mBGAcN8afupbEr5Z2qqVatrkQuLjwjgUopVyl9eG63rwcfy91/qVbi9WEzMiLCBtvp/zqAgBkDQNnsiWpLl7y84moq5EHtbbnKAH13Oq/ttizUJpCTwHACkVqtxvcrLolnZOFZKxRLCp2zUMQA9LxcXVxe3FzfoJ6nMMQF8meypZK8eQk/Wg8/2QKYgSw863aQ7b0/NpESp5bjYIxnWzFJ06hm622Yb5yXO6C+cSO28cz67BsQ8/abGhDJCTK+Jr4JCrMX8izEIboS2JqX+UiIcPETyAivuVycLfSWbLj60NtnjSiGVl5VKCoypkPlUg0cj9Hoerx1hCRs9HCdF6CD0vYlEuzwgipRHD+bJryhS3lZBSH7cV9W/e6mg/sqic/t0fDbt6/45cREe3fb6qdlPP4Aakry6AkAVsgdnIxenDdbVOb2N3iQN0p1x2p4lAizW2JRBGuWu7u7bvdVqDGSDAeXF5ft5+fr629UgfvUs7NzsZmRdd7Vg0MPa4ux0oG+Jgs2ymTSvJPrS5NJGVEM1MSPAChaLI00M0TuF5ncvR3UMtflwqu6LZHjcLcZWrFnWPOpXUoxcLdLI2rn3B3Grqhxtb38ZGQe2Qj7m6l1I011rtTwLynQ3QjGvVDzHndFQXozbdGvTJML0helLSi2LAlw8Pz8jOoO9MEzibUs3/E5Rvb5jH0OLLulq6SP8EEOZG+/vF23s/XiqJiZNv9YhfpWl0WzDvYVaCuPokK0yuaEyQzszpNDqtVAIgHH1qXLE8YM6FAWd0xowdmArFwpS5WjBN0eyC4wGt28S3K2bGB0yOoITrjkeWcdrY5RZI3hRwN2yRrtSbr9Xnh5bp/VWqlU/IHIdqe353R2RiNm8mVyexoTcxpLwLsE5prKeCe1rMTs07asxF6lq6lbzaxKUFpaKdFkySo2RGEAfQehOEjWIcqhoLeowXDAjuz4lBBbWNlKMwlQY7iMsmrN3kLtkdoeE58HKwF6Qq1SqCX6T6Pj+2//fHxQKxQKwVaxjdTAzBY0R0Q8m3JE5a52IRVkPH55B3+kWHfqtZmMEEIumcxks9TFOxQePAlUhXaZd2lJg6hgcG/oHWbSdeNFAhKQ4vHXxKBnkLWzSxUyaEmSPToK5zB2l/gx3/nn88a2fRQyrNijIMWI8mAZmEWUv/1mi0cvggPC9t6TMIF75MbtaN4mZzHp2VXsVZltzS6PMayYlyocT6R9BnamskKLedJQBwJ2395emYTF/DWRwEUjn9pXoLrJosovxyYZ2J+668nv9YvT+/vv9y+jk8NGLimb+OxzAOxa5iQK7coDRQBtAIWxLreEw6OG1xjr0u2JUnJjesOqb0zCavU6WnYsZ3iK9SITTQfUDn23NFU+mIFPXUQNA0mM4vJJeR+D9CQpyEsM8YEPR5MV+bl6GxXvH19O6l+yta1C7RqyR1Gifu5CeGWU96Sl/W6qx4bHTkw5lkCIEggOuC99ZEJsxdaRdiksRhkjpx5v8OZAUy0czzds1PBKK0+0SlpBEky34qSx/86WK7LmDB1eKsV39p3ZkY73kBXEExf1LIGjo2NUrw8detXwsJzOZfZ01gTN8rVKo1wF1+WAcRlPM88bRx49goWPFQTxJm2rOG+nEOT1mse21WIT+BzeY7iEHPQ1rvZEeoIZNRqh0bdYZYgQyiZFbrBqynjgMlM2+5ts9quD18fLAz4dbM8gEEN2L72GDZhKpaW2T1ZX9ULaIe+eDmUOkoijNiGBFYB7UI/AJpq9iTp9yMuhiD1KG89orTwtUhswJjWaJ5tMogvbyRQLbiCgVGcCxm8c/uaY+PF3bk3MCwm4TrQz7brQOOPiZozzLTkLuFFLaouTRQKA0qPSANOqZvPxvf9K11L2G/slHB4u9dIo/RhUTT8kEAl2V/GiyLZLBHt4+6Wbc1Glp7Ckl4Ko28VNpNi0sLtTEqfsGriTxCO/ZFCYqUx9KzBi9Wc3C7hDjTZIkyQIbfUjdkFTLZKEDYX3RO7m++PBUbVU5R3Ds2A3wLUeu6k41rJ7kf56FUwTD6wXNq2829AVLWbjk4hJwB1wX72XRqzZa2FnsdR4bhdnEB6X57C1RON4y3GN6MwTguOJ0bby5GU7FZyp4/mFNOZ1nM0xx+P7BSRhozT/1F2u+eV9paxeqRok3bbRF49xoQUSwMn40WF9MKg0X4aD1+FRaTjrGmVB8W1PouOxoJNWqI4MplWonU2Q2IW0h1XMiBhgr9U/wdwYprsXEWXxBw/C5iUcvWO5XKL4U6vFbsRUyoZK7OHEqvFCIW+Aa9cCFft4Wc1iBFmZOhBjPTNCvYRYF4a2XdbhRmSl4MvbqD3IXhxXUonWmM3Inhkq9sjyF2nG2KZgnjumSPI9fogCYk9NcgHRislEXAJOwD3wHhVxGQTJ3gLZCYy2VWU/t0Wr00Vp03nnXmunNG+DwcvLsyja+Fg/HLF/CxusFHAdrb5xz33WA+FgLmtrT1DNwVxh7RXHFY4lAJg7rgnyw/R9MEycXVylEnvheQakK9sbSVDqaa4liMp9AOBW25GOxZRIlMplnlk8w9gj7ecUAVK/vnZfXjqvXY4v6rvZq3bQ/p/+j/+klqawj8Ibr+gP379//fqNl4H/9X//31C9f/n8ufnwQBX8ERYv/ICy3fyAd4kBn0/MDwKwIToBFWSEkdFE/cd9ZMqzYxx71pIRHQAAIABJREFUA1c/x9Dv9ua6cPz78/zT6tTCpcDopAfiWMW+gqD3fg8433P2XBSwwt2Ii4YrgYyASd93PFzetoi6GwkuybMk2bUwAEZ6i0SmWJR72Uy2XJ72/aKdz1Gj/ngsRrbSCWQedl3P9mVcjFG2rz3byTHw/fTsDPh31z8YNf94dnqKz5XtbIpbrgHpCqDr/ErjLhs94pE9Df5GWa4A8UDMTlTgC9i3r4+OwB0rlE7nBT09xTgfDvCwngJ/s2CUV/JKuYKTddalANzRe3PkhQGrmeeXzkAhbKB2q9V6bD5qVTn11hv1k9Oz4+NjmJxtD84lLy4vrXi076xXsHLKK4fJs86j4DvR+gXFKrfuE1bpPPRqJ0cn2XS0UXusYg+uazSbTTYrCI7e/lByePBdND6G+y6EFFoWJ417aJXtFmF/3X1CBquTYI4EqbNpIkfmcfHSmEziUXvRp2qzVp48Q9GjXt3E+FYbyhPP5KxWvk6wu+UX4Jgtb8HusI+dyFnq7v3k7PrmOpvLHzaq2n56d1poawkPEwBaPW3yYKkg6na8oWv8zSslj6+thKwL53G2oLxO4mHvdDtskkq8oGPlL0YfNYjnmxJbK0Cfrs6AgKkMJjrpTPrk9CSfL+byeYxYCsUiuBrEL5rz4bD58PRw32yen/3hD3+wM8C5MKC2c6IKTZAlMNjaWfbr0hCxztcDih5ODK6wrZ+itrbLh6fOMH9yWXsxGVtbza4rQjbGyLsxKbnmdWsyHh4ebg2vu8Coj65r9ftdaP9m2xADd0/yn+qsjL5TMcupeS4wSZKJEaVaX0zVBYYC0AHcqNwWWcS6q1JPJdpEXupkVuZAfbQR/80Kx+s8kxxtzVW8AVPUblUmObg4O+mPMre3X/Knf1sffZuCqlFj2Dc/wGh5kuSpSmAjA96lpZlsBt05Phx5ePt9tvAcB/yuAsHB6OMohaTZIdVwEWMk8IwquvIj1irPz6KPHw7bXL7iDjIxOj2/PDw85t0A6xgi0UpiJCOK/r7+D7ZHXy+v/VPCf3l+ZmChHghS9uH+4Z/+4R8ZejCXh1qxWKJOiliaelW/sEFjp0jZWxHeOW9Hd/2jevalVAC1RzLEKvbQbgsa99PTs9DIx4RXlIBGIe6wyERVWw06JloS4EUM3JcK097V6EP2S/v5Ijpu8znRYCJEc8bcyqJSJkl0XKjVWYWDAYxTdhW3Sn0mUf24WMYzrHwlRjuuERAPpBc8b2iOzEKR/sVgN9L87Stz2eT7+dnZ2+jx9uYGrXDwTo02LVgeWtA5D7JiRD3DANtUCjM2+iS4GU87fDSzswk6//b1K5/OyG2PZyk5YFo+rMmLtSQZGUDo6lmsNxqifceIRozRtVpdkDS1yPVgcHx8VGs0BKsTxKdUHyv5x8emvRbO4ZY3B+xkOGFH5B9+/PHy6uqq+eHL5y8//fnP/+2//rd6vXZ+fnH14UoXVKidvBKohXU0UwTDvnzuZZ6HjbPcQxT3Q5Ubxf3BJnHiboYtk72iv/c27lG+23ro88eh10dmlbr8cbiBUjFwnxX6go6yIGmajoesk0WZ+UDqbFCKogsTUuZenDGzr0qpXFJQeTI3V75rmqG0IEI/DXbHNZJZ2bJqjxj6MsoTEzBIZDUlrr14zBfc2Kgk5ZNv5xcXOEW5fs4VsolGoT91o6LCqC8+xDSFJ1n8J4oZjAqiuEbV3e/184U8D7t9GSiVAMEfHx95jbFXCD6mLDpv+dRmBisD9HkkpRrMYPivoLtUPByC0rvdDmi9xA5q1aoG7SxfpSzvDOzeYBHRJ1QNA5z33t4uP1xhQE+NvDMcHR59+PDhZ8JPP3/69de/+5//Tj1R3KuhgH2pdURbrUWrU2TDuKTO76OPmdd/O6/RCmOdQBgV+aE5VrHvUnf2I4mwy7CyCydpYdcS0/chAYalSf2DDxrui6z4oG0HIIiBu9UhVrzfQsc3CZB69/UVxRhzMWqxYqFUyOdZamao0ywerRPfNVkUgjjRMN3SyuN0EhkYnu2YTpWSPoh6gqHx9OS0Um2pJLfjQQ5GRBungjlTMdHvdHs3w78stv8Rte7GWQqEAXA06JkJTB4QFcDfqM+JxGIFu3OQ/VRFLFF9eHiYAe5kMwzlxUpF29zoWVEfFV7XtQlglzcG9P30cjFrgQMKcYIKQDA2unGU8bLUtT9VO25tqJo8b703HFEpbrN8GWCrB7gF019eXn769ElIqSdIvTJo6C61YgU0RTCky7dE6aH9dlS5ztUw3YlSEJHLvYoST7vMizbr2uUWxm1bhwS8PrCbwQdrGl7XIXDPdXi9Q84V+KPCF+r2c5tJj0mOOY+P0bhn01o052qI9VfTXHLBJ2h9vMbxAi5UDdo4XtvYELGZbq44wZu1nzYvFfsGm+SnPVtQplTMlRK/drLpr1+/VGuH1UpxC5hezKKy/NbdX+A2QbzKZMHODAXZjCD4WQInJyfX377xIcJKAocDpo3HyMDtSb7LaZI6mwBnDcmV+1OyM8zoUmB1HstxupjQyP5r/FhVcHJ9fa3NhQXXs8j17Y3vf2B2zPOo8+X5hQplUQ1LX8ZeZahBAjXxOoAVkJ1gSOfNfu395e68UUxGaj/UsZY9pHbHZB0kIDunFkvxYOwgmkhELZ1HI8GldyY8tSuw7rlvwN2TlOfeRK9UmM+Y//iWx6zJCiqm2Vwu36g3bNPenLq81jSHzEaidSfVaF4faY1s7MryvIRsC6UnuLXxxuolFI2LLfP9PFhL75EfomuTSnQrwnSEv5dB8brVrxYG5dx2yxGAzjiAuAW1C24XUxkg/NvrK5Yv0jFnQp5v/8Ui44ZYeamgl5EKQIaUWjbOMlde+LXxDGTJpcC2mLBzTjb9RiDwHS07xjMKWEseZeaOQR6IByiv6XOkOgxj9Aa3oHM45cFB2S41JhJwUioXcV8DffLwEGvOhSEV5J3hfaDd0Vg0Az8ZJtLXDy+Ng/pBIy9WOhEJBmR3uJURYXCH2eAbNa3zJ/rtHlkif1MZGGSgiIO37rmoV+4DcA+sx7gnpDRe7x1mRKa44SCTZv5LsXpGz6xLOrD7apYQilwyPVF7rRnoHqxANN/sGW+HScH0Y582IfBu8xc2V8QzCdMPz/S1Gz5niE4X8kN0msauXpfT3XIt0Xrufrl7ODz/XTE94X1lW1oNQGfdChMYDDOHMQ4ojbuYqoObZVhQSbPN4TPRt29f8eICmCYVg3ggM47fKcJsiOWLFBGv8GnysGiVRwrjGF4SIKhGIdG6Q5tzsXgX1D7UC1NlaSomeqx573asaRUP6M/P7YuLse92Rq3Hp1av/356evL6+tZuP1MRDQG4o1mHDdW7pWX8J1CFGvFC3IDpuTtodd/OD8uphJPxm0hk7QEpcG9jw5i1C96qUPq837B0hJ4lHI/ZszKZF8OwIENcHLxJYEGvTO4qcF/Q5sCEZyfEtMjAwUyIn2NGcDRVWJ9ipM6Eas829zwwfufWEMEEPfZp0xrYQysvYgCPGLwKmg8woDj0Tm2ag+lroTgew8dnnmpyIupAwCd1B0pbF1WrFGuVq6e394fHt6ODRiHV3a4moF/v9QW4g2+VXQv2LWk2YEJpDVZWUN65E4CEj49Pvn39Um9gcT7ELSOjjBpeDMie5CWY73jqXUCpv0HzgGelWRe0Lmbs2pQdiRGtrGPEnQzLYTGSYcUqJ3DCalQYe2o9XV5eUdyC8pSSfZ36fVajHh4dYnkPcOfVA8oUtGWTxklQ1dG2MG4QS3Nvbq6Lx7+/LALZN70RMneMR1If9/jZDONG+6Dpdqr1QdqpiPPj6pRTx+11B5mjlZgvrThliQRCGV6X1BlWstdHaS4fbggxQ6Gr4qMwi7eY8Jg3UavjpRGfDXPpTiW4qWaqyE5fGkObuaRVi4ePz0yNgBEuV7SuCc1f2PhGjs+m75Q0btWxez51p9qm43bgup5/r+ULj53X5tPXg4NDrYTeinYxQvBOD6AW7bcC2cpWJoPbRNylW9YmGgeDibXXFzA3rUNZdXh0dHf3ndWivZ6YpGuMMspkGIXUNz3gOEX6vAxAAc06BYHa+IpRR3HYjtd2SAHnIQ5oF9iOtr3Lhk4vGL1nMjnKsBHT0fFxu9XS/ADQ87kc9jwwwDk0DxoHf/zjH6vVCl476Ywo8BXD8s1MILu8F0gQh7UhKNjg+P61fHh4kt/4fqg03nqYrZOt6Ii7y6R0wAgH94M3jYh0S7wLmZFBj2zei8YlnCWwvcDd04Pg3Hgr1g0txgWmTFFU9fsyLY0SYoFayOv9UCxSi07cVLOo/H6l6cFLq98trTyekMUyQMZo0ccjUfdjHMa7m5Og3HtP91+1y33jJlvmqabJog5XfrlwILVyFB3goJQYFS9wPd4andRT33kMV6YaOgFGDHCnslUZ4cgRQxkCinMGkM4L0Jk+nWJ6E9uVtzcmORJZDJpM5jnXiAQnjHh2z2TTxyfHGrgrkCy7JqXeUz2OZFWGK+QXNC9G7BIEtb+/o1+AIEWA8uR9w7IeZXunwxGYDa5vtVp4atfzq4JAAsIx9hu0nzHBx9kPJjTlShnjHl6iH5tPKOuPjo4kv3Q26qRWKUKlnFJFsDJ9bL/2c8fnVZYJBdu5vbNJ/fqJiNJz4b0Zu1aCXrczTVqli0dSCqs0aGfuapAN2S7gHvDtX0yOeZRZFC+NzEbMFkxL4quuWKpUZbNSDzfBS14PZPcyq17kauF4pb8UyKOC/OgMjrLZLn9hqtcs7zqTw/TklaMUfEQu58IHUacirtnneUTjPhy1Hl+yzebN0dEhKmEnihGKUyha75+qVe4C3nn3Jx4uAdjtFqtC89pzi51vkYoCJWyHdHPzDZt4CgJTwP0EzgWvC56V/2QkSau+DRsZhd0ZygDuJKCEB18D2187om4fDd/xSpnJ5vBdM4G2lcN46CfyCYx57r9/zxXkBQlmMO8hDAaVfEEWp+reoetlqKQ5+iXC3oRVzqF583ZULbQa2WePL7+rVOtUlqbSfte91IlEHBeWBPTbbFjUt4eu+9E67sjbc1enOY0+cHffD6fb5ni9mBxaMfYLxDydGY4JEJherVT5qO0NqVPx4mocOYsjvUtAI3jTFN7AEGk0fyAZuYWyvasVQDzW+c6c2NpHmyavJhu5bJhelj5JLZSrRew7VMhNPqwmh+WTh06m326fNopqS1GHnFGIQhWN5lt6ZQKluwRgMZ8LOIE9jE9qLF7H35IG6Q53Q4YkltCgJqfhwFneVQzgDnpWunZ+oU9Qq1C12l2gtGjc314r1QqlsJJJ9EY8C53OM8YyGbxSCivCD4BbuJtEpk9Pj3jZKx0fo6q/vbmlYL3ekI6WFASvjWqEM10vJu7v7xgNCp73eDcd79HLe77dL5/m73k9ccyw7kiLC4cbtG5e4vrsEsAuzH+X28u7afVluxhnz/dSNrNiiFZMNIG7yx7lVpTzyDGN4WABt8TMMminwOv53Ao7rs+rxi2bcb5VJaCHGNxtQsiyqhMHlIJoxLRpn2/RsrbPTQ964A6YHnDzuDIclUq3b/XR47+enJ5qKLxqZwqg/IRIwcWYwWjLOngWpJyWxam5Ap4WRVsOahforF2TzAiJePZPwsLl5eVFv5bijlEDd0oJUAZLm8BdrFbE0t1YmirAXW2SCrrGNgZcL1YyLy/YvGOUozG21D0D3FGf12p4rRXiPD4PD02U8021nyvvBmzGpFrIQdzV6AC+x3ekBvSriBBW70cf0p1/P6/1NrYf6gKHtarlfhs4c3fdE1qhqPtKtjHnSiZz/u7mftwLl7LZD2FE5cmICHB32Tc8SM2RIlML8wpffmWOGQ2ZONE0ufXSOK9yx5rmZY7j1ygBPZRoF5Oo3rn1VK5xvN4WCv3kAtOaNXIa6aqC7uCy3jjwAOw9K7YHubPvd7fp6g8HpUE6GRn33rq1yRTqatGFKysUcDzjD85lLsX3orxmauhsaNwpMikmrlLZFNu0sZhVIexRFt22MpXhkuJymyzgrvXf4G4GOqVxZ9ATkgq4g+O7ned+/43Rj1KMhNVKDf2/vtFSrfmfUriRsavhsTf75edfqpUKtNgeVVcLWRlPVV2sAsrmVjVb6o3y3596x5VvG9sPVUN2+wc7xBJYWOGRWqHoDPuTPYzkmYiZItGN4OFi24e18ufjXmyzhBfLdoEwSLJSd1cAi8UTcOoGgbt1KwNr0ixFphKWe4HYcKygZposai0+GeN+YaVaZ2taiVxceB0SACcxahiO5BXOAejoLV3tOJ57Gw8uod6P8J4ekOj5+cV7Innf7ABqGyVxcB5qW9wTz2bSGJYzIoFy8UEFbIdDRHF2ft5sPrJ6RliVzqe75niuQ9dOLfK6k0z+7i9/j7l5p/uCRQoqRrGWyWYY0wS4i+ZcjNx1UDpw1qiK0TlrT3HBTjyp4v8Rzf9rV6rJZPOlyun55cHhEeifWqQS0f9Lhfzn+4ARoy6hwEiK1v/h/p6tj2RdgWJWESZR6mJ9baVWp6zv8NSv9DrNiwbfxzbx6hUuZPctlTAKyi2eCDMRE6meLqTPEowffRHqcTuWMPmQ8PpEGOr9MYi7FMBuNTp4wa4NuLu8X95aOEuUeREtEbMVzteYzpgLmR15EV91/p6tyRunce7NS0CAy2TgWsMkY+MnsI+xpQ2QwdzedZ2TzyR78ZU/CWQSvbODAlsV3d3dZRu/Pci9aFTqj1pQpdCwA5hB0UB3ELulLGdvVBAwOmwWi6JhcOqlqt+qAw0B6OtRjgU5yslVV8Ft8RqpRykB+oYKHCAtAV+QgHXp3aJfT5TK5aPjk0q1hm94THXswpFKJh8TKwbGQP9Pj48Y23z/fndweMDgKm8L8q6g9e0C3GEKN53+xkvZD/X7U62abNTR2a8ZtdNQxbV3LbuIKA5TEjB6gL+OMEXLdjlX1kkWp+GO2ZZ1V05XEeFcca1VOGqU8Faj10ZHo6He2rhK7vCAu1fJu23FFF0mDb6R8fGXtSl66pJtwgtFFpW6pTgv31RN87LF8VsigVlINMW4PPxJNrQxgt7eFShPRyAJG5sB74IAirhjmCKK8m8ui/b9/G3Qu+ng/eT9sCTbAGyWYYYpAjCXDoUqAZwLZAdvsy40l5MloGBxRjOO2LhIp1PTkbHMU/Guo7CwQb9I0F1a7GEI4v5RDHEEtCuLHM4YEjlCYaAsA2n+//g3f4t2X+SANGwYXc98St1uSEnH6NWygP9u95WFqs/tNui8LXup9mFbZ5Uq+RNf8u9o3P3hp87bqPn0eH7SSI8fwbXcLq1i16jdV4Xr6VX6dvhicIcKzZN1MlGtrjzj75CcjKbME5djS0PrYauqTR25nYx009DQ2jfJylquAgTubkTns0120kxd6NT56MxMwbTEDFXC8DO/sk5ds2avySezcbGISsDf8GEZwb9r2KfsCATfKDRPf7EyRLTZ+81WPj24qAzYgOHz52uQ7uHh4WblwdClAHcC1Mt5viCe2gVEi+921qpmsU4RvfVbj/3dBMELGncA8WoSGmmvSabKPGfMTDY4rnspAB1ILctfxTpGrHSMtwLzTYaCuktr0xfhhiT+RgmW74PFeZ1gMStGMgy/GOO/E9vrKTpSjkFYLYMVoxxK8THBk5B5pFiZkGn87uoUBq0XZ080VsjsXcW+QmX+iyKaAIPRVQKkuGlST09Pp6eFTXOxzfW772Hb2XuWtm+LmuVthJ3plUtFMVPCdYRFGnUOTtCeX54x5KQ0qh2A+qorSqfYsCqbio8vd0gCenHqig3Ss7xlhww1+o5oNRVdzsHxW/T8ryiNbSmONfjHjx9f33rf3j+Uuv9er27mkzouWfBhxQaoaMRB293XLiBeo2QNodV5iiXzWJtUR1W6lWHoImYxPcA3OF7U6SBlRE+nUwOXqOFVnzOGMaMH2iLVylddhfiukTPR41NIznRQK1zhiivRoMveq4Bzhl45YJNDHHlJ5g/9faVaVbb1Yigj+YcjOFOPmLfdl3rvibuX1PHBaT7TWlN3MlTsa6otmtUYXWU15iI10PFYrdaauLRrCbjvPZHqIsva56ZZEWmQV+DupmnLxDM/XVOXuer9/aUjXs+YDJge0EsdHhwyo8wv6j0lUGLeq49LrFsC2FAFXqV+jA0TeXCNurYcyVOdhvgRedoDb/52ESzkcxeJz53y0XXrtZRsVauVNY8BqKJx5sjgBhhGhw0K10DZGNgmuZHtI5JJdPCJoiBrjY85MjzqEVKfYKBC0AcB8AQ5GP8FlXOTWOY5HGFDn8lmMWwX+xwhKTp1OQrCV+Y1Ym7Dy4Ec5Ff+C2H1bUlutaYFz9jrs8Rfysrrw7gslvTsfaHjpcCy0Hx6ea/+1WXtk/siy0jOT9cPJ2/eW6Jin9+SqKRMdljPXAU7Kj48PJydnXtmIi4QqgTcd5Fge0NojVraoPW0YylwX8rnShLS1JmBUNVg/gItztFL8e+gcRD8aB5ua1YSRVw4bAkAlcKuQkMCy5E81dHj0MeD7AXNi5ZToPx6nu2wG7ul9EupTqmWeH6rfPt2Uzv7fSW1LkWvAN8kA91PP/2MSXj7uX1+fqb013pUUijYLlN6iUpBxS1qcnEfKQeIoBY30hQ+F+SuTFUEtYuyfIhGXHTgnCsIT6qcD4cnJydWhJTRAV259Q+krsC6EFJZ4Ui6K+c6JFkFm2R8LpcA6BJFNsjIW8T7O+Y97CTlZtymyG3/uFLMHKQ+m20xagj+x4LswZOOKfqXgO73PsqbfXGiaKxxnxDH1l247A2O9z5KjV3ajkBaMAvcl9a7qpCogEEeBwuyoFSWarEDHyu00rVanXlpVeqO5UNvk2OtcWS0JIBZwvoZ4inV+niN5mFAdKcax6P2VOdEBvIwr79121tjJZ+oXJy1R+kv9916fqD3RVpDc/TCTTTc7321mN6A68YIxWAoEfQGHWGdECExAoktBA+A5p9OUPjZ0nwr9C1QWuFpQdQozsV2HLiswLyAbdufQuiid+dPztXB7JJE2ILmAnHhDEcDdA3coc9o/tZ9PTo5tWV3Pu30Uk/Dw5PsHe47nXMEEiu80pZYxR6INCNERD8cdoaIwcb9xEXfs5eKz7dPArP3frYNEyPWbPKGY5a2wA37AHfoLCXlv6maNBMGW/WhBer3+sw96NRxQFwuV/ytF1zCTYitWVJznBxlCbTb7Y2zxzOpjWcsrbw8fiNxWfOOIhP4Y7rT3jir+8BANdmsHBVxcfjtKQF8LxXCxJE2geJH/erqqt1qqxcGY8Cib6gz/atHb9EVq/+CqmeCtnWRaOX6iF/pTUY2CoCpVRD8Or4yY+XXgOj6TCWoGCmgkuVKlVQUNGlqrdVrmP0A3ElmbKfbassaTIDYsMlgwOkHog+ji0T3p4s6hEOT9ljFbkrDiZk4bpckEHuV2aW7uVJbfDz0MsxFJSxlnwyzGvcAuIcuGL3z0kHng5dGjc5x0QhaT1fDG6wD4DwmscMSYEFzBFvHiIFOULumAbVzCRriIUFPnwHNYxeBalSpWyPI/A6wxGDVaDTqiRQau3bmr+uDXwr5VTf+XCwWNOA4ZKzWaqDY29vby0vZOVVBZXXvebFTH2IUAld+YwSGAqQVlOZUfuVoXqtxXmC2GSSHpmgwohNUSZUkReXPulD5zVidJlWoPMaBHxWF/no0PDw6FJcyilGF21Hti6t48mAqY9Q689MfJL93sgfF20KdxcGK2kyeVSOMtxwlk1VpxeW3SQKsHvHnh3SbGhnzGpIE3AwY4YxY/hoUDHCn1QzZ2BBjp84JSJ1PsuVKuZQphWX9opvrRtz+BBOX2jkJNJvNiLdJjwwgN+0STzug5BIDMrAcmAStfIzjw7iJvDo16iiBvzXbzw/33ePjY3YmCqMiaL699a4+XPLJ8fjkpNVq/fLzL0fHR/ipZKhUljKMoILdFbKVH7nz6keNslwaYZgYpfD0wmufgGnB02bK9K8J8ulfE39SSP1xkKB/zEgdodgwpizWqnY7nXKlhK282OkohkTfjsZd7aiK0OZ9RG0N6p27f2Nf27D2QxVRxVYxIfXZLSBr7E6wBZzGLG6nBNygTWOkDL2B/oE743W/9/bS6ViWjpVy5eDwUC3AC5NvN+ILs/6Y9goS2OTN27gPb39y01BLTJmV8ATUqQWvUJM1r2rlqz/KcakpCYD7Dg8ORo3GQyfTaz0d1QCiAcN3jElqtWoul1em6pmjoyP0/TjE+POffyqXS0eHR9iOp9PymqZ4424boBoYjTYkmWTcJUm+wUg/kD4hvxpzi/JbgvIxo0/VslQDkUNiXpBKjIrG2F4jeD779N+fn9svz23Y/v1f/Qc6IJ8LsLCnZ0JeLOnFA00fV+/Fkni3nJLqMJG6+d6qlAYsxqXEVOqql1JbDNlXleIOlGfVHFuk70BD4iZssQSmBz+npgQB7j0Adz6G4tmXfTiYcpgUOPJlCjAUOlKn7W7E4SSiOG6NEoj6TYq+xt3NzdJPvV7wSn7xra1wPFAOZTwxJBETxODghp0dzIP8jsoD9nXDjmWQyJycXWYTb4G0E3TMyImBOO5hxDcMWutUGgNCrGWwKuRb/83tLUYn+XwOZIyxeF7MTiSv0qYLjtaoXTMj0SPoKODKQRE34br8qlLGilVSZ4PC+xZeV+nqCuiP43aU67CEF3eWJOE54MPHH+AYeIT1ozBGxYL1pSK1HZPkP7/8MCWoTi/x2GqfHR+kE/2ppFUvDRU7ZKI+8qza0ri8CwnwHLnIFWeJJbBpCbgZrpbN34uAOy5fGL4ZqWkoSpxsLptJZw4C96c+T4xumjevbBwflgS2+K5E08Z9xRslKlnLcY2p7dTmNBx5/HWGZePAilzsYHFkeXZ2hrb4ezed6L4cHtQyhgWT/8YylmJOD8LAR7vAdqxNVOCctZ6g4cPDAxS9dRmYAAAgAElEQVTY7Kjaemo9Pj6hKyGJzZgUiC+QAQdc2Wwuk8EppHiHTCrNuwBXMLT4YjdgOugf8C0HpX2fhewSY2SmElmJBG9sjMrf69sbRbF4wVr97Pw8ly/wfgE0x68Au0AxKcCMrExVrwqibheNzjulmS3sK1NlP9Sn13Tp+PIYiQWH2qW/xyp2/51wV0vSD3e1aXG79k4CMsrND/h3sSfyvZORm3GcwNDIZ2LcddXD89Jor3sxo/ac8XnoEtjNmxEFrzJh3zoN0LU+3tLKY06j9fFDdr5ESZoA74XNyI7QTyWGp8XOoFC7f2zzafGgmgWz+msbIBqojRuZtIbtAtwdAuQB6GjlFbAeoTwBTMvioW4TH/AAZWqXkvggGocsOBuIT5J6j5NfDdZlKFcDuniEVGcc2QcVkK02Q00SB2O8CaTTGSxhMDbIMuyrNvISQHVAdv5xBkFNHPcdUhdm+PJWwKQBbu/DYgbVjmlZ1B+m71rDw3qjkAzOlZPY5oiaP1axI4I4TEmAZ2IqJr6MJbCrEshguciG1ozjTOnse4Q6p1QMeUWpJcv4QbNEscmTfbkNG/Hjvskba9Ztx/FgdzAd0MdC8+B4WdRnZo5/ZyWQTg5OD0rvw+RD8ynd+MuD5LVGybM5F8Tw7fLw+BBMLPsnmbp2jdwnS0kcSm6cJfKqUCjkwPqAZvKIllwFsLJyzS5QXAB4r8e1Ruqkg7Mlo7qmJm443FqwhhE+XyjCho7UGYQBUcGbCntdjdSq+oXQUCwPUfOnH5uPJyfHuZR4IBCNO8C918eEslgyJo5Wu/ta/v157ddUMqAtzwzIvi8j1WR/iK9cSYAO6ipfnCmWwPZLQLQseqXUmtoSj71rEvSCavb0Hqxh59QFQo9IEkDM0bomAwgDLQLdgHogtlh9NXPDMqnR6VGtP7y7fXxNDd9OjhoWGp7JOx2B3ht9NJYkNm27QsOCicd/dmtthZYlD/9Tsu2SAt9cSXalepYFrBquC+A2zizMbjiZMXNYyeMTq5A6EfuTCbYFrGvW1K9cqncA3hP+6R//CSsaltUqfb7YyTy3269vvY8//uaud1DKZ09lP9QggFQM2SfuSXwxVwJ0xblpcUIsgd2SAMA9h7Ym3EbtKVAMV6heqMc3QKQlK+riMCMBjdZwNEmK1s2Dz0Qfr+xqwPGyNFJhxZmi+xiRTY3OD0v9fvbrSyU/ah9V024+V7DE8/T0FHMUWY+qILigYBE5/8d/ApQl0gqSpCKNWH2uykqM5AaIq+wWILfU5eqE6HEEWYeGWxfiKa0L6xMICqVxFcaF1GIE3h1QuqfSTBm//vLrn//0Z0xsMJXHIr/dfsEA579/av7uYyKY/VCNtadGw1UT40MsgbkSiBenzhVNnLBzEghCKeIoFMZb688xQxwZogQs0euTEGsKivQUx14v3bCBUYGbbHEe4BsIHn2uHBWaQxPPH2ie+8JJLCJQwlXlpVYY3bxfNJsPi0VCx8PJNGtMZTmpHbcrOCzjJEEf9Zlg5+nx08TO5i/pGuNrIpJdXysSxqmRRcgL/XEd5rlVRCVrUlYu45I86k9dygH4nkpqq3p80r88d4DsxWJZrGi+/TuvCcLBKkE6nXwYWoVGXHbfJMBnn31rctzevZVAcMCdYdb+t7cS3UzD7aLf/IQ3xY2byxXF5qaKYBSBKzK6hcXRzKJx58/A8UA3Bd/B8RrN0yZi9jDksunzzFe84l43X586Q1YLOAqBJUT1RgPzcGVpIhYvIkKRovpVP3JtQm9+Jc76k+xCWUWpC11Un6pEyWCLVGxInARVTJ0ZV9a5ujaujMzqSiiN/4SyIq/qM9jnnFcQWWcLXiewPIq/3lvvy5cvE/RdXlj1kT+G7C6FFmezSSDegMkmjPh0xyWwGnC3RltO4rBuCWxS+va6Z8/XLQl39WmzAHd541xzJaB18BrHa0BPVqWSlyNonsu9Gg9ADBeHhWyhct3stUeHU4JDEVirVtDQi6rdNJIx0LSAZf2nC6lzu+xs5wqGy0EhbMHQIufxrz43CpjxKtK8HRYxVVTTUTQMjiVa/vi1/alTiZFg/IjKnabwpz3kCHDPpPFUSezXT5+9LSaR7WK1il2b6hjcxD+xBDxJgD0HPOWPM8cS2F4JeAHuxriuR3dzNtjepm8f51M3IPQGTNVnvwy97hAqiIF7CEI1FjSijMfFpEbz1AKCp7colbzG9AqZhVF9ZGiWUp2LgyzbDH35et0eGDs40uUwMK9Ua0Bbge2CdhUCNtXuhjJbw3FEJsPqGCCbQFkV0QeNqW0RRh5VSoqroKXCqTqRB1fO5UrHCJVxsC4kj5lBZZUyiqBxUGxryC7YXZzfKLW7AHe2acryg5+ZX376aUx8wRlV8UdPkb8F+eKkWALLJYCX0uWZ4hyxBHZCAsuAux5b9XEnGrxVjbBLn/MQw1RNO3nDAU8hSjAmbZOANqqx0LxOwXEN2lWlm99ZmFZKtq8uzxO9p69PyedBke2M1I5L4nsRnKtRu8BoBYf1iYGQVaQpQvNht3418NbA2oakJb+6lNLGmYBhnUVR41Ql6gtVQJ1ykCQdrBOJ1Qm62JhRRUcoadqqKeo1RKC7qNwB7eLInRz33++fnh4N0o4/UofSr9MRdrYvOLY8jgxLAk9PT7rzejyGxU9MN5ZAeBKYAe56SLWO4dUcU54rgXVI36pDn8zlZYcSQveetEOyCrYpaOIhiOMarZXXviYzaOVHhlZe20oEW+kGqVWLmYt64v3l/vb2Go27sicB7+K4Zwx8hT2Rigkz9KmkqzgDMRso2Uw0LpU4TQStLiy6ugaV36BjnUtNukZ71GSMXGnqKg+XOsY4qgYYFYv2XencResutu5Yy2Rkc+0Mril/+eln5w9cmmKM1223ID4NRAIHBwe+6Oge6e/oq8K4UCyBlSXABnjmzKFPVqYYE/AlAftt8EVgWSF7BZzvYRB/YVNS0Jd7KIuNNlnrWN9NxzWyjSt7OCvjeNNxzUb5C6JyjD9ymcR/+Ou/xr30p8csqnfRuI+DQr8KJCswTIJRqwhDzs1rdarLyakE89cA9xI5kV9nM2rQpCSDVYUts1WLmWhGGBSJlhrVEZbMExUnSndlK5MEtovWXUB7Bkt3yuIg8vb2lrqNIOTUbY8huymS+DdYCTw8PARL0AU1/ZT4OLqgHWeJJTBfAj63755PME5xLwEe+BBDuNRDZDws0nP9hfmQlMaeYXG6d3QRJzieZmNdI41Piiaec9A8x210JM/nnVwuiwvIWq2ODrr3ZhrKKPRrQmcaa3U+EvR9V5EKKmt8rE5NzKyko+N1bgXIkZ9ZWKWJtpsI1UsVOSmulw2LNCVeidXMoCIEWnMi+cXsXDGqsurcEiGlCMaPRu6gdwXflcmMYeneY/fWT7/8cnR0lMHsnyBLHviLQyyBsCTgV+MeFj8L6fp4FtSzupBonLg/Epgxldmfpm+mpXoCC2saC5f6ZiQWWK14/1BgREOS1XCEXdCLzwNjf48IMUdpBC9aeUF93DIxjrdr5YmJbAAas+NSvV5HBw2eBdqWK2Xd7VRT9MFkX/cfAbbyT4eJM4mUTBPBdq1STWrq1ySjCkrRidTJC6sqnUlfGgV0UX1U9SsWJ/jQdu5qkaqYuovSnVWq3LXX7uu3r1/UwlOD2mS98VUsgSAloGzcgyQYMVr2Z9HlecRaELMTnARijXtwsnSmFOKkFSJp57Zsd+ykvzAR3ioCnFGAzERoafmrYw6x7b4BfrnXwhB38oqCtphHcYwuF3yvdfOkREdm/X6vWCqyUy8GJGIAbqxLFQSv4begbgOICxQWsKz6iRkpyN6Ax+bvBFomWZGQLizK9XEn09nkVccIlqk5eYhSl5wqYVFOq9SNCPNHKdylvKF3H3NjELWYspTtupkauGczmT47qg7ev37+enJ6xmcHv3c+LhdLwK0EYq8yM5LieV4xRGdMXbEhu1Y81riHdEd5ZvRfwPQtuqs/lAFzFnlywY7s9huhzhW6EQw1/Wf2hJkSCyQ2m3dBzAI6u5jEZMIf8F3r48HuXIJI9Vi2cSt5oDF2MnQ2bL4NzA5il6B6hmBe1SOMW8OFBPkxTtW1eSGZpYgO+kyNLEQIPUm0F+VC/2kq1lHlMq6kwESAkr42U8zfyVjNCHlV1SZP2lRGWbvj1l3ZurMZUybNTXnv9z9/+jRRVXwRSyAcCUzqZcKpY++oMhC4/9s76WywwTFwD1D49i4eINmJRydIuntGa1PfUu3dYvJcASAFg9yNj3Nu2CTRie7imDSHzJZGAxAJ4Hitj9c4nhiaDojXOJ6jyrWOA0spKtUKjhHF1Ypl2Q436sZomC23Rd8aHS18qRT1ozC5gY4lQoJOFirESJwuYSSpGFukzq0yjw9GBcLGOJiR4xjrTDOoKlKsS8WaHYMZqRzYrv4bZu4auWMwozZjQui31zftdtuiGZ/EEghJAvHOqSEJ1jVZa8BYeuKaZJxxjgRi4D5HMG6j7X3UbRk3+cKi66buHc0T5dVL9ts9/3x+yhhZzeaZuZ2zWdzEzJCJcgSeakCNgHiN47U1CfBdOZIXQE+Lwwio24fDQaVcUep2gbTKBlzkOw2+FfqVSPgwuJE8Bjw2bqgupiNJtYKmp8sZhaU5NhhuxeoykmoLtow6llxyYpZSl0TpP6LNc8mgzjkoJjRyN/A7jRXortzLgN6RBvL49ec5riGlwjjEEghGAnN9DwRDPqYSoAQYO9z/BVjv7pCKgbu/e2l1O3/FnUtZRDmJw4QE7KJxcz5R2LjYhL8wJz5WiHPTdJ3HVon7QvNyKmLzEv3F2/hbw6mF45UjeVHPA+stZbze5zUQNrrdbr1Rx525tvnWGncD4BqoW81ZRmWG7PgxIbM6lYMBj1WKUdIAzzpRiipFu/ErVyqzRcmWXVJ0MAC6eSm/uqARo4jYUyWDupYK5cIgq3giRn5F3a7eUbSDGeXTHZ17liQ+dTQfms0NuOqbakR8ueMSEG+/cdhBCeiBZ+lxB1u+oEnx4tQFwplKkmkrjBAW3TB49U1zzY10qi7KGnffcp1X0EkA8/IuXdbpidjcWiYSHEiuz5pF14QyHpY4yg9buirTedC8jiGPV4ZQLwNgi4UiazSVClrQrIK6ug7qkT/zv10eigfhQ8NoI7/KKef6TxdXUNnMCJOkjRlVnxZ0acmi/sapXI8vpLLJoJOtTLq0XMKAlJQI/V//KM6I5ZegoLuC8LIbk1jLZNK9NOb+vMN8/fLl8OhIcTNZ5aKrRawuKhen7aUE2DBhL9sdN1pLgJHJZdiFgSUG7ktvtvsOsZTUOEMoRMfkN3EW+SZtysZ9EzfDW52+b12gQ6BvLrw1dja3tMJEv9qdPDFaDc9xAPDE4EOw6kLYyzvAYABmxR2iaJ+1FtpAtVKBIqArh5S6kl8dozIYmXROdTTK6HwSo/9URrOkMt/nAp7JwcuDUDQKWsSNE6LnW/tDXTlyN/MqrK6qFOSuUqVO9d/iUeKlXoXb5UcHgLsYzGSz7+8DWHp+biMcXEVOM7To2mzgojx+05SQ/BZeZ7mtYXSdQnGsS3dJx6Q4MpaATQIuB5ZIP3qeRlJb23f51OV99SaCUIh6Y2Hl3FvehmC9yqwszV0gsLYesf5BVCP18VGZxdNeNNxo5cH3cj55D3Gigj8VZd0u8NUCs+Q1g0LUhtQE83Kq/mtCkiCXHMyjWVB+FWiWozqXnKBwlVOhdjvqVlknubNfQQTe9VHix2fqwgDwZiy/4z8F0xUL1Cyc6KBV7QZsVz/K0D2DP0wge++t9/z8XG807Exs8pz2RD9I94oao1NdPvpCjDmMJeBPAj4evfU9HTFw1zfVx01y1RvCouuq8hUybSvfi5oc+wtbJJ1op22+PyqzeC0kbUtjOaAE5g5SwiCqZRTN2mBGwVlB1QpYc6WL8iOn5iWR5pXKoDOrrLqIHGf/pJjkJ5XaVEm5mkTuior3A9Ss6YdKhLpY4wh9ozFyqupWEfBAvE5UrypK664177IZUyYDcM/m8jay3pnazxJK6sE33bq9fkiHwdNKDFmN4EmwzuOTWAKbkIDLpyOAjrrnwN2loL31gVCIemPBY+7t49hjA1X22F+YH6nFZeZIADhrDMDm4wOKrtXr19++/fDjjwaGFcgryFZjXVN/qgpIikVa51PZTKQ+UVBBZYPImKAm6zwNtN+rw8GwlH6y6nBxAkPO1ASYW0Y4nGt+5SjcSAvVwbBzNyyFsBoSk5lqraqbOm6uEytzKnbKGsetIoHFt8E95cBuWDAM0dfshALjzr1A4pyxBFxJwN5P5xVY0n/30KsMUrP+5knNc7xF0c098Uw98AJ2dreD4wBEEPsLC0CIMYn5EhiKzn10fnF5c30jmnAD3VJAI1sF1I3HTUHdCVLqmQQAT4xOtoJCxUjTOHiitFxMPMnDtwf+7Hkmku0Js+eqIolWTFm/+so4CjmF1vWPbpBEANsNU/fBYIjGvVypdF6ehdrCMEHcrHmDkQuZjRNXvkMBizDZ7/ftJD31HHvB+DyWQAQksKT/7g9wtwQR5E0JhWiQDJq0LEb1iRm9V7+xv7C9ut3rbywq6X6/B2w9OT358vlzq9USBbYBbg12eP4mAPbk8yipZgaFgfWFgvM6TYN3wcryp4P5a1wakYNuYtCZiDIvrIJmxNTvND255r/8TSTJlcGl+jEOonbnvaXTecbi//DkuHnP+4P2TDNVUaQvjRZbTQ/hJNLtD5s53/Kdw1g+jzmWz+CeF58VxMViCQQqgd0G7vbnMUixWXSDJBo4LYtLTuKgnH7EYoglEKYEROPe779jH/Lxhx/6vf6nT5+7na6qcfw0yuNoPJIK+qpLzhQqVhhd8LBmEyxsxhvYWXIbiSqL/VyXmXdc8vF1iprJgMkr1ypKM2RnS/C6RvVyhqF9t/vy2u2enl+Uq9W76+v3fo+SLFidx9jexmuZBnXcFzHOkRdbKKxBAnMqNx5Oe+oamImr2FsJ7KSNO49P8CEUosGyuQUsBttgb9QEX8QhlkBoEkDjzr6puI7EeTkwF8/l4Pjvd3cPD/c1CXXsRlTlqiManZEfA/dqZKyiVaQ9gxWrmZdL/k9BceMyn81WqtV8Jj38+7/Pn180Dw56RpOn8huxEz+zVM2qSJn+E6BOHA0Qc/9+D9cxbVzrlCvVg4Oj5sM9bQfBK8gu2eIQqgRWF7GL/hFqC1YiXi6XVyofdGGXt2OrZR60zGJ6biWwG8Dd5TPiVig6XyhEvbGwLPcWsLisCWtM1yBjjRXGVe2dBIYqgNeHw8H7ex/P5ZdXl5h6t55anz79irFWo9GoVKqo5B2eXVGuEy0HEw+P+6xKUdh5UqhTE3/14b4yGN4mEjff70c//HiaSv3m48efb25eX18nyy2+gg3lT8aeS7FmA+/SAlqK05jndgt9J409OjkltvPy8v3u9rndxuxYt0U1x04rPo+iBOSOBhememZwhJ0ptdvtQqHonBbhWB8yX7NgIyy8/WVte4G7jw7v6jaHRddV5csyRZq5ZcxvOh2EsWkW4vp3XALo2yXwI2beo+FgwIq5VCp9eHRwdHyEJv7p6fH+/gHgXqlgSFLN5fLs2SSWJvJvMhjXGruPE8dnZnZrIi8Ph+Xnly9XV8O3N6oY5nLPxWLn5ubH4+N/vf2HxFslMVpurwJ9g6A605fjSLxO4v5yMOhiw95+7r/3s5lspVKjeThrZxEq8awC7/V6vLrw+mKq6U1e49+9kQB9xmuwerLXguSv1+s+Sm1jETeCXUWS2yiTfeN5u4C7mx7r8w6GSNonR7ZikWbOxme0T+PFqdG+PzvAHUh1qP+nNHIX9A6Mx3gGJzMpdiM6OTnhBATf7YKob9hYlMWspVIJ2IG7UtluNJ3WYJeHfkZRbR8IxlNz6vvD5/u7zEHjr/7whxukOBwAvaEJMyDsX3755dN//S9Xv6u+FuowI1IeF3Ulc43U4fn1tctmCIBy3MYUisWTs7N0Ks2HBdT57aenXu8Ns36yUa2qWd5i5IVEWqK+I7iqLc60vxKw92+XUrD6crPZPDs7d1lq57MtlaQlt50XxU42cCuA+9JO6PPWhEXXJzuTxSLN3CSrW3IVu4Pckhu1rWxizI19jKF0F8RuBDEokTYpSK/cRKKDR90OWEedDrzudl9RwwOIAfkAd94wAfHlcoVjPp/LZnMs7GTDViiQH1LG2CBE5f/w+PD8oP7Uav3LH//YfHwCO//444+v//L/8sbwUP9fMM4pn52dgd8/GvkVM8Y5P1YQdtV7x/tgwIrS19c31Of99x5vF9RL6/L5wvHxCS8Xg/cB3BI6fUml1cB3sRKiidJKHfjAZTiTgeFsLmdVFJ/EEghKAvpZ4Hh4eBgUzX2gY4whC5tqDhQLM8WJm5BAZIG7m37lU2AhkvbJkVksupyZHG7zb7wB0zbfvS3gHbsXTEc0WtcYFphNkAPsmz+cihnJKIleGk201rhXqxWwMUlg516v33t7e2w2b29vQcTEgeYJmXQmmwMA4/gONI+vRfmnS1Ewn8v95vrb5w8fMfb98uXz4dsz1YJm/vSn/857wsvZ6XOzWZB3gNS7wtYCtoHbBAXBed/gEst80ZJTIJ3O5/KNw0M2UUomUjBs4nP0613AutKpo9BXQF29koDWx7hd0sVORoVEJpeDIEzGIZZASBJA4356ehYS8f0kuxSPxMh+Ux0jUsB9aT/xL6UQSftnSpWMLmcrNixyxfnKHzmeYoZ2SAI4jQHUam1zKgUS5k/QOj8akovhCDpyOZj/BcQD1jEvUdYkwPBkKpfLFYuFw6NDudLYfzhUWnAxROn1+61WG7QNNlZ6bvVSIHRHj5//6b/8y7+goT84PHjM/I1A8D/9CQbqtdpglPj5p5+OE6nLXPFLMYvUsa2XtwEAeqHIelkxtTfeHAydOYp0/vGdCgjOPxXLgWpV0zhIsM41btet58ibydA09UnyyqF8y+zQzY6bEjEJ7I+Ne3QEvxS8xMg+pJu1WeC+9L6v1OpwqftmLaJs+W7P1hTEOGFreI0Z3UIJALLRQOMAEeStAaxWOIPaRxi7gHcFvycTCqMrDTwaaaY2RgTwvZxjCqNcSmKbbiirFQVAr9jHZzJFOUNFr4MC6xQ0EfQg/5cn/+fhX+B+EnzdarfB2OVKmXr/ot3+uVz527/7Ow20rwRzWzgbIK4vieH9QYKJ0a0zwecKqJuJcqmyzjmgiocowB1OYfjjb37DcQtvaczy1kiAD035QsE/uzHG9C+7uSXdg51Y/HOF6JSwEeDu/m46sbwwLkTSC+tdnhhdzpbzvhs5np6edqMhcSsiKwEMyh8fmyzcRJMNcrUCqFfgOyjbHhgTJiNUosLwCtID4wG+ZAIeW9prOwF9DnRX4D3RGZVyo+FBqfj1/kH2oxmNCvncb3/66deDg/aLWM7Yg2BwgvGjUvS5mWDLrHLari0gLwQkXuN6SqrA64v4k6FtAtYvP348PDqeZTuOiSUQoARW9ePudYJ2eHIDbM3ekVos/ljYUx1ibcB98X2Z4srzZbjUPbNjFogoWyZ7e/Z7cHCwZy2Om7sBCdTrjbvbu9PTE5AEinYNd5W6Xc4nGOJKDRHmr8K6xMi16KsVbpcLhdolqwnfDTKKnqYqR/5//fq18dL5ze3Nff89+R//Y61a/fT+/oz+GyhNsuswk1np2KW4kaJ17pAVykJc/4jpu77SbJ9fXl5efYgHwon7Hl+EIAHeVIulUgiE55B036d5guOwmgQWC3sPBRwqcF8s7dXuJNPCqgRCKx9dzkJr8jYQfnh4+N028BnzuNUSAGWzSer19W2xVDg/P9egG0MRwbwYy5gGI4LHJ9ppRKgf60AhMo2Bu5TA/gRsL/E6SB7jH6fs1ZrP33342Gq1Rj/9xJpXYmZQuMonCTMpgsfnFRBwbpRUJ1zIn47jR/0p/C4G7qVy6Yff/BZdu+be5Db+jSUQigRYsh0K3dWJTj7ni+iNH+pFueK0KQksFvBOCjVw4L5YhlMC93MZegV+mJqehP3RiEuFKoFY4x6qeGPilgSAqnQ2/LX8/NMvx8eHB4csMxW3j2LobkF35hPLtaOcKz27IHAB5mBxMXmXOYeixonQJ3GUwAxFckuQHwHuklVn42gF0X3PDSptnG7kHUfMxe86p4nUBbqbAWW7OMQcDi6urq4+/hjvnKDuUXxYhwR43NZRTah1uAc3xuMfKjc7QnyxULdUkEEB98XCWbUHhEt9Fe6iy9kqrdrNsrGN+27e16i2CtiKyXur9fzQbLIwGs+MOCRF4Q5+B3wDxwWGK+htjCJGhIHgla2MpHOimkgZlVuXUVEmWB9jdhVDEfB/3nRpQ6odjktmHcaxGouP85kpWolu5ufXyCg/xp/G7axGfRM3ke+VWu3j2XmuVkuTw+Db4DX+iSUQngRYVRIe8chRNoaMZXxtKSxd1qwA0xcLMrLy8w3cF7c3GMGuow5/nEaXM3/t2YtSsVeZvbjNEWskO5jCEU4Vf/75F7yvA+UbBw0wvfayIljd/C+YGzxvKt0VBBftu9LFq1bZphE5NS6B03JhHdXZKJkpCGAfsqmTEQzAbvzYsbwJ03VGAPlUMBXqFmqXdHJxDV5/e+10OsCm49Nzvi1YWvZhSkZJsdXnXSWZzAxH77jDsbhWDYoPsQSCkgBdMihSu0PHDVCJxTb/fi+Q32bF5h64L2jC/HZ7TFlHHR5ZMrJHlzN/7dnHUrEf932869FoMwa4BLDF4+PT3d0d/iJx1Fir1dDB4/1d+WtnIlBWMTAsv/IDylVA3qENkm5MHRqxjI/qbFRIv3ECuhagbvyYmJ0YiRSALv8lGJjc+pUTM5jJomQn4NOd95BO5+Xt9RU3lfjg+/Dxx3KFDaQmRknNnTRAhYHC8fDMNwdeRSjdPT4AACAASURBVGggOB7Df8tdjkMj46hYAq4lQC91nTfOaJPAxFNri593GotZScaN2MIT1VLg7oa9eXfYVXzoFbjiYk6mSDM3h+c4eo4E4p1T5wgmjl6TBIC2dEIC8Pe1+/r01AIKsxlquVRi+xh8Yoi3dtl0SZvJyLBv2snMcqhTdbwGLdbROplA6nIxjsDa3gqwY5wbv+MIiRe9+mDQ62EL0319fcVtDGr1Wr1RuihzMoXXZxk1WFQ/qN/VZCbfEXQAwafgKpHEuz0eNAcm0DeS459YAu4ksF+mMu5kEkou88ldRDw8xLqo1silLRbVKkKaBe6L6wpMNGuqxge/0eXMR2PiImMJgDk0INFRossU0wRZ+jfvOC48czaviBkvxB1DXK8porHkHQWlI2czT8Zsq5zRVRcLRdpI93t56T48PI4SoNYU5jQ59jItFvAmiWIeOKKNaqTVWgmv7WmM3iU/Go9rUuZRADfnIHKJUT8SZQbKKIyOEt1Suhtqd1GpD4f9Xp/npdvp9Ho9YmAsk4Gx/MlpQ30iMEZJcq7Snwfq68K7fgBhCIa173eOqOSB9+ohpQmzYZV6oTbZi8Zd0Yzf1n41KygdY7ZrtqU6ZrvbS7/ehcWp827e1sUvBVEyLO17WCCkpeKxgPsCIoHJdx11+GY20sz5blVccCyBbC73D//w/wE1QB7sGJ8v5LudLhYLbLlXr9Wbj03cf3DknBjiSSUPCCadyVAKQkAonGagIn17fSuWii/PL9jNP7WeDhoHD80HjpwTQzyp5KFGlutp4BXXG8t5Tr8apdKpQX9g71eVavWx2cSW5vb2Vq1wbdUbdTxVVyoVlN/FQoGRXWN64G4mnQa1YIpDBuC+yLlebzabB/Tn5gNE2q02zhmxRM/l8ijOKQgR6dKjxFvvDQ3/8/NzoVBstVuVcgU/kicnJ+3n9snxCduv1mqb78+5XHbYf0+kxcoGtnke4+c3Hq+mxufCKtumjmeJ+GwtElgKt5ZC17WwualK5onHkkry//q//x+wRaj8zWMi1EpdEY8uZ67YjzPFEoglsMUSUJbs4v9xccDABX0oQXTSo8HgHXctlssWsY9HUS1Ka1GaolglKHoyyIsS21SuKy2zxChaksorJfr8JEdtogOEl8JjO5bFfG0kFXMalPzaOB75mbY3G+ElrjSWQCyBDUnAwrAbqn+D1YoSMKTql81FIVXrjmykmXPXhDhXLIFYAtsrAQxBsOleCtl1AxWUFkStNm/ie9HSdgte18bkklWK7kzQRvCGKTySoaVm6zhHqHGIJRBLYPclsPRJ32lkv3wOcNkDlorRJZ2wskWdv7DaHdONJRBLICoSAFoyEIEuQwaYCqnv/pDH1Gx/9+GcNhMpWvlEkjWvBr6Pyu3//9s72zTXUSSNpp231jC9/1VN9zP9t6ZmC1037XkRTtKfGElIRMBRZeW1ZQkiToTgNYkQdkAAAnsRKG//HEr8VcK9nMxesbqtx7p9t9byDgIQ6JXAZVaHRtl79dCEX5HuZVR+GopXJxDYy7rYHUyza0zYihEQgIAFAnmhaLLFXiLc8262D4R1+9oTwgIIQGAnArNmxexk00DVqNu9HpUPs420K86xmfQ8C8kPlA24CoG5BPJ6spGsLxXueePnoqh/vHX76ntMiRCAgF0CGubVJs248awYuwRMWpaiMan5MKUodh1TuMIdvo06YpOwMAoCEMgTyCvPzVqTnHDPm5R3Z6dPHZi4EwmqgQAETBDQELva6+thXhNmYcQTAqljjTo+HhGnyIdFetLa+U9OZRcEIACBLIG8QE2tT7aMpx/eC/d8RU+LaLDTh5UNwFAlBCDQhgBD7G241681TpGf7iMOheuL2Nd026u+j2mIPnwpq18nJUIAAoMRyOvYbCtzEe75EkzgdGCiCU4YAQEI7EogDLEXr+24q2VUVoGAVLu20I1e9UFxOXl9WQuRr1AJRUAAAhC4InDV2lztvbwMD+/IHvB4yo57onGmTdyRBlVBAAKmCEiya0O7mQrKlsYo3lGma1ReL9KM+c9T2J36qy1NoGwIQGBsApdHepiCkBo/vWCDAAQgYI2ANFtspqJwi1LOmpHYswuBi44PI2A/Q+96sKvealQ+pAnpsUsgqAQC4xC4n+PezHNkejP041S8IMnodcdJjwJPgwqbsoi8KKA14CExL3RXq3yPc+X1Um+0X6Pyp1t9PyAfXIYABNYTaCrcF+io9R5TgjMCbbNkn9qRgeaT8jJwGmc075MV5plgYAGBdG1/TapdZyh7DtNgvJS9XqcDCgrjEAhAAAIfLYQ7vR6JdyFAKkQQVTggADa4rkJkBFb/cBPiBniHLFL5FIfkf3LrfFl6UrNr4jj9kGBwGgIQKCKwl3CvokyKPOIgawSI/T4RqcWZLwBTvBLOMFGZDQJbEbjW8XqMa8q2NCq/VcWUCwEI+CSwpXBPLZBPNFhdTIBIF6NycGCraJr5wnCZyD4t9ecgXpjYD4HrayCNyscLUje86p7o+C3y+rB+nMcTCECgjEBt4d6q0y/zlqNqECDGNShSxj2B1nkVZ7Hrd1grJv7cm8j7jgi4Ub/R0DSFJixZczjot4bnYzjceNJR9uAKBBoSqCTcW/e5DQn2XjWh7T3C+Kccj9onrMhOwg+SEF4DHdcgTTpebsQfpXBQ89+j8oNEETchMCCBFcLda7s3YJRnuUxcZ+HiYOcE9BCly3LsZL7zUI5i/k2iXr5yTr4nNa+bXLVfx8VR+XjMIjwrTl1UHydBAAJvCcwX7jeNxtvyOcA+ASJqP0ZYWJuApsSEWTHHn6df1q6B8iDQikBS8NGAMLtm2jRvXve8pjk2BeZV7x34JlBAnUMgkCVQJtyrX7xZm/hwYwKEc2PAFG+ZQJDs09PqmRVjOUzYVo/AjVJPC9dMa1DuvgBlld4H9V8vOSjJIYGscK9yiTmE0qPJxLLHqOJTOQFdAYdTmMIefspP40gIdEXgJ/ene1ujpg8Xx/eovPbo7c9hFr1f2Z3Zds4icGyyReBWuK+8HGy5NrI1BHLk6OP7LYE0K+Z8vP2AdxAYnUDUsPqdVp8UkbCEvNT7NFFer65XsOmCV8X+ke8AXWSENyd+se6Zt5C9srdiY/SqCvZDwBWBYxpi5+pwFTiMbUrgMrXme7nJaIsuofC3qkmp3sy9aWpq68oXNCxo/dZB81//7Yi7f38G82BBqzEYIdwdk8Dn6eNLN54yxD5m+PG6JoEo08OovKR7+C9s04Szy2A8UnQO7rm9NnTn0B3jWIS7uzjPvezdOYjBEFhKIA2xS7WzQQACGxCIQnK6xfsjLV/z63z6Ouii04ehh0Js1gM/q8cHfD3whktCuBsOzo9psy7dn9N4BYFRCETJzhD7KPHGT1sEfgfVri10Vfo/Tos/apq8nvI6bwHKqRh+LSRQLhWQ+AsRWzgN4W4hCq9sKL8IX5XAfgh0TUA3nuoJSsyK6TrIOOeLgCRhvNVVj3ENlscbXT8O36PyjMdbiGe5ukDiW4jXjQ0I9xscrd+UX0utLaV+CLQloCF2yQKt7fjFVdM2EtQOgRyBSfeFi/R7VD7Iee3UEvLT7Hm9DJ8iD3MQW362rIElnhvGDOG+IdyyopddFWVlcxQE+iOgIXbpdWbF9BdZPBqDQNR03/PjQw+oqTX6Rzr+W82j472nQqGwQd8vCTTCfQm1GucUpnWNqigDAh0QiMuxay32E9dOB+HEBQj8EEjrS0Y1rytcP9J06PgfRn2+KmnMEff3sUe43xPZ8n1Jjm5Zf0dl20dJY1Mt3dJaMWFUjg0CEOicQGo8r3W8RuXVAMTVbNIBnYPAvUCgpNkfKyMQ7jtcGSVpt4MZDqroidRKX8Zqh17lZpgVE+50Cz9sEIDAkARiY/g9tSYgUHOQnueqVWt0LywN5pCpkZwu6SD6yRGEewp89RclmVS9UusFAqUwQstAddIyXXphTWRfhqGQMYdBAAIuCaihSzpeql3NRFx6Mqr5TppBl5GxbPTb3sRN4iDc6+bZ28yoW53d0gDRJDbLsBtqruIQu9gxxN4kgagUAg4JqAW7eyCU/kinPWmKfPTJUEPnEPIAJpf0nyaSCOG+PhtLgr2+FosljOu5xWgst6lKHFe1Z/HGUy0UE3rgKuYsp8GZEICAdwIPOj44pPZFS8tPc+WZWuM9wq3sn9U9reoVMx4i3DNwMh/NCl6mHE8fjeizp/g0tnVZepwPk07X+Fhc3hHV3jiMVA+BDglEARXVvFawiY1V1PFpVH4rkdUhTlwqJJDvFZdnHMK9MADxsHwYZhXl4OCxvHUQkL5MnMa8Qo5JrMcedbV/yxvC1VVTAAQg4IVAbCjig101V16tkPYcZf20nHza48Ud7PRJ4K3CetmhIdxLIv6Wb0khDo4ZxU8HoejaxPRn6trj69sl8MsWtOtA4RwERiDwreO1Ws2lCUk6Xnv0iOa0gs0INPDRDIFXHdoB4Z6J0StqmVOcfdS/h84C0ru5Wrnt8BF0e23JvjW4hlcK3xm2Di7lQ+CRgPT6j45XqzUdEX7rrtfpIa/TDn5BoAEBhPsj9IZ99KMx9fd07l59YJS4msDU3YWuT9NL0aEzcRq8YInhzBhyuG8CaSpfyPzvUfl4YWosQvvivHmuC99h9mM9wj3GymDnWCeJunWsDh5K2ZhA0unTRPaNK6P4nQg4alXQUjvlxHjVxNTSKvLR9e/pNNodWj3tJffGS4qdPB5ZuDvqfeZlQ7eOzcPA0e0IhBQ8a2box1e444sNAq0IbNoYosxahdVgvd8PhEoZF9S7mj/9w2OhDMbLtUkDCvd0XbkO3L3xfXp17yXvzRNQIsZRdj309IusNB8vDFxBoGJ+8x1gRRwsnnpZenIyLS5To5dx6cn4cCgibjFsTmwaR7hXbGOtxLZDl6ygxY4lBM6fp4OG2L3deLrEVc6BQFUC6xtzhGDVgFQuLEYnjsqnGfPS8eF1WA5Xwx1nTZSvXCvFdUqge+He25XQmz+dXldjuaW/BEutn45BtbNBAAItCCzuGlD8LcIV6vyeXTPVfwgPdpWOVxxjRIhLq7jYr7dX4b64ETMast78MYoZs2YSOGrNtENYUiHci8UGAQj4I7Ds0kVW1o20JPudXo9x0ahI2K9W9krT162a0twR6Ey4L2uCjEatK2eMMsasRQT0t93DSUPsH/phgwAExiMwt3tC6M/NkUjselQ+PAZjWlBX8Kchk8vY/NySOd47gQ6E+9wGxHTIunLGNGmMW0LgfJyWddSsmDOSfQlAzoHAmATWdG2I/pgzaRJ8AhLG44OWDzv0Iu0fM8fG8dqvcF/TDtiKbz+e2OKKNfUIaIj9eDqoZ9BaMcyKqceVkiAAgbcECrvI0WSr/FWTLHrxd/g76NQ4a1dEMRqQt4nUzQG+hHvh9esjOl054wM5Vs4noEEcLcfOjafzyXEGBCCwJ4HCLrVXOZsWq4kORhph4Zqrkfhefd8zzSzU5UK4F16PFni+t6ErZ967yxFuCUiva2M5drcBxHAIQOCRQGEX7F3jRvtvpsjrUVAf53iTa8Ti3cfH4A6yx7JwL7y+HESqH08cwH5nop1g2Gw1L0Ps099dbVr4LsJ8DgEIQGAlgZKOwlcDKWvjqHwyWzcq6SbX+Fgo4Ur7V6Lj9K0JGBTuJdfL1ljqlN+PJ3V41C7FO9/q9q9sd3Wjk6axa6EYnnhaO1UpDwIQ6I/A2yZ8ZZO8NTGpdm1pVF7uyGDpeO2Jr+Pvrc2g/LkEjAj3t/k/169mx/fjSROE4FuDfSm9oNenx50G1a7taTnGu6A13DgXAhCAwAYEnjalr+pp3sRGA6KO12sZr/Vq1CVoD6Pyr6LWZH9b4T4rq5vwKa20H09KPV53HLzW8atzdlgWODz2I6r2sCZBZst+mDnv5qPmXdONNbyBAAQgYIVAvondv+1UjXG9GgFKK1HKSO3XX2e1J762gm8kO5oI93x+esLfjyfbUYfRdmwXlzyp9dAqh+Udd9zq1rZ/V7YjKqqCAAQgkAi8bTs3bQ7vCr/W8dNa8jzYNQVqjxd7Cve3ibeHw1Xq6MeTKjiuCwHNNQ2Dr4+ny8h6Bw893SfZ7rosgzHFJAhAYHgCmeZwozYsFpumyOutbNBP3K8/6CZ9P3xwKgPYQbhn0qmyM1sX148ntUhBpBbJHcrRKLs2zYfJT4nZwRJfVZhN8tg9+oKJtRCAwO4E8m1YxYZERf2UNs2lUdXak0bld3e9zwq3E+75VPFEsx9P1lOHxXqGO5cQ9boqRa/vTH7r6nq6GH96+62pUT4EIHBDINOQrLku47nxdxqV162u2qMa9Tu9uLGGNwUE6gr3TAIU2GLskK6cWcwWCovRNT9RT1AKTWN47mlzWzAAAjkCBtsZXTtsEBibQP66XHCJxAUo04maTiPAGppPe9KLscG/8X69cM9H9k311j7uypm5cId2fi4sw8eHOE6tn34xMcZwoDDNNAEL7SEqxnSKjG5cySWST+E4CT4dEwu8G5UfnfIz/xcL95KQPavQ5L6unJlFeFzPZ2FycnActlBMd14rxgkezISAMwJ12+ekj5xRwFzHBDIp/JiPcU8alde54Wcalderu8e+Ooay2vS5wj0ThdW27FtAP57M5Tau53NJ+Tlekj2o9kOQ7I/NoR8/sBQCENiKwMqWn4Zlq8AMWu6rfEyJFl+kteQjpjQeL32vEtLBQ0EsFO6vCLtk1ZUzhREY0edCNM4PCw9Rmp57ykR255HEfAiYJrCmExlTXpkOp13jMommPErj8dGBpOM/NDCvkasg5vvf8sK9KwRdOVOSmcM5XAKlo2Mukv2490OUOkKIKxCAwB4EFnRGaP09AuOsjsc8imkS1Py0+mT493skXj1kr493fSrcH+E4i+61uV05c+3Y09djefsUQe87wzrsJw2y65/wmw0CEIBAfwTmtm0I/f5yoNijlCzxRcwF9ZB6+3k+x8Uo9bqb2TVJuCfHi1EZPrArZ/KcB3I1D2KATzWAcAyi/cCsmAGijYsQgMAMAmu6QkT/DNDODk1LyCvIypGYJvqdYp5eOHJMwj354sjs56bGkDz/rKe9o/jZU8zW+aIbT7Uo+9fx8EXs15HkbAhAAAJ3BMqbVY8q787Zsd/GAKYwxshrVD4uTCk2dwfYpJVG3G2aV2RV+UVXVJzZg0bx02wAdjdMET+cLmuxI9l3x0+FEIAABG4IFPbCSRjenMwbcwRioNKofAzv8awlHy6Rvovk3dtW/jgW7oVXUCuydeodwsk6qLoqJQyxnz6+Pj/OPPS0q8DiDAQg0D+Bwo7biAzsPx6lHsaApNH3eJp0fLzJVW8V2KdBe7qztNb5x/kT7oVXxHwUls4YwklLwO3YcjypCQiz2KXa2SAAAQhAoFcCJR39zpKwV9Qr/Io6PsUhavc0Kh/3v4pkOmtF/U9OdSPcX3F54pPfXUM46Tc8W1r+8wSlI1mwJWjKhgAEIOCHwNv+YCNt6IfQzpZG3mlUXvHRshFajPLpkjVPo7c+YtaF+1O3d47T5tUN4eTmFL1WoG/uYYHHY5jLzgYBCEAAAhAoJ/C231ivE8uNGe9I0b2bEK+A5JeQfxWx8kAZFe6vHOskKzp3r5Mobe3G+XgKiXA+shz71qgpHwIQgMCgBGbpjXLxOCjN924nKR9ZPuJ/xfjxyOvKrs+yJdzzdl/74PJ15+65jEkDo+PajvqSznLsDehTJQQgAAEIvCCQVynX4vFFAey+I3DHTIAvjPWBXk3TbO6OuSshvk2R0QsTwj0Z9NRc9zs7d899fPZzIE5k15SYL9aK2Y86NUEAAhCAQAUCeTFTIj8rGOG7iB9IE8zp6a4fEgRpf3qR8bOlcM/nQMZoHx917p6PIFixUms7xinsjLJbCQl2QAACEIBAPQJ5zVMiSOvZ4qWkSOWOjUBqT1Tzdx9FvxoI93xwveB+aWfn7r30mw+eEFAySLJrKruWY396/T05h10QgAAEIACBvgjkpRH941W0IwwtUxM3kdMe6fi4cI127ifc81H7ttDtv5275zYurQwPs2Kmq41ZMa1CQL0QgAAEIOCCQF5BjS3rr3W8XgvVtsI9HwsX6ZQzsnP3cq7z2UsCR63uOH04rRnz8jA+gAAEIAABCEDgLYESrTWMuJejWwn3Es5vg2X0gJ59M4rcgVnKCo2y65IKP6SIg4hhIgQgAAEIdEIg0+t2p+lrCvcMtx4yo3P3eghRGx/iWjH6+xVD7G0CQK0QgAAEIACBFwQy4s2npq8j3DNYXoD0s7tn3/xEwaalkuxxY60YmwHCKghAAAIQgMArAhmB9929vzq14f5Vwj3jckOX6lTds291CI1bSsgN3XuqiTFaLoZEGTcR8BwCEIAABPok8KpvNyDolwj3V+70ELyefeshPo19CLNiZIIk+5GJMY1jQfUQgAAEIACBnQlkVOJemn6GcM9YuzO3+tX17Ft9WiOWmCR7mBVDuoyYAvgMAQhAAAIQeEnglTSoLejfCPdXZry029cHnbvnKxgmrQ0Z8j0rJtx7SsaYDBNGQQACEIAABGwSeCscZir758L9bS024ZRa1bl7pRg4Lkcg3niqRdn1BCUmsudI8RkEIAABCEAAAksJvBKlLwT9jXB/de5SW4yd17l7xmj7NSfMitHlMi3v+EXS+A0klkMAAhCAAATcEngqQA7TA5iefuTW0QfDO3fvwV92LCYgvX48fWgWO8s7LmbIiRCAAAQgAAEIbEbgV7eytlvHNsuFkQuWXtcmvf71OTIGfIcABCAAAQhAwDKBm6kylg2dYRuSfQassQ8NQ+zTcuwMsY+dCHgPAQhAAAIQcEGgI+GOXneRcUaMjJJdC8Xo3lM2CEAAAhCAAAQg4IGAc+GOWPeQZLZs1KwYpY30Ojee2goM1kAAAhCAAAQg8IaAT+GOXn8TVj5+IKAhdv2EtWIYYn+Aww4IQAACEIAABDwQ8CbckewessqWjdLrn6dw12l4ghIbBCAAAQhAAAIQ8ErAiXBHcXlNsKZ2S69r06yY36wV0zQQVA4BCEAAAhCAQA0CtoU7er1GjIcrI914qlkxLx48NhwTHIYABCAAAQhAwD8Bq8Idye4/txp4oLUdJdXPrBXTgD1VQgACEIAABCCwNQFLwh2xvnW0Oy4/PkHprCF20qjjMOMaBCAAAQhAYGgCBoQ7QmvoDFznfJwVI7EeJPu6ojgbAhCAAAQgAAEI2CbQTrij121nhnXrNCtGqp1ZMdbjhH0QgAAEIAABCFQjsLtwR69Xi92oBWmtmDDE/sGK7KNmAH5DAAIQgAAEBiWwo3BHsg+aY5XcVv5oIrsku5Zj1282CEAAAhCAAAQgMBiB7YU7EmuwlKrvblgrRts5rMjOBgEIQAACEIAABEYlsKVwR7KPmlXV/L5Ids2KUTKRT9W4UhAEIAABCEAAAh4J1BbuiCuPWWDN5qDS9dDTaSI7s2KsRQd7IAABCEAAAhBoRKCScEevN4pfb9VqoRj9aNNDT9kgAAEIQAACEIAABK4IrBPu6PUrlLxcRSAu76gpMUj2VRw5GQIQgAAEIACBbgksFe5I9m5TYl/HwqyYaZQdyb4veGqDAAQgAAEIQMAdgZnCHb3uLsJmDZZe1yg7K7KbDRCGQQACEIAABCBgjECZcEevGwubb3Mukv2D5R19xxHrIQABCEAAAhDYl0BWuKPX9w1G57WFdJpG2TUrhhXZOw827kEAAhCAAAQgUJ/AM+GOXq/PeewS40Ixmhgjvf41ZnqN6bWRtI8P8DJiDGZAAAIQgAAElhO4Eu5Ii+UYOfMFgTQrRmvFdCLZuU5exNrubkJmNzZYtiUBvrJuSZeyIdCIwCTc6dca0e+52iDZT2FtRzezYrgMes5HfIPAeAQstGl8eRgv7/B4YwK/eJD8xoTHK15TYjSXPUj2T2POW+jGjCHBHAhAAAIbEtiu1eUrwYZho2jLBK6mylg2E9vsE7jMitETlNRSb9dYl4BoW3uJhRwDAQhAAAJrCFRs5/kOsCYQnLs3AYT73sQ7rC8+9FQrsu89K6Ziw91hWHAJAhCAAAQKCKzpShD9BYA5pCoBhHtVnKMVFmfFnI/TKPtGzq9pUjcyiWIhAAEIQAACIlCxh+I7ABlVRADhXoSJg24IbDsrpmI7eGM1byAAAQhAAAJWCSzo+9D6VoO5pV0I9y3p9le2hti1VkxYjv1YybkFTVWlmikGAhCAAAQg4JjA3A4Uoe842Ml0hHtCwYssgc9T+FhrxfxeuVbM3IYmaxUfQgACEIAABCBQRKC8/0XiFwFtchDCvQl2P5XGWTFaKEaSfeGFXN5S+MGCpRCAAAQgAIFuCZR33AuVQbfktncM4b49Y6c1xLViJNmXzIopv+ad0sFsCEAAAhCAAAQKu3v0fbVUQbhXQ9lPQZrFrisxjrIXeVV43RaVxUEQgAAEIAABCPRFYIFOQOs/TwGE+3MuI+4Ns2LCM08/wvKObwEsuAjflskBEIAABCAAAQhAQARKZMaI4h7hzuWhq0OS/fwRnqCUv07yn0ISAhCAAAQgAAEI7EagRJb0Ju4R7rull8mK0qyYlxPZS64Kk65hFAQgAAEIQAACoxN4K2OcKXuE+5AZrTSWZA+zYrRczGNOP+4ZkhJOQwACEIAABCDQOYG3mseWske4d56P9+5pSkzYzg9rxbxN3PuSeA8BCEAAAhCAAAR6J/BWIO2q7BHuvedb8i9Idv3EIfaUhelFOo4XEIAABCAAAQhAAAKFBPJSqrKsR7gXRsXtYUqnw/fyjloupug2bbfOYjgEIAABCEAAAhAwRCAv668NLZL4CPdrZH291loxQbXHWTHledMXBLyBAAQgAAEIQAACDgjkpdpF1iPcHURytonpoacnDbGzQQACEIAABCAAAQi4JhBl/QHh7jqKD8ZfJLueNj2JnwAAAdBJREFUoIRkf4DDDghAAAIQgAAEIOCZAMLdc/SS7ekJSmF5RyR74sILCEAAAhCAAAQg0A8BhLvzWOovJxpl18Snr0/nnmA+BCAAAQhAAAIQgECOAMI9R8fqZ4dwy2m891SPT3r50FOr5mMXBCAAAQhAAAIQgMB8Agj3+cyanTHdlxD0upZ31FoxDLE3iwQVQwACEIAABCAAgf0JINz3Zz63xu/lgSTWj6cPZrHP5cfxEIAABCAAAQhAoAsCCHfLYfyW7NLrUu2665RRdsvhwjYIQAACEIAABCCwJQGE+5Z0F5b9rdd1+udXeNapJrKzVsxCmJwGAQhAAAIQgAAEOiGAcLcTyCu9nmbFMMRuJz5YAgEIQAACEIAABJoSQLg3xR8qv9LresesmOYBwQAIQAACEIAABCBgksCvv//++3C41Y4mDe3LqAfgGmIPPx/Miukr0HgDAQhAAAIQgAAE6hD4/fv38a+//qpTGKUUEZA2v1Xt0uuf6d7TY1g0hg0CEIAABCAAAQhAAAK3BP7vr//99a9//rek4n/94x9//PHH7ae8q0jgmRyPkv30GUbZ9fmxYnUUBQEIQAACEIAABCDQCYG///OfP//889///p//BwrQFgi2vvtrAAAAAElFTkSuQmCC\"},\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":"Code executed successfully: Aligned all viewport spaces to active camera!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvWmUZEd175vzUJk1Tz2qZyGpNbTmuVtqSSAhzCgMzxjwgH3BNn6L5bf8wevZXnzwB9sX1vV6y49nDMZgX8sCg7AtgRBoQoDUmro1dLfU86Qea8yqzKqc329H5Dl5cqrKrDGrOqKrT8aJE7Fjx47MHf/YZ0eEe8WKFX19/a2trR6Px2WCkcASkoB7CfFqWF0ICeTz+UAg4PV4c/lcJpNJp1K5XK6lpcXt9rjsbwuZXC655PM5PqyQy2bJTMik0y63hLAUtItJWigYJEMqlZqYmPD7A16fdyFaVauOfN7n82WzWVoKS+FwGBZr5dXpPA6Fwh6PWzVh0uNF6y+a2s/ncv5AAJYQfCqV9HiFm1r801X0oE/lkU5RHWNHKCV9Kt1JPiI6pjuYjs2m02m/3+/z+yUncvN6vT6fVJ3LZTMIkG9Kiu6uVbtJNxIwElhCEuAnn3cHfJ7sEuK5HlZpV2x09MKFC77+flB7G2VQZ1xRoIxMU5NA6TFo6TyUQuWhIKcussBPGacZv5VuX+CaTXXzIIFaaES+sCYYCRQlIIiNP4CdwuVKp7nV1wSYbmVTqXLhP0FhPC6C2YFxhFxWgKHHw0OrjOT1+3wkJJNJULsMDNyk7OdzHBEdGyjVsdXUMioONghwDjbVwBRWQMDMMSpxPBB1dHSEDKqtorRrKUmmBNNTy2bj4+P1tFzmB97ySQ6g2UejZLIhcs6kM/6AAOuqQXorm8t7PPRUVXQPCdWj1lV3rwLzurFMbwDrEOfW5fWK1CjCTV46XW4d3V2VB5NoJGAkUI8E+EExFZ46J5oHFWfP1b2RlZ7IisoiqcGDyfELlenoEzBeId3t9XVe6vaF7GyeXA7KqIzEqZfl914RmMZrbcATDAbBCm1JqeTkpB4A0FBkqNRglE35+93BjgryKJa0b/yQ3TpnBih7lOnBmajjSj+PVqYjKwG0kPN4Oru60Jm+9vZ2VJfOOjk5MV6qiLHEb9y48ejRo7FYzEmOUhq7JxJxhjHQf0dHx4kTJ4hTx4YNG8h87NixqiJz0pm/OGa2lpbI/NE3lBdOAoWvZ4MV1oL7DZIx2ZeWBIpfFjSRwO+a3wMrp/pUoK2A3khwu9CthGLb8y4UPabZwcFB1FokEkG/nD9/vphhrmOiY9XIlIiLjrXJM4S85z3vGRsbQ8E6sebk5GRnZ6ceXTra2/WohhkeHc4j8pOZyLvvvmuTIkK2W2+9dWho6M0333Sm0/aOzk7mDyS2t7VRKSmhUIiRA0sNkwTSmcMMDw05S/G0ls6HEzrDmRnhkpkqoMw1m51qpCePzLCkZwTmy2yM2Zm6ddIsPFZJuvO4UlaKZ4vjN7e6FBSYEcCGnVJCzdwYCRgJNCgBfqajIyNaD0Sj0c2bN4+MjBw/ftyprDRJlE9beztxT6gzdNknXZ7CvD0U8N5wWR+q++X9586+cmF8/GhVFtBswZCAdf+KGwJrdug8fZ3hld0tJ86ND48l89nk4MGfa2VVRkEQcGen1khEMFOgoNasWWNnO3fu3KlTpxJK8fKorU2s26Bc1DK6dHh4WDdnpPUab+elDBZr+iI97eFzQ4nTg3H0VD41Fj55mukBFXV3d0OBIhSECPyg7kjn2tPTgxBQv/F4nEeV+tnmx5YV9aKQKSxGCB2YJ1lR+URNf+xjH3vwwQe/8pWvwIHzkZ1TRwD3AwMDmDR0HliEIZnEWIHWbtiwgbpJpC9Jhu8upg61A0IkD89XrlyJWJ0ZUbLI2plSGbeZsR9BhJ7mlkFXd5X9aNoIcl+7do2zRbSFxGkLOjPwRezt7bVTkImOw09ZA+08VIEcqBdRaOZ5xLfHzmAiNSXAmDzLv5qkzYNmlYC2QMOdADH+i5GigNGqsqzxuuORPbq4PSUWYiEF+FOBX+vv/u7v/smf/Aka2VG0GKXStWvX4oVIkjMPP/myXzrjQZlqtamU6Vidjtr5jd/4DeLr1q27//777cxE4FwPlsR1LZdccgnag0Q0xqWXXqrVqbMI8b6+vj//8z//3Oc+V5Yu1KzhAGoEdBdX0uG5qgqideRhPlNGSt/avNlP6RuM7vxGpaPU+w3l52I/rxKR3lFdpn7bKoOUlyAA3YpYN3aCVIBlXZeVVBK4KmpyEVN+yaRCUTIXIwEjgYYlYCsidM5f/MVfXHbZZWDIL3zhC2iPMlq2inMH2lf0tN9wWW9PuwDxTDY/mcp+ZPuG1T2CFWsFu7gn1KPz3LJ1xf/7J9v/+gu3fu3/2n75+qkgoijygiYpaEtgWCKRAEPrsGXLFpthIiiM66+/Hq2LVkfL3XjjjbbeDvq9X/jI1v/7szf8jw9t/cvfufG333+ZPeYAod///vf/+q//OqU2bNhw1VVXacUDt1SHxQQTDMQRFKFWM3W63VhuQe01tRVGdBQx5O666y6qrKqp7Zqw4oDCv/Od7/z7v//7ww8//IEPfODkyZM2dGbognXwN0MOA8ndd9/NJAxFCdNwf/nllwNMkQIRavn85z/PWELf//Ef/7F+9Jd/+ZfgbIozPaDZUOOrcN9999llbTamjtBT99yzkzw77tqxctXKUChIGyHLWMg8CrLwwHyjGpH8Aw/cz4B02223wgDDIfz09/dt2bIZEa1fv462gLBhr6297ROf+HUYq0ZExkj9lK4i8857dtLMdesuufXWW9rbZT5XFsLh0AMPPBBtjf7ar/1aJBq58847169fDwNcYRUKVErVfJmQ20c++pGVK6u8aSqjaW4bkEAt3N8ACZN1oSUAeqVKBc6kam1TKWdCITaZ1+kgeFCAbwEUKlBnK1/JkhdDA2hPZRQ78WOPPfbTn/6U95AosU996lPogQIp9fHe974X3XXHHXegqRm00G9XXHEFBgudgtqBGqqVAeCWW26Z1gbhpIyaffLJJ994441nn30WjcTA4HzqjKMl0MAEakFRYPSiXmcGHacJ3//+959++unKR84U6uVVAxYjxjYiPIKmMwNxFClmKm2XKXtU9RZVhiSROZ1FHJkItp4i0C96rFVXq/esT1VQ+t2O6BuZwMkfnWgP1fqJdLrqd+rF/mUVNZ9GAkYCcyCBj3/844DgHTt28GYSNQiMnILoaDw1Mp664fK+X9+5KRL2re6NfO/pwzjHTVGk7JHP69mypi2g1h11RIOfvGdzWYZatyjGj370o+hqdPLVV1/9vve9T5vGnfnR82g81BQaVY8C11xzjc7wgdvXXbmxm5lGMp3letMV/Xdft1o/Qr3TajQbtmyUNv4sGHT0I4oDFIGdmLx3795dG3w6uSjGqwB3mNu5c+cnP/nJTZs2nT59mre0jz/+OMoa4AgHN9xwQyUwxUmGIYT2g00J4MgzZ87YbygYtJ566qlf/epXcI+1hskHAoJv2oC8EMcHP/jBD33oQ7BOIq8P9HvhAwcOMK2husOHD9O8e++9l1Lbtm3j28DYQHV2Wbs1PGUEhWzVrwj+q8lkilfcaO1oJEK2q6668sortwLWb7vttnvuuYfba7YVOsOmSYRhAjls3LTpnXcOUKCnp/vO7XcGArxPcDMTYIUAUzHYozvXrF7NFy2Vrv7Ol7LPPvfcFVcwot/u9Xp6unu2br2it7eP1unxyFkpceT5zjvvvL3/7aNHj/T29HZ3d4Hvr71226rVq+6++y4yUOn111/X1ibuTLx6GR+Xty0mFOxtdPM8/RkRN6sE0F0W/haoJnhwKlYdo4KFBbVSlqIOEyyJPjUe6KfATd5pKlScQRcdOlTuy8gvFyh89uxZslHkoYceQnNyZcaOgkLXYb9YvXr1b/7mb9rVaTZB9rV0LBV9+tOf3rBhA3SgQMsAylxvv/12rtu3b3c2VL+EpQhDxf79+0HbuLXoV3bQ14MHxVHFAPd/+Zd/ef755ylOqarUKIK9h4AR50//9E+hw8BWZllncoILDURokS11nYerneJkUse1BIDWCId1opUZnClkdvSZwt3Ox1bc0emFrwH9KdMyy75vZVTKXVjO8z6lmGhiRgJGArOQACAQc/Lf//3fowBfe+01bIt/9Vd/dfDgQUiiwTB9okOuvfZaZw0TycyhU6NPvHjiZ6+c+vAdG5565dSufeeOnxXfEh2AT5gPUFlgaCtNPpUzff769/Ree2nP1g1d+44N7T44QHooUGLg16qVK1rOWZw4mnDfvn3gzNdff/2ZZ54B7FXqK/T2yy+/PDo6euTIEXwU9+zZAx0UI/ad6y7tTWeKFodUOnfTZX1gDwDt+vXrnXVhzsaMQgrwFZ0D8vyv//ovPUmAEGKxM2v5oBJh2E50RqoAdyi+8MILe/fu3bVrF6L/0pe+9Hd/93eMVdSKFRzkrd1xnFSmjjPfAlkiHRhlCIEOXQhaRTqMXoxhDB4od0AqQsEypFvCSEOvY7hi4OEpRBgg6TkogOlhUpfVmTUDpDPfYIaHc1I1ltyv7X7tQx/68Ntvv40eD4db+vtXYOhJp1P4C50/f27r1iuPHjlSWRCZxmJjP3r88e3b78Q2v2LFSniDAcTK10ixBGLO0y6YHB8bs2csTlJ086pVqzo7OpgOhULhU6fepaXRaOupUydZI+zMacepFCM6BWkp3xjmTidPnpJZU95F969evYq2c0u97757ihGa9QZ22cWPMA4u1t/iN95wsGgS4IepsB2bkAgUq1TBTs5KUKB+YCXxq3fkzIvvuJC2HiuYy4wdVfPSSy+hwRyZJSOaF/XFGAMDgFqlc8RUjxLjh79161YUqa7CySHUaulYjCk//vGP0czPPfccyhnszmCDjQNdhIrGsuVkAJCN2sSWg25BgTDqUJc2iBBhHGWQwLyCYkEdEUEzU5xSaFeolTXH5hCNhxovlUyhWlQ0ZblhdLAtOxBEz9Nqp9ycfGrvI2RKX6GKQdbOp2Vx2BCTuaMLKpC71jiFctL9Kkiq0Ke4MvCrG3miSGnePOw7ZIKRgJHAXEgAVYMa+drXvoatFmz61a9+FbutJoyzO/N/nC80ji+rrbcj/N4b1z72wvH33rQWc7v9ioxsgMMPf/jD+CCgQnUpVHwqlVFr6N2Xru14ef/5Z3efvnJjV0vQh+X70Z+XYDk04Re/+EU0EqGsUn0LgsL0gLbk1tZ4zpxYalBlmoKTiBpnnBlF22gisMpkAJwGQcwoGHQYC/QjtOUjjzyi6YDjy5QkRZj84F/E+tIS0tZNyaREJ6LTmS2h6/XL0A0bNjDD+MlPfsL1H//xH6uidkYC2syApBuMCACpSEpDWBA5ABehM5shD44uTJ6wSPFC+dFHH8UeD3M0D2cgHSEDow6lqBHKjH9EGF3ICRsMltj+GboYL0mhO622uJDFt771LRQx9TLA2Ol25Mzp0wD0w4ePpNOZwcEBxiEcZkZGRvfufQuUDNsgYzuzHUGm6HjM2y+//Aoj68033xSPJzCnAeIZRHt6epmijY2Nw9W+fXvxGuqW18ol4yikWlrCzz//C7hlLGdjh1tuuRlOmIxifc9ksnpMtWvUEY3U8Sx66623AO5I/pprrqbVd911909+8iTvCk6ffhd+eC9BdQgQr6y3336njIjc2iNYlWcmyUhgWUlAkLVCggLXiJepVXlcEuwE9AahgAVRZI6CQFVunbqVN6rYFPgxop2A4Cg3FBpTaE36hz/8IcoNRf+jH/0IvQFq56Xiiy++yKiAnYnpPfoN2zaGBkYvFDQeJrrgFDoW5Y4SIBvaANSOf4su8sorr0CtUi2TQqBG1ClKCYxONl2EGpn5MzagVGkaOpl69SNe2sIVBfWtvjK6QIecP1OBREYHDdN1Bh5hXtLaHlUGcSggTCYVJCr96aRXjAPWcXNnhxe6SfkmuRmraxm/6QLtByXda9HQ3Ve41R/iemP1qkT5L3/Sg2ABnyw7tkoXXGXktphmPzQRIwEjgZlIAN3y85//HNWHDgSJOTUAcV7xgS1RIBol6wo6W4M7r1vdHg387ycP4nPCZD8S8sUnC8smyUN+3LDBmTZoxnMu4DCr87N+YteJI6dH/T5vLJ46eb5kwyvwJGoQjFerPVDGCAtjwELMDUwwnDlpEUAL5UYe4uh8GqJC/vVDgzu2rcIvX+dnE8pX37mAZoEgowPmFUSBHw7jBYoX4Eo26GDdQEMiAQ3ocZhE8TprBOv+8pe/rMWw++67d2qFS5l4fFwjyJtvvhkqzJZIvOmmm5g3lFl0qAyzMU9jsVH4w5UH/lDWWqY8ZbSARU1NcwOXqE7ianR04fdCQb3W2E7XEZ2/7KpLleW0aZZl5pavS1tbieOpzqOL2FcSFU0ZC6agBtd8kxifyE82XUpHbApq9NFEJI3/FUHVokYSoaGMQDa1iswqARsU25856mVQxM/nGGu09U4IMpjJDsf8wxg1hQCr0zepRgLLSwL8HIJBsQrzw0klkwBKp9OLQnTym+JnzFWC2HWUBkZ5ZWVPdOQBSg1HIoVfE+pVWZp5hiLWmhDnOrA7Jm1Kkh+PPszh2uFEixPCRKBAxL6S4kx3ZtClnFfRscqPnI17UZVoVOwUwHSsKlx5sevMrOMAcb2HI8BcKw3gPgVpFPnRxtoIgkfiq6++ysixfv16tLSolGgUo0kZQdC53tamt6cHOoxYaHhIaYMOowO4nLGQUjxigIFJTQHrO2zAtpMgpiZ7/zVnOjIPKp8fJAkF5GNvQ+nMpuN0gd5CDpGiYeUiMTWuIFslXihYXSsxehm8rjpWepZtKPhKaMRAjRCkUnaiDEeqr6mt5MGkGAkYCUwhAX5WQ4ODALyPfOQjf/3Xfz1FTn6Gsrkh+rZt/Yc/8yf8oCdTGazmpNx0ed/5kYljZ8ZG3/y3kZO7qxJBz7So9YTBDe+/7uYdG1a27T544Yr1XWcGE68fEm8ZdpU58/SXbXxbRoSqtR7AIRvLsq1RQbOsX6IUU4WY2gemrbUVDYnJGBWKHsOAwitKVCh5RlpvjfRf/gcfufKydZ3oGowOr71z4Rv/vS8zGQuffAR8j6UGv3FewKJscSnHwIrCoV60K2geltCi6G0Mwahi9DNgu4xPfWvLilvReE7gnk6neEmKtmPCwfhEhEzod3QrFG1yDAnU6lXb0bCDJNqcmQScYf0lGzSx2ZMHUK5HNbvggkXgAUEHgzVXbk3PSVXgPX2x+c+hOoWum/+aTA1GAktPAkA19q4CeaO+kqmUAHfnj0X9fIrAXcA7WqoQBLZn2cRdlu3rIYH28xzYSko6lcZ9D4RaORLgNY720wpzTkRW0LFqH4bJCdGxEGd0wecQzY7NvrIWDEWoZT1LYaRhUNN5iNA8rcCnGBjKCMrQouzxpDNqMQogRiA76RDRErCBe1nZylvKMgkp6QgrE4MLG+RDlq4AQHOQiN7izXpe8inAXe2wVrDP07MQtegWgbv0Kj2tcTv7v8sbbnhmZKUJzOu0lAD0mQx9Kn6Peo5UUpm5MRIwEmhcAvyaxmIxVCkwEreLKQjwY4yoPQbd3nDHNZ++euulbxweTKXFlSUa9kMHi3st4I6GBBbrSb6vY0tw0wf6e9o3rmw9cX783QuFF4ZTAHfUCDtRas3B0kxsGZYWEYdGMDR6iVeRjCAwgxWYbSCpkRYBsrG42yYbgLtsB+lxb1zVhp/PmcE4fvmiemQ7yEe0ssJDhpaCqCEINRQR6g5qaGztPAM17Z04hX62ZQUF0XhO4E4S4kbbEZki8FaTKq0MeVVEIH7zBNolHDoH7OZhznBiJGAkMG8SALFxRCbGXfQ+KhI9YGtkqZNU2y5LXAyyBeSOpgYXchHt4fcXkVyeHVRkLRGWkgncuMWpY979oamjqGNBtNZWBrbYaAV8MseAMZ5yg91aHPF1sF5v2vl1hDGJ/GSjzZQiFGspzUoegp0mVak5j51CBFpIDEaciVXjDGxF3kpzwA8JAb+fD6hNTiYDAOsaqpveEYzvla3fJY/+kJiEkp6FLqHYvXJAFYtf6dZQOCS+9arfkQDnaXm8PipVNMzFSMBIYLYSkJmytTn4FLRQs9ZvF28Bv8tXvlEVZbOTY+BvIjgWQJWfvyaIEna+wcv7Ii53UV8VKkU3JmQLrMqADrE1EnpCVHqFfhPFZIVaOi7rDrl91VRHPudOi2u7RaD4SXWiuIoJxRjqGVNC8b4YY0gqDmSUtvF3IUstPV6kUB4DIvvrUN3lxcy9kYCRgJHAfEhANGM9oSKXJCiN6nSDFt9r5V3NaEQIhEIo/XrIz1keNZEoo0YbMdqgwWWqkc0y8NjjkOSE4bIChVss3yEMSJm0WJoZJ7QzTPW8jtSqIpXxo/R8D0eJeqMQwRwOfSIyJfLIWoJaEma0K+dE2ikDIR/2WKjbLpnlf+EJ5DGE6bQCc3xPFLb3zf9MrFCj+TASuAgkIL/jaosMp2i6O5/GFl2ZQXbz8gYyWU4b9Qc91deVUsqdKVmWY9OpR7+JZphuyNAqxSZrRzz5SVe66I1ip0uklhJWyqoqQTxt6mEY2pilHQqvpNpmueEdQbOwYvgwEjASaG4JoIIZNSItkQw2H5ZGOXzcRVeCVbnI2idxnxCbsQ7KPgScJRk9jrONVjuASLxE8GvEuxGIjC7WTiMOGdhw0ZE2/1EY470CSJf3AGyIhsGFbbKmrZYmcy4EzjMUYv8rXjBgf9aod9qy85cB/sMtsiYMizgWNTbtrdUW3onAbUi5QgnGl0mVbNxJlxGA5XrBgvjGKFu79LD0s3wV8CbFFYdtfFvZz0cmPLlMOoXXP29RVO0lJwzOX2MNZSMBI4H6JYC+nphkF2/ekk3jCVI/zbnKiWLBtW+uqDVEp8ziLl4wVfS47KJVc67TUH3TZC6d+GB36evrr2V9mYaUeWwkYCRw8UkAFQKGY9UN/uipdBr8jWVa6zQeCYzjDxynwBxIDg8KlG9yEo+JxGRiApTn9no7OrtYJIPwyIj/Oj7YAwOD+JozH2AXqUaEOl+wHsY4fwkrO/gbF3hOk+jsqn6Mq5Nb2sphGz29vay7Onf2HEb3LtmWd949f5w8VMaRPtAZT3pwNt6x586d7+6uehAe3ZGm33AzxS4FxKf54iuqAvBdgDt9q3pWPlSE/iXQUgTFBsM9vT0rV63iHQrTnUQ8zhLb8ViM6uS9sQlGAkYCzSSBeCKRCq1b6z4v0/KmDCjSoSFZCFsMpSC2mD67WJkESoB7cNXNrRvuSmbyHOLKEgG/z8MeN16PO3HhnY7YL6mXwrxmJYJBgyuaUS+zJY7yxBOfiIyOyu0SR3u9aSXaVXvgVD4iHVJkgBQUKIILP7YS6BDY5tygdi0KczUSMBKoRwLoKJQJPhBapagiRaWvNYtcxU1CPgoaS3wmxO1ZZ2W5pK4LnYaOAt4D7hPxBMZanV73VVdYd/ZCxiLDU5QUqKqCqFB1PtQUmfUjMLq8clA+lggKhTxtkQXIAGgGUrMAFtO5+N64GVkyevODstqB6sw9ZICwWJf+U3+WyBjh5c/5JplHNJbxhdFE1sgKUXGRQW7KEs+jhfV9KmuVuTUSMBIolQCvymLpllDQ3+lheav14y7N0wx3vL0cHw/w2lMzg42A3bQs5TRnDKK+qChs7TcA3RLg3rPhpgfv2vzYr45du6Vn37Fh1sm+OxC/4T19//Ffhd3B2Z39/vvvhwr8UZhtyNmYTHO3efNmfXof4yUKkZ3R7G2D2TeNnXTIRjpP2Z/S3iOTxbyHDx9mNzH2SWD/oEOHDrH/ji0FVK0mbq5GAkYCRgJ1SgA9QwCTYZetovIV0FOIVRSsYFf5U+sYuQHhgRmVDQIi2N3xO9Qm23QqqfdAqJONWWRT3E1TXreggGAVUJ2qgJYDVxpFo9HhBF2gioimotTgszqo80KDo11hTJl4vOzhgz2Ibcqq1OSRyYZqgnSZ+lPdR9ZSmVnwXTVTNZbRhG4Vw5OasWg6vHJROz/YwqhSp0laZhIo/abMonFzRmgWPCzHoolUftLd3uEfXhIzaidMxQAx56idHoYmRu2awH0skf7P54+OT6T1VprnhycoM54QjI5iQ5/i3wnw51Y7gNoGdRQu6s/pjK4N8xirSNSWeEpBQalmmS1oGzy74kOZPYN448BO8Mr+wUMTjASMBIwEZigBjcmwHwQt82uRkIy1/AfzCepTEbKroNyiScKsq87RlFWSuHCQE3v7RGJC4XleKtYBRYv1zVcMjnHUthjPoaCnrqmAMaxjpGigQrOKQtWiKrHqk6krmvFTlD9vZhFyOBTiMLyqwF3jawHuBNWRVm8y4yLBMrRbfPOpmylX3glj22fNA7MXhf6hg6cUI5EmO2POTcGlJQHr2zFrrueM0Kw5aXoCBf0zHZ/8JEeznUHXUFeAs6jn4D1Y/V1UJ4fTtaD4nAM0OBAJWM/ZsRymqR9s3LiRzR85gdQ+SrZYoJFYicW9tcW/ZX0nJ8c6Kdx4ed+PT+7lYKePfexjzrmFnYfdhUHknB6KlR0NeMcdd9iPtmzZsmPHDnaq/9d//VcS7777bgzz+innSJ06dUofQEgKZw2iQPXRgHZxEzESMBJY+hKoX3nOWVvxYscwWw7IRDeXAD4x2iqzLThQgUFBwxgXgLVkpXgwEOT0HpZysoQx1NIC+pszFmdHSCAqYL3QABwa6x3keOtJm2kaQcSB/b1qm9Q41uhgNt30oWabkTlbJmMJYojhyobH9IjmsKwMXcDorjuNKxzaf/p7ZtnaFYxXDKm2ykxFb/AvBBVw5/Uv71KK+36W1WRujQSMBOZIAvWMARMsNMq3twdGvP56tdkccSdk6uGwanUUrFqWk/K+/e1vY+x+//vfj37jLA4UETvBg3sxf/OU8454tYiuW7lyJQaFZ555hrNmOUX7iSee0BVxLgcZ0FHAb7CxXQuREp09NpF+54RsEe8Mr7wtOB59CjrXdnTnU+J4uXBiNqt89uzZw7lQzqewKxYOtxs7PwFFaT/lrG8HOKtgAAAgAElEQVSK8FQH6Gu7u53BRIwEjARmIQGtT5rhOotGzKgo5mSUHUBcEFsNChYkFeSuwLtAOcnrZtcxWcYDJkTdYQLGEszi1cREQi9XrUFvwZOVcbmA23NydHY9HCAP2kWbkQtxtK4lh3pKT59HSM/oj1GA9cF0mXZzhzds4VXrg2ccLm2wXshTaIb6UF/5AnyX/pSvgKTJDnVypC63YnGnv9UBTPq9cdW6TKKRgJHAAkgAnTSc62UlUVd4ErPJAtQ431WIllWDiLxI9HrRPI8++iggHtT+xhtvEMFy/fDDD8s6e7//ueeeY8DSQRfU7FHqU5/61Ic//GGM42UMlwB3Tqvasqa9LMe1l1bZRYFF+kwU8G8hsz421S7FSwEQOTDdTmHAo2LOiKIUj7QL+6ZNm8gAEWYYBBK3bt2q/XDsgiZiJLCUJaAQhMIMGjks7HUpS252vOPqgvlWqX/HGKAVKZSJqD+lWhVoV5ZbsT2rwOlN9BTQji3PwX3qwFR2EsQ0O/1+i7NjvIHSot8FkvIpUFm/Ipi2PCX0iwUaqAPNnLbUAmRgEsEIhymdCAttMRJNTOAtWj4NoLdgGwOQarTqL8kEgyqrxaj0uu5+jdp1U5WvphKaklqusBNonRsnW7TNp5GAkcBcSmAyHx4Yz7e5B6t6x81lTQtIC5WDFftzn/vcb//2b2NK1xCc+s+cOcOCTxQd6zk//elPY1CX94dqoxdwMl4na9asEeWmArbsxx9/HBs3buQ6xb6WuMokJtNHTuNaVBLeOjJUcq9u3n777Z///Oc6Heq6bm6J4PSiAb1dCkRO4JZlqcwtdPrRo0eJsLaVwJyDOEtd9SNzNRKYBwmokXwe6BqSzSYBwB9oOxDQB8NV9jtqsfCn8F8B/WKCpSHk5ow6IqiylnCYDOwbyE6ROEdjfZ/Dllay1RBxBgbyawUPk9zUR9At8xP9LkLBWV22oapnmHk6/mAH7M7QhdsP7iujIyNt7VX28MF8xZ4zhdcj0n8iBsvDHSEgF0sYqkYtKD1P4V0Kt9L3dHYewz2bu7v06DPDRpliRgJGAjOVAD/fkXjO6471toIAm8KCMNOmVCmHi/iuXbtQOOg0DNb/9m//xus+AkCXRCLaGE9Jbr/73e9yZc0n6c7Xp/izECqplwD3oCt+93XXPPXqqW1bevbrXWUuxC9b13H6iAtjOUZ+SGsSbNpo02JPGAz+pLAOFVZ4C3DgwAH9NBaLHTlyhNmGLjg+Pm6XWrduHY3BxI7vDpvVMBHBZ5+OtOcAdk4TWe4SmG5IX+7tv5jbNx99j7ZJZdKhUFAEW6xAo1wLs1tCFxhbgHIychANBEMUQoFyWA/G3VSSDb8TLZGIIMIiNav8Yn0q27MwrH3BNV6tgxlULG1ERARpt4DeOorNfxYANO9pOVyJFyYMGUiesYBeKKuZlFRKLO7CvHomzbEihcyqq+z+Um2VJ3jFSE4RgfjJpNMZMfDX52VUoGw+jASMBOZCAql8KDY61B7hdy922+URnMoEtWM37ZFHHrHj4HjdWDuFWzJzdaaUCaRME5YA9/NvPPrwuwdcHu+TavvHk29J2UO7XfmJwdPJ09/5znfKaHELUseSX5muH+1VofIppV566SU7HRd5HcfLh0d2um6MfWsiiySB5hjYF6nxS6va5u2qheUMRYLloqxOwXka7qkr0E+hP/lUTs/ynIBiJQXsKC43k5Oyg3sivmLV6iYBuJpJWlJonToptn5VqV1lNLIVCiKCpgiInZOYAOQsUeClB0Mgw0GlAzothWVaIZzrP2mF1QSapKdXSjpELVuT3Mv2/NrLX5A7G4amGSntHBYJ82kkYCQwjxLAVBCb4GeX7umIOH6681jjvJJ2KhCM12JTQC/NdcB70EmyBLjn02PpMy84H+s4fGCaqEwnhUdOvp15ZvDISSoYhNUSXp3EL8p49S64KEUxv41uIkE3ESvzK/O5pY59gmWOoLQKsqhU+QP0SQQlpe7kUy9OVcBd/GRa5KQesKMsmszlmk4X6aYJ3xKwHFe0tEoCClbmKLTZ7WKXFeY2tL5JArxhBcfMDoInIHCOqq0K3Ok1ekQ1QyF2aY4C8cXuJqbwu/Q/hCUwDWO7SRIoqnC7bCkjO/0XSzWJJAwbRgLLVgIpVzAWG29vjfrdyeXRyGhrG0s09c4rKJN5WqhZpqZ8K1asdBq5m0eUMvAuVZVaCReaR65LiZPFl+Pic7CU+qt5eMVeiwKpyo8C7Aq9a+SuwLugQOUz4vX6BM7m89hOwIZsBIm5PRgKO9+BViW70Il8M5V61Mi7fk0J6KXFqFYCWJZ9Mxea89r1MZNgEbAsJhA399DI8GjVvKzEBbnbUw7doVz1Tu52kcK8zfoJY8gnqMwC/Blo2Z6/rb3Dzm8iRgJGAvMpAXcsnnQFW7vbOEthmaB2xIVxoX/FShk/FiqgvuSN5EJWuVBNa6geS7U3VOhizbyYwlrMui/W/l6a7QbIysxf/CYcQZnZtamd1IKmVfoWHSiIVsAs+I4Plqj6sxl2cMdPJjFPRhQHZ4WoVsWKmcIwICkypZB2qKdE5BHWlsmJSZw9eBhuiWCB1hmE9WIo3GmyKoPAW02aZ0xusBVheJZJgLOcdU+q/cCOFMnPdQzHlclkMhQK0wUccprJyj4zlRMwOknaqyVTaI1mhZapZnApNIdmqTaoWYomhTQoDnBHhjhEzXUjDD0jASOBcgmks7KFYHtXX8A9Vv5s6d9X6qh5bRN7b5W4ysxrZXNNvHScmWvqS5Teogll0Speoh1l2J5vCRQRp65JoVUNWe0EAfKCi3VQyRgyKMmONOhilqUSkhMTPb19s2fXqkZQI9S45SqwUs0vNMCkUgJJhU+JqP/SGklTH/zY+CwA0v4V8oygOdRxuVFJcrGeaVTLLgJUrTLI1uYrVq7s7esDymoXGs2kLAtVa0OJCLsq6NWiRMmjL0JH1cvVDpqNmV3ZiHMyNUmPwBjeMswrqJRWl1GjLmFMzWBEiIWY4kbubdBeUk5zKNm1o0wmS8TsBVkiI3NjJDAPEhgbi2f8nb3dbDW+fAzt8yCnBkiWA3f0tVJ99ZJAG1Yq1tqFC6NL7QzL/8niiGBxal3+vWla2LQSQDXZEK7IZBHoSZqF3IkJgCcFVxnwKEZf4ulMmt20MHfPwC4LEY10iUhNCkijKqGvvHG04tRgssCnutEZC7Bb+Fe/XMU1cDkLChfYr9omV3ku0F/Bc0WAC1hfnnjEjV0FQfz6H28SWKApLEkR0nnP29baSq2WKMDlejIjMikG6zmfyrtcgD4gHqM4/7DZY71GXPzTrdbN0LMg4vUGt0uKZ3NKSHJqCfvMVO60AFkZp4RR4VV4l7+CjlMfSjA8KchFRCWLlUlWcxThPJMmVaRhgpGAkcD8SIDZ8Wg8E420t3pT81PDRUq1RG3Fsm3p9qtd7ka2K87EW8ZeC7MNw7ILhXFgIdu1CFUuZPNMXUYCCyQBgWgWeLOr1EpKozy5qpiCfgqgyjnSbjxkQHWyOUAuDx7FHSUQLBy3adOZIgIhICx2egWu4UAhZIWBBQcLkkxZCFPI2BnUTx+eCykq3SqsM6kcUkZQqH1VJdQjlarbzb3Ooz6IOgKnwLZEOElKjOWA+XNnz+LprvCtFCqZAmgiVmVCo8Cf4kLvmOkNuAKusIu9FBQkpo3qYCM2v0d0BERBufo3b4EBpgIC3D3ecEvLWGys8lgW5hvpdEr1H5zoblTIXclXhKoFAv86pq6Kf4Xx1ZSDuQaoHVKFxpgPIwEjgTmVANt/Z/x9Xa1xftNzStgQcwHcRcsjCbGa9N7sCfc0KpXJ9HA4c6RqKbwIu7q7UN8cEIXBo2qeWolo7va2NpT/+HgcE4vOphRyrRLFdLECZWVfCAaPSoNNMd/UsTorm5qIeWokYCSwsBIAPQpKU4hTKzdH/Rqog/dIE7TJRdCm9pn2uNWiVrc/4Mf0yylOE+Lgzp5ldQUgOwEsmMlkKYhGhYh2K6EKXaVFCGcVC1WSxFOnthH0q/OrVHhUu6ZIWq2gW2tfhTbTEMltp+kozennAG1SxQjteffUqbHxmDLNo6H5JI1PBMFVFvgSJK3wQKWouMoo6ZqQ/uQqVebFrs+BHsFgABd6xm/8czjrw1bjkqdG8Pn8vOXgPQB14rCEDBFdgaxVhEf0F6FMpraEhQn+K150BArSzSJp6WsCq2DZdLKMslWD+TQSqCoBvlJT/AirFrkYE1GDsVQo7A9Hgwnrd3gxysFuc97tjUfeMxFa5c5no/HDocmTs/wiOS3ueZensBO+x527t++1iG/ymfPbtve+MZKOnkr0Xt954KWhyzZH3w15Uz85d2MmVwDT+RqO8nTeB3/twcHBwTVr1jz19DO852VE4JSlFf39QHlsWmhzxqe+vr6zZ8+GQ6Foa+v5c+dWrlp57uw5hob33nffmbNnLt2y5bHHHuf0k7gK/f19nCPV37+Cw5tQwR3t7YmJiZHhkRUr+lHxJLa3t7Mdgd/nf8973nPu3Nl33jkAiGdt2fjY+Oo1q6mRodSWpokYCRgJLEsJCNSsNmKoUVfjvQKCB83Jn7VJCX4sTPXBo4BuzLrsLN7R2VmniNB4PT096KVQaCKbSWOvSE5OABAprgGtsKTQpILgZJRPsKOCj/pTDgQC9QqiBBoLkpYIeBm2iEluKeIBf7a2tcFfbHQ0FhtbtXqNelLgtGRWoNCq1QQ1YZAGCyXB5FKbqH14EXNHNqV8ZYQ1OaBUhKNmNWKhp1K8z6UBgt+lIP/ZA0YCoNyO6IfkYwLDH0Ry2fyF8+fxp7fYqPkJHTZ7oSKIQJzWArLLEL9uqchO8VegRVwLVxhUkpJP+af+pL0kSHPkzFTpXBb1FsqaDyOBuiSg9Mf0OeWbdtGG+EQy5W5rDyZQCRetEJwNz3kCp1d+YiyySScOdN3ZPbyrd+CnaCNntor4VE+rSzbgyVzdceTrRz6QyASfvbAtm/cA0wdS7ZPZwGCqze/Jbms//MrwpXZNVb+nIHW2Yn/hxV257Av4Ld537z2A6SeeeOK6667r6up+/vnnb7vtVtT/gQPvXHfdtSh+jnsFiK9bd8mlWy79xS9/icYFcGMf2nrl1mAgeMkla59+5pk77rjj9T17rr322r379kWjURT6qpUrX3nlFSi3tbX97Kmn7rvvvqNHjjJkUnLz5s1wmEhM3HTTjWfOnB0biz1w//u+/o/faLotmW05moiRgJHArCUg+NJWSXZEcJ6Qtj6JCO6TBAXmdLWgT9EPAHfl4A7CCwTVCazTcUWlwFdwLcoKX5Tu7m4Aonh9p1ITEwlMFZzlhF7CdYSXgayJJJvAXr+fK7ZtcVLRQYFNatNAs1a1wNm2tlbeZEIQB/PWaFQAah1B2bBVPrJ7PNhEurtrvmWlUZJVxCQRvWAVmVA7wBegz7tUpis8QmicvIFzi9p53ZNNytsG6qLVbMszFosxDejr7weOC8HagQzJZAZwzWQFwSAfDPAo87ISMCadplM1fMemXpZJJGLJxBKOKqeBe6bdbClTLjFzPycSqPgmTk/V+qJOn7N5c/DjGpoMh3LxzgiLUAuG3eZld6E4G+zeCWrPxAfTY+eoM9i9caDr1vDkqej4vilZmOIr4ZYDKfRzZy5g+i8GroqlRV3qK5FkTgwzqZwv6E1f01EC3G316OSDXmxR50iBucPh0Lp168bGxoHaJ0+ewqr0+htvgKcZ3sbHx1DNnDP+1lt7f+d3f2f/vv0dHaw+ltHixImTL/zqhe3bt2NoZ7DAPHTk8JFTp97lreu2a67Z//bbR48exVS/YcMGXv7ybrqzoxNr/Z7X96xYsYLh8JK1a197bfcf/uEfHDp0eNWqlYlEOwOJDNYmGAkYCSxrCQhAtLBaSUOtX7+tBgT1iR3WeuBy6dPv8JNRjnaBMnNvCTXHDTgbPQaQRZGK8VlZ7kHn7tZop6tLZ6QW/DuAsIJ9s6zslCAoUi21BA0LH1agCFFHDRLVaF5l0eVUWaYI0oxyl5KyspqawruQlcyAaF4IQELnrFodibpSrrSKC7IFEGO6RyGTSKA42aCDsQYTezIpbkK8rmC2glpmukKzW6IRnbOMq6q3iMjrZWmBlynQREKsMGXZIIW4nAKiSc4hrHhDqn4gC1PVFA0uFat1TsnKqja3RgLzIIHyX/p0VZR82afLvBDPJ9KexGS6M4JTtHmR5RS4e6TtKrmXw+9ysYPP9t26npvRtqunA+5OIuVxjoC29JrjEVVEvJM6oact0B7xHz4Tt58ns/6HT+y0b2tFML38938/9oEH3x+PJ55++ukNGzbiP4pXDG7rnGmC2t2z5/VYLHb55ZcdOngQCxTf3D179vT19h08eBCaBw4cAJezscAvfvGLu+++C0+ZgcFBGRlyuY6OzjffeisYCNxyyy3nzp17/ufPv+/+9/HgwMEDvb29AP3nnntuy+bNb775Jm9Pobl37z7okHnfvv1GWdfqL5NuJLA8JAA8A/OptpQNb2p05KJHyUKE7EUEyLJIzOHKopzGUo4JuX64iSY9ceJEV1cX0B89E7Cs6SBdFZRbuM/jx7Uatav+FYjDJsyoq7AmAFMxJYjcdl0hSWN7laYwPUbu1tY2YCgzBV1GFRYSNbrSPTE5IVSkNvGVQUt34gukwLcMBRKxuFMkFFll25b9ZPgnpQsXmXTIaiI9FeH1Alu18IYhmUzxfgHgzkPoiUOO39vb21enJHnDzmsENDZTJsaF0ZEqxzBpvx1hUOZdBeFZH0qSPKIpqjG6PfmsAH3NO2wiv5kvfxLCJhgJLKIEav3Ap2ZJ/SSmztL4U7TBaLolkBnobo02XnqZl8h7Ajm3mLx94c7xY7s6rrjfExAzRNZTboyYVhAyJqhApLqrDCfT9YZGdKZgwPtHH9r0tceOHDg1rlMC3sxH1zz/r8fv1be1rqjpsfHxJ37yJBkYC596+mmd8/x53B0v4NmCyZyUd0+f1um8bAVh73UVXh8cUaidRwxcuMhbZc8TwSWGa39f3+49rzNo4ZDz7LPPkcKZHS+9/LLO+YxKiUYje15/gxTGFM0AI6jOYK5GAkYCy1ICDCSFn/mU45QCfFoVKvSnZAGQBS+CPkGgmM+7e3rrFxHLMVF6w0PD51JnwZ14oaDTiICq8YmBLGZ4QbGF/4Kb9R0xClr2a5LxIVf32qItcFpaoq8WP6LGS/4piM0FbFp4plMEy9tpOd+4deKeIHc3LwfgUEShg1j9JbN8KjqC0SuC2NVVECd2Ma4X/ut0aMEvxFksQNPDLdGOru5Kq7nVkPJPigD9I/j+yDFMfuEDzxnlW29nRRwwpTpP96AG78hDUDqh0POFD5mL0CBYhhamH2YYdIc1u7OpmoiRwPKWgPVjmb6V1i9nupy4T8fHhztaPd6gQe1VhOXOJQOZ2GSgZ3LgcHLgiL+1P5dKgN2DqYEquWsn8d6SoJ/LUIIlW3kllpRIZf3/ffpWOykS8n7xQ5v+n/88rLF7Luc+mWhgPLPpzG3k3HkB8SYYCRgJGAk4JYCKw8WiYuTRgxZX5x8gUxJAh4qCOxKJUpAdtjh9CXAaCsuG7vUH4CkBXMiKGnZTYbk8YBH4DRjFq10820Hw/IEZrUAMAK8hOpgdvMt/lcCnwuwkqcBNMcnBk7Cu/msorxG4vmo8boFwScPRngWllNA0maLw2lMEIJk0Ztfg1oHWxbFEwC4BaK6DcjfJgoV5pKuHIGRxp5FxxR9gL3xWfzJ7oe0OZqePMplB/DBBRAuJmUHZIMUDKhaOocd//uREWDiQmOp6fVUQ3rpnOoYE4ZwInaEmA+rZ9EyZHEYCRgIlEuDHNzrp8eZi3e3GN6ZEMmU33UPPv7viw/62/s6rP8QjjCXeXKpz5MWybFPfYhpxZhCHxYGRZNifYUSxHwS9qYfW/Pzbx95rpwR87t+5f/3/99iRY2cTOZfnZGIOjhK0ic8ywheI0bHR4WGWlZriRgJGAk0oAWAlVmQBuBVBA7xicuFe4B+BdDQq7+iIoSUF4AJAGwSdmjilOju78ECBLLgWamKWlpWayfh4nPWqWKTZ2gQdDnYEwwN50cOA1MLVjlh4XoNswdoCSwuh2BANnEvudYOkXTroqQlxVCVvBohoKqwcPXP6XZ1H+X7zXALo1oHccTKROynFilZ1uCzFifC2kyvt9QeCcmEHR3FxkRcLPHdy1ECcnWTgM5fzSwd48TvC1IRTkJMC1OEIflSi7kirx3WaPEBcSmJicMdjx4MXD+zROqYrcE4GJ00TNxIwEqhTAsl8aGzwVEfPSp+r5IdZZ/GLKltb7PWcJ3She0emYw0ND6UG+s//KJCaid2ZMYJtGNlZ0Yda9PsziUR2OJG1lwFn8t43RzfYwj09OAlkH5/MpjMYV1x+T+be/te+fuRBO0OtCOYZvBXxE9UZWGbEBskzGwtrVUE6Y8/OnTt37do1RR7zyEjASOBikABQORJpAU2WwLIimCvYZ4F8Cu4J9tOwFXAH7sRMrKG2gMVQeOboU8kafAkIB3661FGsWv4KbspFoHBhoaS4iWPNJhW/cHQmEZ4CMXURDZpJ01SJQJkGEtF5UOg6p31VmFY1UmN26wo3q9esofHams8C0onEuABbdY9ylsmEGM6JyASCW+Jgc9UOrjK54J8AcwXPBR8LM3MZoIw88JMhQqeMx4srrHQ1pCMu2qSbZSN3IgVWihxJTDXOg0sPDRHgnk61173L51w2zNAyEljiEkBzjI4n+YGy9S0mjiXemgViv2NkV1vsjcGu28OT70bih9z5mcgNJX/VlVuPnzgJ04WXmLze9ftSMbsVog8Lmi+WSP+vRw+NjBdrSmV9j757h513igibxmzZsuXo0aPbtl3z4yd+ctttt7x7+gzTBbZfuOP22+n4t956Ez7QwhBhMxnSV69efejQYSdNxrDW1ijvc3nEro7ORzpOhq4u2WsZmxbLWDnBGxA/MDhUz3CCgQ0b0dDwMMV5lX3X3XdhjHn8Rz/u7u5iH0lnXZwGlUwlV69affjIEWe6iRsJGAlMKYEihpoy29w8BE8qIClI1BkK0F1DvOINWdAfEojhJ4NyVCBa9jFsa1fbWzmpzEVc6SW5aL1XfNFZnbgwprjTj4VToKfP7wmHwqz7P3f2DA/6+lfobLohOqvzqtC4JPBGQrxi1K4yMIEry8pVq9XTgrwUCC+VnZPQgsSZG+A2zwoBRIStJzPC6IMcilzRy9riXuhJzVVJFpVdl7DKocD10Vqso618S7EgLTOVGAksYQmk8qHYWKyjtcVnDkNtsBs9uQlvZsyXHpkZaqc29vxFMY6OjqK3q3sf+jzZazsP7h7ZTO6xhGzT6ww83dm3+5GTdzkTq8a3XbvtlVdexQmHiQEZOA7pxhtvYB+Y9evWYcH5j+9//9ZbbhkYGLzzzjuHhjipifOYchs3bmRU2bRx4+uvv8772uuuvfbIkSMf+chHHn743zE7XX3VVStXrtj10kvs0R6NRAD9PLWrvubqq5kVPPnkk7feemt/fz+7JRw4cPDaa7cdPnyYoerw4SPbrrn6+PET8JBMpZ566qkHHniA9w67X9utgftVV1/F/jbww96Rv/97v/fd73733PkLq1etPHjw0H333cuOk88+8yzuoRs3bmDLmn379p84eVKNwXb9JmIk0LQSsMBL0zI4F4wBW/lJ4uVR3QRcgvIUclMmaTFTqL9oWyuon43WlW+L9qaYC7ZmRUM6ztGcgsrBCo5ritjCZeNLOauozkqY0GCnFxgsZJnlgI1lqlNn8YXJxvQJ6zg8wh7Wfu1/72SSOH1d1p8WuJdk2lNokszg1D/KuD1jY+NMXbC484JjYdpiajESWBYScI8msm5fnm0Gmf4vixbNTyNsVWqrJzuFoyaKimm62u3iVsaR0dGjx45xJunBQ4equyGy4eN/nb7tg6teuLr9CCcxfXD1r9r98QdXvnhHz5uboqc/sfZZbjW10JTjBZB9fGxs0DqvlMVeAGX0LRuxnzlzBjS/e/funTvv/tnPfrZq1SrUKTQZQ957370s6tq+/c5777nnB4/+EKi9f/9+EDlnM23evOl7//H922+7bc3q1T9+4okbrr8ONG+1y9Xa2so+7tvvvPPabds2b9ny5E9/BuD+j+//4Morr4Q+L16vuOIKzPz7336HDeZvuP6G8+fOv/jCi3bxF1/cBWMPPHA/R7Hu3bs3EmUcb920aRPnPZ08eQorPsPJxg0bbrzhBnhgTsKrbLusiRgJNC4B/SNemGvj3C3BEgLmBOXlnFBXtcPWgkTsP2Lqn+B2yYUCoSBOGgA7gB+7VDWrDGR+opCpNBid2QCfejtzGqwLNVK0gVpml5XWiQsQ0zCP7HvDFbTtJEkGaXlhwlXoPslQ6OfCb0rLiGQdQYFL36qt6+llJ0ETNxIwEqglgXQ+cGE0iVt1W4CTlUyYUgLF4cXKZqdIRCmrkhSXx5fxd8b87eNuT66guVBZ6CzHn6YFMH77nXfIU93ijlIfSbW+MHgFe/1Q1ZmJ7ngmtGvocnzfSXni7I2JjKxI2NLHKiLXIXlVWz3Irl6RSF9/3yWXXMIBTKMjIxcuDKBzDxw8+Bv/xyd7envB38wh2GGdIRM7GWby9vZ2TPJ4qR47dpyyd955Bx4sUF+5ciXLxRhK777rLrgHf+M56kTt5OHs1fe9770UpBbEg5om547t28mGe/3NN924Zs2al19+RY0KuQsXLnB0KwLc+9Ze/c4acz6bIuOug6keBJ85dBjIjn399dffuOWWm8H9u3a9BJO40OzYsZ3NirHfVG+2SV3yEmhKOLPkpTrvDeCnjYM4P3wiWgOWVsnPXaCdfMi1+KnUhQ+FQxoYESUghugm3jrW/oICXoG2qkE1L3ZmnUPUpuB2EZLGvkpaNV3KSdkAACAASURBVIsv/AP4gUmCrHP1evCZYckBPo2lnMj6VCtFOlNtBUmfljRXQ3Yrm0uWECSTanGBjJEmzEwC+pczs7Km1BKSAPqBDbLykfW97dgyDGqv0nWevKs7lWlN5yZ9nvNBb6YhM4rLFegfCK8/4vaJ2TqXDCcObsmMtlZWU6ai3Z/+zGe1MUP2Bev/kDtYr1tn0Oda0+kairuGTrzWnTlQWRMp2NQ5NvX4seMsMAJ2r127dmBgAIM/3wYGRVx2RkZH0KGgZHalhA0wOi9/WYrEjgxkQ3ETwbtdhhjIqWEGiD88PMQmA6yq4mBCNh+g7Mc//tCPf/wE+amIU1pHRkaJMPpSDuKx2Cj2G+C4nDs+MSFrrjjmMJWCFFHGA4A4GR577PFoaytssI8bQ0UCNrq72DiMs13ZdR6G8S5ijzMMNswuiDfzuF61O5Zgohlbl2CnzRHLM+t7XDBRFszz9dJGsVoQ1OFB4rwui0ALAY3Hno+ohPjYeEx+2qFb77idDdeHh4ZOnjhBVpy/56gpsyBTTQq4eYTDKMsgmur0qZMtkWhnV+Fw1mlrQtFFopEV/f24A/Ea89jRoytWrmSqM23BBc6AekfN0ilYfwYHBhITExyu5+SBU/xQ4Cwm1v5CaOPCalkZJmThr3UwFC3GzE6n8x6FQ7Umzpw5jR0H08+aSy5xEjRxI4HFlEDtyVDtJ/PLbzrrisVTkUCOn+H81rRkqQdy+ctGJ4bd+YTXFci5erOuY23hmK+6SXe4/cbw5MlQsrhQ09caj175xuCZyZY2b2oyxwZj0daW2O5r8+lyWwwKjeHKklOpj/vEiefz3jKrhpWz4hNfmSGV6E8PuWqgfcaVN998ixGU01LJi5Ef9SoaVkHw2NiYbFDgcZGNp/IeU42sKGL1SHIS0UOvfHfRx/jcj43h0IkyxvrOS3FIUfDVV1/lEflRz3gxkkIG6Oj8mMbZII50brV1CkIa2ZMSzyce/eF/EoGU5oSC0OHsw/Fx8QhigATxE6FGyFILPHAlxQRLAtXwhfXMfDatBJZft/HDRMHxWy6VecXYR4JOU7ZablqisuM44B6Mx76NEU4BLKNRSnFR75SrjOJAlGJ5Y6diTeteVUo0HmGq3Iv3DL0O2mYTSDoUXT2K+aY0wLl+XUByoXeV0b2Qq6RZ2uwu7xiw2vBaFXcqNHwpPXNnJLCoEij5xpZwUvtJSba5vRmLp7PB3u624VmqiMJvcwrmps8xReFFfrR5fPJ4wMXxEPAx4XXFfK4tsck9XYV9FCuYK2+qv++Cy5MLtXiOvpHIJPOX397q9qf8XcOpcyVGCuigBrEaa4KA+BJcP3TkFwDTisqmScAa7WrfUjUTlRHsRxpJ27dzFcH1iqNYZ0xNxq5mHb1m3Kg6Ci6KNqiDr4smy9LugGblXtxdCkCuwCIfoi9tnUlExdWnvgiOjUajmGzVulQ2sU32hJvoqIravwnhv9GuAO8W/EqU6pO75gssu1UrDWTEYhUuEyqGK+doIm3QnKsrbagiByvJdpghgWEIOpCj+EWp+Zuvsw1HzSQBQOBwuj0ajLf6Rqr+qhpi1voJ1i40fY7aZWf9ZJa6z5vOpn3F15Xo4lFPviOdHfEXE0t5LKnQE8AByRXpxBbtaevx+IMiC09A3GamDiXAvWpW1BwuLljFzw8NJTgyumomk7hAEljU7/gCtXFxqllikl1i7C5on3LIjow3JSLixsJ3RV4KykxDQPwuyMRLP1xogHRYeYsZmy9mI84ZoE/Bu0oYtBc6ctt8AWzN9Elax/pUzmFSb1OdnUIGoDysO7gnWtLr0iw7QUe0jUYtQW6+RhuOjAQWWQLxpCeZznZHxmwNs8gMzXP1tnqYQT2UzVSUT7tdoRwWgar0xMhSeOTJB3pGvC3iikJoafPgLaOi7szYdC973TUWp6ryLp/bfWP/igf+x++fPH1677PPrsvm1l+5dX8y+dJLLzWnrtdsN9+1eh82H5/Ny9ESkOASYLF5+3duOcPZ2dKb0isOYOesRyUrBMuFI4WibW0k4UEBXlQufMVXhc5iTRMvfOEEq1qtrZc3GqyzckapMjzXW3AB89EoYQ0rO46PXi+OiyxJcAJ3yWDNQKz2KP4KbRPEXpCR+mAmp/bBzLMeoLOr++zZszhGMltbwDaZqowEmlcCGNpH8v2h7KmuqPFor6ub0DSBih1K2rP5g8Fa5nZRSd5oMrTqvDeaSJ7riu+9vOWy/d7I2JrLxA2mYzKw/nh3+mT4dDB3Iuixl96XcYM+q2lxD/r9d3d1HxmL/eWXvyz8BQLozdbduz//+c/3dHQ8/pOf2LTo76Fhlm9Gfd6CnrQfLYvIsmzU3PdM84qpeTmb+14wFGtIwEJzVR4LeOdcHhZggWJZu8qCllA41DAarkJ5HpMEq+svNuC1EeCu8C67yohNqPBWYgrZzGMLpicNqwwurEpiJZQG7s4yWNzlDKZCknzyn42SLbROgsiINoqc1CaYvJDu7evn9CU2PGBt79nTZ8KbZG1rgYb5MBK4WCWQyAQnU5nO0ICnxaD2Br4EZ1sCqxPJ00G2lnG5fbnIpuFceyLiCyTPd0+cbS0xKLhcG8fPsI1irPXs4Om+2IG1upr4viuCq0/7O4ZvO9Vy76sr/GkMRimovdHme7gnMCnHWFcJNa1Kn/nsZ0dXrTwQj0OCsHPnTq4syvzqV7/qO3So1/EeGXNIR0dbPJkbHok12YofUdyz/lPtX0aX2UukKoVZSagqxblKnBVnpvDSk4AgNYXWqrGOPtN/8ln4l3eFW1oAcABBtTJ10l4GVI3C4qfJC0/dQGU7t98v1MkZxZVWBxhLUDTqLLqg2YDmdAdV8grF7/exK4yzegTAREt8fnRr9EBld64zq4Xl2ZdGO9io9yqpiYlJdhAqyWhujAQuMgnwGxpOd+RTsa4WNt64yBo/6+aeD/mGo6GNKde6YKr9rgMDngt7jmXOHYnnOg503nCE852dNaQzp937/Zf+YtX7Dvo+OJi8fTS1cTLbOulNHrtk7QtXvm/XqmwyP57NpPK58Wz6mljmwaESjeckVd3YwDqtjZs2ffOf/snOys4wOo4J5PkTJ65ubXt2aNB+6nHn21u8uVA0Fhtz+SKtYdnpxX5aX4TB9uINzdX45uLm4v1WmJbPQAIKxVnfYOtT0SkBqIL3rH/gWHYawYuaLWJBh2wj21268+AM2JjvIgBuqpApiNpat6HqBLjrdZliji6RUUN05jszIFtbgogwrWLocdbIDjH5vKy50n/qs9gWifHfSmBvSPZ/FILJJDSll2WjszxbCfeYJapOsZr4xSSBiVxLIh7raB33+oMXU7vnsq2DAe9gMNx18/Fz+/3ZlEx98ln36PFwujcWvfTc2Nsr7MriHtdbUf9bLj9qqSWX707n1iRz2+KZcC6/bjIPyE/nc//7/PFuf/C6SGdr2H/TWObxLn9Vo3v1GRZ7Aj/5zX/CniEGGaXZX375ZarX8aF0evtnP1PxhpEtGTC9d7SGPWOxMU4+yoqHjtad9Vzt1i2lSD0NqyfPbNtcTx3155ktN6a8kcAiSkCWM9aq3oHzrCwkufLhFlwM3bIXZDqNJzQbv1qPm/xT2ZsbBd9q1oJuFtyOw4m2WDdfQ7H+4NYiXj2yPtULn2JitwKDkcw/pmVefRdwheJPdnaXjfyZm03yh9i4nZaAVaH5NBJYPhLIuzxDk8Fscry7lV1RzNaos+pZfxQ7QFyjdptQ4kLA23Heth3Y6UQYc+Ie94mg91dt/h90B7/bE9Km+Vavf10ociGd3BSOki2YzYdleKoSqlvcQ/n80LlzwWDwpptuQmPu3buXpTxXXHFFX1/f0NDQG2+8Ee7rE61a3Aq3OFKS3tHZiUIcS6TzqfGOzg4Ghyo1L2pSkd1FZKMpmFjE9puqjQTmXgIgOSeO5UdWQ/UVqlZP3RwVJ4aSbA6dBiLkXLa552xOKWobCiTBnXa8nhoU3kUmahNJN14obocar4fAwuWBVRYdqA51s3pYNbbYmboh0n4FvXmg+7pSrZJDb0rDoVpMzBjCOMN7cmIyEAxwNJP6glQWWrhmmpqMBBZYApNp3J5HWaTtdTW7oltgycysOk8wnRytokPwenFjcKjceqa0GjaiGfS7e1KueC5zfDLe7vMdnRzfEIri7j5ZhaoUBrjzRD8sZon7/Vdt2/b6s88+//zzuopt27bt2bNn37593NLVp195ZWp1L/A96s3lOoYGB73hrrawnGqnSc3yWuRyloRmXHzxOZgx66agkcDylwCQjt/odD9TDQHlSnbOggAgYm7gcE1ss4pAUwvK0ToguOOuLq4L8JeCqOXpjdZ10ZyXTJjYBZHLwUniLcMGCfSUo6ZCQwopklXmabY4dEReHXvZFV4CJ6didpftJeX4bA37S4k4qJuokcAykwDf9ZF43pdP9naxn5IxtM9N92ZT/mB7Ln6unBojybSoXZd5od2/JZGdyGY/3rM27PUOpOXEz1dbfRM13h5XB9MXEolbHnzQec6tbOVuhdWh8EtPPcUgZyXU/GRU6O7pbQu5h4YGR8YmRIEqrTqba83K6nkwm4rtsvVUZPIYCRgJLJoE+K0WQ8mNTi6Banm9aQl2W1wpOPTHZ63nKZJo2phuSJUWTsWxMlIrM7X4yij0OlX2xX3mxj9GTTAA2853vMJVsSGqNQ5GSzsYm1c6w/b8KfmfpM1qLiD4ni3/58qi5KjdRI0EmlECyax/cHC4tSXQajZ8nNP+ycSCoXDUGyg68kG+pTeZHe6vs549Ld4nuwIdgSAO7i0e39pg5J2I97+7a3psVneVQb397Mkn773mmsd37dIq8Pz585qD9vb2+666+pEXfuVkaOqBg/fOvT296axreHgA5dvW1j5zXTl1TU6eTNxIwEjg4pNAmatMFQHYoA4XCh7nXfgEope0xT2VTAXDS2FDNEGfKtiRwn09H9JuyivcLjsoNnPAQiQ7Qno8gGyAt80qU46KpisPIFswkpUsbp/XNzY+lssHeZ2CLyqkVEnJFwj4K6nYVZiIkcDykAD+dKNjEx6/p6e70+WquVfJ8mjsorQi9samFdfuHz6RnhwO4B4TXZlsiXSOvNbA8dtPdPrfjHivTGR9ufyxsPftsHcK07hPqe8qLX3ypz+9sb1je3fPG+nUcCy2a9cucl5+6aW//Xu/98///M/xOsztxReWirzf52JdPy8rR8bTnnyyvb3VaMwqcjdJRgJGArOTAP4f9RMAtorZ1evFAZo/jLJtHR31F1+snChPZhp64tEoD1JQioJpXaBYFW+UxgLlhz18MoPBEBHehCQSiZKKxb9d/pckVtzIQJN3TU5Osi41lU4HlCuUCNCVb/J9PyuaYhKMBBqWQCrjGh2Ls2e33218YxqWXp0FMnH/0ItXRdYNRtfH8OxLXVg3vL/hw93eDXj4q6fG6hZ3SqILXx4dWdvR8Xu///st0ejRZ587+9ZbOa/vb/7mby5cuFBO2jJylKdX3LOtZFe7K532sEKI4zDa2tuVU2JFPpNgJGAkYCTQuARAcrW1EVCtlKLc5nGbBsPiJ5ORHUcyzuM5S3M34x07rgBqG+RMQD9FtMWdCUuDxRcuu70FAs2UHSHVOa9Fi4+avcCN6kZpj0Tz7spvAH2KuyYF2RfSzRIGMcUTPK1tbQvXGFOTkcCCS2BkZMTdsqKngyX7BrXPr/RxZx8/3ONy8TfvwSfKTg90+uqoEW14YmTkb/7n/+RVMpoeW0VuoAKyO/LXHwW+d3d3pzO5oZExvzuL9kSJ1l/c5DQSMBIwEqglAVEm0+kTMdTq8nmXLObhNB9B7jK2VWx0W6uexUsXW3khOKJW0nSfbKtIFi0AZNX8FneYxbsFEC9HpZZuu85toR+lP22paBEUb+lTenlw4AJ7FvOMVvOMU7cY2qaTlnluJLAkJSDeDSMj+DYHAhxZUPwtLMnGGKZLJVC0uKMWMUuw6L40g9xxIkllop1SuszfTp4+4vd5ujui+NMPjaWpunUpeJZO3yqTw0jASKApJFAyVlnwTnOm7+TKf9FgbCkDbk/LXpAK1DVFA2oyYYNVa/ZRM2f1B9ZrTmVyBw1Xz9UEqfQFe3QKWAdxe730KDscON8wFHG76krV5XSp1fXWJwktLZF0G07yScHsGNuFXnH4W+y22owuNiOm/hIJaEVRkrQkbmJj4/lQf08PSziWBL/Ln0ne9E0G+jz5bDB93p2f7UvOouZCjW3YuHFwcLAhPY57KJ7rs5E6kJ21s6lkcnBgLBDpjIb0yqHZkDRljQSMBC5eCTSIvPO4UDM+Y3HHRoVCa7D4IsgZ3A6TGr3PAFkoK7UY3MVa38DAvjgQoLAjpEs212eVajqdwcdSC10JYToB2Fy7XZFoxJ8KMNZQUNotErQfL0I/miqbXgJL7+vBmpCRVCTqT4b8LAhZevzX95WY7ldfH5UFyxULXzqea/GmhvJuXy50bWf2VDh1dja1F4E7VFips3r1mtmQm3HZQDDYHQxOJjPMHELBYCQaVZp1xvRMQSMBI4GLWwKOMau2mmcvSC9naoJl2b2Et39+CxTOj+wcPM2iAtGN8qeblVcW6BlShowyWs+w+Cwa0UBRbElgdmUj52yQorEKOQj2VjMYPmu2oThBEbheGFnEll843akBVkxWI4EmlsDYWCzjiXS3cCrw8nZgqPlbb8LOiQfXJpKZQPJwgbfk4HB0k98b92XHZ8xtCXCX0SuTqT3IValFXA99JUSqZKo7KRT0hYI9uOsMjiZD3nS0teFluXVXZTIaCRgJLFMJCDwrCeX31kN0HRtBEvK5AnBviXCEaq3sVrGm+mxIX9uca3O9NHUJNBZfGbxkANzK4l6ymZ3219fYncZNBd8dLYUUzcaWb8vDRIwElrQEMLSPZlpbvAn+L+mGLD/m495u3/ghZ7v88ePjbes6EnudiY3E3UXMDWo/c/rdgYEBZYCplwiLftauvYTNYeotUEc+XE5xOp2YyF8YmWgJeCItZv1QHVIzWYwEFloCzYj5UF8NsSV7QXo8GCzYDBIP6EAH+xwvgdBQG8vag4jA7QVfmeZenArn9A5nnbr8ftlYhjOYHHvg8AikPvXMpVxQ9j0NL5OLuTUSWJoSGE95U5OTndGEx4PdwYTmkgDKq2zbL85TzbhnBWuLwJ2N0EDtzNsaajRvGy9cOD+3wF0zUIDvaTf0W1hYFIk0xJjJbCRwEUvAhicXpQwqLO5TSAGHaYy52HRZBJnL53ycN7FEgqOPHdH6mBfkrs3t7JDY7IZndupUh6eKxd1btn1C5YosGjaNOJTxXuQkQpg+e30SNbmMBBZBAvx42ec0EmntalvevjGLINu5qtLnLnlJCNm8JxDIlx5J0WBlxZmA2GCUNq+kAG5+6KGHKtN1Sq1StfI3lB7253t7ez2+4MDwePnpGw0RMpmNBJpLAqCL+ftrrqY2Mzc+n7xZxlVG8CsuGd4l8qLZmpyI4bzxYJeyyDROYqFKFCzu8luRrWBKhxsF0UuTKvlSmSRZRQp34ixDwZkIr7IGk2IksAgSSOQiwwlXV1d3eEkc9rwIEmqKKltTp1MtJWtH09ENrZPHZsPc9OYlFNwf/uEf3nfffUePHn311VdnU9mMy4aDvnAwmojHeScQ7egL+Yxv4oxlaQo2KgF73G+0oMm/OBJoqMM0UsdVGn8MUBwwcXGYnmmttvm4fgIKs6rsFGaJa7OiV92PLKPivS4gG7bpHYlbNnUylDBP/9XV94XZivK6acyxqk4hm+lAnYIy2WYmgVzePZKOBDOD3dFZeVzMrHZTqiEJhNLnO4KB0bZLXZl4nhEmEOlJHvLk2Fx/5mF64I4X+9NPP338+HHOOp15PXNREncZ/uKJxMDIeDQalWNTTDASKJdAXUN3eSFzf1FKANjn9wfE4K4s7goE1/v9qTffPAq2yEIxVl91sqZT43VVsqR4yU195OYzF67tdJNgcgXcVVS2w6ROHsmJTJW1V0lSmeymQUtOUVUb1NiJlXRmmjIPJGfKyvIuV9rRpXfLtuUTyXR8fLyrGwhoUPvS6OWW5KkW17sZXxs7uHvjM99Mxm7t9OYl9jY+cuTIjTfeiMXdLraIkUhLiJ3j8cUfHGU9xqxmLYvYClN1qQQY6ebqr5SwuVs6EpiTb4A0t07/DzXOF1xl8qC4LEYKwe5TfxdVjukyTU1itk8FdZZ16zRMl9bITEX96aYoiTkylFFe7FuYxItJ2qeBu0D1AkJTYhAXT+FRLoV0m+WilLR8rFbqDOW57WImslQkUPq151ex1P+mHgax1w5PeHMuX09Pp8dV3Bd1qXTXxc1n3pcZ9c5iC0in9Gpa3LFn79ix42c/+xnjWSwW+8pXvqKLrVixoq+v780337S1p5PcgsWxuEfy+fHx8XjaG/FnjfV9niVfHAHnuSJDfi4l0FzdtlDc1F8Pru2AdYAf0JBdZbDg8jeXHTCvtGbDKaBV49bCFGReGZ0VcTGrp4VXDdy5OoceB/gmWlUiJNrphYiCd+LjPivOTGEjgbmWgP1NrSQ8mQuNx0Y626Ne+c06vtSVWRc7ZV5+V/NCdLElNdP6qwP31tbWL3/5y6wK7e7u5mTTb3/722fPnm1vb/+jP/qjAwcO/NZv/dY3v/nNRx991KlAZ8rAzMuhweETHsbGJkDwxnmmtiin0Aa1C5kn8yOBpuiMpmBifuTbIFWAOxgWMy67ynA0Z4OlFzH7rLpQLU5VR6dCRq3SVNbrRWzOVFUzrVKoHB932HTTWy57DbEehJwQXGWdbpQX6VFUl56qbvPMSGCxJcAPdXh4yBdq6+nAPbixff8WhfdZ6aZaHDdCdLqff606Gkyfh2rQdQSUk9frm0InVwfu4OAf/OAHH//4x5988klbu5H4jW98Y9WqVXv37t21a1eTqDza1tbWBjNjSff4wABxZhoNir/5szfynW3+1jQ9h4sp7sWsu+k7Zq4ZxJqrNioBw4nFnd0G57qGeaOHV8BsaBeRLmQU1J0NtQUoqxan0lsY3tkdUo9bAuJpSLEtdlPsEbVCSFaClJUJm9nnYAE6z1Qxcwkks96xRKajrXMJKaeZt3aOSlq/8jkiV4tMaTW20qmVfep0zu1m6UImndaQm4X4gWAwEo0q01JpUberOnCn5C9+8Yvdu3ezAyNxjNmA+G9961vnzp0bHh7+sz/7s0a3ey+tdu7vBL6HXPlgN149Y4lUe2u0yb7lpT089wK4GCkumkwXreKLsZfntc2gX4+XQ3zE4s4RTEttzj/zLyJjjD3MZDNLwFmWYUhmWSh6x8bztScvSEbeJ9iBaPFOeULLI8H8thjsvCZiJNAkEnAPjcZ9rat6WkeahCHDxhQSKGqYKTLVeDQ2NsauiaLllDFCa7nk5CR/7R0dZa7gVFQduGvi8XhcR9gxjZKaItOCGlUvfjIc4s+Dih4dHWFNbUdXn88zhwaV2fTL4gunGThYBAkuQpXNIGnDgy0BvgE1wZnCgh7UpVjcMxlvy5I7d3Am328Bq0sMsAq7CrV7sD/RU4XeVW7qdu8SscRhpzm/Bvph4cpgYT8zESOBZpNAKp2LZSLtbV6/26D2ZuucOeYH+7gG25w6etNNN3Z0dB4+fHj//v36feDo6KhY30sdSaYC7po7IPuXvvQlnN0ffPDBxx57bI5ZnhdyeeA7Q3EsPsnOzG1h0fWOeoy+dgijjuiCymtBK6uj8SbLcpFAlW+WG09pr2A9tRek3lVmqTRXmqOaJHubN7j3vJhgKK7AbRWxNKMIZGMZOouJFrsA6a3cLTYLTeFDtcX6tB5X+1wija7Guklb9hLgVVEsnnEFO3rCY8u+saaBaDM8ZJBDazT6B3/whf7+fvxZtm+/84UXXmQdqd7tFjf1rq4up6ym3w6SLRfZxx34/swzzzhLNkEc/Vvzj+0h2qPBjog/Ph4fHsdGg5FGZ24CxheEhZqisQRRZ4a6mK2T1rTZ6qrMZDISmBsJCBbE9QJ7u+wxmF9Si1NFAvyeZhiU1V2XlbWezR1kpsFLZGkv0dIxq4rhXKC8BOtT31W9Gh/3qmIxiYslATwFhtKd4YCr3WdQ+2J1woLWm0wmWbRDlffccw+oHRz/2muvoeZuueXmjRs3alZwfCc42XKaop3pJfHnn38eu73tOVPybCY3Mx9uGq0NWxQeQmjnsVgsm8u2tbPCo1TvN0pxrvMvnCwWrqa5lpGhZyQwKwnw1dcgrvw3oG3VbArJbpCgd6+vuZTDlI1WiLW8QVOWsB5SUqYp+pabKtjXyto8n8Ivw5n811aoAmskW01pHmYNJ0YCM5DA6FgiH+joCgy63UtnlfwM2mmKOCSgnc9Ra5dcshZP95dffuX4iROotBtvvKG3r+/Q4cOi491uTM9+v98uV9dAhZK/cOGCXaZaBOL1/1UjMJ9pGr63t3eMJ3ODgwOlb1pnW3H9za6as97qqxZuKLHemkw+I4GLRQJsKUNTlaeMGN317dJoPL/9WRjcpY0CeZdGWzWzdnOl6TMKxYIqJrMXA/pnJElTaG4lwMr4gUxP0O/pCKZkemrCRSMBrYK4nj17jv3Nd+zY/p5LL7355pv4GgBWEQMRQtl70RKLO/7vM3h1yH6TS0LIvBbvaHHlQp2jIyM5f2tnxGf7hi7ED2Uh6lgS/WCYNBJoFgnYpy2hN7V+bBbO5pkPhdjlYqkl4lZ0nqueHXnppdnvaUZTZSyU7YSWztxldoIzpZtWAmMTLjZ26o4Oun1s027CxSUBjUJRR5x2esUVl7Oh+fXXX48IsMSPjoyiqURDyZ5aJUZ2MLcoMSUqN1lnYI0OBoPNIGndhmk5wY21q6ubl61DgwMeb7Cjo80evKctuzSGtumbYXIYCVxsErC1nG64pS20cUupRh6gPZeSXGbDbLmpeTa039mYsgAAIABJREFUFkRmtmlc+bs7q6yE3pUphfzFVqoYl2KKk6SJGwkshASAWyzAiwZzrVEge82v7UKwYupYJAmAnycmJtDH7LT+1a/+r+3b72D79ePHT7DDzG/91me/8c1/GhwcREuV7rAy5XaQi9SQ+delbtlZrKe3N5v3DA1e8Ho97EJjW98Xq9WmXiMBI4EFloCF1DG9LtFhU6YaKH0QAAE3/XQ6w3YCtEs2zHG5dGIoFPb7OYfPQ6JWdEWMsBSQKzwXJhrKId/G8LP9tojJvSiJ2VIz5Y0EGpFAPB2cjA92t0csLdRIYZN3uUgAPxfUsihqlys2Fnv8Rz+mZag7vhUkPvTQx77+9X8kj9PBnQzTe7kwIVi7di3ON5y+dPbs2fp9aRZiRJhdHV53rqenmxevwyMxXyDS2lIY1ZbLV8K0w0jgYpTAVFisXGPIPflBcKzYXCrC4tXo0OAgap0tftH1BFQ/mhn1zvZfkUgJFNCwPpVKplKTgoBzuclksq2t1WpuuUSaWQjwCnRnnlLCZIPg29ngpTpfK2m/uVliEsjm8iOTvrB7tLsjusRYN+zOtQTQya2tbRw9pG0TGrJLJXnXz59//pVXXkHjYYMvq3Yq4A7kv+WWWy677LJDhw4NDQ1dc801999//49+9CPgexkVpyose1TX7WzL11VJrUy8g+ju6mC3nZGREZB7a3tHA84ztYiadCMBI4EmloCoHDZM1ppHI/fmxu0odN6oYk3Xmn3Fin40Pqtp2QN46heGqHFUHME+XSqSyai2FhqM6hseGgzxajYcblrjn9U5yuTufCm7qGNHE3/BDWtNKgF25+OH3NXT73EZj/Ym7aMFZisUCubzbWwpg/EFDVzA7krlYWTp6OxAe5ex5EPvadVXqQBvu+22np6e73znO5h2KLZ3715GCHzny0jIbWXhKpmaOok3EWxxL/B9LOnJTdDSph3DmlqOhjkjgUWXQD0mWKWyrN+4+EyAghed8UoGUOWM9Ol0imkG2LqjowM+UVOoctHyUmBGypfBwaoM1ceeuWwnPDw0BDG/P4DNvumkofkFtzPhslmXuPPOalLtT1tYuusbK1ybrHliJDCtBHIuz/DoRMjnAlbhxTZtfpPh4pEARhP8YVD1vE1FsaPVfH5fMBhqaQlXVcXlQN6WFK9cr7zyym984xugdt7Drl69+sSJE7yZJVxyySXE7ZzLKSLw3e9Kpz1DsRReoa1hOWZ7OTXQtMVI4GKXQMkPWm4EBzafzwQOMAB09A/vSQkVigjOBcaWtKburrWhL8UZJzxuT4sKDBhivBiR97Yow+pmmrprmduMqsFqczSLLjIpCKASgNsttDKbTyOBRZTARMabGBvu7Oj0uA1kX8R+aN6qgdnoW22I4FoVr9vce0Tx239WMn7tW7ZswUOGAQPTzic+8YnPf/7z4Hier1+//m//9m+5WnmX4ScjVnd7MOzPDwwMjI4ntSiXYTtNk4wElqMEqmM2reWc7QX0AfxUbnA7etP5cBHj42Nj58+fw3TS3d3Na0AsMRWofVbcFVqtxYRYHIFHVEelVA0D58+fhxnH80WLWn0qQH2uJlmWYrfHPxMxEph7CTCpZGOQXN7b3ckWdga1L5oOafKKUUdY3NlbBtjJFwbrCS5VtXiubnEH7H/pS1/ihc4Xv/hFXs5u3boVLLtjx463334br/djx47deuutx48ftxRfLeJLOx2Le29vbyoj8J3BjMnQ3A6fS1s6hnsjgaaUAFC8xJdCMymJEtPDss24ZNY40AKG9qNFiaCp4/HxSCTa19c/PQPCvPyvO9iZidBg9Yejf6G89WmRCwSCsDE5MYECxHmGl7kNVmcRmoNPJ2+a+UJKWXfrbnTmrl759DmqlzOpRgINSWAylRlLZLo6Or1ejqw3X7uqwmsO5VuVtYVKZO+AkdERXn7qCoGa+C6yogko39nJl6fcqFQduDN+fO1rX9u8eTPoHEIPP/wwqB1ndzZg+d73vsf1+9///vJG7XZ/BXxu4DsSHIylAu4ku+sY+G4Lx0SMBJpUAhqXTjtQKuQOhFVGXMpMW6Ce5s6ECFoaL0TwcU9PXz11CKclKLXhSrWENI0pasSxnj+mE1jfWfmD2+UUmefpEb2khhsxtxPHBXS2Fc2awGwZMOWXuwSYEbPm29+2trcjvtzbOsv2Nay7Zlnf/BSfuU7Br91G7SILt3vlihVnz50jiqc6NvhK7F5zPRbb0DzyyCO6hW+99dY//MM/sOiV2zvuuOP06dMXCWrXzefKW+Oe9iCvHXiFMTY+PkcDvE3eRIwEjATmSgJyIqaFahXNknFBK0bSC6nktTQuU3ISZ//XWEPSqdTgwEAymcK8ja29scJzlbsgjJrk9EsAzEKwCsM1883fAzpJcLv8NTL6WH1rMVZyP12rrULm00igMQlM8ktJ4LLc3uozqL0x0S3Z3Fo5NXxltNL7ydBwBiA2bu/v6/vMZz+zor//oY99FFs72H28gDlt4q6awB3lSPjoRz+6Zs0aKGJlx39m+/bt7Om+a9euJSvcWTHOy2Lch/yB0ODwqBLlrKiZwkYCy0sCtlpZzIgC3xqelYA0RA1bKlifSlFKCvZ2yVueX2WexwsamfObE+wN1909g2WgImVM0bIDQcOcz6y5rW1tsArD2C9gfh5FU0FaplQqwPnUy7YcRZ1iIV64tVMtko4SJmokMDsJ8GMciU2kXZGelozPV+7hMDvapvQylAAuMewHQMPUyOXq7we396HiuK5cuVLrdvKAwJ2Nr+4qo3NQ5qc//ek999yzc+dOjPnA1n379uE2s8Aq28luM8RDAV8o0IY3Ec4zIW+at9vNwJXhwUigDglcFFjFRmbTCkTEIbkVcq+/2LR068iARh0bi3WI8+uMR/dZ92bjTWZ0wWEGX8zh4SHODWHxTx1tnW0WNTEp9JUayepteFn7nBOcuVrhOtu2mfLLSALyo861t4XTfr9AMROMBKaVAKCcPBq1o9xYmApkJ2XVqlUjI6Mff+hj3/sP8UvPZGQLYJtaMWYnOSPY8H/4wx8C/6Grd5d0Pr2Y40xjWKw1MZFl5VZLa1dLsOa7i4tZSqbts5NAvQBldrUss9LiED1dk7RgNRaU3NMWmI5gY88TCQ5hmWTnFjR2YyXnMvfMG82IAPODg0M4wLe0LITlwq1PxVNdxXA0jRikZSWtU+VKC8nz6eiUljB3RgJTSGAkzrkKnq6WBDszTZHNPDIScEogn5cFqUDzq6+6amh4aGBwYOvWK8Kh0OWXX4aafeedAzzV8NtZqi64CWTHyq5MHc6yJu4CvMthCrnshQsXpti7x0jqopEAUGAO/y4asS14QzVkA7wVekvfLwgbnELN8so5Qu0z5XsODM5umkBDhoYGF0BsVjsZhcqmZdaTBpgowfQNlDNZjQSqSSCdSQ9k+iKBbHvEp02n1XKZNCOBmhLga7Ni5Yr/84//uK21dd26dfhxcHTSihUrdu/ZYym8EkU3jcW9Zj3mgUMCLWF/S7iXjXsGRpKRYE5M8SYsMQmU/CqWGO+G3VIJgOymh2ZqpaNlm8eDWu1XUkpnzu+wgIwMD7dEIix2nwPi+js7q2/u9HKamk8O+pic9OHyzr4H2Iemzjyrp8rJ/f9n701jo0nS+04WWcW6eN98r367p1szmtFIGMlzaCxLsrQzgqzDlmBhsV5AX1eADHigLwLsb/5uGzBgwLswICxsLFaWJc8srB2PbK0OG7LOGR1zdqu7p/u9X943i0Wy9vd/IjMr62QWWcWqIiNYzIyMjHjieZ7MjPzHk09EwC6vsYQSh7KF+6D6usMrMeULew3slXOn5dR8/kUqlfHa8BroVAOsVM2khTRr/+W//NfJiQlmf4koPH785NmzZw64x/1kyFAD3HFY5NUSFUsYGRurIZKw1M3LRieJb8YMWl3bPp7MVphD7ebJOJASJXyPDyTvnqmeaKAJOLO7xKH1apWhMeM6nCYYXbS1uTk7N1fXBFe56ShWZ3fuqKwyq3NjamqiqyTEoqeOr7qZNNh9fW62S6I1qT4E6/BcOcc6FdXeJG8nSdDpFqlOqu047yUvUsf1+AIdaIAxhbvnUxMjO5MFfGOG4j7qQDqf9Xo0gBGHyXadI/rnv/D/8A3z+7//U7wjMAT/p9/8TTeglHFQdaOJajA3QysuMfCUZVavR8KhqEWrk49ofp+9l2uTU5PdMa0NheSXZ9I3eZfXnS/ZqIGkKCc+O7gbHQQaTowJO7pr3cpKC4uLjBZqZPjSKUZLm46IwkIj7L8KX2nWqltY3NjcwHbRi++NsrIHju2aREem/Y4EbvIBRvdIcJ90RurS1+pKBYeBxysJeEHhhke6IeECAl0/vX9WLB9vzk14j/auq/Z2EQSUM0xof1+TrQPfP/+FL/zxn/zJ4uLCe++9z+KpJGKmMFBZo5Ya4F5zxh9cQQOs04Su949PD7Z2Jwp0bW5S3+a2v0SucF/4olUN9Og2EgA1WMqrvXkVpAYnmPFdAEBeMgy+P9PHRp1pXqzKeacxN00vqL3Tgu3zO1ZDUdrnbXq2m+CHwaOM9mENKT4s0Po1re8qiVwj2OVygdyvMA/PVVjwZfungYZHsiHh+ng7q4xtHaeL6f3JqS6/1jt4IDvIen2a8TVdTgPYOxhGenh4KAtFKvXkyRPWSopITUxMNi5754F7pJ8uR7gAk/lMJZfGeYYwObs0PnqtMx+3laeP7V5bvvzJPmlggG6Iq7HCc1d1gGmnTIfwgxegSmGBpeqr1d5YIbZ2vDuYQrHxVDdSLs+uJDfpL+Ee2YpzxNzd3UXkrtvdow8C7t3WioFE6ZGPUON3h0Tlfabbq4G9o7NS6XBuZqLTjz5JVNbBw9xB1iQ1K89V+wJXLZ+Uz5uXj7cPa3XhDANQxPDhXmFsSWHluzonGSd+O+D+2c9+ljJ1zTqdgz//8z9/6623bp76eiER2sf+xMtmb39vt3TIi43lV7taUQ+e4K7y54l1RQN9vsx9rr4DFarVa55dMvDvfsriXGXcqFTi5y3KNaeWKJVWmG+gi4ual7cHwa6KbTonHgob7jun0LwEK0mtrb2kleuOK39UiSF3g9wYpS4cBZsAkndb8IhTH7mRGjgfGd3cPS2kTxZmCjg13DwZL9mQRIrovHyXH8Euk4sEu6ZILsdwoTyvDKzvtF80oW2G+7cD7qB2FmBy88PD+xtvvPHxj38cyO79tju9koCJqcnCyGRBn5JT+al8qhl87/zG75QPn7/3GujbVexbxb3Xaac1yKOiWSteryKzzSsjqFB/3Q2YPJj5cbHbHjLGZEy6WPQS/F+tdPMKFxYWmR4Xz5k2L57mJVumhgZ3XVj7MNIyZ4sTyOlEDbfad/uKt6jbJw+9Bg7PskeHh3NTmdHWi80PvZDXLkCXn78W5NwT3yvhukqdVZbA227udba4WLdyC2wH3OOiAj2///u//5Of/CTm9la04vlvd7zFHTQyMj09w4tnd3fnbLQwlUulu2x9v91av6z0La/WZQleXK4PVV7M1I3JIWxXG9B3mETU/bSzjM5DhiMDhbUFL30ED0xwPj83R8t5aSKtC+qrQjfohlppXdMlziAyU7zTaWHbJfGjQcPoVW6gyblCwlZCWm+tA1LJK/U5b4wGtFLB1mZh9uH8JCtc3kBD+425Uq0E6ekTHk531arypOmslISTIfNCUoD2jVaO+N7eSL5QmJyY1PJzta1YO+D+jW98AxLOwwZzO0aUX/7lX2Yh1tsH3Lt56VGpg+8729vnlXOWDW9mfU96vW9zvm5elQv1eK2VXciNz9BOA7VNXPOcIfYL8nLYERxsTjSWCmzl0R6LLVIdO9mlqHi+NCmVdMJ3V3DHEO8IXAQddr80i40F6Wg1Ae528RozK6X+VogfN3TvmpPwqbdXA4cnI0fH5dmZmbFRTfrhg9dAnQYu3wDHCGFcp6nESYY0WmOaODAhvoZHx8d85wHTz8zM1LT1qdp53GOktCbo5z73OZbVcIm/8iu/8sUvfjGeYXjiXdFtl8Xl8szMzvIxffe4cra7ju97Ot1d3/cuM3xpcten/eur6dLK8AV7rgHB0BpvdbBaizuDjEwuqNNkEPrrFnNMwauZpK5nLqkrgHeTN45lu6UA0UF8JrpGFba+xZUo8ybTNJq6VhXazKQeOM0kEwnBeUi6q961i34lCX3hAdMAHu1bu0fZ4vz81P6AsebZuWkawNbuUDtNJZHVlZW//3N/f6JY/PJXvvKbv/n/4jxDKxqfEZI2q6XFnfff0tISfopOSYPk135zmlreQAx0OcvO4jxznhqfmSqO8U1kwMI1MXRN1QyYcj07vdFAgNnYxe6rWDSqNchosFCg8OqfE/nESVM7NzcX1dHLSDOZellfR7R52WBJQqVXfH2oU2X9E9A2fa04cA+uX0dsCbZbAfXVBlqBHYrls3dHA0yLdDi6PD1xnh71qL07KvVUWmmA1ZN4X9AMffSjH/17f+/vlplVBlyeyTx6/PiHfvAHv/XNb7351lvMFFkoFOLt3oXD81tV1910WE3+627V/afGi212dm52enJ3Z5sFCCvdHv6SXLNNcyZVUNPCyROTVuPzeQ1coIEAltVhOm5FgtsGsXBwqhLtRFBSpy8XgP77e3vXhdrFY1WgS3CMiuq0dAkibYugChRyiXX9aqkipQSVp8y5Vk6NzgYnouPGSFxGCRsIbHb3K6qvsTKfMvQa2NjYOC2X57O76QEBR0OvUS9AOw2U8Gu3987f/JufZumld995B3M5NviXL17wWRAfd5o7IuD7OJWWFvd4JuIUZmQqjjdMkpBsLshq21pHyh821cBoqoJjEp/ut3b2Rs6OZ2Zm4x0sXYKmxbqbeB11dJdjT81roEYDauYCcBbHpNzZ4SHR6D63NLPnahPmqCHY0QFv/d5MI9MRFwkyRxqo6iVBqUtlmV9YYHwU328vVToopE8iROXhEljfk1GruaTBgRGCHNNK1rWxyWj6XDdTA6XSye7Z1KzcVpPiopupCC/VNWrg9EzL+/DawrL+qU9+Kp/Pcfjg/v379+49fvzkzTff5BQpmITiTLW8QQH4//pf/+vITfNrX/saUxk6Etjt4yQsHnsPNJzzCXUaaKOsMSZkmC6ened2trdGszNMHHmZV0ubCupY8YdeAzdSAzWALZKw5sHg4FxoUM7OtI3WuDUvFpVvH8FJxjW77bNd/SzwVYbn0VEHZGnT65r1NlUgpqHfALCbwFeSuk1d7hQ1Fgp5lHMlhxlD7u6LiHsNOeKBLBcy4S402Xor68V8+BwDqAHuou398mg6u1hgWo+WoGgAOfcs3RgN/M7/9ztv//Xb3/XR7yrkWShg5L333mMKR3nOGHCva+i4R3l/ufdZzVsNH/nf+Z3faa8UJpzBSIxDWPtsN/hsjcq6J+fY6Njc3Pzp2en2zg6jsqamZ0YHz/e9e+J6Sl4DXdWA8J0DaGxrnlEO3M/Vx6JLLh+NIw8aC19chY+9PdZaWuyIQoShmWCKgsYNaVU2YIzAKdvRvVCEhvfouDSaGs2Mjz987VXGtcO8nbPKXQFXxhLYiKioSmI8AaJkIhTEOkAh0H+QiR0DfGs0ZwyQyZhRqTASkmq3Z9VujO6XBu4RI+5bSlQ1bEbxZtVX9YjoVZ2aniGlO6ETKZpV4dOGXgN4GGOUBMlkMvF7ZOjl8gIMhQb4vMM8o67h/eAHP/jJT3zCjebf3tnmKyV+Lp///Odpl+u+Al2+cwmhH/3RH3333XdvDHCPXg89ud6dU0fDeIgyJ//2QXn09HBqeuoy1veeCOOJeg0MrgaqeK09j5qnBPwWADhe2g49ty/U6iyrVTN+qOlZ1yg7WEzcHRpkTI0xGp2nenQ0PZqh68CfUgxIk+gANegSsgHKjJCmK89WZ93GoXqD9m5DeXdS56t/5ZPy2vpaCGUr49nxh68+pFJxZtjd9gLxZzLlK5xhyMFRXav6nWME0jBeC6Y/dTnEbRiaKgHloKL43AhNszVPNP7P9YFErOmoNtQf155tfmQg7TIFm5PzqcOnAW6m3VKmUi7R3268qYZPHs/xEGoAc8bhwQG3Iq04ru3RcCDg+z/4X/9BLpsFuNO61k0afnngjop+//d/H48aJo4cBHX1sAnuIemLNYc5bW5ypFxObW1tjWUnp4sZ38RcrDWf41ZrQNC9hfWMh1k/h/0E21EUR5YgM/NlA/YLZ24XtDT4e35mRvSRCqPPaXnHxtJsoxBUWVudqhdPlbMzZu+V72O7ELZLtq/icpMldoh81Gr12XeFlCjrRRGoCG6ZtxEzgXoMYzjrhWWtbtjRXjuVkSk+Du+NTwjyIgDbQ8chfPIQKIfsrmbeQxjdLwfcdbH4GQeQFYdR0KnYYZReEzERlOKkdqJb4sVlawj5gxujAe7VnZPCVOZwfDJ7Y4TyggydBkDkeKTjSUjj9Id/9Ec0om+88R20VG9+6018Xj72sY/R4hVrp5RBxhrgzhdYGvHkktNG80bghZS8SPKcFzbGyUnV5+wh6fqqunXM1WUZwvJZZX19PT2en5meGEIhuqUMT8droKUGDNW5Ob8b8tQ/M/FjwUthw84DDSuWEtf4ylgOIWsVM+bA4pChgVgw7/n56Rn5CdTjIm5rR8EmQMcGkQMTuMsdlVFRyxXvaYQYNNxXJRFDCuLMiTk7Nx+crozgKvDtd98VcK8NAdxHFtKt7+E+AQCU9W3AqgGXj6XHQPr5gplv4Mr4wlSPeb4EnGfEX+kErz/APbYlfBKmpqZc2Sp/CWK6VAFwr81tH0xqk+xInLhgezbhMammveC0391CDeyyrE1pf352PDVyM5dPuYXXdHhFnpqepnmkJ0nD9Gd/9uU//dM/c7LQTv7e7/5eNpcrTkzUSVeDuWlmZYzpMEQDWC8sF39PXpi5gwy9otsBC9eTNTOWwqpXPh99+eIZatd6Wj54DXgN1GrAweLaNFlsa4NBQSE4Z4HHLB2O2qzN1+ooMi1jXsfcvrCwCL6lanMhwV4urxJCaH53R5irLQ0nFLmh0NwG/icuXVvSjQqcBTYR4VzXIQi6BJwvFjWt78lJeW93d3Jqyhm2zS4d8FsLjl2rrp4Df9TJpFXKZ+gWQZ48fgxNirBFHeByiwieQxmrAfMKa5tOG1J3O0biAOcF3LU3fG9YX5gejjPjeOCPj0xMQIoAci+fnDx69Ig4LxoqhyClAnbb7kQuwO26WLWlggsb7NrSMexeRfTt8/qzN1IDZ5XRzd3yVP48W+ARiHXmbqS0Xqhh0AANGmMad/d2WSeV5pHmji2ME8HDsBG1c6oGuLeREWOJjPktQqJGs0XZ5sndp9i8nmFMzYyeLy8v0z97ub6Vy09MFtLuxTaMsnievQauVQOxhkVRoTg1lARaTwzDFzID3MZHnJwgVkOQmNs3wahutnKHxG0rJxKD4eZNwrhXQXcaZIzk+huThZtNBhfzTGacGPCXCBAZsmb/Bg2LL0OtasQjXM7n1Pn5OYrsbO+8/+1vL62sCCInDrBHXVH2XDb3yiuvmsMJ7OmFoZ0QPvGK9U9OEfno8Ih6OYs7jMvieILhrIkADwbvMzCmQB22QRxk4J+XyIMHD/g6waAr7PAH+wcY+1GKyxvx0ywSyB7wV9cLcwqyYrHLWyXTAp0F172az8duugb2SqmT/Y2F+Tk3uehNF9fLNzQaoJmfnp7Gk5Amkc+TvCFccxpvqOPCXADc+az5Qz/0Q8+ePfvJn/zJ//yf/zMt/le+8hWa8jiJpPGmbWrSwj5fvQZ4Ry4tzHKZmToa6zvzNsTeX/WZ/bHXwG3SQAuo5lQQAWCLABytcRREdl7pTRUFZHQO3HaWCVgqpeMDLMe4gmxtb+VzOdpGYDroXGNcDf2qwUulHCgvZCec0Xp8PAuc5eGlUqqMcHnTShMlXrpdNSbVdxGfhtuFxENyIbBv2iVAQEF7fSA453WAOwx6ODw4LJ+WSeIkNvtxWqUsn3kZXpXF9i5zPTieKRRKx+hK58az9A74aLC7s8Oa3u5F1VRkcQVfgu1wC+Bumqt5ookXnAok1pFRal7Cp95ADZxVxja3diYLmcl5b2i/gdf3ZoiEicN9h6R50lxhrT9IpmkDXTPY2BhiHfn0pz/9W7/1Wz/90z+NiZfBkTSgP/dzP/cbv/Eb4MUaTTUWrjntD3qlAb3/bGTD5m4pO3rCV5Xqq7dXdXq6XgODrgGHywBqsZYpwHukKDE6YciOpwabRyupMIGwCDpwG9zIKhbH/ErHQFUA6PHRIYjz5PiIz5uyoOfGaXDBq8DWXC4PYAWtmsW5JfFWlV6YHlIM9xcW6F4GAemUvFzofvCmic9PgOZB82imXD45Pi5tbW4yXtWZ1WmpGKKKQh49frS0uEhZAmCeubNOyie7OwD4XWZ8h2CcUwi6Ns31FigRP0u8nfxx2B4rZvhfZNu8GmPZfXS4NbB/PFI6OZyfmxplYiIfvAYGTwO0SPjJYL/AdZJ2Se8vjD7pNIgO20cjv9YIupavtv0j99/6W3/r137t1zAq/Z8WKAw5SP/Ij/zIF7/4xUZaPqVfGuBicXGPjyubuyfZsfLlpm7oF/O+Xq+BbmvA1kAVOpM9WQ0b/zpy2J2DMGKW49SIjN+BY3cDKzSAshDncsxmCLI82N3DY4Ty4Pyx0RFwJ/O05hmbCeQsFLCvQ4rQQKY3CWaJvizpUEGXLd+0HLI7RE5/pljUmCpBeXo4pRI+7kx1z1jV/f093lETk5PgeOzueMmjfL7u4krP192FxQXAfZw4bjYBnfhgXMsRaLq9wgP4zk4ic83d3cBdYFfqui5WXCQfvxYN8ORubW0WZ+/N57jKXPdeXGvI+uA1cHkN0EJitDgyX3RaJN2mNlyKcUGM5ucepp2so55udSd/13d9F14xWEp+/ud//seTaFR0AAAgAElEQVR//MddsX/6T//pO++8g+EEl/lm66fWEfeH16oBB9+Pjs6ZeSZfnC7mawxX18qKr8xroL8aMFDajIXqm9ve5LSZ9joHiI+O0kTWFdHpyjk94YODw53t3Z3tTdy1M+kUpvTixGRWM+GmWLqlT4Zbg6vtMWudPHYokcNSCG9AFmVUNdOs0OXTeBUxHKCYzozY24faNzc3sMdvrK2/fPFyamoS585cPg92B8Ez4H5zc3NpaTlWX2hxVxdAICmOtl2KyxwJoEz1IZam3ppoWZaoUH0BfzzsGtg/PD4+3ON7zuhorYNAlwW7PbdQ7CHqsg5vNTlG+zjUjhbu3L37Pd/9Udq4t95886/ffocUDEaYQmSajYUaw0YsfQQ70y/8wi/8i3/xLyYnJxlL5E7xEfNnf/Znmdik7mtmvKCP91cDfLYmHJXOmTWZiLe+9/dy+Nr7oYEAlLWtWsZqIKAc0i0K+KbRqyuCXzvWdJo7nLSBuJh+cWefX1qanJzCKry5sTHTGWrv2js+fIXqo6ph0I4oxzMbbq8Tu5eH6Hx2dm57a2tl5Q7+8RhE33/vveWV1cnJCTd7/d7u3vT0DOYhx4Wuj3NjgtNzt/pSlX8D8U3ZDeUKNRVmqh7TeakSCk/7/Q3QAPfM+uZ2YXp5oVADd26AaH0VYYgel+pj3leNXVw5bSA+MuRDuTjGfN/3fuxLv/Vf+ET5gz/0g3v7+y9evOTUwf4+7WG8rWsO3MmBTR3YF8/qWAD76/tm2KpezJfP0Q8N5LOj+cVF/JrWto8LmSafWvrBlK/Ta+B6NNBiHvdmlfNRkWQaOvljCPDXv5xwhmFmw8mxyVdfe/XR++8zb1ch/HBJdvzam1G9ljTjNMDtl6vQ3m6SvF7oy5FLWoo+knU2cDRKLy4u4SSDVYkOEh2nQwYTHPCe2oteMeQMhx9oqpu6jxt2xRz3TWUI3t/slDM40gG4nrksG652UhF8voHVAL4Ah6m5+dnJHhvaB1YBnjE00LQ16KNmoqanngfNrKW2SY3Tg/v3/+qrX8WZkMPf//3/FnkM4jND2xg1iZxtDtxp1ugH/LN/9s8ePXrE7F1Pnz51tVH4P/yH//CZz3yGdtal+O0ga4AuFl+nuVhrL1/SmaMzNsjcet68Bmo1cPnGl0awvqWEmKMXRQTXU9g2qFSx0THXgNbxgHM27tpM2kjgCcJUzMS4NKkgyFGWHupvuLrNuF5N1yQPqzq5Ket5b9E1Yl554BZTau5sb6NTPmjE+NAcNbqglRF6WVyo2CkuXHgURcKExr1ktX9HzTpdCYo1EvIpA6kB+tFbG2s8qgsTTF3dvx71QCrHM9VXDbRsZ5hWC8Zcs3b/wQPWrcO5SykjKYb1f/WrX3WfGc3AFBFJNQfuFPva176GV8zbb7/9b//tv/13/+7fQRc0T1hdXYWEd3Dv603QWeV4yxAE323JcT6kdFbe5/YauEADUYNyQb7rOS0wZzaMJtUZ0hW7xjJHocUd23mq0ccdJ5n19TUmVJ9goq60pk8huI+b2zu7zBtzWi7bqqKQM4pNquxNkgkYCdJpHbW8CsxeW7ApZ87oBfESwYaE7yb+SJiHQO2M0GLG+MXlZc5G/DC9pD6G6JJqvhq0HZ1yMLxWluBkrUih24ydDKB7JSAbo+ajQ6wBpjA6GFuZnjnTSr4+eA0MlQZo3OCXuXP/+3//U7d+B1b2T33qk9/65jdpHsH1fG2MC9QSuLMW4B/8wR/8zM/8zJe+9CWmgPzEJz7xL//lv8TU9MlPfvLXf/3X4yR8fCg0EMH39Z3SRLZSN9ZhKETwTHZPAzf83VbTyDXTGvLrh8UdI64yaFYZg/sUrSqHDDw4jx89Wlld5ZsVgNL83cezuSze7UwTyUfI4+MjrCZucSKZ4TX5LuWqRJrV3600qrlKRfa6uFBZV2BWxh4D3NZBUk1ohu8VTJpJ/4d5Zo5L+8dHRzjH4CKDMEzPs7S8UlshCFtTT4rOmZt0P36+nfgmWY14gvBVGH9tlynOsI93WQPcGNulTHo0M5/Zij+8Xa7Gk/Ma6IEGWHUaqq4Z523Csjzf9ZGPMGT/f/zhHx0f8eEomCGt1mDRwlXGsffuu+9ub2//8A//8Je//OXXX38dT0TCr/7qr9Lg9oB/T/I6NAAKKTKn5/7+xv7+xPR8NtPutXcdDPk6mmvAX5fmekmUWsE+0XzCZlMrG/vZJmZxD/wx6iA3vtdLy6svX65ltraY88RG+OTwegdNpjNpoDzPFFAQwzCmERw/AKO0kAQhVs1BLhdyt0nEfIeZDIfWYFNHAGbgkCFJbeg14to2mS88hbCga6dPtnaI3PogIXXpmy2+LnwKPisds2IT8+AzCb5C+aRE5kwmPT07N7+wBKivqwsqpJAHsi7uMkBQF6tlJylQS6gdsRdQNubqavGHw6gBus3MMTozM8f9M4z8e55vuQb46EgbSDMemWBevHwZ+KLTuKnZk69g3XwwF9zrLLr0hS98gdLf+ta3GOl4y1V8M8TnNacZlJnh7qi8t7PLrEHxQQ83Q8Z+S6EHzod+aQDjuYCZfgbRQz7sqsQvjQC1AU3aTKLOVaa2jJWl0VxZWWXs//r6Bv5m2C8mp6ZwemdJJlCmm7ac75vpfI4h/SoBQgVkntk6RDjTsLzQSZmVNXDFUTNswdAmG0F6w55xxkKOW+9DBNoyBz0HQO3YWFO/uOp0kBGUbUkodgLG7UgCgMzdS8WhZ8lhn3iZf4fXjAQTplY2pEb00ikfJdAF2gCys2LVAfYk1l0lGyqcmp6ZX1zC9ShWWxClLjd41io9Z0WrKA8pUbxVpD6Hjq1YuK5Tq4I+ffA1sF0ujlbOFxdy1oEbfH49h14D9RpwX3FpF3ldFQtFVklSuzlS+cAHPjA/N+eGXTFso84EU20E6+nVHrdD7XUWqtqC/mgwNcD7cqIwXszPY67YO6pMF7UC4mCy2g+uOkNR/eDQ19lSAwDHcJgpMK3hUpJgaW5vOWXtAHqqwWwROM1yQvwAoTSG777zDs8L1ndmmMHrLALxGJiDYA4h5MmnqmBU0FNW4wDTC8gblmWvNGC9oC65tIcRItTr2LVIYFwmXZmFncM/5TepQv7pDxid8DjYi2xVTDty1Kw4UUdHJ8LirmtB1QE0Zy9obvCcJCKOpORy0jm7+tk5SD2QEKN66aSEG/JJCe8iskEd5aTGcwtLy5MTU8B9EWkWYIMaxY1JXfcCQy01YjejUJfmZCOx86J1lPxh3zRQPh/bKY1PZ3Yz+XbflPrGn6/YayCZBmiFpqammRWX5vM/fv7zdY0S7Ty2odrB+qJbBe40o6wkwuIXtI/JalQumtH5+fnk+X3OgdKA3TSawIEFusojuelC/ReZgeK2c2Y6fad3XoMvMYAa0LyO9Y0Yt4KbsD3gl2P7YRcnK+jPWky5c7RHgmNjadpZ2tOJiUkWZFp/uQY8xdis+XMLBRA8GJSmVnZ4NpzgD2cRh3Sxyo+mU2l9FKU67SwE6FtM8287MDA4VUZ74WGCg/ZELIN6GiflMuNl77/yiiHpAKarsAU+oxENzflKCkULxuO6bOSGT7x9QOPwZLZt48y0IMTsEL2Vt6rFIshbcXU2Ts/LtkcjUaA3AmTHiHSK79AxmB1LO8Z1XYDUKKtWFQrFyanpQnFid3eHOd0dJ6234go+XYWOpdaZq2ekCKcMi5ly7azUrcB1sYtuB34zNBqo7OyVKmelhZlpP3XM0Fw0z2hrDRj2ntvZ2eZVQq54o8QpFrVQ41wbqsCd3A9eebiwuET7WJun3RFvKD/MsZ2ChuQcixdy2bl1eNviyMvtMkiMB/hmkFjyvAyyBoRiW7ViIWKHf0WBn8JxNH82cRbIuLGVbBQV/MwzMr+wyCmqosFl9vHdnV38QMDaUBCCBzvzHTSblT9NJgNMxJdEHt+x4FKE7nUKmC27tvESwHriVrvkqftTijEfCKudhSBjWI40x6USdIDlm7kHQv1U4ItPB0Eey1xF5qenQuiuu8C2GgDpwQEfDQi0G4FLEHD9tEwiqJ7aEAcNsMoso05zuQIrpKK3CHyjxrDzYtw220gB/EtYKYBuRrWIdOP006xks7RIakWcbjsj0Izo7UkL1dcvicuV9M7B6WQ+l037lZX6dRF8vd3XgN4m8wu0y/omaR6V9urQtFtNK6sCd07z7sD00jSfT7zxGuAtBmTn7bh9cHK+vzM1oQUjuy21f0l2W6OeXoMGBPNscCowI3bDYaR2uIM099MOYAoBzpn/BwkXYxPwI6A1qhZszZMyPTPjUswMjT+IfEM0vyEQ1pC0WeFH0wBP84zXoSUJ0AvNy+cEzE4jHGzsWIDeRdzpGnOMTPbiOGRZwJa/eLCU0EBuJ0YqjKCtilkZcc4/dg6x2IdbRZnIxa1nKuO6TOqY0g2pWzTwhCGBvIhPt4T3jTorU9PssK0TRUqEiNQVj1CBwL0D0PETsTgnlcF4gTN1a8KzRMIDlxadCXOwN+WEGtKxaUh7KIXFY/l9NKaBmN4stZmCY9l7G90t58+Ot+cnc+rG9S70knbvuPaUh10DNEZ8i+SXRJAa4I7NhG+XrglOUpg8vHympqdbtcsJifhsg6MB7p7Ziez5eQbnGV5xUzPz6VGHUfraZg+OgjwnA68BgK9DhI2cxm9igbbUyDk2ZUb0p2S24BA8emF/FUcQzMetMJ8D4gwnmhiZdAwANwm0rtilscczoQrTCFARiWpsNaG4LPFkNsP76BguNs4yX2ueF64XUA/gZs3OuDHp2BhVB+ENuHPsAlUQAWfTRVeco5ERcPzz58+Mx8Azx4zpxIXV7U+QnRpkILfq6XsYy2O5fCE3m0NjmfEs/RHcg1C+uDR+yNw+YISHmYsULrGCDkXt9xBk0SVUfe3rkagWbMdGBSPQf1HZW3z+Qr1ej27KlczW+lMGMGcns9G17G7VwQ0C0QGR+YriVeW5IiFffBA1UAXuNNxvv/3XrILREZs00Kt3CHc7KuUzD4AG2rVP4AZ8T3lP7+7vn58e49SL2WwAePYs3CgNJEN3HYsMWXNcb1rQQd/oFEZ4g680ZBqcWr/ORZQvHsEfBuQaT2kfFy7Hu50nqHbtswBN08mQlV6GbFJk0uZQ07Aw6Yps5SBoMKZ88bFPC4GmSMW7hkpJIacD+TzPjFfVUy2RFJxoGOzN5K/alMi2MoJrnGVRArO7vHj+jP6G0DauK/CKLtR3SGdzefx8iLjAGZdN2rIQErnknvEAdGPaA/eoInVz7LNwTWUhaJfg8SbNqSDShMpYktOBKQdZakj5g4HUwF4pzYCJxYXFHjUXTuj4vXP9agju1i5W3C95ui9JF5Vyc0hV0RivDWbV7VQyXgOdYv1Oq/D5k2mg+08qL/CZqeL5eQ7nXcDE7CyDJPyrLtnVGP5c1fupGhsaqcDCIUILeUaK+EtFQgkJCBwb5AMgAlYNHIdFWuyZhByn8BYnO0iOICkIsg14rQLukDZtNTPcYK5mRqj3v/3e5PQ0Q2PJ5qTmLMu74h1Jb2D/gFWNUhFCVY1aSbusPWLbj6ktX3nloVmunVK0vZ4AGwD39so0C776G67fIs7DQJLjOEyo3ccvtztjXRrBf+u9yF3ehwHWwFkltbV7NDE5O5m94UvHVO/p3l+Oxseim3VemyS9FaObKukFraQtFxaXn/iJn/jt3/7tYGb4XvDiadZr4NoegvqK48eAdRaJBN9sb2+NZKZmivqeH8/g433XQM2NUnPQd9b6wECA7QTQDJuGCtE+NLgHaZbEvS0rM39jYzi0XMgxODiCwhdmvnoGJ04csDrLN+4pwH0WgWKLQ3lUESJgUKfFln/L6Ojh4REgPjqriNB7qBTXaZHneDWlJnMvDy7UJNCcyyIWrFfSqPb2bLs7INgGHyJ0BFm2AWVR92HgNLB3wPyh+/Nz86nU0cAxN8wM9fo5t8et9wq6IWJcUlGJgDvvgJ/6qZ/6xV/8xXv37v2rf/WvLlmVLyYN9Pp265WWwQpzc/NAHNbkAuQ0naKoV3XfULr1t0L98Q0V+zrE0qyOYSBWp1kO9TOkqlPOcAt2BRdyh4cFW+4d0G95+npOBAI6WWqqRDBabA22rVSItP6GIBLSTj8gu+OYVuVChdOhgkeZyDUHZS23cO9mka8qQBfUPq0ECgp2UQbBdy54fXJ03kf6roHzkdGNtZfF+QcLRbqj/kr1/YJ0xoA9gZ0VSZL7uu+DHomBqN2Q5GLgji3nc5/73Ec+8pFf//Vf/7Vf+7UkKr5leXp3hQdOkbxombYfS97O4dlIeRvf90Yb2MAx3QOG6i95/XEPqvQkE2sAICqQ16SFrLtOhljJenbGLIPgV27mJBZ3JozpI9h1ahD+NGnghOcx0g04GLDunkqG3PIN4ejwSIb55m4hUpJlrtNMRK+3EZhHmW3qAGEjDJLaVTqvUztK0OTzLcuHb0gpywXbayPsTmvWsqg/0ScNsLrZUWqKtwzfivrEgq92EDXQ+jG/JLdhm3DJ4pcv1g1JLmi5QO3/6B/9o89+9rPHx8f//t//+/X19cuzO2Ql0W7C35AJdnV2edPPTmQmJyd38J7ZPbjQZnb1GrtLocl1Nd8BIEDCX/2t0V3+PLWrasAs7s2sqmozo8sfxs2QK8szTmBxENyKi4HwjXbSOZ+XmKR8EKPRBpJqdhftRgvFQh3eBbZKD65j07fXV6Dd9sqM4DURhuyaXI534x/B2+B2J6ET0MC6JVjMytGrCZjwuwHQAINNNjY2uNALBS509SoPAGuehRuogeg90JXINSuoncXdofYf+7Efe+edd/7xP/7HPFTXzFw3qvPPfze02IwG81HPzc+Xz0e3tl5i2ZudnWe+uGYZr5rW/BI2T71qXb78DdCAYbkYmI1E0j0T65qFh/IFl89FilnWz09OouytIoNjqQW3W0ekyine7SxyBIcAILMpM8f8+Mb62uLSUgN8F3KvGqOrNK411l6ZGAX0QaCimSiZP4fZh+uYMw1IC3V6AJ47xG75g6juCQRWUGeNflodNX/YLw0wQepRataNzegXD75er4FLa8CaoEuXTlQw1qCN1LeDEQG+rmJrB7W//fbb/+Sf/JPBQ+3XoKhIGT7SUgOZ0XNW/Cqfnm1u7YylTvF9b4IPrHTLC9byRMtK/QmvgfYaAJoJuGlnyy41v8d0q2KYlqsMgF5TII6BDttTHoyzwVJSMAPfJqX4IrK7u7u8soKtnQNOqDOSTheKxfqnklJOkvjbwKUM2NaQPZJV6F8xw30dd+qkWUfNgffwbI1U7kC3ggsiZjFXMEz2+75ogIuxeTCSTefnczhNtQQkfeHNV+o1MDgaiF5iRJo/J7Tyn/nMZxYWFrC1g9qdh0w6m3njB7577sHKs69/+90/+QZfLnsjUsReb8h7qlfWQOMVGk+PLcxOMs3c5uZGJj87XZBbqg9eA/3SAIC1uS2Z2zL6iTkdsLwQoNeQ+5hbZLQe5taJUcWAdSeu6VCAM4CeBtxj1c7OzeLiztcDWmekIGCxZsrFWJb6aARo609cz3FbZYLqnEMLNvJmwP0iVxnrvZkcTkpt+YcsOkQ51yOir6WVBjC0M08dHu020KJVLp/uNeA1UKOB5t8KHz58+A//4T988803f+mXfsmh9qnluc987n/efrL+x//3f6XV+6H/7e+OZZqD/hryNQfRC7N9pKaMP+i1BlpeDL3Ymv+q0Ke2MF9p6OwVxs/X1tew/LV9I/daLE//lmtALhFtVKA7l3/zkGFtIyIc4llr1vc25XTqJMGUkReQ6MLpUDqTwtGjZc5lc5io5d9uqF2b0frZcsgW1i8QaxnDhGvft1cmrCKIuHQW91pXGeSA+SQsxwSObgyGNNTb75OQ8nm6ogEu6FYpx/etpaUlj9q7olJP5PZooDn4ZgmPX/mP/9d/+tXfONrXyO6Fhyvf+7N/+/f/jy8cbGld1Xf/+Ou0ow8+9h2KKOglaBG/6b8GWl6Jlie6zDPwfXFxkXXUN3dLmVSJMawJX65d5sOTu90aABmEcI19Fd+53qhrtZSqWVlseRcg7uhYczt9rSbHM+PQ7u9d7UQLZAkFhatcLic5sLjzVw2jNOmMWYrkiJi3oqGeotPXFYFhlNm+NnP7kY87g1MZVxPPrKvgjquXN3berqVk49/tLCLkbpcP16hYbh+9Pg2UypXd49RMbi8zWbi+Wn1NXgM3RQPNgfujR49Kv/u7H/9f/qfD7X3cCmk6f+9//4+lg+NIalxlMOREhz7SOw201HLLE73jpQPKrPnCqi/Hx5oogNjExESEFTqg4rN6DVxSA1XP7ziBhodGiM9NiwTYdWM6gwGR8WK1cZrEC/PUlujykR6lEKzHbSbCwdlxJwhV0iPRdkQWdzrSceDuGHI0Ikpd5jIBOamxwW09Xs51PQxqk7eCD1D8rOLNmhWh9GYhSkdRnG9WtFkxn9Y9DaD57e1tHrTF6ZmRkZpuWPcq8ZS8Bm64BgDuNIWuNaxpE1++9Xjt7afZYo7RWuWjJlPtMi6fhbVxjdjZ2b3hSrqaeDVqrSPV7lxd1qE8xP5HYC7R9bX1XHFispgfSjE800OoAc0b4n5Vc6w9b2zCn/YYp8806TsROZngmMFRWx8KnK0p0t/v+wEGBZUHrbeuENOkjI2lkULWlrNU+fTUnYTVo6NDvn01XkZ01Ef8ChhHmY1cxVLk9SNru2VtnMCRCxbL3CwaaMphdbshiJrFvY+CN2P05qednI/t7B5MF4vWhwwuzM0X20voNdBtDbT7Vkhrebx32BS1wwZmVAawMn9Tt1kaAnrhez8CALGI+3od28bOVRFDkDgEsnaBRbD74tJiZjz/4sWLvT15W/ngNdBTDch23nz0fIDz7BEOntJoQCq4HZ+Ks+YFq/yynBFFqsd9iZnNmJrV07A42wwzuHM8qplkhN0Dw7wGYpb4+BUWUSn+BZzA7Wz6BqFA4y1WhoI3BVnY9XXBIffGzlJrziPpRCaWjSjyCrgzpKHd60/lfOieBraPRg/3dxdm8o1ffrpXiafkNXArNNDcVSaJ6Cwx+KUvfQnXyWJxIkn+wc8TvNKbMtruXNMCPrFeA7nMSG55+ejo6OXaNovCTBQu8G2tL++PvQYSawD8CigUSIsXMRTo0LrDrpwEwJFTucC5srkzA/oFoHx8PEvrJ1ew/gUnF7JELCDF+HiGBOzuQsMM3BRGVSB+ytQ5Z/iIBw1+pBY7HdG47givjzZqhDcuiCQ04I65XV2RWFCPg5TaxNj5IEouk9d6KSLGoYa18mWiMbNP6boGyqfnW5vr03Or2XwxuBRdr8MT9Bq4TRqoAe50hYN3WGIVONNO4ux9yFjT0tfV3+5cXVZ/2B0N4F5FYBawtZfAd4Xu0PVUvAbiGjDgHk+oj/PsB5BPrQAeG8LscjJJM3ki34jq88eOGX7Nh6MBuXUNy5oI5+dY3MWm5BKkFyh3YUQW66PDo8mpwFvGSgn3Ehw2tuh1b3CiKxTaDU/kiiAMXk94J4H56oC7XUP1T5qGAKwbSo+QO5DdBXD7RYC/KVWf2JkG9sr50+NtpitgEHhnJX1urwGvgRYaqAHuvLEu8jhsQiabbfeSa1KgG0mtGmvRbneuG3V7GlfWAG9rwsHBwfr6WgHze9uX95Vr8wRunQYAfLJBGDQFwNU0CRwI1gYb0BtIDmgoJ5NUKpNOYwZury+ydWrgaE/wMmcd6jYx3BSWSIEvu1CpLTLK2CSYdEGnRkcPDvYj4E6NwrXu3xDuZXi4chnYQ5mtyJhEsrhLpnNgny5QTWaJX5tSPR3IFwkXdFPM3A5BTE711KplfawLGjirjG3tHkxO5icn+/ltqguSeBJeAwOmgRrgHucN6/tP/MRPsGWGmT/4gz+In+ppvFUzrErbnespU554TzQge3uxCHzf2Ckx+zuW+J5U44nePg2AyfANMewWITdpQUhPP7d3qA/oJyCuJKY8T6fLJ+ULFRb5nFyYs1cZQsCKpDBPLTJOV+T2c8rsluURnHmwwnBIAKeSnZVuGpkJ4GzjiWtJaa9G2MY9huunroh9EmlgyrRgAJzLFwQViLolYaISglSJXPEO7pFmehLZ2z8sn6fmpwupkaOeVOCJeg3cYg20HJ3jTO8/8AM/sLq62hX9uFdl8y1vzvAXvVqbRLrChycyYBoAu89PZ7GosdTXUekCY+eA8e7ZGVANCMs1x6SuBQohvIy4isvKLucJzcci4HtRCKZ+vyhbD8+bdFEPBHQO5yBTIqfl05PSSfnkBKE0/Y1NgY50paMj4rUsCcPy36/Q/gOv7OL4tYtFAXc8lOJ8kkjQm6N9kHiBiOiHElZMXjd27dsX9mc71sDZSHp9t5zOTc9N5XkIOy7vC3gNeA1cpIF2wP3Zs2dzc3PtjSKOvnsZBtsQgkdY3EWaAPGo2EVc+vM3XgMMcV6Yn8eRdWN9/fjYG2lu/AXvuYAamyqcVg2CeGGb41opHQq7p87MPQZvb2ZjqdhCqtVizWKsBHShR02zcl1LiySTBCMV7OtwDi4H4MIYTo+sSEqEYxIJ5CcPs7lXOUA9BtuTtPDVUt2LwV77quHZWdzhn8z6pFAbHPQ2DehEFAmRukloB4bXXTJU3SeIWPZasv7o0hrYPzzZKeXmJjP59MWfrS5diy/oNXDLNVDfFNapA1eZRP7HtIHRr46EP/QaSKiBVGoin56bnwdyrG8zbq264FdCAj6b14DTACAPgEZoqpAAssleLaurgLuZom2ikdEkFnemwd3d7dv6FWI6DMSB5eB1eh1C6Abcy1jdyzaPjHA709QH7v4HNd4ywrWtVBSS7+EeBbafTRjeAv+fioB7E5Svl05VFS14DeC7dVJMXPVXRvo7DX8LVoc4+byS4pNpajQ9l923mYCGWBbPutfAgGsgrabPeHXZnt4AACAASURBVGzaANK2bu5tf/TvfJo3w3t/9s299Z0Bl8ezN+waAIiwUgwzjO5x89mrvc2EcR0Je1ouv1x7OTU1zdJ9c7OzW1tb0zMzVMGqrsxyw0QimCkZm4h3gbDcKFZYfZ0vnZTyufzB4cHkxOTO7u7MzMzW1ubs7Oz29s7U1BR+w/Rs6WPQxT09LY+Nyl0Bix5+C5oxcDx7dHxULBT39vd8vdes52fPnzGbOSMouExMQpq364sLu+zQMrmeY5QG7B4eHJJtd0ct2+b6BniObz4Li4s7Oxdc342NdUZ8Xv/1he3xTOa4dEz/Fh6A5U+fPr0/Nra7u8NyeNyQ6BmpuSGRmqeJm5MbmwWY8J5ZX187LpWy2fGD/b3Jqam1l2tg2PX1DQatXv/9/OzZUzhpUy9sq9dh/5sbG/MLC0+fPomeo82tTR69/b29menpo+NjRsjos0M6Y0N1weapk1O+QowdHh1lx8f3dvfy+dzG5uZEsbixuUEKDz66Ivjn9+rtFY/Vi6PJlcLY7tbzSp/ayYWFxTpnqo7eDj6z18AQaSAdWMpbsIwh55XXHh6f5R7/1duv/I3vPC2Vv/m7XwaZtMjuk70GuqMBOpPgLQIgWzh7bu7qjfLa+tqnvv/T+DHbJ/MLDXVtBBEsaHO69alLF3QkL1380gWHuN579++vrK7Q68NSq+nACUz7KJM05lsw3ile4PIELwFqj8jzysNX8CR58fwFWb/zwx+mD9b6OurM5ubmzOyM3U5RxivqWXQggaM6vQsB7lIJnA2L2hE5OznaPwDKHu4fYE7f331bHZBKJZfNLy8vf+D1DwDWnSEZMaEg4STd4f7e/vraWm58HGE31zfn5+d+9Cf/Dl4odFfee/fbW5vbTx4/pZN5sH8wnqFTMD45OUU/dnp6KjNu2jPVRUJ2JQLnq6t3eK7bUMNljovIpcOUm01n7ty/PxFbM4R3U+nkeH5+Hqm5fNjmjU09mFppldPu44M8hsoMOEaBx+ji8LCQL2ysra2+fu9DH/5wm9r9qSQa4E57cTi5PHGokdH9C9xOX/vqX3HrtmWhnxy2Zcyf9BroTAMtZ5VxZHjtHZSO3vjh7/ngD3/v06+9+43f+bNXP/Hhd//oa51V4nN7DVxWA1i4KYpZ7nQ0N50fvQp8x0zO251wWV58uWHSgJA6H07GQOBYxgPgri8pqTODu4C79BhjOsfOALvgD5vDPc0NRn7Q3+hYq/sk6LPxuWZzY3NpefnqSqF2QDbGe5Al2BrAioc6iQ4LOY8WGMdODLB2zIciCLrj2xPN8xilU9xgvW1kaqnAN+VL5VMEJCCvqUXsy6+mfAqolWzuX/7lmtUeE3U+X5icngLHA+jHs1m0enWRN16+mOF5HGvXO4JJFqlFIqpDGuYdjucn1eQINu4SG/eVFJNMUoY5dkaZB1OX1p11/TfX6c7msnFqV5foFlLgoyhWldU7GhnSZ/F5gsoXetV34b7ts5gXV+87Jxfr6AbkuOB5oynf3trKjTxE1DsfefVwZ3/j288e/8Vb5eOTGyC8F2FYNDAzM837G+8FrGhTM/OZdq/7ljJRvOU5f+LGaQCsZtjXXJx5nVXf2g55CtspzbB8WTPJaKYRYCBfFPEKt2+R7ZQCxMdq3y7HRedgDyswbi3YgW2aGuoXR4BjFmeVzxWYNfhTXEfVjc5omKWNTMVODjWeDrZkYYudGV8v+blrQvczkhAQuM6qCbl8deUNyhuoZUiAYzfYwQkpAPqdbZ6b3efPn1MckfFUWVxews7NZFBXAb7ArIvso5qdkyCZuB7A9NouN1LqClpo1LT0YqnhNthLgSLmp4Ns1FkHKdxga+XFTKp0/8GDDor1MqueXB9izdxAKsM9hgPJ2lAxdQFwp3WenZvTy8TCK9/7wUd/8db8K6vPv/VemOb3XgPXoQFe0FjfeWHsHZbOSvuT0zOZdGctNa7z18Gor+MCDUTNyQX5rnga6CYUKyps+KlegLF+wnsh6CM6OuomXzEDPVZmpnI/STKyAjrgQKPVGbOUwk3FzZ4EbnYrGAhqOna1FcJ0u9g2FjVkapkq4H4T4RROCCQ64A52jwIpFKau3NgYTvBksypUDbVjzDa8D6YlY2CkF3GLhsyk6AvgIESg+NT01OtvvIHfuZHqDC9A2C4FHLUM5AGKmTjqhBBRByOWHd5I0uVsEkyPwXW32lBtGCQgfRgcqJoUHLgkJ8lAsXV4mtk6HF3Mr40P0spKdpkHSk+emUYNDMUzB9sD+NjVKPMC4I6hZWtt4+SPDmnAsdbQ3h6u745n0rzVcBmsoeQPvAZ6rwFe3tMT2fNCZu/oDGMgs1IAsxJWi2kzYU6frYUGhqXZFfvpjK2BavA3Jg4iONwOFAwBvOFvbNNjY2msyFiCAe6xIi2j9CR3trdx+WiZo9kJPK53dncYEQuAjp+PgDuJwXsjZF6HUdwdhFvANR7coBb6HpinLaPQt2aFlMkdmzuDcW1iGXnLME9LCv91KUFBYBbgPjk1bcBdqD2IcCIWVT53ShH9M6j3L77yFwuL8x/97u/u1Ni5s7PtXOCMh+Yb6uBhh0+q5dKQyQ6rmV2G1nekqZCNU1S0E+/qwzAMvUprgGOtBewD0yjvxfPn6Vzx7ux0aqSVL1kHjNlF6iB/m6zw1uasP+U10IkGrvmx6/jWbdd40VB+6EMfYuQW7eY3v/nNN9544/DocOV7Ps13Umbk+Df/5t+49rQTdfi8XgNd0IDge3H0PD/LV/zKWHG6CHK42Pru7JpdqH5YSVxzY9RnNYHKaaB4nQdvdNrGUAHac9oSDL6LVQCijWIdZXwmA0PDvO2kKBTyYFDGTrTL1HAOLxGWLAhgqBmMq3U5ziJO4+Zk4RJOGzxRO2//Fa2Qur29hYwIK1kMjAN15SQTCw6tcpYsehBclWaHZnqZ+YV5UDoaiEJ4YHsZ4t1fiOUNu3Pu8PCISWmWl5capGyXQKmVlem6HIiAMQgR3EhTDoWt6WqcV+iDMAjYSReVglUykGjpyONEis5LU+goUJVJav0USURm5p+pZvWxBBo4Oi+sb20vzc3Zxyjp9eqh/ppdliJ0krwCLkvel/Ma6KkGOn0OUi2BO437pz71qT/5kz/Z29tjwqzXX38dxs3HcYonhDdC8DrsqTieuNdAaw0AfcBMfPbe3trEjDY7OxeAoRZFhv8bUaePdwtFDGpyd8VzzVSdrMBWgVuhdos53EcSQzzLpyASHKlp5ZiHpK5g00O5x5tJuP2NV1eWAZdgZYObBjbFkPP4sEh4oH0sTrQmGCo1lC50ynSlzJEDwCUPpcDVNNFIQQqO+ARYLU7gXza7cucuDvSi7HBtpYI/5PS0nNACP5laBK90C3HsTkKA6M+ZVbOzT6+UReI6jWnKF4bkahocYiXy0PFYWJhDUU4Q/P5rxNdBUyclA+qC6SahQXc7cse8tZBDBTPj7WcgaajtFiegtLXDQupk8/58XvfNQAZu8oHkyzPlNdB9DdQAd/cucZVgaP/n//yfJ6kwXipJfp/Ha6CLGgA6Makcb/qt/ZOR06OZaXUsm9IHkDVNv/ZEoaahDkMhANCQJUSF1BRAxgCOiHGhduBj8C//6TEgLoeUAtIxO3jCC4ST98uXL1dWVhLmJxt1RPM2BqBdfNl/GNGxnWtP1sFT5kxEQh4BsAtbm/7cTOLaVLBU2/wxPBZ6LkhhxndFRJqNXGXA7hQMwbhh8vDQ9tUUwXcLrkbqSsKnqgrD2suX8/ML4VGwB53fv/+A2TnxZ0P5bLHKP33y7J233+HSoIlCQRzG4T6ycxjUHl1YCRijLfmCn3QhCC9Nkb1VKxEr7KPSAB0pPGaXVu7ni3hYxZU7WPpJMihlsDj23HgNXFYDVeAOrGHRShYfUfOWOPBZk8VKEmf3Gb0GeqIBXuHzU7mzs8zm5nY6NzU1MT4moFYTAAc1x104iOGFLlDrLYlh4rUbmgDSmWOHuw3YRgqIoKagsX4WsE5TLTZ02jRnuk7CBRAcH2wQcwfdQmtgAY4OdzpuHIJXjXQnqqxaQgOCNwLB7R3FiYBd2FoDrg3/bmcA3h1pG07eRw45ohSKRbzeAeQBKDdkXh+PHUOBR4mAKR87OWeSKMrlQVEscIbS4kXw0S9OsExZoVDh426BQeQAd5ZMYm0kYDyT0OMR9/zZi5cv1x688mB1dRVuURrsAtsdHe2CqEuAJZMuhtuF6GHdeh1ccg/c45egaRxtbeyPnB5sP3jwirtLm2YbkERMjRMTfvqBAbkano3eaqAK3HkysXksLi7xuCavk6Xpshrq5IPXQP81wMt4cXH+9IyJI3dTlVMcA+LzUFz07ql58/dfmJCDAWUrZG9g95iZwYgNbRl3AWlsFcK9bNLuUztgmjYN4K5MyWRbXFxcX19jOaFk2V0u8QUDVCersbB6UJu4iof6Y50LW2gTLgCoLtE13mxrIswqEA8RZqU8pRkmi1u/dXKEapUzQPERWq8mGGCXnyQRMrMld5zf9nGWUuIVU5eHJaaWlpacazuc0P2IwffdncJuvlDYK/KdYPerf/lX3/rGN1bv3OEjAzlRn0jVaSykHmhHRuIQxEsyiUcJqgsz+n0TDZQq2bUn78zfeT0/2cHXpCaEriuJr0bXVZWvx2ugzxqoabx4h9Bi9pkjX73XwNU0kB5Lzc1M4tu7vXecOjti/goHfmwLKqoFRlerq650D0nX1eQPL9IAWNgmUxFUq8sLbgt/3BQKNH2BgzgYPp0WHsWhonbi8Doi0SGz0ODOgdNInSE5ylAXEZCs48gxIZ7szzB9TakwPUoM4boSnIBGU7C0JqIjSwt3HATTKhofIFmYB8XKFF0Rwq/F7joSQAeij5xp0hqbZJMUJcqNnilrkgJ3VISe6z5NQIFp67H+cIqAJuCGPCiThZ+woeJ/j8WdhX5AZkTok7//3vuPHz2+e+/O3/jEx6U5U0rs0XMySzH6C35OCYF8iExdVs5vmmhgfWPjbGzmzt27o6mjJqcHMskG43nsPpDXxjPVbQ3UAHdGBm0xQUEnnz5pYvEw9o1gt6+Lp3dVDTAb4JwmBEwz7fRYdmq6KDSWkGgMBCQs4bMNnAYcogN11oFk67kJ7wm9BxtFnJWdpszZYsGjyZu1hYUF/IDv3r2bVAsGIy0z3FVvtwCDGl8here0CLhHkRCvQ0RA1bbVfQBTdUoDb6uHArImd4p1WYlyhicFSUMSoHDgu028cp5iFCdAPSwuc7Vs8QqG223OSQ4SSr22ttY4GIDVYpcWF53jEGy4KwKsJoULwVKvOMYA2Zn4Fcy+tblVLG4B4vGDp1ulzG1DwJnjUIxKAHgfN2ebtkVv6cmT05EXa+ss3jKRHbKODXfILb1mXuzbp4EqcAfW/PVfv9XpXNc0nSzTzYfL26c6L/HAaqD6Osd4x0g4LRyzsYETMyeq5waWfc9YlzRA64Q/h8ApCDW89IbVqcDhdrIoAmSUxd2GcgIZhUsTG5KhRRGMxEyIknCEnEzBwa9mzGxMbiF0B7GDXXCoo1g2J1x1i6y1gVPx7DqUyArCsQR32u1MU0qzM8HGOc44ndiWdwXmdrO4l8sJuzcoBxWhKFed26JkCEbOljAWYHebtV3gXZPro9s0s8rgi88kM9MzM5s8zOsbztM9Ti0Wl3pNMklCuiSRvNYLMbUmZDtG8+ZHmeX5KL18d3koLXEwv7rKpDc+eA3cfA3UAHdmL44kXnjtzvz95W/93leilKYR2sNOsX5TOj7Ra6CtBmrAStucTU7ixTu/IEfkF9vl9Nku9tEQuzTJ7JNukAaCqdyF3sBvgugOyDm4LgBLzAXQKK0Zg1OFF83NPaHri1MXXtrf/va7Dx++eqH2DPsakDRUWY/cwzvddSzEc5VHaIvZ2ipCTCpqCvaNocZ9hcR4kXifxGnFZdDWYmzqgvnLmJ1dPRoCsF2rOzEwgJxx4q3iz549e/jwYd3Zw8PDXC6LRGYJp0sh8QSpEZFJY0i1Q2d/F3zPZJgDZ3JyAvMq00dKF2jD/ULSNezYZbeN3QLwinYqTNivNVnDEn4/Qjfs6dMnMyuv3cmw9NiQ2drd9ePLv7+QXgO3RANV4B4XeOkD91771IeXXr93sLn75Gvv8HkxftbHvQaurIE+vDUx0S3PZE5KE0/XD7OpI9YR8y/vK1/HgSYwlrZhpgGUM4wqft1lt63buCSBOoCigCNeGlo8tcMBP5iDsWIkGCQHTg2C8KlBSuFzRWpnrYQxcVsfTAZtoCKBDG6HJEkBm45G8N2yuMfNZbZ6XDEVFANGwbaWEpEigk70H+wsylcMebfLwd2N6DVi7TaoZWqqfsYPiO/t764sr0CMXlM6LSccqCAtHSe2rnZO8eHEwDlpwvByoclmsbprWK9UpF88QMYo2QbpdGj/gTgVXOihFS9ym+Pb2zuH6VUcvUZHh3gqdCzuK6urHV9H3XE+eA0MmQaaA/fl77i/8sFXJuan73/PGxvvP1941SZMoKEL73Laz0d//qYH9EN2tXvO7kC/C5kvDAXk8vm7+ZHDw5EnT55kc3kcaUZl8/ThBmpgbFRLxSGYQ4RcZm5QB/bCbRUYg+xAp4BCgDtbXDs61Qg2v0eP3k8A3IVI+Q/xJADc0KWwahWEwmH444S6E9qFIc6bSWfkBK91M1sKB0ZPnurK7mCsGvHo0GSGdAhrKRcLEAhRe4DdMbS7gaqoVfZ2gfc4J63ijDO53+BOyaJRFIcF0L950bAMqha0soGjugSITG1lrSJ1itwjI/okQhXSxEhqV+uqmiT1tZJHP6cJaVZRbQy3ywpFI1Bf6FYeo9i1w1whnbszcTikhvbous1MT0d3g+6ShCEqkzB/j7J1wHGPOPBkh0kDzYH7X33xf2w/W7/7kVe/8vn/9tqnPvLoK2/SYEd3+N2Pvv7wb3zoyV/+NZ9Lh0lWz2tSDUSXOmmBocgXR1Ss58Jv7+D4/cdPJwvjQC5DBkMhh2cyqQbk8WK+HMFrEfgG4jPsLlRsIYDDuGdU5FcjBGnA3SH+pDWF+ebm5tfWXjbOeBie1x4UaZBYlmyN/qzwk0uI0Gb1yXMY3phlnVFBWoF3efKIbTZQCnI7TCpYSqo11A6+q66zcycoZ13VhmGJhkcuXYDfofQQojsW41uBdZtHRkBaqJ3510kAXjtyrbaMSeXrVuNZZorBfA5qL7PwUlq+8iYUKknR4+JCgN1Z4XQsfUoGEo1jpOA3ZgNqpYgW2N1yGXYXZA/AuxuYqjEP3BiN/Ny2lF2FneXllUymcVXa4VMGs8owbajjO3gwBkwIPYGtwqBx3I7XVjL49OvTQHPgTv2P//Lt9XeefvTHP/XOH32dL5fMbeyYmnuwfHJw+PTr71wfj76m7mhg0NqG7kiVnAote13myWJusrjKd/zHjx5NzN2dnfCv8zoNDfchWPDkxMy0hlsN+jm463C7oT7BPwUy47KBwMRAjZewuFOWziG+19vbWzMzs610B44URjaju0F34pqH0fIHD6ls7+pkkEiKwVPHpNgUs4ZjiQiWqi8g3B28bHFvZ+kpyx5liHghj0FZl2AlVDgIjqvIN8bAfCN2N/QObj85KVOz6mgdmAEGhpvOMsxsjweH+0xlJoTOzDZj6gDAAYGIOimjKS5EJp2hAhJDRkwycjgJa6sWSHc/FbH/UGIjDBGm9q90NHqhtoabcIQmn28e50bL9+7dnFkl4naZwbxI7Z6TbnBsT3M3CEGjp7x2k9EuyTtsZFoaS2jhplfnN95/QXs6Xsxlizm2/KZX5t778puyevgwKBrgIUvyGxR2+8VHq/nCaPHvP3gwPlrGz2H70H9E6tf16X69wuJnmmhcWFBNVtBsOUOtgF+A/syMPZrC84MUSjHLLbbkyzGE89VJ6cT5ZTWnUMWgDig7SFm3NXwd4xlSesjDIFhrQROS1weXKVa5gdmoguCEqQRegM0G1QX97RciZFRgUZs/hoMwyNyOmRw7+Ulb0D7ChGPgchQSY6Ua5XmEznGpxI8VWPkRFNHR0b4WTd1n9Cq10MehpyJJTTJIuI+9HFXJ1cfQnUF3h+TtDoAOInGJsUTVZ781x/snY0+3ThdnsnwduklCt3vibpKcrWVJAgI6zdO6tiuc6ZSJhPmvwNHQFb2g/dp89HLn2XpcqoWHw7GOWpznoY1zw/rQNQ0weqkNrWIhVyw82D8sPXr0dIphhn5W4DbKGpJTADuDbw6vs3UPVLAV7DMg6DYMewSZkgV0CJa9nMXdKWZxaen999+7e/cedBpVBZykIoeOrWqsJ/idV8yIInM55mbw9Mj5qDPEO8DtYKj1PQyuindoszO7vDC9fNkt0ep0QhMVZo3QqxHDmG+a0Smd4xMAkD00tAe4PQDtQGRbfilA7TYk1eH2E3bQaRTQpQDvX7x88eDBK60y4K+CxffFi+dE+KILI2HPQHv1Dk5OgPGY3DlPPdKZsc+Gs5QK62YfRtU3cyK5veudmKAqi2zqj91OH3fkf/782Xg2f2++5eegVhdr8NMTzsQ6+IIMFIfRc9UVrngyexiGiderqqHJe+WqJH35izXQ3Vvs4vp8DjSQZL6wiUJ2onB/b2/3ydrhVO50cnLKq254NSBYaQ7lwmy1YhjgNNgcbjDnYkcWALbZBw331ZZJfARJxmI+fvLk/r374qE2CD8aRBX0JIxqDKmAujnA4Os+Kh94zMvgVHxHwPOa2zFkWFGA/ahD+I62xCNE8FQRw7ja2L9QubIYdBWyDYOSDLcL0yoEQN0daDAqCUDmAEs713Y5pWNKh5jcWerlE2moPnv2FFzeKH5Ys/bM7nLnzj3QJPECVcUCfR704xZbJeKs7da5ANMzYtX6DFKfui6NARGrP+udmOxoXsps2qFqJHKTUg7PchtrT1cWFtD5TZIrksUe3ujIRwZRA82e1MvwGWvALlM8UZlu8UplPWDXA/dEF7GTTF284J1U6/NepIH2Fvd4afD65OQIKzU+fvyIFdcnOfBhODUAsMM6a+hU8E1AHgysZ1SYz0IQA66DVoUDbTpCzMkcghcvJzfUWBCU5VRXm0xRV9H0hwrmiW5Q0hjCTSeVkisHAUZTWs5U/+7nXgBOFBiN3EYQhsSYl0tozhfsjv4DJG47aYIgyC5y9AwcXLfs1ajj0VA7hnbZwC0A2vEFMugcqM6o1WwQfGFB66HWpDY7QMOrq3dYsZtJCYsTRbzPXUVga04ZZHfeQOjEXQuBb5xpOCU16ZrWB5Mp1JtJaKLa2ALW1UrRYcjUl7m5xyhg7WQ+dfj+/RV8Y9ylv4HSJrnZbqDYt1Kkxkf+Emq4viehK+wiYYzjywB3WlO19+FYqEuobGiLdOsKDK0ChpnxTr1fyE/Y3tl+9OgRYw1Z9mWYpb+lvDPpN2jTwJs1fOETLGxsjiUOJnOA7RikCsIDEQMVwaRXVFkuly8Wy42TzMAMLAm2y8Qs7EtF/IsfQ8LBRkfk1bhVLM3M2Ug3Yux8NKV/h9qjomRRMIO0URU+txoMkhtar26E2KtBpnaB9WBnEcraMR4rcloxKG3TMgLXge1yYdE3gsjcXquqtZcvgeD5QtIpFxGDr2F0Bra2NvFQYiQr1ywG3IXfTWA2xnulQrZcPufklyi1DASvOPVL7GVnyuHAfYWgP8BY2IYiVY3cpBie3+tr64v38rkczVf8ug+PlMm45iIPj0ie0/5roL7N6Jyj677hIo5TIx0Dd9rQ1z754Xf+8Os07J1LOmglIk0MGmOenw40cPFVtBz7DbPKJKljZnpmemp6Z7/0+Pn6dDHrre9JlDY4eYDggE1DbjSz/LgVtJVHuAFkYi6A54Sj8f82S6/h4EtZ3GO349T01Ppaaf9gf2Ki2uuDMnMpErARGthgrCQsYT8GpIsXNsB0dnZEQ0saPBFSMJTSBIzKZlK4ygRaTEbBbRdCs3tkQHdu7PLScWhW18igrRC+zOqUs0J2ZLg/MrJrNKqNSDXcXioB5cWTmIhJa1edIaUkM0qk03tgPDu+tLysGQq3t7O5HEZx9EMwwdkQqdaFzZ8JXVWF9FANpgcdKuLEM724jWmGKWWyTDdZLXNDY3yxWT8Yr+y/uHf/XipVXRN9+MStucIt2FePjofIB6+B69NAkhuzI26S38HtgHvp8HjlO+7P3lukbmfqYFuYnfzaH3/9eI/1GjT32eLi4uZmuzF/HfHd7cxdV2y3GfT0GjSQ6JolylRP+tLzhQFEZiZz0xNZ1pHB+j638rCYueSUI/U8+eMea4BxjQxydLiNqoTZ2fEfx+0Cwpqm5azMeu9cbcHkAOBe6k6Ly7SwuMhko0BGBs8B1ons7+0XinkH3MWLqhCOxDPGJleUPd3FxEoQKCec7CC7SzNWVZUhFuEW2dxDfE4sMMBbTAfBWSF0FVNBlXGI3UzswvAOxwvCcxAZ2zVru4LZ20uUMlbEPBO/7O7uYRLn4wYeLEy6eq9hrSVX3YVb5OIb10RxYn19jRlmMLoD3x1mx2BkF860ldJoBDLrWkHUNhJHFbCRWIFw7lgpmNttThm3xpZNPansNzSUznNrG5sL87lccfmGilgvFn28+iR/7DUwVBpI/rZpB9y3Hr3kVyf4u3/8DZeSz+c/+9nPvvXWW8ldh+tIXe0wuYxXq8eXvpoGEl2nRJmuxsfIyBXnCwMksI4M3/Q3tze3DnaZTK3p7NRX5dKXdxro0i0B+Ds6PgwGZmruFnBegN25oNVg3ifUDMAjUb4ZGhx6mY+K4EOZ07Ws6JmhxXOGTGxubEJ7aWk5zdpBGfoSygFvjoFAYtCljmV3t4g76eApcXOIj45q75MQqhpClbQG1KvbCLczH2Kw/mhQqVxwDKuTRbb2AMZbtOrYDmI31/YSY1KxwxtnBps1HiCN1k7PKs+fPSYJ5L2+vo6YKBCH9cy4kDf5q2UMXwAAIABJREFUa/ltd8QUM0zqtLS0tLm58ezZM7TEMFWIhDBdZVGsyBoZtgF1Q+6WhgaUC8bcT3qxLgrCcgE64scIDtMG/Z8VHtxdLKZGtFb0LQk8ULdEUi+m10AVuGPbmJ2d3djYSKgULCJf+MIXaD3vt57wKyGpZtk6aOibFfdpvdXAxZfn4hy95bCRelfmC+OVPz87XZmZ2gCLbW4wAi+XyzXWdUtTBu+iY73GCQS4ZqCuiuycuVoAjv8gBONQSRPSZFRruZzkngERAnrBtbiTOLyM8Q+0CWiFiLsTMHMwVRGIaml5iRRwsJxPam2ErJoEGgWg25hY48Cx5xCr0QoIRnTV03A1wAVxDiSr7Mu2cTuX4rYgc1fEtmZxdyA9BO7uyLbqfZil3eZsx9heEnBHJKcf4w5Hd4WXa/i1T0y4YdyOdKXCO+Lg8AD8DL/kAZGjBzB9CLRbPiW4Zd+7d5dxvWT+yz//88JkkQjoPYToFRZvCnVQc89Rs/tJMcRtJ1UQZHDXhzLKtqx4yE+UzzNP33+LRqlY0CfxWxVu1YDjW3VlvbCNGqgCd9rBVx6+ymLdNHGN+Vql8H661Jy4Na1tK+I+/To1kOiSJMp0nVx3UBef1zvI3TYrD8vC/AJPysbGOn1dLPFDD9+H+cq2uVYO3gmxBe0ajRuikqxITTAHFXJFiW1uGLJxFrAuHDxiywOl06B8yrZihrUB8K5++uRpsVgA4/MD3SuzIU0IAm1DzA62dlZq0XM0bduEvLXWarFdBDpBCKC7Hbn+hPoX/MuP3zHpWnqUg0eMBqgqhBPeaK/OhdnajV1D7RSHTzkWwYvYUT/j6dOnCwsL0SOABqmCrwbkzIxU52/BJsrkMVStXg0hO04PRzRiAWovX7xYXFxAbNjjLFq6f+8VVmba3to6PNyjRkQqTkyGbkOxwgbUTTqymEa0D/6te6WvDXwEqKs0TmJo46nN7b2TwusPXsGJaGiFuALjjFf2k39dQX++6DBpoArc4Zp2NjCZdEGEW9l4dEFvXSaR6DIkytRlxq6fXNedIHn9Y9wCCmysr2N9n59fzGb7N0fy7biIl7htaNbw7nDwjeIC7EbF8A3XsBqAo+4MRYgcHR7NzNYsVQMRjNCs7MlMKzhwZACf2bGq/TjAw0aj2QZ78927d996602Mx8Bc8Cu5hDDtR6WwEt86zshDJNoaYQ6Dygx8Ky4BbUukJsi5Wyg+ZnEPyormmCbBRD/OqR0EH0J3OfO4zon82oFFjEk9LRtoB7XbzJUp5uE55dPTd374I/iyGGPaGHPkEDdRIhFsos4sCjNQxh4PYzyVOcai2hQxpFD78spq6fjIiAh2kwFr+0x+hgAPG+vM0/MS8zsZ2gbVbahdohtqDyzudBnCW6AtgeE5Sb/q5ebh9PzyXPrFDRMt8UVI8XAlzuwzeg0MtwZqgHsCUS5qLROQ8Fkup4FEqk+U6XL1D30pXuC9kAFQxQBE0M/mwfnGxlOs70n8KwJO/PXqxSWppQnww7zNBeIG4BZwbu7gGwJIOUCiwsuCnmTDp4MToOrt7a3Vu3dIpFjpuAR8BT/j/p7N5vIFYdfaehxkCu6xJreaJUEWD5A33/wWFaaX5DYjnsSYLO4uiDED8UQCzF63Cyu2O1p0XcRtEdQRhKaT2UztVgcY3fTgCFi+c+A4BMzcrmlkDLibvZ0T9mlAHjIn9FVOpCOztTv2jjW9zNnrOLSoBwKxkK0IPFp/Q+xFZ4xF5DL0rFQUfmRDWiECi3Pz805qlRJ/EkUppm00f+fu/bm5BY0ECKbsDK6Cq4KtyRlsIuSuvov6J7K43zBXma2t7aOjw5Xl5bGxUqTmWxg5ODjAWesWCu5FvoUaqAPuDa+iW6iSaxc5kdITZbp21oeqQt7ZveOX2eoWpkbPist4zpxWxhfmphiV17vqPOXkGmAwIphbUE5lBO34gQUBhOBhh+A5lBmZE0y+KP8NzR3OzIYUOgZXlo6xCjM3OUA/AqUNDIg8/QKXHuyiTJysJqUKhSLGY6zO1fMAdxuCKUYsiCUDvo3beCnFhZmtduHckQCsS2CCHYVJgq+s+hSM6jQyeH4bnDWLu6zscpFxW9nbge6l8kkJ2ztsqJ9T3UmeqYmJTNp5nnAoHi4ISFT7GNKtKuTzlVyOB4evEHy8wvcydGc/1yhUhgvHdCf68p/B4u60o2NXqQhHLAT6MOAfaELQnTLZXNblH/Ytl+lFaW4yX7kzOzPsslydfxbNuzoRT8FrYCg0AHCn1QsavqHgeIiYTKTWRJmGSOjBZRWIkJS5y14UqmA2jLNKav3lCxapwV5rxsik1fp8vdAAyPLgQBBcxthYBWA4UKiC0KgCcBCc6FLBjsxyuLW5ydxBzOXP2VjR+mhEtnkmTsdOQArXE2ZfwUXeoVgHLPFwd5DdwLGirtLGbVR9JJCLGB1IhjhdIkto5ypjOwF3prUJKBjKB6mTQRZ34XaAu23ZlE+dkwx8wj5acqidrSKpkWIhL8EVj4kXMdc0YuqtO0MtzPjEzMKiZkNaT0osTKVcTiIH0aNS8JrOZlyGhoqliVBuJ70jAmqnnMYo494UkRreCOs6759kVua2rZMzvHJ0jXMUcsO+pXRNNZ7QjdOAWnDaPrWYPnSigYYXRrPCiTI1K+jTeqABGTi5Ir2/KGOpyvLyEkDh+bNnwKE7d+4EwxB7IJQneaEGsGRr8KVzERGuU3Nnd4FBTm2EkfVjbkTZ4QUUgamvfeA1eW4kuGPMjNzsxnINa+0Zlu2UJfvkhE8Bjnns4NQboPZwFzBHDou5fb2whsxJNLGCHfH6ECB56YAxqLZuaMATORmAirzmIeNcZQTcBduNy3K5RCViythwW/Jz5LTo+KwqtZ7F2mNK6QpUA3MzscAC/utBUkrOyjw1UCejmwxIVVRLyLuGByqsNzwhqo4yW+lF5fmzgAIoRWBUgj4RhIWGcY8UzzaP86Nn9xanE/Lv9JIw85BmY068IeXcs+010KkGBNzjT7Way05p3Lj8iZr1RJlunGqGWaAOXM+7ISZQ587du7xlge+p7PziDMb30NLZDfq3hsZVnzSBvsqI5icXhgvwHGicdH5VQGro3XLYGa4fxsxguGpzZcebyiZccrpJqmoGEiuYxd04EmfOVcZBZOHkgDORcMA12BovQdUGTZVgfAcREWsSnALAxPilRHxBAD5gifTA5I7RXdPM24Qy5mLkejXU7jgKsLvkEDZ2arQFrYyzhk1VDdK+hImyoAOeyrxNqFrNxkScJS3kRLbjo6NqelTMOhJOG0oL6Im4sLr7ac+/BcF2WzL2/DzDaOLkX96qNQ5KbO/obKc0tjqXHxvtYFLLqsYHRY52fOhCdh5YHW91VSNSfPAauPEaqEcS0TPDo+7iUeRm6CJRE5Yo083Qxy2S4ooLMF1OUw6+g4OYCgPgs7C4lL7pqzbWKmogniUwOPZjAGvYqtG2wRjYLwoGRw3qwb9SmR3y3A2LrBEoaiGVrWrliIkZ5Yil1ZBIjZgxu3ySPnF5HbysusoIvAew3fHoWLIaxVxAjWJhDEaCuDnKhIjVEZax2cWIAM+DQuxIZbmoMivXyNNdYB3ULtDOPJBENKI36j6EtnZUY1DZ9AcFpZMQMlUlHurazjjuajLRlWKW99kZGUo5XT1n40e3t3dNihHGA7DGH2O+I8rIinrskEIq56jblo1+AWw3Eg634wMEemcCm7BsRG84Ily4teOp8dKje3NzcW0NB/edcFm9ExKXooi3uCfWls849BqoB+6RQK4p5JAIT0W0jTIMYCTRA58o0wAK51m6qgb4In9VEpctj6199c4d4ND6dil1urswPz9UzjN9fmauXj361/jUAL/Gr6IgugsAULCdEB/1CbeToEiYO2gRo2NLrz1yWWrTwuLah+dTYGNWIk2Xrfl1mLpSGTvTFOlxuzuMxYG7CBs/RAJuRDSE77aXuTlA6RzH4g69O+AelnClZfgfGZG5XXDdwfYyR5SWDsKAWqjXdKJdJKWzw0OhLsBhmAdGLBoek/PwiAkfK3Ozc6FOSLOoiikfEz6yxToOZH/y5Onk5KSbiIa+hC5OEKp1kDkgILHtP1SstCDBNSWoJpuPsaFSwxAOTkY3X7y/vDIyXgS1+9BEA/i4M6tokxM+yWvgxmmgJXCPS+raVrd16eE30tj7I16ge/FEbWyiTN3jyVMaTg24j+995J1JrFfmM+WTsbW1NRDa/MJCL7/a9/yp6HkF3btU+GMwOUxlRlDOQV32eMEgAggQGMgPAzAQVrjRoCMpxNmGXAQRIGGY0rAnS+uT5HbnGRvKCFF6cYSIBIwxpbmAu4PvilF5ANxVt6AqFIwNcRUwogoNiOvYDkLruoQNA8jV8LwBdxJdveyoFlcZItjXzeAuDxmhdi3roSotCEurTvs3OYIN3YzsePNlp4xFV4/xHFPN7u4Oy1HFzxG3DpORNSmCs0wcmc0ykPf5s+cPXnngEuEL5TgOHGmJbqVCeYXdFUxu+mvAfXokZMPiHujQ0Rr4LUK8OMimT9bu3w/EH3iW+8MgXbv+VOxr9Rq4dg0kAu6NXLlGVk2lhfqGOUy/cB80u+3zJcrUnoQ/6zUwwut/ELTAqj0rq6sYgFkhcmy8uDA/HZ+drwWHXX4GukyuBdODk4zFff8gmsqddks/ABztGME2KeaBnmaJn+NjXK41Q6Iz+jZoqjnsq2sKGyQPz8vyXCxOfN/HP/71r33VmbpdXoFMN6uM4LuAqUz+ws4G1sWl2HQHQRHtIvgeoHbwq+BqFCzqYLsDsfqq4MpbcTLSf6CImduF3XHJoB5VHOH0QAnBrlp6dPSND36kbvLsGHGYDdiz7ka1OJZRE8g4CMmpn6TrUgvITRD6t6xwtrS8hL1cWmLogYJjMCwvrO7iVkZI3iLSAIWcj1Clj5/dIkaTRw4PD1lvauXea+MTA9F2Jef8+nPy/OJVdf31+hq9Bq5fA5cE7nWM1jbWan2rjXRd1sbDDrI2FvYpXgNJNYCnbNKsvc2nO57ZLVbv3C2fjT598j4m2KWl5QjKJKncPzRJtBTlAQaDxQXfDN0J0xlIFBI2c7tasIpQHeePjo5JvXf/AV9I6i9KvKWLqBNpuB5hxmAfng/2d+/em5qefvNb39zb3Q3QqkCmTQcZTQoZWrwhbmyGqN32kiCgbSKJGRcBpYpWFb6HsN3hVwzPDpQ79snH8kqUcaNRyYPIUYUuT7it1jc6Nj4zN3/n7oPJqanwbMBOKKljT5TqAlQwe0eJdt4yK1afW5fMMDfzyb/37W9/8EMfol+hZVPNxz2W2/GGNALspgqpIAhmcecG4ARrZ0VVD3KELuWL7ZPRk/37Dx7wNWiQWR0Q3vzKqQNyITwb16CBqwL3WNNZ5bZpYkObXM3vY14D16CBOQ3q6kVofr8nqWl87Pz+vXtY3x8/fpYtTi/O5M0HIElRn6czDQBHwXwgVEN2hhRBeABLwqhOYdYFEeL74XJih3ZQr6aaFpfawcYAgluBMGNs/GpEyM5NTU593/d9/Pj46Pnz56w9xMzuY+eMW8bHPZzNXWbl0LRsbMrGrEhAyBAqcdCpthbTxoB7AN6DA5cUgmAyuUAZCrO+khzAT7G7a9CnVRLmCOvi2MmYL0wsLq8urazifeRQuUsng/KGPJlmQyLh3uWsIRkUrsH3EUF1JxhRyrWR+/3Ji+d7H3j9dY4yOOfEFRHStz2lHVwPdhwIt9NrY1RualQ+7gMfjs/SL569v3Ln1exoMGHowLPcfwZZ0WxiwnvL9P9CeA6uQQMXAPdYI9uWmaT52hLxJ70GeqmBTizuHd/QHReIScqouwf374DhHj9+hN1ofmEpgfNMrLyPJtAAk6bzage3geQsu9vquoGPWTQeX2rgPEgZTwwXyqPC7mF+FQoxZbg3QqIQRFrC9DCj9lZYGzB4vlB49bXXHj58lQ/9OzvbLBSFLz4gVTxZIE+I16NIQKwOr0PX/cnC7kIE4Q2ykyaA7lxlAo5ZPrZyUjomI3xRoanDWAsqYVGqTKFYnJqenZ6ZnZyaRoH0bYKTtlORCK9HvQo7VaemoM4os7pNcUqBel0aLPNx4KTE2kzH9GzpRx0dHu3u7o6PZ3B8gtWakqZVCNtParCYw+7SB3Af5E5ZQl3BgTrkcjAAppJffeUeMxuWBoq3AWdmwK/sgGvPszdcGkjTStY2ns34vzhHs1Jt0yBJ64pfo5tvoG1ef9JroAsaMB/3ft3vF/OP4++DB6/g1fpsbX88dbSwsOCQ2sUlfY4EGsjn8uh2amoqgHcqohbIQU0z102QoAYJ6C7LOxP/pMGOGrJpE5JQIGwIbV8HSx0PYQ5H3aVZRWE0IFJDAcM6Q+vc6DpgJjXC6t7+Lg73AFZYwOQMpAtqgGNXi3GrRBfReeWSSwx722gngzMJBt7Nfk0OEdC/QfnTU7SAtIg8nhnPF4uMNwWj49CCJ0yxqOlcnGsKBWJBtQYhhtejNE4FQsaTlOq4D4rGTzo3d1LgFcG5KEzhenhwcHh4gMmdAjh8r95ZhU8rjB4gFVITIf5NA2wViQIGd4B7ZTw3zjUNKh68XalcwdDOg18oeN+Yji8PfbOOy/gCXgPDqYGwFQtbv2uTwrXXEWqnfpdybQz4im6bBphVJvaeH1DpCwojh4ejT58+Acrj3uPhe1cuFQY5oLBBWME7QbsgpAQKK0yiollmXDI659Bh9/NxmX7xsRZ8dyt5uoKxNtNoRQRDumEFLY0TIYWwpPaMSB3PMgJifGZ2RmzCkS13CpMEJpEsHZesM8FasM6CLkwPagHcB5Z2wXSrW8XVslo9Gus6Np7GYE6M067yfKH48LXXstkcZnWs6ZnMuHotyG+T6kQSBOTYhbyKgCMRTwvJxtUbZaspHZIWjVDp5IR3iVI+LSHo0fERPZiDA7bISMaXL18urSzpy4BCSDhgSVksm4soHnRXMLar53POGAYVG7zAZdpYXz8rPGDqmMHkcPB0Vs+R11u9RvzxzdVAWo3fADRl1ddBTNfw1TQ9lsVHvQaSamCIJpQw+F4Aszx98iQ/vTA3OQSOuUkvQ5/yCZezfqqtwgOwVdvC1oy2+wf7MioHgXTgfeCpQiks0ZhpySDruwVoOIQYiWIuMg3NaNR4NZyhYHSSaHg+3EenhU1TI2OazhxbuKvOCgalhWYRgw2AF5JCrupbSKxahOoKUAGy6UNnGADrr33gDSmnNhiReJIlNEslU4xvjhzx2rQ4pfo4qFW1wxhmcRQsW/tJiX9wO5AduzsSOnEO9/dxnnHAPSBDPQLoBBJchK26MO5Lg1HFfg9wH5GfT4Ok9exc+7EmmNobm52YKeYO6nV57cwMb4V1N/zwCuI59xq4UAOhxf3CjP3I4N4A/ajZ13kDNdCXlVOvokdmN+N3cHz2+PHjYrEwO9ujwbVX4XGYygLaAIWMqgTgCeOJd+2w6ebzBXvxAwOFOEl1gUSM72l5FFaw2QtcapQjBlxZvV03wJCqStWHMK22HQuOgpM6CvPFy9d7y1dpWO7gtMC5A+i1eLSaO4DRIWnXdTnXNO1RkOBVlN/InssYVMtBjLhj3RKqqc1zVqmgcatO+bDrE+yTAt73zEYpd3Z9VlBgi5oD9YQc8i3CgW+xXcONu2LBtQt2+ioRXDAYnxi8qb4Zl4ykd1ZWBrBH4S7ZsGx5GIeFVc+n18AVNTDQwL2NbN45vo1y/KmmGhBiG8JQzI0V793b399//PJgYvxkxpaIH0I5+s8yVvPj42N8QgTxYMd25nZyZt4jMQQrTOiAINvqUp1Yvq2oDNwhIARbCslzaAWcmFUYy7ERc+nBUXTAuZqswQmlGSy149qjIEuTglVKsRqrZFTQTPGGm2PpDrcHpWPplr9KNKjYvg/UpFoR29QkByJYmm2cfkPALUU6HaI+TSDPn3A7sF0brgs6FsNGx7HFuANm2YdCQMQ4RIOQ0hVVLSJrPzO5s4GKDPm4yKdy+QGaC5K75tl+cWZ8nKVhA9X63RU04AenXkF5vuiQaWBYgXvd12V7LwyZ6j2716wBrK3XXGMXq5tQGNndO3v06BEjLAlV7NLFam40KTzHDw/3K7OzDtxZo1FhpXSMuCEUdCjRgWbDjEKCcrzGNIzpHfXYzCuBedSQujYAUMOgmrUFHM+OQ3c20GhrnO0gqbJVW7FqmtKblFXuaqagYDWh9rRoWLB88VxBArbrUNgwa7hvB9PJU2VZBWoyu/tTSgtjlkXI2oaK4ncOYmejobcagcvz6UbiynRqhAPqAW8UT2fSTP5Tc+cHedgFcN2pPdgG10VXBH8n5vARDwMQtvfPDtOL96Y2Uik/g2F3rgdfaCYn4zd3d8j2g0pwT/ejal/ncGhgWIF7pN34Pc5TGz+M8viI1wAaGOQJJRJeICb/5gfWfPTofZb5nA7XjU9Y/JZnwyZn1nGHqoX0wHoH+/tayFNW6AgQNr7+aVfk1IEC5SJuWcmNnd6BSndK9CwYXHQeNZqF0B3K5xoTMvZg+zM7cs0FiSHxeLoqrTJUbeBiadVoVDBGrLaIYzjK56SQSAo1yNtSLN02VTKW086at3xQuygFitHgVzuyTKFWrHsjCO2Cfadw1yNMIofBb4fBpaaAdtivYJgKF9FqUlVOMXZZwiJwGQPwKNw5ynANGHebSfd/Lkh6KM+fP2PG8TsTzI0Tyue06bdX0MANWoBpAO+K2qf/CpfJF+2KBoYeuMe1UHdzcfvXpcQz+/ht0wDw4GaIzLyWhO3t7fde7s/lK24awZshWk+lAPABIkFOgpLCdyPEgY7ykwlA4IX1q0UxpMgmQO3szAxf87qN0GoUcfDdgKTwpFmdhSux1zssL/e/qMWqtlxVskqrHkWsRug23t5V88VOWxF3Jjrv0HZAzFKbGPipOYTJUpWB8gA+6wxBrNnWUXJSSzqJbRv3GSL4JKFpXnQmDLogVfaJu5+ohiIrUpyYwFspqM7VpK3KBkVEqKpyN0BVV/z8PF/I973rvlvK7Jdzq6vMaHmj3rym/z5vWAmhWJzoMxM3tvqovbhOCd1zfZ01Dk1dN7n5aHrZuQGbpg/NFfOMXlYDcWBxWRoDVG6GMDKyvb31/vvvzy3dm8jVTwwyQLwODCt4yzCdIsY5kBxwe39/j+VSuTFciMHEOMc0GCB+g67WegQvMbUjwpt2mrPaR0HlY22Nw+QRoowiAFfFgbbhTtHwkJMOhXJW9ejPhSBmSbF3qkXDPEHWujQ4hH7Y+QDV892A4IhwUqUCsCxhdBROvqhjS3CZldMYkBpgMOBa4kiqAJPTXXaxcO+OVCIMYib81TAfiketqrIyMjk1IV51HPGgEiEl24eqDBiwhZzIUyioLJG+BNTwfKtczOzfmQZc3uTXbl/US6XeftEvzfes3ugZ71kNiQjXtEmJSvQ+061rQaKL4G6K6LD3qvY19FkDDnX0mYluV89Y1enpmZ3948fr67Ozs97m1F7BDFA+PDqcmmYZJoXdnV05uBssbFKQ1sHguPY6bXtL1EySZpnWRujXndOBYvybczezsitYae2rgcpr4KbKVEPtUW16eM4KBBW6HO4MWxfEh2O8rq07HGNJoyOgJI5X3/Oxj4F4lCFkz+IC0lGAem0cUG7ck8tQ8v/P3ptuN7IrXWKa56E0V6mmc86dvr69Vnev9bX922/i5/Nz2E/Qttv3u/eMNQ8qVZWqNE/ee0cAiUwmKVIiJZICRAKBiEAgEJkkd0JIJBQMqRtmF2IGXIcac2rHRMMKEG26WaNCRQpimaZ3rX9r4D4Pgu9KKBkJGvNOrGTvvAJCwup5iDDYOIpbJr4dXXzdefPw4aOJibyva69ijwWE2BuqV9az3fsbgfR7u+tR8K+9Tu3eO+AeA5QGDEcmrUadTAxTBIZmqUzloADGPFicXV548hnp4GB1HpOL+QesEiSvYmOZ/f3vNguMFRQnJ8czszOGq1OMK218JYSvbJJ6h3l3VxBkT2iAx3AVINTp7XljK8GmFsWLJA2hlGWT0JOIU29RBkOBecGy3DjSc2VXtFqwkH6ZySzFsRWIsBo4MsFDYu+lXIzIJYG3CkFzUAGRCyJTJKRMwmRhap92+eIfX/YmbXXnQsAkPamagnGxmf0E1rjH/w44l/pmCH26g+odvmlBEhbnnPLRsHfyocC/dd4fLkwfv33y5GnhcKZ6EAFMW/TAajaZI9DTCISfmM46Gb2/wD0NFL7404RYVjipNNMDGoHh3i8M0ASPWcVO79gZenf309raesBnA3q4euI2ogTQicm579/3sTUkHq5kAJqoWjgdH/z0q9S+B4wTRUY0ywUkw/Q1B+HtVDitTtCzVqHEpTrE8XKHrexLyO6IJQ5WorUUnjo7LRKMb9qmr4bUC0xvo26MdtRrBc2AIiK3ZKVYzkERraoTV+GAI0Jnd2gUMlTJkRckjFYDcMlOU+AwLljrMj9nW8owRmmAZaRwytywyXaupcddDGd4hha2AU1t3wKNVdc7n/ceP342MXvbXd/C6Pqti93d3UePtvvNq+xPjkAvIpCBe01U098P+/2sUcqsQYsAHnQyaC5fx19tC722s7MDBL++vo6nRV7HypC2AeZbX98AyMPE7fkEtgjkoucyCAQDCDLw7LtA0+2EmSQgB3AEFq3Ng5ZpQhcQXI3wTaJrArNYVMhHMnsiYRf9K0c9LssW+qUmcbC9A1qNM95k4Mok8IsqlvRbiqKKWuSDoDPsQmSpN/rJ5IOQjOHim00oSEpnu74JpKM20YrkNMkOLcmOSHIUO/xvxLaUcQa55iEYaomsSPqvgK2TwZ2p8wu3+RgHHPR3H3Ynxs6eP3k0MjLAu9DqCAxGlmfcB+M4ZS+7EYEM3K+IYvghKdT4c1HUMjUwEbhXM9CA7MAwn74efvryce3BAnY8HJgPBpYZAAAgAElEQVTj1GNHAYmJirGx4+XF2NiE0UTJ+lwbRA8u4INuH/cy2zTrcvLiahlUCNsNd9JkhKXq379FsFyeMnogziUW1pA2xO9M9SXjUqIi3noRqlpLMdkHEXJIAvHcZbFViu3pibcPJD2JHZBmYh5csBamY50Hz2gWr0qVHL0ppaXiRYkl8VQlpbT8YHlszP89YRzlsiJv3JD+T0DYrq0nsU4GFnH5emt3ph5ezO3sfNxa35gaKz2hNvE5k92PAP6Nlr/luh/WbLEvIxCnc/rSu750yn9G5Jt+cvvSy+xUQwTwn+sG3jAzAEnXH8xtb61//br3+vXro/P8YS8ON1Y+f/u2xztIgZk1uy2IHRSKDzmp+AYhGAmIKHZDHkAmcW2kZYBNZV1tSmDVRLTJVirUlnzxxDQJuxXTlKktT0IrbyOmILCJpeU6YqsDypRojMlsO09199oUrDNv476pHcWybqWTCUdi70qdUNHe6oEVvszPpAdrA+XREdyEPTauLWWC3Era5kupWCejFe7cUoYT3gu38sAyePD+/bv9s6mnm/MZtZePUs9reVeZnoc4d9A3Ecgz7jc6FOXfGf70Vzg3sp4bdzUC9/ObHah0a2sL848fdj+dHe3nxTM4p3Bz6sHBAR6CA8Jm3Hmi4aPLC/GiQIWfaJ81Z6mJdxFic16c952mOdrjj0yzxQn4IplxijTpTgHMefKvD8y300ZwxLmsi80+wxtkkShmslxmC6FLQlfsNkmBTVdCe/JISxNUIvKWZNrLilBlGzVMFcyOzNESde1tLM/VSWgmOfsCF5Adn99x28+RsfOoJoPwFfm+uF0rZfBUVnte8sJiz7eUOTod+/ju942NzZmpL3A4p1uOAOZl5ubyvQS3HPXc3d1EIAP3bsadvzAhxd/bwMjlHUcA/0u9Yw/urnusE3i4vnJ+voR/4u/snAHKD/etuq0jjR0hsQk+wPXLVy+fzj7V03BsMQw+wYYIgTNJsC4ITj4rUjAtqQTsLiH1baULwCb1aULN0YKkcssER40MPZqGqVZoKsI2/kgI5aaQVTBZjLLQlM1iqk8LaUplgU8e+7Muade7p0Jkpj0bbW28oTyVT2piXFqSCZbhJZZ1gn708m6oezk7Mzs9M41DxkQPkMwOCdFsHBIXymDjePxfBXen4plNeOCwmvQoG33/aW906acnT5LrwB51lc02icC9WgnZJAaZfV8ikIF7r440f4jKCb83jcyySq71MAL57iUgmK2th5x9//DhbHzp0eo0OD2MeF+aPjk5mZyYmJ6Zwdg3N7c+fviw/fhx6ilQo7Bh/LA69E7m24EPgR+BLm1tPFob7Xmcm6dAFwDCmjScdhTo2BHFRSWIQwnjkqJzw6wBpRYl+A5nVVKA1iYPZmiDr5jYKEmpNq2wV2ZIpJicINNeLgzVYI9losCGeoELgiK8nWssa0BzTOqIytpSJt6ZakLTkD+e2XJ+25LSJtxPT/F81vPZ2fnpadv0M2naJRKn07uj1c0HYzMTH8uXZl3qYIDM8IDeWcImUfgf2p11nzvOEbjFCGTgfnvBTr/W7Be65e/07Tl2T3rCfmH3ZKSth8nZ94cPAXbevX0LzY2trYn7BN+Pjg7X1teB2hGH1ZVVTMx+2vm0uraKLd4D8MIntUDYrMS3ILmAPeJH7F6fA2yqDaCnTdjzs0+Uz1KAlWVNYkcVtpqIRwDr+Bb9kiqnlCNE7OKUDxaqfMUE0hStML5zVKGCZMFzbxwseTdFleb5kr7T6lGWaAp2xTcVdyX26WpkyxB0FxYXcMg43R4n3CnUSzpq7Cvcido53X5xenqCbhawxmaiFxeoo/j/1enJybNtnDk5NZ66txiT0ZHb3DXoFgeWu8oRqIlAvl+tJii3wOIvl3517Ge6+mN9Cx7ciy4Q1+K1soJdznPyCAAAPdre3tza2tk7e/fuHXa7vg+hOT4+sl0FgdqJ3MfHNjc2gfnevHmDNbIA8cKUCRxEUIQyWQZCnMCIMFRS0yEcRZUA1d7W2AGmLJpYaDPJ4pwxeHrmaCEr7fFIPQephUYDxc6V2KP5EjiV0g49RyCvIXWONY/VaFHWNMY4mhrChm+hoGG9ZazogSbdbOzTTYHvrJERPCEYhytF7RSZG95cvipuRO1C7kDV0FpcXq42NLs3yE9OTl99HpudW8KH6AZmctOuRcBuZuiauWwoR6CPI5Bn3O/44PiPZfDCELz/XgVmLhsicJ0rnfu8xr0hgM7ALObDlfGzs3XMHY5OzK2tLAzx7DuQHfbyX9/AdDsgIK7pRoHdL8cunzx9+urly99+/XV9YwPPsMJi2XHsNhNnx3muYWrd5s6d0EQ78aUWy0hI+Ah86Io2425T9fw4c8EMSsubHA2Tu54aQZ+lJXEsQ17gXQesNUWBaGlGOJjtPcHTkKytqRvPOapYb+CYFVeQQbBavOS8WtNff7sj7MxImZVpdQrzbhM83GmKp9sChGPGHcG1ZGGkEg3QUJF0a+o5Z9yxuP307PQUTRaXls3nbuWfPu+fTqxsLx+NjeW59m4F9aZ28FV2UxO5fY7AgEQgA/f+OlD4Haokwgb9PlX4w1W1UfZ2TPdzV5l2YjoxMfHw4aPTi7GPH97gSGxsbY+Phgf2tNP+1nRudpocHhzOL8xjqfSYnlgKSMc53JGxy/Hx5z/8gH8+vH716ueff97e3l5aWpqcmsI1DHSILwnyUfJOVDRIl8cAOfrKGahAx+RNETqiyjHAJDC9Fn2Q5MIatAaLhXpT3vC5h322Drnh1TJyJQ/o1SfkTSNMz5f1ZSgooGsbqFtDYT2FNqhxrNKLjUgYTzKj5XWhSIOyZqVy8UxgDV2FgaECSoD1o8ODA/wb5OJsdW0dCyEw427RoytM3jUacJ5dkN0y3ZnKZ6biXyjAcwsLC9ZA+Y3OIUzrYoEZ7mxeW8QukxkpJnG9Y5KfPX22bsUPfjhyyhG4swhk4H5noW+z48H/irjRL2WbUWpH7b7t495OTFKdybGLRw8fnpyevf+wM3pxjJ1nBJVSlZvRd3oi2CTs3NwDrrjgmgsmAEGCd0AwrJpZWPjLX/6Kk+TFiz/ev3+PfTNxpQe8CKDPyTy0ITLQHDrBJVoLKtCIBAXTRBBAC8KCUPhqPtBgQZmz8tBncvtW8RxyF5mCtOgC3pZA+VqbwIklRPEVmU5QAGPBHrix6m0opbKqdELaIgIJWWCbnurSk2W2l660pMsm9ic1SKGCw3R2eoLNOr9/28Me7HPzc1uPflpcXsIh4AHz+CRd0y9LcZ0RbHBPGYBsEPMLi1PT02XE7807Lb7sX+4fXeBW5jy522nobkEfJ0H57GirT56J10jX6OkavViTa7p47f5ywwGIQAbuA3CQal3EVwc+0ZbXKvSeeZvfXl0YTd4vrJ0gTk1ObG+tYq+MV69fTc1vba1MleDSgB3zYsQH+wcA4vjfAqC6IKBlVDAK06fA9EvLS//p73/f29vD7DsWvuOJmxsbG5iyNQQ/hkU2+NwxIsQJVrB9UYsVoFDjqwH0k8+q5tbRMz/B0qMb+jjDED7UvI21AYfwBxxSZiEHZYk8JBW2+N0YzXLTV59CzdJzJ6wD60MaZhh66Na6YO5dsqVqbFYmaCK+zXnrR22tN5BYD3MJnI1TDjPsmGI/PjrEFcjyg5XHT5+vrq9D4ezsxIC7vvD8FDTL3qfsIhNmJ2zHCwvccYCWV7g4np3dIMG9t59PlqbOHq/lfUtuEMdeNr3e1VSvv8/4Sb1h6qmLXfDvhsPLza8TgQzcrxO1fmhjn7j4ubNPd6x2w8OefmF0w8EObWB9c4cthlf9qmM7NT317NkzzFm+fPmC4HVzqzLZOVihAfAChsRtqZxuT5KAIIeC0dlTVEEDAaytrWHzUAz/65cvuHMXIH5ubg570cwvLExNYjPJCSB4TNID6jMsAI8oCGAT1G4T7SZQsKgY3vic2hGwpjTBywDLiY/xRlXtPIOmEruRvmWokmNFmzlb0lbR0BhsLkuSiJJFqvLlclUIuOPLpVEtSNBCJEvapg0ZU47FMIgwbhcGXD/c/w68jnXps7Nzj58+fbj92B6ZBJ39g++LiwvhwNFv2DFr7i5rSloipNl2n3GH6s03gd3Dk4f39h5tY6J9Vr3nrB8jgHOpD92yj3l3HeOnp1up3/3r1jiHzU4G7kNyRNMPM+GBkEHKbBhnLz6yDZ30E2M4HznUy8OIiD17/vzk+OT1x/2ZsUPcuHnzycs7OSMwj7u8vKxZ2xS4E1YbjDcgHWj6CCQIgI6JdjwLEzc5fvn85f0HIPjX09NT2F5wfn4e1zOID3Tw/CbBeLRGK8ffXMMe6GLiXR9MWudH1D6j1pW3CtidH9yaGXfDvXTNWpFSxUqSkaolINbITC02FwJXAyl41WjryUyT5ktWmPsrlAXDOOoMpHXK6XBcQeGWUdw2enJ8jMdgHXz/DuCOtUqcX3/ydG1zCw9awjmGJtCk8vkZZuJxGcng+sUOvGCKZsMCdy7stxl3zLcDxuHOVJiCZdO/Rn5+cfl+b3T28gK3L1+jeW5ymxHAR/U2u7vDvrr4lY9PZvdTv/vX/RHfvsUM3G8/5j3vUZ/GYr7OPkc9+Yj2fCjd7AA4oJvmemSri996XfIQsOnJ5tTh4djbt29npmdWVruw9qBLrrVlBhhufGICowiztgCBTAKCdeHWUhUpcPYdAHFyakL/dtgAGtzDBOyXr+/fvSfixI2P85iFX5idm8M28IbjsWgek/FcO8/F83X24TU+jVzVHjIfB7pCt0KkbOhrdpNPromAj4WeKQCHTL6dFiMwY4UKydv44KQmzC/TinRt1Zm0ybEoS3PKsQCGOB3gGzgdCbvDHB+dHB1jH30QwPAzM7O4IXhlbX15+cHE5KR7ckHIDvzNR56y5RlWNuHSSAucoOLJe0Y/fhcue/MrA8zSa50MOHMLS9fGc9++7X3+/GX76U8To9eH/sHfXPY8AoeHh3n7gU6j3OTrqTMz/BLpUeqKf/Cthy72aORXmM3A/YoA9au4gzM6PWmtWcrp1wF23y+DYt2329piBweqtaE7luIOgcePH2Njlrdv3s4sLK8uz99NPKthuDq+8Hll9YGm2+FymmzGvZTDvFkksOd0PHN8XrAuBhPrQOezszObm5tElqdnQKF7X/e+fPmMJ9ECN05OTQLFz83NToLyyXh0yxtfheW5Qj6ssNcwYJeT9OgGANT6IqGO+RlNP6dQszYioEY5clFOOA0QSzZrVlge9U3AqlisprTX2Yg9Gi6XLWnJK2NKaNBZc93IiLoJ1gHUDaoDpx8cAKkDxU9MTgGsr29sLixha/UVPrx2bNx9NNQNzC0TsADcjgzbpc/PzypuGr0OTnCLHjIJtBtu9+ZYFH/KHdxxSYALKGvZfg4j797xGhWrxUZG7sXzDdoPTt9q4p9gfevbcDtmX5jXHqM+ztdu3V7DAXCxvYEErQzcQyT6orzh+XX1GNIPCTpD1bpM+VdbGUwNQ0bX8b3nh+U6Tt1Jm9m52cdzjw9PRl+/eoFl31g8Q9jbWepUvzPrFW1ARixumZrEdDu2JeEsuK1y14kPTxpf0YD5yVxT51b1OXjARYD4mdlZrMB58uQJMCag6nfshLL3befjJ6BWwE50NDE5ga4RKIBUm5IHjucSeSYAeiT5pAzOoIocEW08Vw1FE2Hzs0rgLMhrDDIlcAIFKfGMZjWwAp2WFIZ6pNWF1bDbIswB0io5vgZEJ7rW4Ln8hVBdE+vHemTpJbZzmZud237yBFu74F8Tk1PTQOoMuTuvWfJkfYts0bbxME7AeESPYVJSWw3EMhiArjK0Qks5o6n6E654XllbQ95R2j8Z+/z+JbZUwn6gHTXMyncbgW/fvmXsfreH4Hq9+xfr9RrXteo+mOm6i3D7Zl5m4F535HvF68Xxv76vduak549+Uu2H9fpm+7YlftRLvvXX0Si51ueV2Sk+tAj3FL58s4M55tVl3LTXj9EEEsUdyatY2+ObCRr8CyCQ+Di8EHEbAXOulWEqclFkSwx4rY+NPfIFvUziPTONvQs3NjcBJA1BAsofYlnI0bEA/Uet++Cqa6yIx8IbblODreKVwNTEPMpqEpaHwymUN7fkoWeE3EwqkbFM3mKIGTG5WI7Jg5iIGUnQnLPYIQXKUTEWnXPVC/+OQKJbKOI/DPg/A9LS0oP5baxZWMStwBNY4iLMjY7kHR+o5L0Z4EZLuwiIfdFz6HLAINA6rnECN0nBaTrMF+G7AXdtBImLibHxiaVOHr2E/3y8e/dhevnx4ydPGPGcBioCuIQeKH+zs72KwLU/uvYl2iu3Knav7aXsZOBeCecNqzc7Gjfs/MbN/dc/2LHB3OrZHLruRQlkQdw12IeoF4Exmx3HZQ5AdW7++/fvL1+8wErv1c5nN3s3GLOMZRqzvIUU0+0FZNcZYOdBkhskD2eHxaISEa9SU9rexNkAkOhUb8OUAKsXi0uLEWASVp6d45ZJIN5Dbnx4gEf58u7LM243DlAMTfMSc/GA9UTBWFyvXeQL7wMFTwOaJ6HkBB2hN+5GKJOqs8jR7DZWt2iumsvKOX2O4kQ3d3JtOv57cH6O6wuoYsSA0RNY8jI1tba2gXUvOAW4Lkjb7CDGRNtpohOA1cjZpaF24msl9e480IidImprhbg+6WKEq4/QdRihXy/FsaEVUoHb/YIJc/8n6AG7v+MfRKk7LWgckY8fdx8+fjY1jkuR8ihaNMuivonA58+f84a/fXM0BtKR633s7wAgjY5k4N7pGXa9g9tpL32hn56RNuyU0xcuduLEPdsO8pZOVNyaiYT98l6+/7YwdfbgwUoFvHVyiLqpC+gGALe0vo5JbCC/NKEbA5l01eNUUHKiiF4hD6qJlxRqvL7KhZAZLH1OCEWNMIAJttabXC5Zvcg1000wC/gOpAw0b1P1h4f72CkRs9oAzegcUgBraNH5kVHOXZOPrjlAyNQfXHLLWIYDXAwRJvSLzlLYa6PHw2NHx7CsBS2xjoeLeSanlpYfgJ6cmMQlBP8bMI5/FOAPGJodxmHHUJh9Dt0C4P1hTKKsDDUXSpNBxQUL2tGo0yxHOKJkI0hH9bLPLIwj4nYEAxFSDPV/gJWVNYxLRltlnGj/8Hns4uj5syf450Er1cGR4VG84d7mwXH6Zp5y2V5OOQK3HgH73uqo25vjqAzcGwN+jQPRaGTYOJVTzX9k+2+ULQ7e4M/HtBjcHR+JJaYRTCG/+XSwMHmKCvDi3fqEOdT5hXnM16a4nTCb0BO++YtF8FREcJxMgUVqFilWIiFTUqYprVInXFR7KRlABUugk7mSMUDWEOK6Wkq7DbIoNcSPCuAqUTGYyNyzMBAOV87QqUCTw44F+uVukIAZkyvFOpv4rjfyykZET9whI0NuQF1KUijslCl1432xwJv94Ari6OgIHhZHSO1oi0ZlnktkSAm169IG/8LAnamXI+ubG+VuampHJ+cfD2c21zenx7jmZ0jSKHC7YjgI4+FZ2I2EGfdHjx51w1K2kSPQ2whc48NZ+ZjcW+B+jdD19lgOlvX0NLJQppyuj6Wzo9VEe/9gv+uO3dhgE19vbPdODGCZKdaZYr9EPLFoeXVzcf7OdlY2IIdLtbrp9hBzL1EYsi3FLCiVmEUlio1IcyoR8esCgQJdOZAZuhHypBqSEC0yglFWQkHSkviuCZWiWqEkCTyUMcUmxkmrpJ2bkmUabkgHpVA76/6uLeKg0ArjJ4bU23sKhcXMapxzh7KNXgJMGWOan5tHHh5h3j00Qsf2Qs9IBO2WR+jOf1ngXxPn59h6f3W16c2paPzx48fLycUnD/j/imh/GIjBQe086N2IOIzkNe7dCGS20acRSD8moIcPuKcD7NNjMGRuFb/yYWB2DBr5Ju/gCHWgGvpuXvZ4l9+u+tp8FP0vWVpexoz714PzVy9fLj940OOwl+IRj8H+dzxuczFMtwOZxWl3zt4SQSMJrwfMRqAdUISZsbpoCp0IOq4cVK2kM2awaOES60/ysNREmuTLJj4upgpISTvEpUmKHCNiFSqNNDhiMouEKNUIfvXHXowDtwNJjjOlxR7US+yqqDrfWijHGNyAjYp159nwoq7UxEOwzDZiwX8YyCcFEgt13rx5gwfZrm+sc/WHJNDVvxpsot1y3j+ApflcnY8HJEvz119+wUNYca9A7DESx8cnH/fOVrV3Z2QOJHH/lsQ0O0zYVQYbEDWTZn6OwDBFYECBe+UnYJiOyGCPJT0wKV0zqivENS1uyMJCjmtZuHVHr+VlfzUaHV2en1iae4KY46mry7OXWAffwsPuhhg3VwKnA/Ol0+1E6cTI9oq+sCro7KjZaK9ErQYicbggHdyHxkHg9q3rsn1TEc9WY9NE7bLsAjQTbEckjelm1cGKKcLqyIl4FxzAaM2bg6Ai+pZUI7QGJKmI3BAwiSYJA6iTkZ2+0do1vb8QKmtNExSg0GSxSEBS1XGH7u6nXTwOaXt7G8fUHePWlOauz7nH22txYwPNjI3jfoDffv313/7t32gnSR/3sAnO6vb6N1zPJewBJDNqTw5a3gsyCUYmhzwCtT8SfTJmfKs2e/WJh/fIjWZHosL3iFS4SZW/lfa69eCtrFSegBhdaU3cuqOD1mGz8AEYrTx48Hh9HouOMft+sL/fTLO7I97f53Q77qUkVC8l9QPQyESoCH8aEnnOD+JQBt1Yj0TRhjoBELIXpUhQmHJcQQWy1CPoJeZN0eyY85RXkv6zgBsyK2zqh5T2EHilslAtsZtVqH51E9cIg6hpAQ1JWcpkJMDAf07Ozy5++/X3X3/9DQuxtIMkUDtWxyDjn92TyhUy2AwHu/RcjvDpTuPjL37/A7c6RN+xiub333+fmzjamv0+8KgdoxqoJTHxKPSIwJNTe2Q5m80R6LcI3PmMO7+lc7rbCHR2DDrTro6sbn6uqtOj+u7urlDBzQbQI+f6wGyP4gI8hkumlZUHmDf99OnTxsZGT+8SPj45xkNLsY1gOt1uSJbYsACuGK6QModtQw8BKDg6KoEd9SLXm6lI6WCQiqG1ujZNZ4UWQSPqkhGYLDnxDY7NjYOBGvPwhoxJ1VTH2GkOH2wqPTLNWqySqLAq1ZJqtSLd2IBErDTYDW0TDejYtDt8sP8FkPDKKOE7dqp8/foNmuLxtNjyBtveoA+ubkfSlDue/4TVMmPj2LAS91ecYOfNf/7HP//rf/tvaLJ7Mn/46ffnz5/zSAxoylPszQ8c/hvTXJglOQJDFYHbAe4D+0U5FMe6s+h3pj1IAWqYcR8k5zvytS+P4Sg2esdrZ+fjx2/nG4vjvYDvgKVHh0e4SMBsK/BZSJrM9aCgAFs4kjENoVJZQXRBFtRYT3hFc2OWRV7DtUG8ViBLbDkAOuoUtDFdwB6QuLZFaF2wHK2wXAQaWuYCsVC9V2uui9G9IX/Zqs9orV4ijxtkFX1V5VGdnSCoNPJQwjYEMfkksi3hiS0dv0OT8B0o/WD/EGvaww6YIyD12NZj3Am8sr6F+PLu1PNz/Nflzes3208en8z8sDTycfXR1fvMRE/6iIh4PU+xNz8q+HdKc2GW5AgMVQS6uFQG37LNXkMVsn4bTLOgR37hcGS1IArtO6bgY3fTly9fumvwdqy1OFbNRLfj2PV6WV/feLoxjzvJXvzxx9FpAyS8ntHQCrsH4slFmIvldDsBepLw9USWI2OGLjnDEjLgSLJSdugjclNhQaMDaiKL9gsh2JErLaspp1YkKCyS2ww9hw6CQmwZGOXS1BmJlF+qpAKj2xC3UmmQOcMK5IlCqeZ8FRRU3/BOx3RsVPvNT2LLSN55jDn2yanZ2fnFxaXp6RlUuXc9ZJCOjP7j//2fWzNfFxcG9s7FjNcbT9AGDg53Ay8zcgSGMwKdAnd+jzZ5DWeA7nxUzcId+YWHkdWMKFQHgKpgOozphqkf9gtrdmRa8G846j5sDuS1ubn55OnTb/uHr1696tbiVEy3n5wc4x618nS7IdZKgBEVchqAMpjVFFlOsAhvmqB+Igp0YZ/yFDRbp4FjTWVQGc3JIGomM4bRSXcuZJHoSZkZeBy5dVarkTZMjMlCvFRQ7cqs5IAqKSfQVoYa/dMr+g8JOSokldvUD2+SoqXn7Z3WYInhuZc+XzgNkLCn5P73/c+fsUwup2GOQGUR2DAPNY/t3kegslSGX4o53UIEOgh0B6q34PiddZHiePzrGL/lKacdtzDL247alTr5gFwZonYUALE2HsxdLM3s7Ox8ORxZmR+dmZ5pp2EznYODAzziHkspBN6E3RzCEQw2vqIdx4J2XJmHd0rWHPWiQamJYU+3bsiZhuRAqYn6NSRqufotd8Sar3G3ZShgYKEMl8uYIgu9rUOJJecnJHxEjOsuxUJWYk1EDauskNZqrJZYsULCKyiYVET3yHA5P9WktUGkSg5Xbwq0aSUyklhOkyQgdTKI2XF7ridOuZ9PnJxfYKU79nQHjmfnOQ1jBDJwH8ajmsdUHwHMuOPbMb7qlTK30wjEgDYjSgabKRm/pJorjABQQIAkHQSk9X5hrQ9CKu2gy6x6VQSAsDD7vvVgBruFvNk5ONHD6q9qVCPHrYnYToRPXBoHdksTlNOjF2lyJYrWyBDTCiejuMQ1YTXH9YGpR4ImBUTdttEpJyiwZcJHNbzcmNsJOkEeurSOvZsGZpQaYaYDk123TvUa5NZIUlZKSxeMEq9iIhFzoHxRnW9LqNqfZAwuOdITbbid8N0SLuQw7b73de/tW97VmtOwRiBflQ3rkc3jaoxAp0tlGi3cUw5/KZq/SkFpoWeiknau3DQCCGptwpKMFoeitklm3k4EMEkK9P5wbQ7PLX/9+hU2Bum0X+w1ia3iOd3OSVdhORUEfgJ2wnYlBOhoMD1dSKf1ciXxyZRKqpJGDgrHC+4AACAASURBVAj26Ik+hGSUcxJ+1C9UiybO87bgi0LmAtNkBRZLvGCjVNbrpM6U1FtavLo3syXnUrNg2CvKo6mg6ww6prfHiO7z6CLJhGqBIzZAu7C71sogg8Yv//o537+Yxn/IaOwEOmQjysPJEWgWgQzcm0XGf1T4k1H3qjarVTJmVTXXexuBymQ8DoKlvF9YiESflrifdGtr69H29s7Xkzdv37cP36GJ5ST+xKXKhDsPv38Oie+EAFmEJFL4L3BQJnLnOodFKizqgUswqYQi0mxFkClRouB8Sq2RjLNleDk7KAQ1iYPMSopcXGiVVa5dq3ykynZCb6E0aaiFstwmeOrcOPyoJQ7asqQJI5WzirrCCbFeQuukULUrN868ixrnbaoA8gf7B7//9lvsIBNDFoH89T5kBzQPp0UE7jVw589A81c1ai1U9VNS1c/1/ohABB2YkrHHuveHX9mL+ghgyhxT7w+3Nj58+PD27buzi6s/XfuabseCZk6322caqC1+uN2AFcj5gliE+xBlrEdRiXDNUFiLoBw1gziU1pHVoGStvK18gEhVZskbpF5kpUn1IgsimtI7MFqWZtTyVorRecWypSaF0WAkGpqUeybM1ivRKzg+HhVoVzR1t8QDzaQDaiQqmm331TLC7Zhz5/agf/z+x/ExtozMaQgj0K173IcwNHlIQxeB4Qfu9pVfm1ePZq1SZFa1c33AIoCJN24PrQQE79SADeK+uIspUjzifmNz6+POLlYnn57H669qBLD+ATOqU1NTWh4hCCckRywXYKET9llODXRwEhSqdhKFU6kwFzjsN0nmjTPKbV1RBdskb5CygiFY06DLmrOcjM1NMeaJVspzQ5FVQ9S1rFFLWKFFyXhgpv6KhsBe0QLa2StySLgB6pK0wEYKVUWHYmmQoaT/uviUuzaOxEUdpuBPT05/+fnnUg+5MiwRaH0L07CMMo8jR4ARGGDg7l/X9qXdPPfj3I52PiWGOgLptgNA8BUkaCfIUAdg8AYHuPVoc2Vtbf3Dp29v3ryuXTyzv/99YXEB24cQuGuFO0tBt8ZvBQN4MRCoUodFyNUmKlQI6Zd4xgl8M0JrjkJdAD45SkaEamxh/VInGKM2aGoYyywodz0vZLeSScRekUyE0l4Vzbar0VJbLYJv3nsxruBOtBI1Shx5GzyWMelxCIqHERpfminSZGjaHeeEEmbcuaf76MirFy+/f+/O7lLR20z0QwS6tWlYP4wl+5Aj0DoC/Q7c8V3d7FUdWDM941e1c/3eRSAF7o2DB46PUB6nDJLljZqZc8sRwGz6480lPLnpw4f3b3cO8ODM6ACeuDQ1PTU5MQl4Zrg0AjcDqWlueA8HFsz08JYOdxQZPFRPfiZ4Edum7RpU9L0FbtHI+42+ywvIqRE9UkWt0obBSsIzLwr1aLaGCM1CWVZp4JpjZaVr1Brtwogzi8KomINwmXr0Kllix5gFAnwedx7SQKhmma7gCuQONI+TB1tDXmM0uUmfR6AfHtPR5yHK7g1NBPoCuNu3c21eDXStkjGrqrmeI1CKAGbdSvXmFUPwKY7Pi+ObR+uWJIDv29uP15en32Ll+7u3uAxDOjo6nJ+b10KIEnITzovfC/6twQLvmFI6MhsJqkVVw9mBYWzlwTIhZJKon3C8gXNSTdLJW6Kq3LsNaoVXSYeRTBsL1EZJlXDNUoOqzpX12JpErLRqViiJKqpqlFZBM2IKbYyvEeIzxvwzPQiU9E8XX+mOyzquc8diucuRD+/ef/r0qZVnWTaAEcCGVAPodXY5R+A6Ebg94M4v1SavquPN9MDPKUfguhG49mZwnIx3qIVzmHg+L5G/7kG4abvJyfHHjx9j8cybncMXL15gKwlsAcl1EYR1ngLEs77Sb42U5tcRv5BYGL+grGXge61QCoKUE3hUDieLNYQWHAtGAqG6sU1KQXibkPWgHko3Y6reIvIiUdVGvcqKuj0n2HOp94QBkjEoicnxeEnkdKGkeLFJElbRrOsFwpKWTmkBFefdefsyzpSRkV/+9a+L9PFPPY9B7qDnEVhdXe15H7mDHIH+iECXgbu+aPkN2/iqjrdRI3KqqrmeI9CFCHRlvzA+xlE4HvidVDjVRebsliIwOTn5aG1mbm4W02yfvnzD1DuBG1GbJ4I6vQOUU53exYPmrlq9gX3lQLxdqscumVhYv6paRveUjAjVxIyauY6MJDKwraZcpspSa1fKr1QI2v6vJS8C1zuM1Y6ItvqmUlC0eKVRIx3kHLC0CwJ153hALeRV5M5rOi6YUcKMO+9Qvxz59Gn3/bt3HY0oK/d5BHZ3d/vcw+xejkC3InAd4G5forV5jVu1evzKzSlH4FYj0PX9wgzpIDciT8Pf5uE8ONhffrD8/Ifny6vrb9+8/vhx55LbAEaADl8qXz0uIvYzoUrLki8kJyOnWnfE6EhSzQMKL8yGRoYuQ0fedagmbaUfGiVytne2g9hYS7QS8gpxolmQHbZprt5cUgwidEvdFvquBg1/saC+ZRSrZnoWJorw1ouHBG9LEblzS3fuCwnFX//1c+29zmYw5wMXgZWVlYHzOTucI3C9CLQC7vwarHvV9FSrZ8wa7czKEbiDCPR6v7DanWruYJz3oMtzpenpGcygzk5cPn32bHHpwYedzw7UHLwZtgt5GSbiyykIRFaCFnmRKPRrDIXWRIQhgdRLzogZhaYm/Bi1XRh1ggAlLLBWJ0q0Ikm9dnVjozoiGAllnU6ZV9ZETYwSl5USo1yFyF4t1Bg+hRCZHWoWIq3KvJRsxQwn3jXpPoZ/0WATktevXpXdz7UBjsDXr18H2Pvseo5AJxHgir9mr8JOM43IL1QzlSPQpxG4/f3C0sUH+KwgVTeh7NNQ9btbeOLS/MI8VrcDigmhjc3PTW9vrWFhRPp9xu+nBNBJVBqa4J+xpVcWhhoPnR8+KyRwjiuxubFpM1FTBT4mUpBlhVBjiXd8mUFryVx6RRYEUZ8iiYOko7LSMqkm5BUWo2YgQql2pQo5YBjPiFSe0tRSWBlHhZL6FlO1FJ+cNOnU0KaQWi8TV8vopgjeq/Lrz7/k5zFdcUAHR7y4uDg4zmZPcwRuFIFkxl3fgP5VWqFv1EVunCPQFxG42/3CfF1NAHj4hOV0vQjwJuPRS2wygzsNA1gjnKu8ZNy+yEB6vB31laNfrrXjlB3FUrukEjoJx9o8EZeeSDPk1ox58gYpvjKJAkVTgbbSaygCnzrlijjNslKzZkol/lUtauW1TDfbWoZQScFDhrEZFYhwMKhE3fCiHiG73gTwvtSdk+5j45cjl0DtL1+8KA0tVwY2AriYH1jfs+M5Ap1FAL98+pJv9dXZmcWsnSPQnxHok/3CHMEnMbKPYMLIZKsI8IlL83zikkAZcJkhNKK08F3GrzMDcPh+I7ucxDBuRebVyE3qgRdKmiSNDsx6JMgseIm+c40T+KGUEa+oKAm8i2YCE7snNQ1d3rJI/z/UUrEjYVNngoBBUehApC/0wqp3VinDsYYKdZI/kP5ioTcKTxG7T2BnWJr847ffDg4OOhpQVu7PCMzOzvanY9mrHIGuRyCZce+67WwwR6CfItC3+4UBMUXQZAAlwJR+Cl9/+IJZUix1wK4yYZEM0ZklAj1hNnmKEPLl8WRhZDIMlyUcIxsUEw03mCpG2+V2qNkrONHggPThMYyFN+2KHZStYt2luTXwZiUl27A01b1luuSN+i5x3OeqUyUdCgMDZSAVyXAEeKyjEdL2x5OAiWWgxYhZutL99PTsl59/jmYyMbgRyKueBvfYZc87jUAG7p1GLOsPagT6ZMa9dfhq5+NbN7lXUmwmMzc/x71BbHG74BignZfCckJsMSoF8AM/AYEJ7LNWsQUJYcKgr0raNG1LZVcQWKzISs1cE86qi9DM+zMmKp6IREMKXRScICnKIAtlIamh2lJSu/Y1QzehRSgDv1w2k4IvEbNGHUoZDEnssFtsLFpBBp4JvWAFi955uRcXu5s7b16/3tvbK7uWa4MXAVzMD57T2eMcgWtFIAP3a4UtNxrACNztGvdrByxPxsfQHR4eTE1P8eZCvyeVcExAzpCcwBwhHQlBNwd40QIJCSPH2hgOjMxawjVdNdSqqkKREiYactM0E26pKfmNskZOaGT6VXm1HrQby3haNYqMc6VCs4Y1fHO2ZoDyNzoNItKwktIyWjAMl8eePL5W2IEPhx88S+FCT4vebXsZ3COBpe4Xl//xj3+gZH/51T8RiEe3PeL8/OK2jl97DmWtHIGeRWCiZ5az4RyB/orA7e8q093xN5uM7ybC6q7HXbWGLfyOjo4erDzQZjIBjvk8KqqO1B2wBQhGHII3qsyZvDSmscp5UAzcaj2YkAUZl9VCjTy9PZcheugEC6uluUkbcjhOUFlKsFTmNDAS9VYy+dHwCNGy7cSUyNb2ojaGdnnZoNvAcH3wkdKOE8147CwQqsppxBRnRczRoVXQFkwmoHSyRI1dkL4MM+7A7hMTpycnMPfp487n3d3VtTX3ppuFjaybFu+LrY4ip2N8W5HpyLPbcupG/aSfvBsZyo1vJwJ5xv124px7ufsI9Hof99sfIb5u7RuXSEXdD99PSowqdo2YnZsVag+Tp/i1FkLT6C0GHgkiYo9FWjgNmwk3MkXEWuyYhHEdQYZKjUZgRRWD5s5221GYGA5dBMeCoaL0xgWjIwqt7YVWItxc3S+2izqy3wVl80+H1K9r6GsRc7oVqlGBpwDZ8jlUrJ7YgyBJcbWMFs0A7cPCP/7nPy4uQHij7hGwndNtRAAH+Da6Gc4+un7aX2lwOON4a6PKwP3WQp07uuMIdP3JqXc8nqR7IA5DYJabZJh+x84vzk/PTmZmZvzJlwbDALP4a138ZIffbjEpSGLUaVAcMQYLMtVojwgvcMvdBW7iXtQMRqsl29iYqpIr67G7qzXTk6ROu17edgd1zauNFSobbcmDRA9kCGjkkhdfZamfBCqkzzhaoqGi5kyCdpxM49hehmlv7+u7t2+MzvkgRgDPZBtEt++rz8XnOH6gu0oMeVwzcB/yA5yHFyMwPT0d6ftARAAlHHMlaOzrkBzgiUtzvCcVs6WOvAosRs9VcyIM1cZNIQV1qYmgxLZKieWmnBdELAkmQ72hw0IgirpsErNQScoySc3GVJhtlIETTwOT+qYz3sh9qG1YYVbsVKRtVIOfoWxoUghEebXgNjYw9wnLmbxEjS8FVrRn4bzRIncusTLszlZY33458vM//3WGRwTkNJgRyDenDuZx65HX+EroxatH3nZsNgP3jkOWGwxoBPjgnnuZDHKlwKsFGOrDCJ2dnV5cXExNT2OO1NCXfSkbIGv8guYXtn1pJ1jOxqWBO6YrRtpBOEJbMxQbOsECb/YecvXikDLwXNugpXLpS/VmWXqQ6yzJQXNP4qv0ExvB6YTVFpn0VqdfMYtq5KR0XVPyPLIhwHZ4yOefnQUoiqQLP3+mKhC8YXesnUcgsKH7H7//TqM5DWAE8naQA3jQBs5l+07qVn794Wfgfv3Y5ZaDFYH4b/HBcrsX3ka8ZiCpHYTUNTc6/9LD6vY5TrdP+KNSichiIkq2Fz208ahwsvC7YDRqRVkk1C7iQJkuywrDsVuygm2VNaDV5LWmUmZKpz3V0HYw4yGt0QAriG1EhfXAr2/VIfcKY+y16Llsu4FfZRTHOBpxFT9IOCNk304HdKQquf7mKRP/XWPYXbhdJCfdR0Ze/PHH8dFR2bNcG4wI5AcwDcZxyl4WEcBXzjVfGbgXUczUcEfA7kIb7jF2OjqDWsjxItBRikRgNC+v8bXT3Fit5OT4BIAL/wd31G5AzNAZoVp8Cb7DhDGK0SRWIWo7JbqGAd2i8QspqfB241FY4bfqO7apUWqUNXKqza7A0FG9lSW30a6p5AyK5tsjghOhTCwZyzE5+CkMJyLnsfHjQynV8TaOVeUCedVEwI4X11+NjXHS/XIEs7a//fpre05nrf6KAC7v+8uh7E2OQM8ikIF7z0KbDfdZBDJwrx4QQRwDOsi5eZ9eRggTRQzkoqjsRNVi9+s23W57t8f50gSDEcvRmeAZMBsZ4ig3mo6RZ1loQEaapEFGIKwMtVTV6CgRfIw1trdKBVRG2+ZaxUij/Y44hrAbNo8MNiQ2qWPxwt+gc7MyQHyWgQbZVjeJkg4gPSHPjmeMZoMxa6cTQk1CY3DYupQcqhtgZ+77ywC7Ux2bSV6OvHr5KkNARnLQ0uLi4qC5nP3NEbhmBDJwv2bgcrOBi8C9WCoDtNL+q+UhBPIigg85CBpO8FjL1t0RYiMgTLVjuh1zogkEczwmj9gRHYujdkZ0QGNowHuNjNDA9EOtpgwGVVa1q/VS+1ILVhLthLQ2BaMx5lVOqIey1GulkvTbTF0bJKbQu2LiqmrSRVAtRgNOlAduKF2mRgGBpzwJqhlPDG8hAplxnK8qlUxgVLFoxu9S5aQ7h3x+dvbrzz+XLjyqHeZ6P0bg69ev/ehW9ilHoAcRyMC9B0HNJvsyAgN8cyqASZuvXkaeyCZALEPw5tS1+rx6PICPeFTq7Cw3kxHOIjyP4ItQnc5EO4Eisy5FfiTU3jIDk3XNUl5oqTJUogIZeMMvS42E9xUUTN+1G4pmqLpBsY7BxnUGGniG0asmEg8LUS2z0ovZr+udrRssNLhT9GYUWjQ0quqwzvNCSSVp49gJQ4ksBQxvV4GacKcGl7rjKapICse7t2+/f8/rLhiPAUorKysD5G12NUfgJhHIwP0m0cttBykCfbcdpLCEIYor8v4Ls0/GCx4aZgKUF9HmqK4e0snJydTU1MTkBJc0eCIeQyIuMyxGKCaqiKA45ktdJ1CvpMiJhBTSml0keLtUQE+KulPBJdMvxJV+26sGcBtKtErIxIZx62Wu1ihMnHfYStVGvaSfMmmjCw2uHis0opI6D7VQ0rwppRznxr4bNcJxYGl04LCq88RLFlwow9ISlsxwoTsn3YHdL84vfvn5X7GrTAxEBHZ3dwfCz+xkjsDNI5CB+81jmC0MRgRu7wFMEVW0JgYgbK0H4FIhdj1OXpgMj6G3kUF8k4RNtXGtpXsHA8BCiS5Yg+Hom6iiM6eCjrtQyMWoVJv7yQ6LlNLghqr6Mi1nBUnRtNBOec3ogISr8hI/LGcHU3wTllSq7Q2du0qdk40NAqfebplbroWWNyvhJV8qAu04PJwK3oGdF1bxQ8JzBY3Y3EgvVRF811WhJt/1MCYbwfu3775/+3Yzv3PrW41AnnG/1XDnzu40Ahm432n4c+e3GIH5+fmb9ua4wZBE8/ym3fS6fZvDIOC5Rrp0zEQwaSDe+uvU1MXFOVa3l2ZGDX4ZjhN2E3QO5o1T7sZQW+C1HFEidFKF0YkwGIuwPTKcKHRDJCqqhUK1aYd1osyAla0MtdaG3AEot7xjOxhTGSqtLd9MSr9izGTKPZWADBBlhVAPipTiRDBVj7uwuykYYC/nBt1tEn6Mq2UQlIvLX3/5RR7kbDAikNe4D8Zxyl52IwIZuHcjitnGIETg+/fvVTf1Iy8wYJDgqrzavh/qnY7BAMwteW4gngAxdOhQPszKB3a1BHY6Oj7GZjLFMpkCbRl248DDG80Nzxlmq1pzAOdKTXRio+YRChK3EKpsWYaT9EsvM1r0aE2KhgUVu68SFjrlFjUjXS2RFg3JDO+CK6rUuCwzU2Ve27UrG4eRhrJkuZYpDUgoNAWvlJpSiKRMWqStFvhSKVhg20oZbiqjdVhYJaOF7qPjvHfDQvRld/f8/KzaVa73awTyrjL9emSyX92PQAbu3Y9pttifEeA3u/3yx7w/HXWvopetib4eQ6NzDuWFczEwpLi0JlXGncSQAk4RfIXkEM2OojUWoiOZvonRUmMSlhjNKqVmpUpsIW5ZhFoNN7a4BlHAYKcKhllL6ykd+yIzvCPTiWb8ql6i3kRUsMsRKfi1VJvKIahtqqMrnil+MNix0yWmcH0JxbORQXgCeMhx1Xg5csGp91rvM7MfI5A38ezHo5J96k0EMnDvTVyz1f6LQD/9L9UQReu8/yLYbY8MF8WlNRHBIy4A7tMz04JUnhkqI+yyF+uG0Vjg3QDPOfOapFIl4de0LNtq0bCkCD35UbZdqrU0FTULvFhQUVigyTphodacUrvWnlRMV6p1pttQqWkmL9yVOo/qeMEMZLVinR+U2MEgEhddnEeUhFOLc+/2Xx3NwoPWLao8A0++Nf6PLnSey36LQH5yar8dkexP7yKQgXvvYpst91cEbuvuJQMUrfP+ikyfeBMRPEDgxfm5PRZHuIvBFNhKPRUeC/DMS/KKVK7V8ZtpFLqRMlUDgwWz0UCFU6nGllUiAt9IVDXCGg7yqdRC0ZoGJS7aNk6Rp5xEmLILZadML9FuotHAjow0epFOI5TQJEMVpb2MZ7A7wO/CeqDUVPZ19ujcKWw5ZI/QnXVhdyF3+x8PIjY3t/Dqxcua0IVectlXEcBTb/vKn+xMjkDvIpCBe+9imy33VwS6sV9YRBAtiP4a9YB6g+fQHx0eXVxeFHOjRGLJC2REc6RZVdkw0e5SqUf8FoiS0MxYboLG3HUqvbD3KIkkOYHtUisamITDeGt1RoDGznOuA/UgLJlrWkm0E7IC+kvOlNVKtaa9dF9QcsnMgxW5kaAIR91epF1XZYLaA9tYqqUnFrE7kq1452oZHAf8w2fv65fziwtrm/M+jwBuZO9zD7N7OQLdikAG7t2KZLbTbxHAj3fptbKyepWLJf1K84gKrjKS5V2IwNj4+Pr6Olc3EVEZMouEqjgegmdeOs2uE7LkSTN+SanU3Htos2FsmugnpHVjjAiII+G4vexLIoWgqJEqakkbY5ZFtlS7zEuawFILWUnxxpWrO2qIV9EnRTwg4bCXJOTbaWFinivSt1Y8iUI7oz23WXZWtF4mVDEDPz4+cZFRexHkfqfOz8/73cXsX45AlyKQgXuXApnN3EEE9GtdRucCT8avOqQ17p01qZrI9duJwOUlkBNuJr64uMRSY8NYyZElGFM1grgAygr3AOO8EspC1opqWzsqsu9YaWW6mawWzhJuR0EkjFVUWU9fsYtCxSjlBTPqOdFcUtW8dj120XasXJGFH1+QV7Wmium4Ms6e4HM4kbwuVfE4yQ7obm8UI1PTU7xBNafBiQAO3uA4mz3NEbhRBDJwv1H4cuMeRMB+cNvJO+s87xfWWbzuTpsbenAe+HJzY+PLly9whLjK592Fy/xHmj/VJFmGXOQVtaDTUWmdyJe0nXwoGEGr4Djlgo7RYMDvaljOJDKDyO2FwjgNDrQCoo1eu5FgK5QNVq9k1Lds7LAwRBnehUpBFUqiSlqJzK7adMawrc4enkAJbecTcptk1yoZrnQfueTamK1H23j+F+oE88GbISAYgSFNfnyHdHR5WDkCaQQycE+jkenbiUDrX8Be+ZD3C+tVZHtg9/Tk9OzsbGp6enZmdn//QD3YaUOSlNdQGMQjQdJKVVxidORHwnXqi7a06pXqubUAto4JHsF3FIkw1B15ksZaAPBkWEOjGkZWO4tcqxttl2wYt15WUpQjVU61Xh+nqlZRT/VB81ATmxcKBUVJFBREYKKUBksl4nMkLLfA3u3Pf/xxaWmZG5IWNgrTA00pbAxZf75uEtu8VOYm0cttBysCGbgP1vEaFG9b/y7czSjyfmF3E/fOe8XZc35xjm0i8ATL1bW179+/acsIA4x2asGoUBVqAbqR9IqRRV1UyyxpEQ22bNAgLFlokJYZJejLCt7xlaom+N20oOf6RQMyQjI6qARuWtZPvKc2TLvEKVVSa+3SFQMNwRKjgVtnHUqu7OrC4ILwQvEF2DbcbXVTkj2H46EzVJXGsJHR8dHhk2fPH6ysftvbOzk+xh3SENU5kXk9iQBife1Xvjm1J4ckG+3LCGTg3peHZTCcavEd248DyPuF9eNRaebTJbdyPz09A3haW1t78ccfePAtbhYkjsKrDNZrbFCnlMRo4DaqlDitK9fAdIZfI4oNRCjZH+gErruERTLpnuq38jHqRYLmW6cqsE8aGBkYoWxtrl5atE0OSUKWWiV8JwnDExXQ4VU5Joa7lbOBEHooVaElii8uznGCbT9+sr6x+fXLl52PH+3miqSXTPZ1BPLXe18fnuxcVyOQgXtXwzmExsIvYvxpLIgBG22ekhmgAwbsCph+cnKCfGFh8emz569fvd75tAMsj1EIbrHkiJR5zjqT8YxumrdQKkQFVbJbYrNSYjTtskbgGBaFvVRKL+L3AuZae9WrzIppGqNKnVoVm1ealqrRCXHrrJUEdV02bYSWClsau5QOcvZAPt7hyJPVLEEnORyE5d6OBEkrVVGGkwo7Pz7a3n705Mm3b3ufdnYOD/ahhCXuhVKm+jsCMzMz/e1g9i5HoGsRyF9MXQvlwBrCL1SL18AOq8HxvAiyISR9zABgvLzEguOTk1MsmFlaWvpPf//70cHhy5cvjw4Pfa02AZihMFJMobRaYDRwo5hESVqqFGo2w9tEGNSuEEutALFOlZAuKkEh8gMhQcTigQC38kI3UjWvKLRoFcxAhdIbxGokwsBY1jILhXbGXmhfRaXWUvqqdkGuNsi8raA6aRAO40VpUfve169fdne2Hj7afvJ0/9v33U+fvu195Xg1Fx8s5rLfI3B4eNjvLmb/cgS6FIGJLtnJZvozAv7L1Z/O3bJX9pt9y53m7hCBjs9CPAFHCdPtWLGA1TJTU1MTE+N/+vOfPn3a/f3337e3txcXlyYni68vdVGZSk26Tci2j0hoE8qkYSNLnEY28W4NF6YSAckEF4tMJroNozvXWgpGk9/4kii6qlasyUhkC8xHmbErVbYp9MtUU0FQM4Umak3YoS1KROyS74RlXOQlpq6nKkNTI7vQorpFlg/lxecfJ1UocWp9x+z61y/j46M//vmvYE6cRwAAIABJREFU6xsb3799//x598vuZ/yvx84kbOYuazkbgAjML8xXPv9dcbp0vrVv8ZrN2u8ga97rCBS/fPc6DIM9+HpkMNhj6oH3Gbh3Jai3c7Zpwp3gHQBLd6mOTE8Du0/gqUwLCwt//PE7ViRvbW0B0GNDv/K4ImhrhpkL9SvG0iCOjEaiMFqmACureAI/6oST2Hkwetjsdz7B7wCh0mKWvEGmL69IwxyhrlMFGVhF2UpWaAXKtJO8rjkGWGVX68Fex6UOAB9wijCOEuUXqRQzSqVkVwPA7qN4HO/hwcGXz7v4f86jx4+xrh2nELacwsajmG4/03MDcGBgE9eKhdlM9XcEvn37Nj8/33UfS6dW+9abN+vCR6ALJtofSdbsxwhk4N6PR6WJT82/DJo0yOw0AnmpTBqNSPfhWWWXWJpz9wzz7qCmpy8nJ6fm5mb/+te/vXv79tdfftl6+BC/1oDv4+MFxmpjRE1UAjuUDFJKx6B1SAA7xssJ/Oq6SVJFLTHp0JO/z3xzfjyQYohJgcuNDPWgizJRDKYSXbebclK61DgVBDpVwJDSqlQaGKFhLNkqtAxlFBpRxxYvZoEIDYnV0ysGRR4M6F1cnpwcf97dPfj+bX1z88nzH3HmHB8d7X//+o2LZHaOsdwCangxjY5P5N9HC8UA5MvLywPgZTy5buKrzs+rP10dddFlcx31nZU7jkD+Yuo4ZD1u4D8aPe7lPpq/PzenDsE5BKBF9IV3SJgf5f6QlyNTU5OYCn385MnygwcvX7x4ffJqY3MT9DSX00zU/l+lhwHpwLTDd/xEqhHL8A6fR/18VjKXGZe53oEILUslhAhUYxKzTlBRdZWgGUpqpXTSqgk70agjr2iFMJlGJIIRTaSjcoU4aiD0Z6dnWBjz9fPn+cX5//Lv/31+fgG3Pn/b+7aP3Ub39iA62N+H2pgdHPQ4OjKmh6mGPnPZ1xH4/PnzvdrwV98hNzoipU/fzc2ZLyWjN3IvN24RgQzcWwSnp6JufVB66uRQGR/0/cLu1xkT8DqhesCbtuQdy2empqaB0e2OVRzWN69f/+uf/1xeWlpb38CPN9a+234g7UfsSs0rFVp/VPBzBguWU7OgROrXTlliBuOWqo2+UCHltRAYVOPLLZxeTCF0k6PHQeR8NXGaRVqXVfLMfqIlzZg1UUj1g6mU10hbTBr5kRMVhNTbM2qNdcAwOF7vHR0DnQOe498yf/3731fX1gHi979jdcx3Q+3IMROPcOmqr7qyKTqTiX6OwOrqaj+714e+6SNyTb+afhRvYtR8aWr6mq4OZbMM3Ht9WG9+Ivfaw/tiv2/nY/IpUnsKGnSHCN/kBFVAYViifHGJJ6qSeXkJ7I5H0mPlzJ/+/Gfs6Pfx48cXf/w+MTm5ubmJdfBYVAOg1ojCbhrtq9vD39ZKNhKbcMdQOO+eJNUCai9BaEoEtCNhdVTjy6mRw/Mp6E5OALg3JNkwLi2FVI/XC2mgilJ+FNVWVNpR1EuYiJjXCirqdU6cn1/gCUoHB/vf9/aOjg6wJObp8+fYOgZDPzw4xBp3oHak/e/fTo5Pzs/OLi4SX8Lxw4R75z3nFncTAcy4P3r06G76vn+9XvuDUfqY1cbt2qbN2tUd1PY6YMwM3Lt1wG54unXLjWynaQQww9ZU1htBPieuHVfMfQq4E6CXEu815KPpLy9P8HTVCWDzcc6vA5k9fvz44cOHX79+fffu3Zs3b/DYppWVlanp6fGxcSg0IvgmvhUHraCaqF7Fxm9Ig40aXmSF35yIoAN8N7ROsVQCgdJJp8wh8S4PXjN2S0toa82jt5QHlCxSDImlKSrJgjuFWiIEWeaXaw3ictNKDdFqaF5RQURxg2lwSQFOmuB/Mpg7x7ahmGAHaMd+RA9W1/60+bfFhUVoAcpj00CgdkB2IPfj4yNcBKIJble1GIW+eNTwzvu4h4AMQDkoa9wHIJS9dLHhC7GtzpKP+FX61+vArHbQzVVu9FiegXubAb7J6dBmF1mttxFYXFy8SQf5DLhJ9DpuK+AORFVC7apIAqB1cX6G9/n4OF4T+APMwhy84XXAM8D3f/3rX7h1FfBd62e4oSQ2+KtZBN/JoW1HF/DXrhOMUA7XdTGSSux3ghbDL4ZKzwJqN6EgtTLVqYO3Fx5dMtTK9NKYW/OCA7WgRBtFKtcKfqSaKzSXxMZdJsICGpwnZyenR0eHnEb/tocY46A///HHhcVlXLGdnp0eH2NBO25DPTpA2v+OxTNnZ6eE7CHRMRyI+FIV51WXHc7mehYB7CozNzfXM/PZ8F1GoJ1v3db+tfXldPNuzIm2Omvt7xXS/MVUCVC3Dl3FbK7efQQwF9vMiXzUm0XmzvjAlRecB7UU3SAwxdvAu7iYfQf6Oj8f4+Q78DvTGJbK/OlPfwJY+/Tp0/v377EcYmp66sGDB7h4w/p4LIKHpu487N2Rx5e3GY9EGIQzOAyw0oUy4QufZaCNUE1s4u34dj2ykhYjuA97bW119B//gX1R9mdnrWM2ClZJtk6moTatFYP0apNBs3WJmFhgorP1+jgNcNyBv3GTA6bPcXcpth7C7v4//eWvq6tro2Nj52fnWEB1cgrEfoyXEqD7ERa443zhJaG9aIjOo2N7sT8emZG8VIZRGJDUi70gB2To2c2rI6AP9NVqqcb1v9F639m9Be7XCG16TDM9eBHAJByOej7wg3LkDLJXckPstt4dA4lglDOnfNLqhYC7wPvYGJ6Cjkc1PXr4CCsi9g/2d3Z2dnf/AG7Ds1pwKxvm5wBwJyYmMVWPVKylue4pAmcKI02jLC38JrAXFg0/D2QEwIzRy1DKKwNaSKhjLz5v6PLZu3cn//W//P7ixeny8rNve48XFn4fHwdgTczKJjPaZQr9WY0MUUEc2QnRSuZq/j+DpFEwnLBa2UGMCOJ9bQz2hb+4xFXa+RnwOBP+r8JF6icnAG3PfvgJxxR3OOBEAF7H9DsA+6km2gHZoYNzAFD+4jz9H46QO64POdziqItCNop/0CSOZrKvI4CTAZfrfe1idm6gIlB8I7ThdquvsTaaJ18/bWiPjtwH4N5R/NuIWlYZzAjs7u4OpuP30utRPCjHE8YfSJZh+Tu/KgWUhX3BJ/7CFCzgO6bfx7h6BnPvSBNj0xOYZ+eMO1AdMBx2/9v5+BHTsDCFyznsTjM9PT2J3SSxXH4cNySC3faXRgny0dMIAQGGoxnRwLGwzaNJJVdEUU4FA2M1kUrSxjGKVb2oYxRsPv3llw8Pt/bfvQeOwa2pH1dXdy4vfnzy7MUfL4BkpcrmloJ9Nm9IgRfKioKzrWiiU25Sq1TLLNrxeHM91AWei3Ryisn1I9xairUuR4eHE5MTCwuLT549X11bn5wgXgcwx6gN03OeHcDd8Lr+J6MrO1rjS39c2y6Ch4zHpTjooPiKx6/wKFN9GgF8hPvUs+zWPYhA8d3RxmCv+NZrw8IwAfeOQtdGbLLKcEUAEG24BjTUo9GSGOG2IsOArQJExe++8OYnn1PxDIiVwHpYwnx2NurYnfenohEXwSPNzs1i5xnge9yniMs5gFrgt9mZGWwGPzc3z4U0UALoZ2Pd2qoOaL2NBDcSvAd/zVmagIP6kmIZ3olF+a9Muk7BAquBb5SzTDR6enpxcnoxPbW4uLD/7/++//kz9DEQxgoPHTq/+J//9/8z+ep/nP/nv40dLclONJYaDp4UXQWO3GHF2iWtE40oLvM6r8FrTIwj2cQ6wDpWwmBlOv5Vggl1XGU9//GnpaVl3HYM20DeUDw6PtKiGC6NAWDH8hi0BVK3xDh48otBwXWwhNw13nCEedVmLmOUWG/Tufu5xd1EAMf+bjrOveYIdBgB/4ppo1Wz79pBBO7tj7qNwGSVexOBFmvc700MBmagwNnY8APYyqC6+Y2qoWASEAlt8atNb0NcVEEbZZBggQSajI6eAYRp+p1T8EyC5diLZml5GRsF4lcfT+HBMzXfv3uPLgnuPc3heU9IBuW1hp6NeYHQ6nuIqK821vSaEiqY56ZH2pNIOMGqFzZAIUw1UmbqFobTr19fHR6sr61iTf/8wiL+t3By+A1ROjw5x5XJ/Nz8f79ceXG8HK16V6VCHQZwHiXWJOTepwpzwluBU1CxsRNBEspUDicJn/kmXD/VonWugdG0OtjT0zM4Rk+ePscjkzDLzo6oyvUwBPdKp5hcRzphazBp7uoE2/zTlRAPgr0DxX7wb5rU1Uz3cwRwld3P7mXfcgSuEQH7dag0BLNvgXutwxX/czVH4IoIpKfR0s12lbmipyzubgS0VEbLGi7HyiAMkB0IUBm75CEWVDfoWMB2w+7KiW41jSsQD+RNGK91NNxkhuvcgdRnZjY2NgwLYlU0cDw2IPny+fPp6Rlg68z0zOzszNz8PNbN28p4Lo/nvDwTLGLzQCFyjwJ7BDLX2hijw0WHrjjotMNYu/6IVQqixPEyGWLaoCm2qrW6mJiYerj148jl9pcvL548/br3FbPUGy/+D0g//vi/4/8MWPt7+Ze/jO7tmWFZYxacEMHsyhSblDTNzZRVa8wOI2fBufolJGBtrW7hni9Hh1iOjqgi2rjB9MHK6uzsHFY8wXIE61yqjsScpAF3TM6DJ7heB9ntNDIx+mb/SkLuMB4PnH1d2BkF9sLCfDqoTPdzBOIXQj87mX3LEehKBO4cuNtXZVfGko3cxwi0fwLd/j7u9/F4dGvMAINCWob2KjlBcYC31iHxuU1yp1PuQOXQ4+R4UWiVMx6oyYU0QvA2/W7z8ZxYn56ewn2rmLRGG+A7QENM5nIO+Pv3nY87hI1nZwDr0zPTAMSYl+f6eE3JaxKfMN7+cGnACwQu0cFJqvMUeNbWyJivfu4WKDcZE8GwBMYzEmO1cbMKgWSgWeJ9+H3/686nnfOzBw9WTh78b+AsTE9hnckff/wBqPzy1es/z8yNPVg+1Ly1WWf0ZJtEJcl6hXdlNXoNghPjwuiG0w1k4x4DYHTspm4L0GFwArcXTE3iHwV48O0i7zeYQdQwTiTEH2pE6oi6UDoKkTiAmll3CC5txKBlMugOFaJ7Q/CYcbdDE8E7R8gDg+Obl00zGAOScEgHxNPsZo7ATSNwm8Ddf6Zu6nJuf58i0MWTBhjrPkVu4MfaOHcagVmctcYg+XOt32w7VYqVMobuDbuDNqhPEI2feCxlJuw9Px8B+hsdOQfXJs61oAaT1EzQBR/oDQ0WlxYvNzaAEgUbzwk99RwfLEQBHkXXgPywgPl4zBYDhmI1tubjdYvsmAkhtxl62NayHeugBBntqEUQYoSB4QKYiHKYQlohOLr8+v+d7/3l0fNlLv6+PD3Fo5cgGF1ZXX06P7ewuoYl25fHJ7yHV8nbW2vjxB6izHWrBSxXEwG0gWHGSPPhXLvC20kRqYMDyBA6gHKEBTeV4naC2bk5VBEmXj/Z4RGkhiFiegaamRJirHXv3MaxBL/lBk8AxcGc4ulgVEOuc4qOmrMcp4WfUbHIKEeGmwS0Jqo69lzvzwjg09WfjmWvcgS6HoEeAXf7Cuy6t9ngEEagh+dK2TR2mRjC8A3pkLAu5evXz4DBRGQRfxm4ClUbOg8yYB9y4U6vEptjoUo6407wTlMC8QC7nPUmgGczvs7OZP4Uu3cLsxNdC18zI6yf8FsV6dDCArCfJS3YwPaDuD/ylHueYIHNF20+qHlmdIr2QOwzM5ybRwKmB1q15fIG7lFjZ3alQCBJB+imDcZyDpND5Lg0WOZGqpwZXZp+ujSxsrI1MY6V+phoh5/Y13xuf3/9zZvf9HzZ05lpixKborEPnYQlYxqN0ZGwwoaqmkjOdzuUBrLGPyW44IXLzLFjOuA2ho6LlanJqenZmdW1jadP57HSCONGJNWzd27mYYCdaOW6o35Nqit+FFFaSRoG3Cfb3A+DUEwq2mheWHCKNjHdXiSdDQyvpYXFRTsAgZHLvo4ATr6+9i87lyPQvQjcHLjHL7ruOZUtDVEEent+dGIduGGI4jrkQ8FcLIBqitgwYIdjhr+JYYVkFQmeCMK5xKIRsUeKKJjNqZRMvQP32kIaEkLxsMO5YdIXo+cjwAJooMSVL0LwwNzE8UDf4EMOs+ZnzI0QHOV8MVbJA9EC0GPpPGboAXAJegVGYQvT+0Dzk1O+nbx1IhjP7uwawv4F4H6gU6NYOsI3N+E7drpcm5zc+uc/p/b3z/7X/2V9a+v4l1/+x8b6xbdvArgeQ2iGpLBYhVzCZzhHv4XHbX0LkDmWuGAUCA56hQxRRhjgNrzgvxImJ3HzKPbcxFhwicK1Q9pYE4E0ZM24aqZbA0fmyaKUVoIEwFq+6eKEB1nV4LZK2vahULWdlDgBdYwFBnQc2QMrKiDB9jVWy/lARAAn3kD4mZ3MEbh5BDoC7v61dvNes4Uhi0APz4zumTa0MWSRH+LhbG5uffr0Eb/HmlouATNHWgF4EYGFRIAutAgdyontRBpopxkBtVAaA3psxz9SKW0dc1vFcxOd2NS4zcLb6hdMmWti3ebMzRUCeiU05CQv8CjOQKFS0oaNMU2IyerDo0ObrObaErbBtuUA91jDw83sQYMDjI42kBHMj5FGxVE+OPyPACezrSeoYYHKyP/5fwFY0556V4HRs2LBgDKW8gj52iipiA8cpJgsRzf6hwDX/0xOLWMdOpcAcat7dqZrBgtI/IiiucKpDBVcAqhn+UqPhZp5YWBxMCDPTvH2hDGIUhDhSOCj1OEBI74KWUmvYDel5Ai6iK6rO2M4ll9YzMA9RGUQStyFguciD4Kn2cccgZtGoBlwb/hCu2lHuf1gR6CHJ0QPTZdiHufVStxc6dcI4J4EbPeC5dKAj7UILDpuRxaITpiTaKxY6U6YTqRHHVhpKDXfapjQYGhB0wxrOkEpNBFubKV5oHD9cx6GbZE258eJqYmiUQjeEmSP48lOwvqE9XRBfgjRpmg+Aa+1o2UDCPjmlYBAMP85cHGOuXCDxBwqwSj/9ucX0B1unzVXwKdrEoW4URs64FE6OhYJOA1OEBPHQ8XCq1wDYMYEV8B00gohdHrIxIsVpyXlMGDd6EigCjqtmkIlRzM0ja1Dx8GXwnDRLsbV3FDcjWcdKmY2WGMwxzoZPI21qGeq7yOQj1ffH6LsYNciAODOb++u2cuGBjACPTz8PTQ9gIHOLncYgbW19Z2dj5joBVLmq5wqxoTADBkLaHom2G60wCihX0DQZYqQkNAPyuhNlFA7MyH4ICJwtM5xfgs8cuUI3n66ByyIbjwRNPu0eIT14I1hsTvmsSFDUxiiORrGOK0gJlaNcLcxAReXmWxlaW/vKwhb7yFrtOh2SahmrEJicq+b5UoHzkz6jT5EAjpOQ83d9w7Z3LwQJSEpJPfFKuWcLdLU4FNksF97Qd8JCsFHwWsMlqwit8MUjhprPA64TXli4k9/+SuODlzKaVAi8O3bt4zdB+VgZT9vGIFmM+43NJub910EHFN03a9e2e26owVq6b7pbLE3EcBS6cnJKTxWB2gXSMsSuopEY7dAypCCDwQmbMaMJGCxi1SVhvGp6xQBNMgSgmd/+NOJLkBP67HqUuMI4tM9asiN5ONBy5bYnToFwdl6wXrSloJIbikzt8xPy8kZ422v6so6Y4d4s8Aei7NrILCmRlVmeMeECgTmYsGUGVYN2EYKMFeqxm9sW3QfLcIDs2tEqWrqgYVSydR9OODIPUogQPh5DEIyWahJ7pVCh+OLSTRGQdzOhEJ9It4sLRdnBGfa3/7+n9fW162a80GJwPLy8qC4mv3MEbhhBDJwv2EA+6t5QAbd9qpXdrvtZ0t7WLvQUp6F/RiBlZWVd+/eTqzqTtAAPx1wBX+J9iAKCVJANtQExwmXUQlMK2WASgaJpU4eIaIMScGrBu60jyKllSr7ES8qmB/2mTFjJjfLkJp3plbK6WdgsKVaB0Z9mYBOu/gw45cnZ9wKfe8Eq8y9P1SRZIWlaqiK5wLnJ8xEajpN8tiy6Mzcj/rWNaoJR2M0lrPdLemYc8rBVjASXZcWnDCkhGOkptcTruohbnbsLO64ihr9y9/+7eHDR3b46yOeuX0Zgc+fP+cNf/vyyGSnuh+BDNy7H9NeW4w/7V3uqFd2u+zmtc3l/cKuHbo7bIjLLTxO6N3bd1sPt7D9SgrXolc8cwHWhb4A0FQDdkfJ2XZwLCd4F65U4VrAgw7fraWUHLmaisFnUyMnBej6zBTraijGW8ln30kb6DT2VTmngul5UiRVQ7+mYQOkXlBwwgfCXk9lzjTLaqbkqrQhIyVuWmmgaxhyhWaCLBKBwU5coWCByZe1A7t1ki5bFKlUCex0wl08rZIBl7AdfcUjZYcQOc6Kp89/fPzkaUbtis+AZaurqwPmcXY3R+C6EcjA/bqR6327CAG62VVPjHbTwd7ZyvuF9S62PbWMpavb29tv3rxZW1vF3ZaA4YbOKp3i1AYic4wuHEbsnKJ2NCsUgqLQPFo6fCcKhGGbiQ8Y1OB4BbLzo9QA4mVG7emNIcTEz2Yfv0KRVGPNObQYhJE2h72ZnC84hlFDvVJatWLHddIipS084iRsIxtyjQXcUNJHdKckOsgUK2eHoUrT9GMeGqudmWVEWieD8Fwno6UyhO423Y48IUafPHv+p7/8pSlqb3boMJCcLALh1LyTeGDG/dGjR3fSde40R+CWI5CB+y0HvNpdr34OemW36v8A1bFf2AB5m11NI4CtwZ8//wFrZo5PTtbW1gx08xzXRDuBXZICJCfcDgIgNClZKbDOelQVJVhskD3C2VCFffKQAkcV55BNki4VfFQsxU+jZIHbUBbSZEBkhmpTOio0IQBtrTcj4ujqq6bsMla8eSyck2i4SsERxcx8lklUlehKmYh8NggikqboPAhozhSSvJEThcLrBOzGIXiHAfQfDwlOAdxjsPXoERbJ5NV0dpZcM48xvWb7GzQbHclr3G8Qvtx0wCKQgfstHbCefKf1xOgtBeT2u8l7Dtx+zLvYI0DV48dPsMnMu3fvtra2cNcqpt4B2fgh0MQpoBkJgr0CkBsyL8P1OiViPzUDqhM6JLTjX6zSag2HbJ93B8m+mSqfzCvY1ka5a2oQxq5wWA3SEh2ZZSLqaIA06USoWxmGWRK6SAYb6AaGGxafTVyBpTnhhCQWY3KQoKLIWS3Jva3Enrl+UJJG4MWKS4nXidgDaucqGe1FaYcSOSA79uDf2n7857/+DU+3tYjnfBAjgF1l5ubmBtHz7HOOQKcRyMC904jV61d+qOuVOuX2xGinTgyPPr7Zh2cw93Uk6+sb+/v7b9+83djcaPyd9ptSFZxiMp0wmtDRk9cCfI9gvyCgCIxum7hbI0J2GAmbQgYm19DTdmLdRE3yK/QKMVBqSKTCO5AuBSY1LckDHZllgl5KJRDeujAi/TLXasy9VcEgSoZn1klCVzRY1V/kgyAdmLIiyF3F3aaoXNA7nTdPZAbME4AOGRE6YLr+WFqyEiHDUcPpgadJzS8u/vDTn9c3N/Pmj3YmDW6e52UG99hlzzuNQAbunUWMP99dTz0x2nUvB95g/l/qwB9CDQC/0Lhd4f37d4fzhzim2AodzxaCJEyuF+g9cIAIXSrMRqgYYHuE95GQoYBTUdEfUSaSMgPxYLNGW0zlz3BcNmPClnmwEJXcYsJXR5AHCck6OjKbEIaW1To0lyaQrvVuRBv51SqKGNX0lxLe1mG6jYV6ShwYvbFazMWmKelVpVENhCbVU4bxDMNrnQyO38T42PTM9OOnPzx+9gz76Nvgcz7QEcBKSNwAM9BDyM7nCLQZgQzc6wNV/h2u1+mY2xOjHXtxbxvg7qV7O/YhGziepbq9/XhnZ+e3X3/d2NzEDzZWzsRJU2JwDJj4zxF6ROWG172KghDQkXogBBo9XrIErBiqNSDeRLZaxtVggVRoFbmtCCoXDRKqQnqVhY2PzdzDEjNKy0Sh7HwFgMbMspUps5EWJ2V7a7KSv1g1VUjEcQ2TqmPy2RQe6G3mjBPyoOKaqpYyWU84NgOfzMPb/Dt2zZ+cnNh8uP3DT3+an1+oXHC1OkJZ1t8RyHsP9Pfxyd51MwL3Hbj3BEv3xGg3j/r9tJX3Cxum4w5MvbGxsbi0+Pb1m/fv36+vr2P2nU8htdl3YEDBcnwWDZFi7BGvCx8Ss0nUMPluqgwWgCALTqjzzzBnwWEnVqOkktr8Fqi2Cz3CmosCp6aaiiItgspNOHEUYXReWhGkXjNj1VzCRk3jKI/GSEQOCdZSJuViKdoUBQUrxQk6VrHBOV1XEK+niStlsE5mYmJ0aXnlpz//bXWd9zdXDlj/Vfvfw/6J2Wje7bd/Dkb2pNcRuC/AvSdfgT0x2usjfn/t5xn34Tv2M9MzP/7008np6Yf37z7t7DzA45pWVjD3hjtZ8el07J5u5U4MTvToIL6hdIwO1OfBqofsBuKhUqiZfvGdECTNgl4jL1ih+wrHq6m0lo7MMsHm4gAHm+c2zmo11ZGGqxk/zWXHmruiuggq5OkvJcq0eQTPqBqSqCIL7FLJFiGJLKF1m18HXj8/Ozs/P4PC7OzM0x9+3H7yDDPuzY6J+MUhbKmWhf0VAfwXrr8cyt7kCPQsAkN4rvfke7cnRnt2VLPhugjkNe51UelXXiefuKmpySdPn56dnmHPmd9++21xcRFbRs7MzOC33KFueVY9Tr37Upl0xbtDUYSlBrJrltawqMVNXtpkvDG8vw6iKjQd9ZP27KcwKixcVOUmqmrtepGOBBUMRIvy5sZxgyaPow7VQscNFAy3WebTdJmDmkWqQhRMmpQSNZhQs5dVVSuzqB9SCagb01mcXT8/Pz05QQa/Zman11Y3nvzw03xeA22nwDDm5+fnwzisPKYcgZoIDCRw7+Q3vWbM9ayeGK3vKnPvJAJ5V5nbDvvtfqYmJicePnp0cX7x+fPuiz/+AHC7NNNUAAAgAElEQVRf39jAU9AB37F+xsFtEgJ4Z8yA6n0y3lUAx4kxY6rWfe6+kR1bdEIAn4bkFIuEZC2tum+BFUohcOk257gdYF126UUgrNo6N116UPtHuzSAt+RelOsUJnpWVZ5kAZuHkreeMrEQGetYCgO0fomjfwrAfnx0dnY6MT7xYGVt++mzlZVV3oGqqy7cucwxi+bocxqWCAzC2qdhiXUex11HoN+Be/d/+rtv8a6PYe6/vQjk/cLai1MbWn38IRobH1tbX19ZXf3+7fvrV6/Hx8ewfmZudnZaE/BA8GGRjA8zViNBQbUClmFUa8U5dq6WcbYx07x1gNQwVRctRBm5rpMwRSZsqAqFskzpgkmXJQmtosjgq8aUKAAOF7WkYqTAuMchcGgh2GJZNFLfkpIqEdSSIjORqloNDPFUFtKoJoJ8lxl0v7jEkpjjo8PjoyMA+Ln5hSdbPz58tD0zOxtvWWYk4GAC2bn9kKF5i6Bp5HwwI4CP7GA6nr3OEeg4Av0C3HvymeuJ0Y5DnBv0SQTyk1OvPhDD8pEBXFtaXsKtqwf7Bx8/fnh3cgIEDzSP/WcwE19sQYMfe+JN4nAgQcbHfv8JDJVYjZVCLCxqGmQ6pVKZt44aIApWQZm8ru68UITSGyTV4ChZkY5E8DMOwYiozEHLmPPVMDDFs8zVSlqmW8mtrXIoUz/QkTC2oio5+ocONYsUaEhSkSrQ8ol2UOcX56fHx4cHB0Dtk9NTmw8fPn76fGFxqZ1noEYQrwjgDHAcb0HO+WBFIC+VGazjlb29SQTuALiHX7mbuN3QtidGG3rJjEGOwL3eL+xefkCAuucXsOnfj4Bkx0fHQPCfdj4BoAHRLy0vYxUNVlDwNlZuREN8WULpFQSvOfYKiOenIU69gzYI3OFnhI0MObJhQjkZikRiimJ4FmnhaNqJBEyaNHJEkFvLN2ljLn1r1TJnS/aOP9fzGrmWXEEV0dS0WqSsHrlBAQysX9d6mJNjzK9jVQxGu/Rg5ce//HVzc2ticpLBuVaKOB6tI4jH50ZRupbF3OgWI4Cr8VvsLXeVI3CXEegtcO8+Wui+xbuMfu77NiMwtPuF5Q/FVacRQPgMNhV59gwo7PTs9PPuLp69is1GgN2XHzyYn5ubmp62pfAG10sIPkzCC7laT9dYKgP4dwUIBGYNKaGcDEUoTVNNAiuWJPSu50SR4Woqux0H2t7Y9Io8VWtoIGBtlmTFBiOu14OGK0GBOgUyF0NMsl1gYm4Oc36BZevC6lgPc3h2eoorrrmFxa1H2ND/KZbE2IELAbxpGUE8h68UoXxg5LK/InB8fNxfDmVvcgR6FoGuAffug4fuW+xZFLPhQYhAO/8978dx5A9CF4/K6Ahm5ja3tvDC/9Zxv/Ln3c/v3r7DFoFLS0vYjsYQPKfhR0fHRsc4oe6racwJw4fAlUK3hWOE8kmKeC/hNbSRrEbTQC/RqyXvq76aKoumWsEMnpZFgUt4bF5Y6Qjela1f8oJ+IW/kCISbJturmbd1umAmclpnktOikHEzR9xoiksrrFxHOjw8OjrAhffY2Pjc3By2dFxZXVtcXJqcmuouXveA1xURykOYQXxdhO6Yh4vwO/Ygd58jcFsRuA5wL/1CdcXR7lvsilvZyFBFQOigz0aUz/y7OyBA5w+UgBIPDg++7X17+fIl1kxPT03PzeN21rnpqSksvcBMfDoZX/bXpt7BI5C9RmKjomFBBXsFh9CWKRShJKtKsx6ZIiKnIKoKNgL7kPhHxdC5s+SSc+pp64k5m+DNItAiyDBRqLoKuFy3zgclnQOgn5ycHOGWlMP90+MTXDlhG8dHj5/hJuP5hUVs/YlLKkbi7lIK4uFFxvF3dyiKnvf393EHS1HPVI7A8EagKXDvPqLovsXhPSx5ZD2IgDBCD+w2msynemNMboNzzbhjah0L4fECqgRmxP2sBwffv37d4/6ClyOTUxNzc/O2nAaz9QDxQPy4+dXn44FAbzg0gmFLoVSNoJcpMF2trpqI1Io6RXNKI6cgooI3CUrqwD4r1rIxF+qW1UaZI3EIlFyVfTgjFtptHevVMa+OmXWsYDo9OT2x20wBhoHRHz56/GBlFagdYb9zsK5jUZ+lOD6D+PoY9Z6Lf5f1vpPcQ45AX0TAgfs1f/GaDaHL5pp1k/k5Ah1EoGtLZfLp3UHUu6J6CxEnXsX8+dQU5tmnMbmLKiAmEOXR8dHB/v6HDx+xPTiYgO6AkoDyuNfZcHycj7/hso2IpNOQVZgOsk2DqBlJGb11OhIOrRtFoaFr1lYxeDNOaZt/bON/RpbAOtepB5SOWXVOq+PWUtxhenx8gk06MbOO1S8bWw8fPODMusD6LRx3DrKLKQXxXTSbTV0Zga9fv2IZ1ZVqWSFHYAgiMIGvxpt+O960/RCEMQ9hACIArNCul/mUbjdSN9S780A76q39FgQQx1IZzPcuLHAyjzj+/BxA89ve3scPHzFVjCl5zLzjH/RYWjM1OTUeJuNtibzlMIJUaz9i7jSIEX5HpiHoqFyqEibTNVOWSAailQoR9SPCVvNo0y2kUofg4cJAooJnvYWc/BFf9AKYrjXqnE0XSAdKx0XQ4fHJMf9fMT6Bf3HMzs0/3H4CvDUzM4vnZ4GrWNlohjC3+XicDQxYTl2NwIoutrtqMhvLEejTCDRdKlPv753/zta7lbk5AldHoLodZD6Zr47ZTTT6PL7Y/REAqgMngSm52n2Ci2osLliRDUyKxfGfd78A0GNlDS2OjgKy42TD3D2mjSex1EaAHkwkYlYlqFmqDXGE4FFa4kT8LXEqinSViE0MlMNRoW2fIYcd41fUCqYoAXZkwuYsdA8pV6Yr4bpGYP0UW6sjAaYf4WoZw8eGmwgatuBcevZ8Fnv4TGEPH2D0O16qHmN7a4TNx0fUntfVdDHyu7u729vbXTSYTeUI9G0EmgP3Dn7R+nZ02bEcgSICfAATzup8YhchuTY1uEEsUNPNpz2BwKenZ6Y3ZtY3NjghPzJC/Hp+foq9UHC2HR2eHO2fnX/FkhAgXJ56YyOYmJ/AW3+TE5OYpDdYH2A8l4zwD0mEna+xgi44fZ+mCLXFrOL1EkCHhrxU5nQE7BTyj4m7uvifQXKMSqvRBczjRLoWpgOtI2H7F2Tj45w1x8UKJtFX19YXFpawUSMuX8a0S0/qdaYZ73AseUgR9lDNwblGBPKM+zWClpsMaAQCcK/8GAzoaLLbOQLNIzA/P99cmCVpBIb064BPR+3Z0Ai1R2xaHdsUArlymbwlgWEs8EbCPDQ2I8c89Le971jizTlrwv3zcawSwe2uY5yShx1QSukcPcVYCw41e8dCMB81fzyUehMCN2xu9QDKhcwdoBtKdzkhP93RhQcuPbBr+inwONYDMT8/h9rExCSa0KfxcSxumZ2Zm1jEWLGF5gyvP7TWBVK6kjFoOPjtlAh9vCDz5TQId45hO7ELOljjnneEDMHI5ZBHYIITkD37LRvy4OXhDVQEsGn3QPnbXWfv64ccYB3J8HrvUHvrYyUgi7loaAHmjiS7XxhoBlbGVDawMqrcYuX89BI4eYRL6sU8A7IHjAbux3c1lAXqRtGES1ZGLoHngcQB6DlQMoi/UaVUcNzUEAhgf4gnJ6akyMhIgTgelwPQhadA3gDl0JmdnYe3SJhHx1p/wHVcMPDaAc0ypkQQepN8OU05wnlRzZXBzrvKXBmirDA0EQgz7kMzoDyQHIEmEVheXm4iGQ72fYXm9UdPS9ghuiuwXu9VlStITzg8MTI5PV2V1tYNahOdC5Tb5DkXsgDWM9EkCkzdk1RdfGUJHHQ9tSgUKvqpINN3FwFD8ziaugzNO8fXHAns457/p1oTl8waxghk4D6MRzWPqS4Cnz9/rmMPEC9D8/YOli2JMYzTXosB0jJcDlA+njgN3J/UMjmcEYhndLqKJk/G28HO62SG86TPo6qLwL27r78uCJl3LyKwurra9+PkDGnzV9+7f7cOclWMsE1/z7LfbZBy70MWgQjiudQJ3x3Kh2yM7QwH2xi1o5Z1cgSGIAJ5xn0IDmIeQlsR+P/bu7f2tpE0McCW7W512+N47Nvdn7dP8mezd9mLzV2SnVza43i8brfarRQJkYZIkMKhCqjDq9bTpkCgDu9XKH4qgWAeK+5WzUcFa+JOk+/tOLF8uxPIXaB/cXw3yxxX6HNveoz2hXsZ7Zc8YpSVsIymYpLQsfGiJe6ND4CGur/WNe5S87UG1e6SmFDX/m8UXhDXUldP/gInZ8Pxcppwqpw8lX9fRrYwvJN75J6b7lbQq0OtI2XT+EeqXOIeCVIx2QtEvatMQfNv9oGZ2sAuX9/lIKIw1c7+LQocL6cJuVitSfzDez9aDG+iPq8/u/pVYWwoJe5jpexXukD4aPpxXVh/whrXLnuF5cJjvm6SNx4ITBfoJ/Hd0V0qf0zopxeZxRES9yzCsKgRG77yFvZyInFfNNAcXJBAuF9Yr7UbzhG9Vng4RsAlMWOU7DNFoNnz/zxDqePi+EIulZkyRu27nkD0+eD8PIvZGYl7TE1l5Sywv19YOD+jn6I5d7r0tvWW2EvvSjPtd4LlHOox0Tnuc1yGD1vSZiJDZJNq3L85dagU2whsIHA8hxbWPXwSSNwXsjq8GAH3CysmVN0lMaG54Sr24YmrnK5k1tJYryeZdUtzkgj0r6s55u7HB0mq7BU6fqyGPU3vPTkPqxEYPAluJO7VBFhHnhCwJPMEUA5P7+7FLl9/FInBmfvRHn4gkF7g+Bv08UH6OifU4AOYJmDZtXABH8BUeAA1f7SAiyBHU22x44+UvapMNXRm4fcWwVAngckC2563j9/CNLnxDiBQkIAV94KCpamLBNx2YBFfooPzy9e3zT8SMSuWQGqB/kr88eL41JUey3/z5s3xsQcE6haw4l53fPXuh4DE/YdFDo9WSdlnrHbnYKMNBIoWOF4cH3qxzm/Cnz59KlpM4wmMF7DiPt7KnmULuFQmi/iFfH23NBfedTrnBX3OMVl0WyMINCrQX4nvE4Rz+dJT/d1GPn737t3IPe1GoHQBK+6lR1D7xwp4c+pYqUT7HZfYz9bgwkv4yO9ETVMsAQIrC0TM2kPLP3z4sHL7VUdgKwEr7lvJq3dtAfcLW1m8Wx2/vwn/hZpnLrGv3GbVESCwocDsi+OtuG8YNVWvLGDFfWVw1W0m4H5hUehHLo3vs/bw8Um7vH2ty1yj9E8hBAhsJtC/OD40ovvlf0xr/v73v4/ZzT4EKhCw4l5BEHVhlID7hT3JNP5l8lpR/Uti4v45/FqtniNAoDaBbv4I81J4cH0x/u3bt7V1Xn8IXBCw4n4BxubqBBq8X1i31j3+/0tivr8gZv86u/u40zi/Aixpj2MJEKhDoEvfj4vxg5PL58+f6+isXhB4UsCK+5NEdqhEoL77hQ2+gG0RrXBJzO6imC2qVicBAm0JdHl81+cw6XQ/vn71ui0FvW1YwIp7w8FvrOvFvXspvCZd/946gLtL2Ls/Ylti3zoW6ifQosAxif/629d+/60i9DU8rkzAintlAdWdiwK53S+s5JeW3RL7Llk/vmxeVPcEAQIEkgvc3t726+jPTGGe2k9Y/ec9JlCwgMS94OBp+iSB9VfcS07NB2kPL39S9kEeGwkQ2Ejg7u7uUs2HaWu/2nBpJ9sJlCMgcS8nVlq6TCDFNe7VpeaXicNVMfL1yzyeIUBgQ4EXL148WfvjZXh/L3wSzA6ZCkjcMw2MZkUXmHRXmYYy8ivQP27suL8w5sqeniJAgMB2AvfhcpgpX5f23l0DOKUc+xJYX0Divr65GrcROL+Pu+z8YiT6KfvFnTxBgACBLASmJu6XGt3P2iXxl5Rs31ZA4r6tv9rXEwifnBomYsn6NfGHfN36+jUkzxEgkJvAmEtlprb5mMR3Gbw8fiqg/RMJSNwTwSo2O4Fv375l16Z8GmSJPZ9YaAkBAhMFrrw5dWJJA7t3GXz3f+n7AJBN6wpI3Nf1Vtt2Aj/99NN2ledac5evh9b5+KRcQ6RdBAg8KXByO8gn95+9w3EZvivBvSZnSzpwtoAPYJpN58DCBL5//15Yi5M2N6Ts4Xt3o5j9d9K6FE6AAIGUAl+/PvoAppRVPSr7eK/JR1v9QCClgBX3lLrKzkLg4bL2m5uHB1k0aqtGWGLfSl69BAgkE3j9+nWyshVMIC8BK+55xUNrZgmEjPzK90ORbSfu+z/wWmKfNbwcRIBA5gKfP3/OvIXWjTIPUEHNs+JeULA0ddHU1+ilMje7+xvv4MI/LmR3DhEgUKPA27dvM+/W2cXxbhifecTybZ4V93xj03DLLi2fLyJp7M2p9/eHq9hvXMW+aOA4mACB3AU+fvyYexMft6+fx4e3GT1+0k8ErglYcb+m47n0AosW0Sc1r6HbQe4vZN/l674IECDQgMD79+/L7aV3uJYbu01absV9E/YGK02yiD7JMXwA06T9C9u5W1/fp+xuFFNY7DSXAIFlAsWtuI/vrgWY8VaN7GnFvZFAr9nNTOeZL1++rKmwYl379Zrd31ozlV+RQlUECLQokP817rOjcnIZTZjlT7bMLtmBhQpYcS80cJs3O8wel743b9twA968eTP8RLlbu1X2EIjdVTHh2xcBAgRaFMj/rjKxotLP2l0cH0u1rHKsuJcVr01aW0lG+OnTp0344lfa3SjGW07jyyqRAIEiBdq8j/vx4vjwIt1P6IsMoUaPFrDiPpqqiR27hduT/1fS87/+9a/F96RbYg+ztTeeFh9LHSBAIJrAVp+cGq0DywqStS/zK+xoK+6FBSxecytZRx8PUvC7l7q3nIauytfHx9ueBAg0I3D7y21GVwtmmUdbla/mbJC4VxPKKx1pLkcftCjxfmG7e7GHq9fl64MRtZEAAQJ7gbu7u4wk1nnJnfjrQX/3cHH88TKbjNw0ZZyAxH2cU0l7rTNnlCTStbWkFfeQr4dkPSTsUvbyBpoWNylg3t0w7DfPXrx4sWH921S9YMidZO3hbVP3N/vi+tn9Nr1S69MCEvenjTLeY8GJm3GvEjWtjPuFHd94GhRk7YmGgmJrFTAj1hrZp/oVMs+ndvH8RYGHrH1/b7I4LzuicRE7whMS9wiIiYvwWhQHOPf7hXWr7McZNE6nlUJgUwGz16b87VQucY8S68GsvXtpmlZ+ihPfLwOHGEjcDxK5/JtivOfSt23bken9wnaTYoDp7sW+rZDaCfQETEU9DA8zF2jxUpm1QtLP5sOssFn+PHtG2qzFqSIkcU8lO67c2SNxXPH26glkd7+wwxJ7r40eEogtYI6JLaq8DAXyenNqhkCRmnSSA89ZjI/UkgnFzJsDT7o6ob7ku0rckxP3Kpg3fHoFeLhA4Pb2dsHR8Q61xB7Psq2SzB9txVtvJwjkMr1PaHINux4X48vI4CeRz5hv18r1fQDTpEiO3znE/Px7/OH2jC+QwZLM/q38u6luxpQQH0SJWwqcTw9PbtmyueomkLXAb1+/np9AWbe4rsYdM/jQrZDEN/p1PgSf3DJLyor7LLbTg0JwfOUusNlFkN001uXrzc5ouY+OGO0zDcRQVAaBGQKDb2FKfUaazgcj1U/iww4VLsYPdnvexklj9DDgJO6TsCcZTyrZzskFVr7twMM92MMcdjKNJe+oChYLONEXEyqAwJoC//jHP16//suaNYa61pknDtnayp2LVt3gC6Bsfo5vN+Bunkncr+itc1ZeaYCnYgqsl7jvl9h9dlLM4MUqyzkdS1I5BHISePPmTU7NidmWhZNWnnn/YDYfU63qsiTux/AuPDuO5XiQqUDyS2W6S2JC781J6w8Bp+/65mokkI3Ap0+fXr16nU1zMmrIkqlx5aTfMvzIcdNy4r5kPI/ktVtGAgnfnNql7PL1RNF2piaCVSyBWgTevXtXS1cy6se8qXd2un/yEiqPvzQU2rmrTBiBJ9+XTGyvUyD+/cLCvNJ9h/nmZMqpkzBNr07Oy/Mf01SrVAIEqhH48OFDNX0pvSPnU/iTWwa77EV1kCVsrHXFPYwTXwQeCUT+ACarAY90L//gXLxs4xkCBKIIWHGPwrhVIVNfJbpF/XDU7NX9rXoapd46VtxD+E6+o+AopCqBwfuFTe5hyNe7L6sBncPJmXf+42RiBxAgQGCaQLjGfdoB9i5ZoHudCT3oHjzff0TK+YvPcbeS+zrQ9rJW3EMUfBGYKfD58+eZR4bDuny9qUtinG3zh4sjCbQssP7ccVPxXWVaHkkj+36yjHby5/DZw/GwSjeyFevtlnniPht8PUE1lSLw9u3byU2te33d6TV5QDiAQP4CLZ7YX758Wf8+7vkPhTZbeJLHz0YYfyKtnOJnlbiPV5odCAe2K/Dx48fxnf/x8Unjj8lwT6dUhkHRJAI7ASdnzHHw66+/xixOWQSmCIw8mWPl99sm7iM7O8XPvgQuCLx///7CM73NhyX2kj4+yWnUC6CHBFIKONlS6i4o+9u3b/V+BNMCF4fmJDBy+ngyv185cR/Z7JyktaUWgSdW3G/uw9mSb77u1KllHOrHdgLOou3sE9f8008/Ja5B8fULnFwcv1WHr89T4dmkifv12rcyUW+jAsPXuB+W2MON2Lcfr9u3oNGxodtFCThPigrXKo39/v37KvWopGaB/sXxmSTxg9yxEncz6SCvjRkJPLqrTC9fX6+JzpL1rNWUrYDTINvQFNywmwwWXgrm0/QzgX4Sf/bkxhvmJe5m3o3DpvoZAg/3ce8uiQnHJzovnRwzYuOQggWM+IKDV03TJe7VhFJHnhQYmbibmp+UtEPuAl+//ufuduz3z+OM5jil5I6mfU0KGNxNhr3kTrtUpuToFd/2la+ruZS4m7iLH0k68ON+a7uz6tkvv/yyW2WfOrSn7s+dQKYChnKmgdGs5QLenLrcUAmzBfp/v18hiT8m7ub02SFzYAYCg+M3nEDhK5xS+7Pq97u7aw0dLOHaAZ4jkImAsZtJIDRjM4FwO8jN6lYxgZ5AP4nvbY75MCTuYdI378c0VVZagadH6/1uRIek/fEJ9OLFi4eGPV1C2h4oncBoAYN1NJUdGxbwAUwNB7+5rj9vrsc6XIpAyFgGv6+0Pyyx71bZuyX204znPtyovSvwSgmeIrCewOD4Ptm4XmvURKBcgS9fvpTbeC1vTaC7GmB2r4+XyswuwYEEFgicZtezinp8ScylInaJuy8CaQWiDOi0TVQ6gfoE3vjc1PqCWm+PHl8N8NDPkMgMbj9nsOJ+bmJLGoGTlcTux0VVdevr+8E+NN5PKvxxqcyiSh3cssDJmDr/sWUcfSewmcCnT582q1vFBGIInGQxV1blrbjH8FbGiUDIZ5J+PSyx/7iKfUyF3//4I2mjFF6+wJhxVH4v9YBAdQLv3r2rrk861LTASR7ft7Di3tfweLrA+Zpj0uTnJgzm+/C/3fez3Wfldd9j2u1+YWOUat/nOGQGH9Tee/0jUKnAhw8fKu2ZbhH4IdAtWlpx/yHi0RMCIdVZ8etRbQ8Xf42+BGyone4XNqRS37ZHA6e+7ukRAQLnAlbcz01sqU+gW4a34l5fZCP16HxFMlLBJ8Wc19NtOex2HxbYD2/ZWJSTuV/YgbSCfy+NmkUjpAIXXSDQpoBr3NuMe5u9tuLeZtwPvV4rz5lcz83uFjD7vwqFq2IOrV32r/uFLfNb7ejJg2W1lqmIAIE8BdxVJs+4aFUKASvuKVRzLTNkRCffsVt6Uvzxxyn17O8Vs7+Kfd/cKYde3dfMfpVn5SePQ+P8wcotUR0BAsULWJcpPoQ6MFrAivtoqrJ2DOlQ+q+olew/HWl3AVd492mSpvtbahLWa4VGHSDXKvIcAQJNC7gSsunwN9Z5K+5VBHyf7j5aTY/drfMaoiVl4VKY3dUw3cedxm53rzzvXuphRHw4ODS6jRFrURQBAgQuCrj3wEUaT1QnYMW9wJBGS5mH+564+F6lIV8PGfuVu5X29l3+0P3ClhmuNy6WtdPRBAg0J+Buv82FvOEOS9yzD37KfCll2VdgD1fFrJWyd02x4n4lJL2nNhoUvRZ4SIAAgUkC379/n7S/nQmUKyBxzyx2KbOmlGWPYTzk68muYr/eCNe4P/bZejg8bo2fCBAgMFvgZvdxfL4INCEgcd8ozCknmZRlz+LqLonZKF8/trixu8pkNwqOgfCAAAECBAgQmCcgcZ/nNvGoZElUsoIndnB49/DZSeF9p8nfdTpc+dnWeu8XlvcoOAuEDQQIEIgrcL/75A9fBJoQkLgnCHOyPCpZwbERdkvsobHRPjspSvvKv19YMfGPEi+FECBAYKTAixcvRu5pNwKlC0jcY0QwTUKVptQY/b1UxsMlMevdKOZSQwa3F3W/sPKCP2huIwECBFYQuLu7W6EWVRDIQUDiPj0KaXKqNKVO7928I0LKHr7WvUvM1JZmeb+wssM+NQT2J0CAQAqB29vbFMUqk0CGAj6AaURQQnLV/x5xxJO79MvrHj95SJY77G7DvvvO5kL2K0pb3y/sPOZhiy8CBAgQWCrw9evXpUU4nkAhAlbczwKVIJtKUORZs9ff8HAh+/oVz6xxxfuF1Rnwme4OI0CAQGKB169fJ65B8QRyEWg4cU+QXCUoMpeB0rXjfnePmP0i++7ejoV1N3biXlj38xpJWkOgAQFzxKQgL7kvzOfPn1+//suk6uxMoFCBlhL32JNo7PJyHkL7z04KVwyVlqz3TZddKtNStPtqHhMoU8AZW1zcZocsHPj27dvi+qvBBOYJVJ24z54GhiyjFjZUQZ7bunedhraVnLJ3tKPfnNpoqPMcgFpVvYDzrfoQr9DBjx8//vrrqxUqUgWBzQUqStxjT/+xy9s81hMbcBM+0CJcGlPP25eHbgfZepAnjgm7E3gk4Px5xOGH7QTev3+/XeVqJrCqQMlpWXjR6H8vc+uX1D1eVr45mK4AABM5SURBVF7JR4dV9v0bT2vK2kM89h/AdBLnksOk7QRiCJycEpN+jFG/MghEEAgr7hFKUQSBEgSKWnEPLymRvuKVFKlBORSzW2LfX8Ve/lUxg5xfvnwZ3G4jgWoEzGzVhFJHJgm4xn0Sl52LFsg1cY/3+hOvpKIDfbnxvXy9bqs3b95cVvAMgSwE6j4HsyDWiBoFwl1lXr1yR8gaQ6tPZwJ5JO5RX6yiFnYGVtGGw70dbxoR+/TpU0XR05ViBBo5v4qJh4bWKOA+7jVGVZ+GBTa6xj28lPW/h9s2amu/mO7xqMNa3ml3CfufAWB3b8dKr4oZDO+7d+8Gt9tIYJ7A+eQzuGVe4Y4iQGC8gE9OHW9lz9IF1lpxDy9okb7ilRSpQeUUc1xi3//aVE67I7X0w4cPkUpSTOUCJpnKA6x71Qnc3t5W1ycdIjAskCxxj/TSF6mY4c63srW7F/vuVjFNc1pxb2XAX+1n0+fAVRlPEihX4O7urtzGazmBSQLxEvdIr4eRipmEUO3OvSX2avs4vmOucR9vVe6eJpByY6flBGYLvHjxYvaxDiRQlsCCxD3SK2SkYspiT9za7kYx3VXsiasqqHh3lSkoWJeaarq4JGM7gZYF7nd3M/ZFoAmBcYl7pFfLSMU0EZiZnXy4KqaVG8VMUnIf90lca+5sZlhTW10E6hOQuNcXUz26JHA5cY/xWhqjjEstt/2HwMMlMWFD21ex/xAZerT/5NShJ2xLL2AqSG+sBgLtCrhUpt3Yt9fzXuIe46U1RhntBWFJj/dL7I2/63Sk32+//fbn9++DCzNh3O4+NXb//5Gl9XebffjsA7vaZx8++8Ar9YYyfREgQGB9gTCre3Pq+uxq3Erg5cP91OfW79V6rtyy47pLYkIZlthHQ97+fPuv//rf//zzz5cvX/z++90vv9x+/c+v4WM7wkfuhY/L/vj3j+/fvQ//f/tf3oYtr//yOjx7+8tt2PPly5fhqFDP8+fP//jjj59//unbb99+ffXrl398CdfNf/p/n9799d2Hjx/C/8P7X8OWcE1OePa3376FPf/443s4KhyrXs7GlfPIvJFonnz16tXolwI7Eihb4OZf/ut/C7nI+E7I1MdbJdmze+OpfD0JrkIJECBAgAABApkKfP/+vXepzIVGytQvwKy9eXche7ie494bT9eWVx8BAgQIECBAIAeBR4m7HD2HkJy24XijGKvspzR+JkCAAAECBAg0JPAyJOvy9RwDfrgXu6vYc4yONhEgQIAAAQIEVhd4tOK+eu0qHBA4ftypX6gGdGwiQIAAAQIECLQqIHHPJvIPl8Q8c2/HbEKiIQQIECBAgACBjAQk7hkE43AVewZN0QQCBAgQIECAAIFMBXZ3mPa1mcAxZffG081ioGICBAgQIECAQBkCEvct4hTy9WPKvkX96iRAgAABAgQIEChOQOK+bsiO+bol9nXh1UaAAAECBAgQKF1A4r5KBLt8PVQlX1/FWyUECBAgQIAAgfoEvDk1ZUxDvh4+6zR8yddTMiubAAECBAhsIeC+zaupd+nUatXlW5HEPU1sjpfEpCleqQQIECBAoFUB6XKDkc8w6Nv8LiFxjzv6QxRvdm88tcQe11VpBAgQIFCYQIaZVmGCmpu3wDoj/PTXA4l7vFFxzNdl7fFQlUSAAAECKwqsk4us2CFVEShb4OSUvJG4L4rnffcxp12mLl9fZOlgAgQIEJgqcPKiPvVw+xMgUJiAxH12wMK92MNFMSbN2YAOJECAAIGjgFeTI4UHBAhcFJC4X6QZfuJ4o5iQtp9edzR8hK0ECBAg0J6ARLy9mOsxgfQCEvfRxjf3u3eeWmIfDWZHAgQIVCQgEa8omLpCoFgBifuI0B3u7WjaHoFlFwIECBQkYF4vKFiaSoDAM4n75UFwXGK3yn4ZyTMECBDIUkBGnmVYNIoAgWUCEvdzv/uQqO8uibm/MfGf69hCgACBTQVMzJvyq5wAgU0FJO4/+I/3duwujfnxhEcECBAgkFBALp4QV9EECNQkIHHfveG0e9epN57WNLL1hQCBPAQk5XnEQSsIEKhCoOnE/ccSexWx1AkCBAhsISA130JdnQQINCnQZOJ+uBTGEnuTY16nCRCYJCAvn8RlZwIECCQUaCtxPy6xJxRVNAECBAoTkJoXFjDNJUCgWYE2EndL7M0OcB0nQGAnIDU3DggQIFCDQN2J+/3Dq5UbsdcwVvWBAIFBAUn5IIuNBAgQqFCgzsT94ZKYEC8pe4WDVpcINCggO28w6LpMgACBU4HKEveHJXbvOj2Ns58JEChAQHZeQJA0kQABAhsK1JK4H65it8S+4WBSNQEC4wQk6OOc7EWAAAECjwXKT9wPH5/0uF9+IkCAwLYCsvNt/dVOgACBCgUKTtx3F7KHS9jvb7w8VjgwdYlAMQJmoGJCpaEECBAoXaDAxP2wxO5C9tIHn/YTKEpAgl5UuDSWAAECNQqUk7g/5Ou7G8V4/axxKOoTgc0FTC2bh0ADCBAgQOCaQAGJ+/HjTr2oXouk5wgQGCtgLhkrZT8CBAgQyEog48T9sMTukpisRozGEChKQI5eVLg0lgABAgSuCuSYuFtivxoyTxIgMCggRx9ksZEAAQIE6hHIKXHv7sUePkPJx53WM8D0hEAKATl6ClVlEiBAgEDuAnkk7g8puxfj3IeL9hFYXcC0sDq5CgkQIEAgV4FNE3cfd5rrsNAuAusKyM7X9VYbAQIECJQpsFHibom9zOGi1QQWC8jRFxMqgAABAgRaFVg3cbfE3uo40+8mBeToTYZdpwkQIEAgmcBaifvh406TdUTBBAhsKyBN39Zf7QQIECBQv0DixL23xO5Vvf7RpIetCDibW4m0fhIgQIBAVgJpEvd9vn7/zI0ds4q1xhCYJyBNn+fmKAIECBAgEFkgeuJ+H27C3t2I3at95FgpjsAaAk7cNZTVQYAAAQIEZgjES9wPN4o5Xh0zozUOIUBgLQEJ+lrS6iFAgAABApEEFifuxzzdx51GColiCCQQkKYnQFUkAQIECBBYV2B+4n7fXRAjX183YGojMEJAmj4CyS4ECBAgQKA0gemJ+26Jffe+0y5vL62/2kugPgFpen0x1SMCBAgQIDAgMD5xv392s0/Yd0vsEoUBSpsIrCLg7FuFWSUECBAgQCA/gRGJ+8O7TsM6+42UIb8IalH1Ak676kOsgwQIECBAYJTA1cT9IWWXN4yitBOBZQJOtGV+jiZAgAABArULDCXuN/e7a9hDz73xtPbw69+mAjL1TflVToAAAQIEShPoJe69GztKKEqLo/bmL+Csyj9GWkiAAAECBLIW2CfuvZQ968ZqHIGSBGTqJUVLWwkQIECAQP4CL+9v/nRJTP5x0sLsBaTp2YdIAwkQIECAQOECL2/unz979mfhvdB8AusLyNTXN1cjAQIECBBoWqB3jXvTDjpP4EkBmfqTRHYgQIAAAQIEEgpI3BPiKrpYATl6saHTcAIECBAgUK+AxL3e2OrZBAGZ+gQsuxIgQIAAAQKbCEjcN2FX6eYCMvXNQ6ABBAgQIECAwDQBifs0L3sXKyBTLzZ0Gk6AAAECBAjsBSTuBkKtAjL1WiOrXwQIECBAoFEBiXujga+x2zL1GqOqTwQIECBAgMBBQOJ+kPBvSQJy9JKipa0ECBAgQIBAFAGJexRGhawgIFlfAVkVBAgQIECAQL4CEvd8Y9N8y2TqzQ8BAAQIECBAgEBPQOLew/BwYwGZ+sYBUD0BAgQIECCQs4DEPefoVN82mXr1IdZBAgQIECBAIJqAxD0apYLGCUjWxznZiwABAgQIECDwWEDi/tjDT/EFZOrxTZVIgAABAgQINCggcW8w6Em7LE1PyqtwAgQIECBAoF0BiXu7sY/Uc5l6JEjFECBAgAABAgSuCkjcr/J4clhAsj7sYisBAgQIECBAIJ2AxD2dbU0ly9Rriqa+ECBAgAABAkUKSNyLDNsqjZasr8KsEgIECBAgQIDAOAGJ+zinVvaSrLcSaf0kQIAAAQIEihOQuBcXsogNlqZHxFQUAQIECBAgQCCtgMQ9rW9mpcvUMwuI5hAgQIAAAQIERgtI3EdTlbqjZL3UyGk3AQIECBAgQKAvIHHva1TzWLJeTSh1hAABAgQIECDwICBxr2YoSNarCaWOECBAgAABAgQGBCTuAyjlbJKslxMrLSVAgAABAgQILBOQuC/zW/tomfra4uojQIAAAQIECGQiIHHPJBCXmiFTvyRjOwECBAgQIECgLQGJe4bxlqxnGBRNIkCAAAECBAhsLCBx3zgAh+ol6wcJ/xIgQIAAAQIECAwJSNyHVFbaJllfCVo1BAgQIECAAIEKBCTuKwdRsr4yuOoIECBAgMBEAa/VE8F2u9/POMYhkwUk7pPJJh7g7J8IZncCBAgQINAJeAktaCRsHqw2fnOQuCc6JzYfv4n6pVgCBAgQIHBBwEvfBRib1xBIMfzy+2VA4h5lLKUYLFEaphACBAgQIDAk4IVrSMU2Ao8Ekp4ms34rkLg/CtCUH5IGc0pD7EuAAAECLQt4OWo5+vpersCMM/fmmcR9asBnME+twv4ECBAg0LCA15mGg6/rBK4LSNyv+3TPmkTHKNmHAAECBC4LeCW5bOMZAgRGCkjcL0GZYi/J2E6AAAECBwGvFQcJ/xIgsIKAxL2PbALua3hMgACBtgW8JrQdf70nkKFA44m7WTnDMalJBAgQSCNgyk/jqlQCBFYTaDBxN3OvNrpURIAAgRUFzO4rYquKAIFNBBpJ3E3nm4wulRIgQCCegIk8nqWSCBAoVKDixN0cX+iY1GwCBBoWMHM3HHxdJ0DgSYH6Enez/pNBtwMBAgS2EzBJb2evZgIESheoI3H3OlD6ONR+AgTqEjAr1xVPvSFAIBOBchN3LwuZDCHNIECgVQHTcKuRz7PfGY7H+zyltKpkgbIS9wzPypKDr+0ECBC4JGC6vSRT+3aRjxjhCjD97hFxPEQpKv/EvYJhHyVSCiFAgEACAVNsAtSVixTDlcGbqi7F6PLLwJIhlGfinmKcLFFyLAECBAoXMK1mHEDByTg4mhZfYPmAbzn1zypxXx7K+MNLiQQIEChJwDy6dbREYOsIqL9+gdlnWQUZ/+aJ+2z8+selHhIgQOCagOnzmk605zBHo1QQga0F5p3OWaX7myTu89y2jrb6CRAgsImAKTMBO9QEqIokUKfA1OkiaaK/WuI+tdd1xl6vCBAgMCBgghxAGbsJ3lgp+xEgsIrA7ElpTMafNHGf3fJVXFVCgACB9QXMi1PMaU3Rsi8BAmULPDnjhR1SJO5P1ls2q9YTIEBgrIDp8KoUnqs8niRAgMCpQKzE3fR7KutnAgTaEjALnsUbyRmJDQQIEFgksDBxNy0v0ncwAQJFCpj5DmEjcZDwLwECBNYQmJG4m6jXCIw6CBDIRaDtOa/t3ucyBrWDAAECncD4xN3sbcwQIFC7QKvzXKv9rn086x8BAtUJXEnczeTVRVuHCBDoBFqa3lrqq/FNgACBygVOEnczfOXx1j0CLQo0MLE10MUWR64+EyBA4ESgS9zN+ScsfiRAoFiBquezqjtX7JDTcAIECKwlEBJ3LwRrYauHAIHoApVOYJV2K3r4FUiAAIG2BE4ulWmr83pLgEBhAtXls9V1qLABpbkECBAoS0DiXla8tJZAYwIVJbYVdaWxQai7BAgQyEZA4p5NKDSEQMsCVWS1VXSi5VGo7wQIEMhdQOKee4S0j0CdAiUnuSW3vc7RpFcECBBoREDi3kigdZPApgLFprrFNnzTcKucAAECBNIISNzTuCqVQOMCZSa8Zba68aGm+wQIEGhIQOLeULB1lUAqgQIT3gKbnCp6yiVAgACBUgQk7qVESjsJ5CRQVNpbVGNzirK2ECBAgEBmAhL3zAKiOQTyFCgn+S2npXlGWqsIECBAIF8BiXu+sdEyAtsIlJD5ltDGbaKnVgIECBCoWEDiXnFwdY3AOIHss+DsGzjO2V4ECBAgQGCZgMR9mZ+jCZQokHcinHfrSoy3NhMgQIBAJQIS90oCqRsErglknAtn3LRrop4jQIAAAQLrC0jc1zdXI4FVBHLNiHNt1ypBUQkBAgQIEFggIHFfgOdQAlkJZJkRZ9morMKmMQQIECBAYKyAxH2slP0IZCSQXzqcX4syCpemECBAgACBKAIS9yiMCiGQXiCz1Diz5qT3VwMBAgQIENhaQOK+dQTUT+CSQE6pcU5tueRlOwECBAgQqFxA4l55gHWvJIGcsuOc2lJSDLWVAAECBAikE5C4p7NVMoERAtkkyNk0ZASaXQgQIECAQJMCEvcmw67TGwrkkSDn0YoNw6BqAgQIECBQnoDEvbyYaXF5AhmkyRk0oby4aTEBAgQIEMhK4OXvv//+/PnzrNqkMQTKFtg6R966/rKjp/UECBAgQCBPgbu7u+f/92//kWfjtIpASQIhWT5+b9HuY+Wy9i341UmAAAECBJIL/O1v//Hy3/7tf9w/e/ZP//TPP//8c/IKVUCgMoFN0+RNK68skLpDgAABAgTyFfj27dv/+d//63/++7//fxXt00MpjXrwAAAAAElFTkSuQmCC"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","content":"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"},"cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. 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Magnificent framing! The lantern is perfectly centered.\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\n# We want the background to be completely seamless and cover the entire screen behind the model.\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\n# Scale it up by 4x to be safe!\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.scale = (5.0, 5.0, 5.0)\n    print(\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}", "parse_error": null}], "tokens_in": 24435, "tokens_out": 208, "cost_usd": 0.0072072, "cached_tokens": 23198}, "error": null}
{"ts": "2026-07-15T04:47:26.256642+00:00", "type": "assistant", "text": " eyeint", "tool_calls": [{"id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "name": "execute_blender_code", "arguments": {"user_prompt": "Scale up studio backdrop to cover entire camera FOV", "code": "import bpy\n\n# Oh! Magnificent framing! The lantern is perfectly centered.\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\n# We want the background to be completely seamless and cover the entire screen behind the model.\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\n# Scale it up by 4x to be safe!\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.scale = (5.0, 5.0, 5.0)\n    print(\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:47:26.256642+00:00", "type": "tool_call", "id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "name": "execute_blender_code", "arguments": {"user_prompt": "Scale up studio backdrop to cover entire camera FOV", "code": "import bpy\n\n# Oh! Magnificent framing! The lantern is perfectly centered.\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\n# We want the background to be completely seamless and cover the entire screen behind the model.\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\n# Scale it up by 4x to be safe!\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.scale = (5.0, 5.0, 5.0)\n    print(\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\")\n"}, "raw_arguments": "{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}
{"ts": "2026-07-15T04:47:26.292881+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "name": "execute_blender_code", "text": "Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:47:31.467677+00:00", "type": "llm_call", "call_index": 31, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:47:26.294705+00:00", "finished_at": "2026-07-15T04:47:31.467677+00:00", "duration_s": 5.172000000020489, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "content": "Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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{"role": "assistant", "content": " eyeint", "tool_calls": [{"id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "content": "Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:47:26.294705+00:00", "finished_at": "2026-07-15T04:47:31.464672+00:00", "duration_s": 5.1560000000172295, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "645368", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect camera composition framing\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\\\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\\\n# Currently, the height of the lantern goes up to Z = 0.50.\\\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\\\n\\\\ncam_obj = bpy.data.objects.get(\\\\\\\"Camera\\\\\\\")\\\\nif cam_obj:\\\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\\\n    cam_obj.location = (0.75, -1.15, 0.40)\\\\n    \\\\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\\\n    cam_obj.data.lens = 60\\\\n    \\\\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\\\n    # Alternatively, let's create a Focus_Target empty!\\\\n    \\\\n    focus_empty = bpy.data.objects.get(\\\\\\\"Focus_Target\\\\\\\")\\\\n    if not focus_empty:\\\\n        focus_empty = bpy.data.objects.new(\\\\\\\"Focus_Target\\\\\\\", None)\\\\n        bpy.context.collection.objects.link(focus_empty)\\\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\\\n    \\\\n    # Update camera track constraint target\\\\n    for c in cam_obj.constraints:\\\\n        if c.type == 'TRACK_TO':\\\\n            c.target = focus_empty\\\\n            \\\\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\\\n    cam_obj.data.dof.focus_object = focus_empty\\\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\\\n\\\\nprint(\\\\\\\"Camera composition refined for perfect product-to-hero prop framing!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Reposition camera to cleanly frame the entire lantern and background floor pool\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"content\":\"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Verify updated camera composition\\\",\\\"max_size\\\":1000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\" eyeint\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Scale up studio backdrop to cover entire camera FOV\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\\\n# Scale it up by 4x to be safe!\\\\n\\\\nground_obj = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground_obj:\\\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\\\n    print(\\\\\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":"Code executed successfully: Aligned all viewport spaces to active camera!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","content":"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvXmUXFd1PtpVPXerJ6m7JVmSZcmWkSzPs8EYTxiDCRgwQ5gTSEIegRcWvyR/5CVZWetlZSX5kfXyB2HlLRIC4TGGGAgEggEbG+x4HrAs2bI1z0O31K2eh3pf1em7a59hn3vurapWd/vWkrr22fvb395nV/W5u07fuje3atWqnp6elpaWfD5flz2yCmQVeLVWoK99dqa5ty6Xe7UWIGbei6suMzMzmE99fX3MrDJzVoFKK6D9ZkxMTjQ3Nc9RapZKw9TafwEn603Na6x10eaBf6nPL0EJp6enT548eejQoYbly5c3NTUVCgUs9LnSA7KfCShgFAztvhL8LvNvRZKzs7PzHzeLmFVgkVagMINf/IX527yAKjoxMTE+Pl7rhBoaGtra2rCIIRBW5tHR0djVDMt4a2urSgyvYmNjI2TFQNli3T99+jQNPYLBhnwMKvgisbGwUuDzQ2NDgydcWlPcMT3OnjbuvPot+Elg3SjnWJ+vx9sMb5h5rVE1gmnTqAZh9Ti8qXmN1cshAROWyLGxMb8D1pO29na1TGG9mm3qrWvtt10mT+6cOHPc1mNJWbZsmdpuni3kZpedV1ffYsIKMyMHHneunNiqptUSALAZ61sxpdlZpYQMwbm1PdG4MtfcbcbFeHaqfninc98EyyaojHCKQVqfAW4v1Qq/VthqB7gB2YNIucGMI4SS1c+Ojo6NGzfu3r17aGiI63FQwWSgwdxwGFu5cmV3d/e+ffvwaoFkw4YNMO3Zs8dZMs5TOxkvTO3IM+asAkuvAvX5/Aw7AC+9CVY+IyysJ06cwLKGZRSPY8eOVc7pYUAIWE+dOsWX5ebm5te85jXDw8NYYNUirBiw8Pb19zeXdmGwPqPzhh7LO9ZwLNHAAwzBWMkBu+GGGwYGBn7961/zTIpsfX2IBS/FBo36w+zk5OTU1BTA+AxzSv8YgAOStOa3t7UtxmaO1+RsyQuvMfNVAq8y3mbF17rczPvwmS2sAt53gdcYxl81FBZJLIxqHUBvfcEFF2AF27t3L1+sVDAsa6tWr8bCMlvfUX/+u+ryxb0GPFqa6q/e3I/m9vHtR48MHjl9+mWlN36CsKurC8rZ7kvr19yorP09ratXtO07emZweKIwM3Fmx/1qsTJ8sR6iA8bqhzyxvuHtiqVy7dq1BDt69Ojhw4dVzljW1FKMLhcR4Ts4OKhMkx0X1/dciDZ8bX97b1fr0YHRQydH0BoXJodbpvbjYwl8V6xYAXK4wBH8yAcNPfT42dvbixyw/I6MjMCEXxxpYwW1Wl2qFeKCEO7lM2SMNRfL9Lve9a677rrrc5/7nPpgRLNSSRcrXtrVRnOP4xleMAVAikgIiz7hMVt080gRSryW0CMuNvsJYAsoosoN6RorPuLi9B7bhWuMucCEKXR2dkJAApi5MSPua8t4adevX88/DGAuUNpIjwbTwSccAuBNoGTkY0yQMBQXvip5mPx1I99MyCqQqAL5+vJSkMjxVQLGwRF7HFhY8Nv6sY997LOf/SyWEefc8du6bt06tTXCMVgGjd90/FJLCxEtpyQgFpad97///RCwHN155508Otbk2dIWDAQV5dxzz8UqioSx1Fx44YVqOeUukPv7+//sz/7s4x//uKEHidrQgYADDAjRx+MnhsiZ1i7u1dXZ2dfbK62K9oLMfdPKeE28jzi71zkzBlbA7NDxTiu+3Iuw+As7ZW92XmPgC1kVGF764qtfV4c158///M83b96MHvL3f//3jaUPAKwwWE+KQZs6VvV2Xb25r7eruN86PVMYn5x5x00b1vQWe0XpUe7IW+a6weu3rvrHz970N79/wxf+101bzvO1iJQkElCJoTVHc4weWj02bdpECyYE9JxXXXUVVl2s6lgJr7nmGlq3mxvrf/8dW/+vj1z9e2/f+he/fc1vvWUz4CpndIxvectb3vOe98ALDfAll1wCHmVCOOyYYAsG0VEoPKRpKn25Vthub2gQD9VoMXGYAd3NN9+MkM6VmiIdPHgQ3eRXvvKVb3zjG1//+tff+ta37t+/n443OHQhdfTfOOTgQHLLLbfgQxhqgaSR/ZYtW9DNowoQEOUTn/gEjiWo5qc//Wll+ou/+Au06XDHxwNMG2x4K7zxjW8kX0rDL+ClUoc6+OKwipqCc82aNYgOfvXRBy+Jk+Ttb387Nq5uuukmJHDeeedhsvBFGkgYWWEuOFzhlcbwQx/6kPpkYvPgxfvwhz8MHtQd4De/+c3AoLagdZYXn9IQF6/CPffcA6/bbrvt/PPPRwL4iXKBAUERGgkg//e9732Yix0002QVCKxAfbTcBOJftTCsJD/4wQ/uu+8+7F5jEfvABz6AX1JejTvuuANr14033oiVGgctrDAXXXQRlgWlwdKKX1ssrVhtrr/++tg9CM6MlecnP/nJc88998ADD+DXn28lcBhkLFNYgfFAFKwk2PTiuwYExhS+853v/PznPyeNU0BcnFuJHSMc2yAAA04DiQ8VR48dw7Js6LNh5RVYMF1Z0FQamxqnpiaDoAsPtLBL7c3Oa5z/Sr/73e9GE/yGN7wBG/BYBtHqeHI4PTJ56szk1Vv633Pr+e2tDWv62r/981dmZhNMqaE+v2ltZ1ND8Vs93cua33fbBZ5w3ISF8Z3vfCfWaqzJl1566Zve9Ca1Nc4xWOex4qH7woqqPmxcdtllCvDW162/eOMKfNKYmJrBz2svWnnLlXOdGJZ3zBqrPfaysWifOXMGnafygjuWSnSz2PJ++umn0dGhBeUR/bKjcUdyt956KxpBNIg4Cx5nv/zwhz/EYv36178eGVx99dX20oyTZHAIwfzRc+OBNhR/ZaDPQzho/exnP3v44YeRPXZr0L+iQMgbc0C9UI63ve1t6FCROpTY3VFnR7300kv4WINwr7zyCqZ3++23w+vyyy/HuwGHQIQjX5ohrDiCgtb5FsFHFvyRFx0w8JgCDmxXXnnlFVdcgYTx4QQfjCAjHLGRgNfpwIED+LCxfft2hEDLjmSQAAoFLxx38fJAwBAfyPATUciXC/BFHS6++GJ8dMFkERfvErBJL9g555yDiHi9d+7cidoChljXXnstPvzgvYXXAkERGtVWn9vUX1t4xEzOKhBYgdI+gGM1CHR/VcHQuONvmqWueBpr0csvv4xfRl4B/LaiFT5y5AhgWD3wwRsA/MQndixQWOvwK4yP2R/84AfVMYB80dlLaywCYVMAKxt4wIB1Bo0yfr7uda/DT3z4JxII6o+wcMGhAmsIum2c1qKWPvCrgwfcsRSjcf+3f/u3hx56SHk52eCCxRMPrJN//Md/DB4c2IyddZyigxNoijmUzgctClFzrw54SlODn3GH9jh7DVJ6tVGaJc7V4VtwpSKYloVeGSyDpZVwIefpranXOG+zQm+D7eTPf/7zWACfeuop9EV/9Vd/hTYGCWAFw18CsYag3eL5jE1Mv3zg9I//Z99Pnzhw940bfvbEgUdfOLr3SPHcEvVA44TtAyxZ6HYiXfn5qtf0XXFh79YNy1/YM/D0zhMwtDRp37JQSyt+Ym0su5UkrIQvvPAC+sxnn332/vvvR9OLFdXAYN1+/PHHcR7Lrl27cI7iM888Ax4sjNhcv/LCvqnp8tcpJ6dmr93cj7cRejw0mZwH29lqaxjdI1Z+dHTf//731YcEEKEsBFb1wfECCZOSC9rxRhnA+Mgjj2zbtu3RRx9F6T/zmc/8wz/8A45ViIpdcHTeSRtEfN7CrjCqg0RxCAEPXkL04qgOjl44huHggcPAiy++iKJgZ0jNRH0JDBtXOPDAChIcIPHKgQE9PZJUvvyzEfT4vIFPeOiz+SRJfuyxx7D3jxcJGvTuaJpRL/TZeA1wShM+NuDFIzAJwOAF+973voc9byQAL+SGBFBWyJgIUgIYyePjCvKhTyzEAAEhMFO44+MHpoPXHh9GUUwImBpHkoyg4IcjDrQoCz6ZAYyDMQDoDMCGWJgFlDiBDDIe5JsJWQUSVQB/eyvkzBUtEcOrDYxfTPzxFEsNVhWsYHz6WByw8uKghWMMVjnsuO/YsQMArKJYxPC7vHXrVvzuY2GBkh8kwCatsdhM+dGPfoSV+Re/+AUWZ/TuWMewx4G1CEs0FhOeAJpsLJtYMY4fP448cdRBLLUhAgHHURwk8IEfSyh2ZyCovQN4YSkDmzEdyhArHpZxlTYPB7m1rU0tgyOjo9TT49Qd7DWgGngY+GyYtAKLq4J4n0gbWEknPp/4RVJkb5pe4/wUE0sNlpEvfOEL2KtFb/r3f//32LdVodGr4PM/Tr5QfbyRT1936x3XrPvBI3vvuHYdtttn2bqB/uruu+/GeQpYQg0vDC9c1/349mMPPH3o4o3L25obsPN974O7OAwr4ac+9ani9kP0lU5uhYw9DtUKQqYVj2OwU4MFXDFwEqvJh3vRDyRIFR8G0JhBRueGtg3HAmXCavnNb35T8aCPBzNfJOGCDz84vwjfL+U5kFyPtRs+NEZyWNOxo4yWEQ90mddddx0o0B1i0x1fYEJzDDA+TOAnskEwxFY7zfhwgKMIHoChGcVxSzHDEa0nNrPx4uEAgyYeE/jlL38JAQchyODEQQhHJvii90V3i0MOJoO/IIAE7njN8EkLqeLzEPQ4RwUabEUrXwBU/uiYEfr555+HgMOVXX20wiDBwQ/5oyL4ifzxgQFx8X6CF452/CVRtMCgFNieBzkeCAoevCQoC958aJ1xVMYbFH8fwKwBBo86hil39RMHcrx18KENFcbHA4DxRvnVr36F8qJKSAZeHA8ZH5AwWdQNWWH3DhvwaNbxaQrHWuzcIyUc5nEIBwMmC05Ypc8ABnM2zCpgVKCxvtDW2jydm/t6kGHNhqoCWBzUtgV2prEDhE/72J7BWoztAKxs1O/i1xnn42Gl/vGPf4yF64knnsBuAlYzrBv48yPWCiwjWDOxIOBXG8sd/dqip4dJrbGgxa8/4iIi4mJbAds8WCuwNoLt3nvvRXRYkQBWQpysCBkds1rT4Ih1BodPrAxYLYHBuqcWGYTAUobVAy01fkKPpQlKtYpinQEb9lY4G5owfMwAGxaZBx98EKslhliZ8VDVQCuPyaqNg+mpqe6entHSd62QNo6FamXDVWWQDGhr8DC3x7QQXqOGXPCDhT0VLTscfPG645NesaiaZcFXedHk6y2r11jT1wC/9VhM0CZh2wLNGLoUrHsUEQ0SVhj0e1g6sG50dHYW28iGjhXnXn7XDesvvWDFt37+yvDo1GvO7d5/7Ax2sieO/Xp86Ajc8XZCo4X9e/ArNqxLao8Al5S59OLN23YPYM/+iR3Hntl58rsP7n5hz2BdYebM7l9QF4pmCX0jZYIlC+9P5INGDlRYlrEUI20sjMgNU8B6DivwyBP7s+hXgYeAoKrjxao71rS2f9Xajed00kk9TQ35nz918OV9x/ODz2GBReOOrg/NG2R0bkgARwpMAWdwoDJgxlqKRRhbJ+ixUSjagsFiiypRc6uQ1NPm0EHylhFrMRJFL4iDB2YCGedmoEklOjVt/PUTUwKLWppxwoZ6JVRNUREcLVAmtcejXBQesqoFPhJgTUe7zPWUlnLhP5WXAoS44JXAVDmDkskXQ86phv4EKDrAkJW7ksmd80NpPxSAQpNgI5WGCBUSfQAOtHhFDL3Kx5O/xJ/pswqgAq0NhRXdHeM588TlrDi8ApMTE6rJfu1rX4vz3NBDq6MCPl1jO1ydcKLw9Hutfk+dv61Aen5tsaBjpxwYNOhYKrGiounHZwD8DRo/sazzxJSM1R8rM1JSW+xQqm0OtVmA1RjLOw4P+Azw5JNPYscBf8nFKo2/3mLFRltvEKpDFNiQCXhwVMPxDFQIASSODujgcVI7ZHWoQ5KKAQc2HOHoY4xStra0YE1Wcg1+yn2KbKlBGjWnXMCzMVNDX4ETCerrF1/vbs6k5q9qugDeNL3GdPECvbAOHD50CA3eO97xjr/5m7/xeOGT/Dlr1qBPm2lZfddv/p9YDscnp7FrDpdrt/QfOzW25/Dw6V9/7dT+p50kWLXUnwqnV958xbU3bVjd+fTO4xedt/zwydFnXy6eLYOryhz++V/y/pbzoJXCsoaWFVswkGlFRTeL7y/BCwumamiRJ1ZL/H0VGuynoL1GXCyhwJzquKF95Zb/4x0Xb17fM4vLUuZzT714/Iv/+cL0+FDr/m9i5cQZQdiMRs+GxRanlKOPR31AiCUaZ9UjH6yi+JsndkywFGN9RmvHkyQZLujvqafVGndokRmOMViy0amrYw8yRvaqoVcsgGHtVlPCgQcmfGRBZvgAARka7NkDg6ZcHdUo9rwJyAEvicp/3oJmgbIKLN4KdDQVOrq6J+tq11ot3trMZY5DIRY9bJPbRwL8RRGrXxUXHKyfWISxiCE2Lrl4pnQxRxxdsOuDlZ126HlN0SujBVd7SLQXBQDaaKzDagHHT+nAwKkggwcHJ7V/r9iwqGLZx/EDJKoC1LgbvvYQvjiO4JxQ21SxJq5DibNXnMA8ESyGeZg54q2i3sPZpntt3iVmwYtRXLraRHezYpE8dvQoPuFjBcNq6QaVtFiaevv6sDjM1DW2vuY9l2698LlXTk5OFTfUl7UWz4YYGZ+WGneskNhKQO8O8FTruQ3r71zZ27Vxdce+Y2cOHi9eWhEPT+OOlQ2b61jl1MYEdmGQhvKCBj00ZoFzIpQSmWD5Umsymmz8aZG2bNC4Fy8Hmc9h0x3n+Rw+OYLz8rFNj8tBonFHFHghEFZOdNT4wyZCYPHEKgo9osOETwJgU9scnvUZ17zHNbsoSa1xBykZ1Bykn/wQFegiUdVIzzOsUYiMNqvAkqnA8tZC07Ll03XaF3qWzOyqMhG1rmPZxaMqhB4SrOxY0BUAJz/ihiaxCxoOEnjMuZTOxYRsLM44JuGA6olLJoMN0e0EoOEnoZKvLeAwhaOUra+SJq5VibNXKY2a0yyGeWg5YnOxobEhnyt9YNMsNa9V5QEWdr7e7LzGyisTy4Dz5Sbw9zd2hrrtgqUJ5wJioVOm6dncbK64T2E8ZiaGZqcnDCWGcMRnQrW+YSGaqmsu1Jln4mF9mh6dO4/aYMDqSqslenQsjAYAaydO7iP+6Wl8nHB8e2A615yvL1/3nEgQOjc9jKtIkoYErJnQGiuzsiKNKdf6DHBLczPVCmCzcSf2TMgqkFXg1VOB/rZCXXvvbF0t9kSXThUdy/BCnRyORkiNr/ULNdPK84p7WeLslWcwPwyLYR5ajuioJicn8GWNs74NnOIF0maSwr/mLt4EvcaapzaPAV41E9VqWrtdEC1MJQN7p6cStsw3q0BWAbsCK9py011dWeNuV4ZrKjhIBLsGA3litjxVOuO8sYanldsxa6UJKEkcJM5eq9SrzRs3D6/da6xeploYnOXVvqwd94tPwa8RpfCv2OWsJxA3A2+CXmMc80K313hyQfRTU5M4neasVEpr3HO5fEMT/lphJz2bKxT3b2r9wC6R2iiiQK4/YZAxE7IKZBWoTgUuWd00uHL9jP7XRnshSBisYgKKVz0mokwnpE0k2C8Y6M9fXdgKZ5H6YdwqRBbU3BNyGMpwChwGcMdB4uwhmcRxeO1eY0h0hQmgkSGyJTaBMFcHqvSV6LEe713SPdEdjGW0bJQtZe8wKYDJBXHpYgNW3ykVo5FnWo44vzi7kYY9rJjApuSaIHpc9eWVl4vXp6cHzkWsxV4zTjXkZ9eUG3douy+4uev82yamC7iJK74igJPTcO9Z3E9x9PiL3UO/QmbAqCsDqH4aTbb6mi1MONdenTOEpPFA9jjRXp2miT/XqhMcbRP0oAIAVGCAC66wQ6eQAk/lyISsAlkFaleB5uaGBb3djpUgaCGtXYXmi/nszTR95IX/0qSf29zrvkCmWPE80ryNg+fuyK70tbyTPT3La/D76wg3Nz3Zkmb+Ph+hNqkSqL5TKkZjutXgMCirs5jXJLFypkH0+ILskfZ2dVVcuGID/iQu6+I4Y77Mm0JC492+bNmK5cupd9ca9/7zb7jr5gt+8PCeKzb14iqY+J7swRMjV7+m/9+//6IKhqtR3nnnnXBWvTWuqYkLkykTLkup7t6HFhy9OK6MhksUKxO+sYsr6UCGHtb//u//xgVnlEnd8wiTx3UScP0gXPEAn2CocVeY7GdWgawCta4Avv5esA6tQUtXrTMTjoy1Dls9/kU/geqVIiVTwPswAJIyeLibNwevMTxGAFKOJFsCaNND2tra8feftvYEf/yhYOlTTu9Jwa0FsWyplVSNrKuf28LMCvNEYnjUbIWNp0czzL92jw6+6l17cZqFwtjoaKGnx9G4w4zr3n/vod1nxqbUpTSPDY5BeWa0eAkFOOA7vLgmjvrbKz5GQ0kb6tgyx3a7UkKPh9qYx5SgpG/vgoF239VscfMjMOOaQfjFxpXgA694oEJkP7MKZBWoSgVa6guFfJrDalz0BbvgxyV+du1no2zCwU9Qn936nI3oZ+M1cc8zfSZpX8wkER3Y7u4uXKa62DmkTcBdiKLWEU4G18IiJyBbqp0HIuEhFNdrDM+kyrOpKl1VyeySpKwg7ryBGyLhRBLcO5buo4Tr+eLij7hyPN1K1o4XoinvuAPd0da46bwe3DmWe16zpf9H+7fhFhvvete7kAQ3KVldxx4358MuO7pw3D+VMJs2bcJN/nCl+q9+9atQ3nLLLdiYV9YtW7bgysfYjIcAzXe/+1208vwOW0SSCVkFsgrUtAK4Zu5sveNSXDUNGkpe41U5NA3xwBhLsGAm4M1UyFJQG1RhKMMp0TAgghcidDWJcggAe3PwGgPIAyHzNNXAbADDd+fwwB/Si1t4ybOLK1ucPTxRCxlGLaNkixWqrEjlVHZ3S5WRJn/R3Flo2spS0qhqvvWOaInTxZ3yvvzlL2Oz+y1veQs2rLEZj/YY17ZH34sPsbDifkc41RwXd8dNVfGrcf/9999+++243R7ut61mh5s0AYBfHLTfRm+sNeLDY1Mv7iteIp4/cAtZDPGLh/1ytY/OrZBxlgvuxocb6eGO3LgvFLeqnXiki5PX8cB5MmTduXMnXJCueoBf7bsTIBOyCmQVmJ8KNNbXFfLaZ3iKi+Uq7QOrfU0W/LT5VOpXQSkqDZ35x1VgPt5sC+QNkP6XqoIJJHF1YHE2rLr7TNzr6LY7GN1Apk1fJkYSdYSaKtEgTequC4bHB42LFGf3REjlGuAESADKk5hhqiqZwY1hgnTV9zPxE2eRoA1G53zvvfeiiUfX/txzz0HAhvXXv/513M4JDfAvfvELdOfqwb/YCa8PfOADd999NzbHjWy0xh13q9q0tniTbf644sI+PlQy7pmH30N14QJ121TC4I8C6MjRppMGd7dCYNwjCl4wqTtjn3/++QCABJ8w8IBy69atia6BQPyZkFUgq0AlFZicrsvPVvemQlU6bFYyq0XtW+NDkLM2FcWsyNmZjqaMez/FhY+za8EqGngjeY0VhS07z0eMcjS3ZObQ2Ng0PT1VbEpMi9vf0HpffcGYKpARVw0DmLwQr9EZEcoqO6FIQp2kBAx92nwC/AIgRjKeYVXJnHEQIT4Idquxi/3xj3/8t37rt7CVrlpw0B0+fBhf+MS3PfF9zg996EPYUIcMPTa10SfjrJO1a9dS74697B/+8IfY48Zp5EYq2jbb6PjUrkNDBuL5XQOGBsMdO3Y8+OCDSg92FRtDCDjpRTX05IWOHA8M8bVUfLZQ+t27d0PAd1vxwGcOyPiqqzJlP7MKZBWYzwqcmcRdp4tfaFmgD6yTlR11zva8Fv0EznYBF0r8hfBCnq0cKoy7bFnH8PBQZ6e5Mxj70qb/1a8w49jMNED1gyVkTF8nbR7yIGE+RBTgFwAhulgBZHjUoBzJKHGK+KOPPooOHu04Nqy/9rWvFc8Yy+fR6EIJQW3GF1PN5b71rW/hJ77zCT126EszKP7A+Sx40JCEcuOONr+pMHLLlZf97MkDl2/q3a6uKnN8ZPP67kO76rBZjk1+UCvPgYFyN4+/gmHDHxp8DxWp4K8AL730koINDQ3t2rULnzaUI79Y/fr16zEZbLHj3B1crAYfRHDOPnLgp9NQlpmQVSCrQO0qMDVblytMGvzJVinDeYkOq3p8kWu0sEo/T5OWy7HoLHLF5umVlROQLdWushmpo6Pj4MEDnV2JG3eTyEw0zm7i04zDYsgo2ZImG7ePN4bX6OZzadPSBPgFQFwZibpq8yFQPGV9fbmjRtOrtqTh+c1vfpNkdV0WKEkDWXXIXAMlf6Chp/Yb+tw111yDc2sUoqltecvqK3L5cr+v9LNjJ1sm9nEWktFn888HpIeQwoSPB8PDw/SXAggkc+ZMziqQVaC6Ffj4jV172q7jnBX3GBUTVDUbTlaJXMGsgl2DgZ6JqD97Jj35UIgsqO3wwUDbNVYTwB0HibPH5qAAXpr0xsDoATkAIqchW0ISCPN2o/DH+a7uLmzkhQQyMG7GMki2y5ayd5gUwOSFeI1GCkmw3NXr5zVyFr+clibALwDiz82wVptP0WusO196EXvQyoCda+xEV333GS07PvfiW6Y0u/LnA6gmRwcmXv4p2bhgnkDDbdWT+UeKrGuvXl0zpqwCi7YC2iK5GGeRfALxOzu1qoMQWVDzLJLPknvPkxwwD38mAbP0xvAa/aETWuVIsiVhCA/cHaO3t/fwoUPnrF3j8ZRMbsYyWrbLlrJ31SQ5WMBbh2cBIjwSOsFDTiDOWIwX9vDG8FAE+KWdth01eelsDkmjZblq9TlnRkamJot/ssZuOs5AEW8cmjYn+PHeuBgIZ6XjISU4z/psf32eC56FyyowVwG1FrFyBKyyDO0QKyZwcJ59VfJZJfc4+7PkGYQdbWo/y4AIARA+s+RyzQMEp1RRJmmdw94K4hzQfOTyucJsAT9F0AI2BJRKSUtNAAAgAElEQVRtIczLm6bXGF77tDRp/cIzi5C1j4QIeBT3wi+99DLcXDSKXPNnXMpF23GvecAsQFaBrAILtAJqGVqgyS2QtBbCYbmmpVjyE6xp9SJyb8/gNUYMVXmufqQkjG5s74peXHeut683xQzdjGUi2S5byt4BUkW/HalySOWEmXj9vMaAMsxB0tJ4/SoqsZm7N5IJTjtGkDqcLi6dMe6jTT5Z5YENbu1ykL4YmS2rQFaBrAIJKpB8WUpAvtShxcPBAnksoFQCKuLNthpvSW+AgASrB0mfSTXqEDAPR4KNTU34JlvA9fQC6BNBHLkk8g8EVz9M9RkxlSqRgiYhE9553jdfQrrYVyV5hrGUTkCqvAOcVL3oJ8U2G/fiH7MSPogrE7IKZBVYtBVwnC8XsLA4pxu3OjudJGXaJCS+s6FfCnOIr1vtZxkXoapvvPgJexDeTL1GD2lCE6pR/UfluXd2Fq8LmS6zuOhx9nRRI68wdhklW6II7udUfnFOcXZ3KpY24ZssIGoAxMoiRlEDSjsigsTFUesT/2nRcKOnttqpMstWXdS2/qZczryqjEVeVsxODp3e8YPJ0bkv1ZYNmZRVIKtAVoGlVQEszJ7FtMpznddgc7kLMQV1lSccTxdX/Lg84+zxGZQQVaIJjCbC4qohOhYbjPTOMq3D4oiEC7ofOXKko6MzXQ4ORkdcXVW9yaaJznNJ65/KL84pzs4Td8qp6hoQFRA8UrE784SyBpQUSkhUVxfqclN1Lbm62ca6CfKEoKO4xSeXG3eco9O+8U35tmQnn+Xrzmlfd2zyxf92BsG1J9esWYO7Q+E6UNjHd2IkZWdnJ76fi4ua4b6qSX3xdVucBrRixQqcTpfm3CMpp0yfVWCpVkAtbKXZJftFdRQkYGl2eMmqavPJkWpnWQpziK/Oq2OW8XUoIry18BrD+GNRtYqRkNeE42je2NCAE2aam5tTtC3e1UkwminEls4HCCDzQrxGT+BUfl4noVqeHAwT2PFIThPm583dyCRwGBbYQybPVbaAbqDQv6Nw8XChE437ityJi/LPtdWd8YRxmXiAXD1ufqSuKoMmu339jbmG4qUi87nZN6588tLuXUfGl9+x6omVLYP1udnbVz41Mt16dc9LF3Xu3T26ejY6P37mzOGxE3N3XOLxQPvJT34S/fett96Kmz/hxkxoxEdGRjZs2IBbNa1atUrdOGrjxo24MVNPTw8ywS/z+eefjyFMH/7wh3Hdyje/+c24x+rKlStxnR18lxZW/AQD+nJcrH7dunX45ceFM6EHv7qXU39/P+7udOedd3Z1dZ06dQp4dPC4Vv2mTZtwfc3x8XGeZCZnFcgqgApcua7pVNM6KgVfJEiZRKiYwAhWbT6DPnxYQSJJXIFNArfzV3fn8NzRw3YhjRBZUJObEsJQhlPgMIw7DhVnr14yQiSoBUtgaILF0XjtXiOFkIQk3ia2qbn51KnB9vZlKepgcpn5yXbZYnLEjcOYvCiv0RM/uZ/sIVs8CThNaZnC/MJQzsRsZRIyYI1/EZ+htki5/XSh95HZG0fq2mfq8jN19cN1HUcLq9fl99fX+S9EwzmiACUd+tjyjnuUUfG5KT+Nrv3/3fXW0enmB45fPlPIT8/Wn5jsGp9pOjnZ2ZifubzrlScGL+QutoxPArj9x3e+853iJ+zGxg9+8IMXXXTRv//7v19//fXore+77763v/3twLzwwgu33XYbhOeeew4NN1rwyy+//Bvf+AYI0fQvW7bspptuwi0b0Kx/97vfveOOO3AX2de97nXPPPMMWnY80Ls/9thjF198Me7e+r3vfe9tb3vb9u3b1Q1cN2/ejP4e3Tw+ORw6dAi3cf3IRz7yx3/8x3TnKjvnTJNVIKvAwqpAtGQtkKxqsA20QGa2CNJYVMWX37jVm0Yck9fuNVb13WBGwiEY+3qzhdl8zvyWXWxckyvWgQDpPYliTghj8qK8RjNe+rH8JgTnPOXgyT4sgzCUJ4y3CoafC+vSGW4YSqgXC1vRr4+feGX00Av42X/9R0baevYULtiU28ZIBG9B7f61QZv+yxOXDE21TRfq8XNkumVithHC5GzDmenW0Znmy7pfYSHdouqesRF+xRVXoPlG540eGieuvPTSS9iAf/DBB9GXn3vuuTiXBpviAP/qV796wxvegJNb0I4rxp07d/71X/81enpg8GsO4de//jXcoccNX/GxA8MTJ0685jWvWb16NVxwf4f9+/eD+fDhw/v27YPm/vvvf8c73nHgwAEAEIvuEevOONNmFcgqsKAqICxbCyrHgGQwjYQzwbHq7D3SB084y9pMMS79OHtIVgtioiGJ1gwTXEX3m7+zs+v0qVM1yM6bl9eYKJkwJi/Ka7STcdfRxmmauBhxdo1MHoAmLVOYXxhKJagKxX/KidseJQ13hmw9DLsaWiilyA0WuiG19J7f0rexfd0VDW09GJaUnCby5jrIrgfU+qky616rTpWpz89uWnZo90ipG+5sWr28ZfDMFDHMFOpfHD53qjC3Wz8zfMB5qgz67F27dt1zzz04ZeWnP/0ptsZxsvuTTz6Js2VwMgxMaNDRrOOcmaeeempgYGDv3r3YWe/r63v++eexQQ756aefxpkt0GMn/uDBg2jTcS7N8PAwPgns2LED/FdffTUa/W9+85vo7HFWzI9//GNs2F922WWPP/44MNjLR5ePPXu079u2bQPJiy+++PLLLyc9Y54mnglZBZZqBa5Y13i66Vw+O2HR4BC/XDGBoq8SjT/XRNbkGSX3QEKpnGgilZwq4woelk0YipJMJwQEiYPE2WMTCyCIg8TZY3MgQByT1+41UghbSOLnwOKP8DjoY/POZg7ROBg1N9kuWzSCsEEAmQyRLVLw5B6KKc4vzi7lY+vTMrn8oOP/7GCRhqNcRBFu7tmARx5cbXiUhnF2l09Rl9tduGC6rmFm7PTQzge7Nt+azzfiPqjdudOrcwe1CaoAOg0FhbqAO6XipqyFAvasc1dddZVa4rEX3vfaz+aau4Borp96x5pffmPfLZDX9Lb+r3s2feEHu146MHc2fVP99HvWPvDVvberEBMH/2dg+38qeT5/4kwYdQLMfAbNYmUVWJIV+MCVrcd7XzdbV/4THJaMyh4VE6jwVaKpbC5z3hXkktw1uQefI77WjyFOVuTKEFkIK6gNxjCU4ZR0GBCkOhB/YpXGCPD3J0DWACYZIluI3ykk8XNjBwcHW1pb8IU3J79f6WYs+3jtXmOZI0AKYBIggjo2Zq380vLaCYcxuVAunc1PmmC4ABTUyfnJgwSNekfh0pdmN42f2D12dEeuvrHjvGsbWjqvq3+kP3eIHJSguek2bHZjz3oOhhsQ69a50eRM438euoFM7S31n3r7+ReuXaY0s7O5/aN9ZD1bAjbdcdr62Yqexc0qsJQqMDY+lU/9106tEFh81D9Nmw0SVyDJX4cTk9fIYV5yDgjihczf29ObhtdYo9fnLNG6p4qvpQ0ODKRLyc1Y5oqzl5HpJU+bFZHKkLQJ1sovLW8007lnecIAqF889TPyc+kim/bMgTqFBosGHF7ScYXgb0AiqpBny5UpLshv78udbOnd0LP1zd2bb29o6diYf6U/dxi8DFWUPY/ibcvYw924N9dP3rP2QQara2rI/fad5523qrh/g225/aP93Hp2ZXwDFWfAn90csuhZBRZ1BcamJnGlqoU4hSodUaoytQpyqcC1KqkHkwiJCupg2nkEqqOhHHARTUWeBLfETUhuCWQL53fKcUENJwcc56zifNeZaf+1NQye8tDBWDZCku2yRSOIGwTQeCFeoyd4rfyS86rfNP6Tpc3VSi4ZDTVz4KKBwtD7sOCWwnYPgIQ4lWi8XA11U9fVP3R1/om+3Ilz8/teW//w1vyzuboCnISHTTeHxe8Lrs0IL3fjju+k/vr0BiI9dHL8L76y/f/+/3YcPlm8lmJjfhqXhiSrR0A/zb8PCrkWHTZo3/3ud3vSyExZBbIKBFaA1oxAfAarYQWSH0prmEwgdY1zVu9Pby41ziCKHRbGi/Iaozjxz3NH9XighahSAhZvqKK3t+/kyROeHttPFJd+nN3PXh2rNwev0RO/Jn5hbyM6QAhwskfpk0IJkdp+5kDbqms4FnLpwXU6GiNuVLIFcSoMvxLG0GHoenAUrvy4Jr93Ve7ghtzO3twRdO26B8cyOq5Gs57Pn7d+PS7lAl934w7aQvR5YGh06v+59+UjgxNnxqYnpop7cpMzDfcevFEP7B7hq6Lvfe97t2zZ8vGPfxwtOy7wgus2AoqT69/5znfiKu/4din2y5Xzeechq/U33mgyA7B27Vp8x/SSSy5xhyldTwYmfIJ///vfD9oLL4y5VCXx4OLxuJC8GuJ7sZ/4xCd++7d/GwVCRMIoAV+uxXVpcCVKQ58NswpkFWAVMJYkZnm1iwkro1bts1E0IVFBPb8ZVieJ6rDM78zTR6vVbBPyOuC4LuTU1HT6mcV4sh6IIwU1hwTKjik5PL0or9FBFqmS+1kLilLQz4jZfiYIBOvBjZGd6ywHrkgFjKKASPDnaiXzoF7ZcI2wXB3p7Oc4lHrROErJEZNhidTqGd8GQYurvrzkvqoMvn6K+y49fWoTHCanZscntb+h4zrub1n92Lah8xSddFUZWH/jN37j4YcfxvfH0a8/8sgjOCX99a9/Pa7Xfumll+JiMl/84hfRuOMDxG/+5m+iJ0aDjuvPbN26FV+oestb3oLryeDGSbgoDe7N9OlPfxr3T8UlI/FV2ttvvx2Xlbz55ptvuOEGzATXf8THgOuuuw7Xq7n11ltx/v63v/1ttNf4GAAAvnF7991340ZOaMRx91bQggTb88gHp8h/7GMfwwUocUFJde1IMOPbrrgWDbr5P/3TP0W2yAHXncT1aj760Y8iIqzIDVewAQ9mh6GqQPYzq8Bir8CmFYW6no2zdfWYCFaPyh4VExjhq81n0CcdVpBOEtckWHsK6k+d6W7ABDYhuKC2wwcDbddYTQB3dSCxmQBQhUgBFLGZxHF47V5jxaE5gSMSjtG40By+pBpSSs4F2UGnIWS7bNEIwgZhZF6U1yhlEeAECP2LaCxFZNCeCQXBelhGrnA5EEMwEB4cWyLgCiUTrwPNbD7RII2ggjoyzz0nQg0Wlrflxlpy0T1ADWcM5cfY6CjaUazq/X19Q8PD7h33iZnG7x967dvOeeTSrl24E9Pb1jzc1Thy1+r/ubH31+cvO/TedQ9gqEK0NMqhcHWa5ma0xeqOSMDh/Hr0xGjQccF1XA4SG9vqNky4niMad0WEIw2adbTmb33rW9/znvd84QtfwFUgcbslXLsdO/do6z//+c/jrqjoy7/61a/i8vC0YQ93fNllz54973rXu9C4A/m1r30NXTvw1157LfC4viRu7YQPDLhYJDp+3MsJ/TquIEkTwDUr8ckBfyLApScRER9uMMS1a9Cy4zqSsGKJQQ633HLLP/7jP+IvA7U47YeSyYSsAou2Ask3g6SpqqVNsi4m/SKbifASCur5fSECkgiAzF/OcjLz96aQc5i/OiCSIw3c6QXXiS5m4TDWLLn5jFXVSai3jPeNY0G4ArL84EAdxS2MgtQ62h4FAAkSCdFzuYG3eDnEMkoK7gQ5egjqyFzOggPJygRuV7JgZOpAEdeW2X/gYK4u575zaqEud2qy45GTF+FWqfh9Ojy2AvdgenRgC859h+bHR64ZnW5BpE39dbOFutNyTPxCokdXp6PgwurYNT9y5AgaYrTFn/3sZ7ELfsEFF+BS62jEsa2ODxM4tQa77MBgY/uVV17B6TG4uyp+sREBJnwuR/ONc2zwYQD3XkXfbLTOuO47TpVBk43OW1mxy47zcyDD96677sKpOKDCDj323YHBFjv6ftyKFVvy0OOvAWj98ZcI5IxvAKBNxxY+GncAIOATxQMPPIAkUTtw4tMPJiJPPbNkFVhcFSieHldcnLJHXAVw0J+POs1TmLjZJrXPR2mS5uTCV6m8ATRyRQKcXak7dBUxVeRc7LflGTpStVUtzc3j42O4MKRt8mviQsfZ/ezB1rAwAiqgdmkgAT6YXxzKslsKZ5EqQsnOssWZBVe6XF067qPkilBhznZQaAzXfB5/DC/r3NdxdxIZyuaGurU9dQMjdUd2itdxR8uOnW+cG4OGG038RRddhDNbdu/ejXa5o6MD+9m4hSp6aNzWFOelAAAN2mXIaJ1hQocNE27TgBYZfTNOqoGAFh/nqMAdZ7CgzwYY+k996lP/9E//BFpY0eij9cdPbNsjLlp8NPpouEGFbht4TAR6tPKIgnAgQU+PWF/60pfwUQFIhEbjjgYdZ72DHN0/SPD3AdzsCVv1+LsB9MAAadQkG2YVWKQVePOm6brzbp/K4YN68VFeIdQ42c/KvHms6jFx1tRyBemkck3lpGaX+jruVBwruKUgqCEEAw2/RMOAINWBeLIKCABvL8pr9IS2TQFMXojXaIczNGHebhQOvjierj6neMPHFA83aZlItsuWsnewJJNZFkthBImzA65D9JHBxodeoGW0FJyK5IpQXmevkeLbgsvPpTM8AyDwEFCCmofYNbNxRf5kV07b6y7kZqbzg7O56cbZzvpZ+7Ybc7z4HRkdG1V/lZo1bsC0bO21hfq5IzeP55cLI0eGj77oxKCTxgMtr7Ji5xudNx5OcGolGmh8bxXnrKdjQIbKseqJpcsn88oqMP8VqEbjXuXf67ki1IY1dYUrSCeVayonzK7Cxl0IK6iNaoahDKekw4Ag1YH4E6tCjAAKfw5kDWCSIbKF+J1CQj83HH+M7+3rwz6aM4Rf6WYs+3jtXmOZQ5YEAkttKQzKNPY4HxXCi7KMlsLIM4CTPFxcLh05QIizcyzJgpOgJrewcAJLSZ2fLfQcOpEfQUtdKLQ2n17dN9XkPo0FsahxJ8ap+lODbQ+icceJLblCU8fEFcsmLuLpaXLkhr1jLcbArl+ir9egAQN1nokTWOrSo2i4hE3UwTvBqZXYjE/dtSNo1q+nrnzmmFWAVUD4WzBDZOKrogLz8kYICOKFlI9LtX5NvGlUNXhFkdI6p/XTZt7d3TM4MNjb16tpwwZxCXjtXqMzvvzGsSyWwiCMswNuQSyFwamGXpRu1EdONlceSYDeEF6jFEXIKI4rzq7CCSiXuu+V/YebGyY7i+da188Wznl53+CF66cbtM+f5KcEGhbqZgfbHhgZHxg4PNm7rvnYntNrLnysYba7deocvCvtV14lp35qjTs3kIwLNuFKiHVnzhwbGBidmSkyZo+sAlkFllYFjDVlaU1ugcwmeY9w9hJfTLm6q0THR7e5Cqdml4irUKgqUAhzDFfHVSucKQ7pnq26jkWcr2h3k5bhLnvyKQseltpSlBMpSYntcQ6KX0C51C6dkaSX08C66Fw6cvMaCWUILieXznDDMAAlQAS1CtE1MHS8ITfZONemz+Rzh/ube7uePL2mPjfRVjhyft2EfQILGOdIJxuOT+dPNbXmB49Onj4+1X9u85X7el6/7UD36WOTTfUvrVvxxAWrpurd14/xNe4Nudw1K1e9+fd+d/+hQ9seeGD9zOx5F2/dPjHx2GOP0ekldo0yTVaBrAKLsQI4uFX8cB0gKyStAWUlGc13OvMdz1+b4GyCgf54Hqv3kAq/2mcQJRcQKQASsdXyWa5ZZQkm9HbD8b21M0PDyzo7qloBa8qWwhNOwLrULh1nTmNP46NiWp6WgufG5fTAOM84O8+CZJeTS0cOEOLsCiugBDXnJ7l1eORoS7k1r181OLTy6ZdeaJreUV/fPNt/0XP9nTfUHV9PeAhF+uJNU6dnG8Ymmw4UR7m6c7e07Xp25O7hdbc/tap0Fvts6/jsZTuPrBga+9FVG/F5gBiUhJ/addxxngx15M2Njbf19h06M/yd++578qmnjp458wr+DQzgYovdy5btfOUV4sKWPHmRMhOyCmQVWEQV2LRitq5742xO+xtfqvzLq0wqd5dTDShdYUJ1FaST0BXwhB40B/XVeZxGSJqkghBZUNvswUDbNUQTQF8dSJWSAY03H68xJIe4AIpDDiNbqhedmBzBcMUI3EW1o6PTXyeioPmAS6dTCl1NOsPfNRSwlpoUEIQHQULt5CBweu1kjKJZishgPgcACaKEiIHUkYKeyaI7kF0SuF+Eceki29wzQQw9GxIEAnsIaoYwReXRNjw2VJLwI98yNbzu8YNPt8xOFffICzO5M0cacr17ltVvqJtqKr5Bc3WdQxPnju1sa92Wb9pTqBtumuyeyh+GZTZXaB6o/72dm+pncg8PnZiYnXlk+OQ5Ta29o1MD3e2nlrWUvMtvcjTqYuOOG4juPzP87L59KuU3vvGNL7/8Mi6xgmsjbp6dHRgZwWkzypQ17uarmo2zCiy2CizExt1YrhZMSZFX2kdC14RwnlXljTvYhPiCmodXcjDQdo3VhHHHoeLssWkQIIDJC/EaKUqsEEfjtXuNFYc2CMxg+LLZ2Ph4Y1Nj7FdU4Un/GKmlIwWEuIeAtdSWwiaOg+h2PoLsenCIbucW5kxqHW2MCMU8DQiGhIpMpFBCpKZnspMmTiAPnZGrLQpu1N041EBFJkEdmR3PTo+Z5qauoydHSzczqt9w6OCe0zMT2s7X2Kl87wWF3NAqxdiaH2+ezLcOLe85sWLV4bZ1JxouPLbigiP5i4503Ty9sn+geKJ8d0PTvx3bs665bXNbJ4ZTLQ17+7qMhNC4u0+VwVURN55//j//y7+QA+3cwOehffsu7eh8YOAkWTMhq0BWgUVdgYnpupbiXRmkXi18ctXgCI92NpCpZoiVP/kjVaTkYWrgkWq6ifKoTm2qwxKYuBys9uWKUpRzAMJrjBjE5zBva6pMgbu4nMRNWlautGMwFBktnaUgqC3IWMtiKTib1wigbtdHnMeQZaBlsRQGFQ0DgC6IS0ecEOLsHKtkl4dLxz3j7K5EAnx4CJJdfg7dREtTy4qe/oHBU23NU/mhqXGznZ6ZzM82jtBZ6hNNDceXF88EI65C3Tnjje1jja9ccHR2095i/MnCDM5RV98mBayAM2nYZRiVI34SJ+VcFJbX1f3kn/+FX70RdxuFXl2AZWBq6qaPfBgb7ZpPNsgqkFVg0VZgeCLXUje5sNJHI7DwHlg01eqZMLUFOZm4OSzwpAPSC4DEFSHQHhfJ+8aJcw7MAbDqMYXHnEPKvxdq7qwClkJRYK99Bm3H7KwaulBeXUDK5K9jSQ2h9LAUHO81AuiyKx1nsWSXmwK5LKSzeLiCUBDkB6EiCClcbtzoskck5WfBg6vL4LLktXOjkvXCQxf8MLgiP66OdOo5spzu7xnfsHZ5U0t3rru+2bwkY75hNj/dFmHpbVFW5HL51un1y8duHW+5aQK3Rqqre/bMqff3ndeUzw9OFw/H+/q6Sp276t9LjiXR3Xy3FAoDR4/ii97XXnstfpe2bduGWxrh9km4DxFuh4QbKrX290OPeyfps8lGWQWyCizKCgyO5S7MjZ6uK/55LtUDa0r2WFIVEF5RQW1MHf1jGNDwOwvDaqQaMNdqhKlOdbyZeI12fO/ELaOlsAlxP8RTg4PYetdNlqel0PHmyAXXdfrI9I95O7ucXTqbFhoBaKkthZNNJuRwgUtQK0+vkZPL8DgKr10wCmo7IUPj8nPp4CaolWWquen0qhV1k+19m3YdeloLsmLTZMMgzpXR/LVBBB9ranx8y9rXPbvn1u7in5vubCrejGzPuuX7+8yvfCh3d+M+0th4yeWXP/vAAw899JBivvzyy5955pkXXngBQ+zSH3riiaxrV5XJfmYVWAIVmJzF73VxwyDhgZumntaPCJa+kKpEqZyWfi2jGQaUJwASsaV+no8YwckFJCNAnD2FK64XqBv1kYtsTqeAba2taNxLKsvTUsSyWQCLwlIYLontcQ6KX0ZZFkthZEjDAKAL4tIl4SSsElx0Lh25pTF6fYjZEFxOls5SgMSl09VATLT2L7uxcPmDR7c1FKbzufrCigsn1nRcX3ekk7kzUSfA6IVze0dbGy97+cjyU6OjbU0vrut9fkM/mv6Sj+5Yl3M37sdHR6+/664f/s//jI+PgxEPXModjbuS17S0Pvazn+FkdzXMfmYVyCqwNCpgLA9nf1JCg3EWE1t4GdWqGMJMBbWdRTDQdp1Xzfy96b0V8RqTFkQmc83WpbMjelGW0VLYhNC4ULnW1rbR0bG2tuj27y5QMJsrSBxhYnucg8pWRrksLp0965Qor5vXGJBCnH9Ke5ybkZkFZwomkle8zkKYipPnrGq9e+Vrd05MDjQ1ddaf2FQ42uF8iyOo6VvKY29fN/5RSk4QHNHLu89xx9VjfvqTn9x+2WXEfuzYMUXX1dX1xquvfn54uMyeSVkFsgosgQoUcLjPHguvAtnLUtFrQgexilhinRfIq1Q8rpttAelYKVw65xwJaFnJ4qK10FxBnkxZ1vX09AwMDsxNg3EzcFkkNx3oUpOu7K1JXjsZIUQP0kUK57OAIrWLkOlsTpdnGIo8bXj0tvFGVm7EooSSkuu85AK/4C+oeQQOKcmlc8Dx7pl7JqE4w8hWfnORy1wBNC9dx0qkmHRzSTfemtt7acvhm/N7ryyMzH0P1ZlhmcwVcS7VMqicsCq3e8cdtp/cd981Xd03reh9bmpycGgIV4FE+C0XXvhbv/M7//qv/zqSbbfz904mZxXIKpD6LBt/6eTNQ79f7awVZJTcFcvu2XsI6Qrqs5FnFcozr7PxBvMapeoKFbDUliIhoYJbLJbCSWuhYhS49AX+pC9dF9JyFnKDWoBSkl67ZbQUxMMFGWVZLAXn4XIYUEfpo+Rs5OEicunIAUIyu4zWLWzERIobr7MQlsLK3YVQEV0WXaePKE8rBrfoRidDrk5s3LGGPH761Lru7t/53d9tW73hNIcAACAASURBVLZs9wO/OPL887P1DX/7t397/PhxI042zCqQVSCrQDUr4Fywqhkg46p9BVJ1otVOa/6SSB8p4btdgLvULp2zwl6gbtRHTjYoLVS8gqh6e3tPHD++ctXcBbCV3vIX1AKOyJPZ49BxtC5/l454SAhAWRBLQWwQvEYOVLIL7tKRp2BkaiaSlytYhIuevWAfATm6mHSdPiJHCJZFV+gj7ujyVXbLx1IQj22BRmzc4YZlaN+pU3/7v/83Li+TKxQmp6ZmT2QtO9UzE7IKLKUK4Ncd/xbMI30TtGCmYCZir8AmIhsnrUB13ibVYfHnbr36lsLvD6vg4VK7dDa/F6Ub9ZFNRRoLGK8gXxKw405Xv7D8gQrVEaHgQ3aL0FIQ1BAEoEvt0hlsGHpRLqNLR7ReI6GUIGBD1QzHRB5DV7MREwnv0sHI1EyUJxAE0nnlFCwyOa5sSUxiU7Evp+LiqcWrqLrOgZmYmKCp2MLc115tQ6bJKpBV4NVVgfnogBZCRedvnvMXyV1XIb6gNjiEQ5SBqv0wLNsK8hAm6lK7dJ7IAtyldumczALQpXbpbE4LZSng49LZVEoDLM50P33qFK4OyTAWhaVg4DlRhggWQc2ZBYhL7dJxKshpIF4fr9EI7oqv++sjHW/ZFLuuZiMm8jwsta7QR3oGAo3lIuBIbTlYCjmuYHExCFBKw2W3efipMoVCIZ/PY3Odc8TK6Nrpdgmx4AyQVSCrwFKsgL20LMVZluZUwVRr3j7OV9ErqMF8pZg4TvIXR6iCS+3S+TMUPFxql84mF1DJ1Aat5RyvMBiMIfdvb28/eOhQd3ePgSkOOc5h9kNczi6dTSygLLWlsKmgCUBZEEvBmb1GAkao6NkyWAoLaSGUguGYKIBdasvLUsBL1+kjIlWCy8h0TAxwdFHKDAZ6qtC4a7R3eHQCd1zq7civaz6Zr5u7y5gRem4Yx6ydKpOiBUe77w6cabMKZBVYXBVI3rvUdn4LLZ/azlZmP6t1iDuCyGnPlyWsPAIq4fQEeDK1VBiBBXCXxaUzmGWIy+LSGYSuVFxuLp1NpTQCtqjGNuLY2Fhra+ucrwAlZq/dZXTpiI0EC2UpAHXpiIGEAJQL4tIpzniLC6Hr2IiJlDMEXc1GTCS8S+fjUI6Wl67QRxSLBN2uj6zg5CVbEjDIJHNxJgtNjx3rKZx8IVco3rF0z4m2gVWbLu8+kqNzU61oc576E6EgaI07YNh018HxoxTtfjxphsgqkFVg0VRA6IoWTf4JEn0VTVWsSnANgoFiqCoY6HjHuFw6ZjZFAZ5MbZJG41AWARfRlJ9loMvi0pW5IsmFsnSWIvJ2PAtYTY2zZY4fO9baFjXuDpqiSvMxMZbRUpgeIqHL06UzCFNCLDdLoeIwNRNh00e6wrJRzrqFjZgogF1qy8tSKC+mZiIxkmAZ4xVWACKDYLkLOi8JJ3QAXx5ZUTi5LVeYu/FR/czo0NHdR9rXrG46aXuSxpUZGfXGHee4T01NJdpER6OvrtxUpsykrAJZBRZlBdBnLbDHguj8qliTtPNJ61fF1BcglXBsc6ldOs+MZLjL4tIlJHdRuHROWi/QMloKJyeUFjBeIVGR3qLwxSleDjKXc14X0sUjBPFClY8AcaldOgpMgoxiFiaGpqG76CNwMAUTKSsdoY+C8IpJh+ojF8Klc3m5cJZOdgRUNloWS6Ei8Z8BECs97g+5RDEwPJ2PunZlb5geOj62YXVTGR0ca86lvOOOXxLcd8n55dQyvSU5f6ksVKbIKpBVIKvAEqnAq6qLFiYrqO1XOBhouzo1whHOpXbpnJykFDySqYmNCy4Klw4+gpqzKdkLtIyWwiYUaC1PSyFRkd7lYeksBdx7ursHBwZ6+/qE3GS1i43ykd0ESxo25sNEBNBHlBRTMzE+IQvscmEgJrpikw6CDtVHrihBLgkCuCIKcYlV8BHU5BZHmwRoxMItlugRifh2aCSSzS9weC7oxBjcfPiee+7xs2bWrAJZBRZvBWbwVZmKvq+CBq2qDyxT6l9VWRcAWfJCLc06JHgp6L0AgT3i1AzqEeNYgmJK/ETOAF6dHo15lUWXv7KSJWKxFGUWXXIBSRdBLUVkcD8THEL08OoiEH9uaWnBWQBYmpQnN0ULRAy97lIcURKWSbfQiEVQLtxSkot3vywS63fBLI9Y0BIZI7BAEZfGaJPzaTC6chK6C8uAieTo0Nl5uECaTncpZhg9KBATLDSzzc1Odo8seDbcBAtTk2h4Yig/DKwO9BpXLGso5PG3o1KmJb+phq7+lnGdwh75SMs77raf0uCDwSc/+ck3vvGNu3fvfvLJJyVYps8qkFVg8VZgfDrXUJhavPnPc+Zp95GxFi++R5rJVjxRgSCZ2lPrICIB5KFVJsvPUgg4D7NAAQ/LYimS0FrOlsLD5srG0gUQcgguLzMyMrJs2bIoLjeWdJYiQmrPFoopmKj5mMWNcNEzgS2Fy2KBdAUbMZGISIiM0bMy6CMLTAoL7XKM17kQFMMyxivIlwTLhywQBKOgDvDkEJIFOkFNbiQo4KZlxwd6t86e3JGfnYRppqFj+aoNK5sOEywSwniB4peDjJzNZ5zC/vOf/3zv3r0DAwOmLRtnFcgqsCQqMDSRa5gdWxJTySZR0wrIRxfZEpiQQJBMLcVKwCJAJWaldzm5dEALapvfC7SMlsImFFJ1ebp0tSZ0xuzq6jpw4MCyZR1mdCc6AulGNmJihC0+W2qmYKILqWgYiInKZnnpCH3kcrEQlsJKIlRh5WYphFgqQCg6jkSxCShB7fWh9KwMuYXL3igVsDTmpq/vO7F72ebh8Zl8rq63vW5N0+HyJWXstx5Pissswfgdd/yhateuXe973/u+9a1vcZJMziqQVWDJVGByNpcv+O6zJs+ULScyKLElzTZv4iApHGozW28iZ6kUbKZMNDKVLQbQM3RxuHSgENQSuQBPppbIld7F5dIlSV7wFwJ60Tx5C2gpKk0yJaHLjRIvGpuamtCHNDY2FrUWWlewERM1OhrMCQzHRFco5cBATCRWXcdGTCQwCczIRJj1kQsv6CxHS6EcLbWloAAQBGMytRCYx5FDeS1hzBRISJvsVYrVkJve1Hakri0Jrze0eI47zi1705veVPxad13d0NDQ5z73ORVz1apVl156aeIT63nCmZxVIKtAVoHFWQF00UvvgcOX/s84/TSasQ6SjuER2v0sc3BL5Mt1kOMeMpxbGIugZghT5B6QowdXu3QMGpm1Z5e/AnBLicVSaET6gGNLFl3BR0rW3Y0RhzMTqSMdKSAIDwvCFMV3H/0rvjXx5dSTJ0/M6Zit+A2/MlK5zL2Vi2rtXe3wVkwage5SHlmgiE5F0c36qJxHmc7yElyKasGrrC776rryCCRBTHPBrJeM51AycoWStWDlpOOpOMKgjUyGGkPXg6NcdqXjKIvIMKqhwEVYwW6oCU6CAYiGZIcgP9w77h0dHX/5l3/Z19e3YsUKfNj98pe/fOTIEfzF6g/+4A9eeumlj370o//8z/987733JrpwpJxDZskqkFUgq4BVgbO002zlUUVF2iml9fOkrh8X9BF3ky0c5ZddHC6dYpEtUhTBI5laIpeTqgK/QIGYLotLZ6eto/SRPBmbhzShFC4cSCx1pIieKZAFnkPU5+tnil+fL7WxHF2SdRo2YqJyshTEFVmiZzK4HHWQPuKOzMJEIPQRubjUls5SJOOT0ZSGAHEFFqBxVGQXOANoFYXsTyHCgGFEYSgeOsyDoXCdRjzQWqPxLn4oFR7uxv3MmTP/8R//8e53v/snP/kJKPCAO5Rf/OIXzznnnG3btj366KNKKdBm6qwCWQVeJRWoQVO5sCs33xMWV+/EZdKZ9JFB5jUaWOdQIEimdjIrZTIiAe3hl6MIXILaGULGWhZL4SSEUgfqI3kyEptFqCtc9DpCH7nwuo6NmAgW9ejt68Wme29vb6RQzwzKRMvGnXScPgLOp7BsAq+O00eCC6l1tD4SQKSG4HJw6biPkl0ol05Gc07ZE6j0xrDglIgcSLaQMxeSwFNicZPgwcHB8TFcbabYcuMWSW3t7cuXL587Q4xngwpeddVVxcst1dXZ13HHt7lHR0dBgS90o4n/0pe+BBg+B+BWqdPTxXu3qgdOqslunhoVI3vOKrBYK/B7Ny3f1XJ1quyTLFWJAtSMOFEWNriyvOK84+x2PrYGSzeUuJIvI2Oi7QBNnN3pxJUCgaBOFdHF5dKlIqe5WIyWQoaSxRZcLKE6mw0ay9lSuEBOKgvIqJiofGMUllkkdyGlEAcPHlyzZk3RanlZCsWhQ10gXecbEaNOGjMiL536bKopNglWbpYCUJeOGLggAwWLoOacSg4DxqHi7BQ3GJjQw8WLS7/gfgWKCC037bVDWLlyZXv5kkpFyPj4uHvHXfnjGkxKQE+P7hwUYMRNmpQy+5lVIKtAVoGaVMC1tNUkUFrSJJvucZOJs6fIEZSK1X1uATFWI7TAIagRWrZQXoYgeCRTG5zOocVoKZSboA7jFCgq4nQ5u3SUITO6RUJCYIiYkc+ss3B+nxeztba1joyOYD9RYNLV+kiFs3RMwUQjN5aCLlojw1GmdFlCdRTE5RCXknJ2ebp06dnikpTzIE8ShMzIToIX6DUShRKSYOGREG4Ew5dIT59WXTu2xbds2YIz1Q8eOLB33z5siKPlPnbs2Kr6+tbWVsoN8epx6ovaL8fOvDq3xqBFy/5Hf/RH69evBwAnuBtWDHG9SLDb+kyTVSCrwCKqwNXrmwcbShtaaZKudPHSYlaVTGOueIDU1D8vE6FcM+FGl93LLBoZa1Gcmir+UbSxkd1WG2MGquRww2lYQoLaiMsc/CLR6bBkat3XMSI6CNGDdJECz6RjOGbWRBlLFoZXOqaQRMs5UkTPziSZkX2NkYllL32WJUf2pcY5Fw1kk8+ZbXA0K+aiIrMQUOijoiIK2NrScuL48c7OzhITqUuC5QWMDyGnpztaOJ00mpP5rFBMK7hxtQvNdCSST6QhhRIitfPZgpCzDhfUOmiuwoYyGlqhlIGYIXgfBAxDyXSKyMtCxiRYOCWBKyx+6g+cvXL06FG00C0tre++556Ltmzp7+/fvHkzGm/07go7NTkZve2LIeEiXlWGyLEtj+u4g+X+++8nZSZkFcgqkFWgVhVYYPsAtORaqy4VgENcKG4np4qFiNVoW4i3lEkEKi75FTxkGrLo7KROGJf8GB3pLC6yMHSI6PKzdKSAEPAguIV1WVw6wzGC6O3j3Ii1tCW3CKy6CQZi4lynAajgorfdLIQmgsZOKWpjVB5zEcoBo/SiZ4tDQZl5zpeYcrk8dgmnp2fs5h4Yy1FX6KOIs/wc2YvzYmQRgHSRwngmuxIiK6kjhXoW1DDq7gbaMloKPUxxRKEYlut0D7LoamMko8gCwXooo6W2FWHAYJQdwKUJo+Oe8KjCY3RkBI04GvdrrrkaF4PB7vn27dtxessll1yCXfXSC1iH81zQhyMYhfSdKkNJPfTQQ+CiM2dInwlZBbIKLJkK0KIQPKPkHsHUZwsYMKXqQBJNkIVkopOC7EqgoRMcp3R5u3TE4zUSyhBcTi6dcpMtBi0fupxcOvgIas7GZRnOLEzkvkqOjNEztEyU8TrO5RKvS4RwgSk93aiP9EyVi4UQ1Dpu+YoV+IoqrklNcd3F0r0YWBMjVPRMRktBFi7IKMtiKRSPoPYavT6Ungvl0sFBUBMVCV6gYBTUxMmFAGwARDFWH8gzhRwcIA47OjZWOqM9t2rlSvTYuPTLkaNH4XTx1q34ZuqhQ4dgxQOb7tg9pySCGnd8Gjh+/Dj5ZEJWgawCWQXw5fdk61eiktWSmycSsABXB8KDxsp6SH1kOHuNBjZ2KJAJakXnNUoRBSdBDRbZIoVwOcksssXJr8PZiImGY2SJnpVZHwkuUDMcE2W8zh7qwnBMjIsSn14omYAjdVNjozqhF7vvRla8QqaJjSOq6JmZQhhcbqAQ1LJFcBDUMg9PX8kWhaUQcDZVHFBgJqI4exw/EQULYRE5XfLjTJVjoLvGAwfTkydPYosdV4t5ftu2Sy6+GEmeOnWq1LTn8ROnu0NT/F96aI27umJMZAp9xldXs6vKhBYrw2UVWLgVoGVh4aZYeWZh624cKs6eIk+dUh8ZdF6jgY0dCmSCWtF5jZ6Igl8ytYdfyK5S/sg/ehbCUGIRLnqOw8POoEzUDcRvqXUXy0yODMdEGa8cfVDd5gpEOiGMwCCF7urqPn16qLu7uwjw+ioGncfl4NJxX28cl7NL5yXxGgU2nqEL4tKRj9cYhpIpZAsxkxCGDUMRabBQK97gBADEd0fRl+Ot/PgTT2zYsAGXcNyyeQv0OD0GG/Bo6QuFWUDy9eVPqjhOa407oPg4myRoEVuKmtQpw2cVyCqQVcBVgWqvpmF8cag4u2sm8TrGykSnX5zd6SQpZTLBIqglftLLfi6LS0dUfsHl6tKBRVAr/sgYPcfhmZ25MK2i5T9FnG4gF5da1+kj5ajrfCMK5HKELt5XRxh8FoMQRlZr9Ohs9u/f191TatztUEzD3JhIAJeOjCS4UC4dHAR1GotMRYmRoGP1EYGU4DWGYZNQGIz6sGpEyadGiaADrnIaRG0Iak/MFay9rW14aAh5DA8Pf/0b37j8sstwDd/DR440Nzffdddd3//+94eGhnB9SOyqc0qtceeGTM4qkFXgVVWB0t/iks64sqXPtZAlzcDAh1EGoAIgRuiQIWNlotMzzu508ihlvjQWTyCYXIwunWKRLf4orkCMi4k2T2SMnglhKVwWBmIiIbnA7G6Rg5XMcJbCspE7szARZn1EeIs6VEEMyYjhJjtYFkvB3NHZ4G41dIE8ykcJzJOJBHLpyGgxhFkETkGdMkhAKikDcmYvBQdacnpPi6qsCCZNdSAKZi8nVJHkSrK1rQ3X/pqamoRxZGT04UceoRCzM7O33Hzz9//zP1taW9DHkx5CfOMOh3Xr1uHSkrhmzZEjR7KzYnj5MjmrwJKpwMTEdN3ctWJrNqdqr5RhfAGoAEjqojBuJjrp4uxOJ49S5ktjOeuB9KTZiIlGkrrFN3I56nggLAX3YkYmer10nAW1zBSOWZiozJbC5UU6CC4Hl06ApqRSbiwOE72UPcuXoxWxG/fIP3omEktBFltwYV06eArqqlvikpTz8KZi0FaHxSB1Db2BDAdXq2tA1DAJKREEs5MHBDjhkSoeeZcoFAdONe/tXXHkCL6QWjzXXaUECWeyPPPsM9t37ICAq80oD/rpa9zhcP311+OKki+//DJu7HTZZZfdeeed//Vf/4XfGfLPhKwCWQWWRgXGxyZz3Vg6/EuS38oqEQxkPkFiHHGcHUECIEGpCCBGz0QBXPVkVEgrsKWgfGQLQSRBdhUsgtrgZygmAqSPDC/dzqBMtF2Yl47TR7YjszOR0XldlDGdY6gXJaA7CGoB5EqUGEiwnC0FQSFYRkvhAhFD8bTguhxO60XTEwFdDJGNHCUhsbPg4A2YxsdI2EXh0pGb10ioKghJAiVpl4N5k5Dy+c63nxG7NGxvX9bfXzhx4rjaFldduwLiSpGrVq82zpOByde4v/a1r+3t7f3KV76iTnzHdWq6urroOvA8gUzOKpBVYLFXYHxqIocP/XNH1eAVMxiYqD5hrGEoBA4GJkqSwIyeiWTmQpydYwNlFyV0+CcclVwOFcSCq8VoKQx+3e4byY4JvKIUdZdIa4TgQ+bARIWwFOSoW/RRLX0pAQihUV24uBwFelltWYSoglrlw38uX96Dm0329fVz5ZwcTAK8C+vSKerqWoTwc7NgT66wLh1zcU6M27lcKVecP49VQxmrHR7Jk1F+yV3JL7lrMVHtgVNacLXH06dPjY6OqusmoVnHTYI7u7rUp1MN7WncwXLxxRd/8YtfVJ9r16xZs2/fvtOlx7nnngvZIMqGWQWyCiz+CmDZ8658XmMl0w8jriaqetnGZRVnT5qJwCeoFbvX6E/ActUV+sigsoy6Qh/JvjpOHxleGEb26JkQloIsJESQ6NkykIIECwqLrtNH5MgFBmEiIVw6MpLgQoXqFIkLTfQQXHaXjnwso6UIC6yjcrjNJC6fV9pnYIRMpARsQUYJFkGtp2THgUbwFNROisTKapKHcQn7A4kzdzgkp1btdFjiRsAKOnFyTRW4lEdjY2Nvbx+22/GAovhXJfX+IW6VbilC+RIzfA44r33Tpk04Qwbf4Ma9yt773vd+4hOfQB8PzHnnnfd3f/d3+MnxmZxVIKvA0qyAWj3oZzUmSWRccBFzu5JdKOgMoICqUM2CMNF5bI6zp8iEUzJ3QU0IspNGFggbCbhKWfFfsb5zYllRrnlEGHlpDnOvTdldf7Ecvjp0buT2Ut5RXObo9AJOf0SO2vSiyeoRdV/mWPS1oJZOj0sj5slEMkMgNVdasoUiBQT2IDXTkaiMNNQFwTPAJ+LxMsAY8CihyiGxHzl85kzRT+B2UgqhyrSml+CgYLJRJjQDxIytEFVjjpsC7EbPGJNqbczIIVUaqZxoCipqcg7y4wKx+gScJ4MLtx86dBAXTdq7dy/OSMd1ZhwOJWL3qTI4h+wzn/kMzpP51Kc+hQumbt26FZ8G3vCGN+zYsQNnve/Zs+eGG24Atfpk4KDOVFkFsgosxgpYR4nKJxFMWX1ghcmzhJjoJI2zO538SplStihGZcfPghupa/URGCyFnSeDMJFHt30iDXNgYjLfxI6gZz5MTBY3mgMnI50eg6u5rMfWR4QT1AF2wVNQg1C2yMZkPgJaUNMcSSgBHeju7p79+/d3dHYQ0i84KOYcBIugVk4pjV43f/4la5x/nD0gxHxAVEOcJFl4JIRjHsnD2JNXHEqfJAPOxDmgd9DgDJljx47ijPaSW/ES67iI+5kzI4ODg6tXr0b7zekg1+NeTeqMeDTr6twaaOGPW63i8ctf/hJnxxw7dgztO052Hx8f3759O05z//a3v01dO7bkSTbYs2FWgawCi6UCm3sL090XROe4V5Q1Vib6JxMRRAlVAMoUQRaWUHHPNmYScfagkBaIWJmFdEqILIaahiX71NQUZoA7TUZqtiGtphawLR35ag7lyhhFUugoOzwzd2d0DVFE64/I3faNcXSGZj5MtHLmKUQJkE5WeHmUfwQhEgjsIagZYk7U3aAUPAW1YiGjzc8IdaPXh/KIfAS0oI68tOcSVjlo+uKgpB4fG8M5vWhdLLOm8FHYbzvlCh/hIbCRm+wJiNdoB7TglsLwibMbcDWUnWSLTZQEy70T+iWEVxCJu3rkChKaY8W1TY8cOUwXbETbvnz5ciix8M3OzuAakTjzhb/J0Z+7G3fwHT58GN9GVR05Gvenn34aXTv0N910E1p5fNKlmWSNO5UiE7IKLN4KpGvcsWzZ/1xFCEPBMxjoipJMx0IZTaiLh6GTHoBddGUdJ3bMn5u5HBFwXUkuzmV6ahoyrhBcJDQmpxwid/XMSIpozUcflV8gxsDc5+KBYW4uCaKXQ8+5B/g682dljLKM0inmJTw4pISyFMqR1AIPqSMgnwYZS0KE0LXaiCBKiGykjhT0rONIDUH28RoDCAGJHgJaUEde2nMJKzvAUnrggu44o8B/wYwIq/GXBoJFDqsoBDcYZUucp7IbPy06S2E4YBgASe6UkDQhPG3WFfjxEiTPlnt7ZBCH/JkA/TquBan22tGy48Ltp06fxq2XDhw4cN111+HbpNhPn5mZXrasnWIBLH5URcuOxzvf+c61a9fCoQjN59G145rujz76KFFkQlaBrAKvkgpgJbL/WXO3IUpjAaFwYl3ACnV6nHJ3yDKwIug+6Y6IipQxlUPPNcdYqk0dS0p5mgrLwVLMFRbhIwYSSwrTodgll3WRF+VdmgaNnAxFH4lBVUEjKUOVW+TrDs0IyiLLp8jGPLVIZUvZVZO8nhGSgyDLDwbkWTEHhijmJj8IyCCk0z25WrcoX7IzKhIFI6mDCAPQFFAWSixUNxdOxYks6ElUoxIptGcdSyZKlTRMgNH7iLPLzsk9jRMsojM/5BCwwMdy8zoUjXFOcXYjgIInSYM8uGCwuoYcniQe5zI41JAD0soqISc9V+IkmcnJCQQpHgjq6nDJ9uU9Pflcrqenp6+3F29vKEuYSZ6I+xx3hYDPfffdd9ttt916662Tk5O438ELL7zw9a9/Hf0/p8jkrAJZBZZeBYIPNNUHVl5MlhMT/bzBQCeN7q2P4GApak2CgDjrqdTRWqECkmEQJiomS2EFmFO4oyck4eQsMhM5ArJskSMLPoLaCKjHlH1kCyd0oVw6eTLEJrt5nRO7CQ6CmtJLJghsvSt6B06eXNHba7AJcEFtOAtDr7PXKBDGqtG11YTXFTguVrGDLD2CM0ruEYUoPif3Jg+iCU6VPJRgMxEgLSURGMLI6GhRU+ra0XJjux0tOxT4iim+nHrLzbc88IsHMJyYnGxswp9Piw9k52vcgYDnd7/7XXyuxacBbOmr9r/km/3IKpBVYElVACuS+uedVfC6FQz0hgs1RtGi5xC/JFjiY05MdJlJZwiWm6WAg0tn8FgowwdrdtAXFpgbEymYS0dGQyhhZQfZYvDQUPfQRwrk0pE7FyygpUhICDijYKI3KjcaskVhKchBtiiI1y4bZQtoLaOloPRcaG605RKXl9D2KWlwN/jjx48ZN5ZMQwS2lG4VeQrTktTo2QKyDEPZMahf9cYIQ3H25B5Ob6VEbsHzo8DE550ZoTyCTekEB8fBPnhxV71Q2Hj+xqGhYVxYZuPGjfjmxoYNG9B278WF10t/k52Z1rbLYxp3lRKdNe/MMFNmFcgqsBQrELz2qMknhFdYsSha9OynC0MRB4Mz0WUmnS1YnvEKm4Q0zJmJZIZgqDG0jjE6RB8pKpeOB7HlkofsJltsKisFr7PXaFEJlyScqwAAIABJREFUCkoigM1FIbvJFooJQUAlUwcQunIPduPASBYyhFm2RL7acwnu9fEawdXR2XnmzBl8gU/xynDZAk+vUcvYMcBvWpx/AMRB7GhSw4jodz8uLymo0nu9k8cgjwB2Z2JQKo60TIYfD+KdKweGyJ44unvxXMFi5By22N96113PPPssLhiDMc5wKZ7z8tOfFmZnc8Wtc80tqHHXPLJBVoGsAku2Alge9BXCM9NgoIcj3BRFi55tT9liY6HR4fpIObh0cVSCj6C22RiQiRwnqDmE5AgbPZOBBNlCEEOIPKJnw4yhbLGxpGFOTCSzEmQLB1ooS0Fo2UIQEhiWiWSGIKg5hGQX1qXzOoQZgaoic3oqyjaB4I2mePDlVHxFlRp3gTyOCJ1WHERgVuoA/wCIM4RqAll2lsLpRnlBYM4erG1SkZRe5iCUDLGpoSG/uABOb640mLgJclhafg6iDCMjeJzQ2tpy5gyu11547LHH21pbr7ziCvI4evQo7jKG/fiZmdniZQYowYJ+qgxusppicx13ZE3hRcllQlaBrAILvQJVXqxiphtFi5453KXjdltmHkwknEtHRi7oQH1EOEFNdi4wLBMJ4dKRkQsWsKjA/9KJMk4j9w6SSywWFbnKFoI4hcgvevaCnEau1Fn0EcdB9hplrNfNayROGeWyuHRERYKMki3KOZk9GZrS8wi+b0F43HQTTuWtz+fxNTw0MLqFj9D1xOXP4S65OhRgTpWIatuYq6Vw5TynU1gCMBbSxQoBHAYElEki2d4JCaQpOImd4IB0w8l4BJG4ra29vn6g1EIXfvHggzgf/dJLLm1oqMflIH/5y1+hZQdLc3MTTp7hdNqOO976Kb54qjb6OWkmZxXIKrBYKyCuMFWeUCmOK5hL54+te+gj8hTUZCdBB+ojAURqSYhYomcDJ6gNlDHUnfRREQpNdIyxjQaXNSx5xLnF2S3WokJ30kfkIKjJzgUdq49kHLdIcsQVPds42RKGTeJvMaZ3DvMMQ5XSSgAtT6MqXbuiw/f5cKualStXltlrI+E3Sp6r18jzCQZyJyVHv9KUhqWwfWxNKieDhjhI76qMgXJByN8pGAQ2JjmlzUGa2GhAUkCASSYGQRCJGxsbcJekgYEBOOLcmAcffBDXgOnu6jpy9ChOAEMEnCSDt7f68mpEXtAa90ibPWcVyCrwqqxA8Drkr06JxsXl0vmpYGVOTORugppDuMzgTBQQXO2RI6Lo2Qn1Gp0eUFpOloJ7ciNkPuQwS46A0bMFCKfirjqdPuI4yF4jYV0ol87rQEZbYFxMNHCyxQCqoQW3FIZbTe1x5IlzDiNMOEUDHjNsam7Gt/fQ96DNSdJO6bSquYqbDrVgLqDXyKMREEoXEcc6ZYuAK4JJDadgv9iUOIDNz47HgSo8MMzDsNvDWEruAuKE9NxbyTwgl21koKaru3tqenro9Gng8QY+fvz4iRMnIt/C8hW9be3tKhDVJWvco/pkz1kFXt0VaKxvHAtbMaPlI3o26iaoDZQxZE5M5CBBzSFcZnAmcgRk2WIA1VCH6yPbIc5ue5DGcrUUMnTOAg/vUaXEKNMqljg7ZWELzJWJBk62GEAMLaylIB/ZQhAu6HB9xHGQvcYwrEwhWwxmeRhHEWdXzDpKH8mxE1nw3qwub0dnB24Nic1LOY2wmEaL5KWDUZhFWCxFriLKXHIKcxYiUONSSqQTEpRIyQ+AhK4hlDZGD6LC8yQMDx1uGGOHsfQSQ2VhJValR7Pe19fX2tIyeOrU5MRE8cNn6SNoS0trz/IefEuV3Cl9X+N+xx132Ge9t7e3P/PMMzt37iSuTMgqkFVgCVSgra1liE2jtFIJy5WgZt4OUXfSRxwuWzhKyRbWUgDn0tlUTg1zZWIc1Gn3Ky12S2H4x9kBtyGRJno2OJ0+NkbQMFImOsFxdu5kYS2FF82NTjmii569IKfRqXTRuXROZ1lZEUWYs47SR3JiZ93S0dGBG7qrxl31N67UYXGp7exlCgMrA5UF8LCIAJJHEicjH4uGkypsqnx4mGAC7iTJdn4GUo8WCydv3Y/U6YTwsIH8yI44S/Kyjo72ZcumpqbU346am5vx9VHpveNr3NG14wZMExPFuzrhsWnTpmuuuQYtu3GavLJmP7MKZBVY1BVoasZqkC+vdmUpwbSYExNtAq8xDi47yxab06lhBEyMgzrtHqVFbSls5wCI7QRNyS/OOc7uZCYl82YimUnwGgmlBAtrKbiD18iBJDMPJpKZBK+RUFywPCwFRys5AAJgHCrObsd1adBOVIfIRc51hbpC/GnuSbLB3uTE+ERzS7OKIrgKap4ZycDiEVAOBRSwZBTsxRjWI5WTxVJuDpkJZecPjILnqfx4bpwJsk5tGNMNPdEMQj14uJ/KG3idwGCv7pBnp+RC3dTk1MjIyPT0FL6iig6+ra2tsbHREda4qowDEamwe3/DDTdcd9112G4vfg7IHlkFsgosrQrU1+cDFy59edNHVBNBTXZb0D30EUfLFo6KlRkNE/1uwUCbRnfVRzYamgCIxw/e4p1TlVtafj21AJYACE3EwloKgup5cLVHZnRMlBwCINw1IbzkGuzjBXqNKsUACJ/L/MhBvVIQqJgvbhd/6ODBNWvXUvLUHemzF9TkZghJ4IQFhx5UkZLdZTTi0pCcSJPEm5xI0PloVBQYsRKhZDqikAQikwCkT8JKTn4hPDjnQSKlz5DQFW+FVNW8StxzwUhWEVSyc9Gmp6dxXju69rnvaRQKZ3Jn0HXj70h4V+O6STxhyL4d9+3bt8NTXWIJ2+04Zf5P/uRPcHH4rHE3ipgNswosiQrMLSJqLmzARGOessUA8iFzYiJHkBxnJ2CgEPFFzx63AIjHGyaLwFIY/nF2A24M4zcvXTkZJLFDliMTbTevMQAe5x9nt0NAw5yYaEO9RhuuNC4nl477x9k5dt5kPamATiYAIiePVsm4t4wLqzU5LkBJh3YFz7iynt3oCARKDSd90lIEgod5eOHcGEbHs6rMmzMZMiMmcU5w1cilMxilIdFLgAq4JUqnXiVC6ZDgBCdUcrI5OVf6eBDxFJXo2g8fOoSLOkIuvYcLuBxkfX0DNENDp2dmpvtXrtTvwCRfVQZ/dfrDP/xDXIZGBfjSl770ox/9KAqWPWcVyCqwpCrQmC8U8s1a85d83bQ8LIVUs2CgREB6xsREMktCEqzNYXlbCtsHmjCU7BrnD3scxEluKCOO6Nkw0zDOTkASdA99RCAuBEA4nOTIL3omgyHE2Q24GrqcXDrDOQBCHkmw5JRSQCuROFwaH0qvuM1Z7FhIIQklXAkrIer6+/tPHD+ORseJkAnIovzic9HOQUkId8UwEuDpp2JPSMDhkuxK0aFDukrL8laipZYiKb2D2+8QZ+VZAMvyi/Ospt2YFrbYTxw/Ueracy0tzbgge++KFbfffjs68B07XvzVw7/CNvypwcGe5cspCTCIO+5o/PE7gLuwKnR2XjtVLROyCiy9CnQ2FWbybeFrmbXoWQpnjcJQTlePMmKNnj1QZQoGSkwWgaVweoahZNcw/zCUM4pSMgImhjl4UGSySC0FQUkIgBCWBObERDJzIc7OsVx2+bl03CehHEcXZ1fhcLQPA1rZhXmGoSxyUhS33jEIyhGxBGhjU9NEaeeyhBB/KAJldkUku8tosxKcTAF+thO8XX420IWi0LZgExAmIRP5eQSKRkLUySsnpi4pXCm4dJ6QgSYjsjEMJImFqdwNcpoQ15eU+FbG6OgI+u2NGzfecvPN6k5KDQ0NuBnwVVdduWfvnv37DwwNDXV2dfFTXcxTZ2KzygBZBbIKLL0KtDYWpuvd9x3E8qL/w96YrnAeblCjMFSiYjLKYhrqXxRJYGI+cykJQI9a59BH0vRBZwA9AQSTPkcBZASSUbEWlS/LW/bgU5NRhiXiV2qiMFD6UPfRbeKIOTFRggOS6uHyc+ls8jCU7efS8F7AZSddemCYJ1BhQMpIF4r+ONs4lEbhrIhdXV2nT53SmX0jonHF9Ro9rIafi9rp7fSDUn/YKGW3gLqbY2QzcQ0c1NDhWTUVDxjJ6m2Q4Cd750Uclb0X00/PWTFKivOWlCMjZ7Dpjsfll132/PPP48pIOM8Ft146WbolE3bMcZCDdXx8fO61KHmJO+6cHzI+EOCbqfiKK643mV0L0ihONswqsNgrgC+m1uXqWTvBxMC5JffwE+t8+kjyDENJ3lxvMVkKjiY5DEVwp1DiCCAKgDj5bWXEFD3bCNIEQAjLBebHRI4w5DCU4aSGkWv07ARVrBTYcVwVLBVHnE8CTAMPNpPgeVmeydMuUqgNeD0HgUlFVMZcXWdn58EDB3FTGwEdo+ZkgLIKYGQYLbufuwJv21XFhr6UIdlJoFRgJ6U+G4JIAvmRICGVPiG9nyyRtZSgnaWt+f/Ze7MgWbKzTDBjzYzc95t599pLtUpVIAkhCSEk0ULAAGMYTNsMNgNj04zBGEu/wRvv8IYZD8zDGDw00IM11g9gMAw03a0ZkJCGRaJKtdfd7819z8hY5jt+3H8/q/vxLSIyr0fdyvjPv3z/f373OP7HiePHE6G6KvMUe/NZzCP+9zKhp4PHI/Hb7TPmplL54MNbr7z8EnaBRGt9fX1tbe3Bw4cffPAhW/VeqXTOOkwteFkLd6y5+Z3f+R2OAuVvfetbeLoBgxgbOzo6CszL9zIDZQYuQgYWJ8ZOKzPqdUrvmTTm6OJMnAA7eHcES6huQ5Vh5JbNhvOT6FqQvIHVIlPZru6gF3XdCmCCd9WN1nZWVCxlO7mlqFLTTYvUMxE5+8oZDl0L6oBMvfSNE2LJhYbcig6H60InfT58CO/NR4kH87SbzQa2sR5vjmfwHiZM76gQRrZ+krXiQ3CgSMQmt7ZhcE1M1womIi2wQ9LNcagvU7HwsrrUyuZZghp4g/cbPxaRZ48Km8T3CBO/P/b1r3/99u1bTz31FJ7EBLV79+69+Z23sHIGVTc2afZ+iSJD+82puNH1r/7qr2R/agsbzmBWHwW9KijbZQbKDJyrDMxO9E/G2HghvQobTAPg4F3yGtlIbmGEM8GYeEZjMJPo2jGcUZwVbb4UvofnBuqmpeDzpmwqt4wGYLppxVk7ozgr2jwOho8rdmSk/IoeqUKBxmGRIhGyRRJfgODqINyiI6cy4aN4byFQSMnaCwuLDx8+uHz5Sug9awCSA+oTceX6mNicsIWpqAlN3YEgTJTJaCRCRYjQjOwF6YpE8q6J1iLtGKhoEk3zLkGHx8jx84s32rm7FFU0bj/ln5Pr16+//NJL2L4d5gcHh4sLCy+99OJf/6e/6XZ7jbo0yZ5+jTuWz//AD/wAbTvjHmipWWagzMCoZWCyWelUvee0YWijf/lFGUB6P/u5OwjM3C0iQhbBhE4S225KKkTYdaMltGbd+101Upd8gcjjFeCxQxBczSJx3bR0iMARl8gtXZs4ad2JAB4NILeXs6IbHLScEZOXKQ4WDiq8J1Dk/5w7RhaBD2Ikh3C2sCiS63BBfBCVb4HiBMcC+0JKCKFd0H3FTNJO1tCxBXtdSBxoJQ+CrI0Ed5wQlasb8SKZkUL/JNN1KESICnsRNvcv+tQjGh5namrKvyhWxlDB85tTEexEa+LLX/5XmIAHjUesTEy0xHRKVXzSFP7N3/wNVtSI97omRSj1ywyUGRiFDDRqjf5Y+q/xvAte2eJcu1C3k1uQqY2QIeWWzUbkJ7cQrQU6yRoYmOXmNwzBg3TGdVYMHQiUbC23BDUDmUTXYB5mLjOQEd3ExLU+kzPY45UEwsGCqzjjkrqzBRRFI7SEHggk9Gwv2Z5pudlZ8Hw4AZXBzc/Pb21vLS0tx2ALZhI+zCCKMZYslIYNWFZjWm7z3M6hkGMiZJdOreTenGB9JTEykfbFPPcEKMYCbbFJOhmIvPEShtIcb05NTR8c7KNnWJGOwv3G9ev4SvrB+x987Wtfe/6553Bn6uzsnPJoAqlwx6S9+iU1MggspwEcCvdEVpGQpbDMQJmBIWRgujW+W0n2RORgwAveE0WdyijCg4anMSKMuSi5hR1y+PU6rm+VPp9ct4cpSrJ1X7aWW6IXI51Q3YjhMXMDsruIlvAqJEkYySsRqnQi3aTFjQQ1dl7os1toOgrZQZQ8AA2PwbUmW5ubG2NLKIH4S8KVGpo9Y3CzwFhVibdXLSLaNieyia8V5zlOLoNaW24xGczhn2zTxkIAHD+6aYghGUuBT2ZM2tRXgqNMRIsgXVpeOjtr464MnKxveC8U60DG6va//8Y3UNbPzc3zZ7sG4PIad0yf00Q9BRRL8DtWY9VKhTIDZQZGNgMTE41t+4x7MPQE74m6kcoo1oOGqjGSQ8RaRCjEL31RjJPHqwAYmx5qcujkFqJ3wVogRY0IOrmFDpYHho4az+HX0azegZIKIs57cJVPhJ7KCJkiOy9r4jQ8GEm6JwMxtCTWnnf/z9T0NNYeYCmC15ZwhUaIHlIiipEW7FV5AhTV1KUd4dkzZ3IeAii3WNy0XIKDjhifSDuau6tRJ2GSaw/cQxA19b4ShwjSJw4nMPe9fvny5sbm/sE+SnaqqDEtPjM7y5ajex0UraQZd8LVCWwnyTaSLF9lBsoMXLgMTNb7teYsdSsYBoN3EjgSae2i4TVUjRFtT9K0dgQQEAwoGVgy7cCP27uHHToARQN9FEBoEaUVIRMABDLCQBQltxCtR4RGnuV+8MTLvOhY6VAlMeKQDs4cVPTwKCSIEBWaSWITrfmZKFvLLd27yJGxQkkcBlbL3L17NyjcQzuZCtFDytTVOG8BqoASsOR3VyDZKkmLQiAi0lrVShJgEt3IIBILxahFOjHQSBhUK1XstI4aHQ9jOmM7P/bHxyfw8FR/IbrWwZjCHVuift/3fR82pvnhH/7hP/uzP8N8/De/+U2skBmJvpZBlBkoM5BHBibq/X6jlXjOGK6LHLdlbLnl3uu0dnYPCZfBcKD8w5ACHNaxE7olkFJog2jgupbGfUozQ4/4hVWOIRW6CcjgT2NxO86WwyBVUQVMixapiwQ3FQGSWHMk0Zq+VHowybFMiKxD8CGAefvoVc7OzvD8GbE3DrQcrGcgswQ3nlRzbnciA9n1Aly8y/2KMcksThKgVZcnhGJR80WCBATlmDvNAzKB90GookxvNse9xeco3MeVde1iBFGFO/am+dSnPvXnf/7nP/qjP3rp0qXt7W18En7yJ3/yj//4j7EcR0Qp6TIDZQbObwaWWv2zGjZxj3sVP1QGHoL3uIgM8gymBjSfxUDTAKexsUehSdLU6yl7ovmWEpKqn6mMDHFkYeVaEmlgVNYk7GpaO54JN2s3LVtuyZorJOwfoXowIZYMI7fIxkZwmBCM6S0vr2xtbqF68Y0SQtpc0RcPUjA5NzszcwlIIag7RCgKejOZA90+H44Sr9JM5YNjEBIRVjBkwqjEM8RFXGfoOcMKmb29/d3dHXzPRJnNF8ygiF9YmJ/EtjNaF62FO9bGfOYzn/mjP/ojLHz/P7wXbAGHRWOf//zn//RP/1SDKhllBsoMnMsMzLfGTqps71j/pY8TgSSvd8GDQKZAz2Ztd+jjpoFPY2MPxCJJWbIDLafwcoKxdC85G5dhb3eO5HHRBTy5U91CLBSEUIgt8HRjnUN2JEoCoFsDRgMwapkUKQadsGFkgpFAedhgeYTc0uMJOJhr73TOwnXDEmSgpL9rOdJVHDhmZ2auBBfjPkbs4EDylq5BQcAd0emgCrOyZULkc1rkFBaOFRibu2w8enRwcAANqtoRUrt9+uDBA6yfmccyd+EFkbVwf+mll7AqBog/8zM/8+Uvf5lb/cZv/Ma7776LOXxsEV8+P1XIZEmWGTjHGVicrJ1WJgodf+WxXW4lzVw2a7s3HzclfEozezhmSaqFOhwqvwhlJLllDtvExfUnrakJjs+u4bbIVKD80p3K1BgMmBwShIBq4tnsjXyeNP7XqBDHpAi4ohCbYumsqNgpTQUmzqtiTU2C8Qi5RUphqsNuYUeOnZ2dZA+cIXgB2UCGTgzCDKwY93ZxTEBcTOYx2tEdIBSoiXS0Ve5S6gPFQJzcfRUGiPMTVTtKdnhYWV5+5plnUL9/+OGHt2/fBgdLXeqNxvT0tOjfunMzJtp//ud//tq1azMzM6vBC99ff+InfgJLaJIvGhOdlnSZgTIDI5SBucnxbiXpMtCo+DECCf/4poQCI8rUIstmbQEF28dFtUce7MomSUozE1QMj3nC/ylf6S2jHWbDpctttJMkUvHB40nsPF3EU0hIDFUG5gyZ5xguNyIAIhzNZTWyJkKWU4vkRJAoOUEYOpEcTLQI8LCdnv9vanoKywS8s4IJA0I0SksHzvyjG91M6ySJXXQEfkK8M9FPBWXJQjj1TPfKYwa/8Bf5Jk/EOSdE+7S9u7vLv3Jincvzzz//d1/7+t/+7d+ura0vLi6iWyjod7Z3+r3gzPX6ZZ5xhyrm1HFPK/8SQDkBgYXzuE0bk+4is6TLDJQZOKcZqFf6rcnJg4SbuCudFco3gVSUEjVzgjH59KEzechkbAoqhpehZAdy3tHi2pE3ZEz/E4m9S1yGjKF79Mq1nwQsoBIPLgU2BeBOiEjcKhWeM4yumMErdbMA1PHxJu7Ko6LF82Bzw+IQ0iaQFGE6IsphJCIPAeb5xRLpTxSmDJrMiBBBHWjqKgegpoPpoFQQU7reKYZYutLrskf84nvV+tr62++8g2XuaH7jG9+o12pg4gXO8clxq+UvZwXPXLhjTRi2jvnN3/zNW7du7e3tYU8lZs3W3LT//b//91/84hf5chzOLP+WGSgzcH4zMFEfa7Yc7kwVeqiNoxpDUE5A5gRj8eijZ3WS1d4SnZXN/A3apzUYXYCLSLbo+NUvG4Yelncd5OwMFTwAigmPo4phewlQ2JmTkhOeAqOFLTIoZQoTTXQHSGk7FREEubJgL6+s3Prw1rXr10gxmhBcCaTFRvApkBblNGwKgYg0KIENjxFQxQQbuMn4rnRVaWYEz8c8dUyKIdtmnS3vY8djbe1Sp9udm5sDjUlzFOFvv/0OqvFqtdJBNd8iU/kBTGKH8PBVrIp55513fu/3fu/3f//3gQJ7vNbX17HwvVzgLuaqpMsMnN8MLEz0Ow02UkS85EFebkWYxYryQ4p0xdxkdZXVPjJAqzCPyK3goyTAJamoDPNJq2yZpCsmz1kBoYoeAniRl4djHS8zqg0SwEEvuA/85bpGC02XjBIRRmwPgW2x1+2F++vl5JB65Tmxu9d6Qf77Xp6oqSnmzaAYicjbg4rH+8bdET24DqvhjF6bLVPyfiQcazSb//zNb+IsRb2Nvddfefnl99//oNvt8PJbjNy6xv34+PirX/3qj//4j6P8/7mf+7nf+Z3fefHFF59++mlsKfOXf/mXIkRJlxkoM3B+M7DQ6p/WpBtfeF8wtHr/MGEZkD6Rra8iWDakSGvfDS1ej1SOFHKkSJUChMxrPp6BUsALF+Lg0h+8Z/QiIGZEMpkzdHaFpKhNSq48Hir9dTVz1SNgkYCxF7zI47QrrFWvAEgvWpZr4z8tEqOWyOQW4KR8rawsb2w8Co1FbEc6NM6BIp88Q/zMTPuXn9M+pICcQ5w5QPCAOBDRFOVjT+ArJXKD0hwvLInBencsbb929SqWurTZmpk+JsrxqlZr4mfJvFSGJ/m9997D7a6f+9znsNoGJTsexoTXH/zBH5QPYMrhbC4hygyMRgbWZhrHFb5TbAElXgGQprRJbqSGSduVlxuQq8NAb2iOgwBc33HZxSvlPoxGJxzRAzXKszPl2j3bQhoeDcVMwRVwALkT3RUu7SZvJh6FF0uY3JiNUvlxhw+c+hbemRbwnN79+BqNJhb6OlnYlJIHbUPy+alSZ8JkkQnR+aTAMRkVyEPHmHP/zXdEvaXTtUIRkoyC4iKdzxUgtYkI4bwQk5NT2MGd94h3anNzi5a0eMtc+qjpcYeG2Ouowh09x040f/InfwLizTffxK3Z5yUXZZxlBsoMOGZgfrp2XGnmORIObkyVPEkNx87b1PLEsvmI4ecWgjjex/hMKYaHoHzMLeqwEskP0tQ9vpCGJHnU8SwZBJjnB0sAlUjRWyBgPEvmLOzAMtm7yTdD4E64ND+HNm8Us+jWiyK0wLQjHnDDFxCTvonIL1wTesgLQwt5mahBBR4XpN8xuX9hK6CCd/njIoKHGiLXoyNEmu5IMyZaE03v5mn0aKLV+vh3f5z3D5PuOGNZ4T42ht0j6g3U6v5ghamHmMKdelxW7ZSKkigzcGEy0KyNzc4uHGbbUsZWIBSWJfUCpbazOM4TK10cBUSAsb8AVKV7nhP8wStXZxySO8sVmEPKf8U6voAiXnRWeF+krw+CZz+hmn+NIdgkJsWjJtI6ELmFGtG6mhtHcSU2p2dm7t27N+vd+RcBlnxSn4NlDj0iJkeR2FtHk0LVKCU8MDSJKNTvuQLHbPry0jLOTJTof/mX/zfWt+NjABqjDwYjLJLB3ut8X0ixW2HhjrXwzSbbNckr8UWdKJqvo4/SKGVlBsoMjGoGphr96kTMnanW2GlctmrkK1D9qe0s3vLEyicOXOPyDCpnOHMf+XUZ12cv8jzD9/0FDlizAHi5V2IRzyWhy5CSbRK0xL6IZjlAi3BWWvMvMYQoOAmpwLOiJheIbkXaiEQhJA8HFRL21xP3hTR6iA3BaMVrUorOopOInStYIs95KSuppCYReTk65zhYBoNNXx4+eoitYzodtt6dOgQRnqJU4wvciduXt4PktTtJXQhU+bBy0Sx1ygyUGRi1DFyd7R3Xpccpx0QYDikxivmJDS4NrPz8DRUJ17TCOsevl4XBi3nzXLE/nrdiXPLuBD5E74XR5JJPHYb9CqnsvkMnAlaeDgRYOylEQSQRvpkpKBPP7iWxRAxBpCOAKKKfh4s0AAAgAElEQVT+2OLS0sbGxtraWoR6FpFjRG4urGDUITccd63CgN1DeFw1UaBfuXLl8ODw6OgQN5FizgCcySn2nzEl4Yw7F2Nm3qhXMssMlBm4eBm4Mlc/rMTNuA9nPDd7NXOzHJj8EbNEA1tcsP2Y+KU7NsB+v9fpdDF0Yw6l1/cHcDyQDzM3+BEVuxaEu+BxePyNBc3YicDc64Jfgng+C3DswwcuqXfgF+BNcBM6DikmZl5z9iw7EGIIyJz9BbCR76agTDxzNgYVsRBRvVbHEyilfSEjO2gQDipqg+uAJXQoYOXz7grMcyBqG7PCFYwiY7xMv9/HPcSYDsYzRI06F5iJPSBnZrCeS9nhTUwz9V5e447xXRriSc1OIMW4YCRaXWMHKyVlBsoMDC4D+EVueXZiu6o9Bdl9rC0kWIN7AysX10XhZgwOg3UYmeX618fPqvjdv9vrQrVaq6M8x59GtcktMWHT7XRP2+1up4MhGuU7OI1mo1rxtgCmy0HoJ2PM8eaeT3LM9PljR+Itk2qQEyI8ZwwGnMK7zLyKnplf/6X6VtuBXuJ3iz8zTm5ezfA61xSdiadbyhxT4CaebCW25ufnsOXG0tKSyExAp4k6AbxBlfdP8Zus0wbULCwlFkDpHMKPEJEOJzY3N/HNCl+sMEZVvFq0XldnlhWTx7YZ5gXTM6i/aRsax4zACt+NytUyjukq1coMjE4G6pX+3Pz8Ni9khnol8HJijsDMHZ0kDioSXP94KnBhw0M2ztrtaq3WRCVeaXRQoXe7Z+1TNu/OLnt4sSIe0majXm9N1Gp1KJycnh7u7OCSODk5iZl4P3DxujrwXMs7MxZWx/OuUk+J4Hzea8pvsQdU8a1UPOEBAFVgRGoUkX0Og4pUG4jQFLiJZw+m1Wptbm3hxAu+NHLzUeqkEryxf0amYjiAJk+bMRgxo4qCKOJB9se2d7Yx9cB+IazX8GshFo1gWFtZWfE7AQTdagAddHfB+xgdZGwe4E7R4QGIsFAQ17jjG06KjU5R6+Pl3rtSs8xAmYERycD1ud5RY3XYA6I4JqmJiZKpusnbxaInj0ey4IOqFCJ+2Tw+OsIojcvb+PjE6enJ1v7mwcHB7s4OBmFU6+DSHBU4vLgHZ2FhAT/BTrYmp6enUOTvHxxg5+CJ1jgWUMIqdEsDucALpcVTYh0f1FWD8Or7oO6Tz0HnIYyAU2HbD0kNSG1T5DkSWhAx2IOIKSaECDF6gy+umKDEX0EtaSfZqMlteHdBewS1gM1pwcnFIyPSlkSEVd0T4xNY64evVZiVmJ6exvDVbp+eHJ+Ea2YiAEcnsYmCdFfWNMMZ9+i+Y/T/yle+gmem4joRrVlKywyUGTgXGbg2Vz2oLQ411KhrW5Qsl6AxGhbuI2OgYYgnJyeo2jGFjtfBwT5+7t/c2MAGwPPzC08/8+zU9BR+ZWZT7UEhjg18sYzRq+9P8TS+O7duoe7HJtYLi4tzs7OYptnb38eNelNTU3IF4wVM14nh5Ucs4oUkBt0TWAWSlAfFx9DSogaktlmc5uDMXKVfuTRNMcUADy44Fsj8/Pyd27cNp31MlJKYeikT1IKySEu2WRpIFeHytPW9RywHmFw42IQGvlO/4/dADFxTU9OPHm1sHnRWZo+wlml/n5WaYeGeGv0iGjoV7qjaf+RHfuQXfuEXrl69+tu//dsXMQ9ln8oMPHYZuLbY3PSfmTr4vkddWqJkg490yB7ZTUQH+/uIAmX6wf7Bo4cPdvd2l5aWX3r5lZnZ2bBUl+P0KtwqK/MbDcxgYcsCTGttbm7c+vBDVO2rq5cWFuZnZ2d2d3YfPHiAUgYr4GUAr0U1ApcFlYKlODQA5M2y/bw7GgU99XaYZ7ByzPyYNG5UiFEy6mOOhBZcemyH0PGRQUmDjwP+pnc0JEsxVQGtfygCyZCCTOp2b38P62Qq9cnW3Gpt//2Z2UUMemzzcrapvtIXHGCFQ97EYx+rE6sAWJsOROQrQofUonUoflcCOaldvnwZOYIFRnN879HXveBn2V/5lV/57Gc/+xd/8Rd/+Id/qC+Cx9mvW7mGUOqVGSgzMIwMVCv9z764utO4MnDnGPJo1DM7jxGbjdJyB+oscZC4/XR/bx/FN1bI3L2D1y08QeYjL7ywtn55fGKC5tcJl3rTPjsDM1zL7o3wqOBR8bdaE5hrRxGPMX9+bh6raLa3dzCjj9pdByRkK0EurRrDErBqnl8zRzHGUYwp+kg5ReykFO2neGmrNbnx6NH09IzZFfqg1FrnolfmzpwDLn79qNQamzvHC9PVmWl2B86jhw93dnfAx13156ADgw0RhXrMN05k8Jd+6Ze+9KUvvfPOO6jacdvvYCMsvZUZKDNQSAZuzvUO6pcKgbaCjuTVb3QruzEsd0G9jhtQt7a2ULVjNH71Y6+h+PYSjLgN+VTqDV0JpTl+kn7yyemDw4OHDx5sbW5i7mZ9fQ2+Hj16NIOl8NLaX+uxDAWKS0NQoe5gqXAmUonRC4MFOsxgTTFZ8zPMQCkop4hNSogebNc+uOpRXMkJlIO4k9tqp/dB51iNixHwpIhhDCBNxXRFQcXtOafd6ubWdqvZx68gmBrGbMXU9PS892uholw2eQaiZtx51f6DP/iD77777q/92q/ZqvZyxr08mcoMnLsMvLbeq1x6tVNpDCryBNeZBKo5Rj8cr+YO4DfMvb09bDiNX0Xv3b1z797da9euPf3ssw4rPv1unLXPcJXHGK474BoQYX086nhM4+N5fbNY+T47u7Ozc3LKbghLM/Wue7JxRinVthg9vhqo2o40Hjnh+Y4+Np1K93D6KxwJAbU7vqxOtFrgKnq8PFaYknHZyC8DO8fNmWZndrqFYWdvd4+tA8FPgdXqxAQ7NOVLzwBm3K2FO77t/PIv/zKqdsy1//qv/7qtagdoWbjrmS05ZQZGPAOffaq1N/v8oIJMcBFMoFpQ9MOOgFXtu7vYyRjF9+1bH+5u77z86qurly7hYube4zNtqYzBlm3m25pfWMDdq1g50JqcXFpcglOM9tjeIZE7A3hS1rDTnjReQd8QuoElGJwb8oJ0w5Bv3OeNH7JmZi2rZVQLnghxxhsaeWancAdqj4bf3jvF/fRj080ODwWTBajdG/UGFjI1GoYZh+FHPBoRWAt3ZBAl+wsvvICFj6jasSASAdfHG8997mPPfe61xnhz594mnnHFe1EW7qNxNMsoygy4ZmC22X/9+Su7jaIe/S3HkfjylthA9pdPa0hBoGrf2d5G0Xx0fPz+e++iLx977XXs/ZK0U17h3kd1ohmqHYMvzLVjpgb3reLbwuLSIha7379/f6I1ga3iVW0NbnCMEQolS6cN3TCwsngYZVt0dWQmtLGhKk54nPmjnDC32PQziL5j6CI3yCK1UDxunTTnJ85qVYqT+cPohxeGnSKdn3tsFO7mKZybN2/+4i/+4ne+851f/dVf5VX77KXFL/7yT+3c2fi7f/d/IbPf92/+m1ojZn38uU9P2YEyAxc0A9jB/aA5sAXu0tDsklEYJLZxwU2kw4Ogv4ls0ypjaEXVjuclYaHn++++Mz7efPWjH8Pu7GnxjHbUJU5AB8QYbtR78qmnsMXkhx9+iHL/ytWrDx88RBiKNtkYoYtlGkPRmcUGkR1djxhljO0fOzbGf9njGA4COsNfxl7lwgw8xL4vLi1hW9VYtfOgoCeOotZFQ+bsnDSOO7XF1ql+auOD4MU95Aj1wEaMM2ZeKoNblDY6+//nv/ujo4ND5HH55tp3/9QX/sv//h+3bj/sdbo7dx5hSn5ufRkEpOWMO31ESqLMwLnIwGefqB0vvtyrDGxiI/2sT3rLgo5EkQFhyQqbaz88/OD99/B78cuvfjT1pgpOS2XkFME1ls3s7+2hmpmbncNjmzBrg90ozTtFyrZiq8gMiX4y0+cm0HQ9Rfd4GaT2k7fNsnSuzqcVyhgsc2erwirmGcwE3TLmFClWc58A8uKpdvuVreNxTLQ3a2wnQ/3Vx8MmxvoYiHRRyaEMYMa98vrrr/MhHlcI7GAAFhevPnP1+c+9drRzgCfQ4uekb/6H/3R6eEKWICrYTw63Tnk75PMNJUVpSZcZKDMwmhmoVsb+508v3pn/3sGGl+kKlsl4AP3MIz52N2qvd3pyghUyWHr+0iuvpK7a0eOjQ0yW91OssYEt1rvjQXtXr13D4wzv3buHNTOYytGymL7P6S21IAbEOH8R55IY3m3X8hParqq5RJcHCGqe3d2dlZXVPMAeYww6U8Qc8BMi4Oy3G/VqDyV7rcLqRukrjccAr9tj9Scb9wKOpBnB5AEwbe/FNUVmhG1g5IeUTpNAiidw0lqXuzx86/ajd+6OT03gEdlnx/hRQ32hasdX1eXlZWwipsrKdpmBMgOjmoGrs73e5GBWt4sp0IdSURpDi2MpVxXH5BjjAYj1+BJGiUUpnbMOHvp9+/YtzHC/8NJLWar2jD1eXlnB+pwP3n//+vUb6+vrqN1xHdVu47P1WXFuOFCOlgoQmsCCrQFRV82XkzpiYxjUjeF0xhiTkUndJsKo5jO5kpNqFAxkaY4wTyoBcwgEE42FvZVQBmGJGmbfybYkEmfAeNQDJt6PzzAB3G/VhS04A6noC1Ulm25XREqTGyhMpWnUIU9GZUh1vs6JRiYXBRPmpTK+036/0z7D2hhjDLi6YH/34+Nj3JqN896oUzLLDJQZGLUMfM+1sd7qy53KcG/bz/MymSfWYI6WHDF+8zw8OMBvmHgSe+fsDCtkjNs4JgotxVIZER/DO2b9sRNla7K1uLC4vbODvSmTrpkRASNpng5cROS8RNpECIHCL0j5wEV4Gq7ogndvUMmtVFC7e3eSiGUMT+6AziPRGbpNB3ZA7ovM9EmnVvem2CfEqt3ike2Xghviyy9RlvxwNstS7JNTbQgo1t97772HDx8i0WXhbstSyS8zMFIZwC4h3/+Rue3JZ4SrwxADpCtUbjHkj5hbaGYgDJ5YU46nLOFxSNubG69+9DW2WXvmbmQs3BErdttAJPfu3uW1OyZosJKnsNrdnJw4buY0mRwUAmpyNKK8x6z/+JKMtWHYWGlED0easHAI6UuIeDhtTFEH/qCmcNIEcXJ8fHLa7lUnmvWesnuMDa6DkrRuXQZis3rc+CjcpRzhDE66Wh13pqJwT2r1uCW67G+ZgRHJwJWZ3tj0lX4e43IePaILCQfL4WqhIFKQOUATVq4Eltiiat/d2cH09suvvOo/uNTWDTFPMZfXaAinPmAx5Nr6On4HuHbt+vrl9Vsf3qrX6pNTk07Gg1BK1EfXUyARaEQvXf1FQAxFlFf/o4NHdhRHQ8oX5ncx3Y6ndY7Yl9Lo9EVLxcyKNFkpTKUJNZ1Dtk5Et9t7785Go9l8+nqi4QJ+s7p2iu98K/Wlwh0/GNHNqe79Kn/XcM9VqVlmYLgZeOFSdas++AXujp1Whmx+bc/neq5AiwHl40BEdKax1BD7KLSPj/GgpRs3nsD+dE6m1BkijGaQcoUMPUTtvrq6euv2rZs3bl6+cgV7va/V15rjw11nZextLDM6WTZzyh3MibYpS/x0/kQI7o9wkrkXgUaQpl5RbDqHRAUTi4uLjx4+WluzDIx63hGqziw4yHMEj1vbd9pTzz15udwfpqCjJhXuog/Mvn/lK1/B31u3bn31q18VRSVdZqDMwHnMQKPav7Yyf6+a+Gk+Q+osv5Ir1/P8L5iKA7GzcFbcNRqzJIdHh7i2PcDOLeMTN27erBRUDkT0UOwtaDG7Qc+npqcXO51b+GpxE7X75bt37uIvfmtVTC9oU8ydSKforphcJ3PFn9J0gnBWShycM/LoK9aqbLkvFg6YC01j3o3M0e9qwREiKw+3Txu12tUFnFCVmNlzfs4pmVSaPGA6O41SvVPp9B3BKaRE+nqQqTn9Met+mXzq/dOf/jR2FUiNXxqWGSgzMDoZWJ/uV2Yvj8w6mXSJwWAZ8S8dptWKj8wR/kjBCmEXYP/HRr2OJy4d7O8995EXzEWD3bwQidhVOAiac3Pzk5NTWDMz3hzHTmIPHjxkewEHUp8oJKCLBKrkK8emmCXApnnlGE1GqDTRZ7aZm5/DJzEzzGMNcHJycnevNdfqL862nBLBTxRR1Xby0iklKkfQ6fQjAHWRLVRdswCOddYEhTt2AcNPSI/NzEoB2S0hywyMUga++1pjo3ZllCLKPZbY0RRTMaRD0zKZwuBwBOqIhTW1vX7v9PgM0+03n3gq3Ybrjr5yUUO9fvfuXexGcOnSJVyhsQswltBIyElTIBlrDX5wRMx8Dpfm6CIwxDShP0pzwD0UP2LkOsHBG0r0eN4Z7hEfimvKkQuh59EYs67GwRVlm5qoHK1DMW8eVrvtscsLx5WxCWJeeCJRPikbihXxQdiyLZqAthbuHAtLZfybpUTski4zUGbgvGVgvN5fW1l6v9IyXNZto8V566NDvMoAGGFRYFLwu/zB/j6ebffo4QM83ujS+roYVkRMoqjA+EQ3Ao1FwFg52WpNLK8s3751Z39/3/RgJsEgC6lnROckxUfKFJDBJzFpzOdPX0kx74CROVp9a020jo4O8cuSEBbCHq1TxDGPOarFQnV7lQd7/bnm0dQCK9lj9YX0qiRss5ircANvZw/eESGmcMePuVv7Oy//0Kf63e4Hf//G/sbuwFNROiwzkE8GsBQBpQa2/drZ2cHj3DG/Mj8/jzN8enoaj7/BznfsgWT1Ot8iCcqdTgc74oEJERSgBmWYwBDmAAEU7sLBN1tMQOIrLvT5agcgAAf78YEJERSgNnS/452ttw+e/vDBO9Tf0zaeZVfv9nrYNrdSqXa7rL+np21UZoeHRzPT07t7ewsL81tbrL/Y/GRmdhbbjaM7xycn483mWadTq1bZUNvrYWsp9BfbMkA0NTm5f3AwNzuLzb8XvTzPzc/vC3kO/eKqWK12vTyD2ZqYODwK/M7Pb3l5xnYrI+mXHV+vv8dTk1P7B/tef3eD/s7t7+0LeT5t1Bt4KKCX5goeaYf1tDglcLbgIUcvvvTye++9uzC/sLW9xfK8s6v194ytwR2DUV/IM/mdE/Ic+sUxbZ+2sTyd+61WK51Ot4nj2z5FgYK19dPTM3t7u/Pz+CAwvzs7u9r5bPB7cnL89ttv4/eB6anpf/zHf3z22WfxQZifn9sT+ttun9brDTxMivrLzqtsfo9P/P7Ozs7BI/KM02OE/DYaPTyH3Hvhx2rvc3SK+3oPDw+nZ2b2dnfn8UHY8vK8q+X57IxvqYyvc+HxPT5Gktl4NSf0d25ubz88r9qnp/XS76mfZzzoF+NtlkvFwuLCndt3tJlKx1Iqi+dRtuXfW6xJOGw3drYerl1aqlXG5W7YTGxfhJi+J9MNbSbkUDRxUeY6olXgnCCtRFIrRV/EjQ41wrBfef311/mOvxgylF1lPvnJT/7sz/7sP7/9L29279/+p3dwie2cnr3x19/AFUR0jrKG1zois6TLDIxaBlAqXbly5fHcBKlaGfufPjFbeeW/YwcFwwU+weJfI1NUiKZTmweGqDIxqODQoHZx/AsD1gmTCcfiUJz2/1K3PUstC9GdFKVB3FISRQUjDSvmGF/wHjy4j6r9W//0j8vLKx997TXvYECa5wtfNQGnVSE5uMBXuPv37r/w4gu7u3u3b99+7rnnBvSZ0pPKe2PjQ6qLXDiBIT8tdZDgdA1PLl9HNtTVIjg4NfhZ6X0QOHLwcRjr4ysQ5Oxv+AHhZzPje24DBU/N+dyGKX+55EXXga3OdOEEblOaw4X0wpel//Kf/2ZFWbslqTg1Hty/j8cGoxZy0n68lXAqPtxp4zvq4rT1VsmkGTprn2Fv3KRWj5s+m2GM7jOmlA5Pj5/53KvPfe61u99671/+6u+f+PgL7/3tt6KtSmmZgRHMAKYVUWEMqMgYsf4/s9R98fln3lQuSOrlb7SClqOjFitvRivQhNHgSUaop1Hynp6cvPDSy7VazCCcEN5XZ99rvBncdOYRVouLS/gOjDugbty4sbe7t7GxYd1HLwJlFETDO48Uz6z0HsRprbgdhWOQWwx8/jEjHKp23LxxXs/njJ1PYt7uVh/c+XD1yvXxWi+JXZQuG7LKl1sGYq4ZmLbBrdYTYzeBdvnFJ452Dzbfv3f7H946O2m74ZdaZQZGJQO7u7ujfwtgQcn6zPXq/fFnVXDHcXJIl3s5OrEl0mqf0BbiJRImRBtMBsbqdFBgtPET5e0P3n/hxZexmqIw1zFZyuL36tVrb775BpaNXbt+7dvf/hessSliaj9LhE62BWbI4h/noOc09CyclSHTYp2ZHe+Bh2PTE4LNHIsZQPHAA1GYZks2TW6L22xh4GJNGlbuYU3aOZ7foWzZsgEFiEhNSYNoZdTpj23v7re7javXrlYqQdVOVkYTxcX5bUbkLbZTESmKEHFYUgiaMb9xoNBZWFykY3HjtedQuy/dKDeIjD1KpcLIZaDAG+lGrq9SQNi+/fXnr+5WFySuewOjRl7/uFNCc48hiSbBszln/x96QHR6wi+7WDBwwv9ywms5/MENEliJ++jhQ9QZN5980sFiFFWwzufGjZvvv/c+6pvr169hpX45W+Z0nPSTRThZc/uUZcDknw1bJOk/OfrHTx9UmI73Cj+qPJB4twDDy+kQxCktLCxiMVic1gjL6ejbYuR5IjWFEK0UUX+s0+ndfbjVmJi5tDjBykJSICvipCNEzHQIhVplCS8iRREi3h1SCIiYGff79+9vP9ps/+0RHu9Xq1Xx0Tja2Gs26lhCg+3MApDyvczAOcgAbhR7PGv3zz/RfzT58kgcIQxD4ktpiqJYmqYTYjXzU/DipaB1IsYTVnDihfW4d259+PJHP4ZdZVCrBDZY/3yeXphixw2Xt2/fQgW/sbHJt4k8Tx0oYx1uBujE18OIEOnKnOMV/TZhIn5rku0LiZu2E1k9Dsr7B4cHndbq4mxBz17DV69z/EPHYM+AqMId0yrPP/88HsqNK80bb7zxzDPPHB0frb36qaWlJZzZv/u7vwv+YKMtvZUZSJ+BIpclpI9qAJZfemnurcaNATgaqIsUV/eI+FA1c0BePoMuoI7GLi64l+v+vXvYDOTK1atyODlNGAagvDfCF4NA4HUsl85hKfCbb7xxeHhw9eoVXCDw0A/0jtyURJmBgWUAZ3u1hq/B+bwmsB3Q0dG5XP2VTwIMKA+2TpoTM2vzWBvDdhIr4oXZYeyAUhB4EQEPC5Od7TbfqHKwq8zXvvY11OjYBuHpp5+GJsZlLGfEj7wo2fP6ZcoWQMkvM5BvBh7P34heWe32Fl7s2T/p+Sb5vKLR5QIEpzmR31+MotgNALtt3r1zG6vb/SW55C73xPFeGGCZSzZBmfkfvgJcu3H9gw8+GB9v4qlMH7z/gYbJf0snX7zp/zWEVrLKDKTNAH7LSmuq2vFtglXu49pGYm/tTi5MVxcmgxXtxaQC2wqXM+6OqbXOuGOi/bd+67ccUUq1MgOjn4HHcDoQyy9+7KXWB+MfGf2jc+EjxM7uOAPv3b2Lnl6+ctn/esC7bS2yM2bFNOEuQmaeeJ9sTbIl+95TVDcebWiPZFI6JjWlhhhVUTT1VvFMfMkxcUVtYkqqZWMEMoDlu8F37qzR4KEH2D8X95GPzGPj9fNOPCvF/uqaopRomLto9neOaicnnWuLh2NjmfbIJ8cRhLdUJkJeisIMhIU7JtExBGNOKNFUOpbTlF+SwnSW1Ahn4DFc2fXqanfq6sv3K4/RM6hH8wTENjKYCsFgee/uneeee76gLSAT99129U8CdPnylXfefgsPkLp69ertW7efez7vbd1RYChxupQchi4oKKRh5hu5RiYB5UfofRaxjf3noRlFom1Ip/DBjSkJCZyFboui8PnKEZo992p7G7tD5oiZAYpSHouRm2a3N/Zov9aq7K4tTnte3ZFjgzQrBNMMhTsyuz9P3H5YuCNsVDYpvmI+hvXQeTrEZaxBBh63b5iYNPrKC63bEy8FCSjfh5YB3BjdaNQ3NzfP2u1rN24MLY4CHONnhKXllXv37129cvXuvSqWVmKxe55+9Ou4zsnT37CxWDls6iHn2aR+1CZDS4eiVaOlHNJFx+I8I9vwlQEPMDamLZ2nifGJ7a3trPtCGsI0H9swSKNJKDaZx5qI5pymI2exPTyt4LHZq0tz9Sqv2nWIkjPMDNQxxS4WNImm24cZeOm7zEDCDIjneULTc6l+fba7eO0jD6vYKZyN03yI9kZsf7S2DNrnsrMjHjQKd+zK9eDevWs3bo6PX7QfQLBdwVtvfae92r529do7777Dn3Q24kdkdMOjosoYYrTUaHIBmXoW+limle83RuyFjQeNZdqITA8z9lgMxoTCMLl7uH1Sr5xdXp4ZG+vl+F2IfFoJBGOKx6r/2Ar67FbsGp6yhPIdScC6FzSTvh63euixPVvOe8cft5+GvvRM8/748zQUCqMiJ+mWwYII5ln4By/SS2lKsovV8EbX/tHhEXaVuXHz5sXqHOsNLhwrK6v37t+fnpmenJzC1pAXr49lj0Y6A/2xubm5fCPEPhz7+3v5Yo44Gp6Heuv27bnZqcV5VO3la3QzUEeZjm2PsOPBycnJxMQEr+Dd40XVDqvHrSRyz0+pOToZeKxuTl2f6r3wzNPf4g9dGk6N3Jduf0IMlWHV7sKPDVJM0rkp/v5ACROZknaSBh7Z22g07965g9piZnY2iWkW3WDJaBYMZ1vMsm9ubJycHF++vP7mm2+urKygmne2LhXLDGTKAD6w3ubr85lQNONGvYE9VR6TC8f29m57bAIL3iqVcptv7VQYMYY/tmJpO34YEuvv5ScvP/d9H4uNFktrytU1sVkqFUYhA4/PdpBY3f7fvtz4cOq7hpx2XE7pH0IhetAE36WQB8Bp4W8QFh/K+F9/p8TMGybCDTYnxvYUmOcg2JsAACAASURBVBDB01KfeOoZzHQIviN+60BYojSIMi6L/IgbExx7MsAq9Qv9Wr20igf2TWGt8dT01uZmaqjSsMxAigxgnYzxtM/CXFpZ3tjYiEaICDXaENKkLwUwqbmuzwF7/cqd+9v1ycVLC+Pn7DlwepceD450cyp1efWpq09+8oXVp68ebu3d+da7uPiQqCTKDJzTDOCnoXMaedKwX1rpPvHsx96o5Tz/lDSM86EfMbZFiJz7dnJ0hF81d3Z2up0ONjtnBbjTS9FTmlaIUI9Two8cjBHxC4KnH6NjdcsE+DHh0cNH2Kh+fW3tvffeXVxeDooA2y8ePMSImCL9pRLanPFQFEibsqJWNkchA5hxx1Yw+UZSqVQxm4kv8xHrgY1njmMYWWzhIqM5D/Lo+HT7ZGJtdbFW7eYC6Nh3XQ3ehxuAHtJocpAlc+F+6dlra8/dmF6au/bqM5sf3l9+4jLrAIaxIK94wNWt/+87ZUE/mse1jMqYAewXlvs6SKOj4TLxMcXe7e9NvDLcMErvPAN7+/vNZgPLSLAKfPi/uQcDeNTRcdEx2VfGKkvLyw8fPsC+kHg07O7ODh5k4ymKiCLNUXSOCT0nnsEZleeaTGWQpi0YMojVNH+FIntyEA9EqueRoO7pPbd1h0wUhbk5LEJzh1Gsrc2V1ZWtzQ2c2FaNnAXG/kX0y6jvFBNAN3dOK43WlYVin6zkFE3krIIjwuOjZi7c/+lP/5+dextXXnzim//hPz/5yRdvffM7XTzUKsjKlZefvvldz9/5x7e7vXItVJCU8n3kM4DFYCMfYw4BYrp95vpH71WwmUz5GnIGMF3X63VPTno7W1sf+65hr1wqPhn4Yrzx6BHumFpfW79z5zaaEVOVxYfj5iGiKFIA8tR0xHJUUwI9N80U3bOZYFeZ+YWF3HuOh9sMdo2lrX+2niXV93HQqc32/MLEcWtiVKo4LA4s4quXLXHnmd+33j90+x/f+Yf/+F9f/vInH3znVgXPC2k0at6/laeutg+P7n773fPc7TL2xzEDGNkvfLebtbEfe3nq/eaL4nDOh0ORc+HzMCIdxIZdzeb4/h5uTm0sL4/Iw1wKzA3K9IXFBdTus3OzWNyP7hforIQuMxBmoF/cvMzU9DT2hQxdnX9q57i2c1RZm0LV/rgsHz3/B03qgbVw7/d6c+tLmx8+qNaqzamJ8akJ/MW/ubXFD77xnQJ+kpLCKhtlBnLPwOOwTubzN7uN699zisdTC/da0l2OIY9YnAgF4g2RqWl/5oS+MCQi9OMumuvSUeagcsXTUvHcpbX1y1jpPvBQh/BlbWFhEY9uwfO0l5eWHz4o94Uc+DF/PB32x46OjjONO+IoI9OzM7P7e/sFgQ8YFo9wvrszVuseXJpvYg8DV++2k0pOlCuaaGVDjuaLCEY62jyLlLvLgpCHrXmpDCFv3Xq4e2+DmiCWb66JzZIuM3BeMoC7l4qblRmFJKxO9r7wyvqbzWfig8HoY3tFiGwmKj8TRLSxVUor+SgYUtVFpCMRRj1CgapRQYIQG7hG4sKImzV3t7dfePElEUhUI9qGrhvaNAlqiAR2gZxsTe4f7C8uLdx/cB/LZlI8jXuI8Zeuz2MG8BkZHx8vKHL8jlSr1y7AmXx8Vt3c2Fpbna/XEi6k1MegvHIdILM7gDHABs2s8Hnh2OIoGt/ml/P7lptTo61KaZmB85iBfJ+rN4IZ+NHnKw8WPyNvnz6CYRYTUsRIGiGSYonVi1WQ4E5PTjDLjk3csWiEfWMUNniR9IKGO7qLJi6EAGZ/1TJfbfuBaWwvLjM3CNn8jjv57t+/98QTT05PT29tba2srpr1BsJ17ICYUkeTgYRfOnHNADZcd1VNrrcwv7C5uXHp0nmdtcQ4sLnf6bf3r64vJu/9ICxY4R7sQjUIf+fcR8yM+znvXRl+mYEwA5hxx7PGwvbFop6a7z377Efeqi1drG6d495gnUytXt/Z3sZ+MsNYJxOkTqxJGU9t+3pmtpkbQJvfcT9fr9trt09XV1fff//9ZbYv5NCK4RQdkE3EyGWJuffEDQyDdxKURBEZKPQjhntUej32TAasASsi+EIxz7rVB/dvL1262pptFOooCziyWz6yzT2BaQr3aq2Gr0e0FyR+EcZGe9gke4ijs3uHS83HNgMXeI37RL3/r1+f+nDmu/X51cf2cA+34xgg8ds6qmRsBPnR114fbjCD944HqbK59hU21479K87zIxQSFetipgPD4F2UlXSqDEQVzawmSQXqaDQ7O4tfz4IdTh2Nhq+2e9g7Pjlav3y5Whnpx/Hge1GtjofTFfISz5uCXOhxi051aToODx5/rTen2nBRtT/5iRfe/X+/7a3gZFr4noTlZScnJ6djEwV/dmxBlfwyA/EZuMC7ynzmeq9+7ePHlUn5cZu4kjn+S3NbEYYPl3/xB+YiauCHewyMuGEO5Tuq2IvYxag+4WFMh4eHuDF3fm4ez56MUi1lZQZcMxAx3nirwlxx0ujhgcBHh4dpLIdkg+eh3t/Yq9TH15Ym2X2oo/1ipWNhv2aI583A0iA6zYum4KNm3E+PTtaevbZwle1ihpTyxE4uzHzr7759ss/2+cLCzZWVFTzmGpeoVquFmfeT0xPMu5/n+RXKTElctAxc1DtTl1u9r7x27VuNZ2yLIBwOJAaWol5O0GmuK2Rj9EDSnPvliIuJjEajvrmxjxK20B/xc+5eTnC4ImCZECZzsDvku++8e+XKlfL32JxSW8KYMtDv12qYhTQOBSb9VLyJVusEiwtaQ9lC0XHg8Tt2eHSy255YXZit1wpc+p8qi2ajZN0zYzxG3KjCffvWQ/xTkvHe3/0L56BS/9KXvvTWW2+RAuZXvPK9xxZ31mrF3eVNHkuizIB7BrCgCz93uuufC018o/7Z1xvvz3+ml/zXsxHqYJoLbrRNtDR91x1xT9unzUaDLXD3tm/3f4pMdnUi7WifpCZ1ittEW0oGeTTEUOZm51j32Z2p2FpHWi1jjEq0zSOWEuMxygDOKG9lWrFdnp+fe/jw4VprKLeoGj805v4+2j6uNWcuz2Op3rl5ed1L0MeEHaPRJV8XBJswnMzqYeGOh/zhpiJMFDliQvNP/uRPMLOiPMcbHNwCCLSyfHfMZKk2mAxcyG+Sn7/RnX3q0+9V5gaTw9KLSwawjLDX7WKma2937/kXXgxNkl01HLUtaviFFC/+N4ygWEoMBY+t2d7ewjzo4tLSgwcPrl+/Hu1btFU1zddHM1e1fSzbj2FqWB0SdQ7lcB5UK1Xc2tftdIv9DS3DweucdR4czy612hMT56lq945NoQevIPCCYGPP1X5YuEMX1XbS4gYzSbDS/eDMRvmOL8Fl+a4np+QMJQOF7hc2lB4tTvR+4hNX/r75QoahfiiBX3CnONPwsOnj4yM8ve6iLtCKPYTYf6PZYDNBuJ/vnbffGYup2yPxzNdHMzcS6HERWlKjjRMa4/wmCL/qDCD4paUl7E6GjZIK9GU5eLEe9w5PD9vVy/OH+OQV/R0mNpjECuh12o4n9pWXQV4fH/eOBx6lwh3doVtOc+kaHr2BFy5jKN9BYEY/F9gSpMxAigwUO02SIqBsJvXK2C99evyN6e8v/LYs9uShYMDA6BqSfgc0RraOnX9r1BD1eu1g/wCzzhfsrEt0cKamp/AU1aXFpW630263y/E/UfYKUNYKBI1RgNMBQWKZ7gA84RzGmYz5ypG6ZwNl26P96sTY8frC/ACSULrwMzD4jw/3OJgHMOE3LLx4+c7p8sCXGRh8Bvx1xoN3XIzHf/1K7/Tq9x9WpouBl1CxO03YFkjO1Bih7shTsV86qHO+ptlA5mKsazYb+3t7w93CfOjJn5yc2tnZqa2u4t4SENjWfeghlQFc1AwcHB4uLJqfYiF/OrMmYGZm5uBgf2ZmVG6XOj6rbO/sL89PNRtl1Z714J4Xe3XGvbi4eclelu/FZbhEjs7ARSrcP7bWe+HFV79TvxHd5VIalwGqy+MUg68uZgOBi9OMrXDvVg8PDp586qlhnXXcb+A9eekSaREpDDOJXxswMdntdOYXFh7cv18W7mFqSirfDPT7qKdtkMKn06aSgD89M3P37t3p0SjcH+20q73j9RV2j1O+3UyQkZxUbfE7jjY5RXE+YAZXuPN88PIdPzZh8Uw5+34+zpGLEuWFWbSw1Or/9HctvdN6zXTrYTDKBe8X5eidm3549/xUTk/bZ50zLJUZjbht10R7dJEWkUIJc2KihaF+enr6w9NT9m2mmvjJIRJcTKOYkz4JahLdmM6UYvcM4ITc3cHTkQb0wARsGIUpSBQw7hHmrtnpVR882llanJtoXPAVyO6jTe5JHk1AJGTQhTtPBBaK4YWVoEe4G3aM7R05mgkqo7pIGcBQewG606iO/eQr9Xsrnz8bM142glEueB9Sl80FjJmrhOikpNiMUBOnGRa47+4etFqTw720j0JSULJjb5nLl6/U642jo8Ppaeu0aB7RFnPSJ0FNoptHj10xhvyhUt2rbdduROh5jzkbUPpxi+q9+/cuX74cEU9akVNq9o/HDo4O11dnq0U9bDRt+GntBnTk0oY3anbDKdx5FrwdbFC9d/A/ntlU8GTMqGW+jGfQGUi6Y9Kg43Pz99Mv99c+8n0fVAc0t+QWlK5lHofNXMXaSUmxKarpXUXjLqWyHD8n4kb8w4PDmdmZckzDBA22z8OCmdnZmZ3t3YIL96JOg/OPO+QPlepebeeQ4K2trYE9pgPnM+7Xx9aQ7D3nlyk1ghPcFnv/4dbU/Nr6EjRNyjnHM0i42O4IiRhkXKPna5iFO88GL6dQu+POaNDlpW70TpILEhEewDQ3d773O//y050XXnz9O7UnL8ghGflueFeSuMuJLPdm3FuHB/vLK6vBEvPi+mm9kkVv4241s0Wa2MAHYiVOpdLpdrBq6NGjRzb4kl9mIGMGvBn3jBgJzBcXF/FVAVPvCWxSqwYjzEm7s3lQW11abNROL1jRzgaYoJthntRhR9cIdR8nSt7HfYg9R8mOixzKd/wty/chHojz5Ro1AdVGoBE8b3JaadKO2rGaiqHRRepEiTEnAnl6sftDH3/2mxOvUTyJzFMo+8OmNHpKjRSYF94ER6fb62L/8tlBfEuMuJJxkVnBzI04NokNQqzm+PjJ8Qke63F8dITZGfpshhoxlNMp56QkOkpsIBqX9MhlYHd3d5DzMqhSNjc3B5mFrb3Tbm3qyiKerJTh0zjIiJ19Ycw0DwsXraPOGYlWFLeDpMTxsoA3B1YiIE54xIIZeOTl+8TkTKV/7p7+FZ3vUppzBsTz00bDJRft72MPL7bENlaTohQ1FUPSSUoomI7mN+a6/9tnl/5h4tO9sUJv75PC8YdNafSUGpL2eWl4X/AKKtu8wnSsc3aGZ8/RF8XzkpiC4kQeDg8PlpaWMcTj5wgsnknoyOmUc1ISHSc2EI1HjS7odDZ0M95TvIYBNiMLg+r0zHRBh9TWIZzY2INyemoqY/Cx5vjUPDqozE+MTbYuZkWU6vt8bNouskK4VEapJ5TmwHIQlu+dfr99jFKevlEMLIbS0YXMwCDnY9wTKH5DFmkRYarZ/8VPz705/6/O+g3DVcTAEq1LWs6At4ikoGs86vVqrXZ6fFJvNLHSPdUoyg6nf0gvxJFFpb65eYIdCKYmp/a8ve3l41G2smegoNPZEFi8p3gNgs3z/D7EPu4LiwSdI2Hr0Mzs7IMHD4r+fr53cHx8Vl+drdRq47ZIcuxsIqi8jh/GyXKNdKLMh4V7IrOilVn5Xuv2JybwizN8DeahaEV3ajTxkWp8bPhfitCFCR2uz82JBqGYi01O6zqcg7+2YCDK+MKjqoseZFNEiP6SlUgTc6Le/6VPVm+vfOGwMsnSQwIiDCySZSfYUU45QKc0yx7z0BDY80Ebjd3tnfFmI+2liB1O/5BmOLL8XDKeUXlnx3qY+amDPFQq+JmoPzU1ib3ti31cfN59K/GKzECG81sLyysS8gTUPHBGeLbjWoZT++ys3ShmQ0Z8eB/s1yb77UsLg3gorKW/Uey80t1D4Y46JMrV+ZOFJ0oBsbPCXamlCvCSEhKB4dOI0/e43avWGs1qB5yUWIIZ9VfghSSXRuuE2uefMl7gXZhchydApMFRzMUmaYpMBYR0FD5vpv6L24lS2xZqyM9q6rXYbNXH/tePV8ae+MJOZVjBs+E05ZCa0syWbPWzr7ZtdiI/jY1oH0Njxh0/EmK6YXp6VB6sGBNxDmLrYaZTp16rdTrdiVYL9/PReZ6D50wQllPBwiZXcXJSLImBZuD0JKf7NWMOsHS2Ly4sYrfTlQIeCXzaqTx8tLO+MlOvYySRnA40raqzmOyo6o9xu9Bjxgp3GkmJGKlso45pNSu93tnJySkmb/DDK69sIoKk0ocIUTmim9DnUkXHiCNiXgCa+o6+ZOkv4RBRUHKS4mPGHXfIFRRMFljlZKNmq97/lU+Njd34wu3KVeNUu8UpG1sN46uBZQEYUbY6Eqptl7DT2Nhw1YSizZbKVCsnx8dsj4ukvlQ8m9/zx683GqenJ+PYGrLbxenNh5dhd8NyeCxsijZOTooFEcnPkuQWCD2VUUFdjofFSZXbMxOSHGAsh+t28aDkXtqf1wxdQ192TltnB/evrc0zcZJ4DHA2VsoDXFA0gC0I2db/CH7K1EQg5i4a0aUyej/xwcDsO8Z9zGaBxj3dug5xqPQhgkQiIVZ+nCZ9UcRNSCQiKDRdkKBMNHT0JpjcBTni+tyLaKu4oKYNk7sjNR1WcaE0yZDzqWkjKFTS1wmbbUY+OXLEGc017rbgedXev/GFO7XrNh0Ln42AhlHQwLIAZGJbfhQ7B4Nh0m6rCfXaOCv77bP2FLtVTlWIcZBQ3YIWJNpzb1pZJdkF2hIzbMSIQ8VoCsM1xm0sVMNtdvhuk1uNFe31YkqTnyXJLZC5VEaOGc98VpkAvCcWOwaQpxo2PMC2B3ldXPA81Efbh3gCxMKyV7XnGamMVeQBlj05tPr4olgp9JwLgzCdPKGUUSOVGjk0v2XfDhIFGRVGevHHOuddE5S6LWmTXOjRUQCid9zhhElTDP14jDa+7CbfoEDyI3oXaeodaStS4iuEqCbSOiBxSI0IEingelM04dJojigVad2jItVdE8ddk0yGRWB4xXMc+XmlnKi5hKRgiudtUnwUv7/6qX4Pc+3V6zSMqAOO2k7qpCB9S6046MEwzE5IKT22ChQ91yY+C3hhBq592sZjU13NctajRHOCmmY32cRmTJ3bbDSxsczs7Fyj3vAeUGV86K9vl9thyQ1I71DJyZKBmJMuHtoEwAb2eMv8NSanpu7dvZtx71d+qu6fjB0c7K8uTdeG05X8kzOKiEM5S/JNhLgdpIKMKxBxbDQURFHGJrnjBCETQXyxfG/OXamfbSu2ZbPMgJ4BfmcqP52Uk0pRZteA4PyPrr91KTfU+SKH03CqK0OEu1F/4eOo2r94u3pN/PYffiB5uGpb6URxTXNBJHGlRnGRRCCH2QkpRd0qUPRcm9jUrFqtYeYPPwlm2VJG8WfNpVXAAPLunBJUsma1VuVpwVTLwf5B9Iq13CLPDcils5EHwwVA1omBixHLWI9Hy/ulD4d8CKnBbS3Ywzp6FUD0QeiNjT3a3JuYXllbhmJw7Ym2uUBSXAdjP6xDOK4jnOFzs1RGzyEujXh1OnisxxF+ey1/ftVTNBQOSk+Hj6FraGKNmwhZN8STU8VnYkegifGLtB60KLXR3CpCKorGa71f/Z5e9/oXb/nr2tXxKq6tx5g7xzzGSlypkXsAHNCyJscXFuQ0BhYxYY07KvheL10KDFYGFo/CKvDE3qVQPLViQg/F6ilGEquANBQiMMBnDV9pIKzVa/0+qpSL94o+GIn7GwMXI452FxyVaK2E0pSgKc3U4HCydztd79tq2tRkiGR+Yf7evXuXL19Ww3Jrn5ycbR43VudnGzXsoZchDjd351Qr7XE9B91NccjPceHOD0i9f1KfnMTSyYtavkfUl4lOSaWQ5bYR13XRL9EiCBCIr9DU5PrwwjVFfR5A7F8xQpFOYajMiCRCi3WXiwLm2v/t9/Y71754h82185c6XsW1A7tk71FDh1lm5ibzmk07cpJGTVM2V5FX0zATWFaOxZoV7GwGXs4RpOhA2gisdlaBLTjBwKvX+9j3Dby0H70w0zaH8Rrc0lXP5udc84Wjkl8/UoJazZIeocw3p1ojic8R1mfjH05ufr2LNxA0NjY2xlprl+fPWIdZDJFxJM2K4CgPsij3kX3OI/DRxkjafeizwp2fbWkH05FICZ9xx+pJlO/4NRYz8RnDciwx6YPKs+doRTkHEWuY13ERcUTalihRh2giuJXY5DRlgDdJQWnanBbKx7e7QvEzguNu1H/7vZWzaz8g7yETM1aG4pBKEUjU0GGWmbkpXJNJ2IGQIiEnrAJFL/dmVG9Jxur2fg8fAbwQAZ38uUYTlQJRRlHl6j0LGPshrt/LMt0e36d4Dd4DV73o/or5jtb0pVYDq8AJ9iIqJT5CWKwyxDysrK5sbDxaWVl1jwGLxx7s9uanZr3nobqdAYmz4h6Oi2aR7tmch0sM6XQKhE4XUEYrVuAqFxjxqsNprkN0RpepzXmcFAaaRPMIMaWKqj2X8l3JiS1mRU1p2qx4tKLU3VC0GkF6lDuS/etccQm/Ptv77z9W71z7/tuVK7KXmLEyFIeUDODUihnXzGIz18mfSSnsQEgpelaBohfdjInbLDZzJUcYjqpsun2MLZSJ/D1AMkvWiEqBLGPbyhQTRlQuJJnUYJOJLJ6iMpMsj3loy/l2QLQaWAUOoPEqymEwGzgpmU0Fbj4oAqAbiXMKTwkoNouRkdRqdWyYhABcEgC1w6OT/Xbj0jzu/mBLyMoXy0CBx69AaOuxczkV0hr7M9NUAWNgxQtNzgENZN7ktNXRAAXGSHjA+EvlO9Y0g8adrGJovHecI9LgRDS5RzEn0EeT80X8kh6dDCgHCOUUxaaIiJ8v4ejl5lzvlz83/53pz+2MzUWWNeaRwMzlPYmSiX2NGdfMYjNXhI2g4yNjGvFaES5sopi4zWIzV3KB0QBlKQ46bk5ltFqhunbGoGdgSa4NDYRi4ObCigKWZHIDLS8n2YdN13TE68Vr5JKx4YJIh8EWipOSzZj4iVFyOAAeRB/bFi0sLgSB5AAbQLm+z8zO7O3tibdRGS3xEdjYr9a7x2sLE1BInDAjaAHMgWZQHSoL6M/gITMd2mjjYDtI5RoT3Rx8BkSPYmwiLeqA5jtF4uczvECL5btoJdKwim3iqkyOFGVRRDpJCaXUU5pJ0UR9JVpRdFFpdNmWwMFkw8XLDz199oVXrnyr9emDsUmvamcnWHiSSccm+DDL4oArqfqNKJlJ35UnRyBYWQXQkWTxkTGNeC3Bdw6kFKKOFy0OTjZWuZsOvWtnDHoGlh6dxGEWCCKfK2J0v5lfg4bMQnJ4QPhNQgo0TcM1HfF68Rpp4nOzcc1DjF6M2C2WoWnlcAA4BPZTF4aLHGCTpmRqavrB/fuzCMP+wvNQt3YOFmcnxpvz3mhvUlUPqNo22eTPG3AGB+wuab6GcwzsUWZdC25HHgkJZtxx6cLiGZTv2LMJM2G2sIKLrk3u8yMuhBB5Fye1+o9BlMUKvtIUdSlg7hQiHoBoQiJIRVrEEWmuQwhoijR3wfUVTREkBc29UITkNAWUYiJCKUef3JEJlIlJIXGmiAOdiCahxRLjtf5Pv9R55SMf+XrtE8L6PjaCxYxiMeJoz4YhyMAiDLPMGoFVEN8rcqkT5iC4nllm5urICicqfKjGivnZws4iUPFFc+IoJQOpoXSEDQcaKzUjHsqgobEQEctJAZmxZsIqSJ2KHA21BFmwY/RixBbQkJ0+RwZLAyv0VCi1u7s7N1fwQ4siO4CuYzXmabuNBbtGxe3TVvfo0dryTMyjhtQDqraN4MNk+gc95bFnY4IwHzrMjth9j9QxQDBDK9z1CglJi7jUKdUSZdjGFxV4+Y7aHYsl8PhVck060a5FtWg6Iv5ow3RS3ndySqnghNgEvq6mOyUdLhKbIi2i6SA6R4nEqMCZ0CQp0dw1gUBBF4EJNeLzJociQ+9x9HzHDy7x/1LXRHNOcxEpcIPopgRtb6xM9v7Hj41Vr3/+7yvXvJ0DU44M7IFzES+D0ODIwCLMKBkpGQnVt9rWjawaUUGYZWau7lLnWIOAaqwM5wZOYZw84ros3UfASRylZCA1AsjgnQuFczUq9sDI/C5ZSg2zvs710gJLFlTNaeeAyL5pDqzaVoEG4TNSdU8GM0OYubLlcFqJc0RhGiwNLFJ3JNJkCifY/MJCDs4dY7SoLSwsYJeY1UuXuJx60u1X728ezM+MTS1M80+BBeB8sv28p0w/blhnicpzosGSRuaGjolF55ywnQp3sYjJq1/CFYVBKk3di03BxlcQ0AXMuIN5fNquTq832xu8U4raOWrqHScOJ5QmdY34nEN54HyxyWlFn3DIHAqiFfhKk3OghpdirjRFBZGGmtgUaVGk8Dk4MbH1EMopik1xrfOJQ4RiIjbhxUWNm7x2qfNTr81+OPu9D8eWvGKGp0UaU6QGNzOwYB2Z0kihGL+N9nyaHJOBVaj6VtuEQES8BqkSYXBvYJE6J6I0ooKIlXnbt+NEqJx1OvixT3ErN6OCiAkz3tQDkAKIDkYOTW5JllJD1rO3eCAwZRdp9oOVXVWQWHtpFQjGKUm3yCLBzRBmbiRQtiLDnCQzNzqMAUvTZAohbm1uzUzPuOWsqCxUazVEj6sM/3WX9+SwXd3bP1hbmqq6nviFJ7yo/qcKPNGlM5WHwIgdj5RnVwCR8d058XGKToU7lT4UtTf4skpFFClNrmzUJJzBE63xZr+zc3JygsAwE4+/g48BHpVcURjRJ7Eor+kiXwAAIABJREFUFWnqgng4iBlNKCZiU6RtIFxH0YxuilC844q+qCDSStJEkQuNgz43N2fT1GMgDhE2W853UatXx37kmc6nX33mn2sfPeq3QkB2GkpjitTwHYTqqSjDqS6xpIbvwQvDEEsYQKQwVIui4j6EpsACPIN7AyvQ9t/jNcjA6tsiYKMiXg4z7vFBWDWsAgrb/6JrOSctsYfWWv0TbyEYa2Sv12W8PqtpkBzlVNfUfYa1l1aBgpQt6AAs7uwM9PJ/d+2n0bPZ2Mw1AojMTJmMN47XEIMx0PgqiNluJnDqoJOSwY0Da35ubndnhweDJSAbW7v1qUvrSxjqw60R4mAypyPOQYH9j3OtyzFGsZuCdIHHKTwXFr/FsG291LxFK/bTLpXh1wPlqqA0eSxGTS3MgTIq/S6euY1LCJXvfBeI2CC8S4409auYQIH3N1ZTyZXYFGkFH01RKtK6Jg+GQuIKRqZuS5xoc0VKVu5EdBcUnETKii2aWCWlM4vjUKrJxXSj928+UR+/8vGv95/Aj6fSRSb6g0oQKiENa1JD1PQFBh8SS2qI9o60wb+BBTCVGzcDm0dgqs/oPoXaVt9GAZ4PClOMJxheMpyuoXs9zCgZ147XMMYuuVI11LakbG+EoeDjgMELSYGy8XiHqjpclEzXJk7KoMmeE8ZoFR1TM2XQOlQIFFK6VqGcTJmMN1Y1UvSzv7O7G7ujS6E54uCYDdza3MTXiPZZ99HW/sryYrN27PZ1gqJT00GCrESKvPou01vGxozeRqAXlovYuFIqRPQlJaLJrI7xlA2pQkUoNkFnuPyYHGbmUUg8TjFyBVvRFDsCGlJUcri+YtdIXGhdZt9FBMUXb5ICEUa1wTB5DEokRmZEPNHmihQ4/KBwfuwBUvzajpeIqZgkah4eHuojOwVphIqWiiYUvMjkNES1Sh8T7Z96dvmN1qd2+962A67lgHkcCLjSsMYagUAKQ9KSJHENCU5qkGXINbgxsGBo5hKikQjdiGIzFxqSgPlL5jOZtuiM3WbsTwREgIgWYn98OsLSoSOetcMn3RqEVUChxmtwVb8jfjCscu+xje5NncjWZ4qMCNcQY/RixOROJ6I6pGtHcEKgkIpQV0TpO6AC5YakAOvNNP3ErjJpzHTnCTl6VqZmZu7fv1+bvnx5dQ6/vSXEK1I9fYLSW8b3B8UYU4p1oWc6HnvwGrHdyB4SXNQxpPJRleDEJmheu4hM0hTLGq6pqPFqhtRIyvmEk4ggECJs5qRAhK6JSyxm3/EMM5Tv9dZcs9ob63u/6uqqo8qhJKOblNhYZkG9EVMt0i7uSJ8IbsWb6JECwjmkTH2Hmq4MJqp2HG6I8CIrcsGZiojUiFAUlJD0Jva+uznb/R9ea7aXXv/a2BPdnjfRrnZFtyOOcRyogGvG4OpmGWG6E1Y/IYQYYG5+Aa9gMTcKCxzfuVUQhpkHpbvhqPzCw2hMKSNMbD7b63bZOWO1yCMcM4bvkn8x5H8tUQhHTtYQBGYfQd4tUo2NVPAPDiZK8OGkT1OgKLsPuMq7k1JoY09+qMOomM7GiGWsqFbC8C1QKora1s1y64DrVIMeQgwnvg8xADiI/f7hwQGWqcRq5qHgBRxEreQXtcRhp3XS3r85jZKdDdQj+wp6MBoBxmcqXmM4PbHm0SrIHmf8GndtkA2dKiKlCT3OsfFDIDuF4Z5wOM2bRCtN7ku0smOHElxo2eKZWvP4aK861sHseygbeUpMMqU6lplLtyjP4uEwIisKFCeUFRE4otSIJvaOFEQrkSaF/f39qakp3R1XMGKSLRFGZJKKBBzNNns/+FTvo88/+U7thZ0xvr+vN/okHoKkIYDvJmbAIC2DTAwtivYwCAiaIRajRIkOE+rqMoVjAJJZMpYnk1kCoFUg6BjI0GFI6WqhzOqGBHwpCDZyx/Wb3TdMAh02hhN6DSnFxCwgl5xgf4mlAITNeI1QV6ZYEOZARD1U0V69jvywR0XCnWTj5N5JSXYqtgJachwwDe956yUO3xCTfiidUF27YvRITAOKgUXq7oRTH2LhBrgS0gvYFPXB4dHB2cTKzNj2WeOs3cZ2CLFhp1WQUy+3HDFNPXA0zVONjQnyiZ2qN3mGlAzLmkerIBm+rt0PHsCki0aEI9ZJNhqhiiK9GdEXXsZx81rvaHKi3umMYfYdpTzKdxFW1ASg2OQ0+UVTNIzwPkgRBcmDFyPkAYsKJOVMNI1SHr8oAofbgkkgxOT6yl9RTREpTQVTkcY2/buXYvUChSzuMPR88vLZj73cujvzia/31saw4VXayRdvw0dpCAgbyggXCoI+OL37KPzNw5CBRC+yxAneV1J+MTEAhRPXOq5BXVcijhhx4J6EIeGBerpR8FGyEEuk8Iwh7D2qz1AawuJmBkHoNaREH6CtAqbHP1aWD5fBH0zMXAbmvcxiFkRkIMy218cvmQiJRYXkJPlKY/Zq5nphWv/4NrHB+gC56rnG66TnpCSmwbUroo1OG1AMLNEucaCisUgbgGQWTi3csRYTjoiYgZY9h0APDxuo1i/NT0JhfmF+e3t7ZWUlFOdMyX2VWzm7ssIJmRBIq7pNgIMnXxyG0xs5vCwdkpEKaflr3Gl850WYrVlICKlAY4uqWAXulnqKJqdRsuO7O667R91mrbPPn8BKUgpWNzSKiCkStiRzvqgJWuwIGYqaFIkuJZGCCb4o4rTIIX1iEkGiaCKpvoJm7DUwqeMcX1QjBNIhDie2trbwuwqXkjkXKU0yJHzC5JqkoBP4wF+f7f4vnxw/mnvl692nOl3MJuDlTb6mGQy8KseDMFjzEc4g8Azsf+Td3/1xUhouRUxJYAcNJaIxlYN6HauVihkcSS71SjLikSeJnaKfsreg4+DiPME1COuxul2UqgF0oB60AwN6twpIw0YE0J5canBMBdnXULg+uJlLnmPEpEdEGA5WyLB6nd2bylB4YkIxWXBCEpi9mrkKjtKMt5Eci9ZWgahEtEE73je3dtJzUgKeIQ6KUSTy1iNs10DJwEYYgFRWnz32iD5xNiAj39p/s0D1PDZ21q082q+sTB01ptgToKCAhxV0e12c7nThMHrOzjSHmB3XCUHIhEA6mQpKbExIc+AsXbewBYdOZIYOOeFnUUJs6hp3pS5RmuSMTkcoEA2p3gRTVCAEoyZJYwlbYGQYq0CaRgKPQKuPnZ5VKkdHR/jBi37zUvqCJjniIlu/SMrdKThg6hyOTPjQIZoIMXiRKdKiDncUIVWUEzWpCxzf2IwQwRcFRoTIVGi9CY6NCb6/RZcwRoheIgyjRdRNFIdPzPe+8FTliRtPfrP3ymmHPz9PGAEEEphuF1bfxjpjj3FKgWXQES82sHlfI9z8RyCpIobsDZtSQKzhcVV1tCVFg1xjSUBiNa4iSYopHHHPEorQkL0FAnD5GcVWg4Ck7snqWqcURgAXsNV2wPfeBWgxHazHTBTGwK0EdQlGalgdWgVkLmmEzpCWaqWKjHh3pkKbx0ZmMhHayfyoluRYV7SKJYHVsVWgewLHSVtyrMPEiEUDq6pTHK7xOulZQxHj5XQCVReD/tjZ2ZnuxYljzZNVIMLutidO9x6uL89VsEefcHGZnpreYw9ztS+7t6bAKhD9ctopRN0sMydBiLG+5On2WPVAwdJ1CzuwyuvdOQHOiokiM6xxRyGiFDQ6oqgg0tBUmkYOB9Q1yREVQ2QuckgtlqC+cHN45BwjGsVDVphux6vdbh/3W43uPpXv3C/XJyudEMMjqWgrKuRIK74UZC6lDIhN0NH50aVkAi8citxFNCNEZJ47gWdiY427DZYnhHeHdHiclCuFz5tc58n53o88X1tdv/nm2HNf7eLxeHiF+wl4H17tE8yHGI3NYZW/qMDkafJATuOUGw6VFdZvAgAGFMEGfuLefRvYGQIhNIMsDpgBkhkBMSu/QUIRSVIUBTqt2gttCYU1BFkIJGixmy+xHWTNW+MuXMVDZYES+iVwg24Ry+qXNDghhAEGPy3530DRGL0vFGQyUGCMvlsEpEGHROB4JNKCXzLZljLVGkIKohJ8ChZmLlewymJCs4qtAiEgibRGEBOgahfjOEYshuSkqroXAaJkop5Oq5ZOoXCYBKpWg9A9hkf+8CM9xmhOCBGtZ5JiS9+NnZOpybHV5Vn91Me15v79e1GFuzUFVoEpigw8qfNSIxY03xDToSWLOLZLyRScQ3ZWTODfuMY9GFUT4ESo6kWPrkw1ExdRkxM6AkVImjom57BLRFCMgiA1fn1FU0QgR1xKIm++/fis3+ez72wuPniJmAEvh3eKhONTJBxabCqa7r6VyKnJCWoqgEapTVmxTdSkfsEK+GKTcLhfo4jrkCHX1PeCJCjuhaxEvihS+Lz55HwXJfvK+s03es++02VfDPQKxzvzgtNPGW8CNoGb6zkGqqmSDQhBaEbgyoH3CLRK1GJzwWUA5bFC9yEl6LKPGppGmYQj2fCGJdTAzMMMGgZzc7UdKqoxBW3TRFAgY9YmlzjT8MIQcXBwACJ0olLMWO+XCdKzJCSrhuqAV+5yDEAx2HMWeZCARHWzhqRua3Q73WazgZXueOEDGyRGRYyPRLWwOUzBF7sammvcIAJNwG0CcYjgU1aBpmllWFxa9UOBOiLJSOlDS28pRxBGqlKReqF7nFHsVA9OLBXF3g4hrDrmEI46NUyoLy/O1qtsJynjq9EcPzk9zbjXhdm90V9SphS31EiKlEnf8cCxREjJGF7EaborhZ4GQLLxa1Bj9WNkStZeQ1STrxOqLpfyv97w7WdesaImESpQ0I5V4IqimkhDKjZFWhGhycr3RgOz7yftbnNyvtY94ODZ/yp+ddeKgtgU6eyRFIEgnh4cn2LmIt4kNd4kHcVEj1DRVBREKWqp6Wk+F65osaZ4QnJxRHhQmG6OfWS585WPjFdnb77ZfertM28u37sDVS/IGD4D9f7o4438mdbMmTjq1lbZHMoKglTHk3fNigXovRRzHrZBnUMZBAFQ+A4lchxyfYokTlBkDrOwtiYMEjOCA5plkqLcYGaodRLbYW0rNoLEvLL3Vzz3BHwfXOCEZOjRlopQI7SSKd+SR4+/MpLBPuprmkFd9qa2ZG+eFCykAjuxnp3hhl3vZyhKrawe5S1KpgYRHHidr2RDVPAcyPFAbHVrFYiYCq2hB3I3QRqX3INqqbaDONi7NRZRyUhbLQ2CqAhEcFc9thFr+AuniJCZNoTwaHO3MTG1toQxv2s/RcYWFxYw6b62tp4wBilfkntJkhDVWX0gTpyjIUWWCCkZJCmKCC8vOXjIMXRA1TGe4iXGxa83+MsJUZQjXSi4GKdYkFF1CAUEYKzMdBE4BIKVM2igej85OcQ3aVynRV/ZaXJEoVKcHJznjaSiRxJxQhQ50uQd+twFQYlN8k4eoU+aii8bXzGJUFMAUzej9wvTAxA5In11tvdDT3efurx8r3bzm931dpt/++UVCY/O8In3PrdqGeX3xSudzMMl4zJT/WMfluOKTANSCnHfULHyQzFeuKUAQr/cRMTRXAeoEoLHNKQo7KQVJ8Bj70CgAlDkCzTcOkEJJh4JI7FboVhEM2uwDwIbFvDmRyfaIBw9FSG8T1nda5oywzs0fljsoIMMggiRZRPWsnREV9SzKfVNx/LELAr8h0fR4GmyHmh4QiZwzcPRHOoxiWFo6jEOY8Q8BvzVcEkSJbN8/COOgGs85D4qsKi4Q7vELsm31dIsCF0SQixhs8FHja3FMpzKNotYVwYFYLXPOhs7R8uLC82a0/cE3NrBvsnLJZYBWmKZ88VU7BIJIFMjmCPKM3NRAdFACaVB+YyKJ5TFXV5CzfwpLxP2dNRxVuGl+40uzkSpSOs4iTiAogsNh0WT8AHFpS4i8kuAZJ5UpBr2+83qWXNyEsU7XigHE34syb+BoGiJUL17RqIUDDFFetOzcP1DuVUMRI8iDTWlqRiOTvP09BTBoIM8YOqpmD3qC0m5Ceqttane91ztvXa12Zm69u2zJ/6qOx08qovt9Sh3M/jEK2x4ZyOvxvXGY/PEJ7vW6/rMWz8YYGXXfHA3lcWBZ9vlzXejXBs057K57EixDTyqEbJ2kCJPpvZRxNECCNAkBHZgA4H0zqHMMknRa3AQBVlQo8AsgGzer4etyqvedpBcG3+ZNrcgAAE0kBHLrERiAhM5DF86NN6pTOczU/U0JBvXBuVWjUxq8x6KmIEYlxiqYLzkBgJR2c9QyNLx+MkdaoDi34QkltAw+hHkGqn6VNu+gYcbJdOAOcM1Hgt0gGoXW78bxPh3DQwwdufx4QUa7D0mVFGVaHuY/bP2mamutVsQqDOxs7vbri+vLbFdo9hpbMiEylpYWtra2lxaTrwvpArkHGRmxSBjwXtmQBuA0MXAV/AemAgqAavA98F6i+6Ilwk1HYGJcY07F0ojPrvMShhoUgEUgPnvYhmkiJSmDYGrkUciyJw4ROgi4hRHTExMABy1Oy5LoN3Ld+o4zxV6ISaNd0rkiF0gqdh3Gy0aJqVFTMWWYqNgoED61CkwozUVWPdmrAsOhZBETfZriRAnBUyEGABn1ipYEtN7bb376Zvjjfkr751d+euzhbET/gGnr7soa6RPB8PhKgKb16YeQ+KGTj22ZycNIH41JvE8I//CpwsgRc9D4JCyfg1gvi0mDMgA5rPkcSH0FJR6Ql+Z0ADE2ELFGRgytvdSEMAzg4iR2EHMtr4rljciPUL9RiFL9RZOG7xwc+oZ28edg8ElS62CLNmSosSNb4i9UfC5dyGx0RGQLxGSMb22mhbS9glupEQgKOFxVHhmMZshYkhBYpgCswxcqvZSO1ASUD1SUlKFQttmT965rgrH2jZTVVfwZiBtKEb4ANpiFIgNbiLPM1HfAu2pRMlsDgSbqPDEEGL0BETRykTjtGLPXTGJonmOPnDqPtztTI9Prkxi75rAyOBPZdVrNSyv1caV6KCYVAUiC995EAPx8yYKd+B1kY0FbLS2dNfCztDXqG6Zr4AZnCUyjYpMA2L7uBNTrHKIL46ypAmC+ERwqdIUTRTaXROGemxkHi0iNcV7iiYcERrlBzPuYKJ8B+D4+ATSKcZj9EIgOkH6JCKOSERLRc0iaMV7RJOLKFdiMEamqJCaFuMRaUyCOmLONPs357uvr49dW27VJhfvdS59o7N4fNDwyjiMJfJwEn6AfHhDtZdKh531xoglrosOQ+FRSZWcxw09eN3ysEUHjMv+F3k+i5kaOsvY1m8O1k55WMyQmesv5t4PQdRQovLtRBA5P6JtiKi7C4BY1wXXpEg4cgDYrbzX69aqbGO44NxjqqQe1X3fFfmIIOJVvRSwndODrEWgkQid8SOlbgmRe2okICMQqpIoA11BGJjXQGZwRvNjE8AwS6t1oMTgrEpMaHoxYwHAah9/tbaacreCE1scjG9BMXzpF1AsRoIGJ80xmLm+sQYtaGsyzaHOkGwELF1T4ETqSYjisRQAiOwfHx3Nz7Nt1J1fzLnkwxLN8fHpLnseag+7s0v6bp5mZuf29vait0ZwQ/K0/AhSBOLsxMtDooHDGVpVZFvdq7xC25F5ixQmDytZz5hzZwu2jzsFZKNJYYhERGy6SCwKRTpF/Bycg+iOCBAz7vhS3m6u9A/ujjelr0Okc94JJEHMhtIdSk5ErkgHtiKtQCVqKjhKUzz6KB3QxIvrgCBHeDjM4kT/ykzvyYXe9cXm7Oz8bmXp1tnS33Smxvb90rQibO9IHzC1bA0hoSI0yJPAMygIUs9CCJEQQEhqLjrMxlwhaFDecBCOCcwt1xF4YuThNwEJyr8kejxVwJAUHnMDE0tfIPLWBQkhePqeCRMGLend5woDXCj2ZYbVRgYs5jZwzcSSSiDg2BDh0UuYcQcbA4J4jgXOEY8PIOFwMcwM3MBUemeOA+eGvPGTnB2dQEmytjTIudWIBKRqgZLZ7LcvZKaJh8B7B4RgfDUjmqokQ0Zmip/tZgDFl1lJ8SU2VXulHap6ghj4GLEVO/TCThkzSrKayIzhFACCkfU0LFlMwWt6JNGIUNWAhbN9cmoKKgaZhhQwQkSfozP6/e3j5livszaD31fZjSuBbYJ3PPXv4f37MzMzfnRJQnR3kyeq38s0nXUP2Nf0hkNhkMqzH47BFOYyeQIdLSKWyjj2OV81us6JpZVIw11Ek4tIJ8fYCJlj8jiVSHBzTK2z0WvW+ew7ZuKV7uQYTyIoCoOslMjFJvUUVkRzQ2oSQYAiES0VNQdAK8FM1nqLk/2pJruiYVp9fbq/ONWfn2i0JsZ79Zmt/uy99uzXO5Nnu9WgNm17NRH7aPsEEwSfdJlmOlwk8j2u9AeNoB28B4CeJMAIhJwpt8IYXKQqvNxOgsy9GbzLIHJQmjuTsqdkKKR9KBOGd0zYUGce7iwmHqBf0hgMo6xwZCzuAitchrrdDkYCbIAYFO5MFsh5b3y35roqjCk0CikOQH99gVZQ9MdO26eY524FjwrWYiAIMTgNR9AKSOaS/R/GGUjoXQ0XgTDtXreLN3n3j+ArRwQawXIiBI+y8WShakgBRLSTBIon1ybhqWAkICRVgwQ2QoEw2IOlKPlYVoHNl8LnvszYomqUHpdBOx5GhBRpgiCmAQvn1/4+m9U2yMjOTOj4nl5lrN2tbB/W58ePxicbSWFFUNCNZhN3WPn7QibFMoetcpOgBtEF7ypWAW2bK3aBxhgQRh9SBURhhoxyGYxPZsvCuLZ0kcNwS3JiORJUvIp1Ia+TRA6hUQlFhhCBJj6aIq00I0SKJppFv8RgRBp+0SOU7LhmY9N3TivBUJdBQETmvKkouzSBoEMRrOhCQRN1dDVFqtiOVBOhTjfGZif6Uw1WIEzV+6jOZ7wVj5VKdbJRr2KlYa3yT/d6X359caJZP+o3cJyOO038APpup3HWqbf3sSMBXvzz0mWbfCkvqVb11OTq3DuYxPehiEkHlzhw5UMSwU6eIAYfKWyaGTxIRcaZgan37mkQXxAllkpIXrwirIbsZVUeGDlCyPNBVE3JEbvwB4zgnfn16kHZKWPzAxlRF4tXCm7gGTHoMLBQQO41oR9M+IUDv8v0sI97FccSg4B33wszkg01K9EVp+VQdHMfQhZwUywJOzk9qdfqj7Ye3b1z58mnn8KI5MUQ+FXcKSC+lqJETWiz7sZokToRlUqnyz5W8sDCrtuhClEKEaqoqVQUgyY/qcKOhVSg4b9zgYivKKhNqPpgupHBi6Jk0BAdKNqiiNHG0zaEVKxDgYqjtRVLqy/N0BIT6bnGYAiAYxi77MOLRv3+7Nyc0ZuoRXEJhNFobK/dOj3YWFmYrsrPQxUMo8gA1Hc+Ozu3tb21It6iGhdWFLosS44URBe8y3h5taS4QlcSm32WwAilmZ3L8JnhgitMZqBogCDq4D06IZCyRR140WAKWvfApaIadMiECDLUOSQSDRVa1DnXNBKFOhE/kPHyHRdvvg4eneLpFZNMWUXSiHbvPkzwgj7PuUiTOwXNqKnoDL6JPngnK/vTqo0tTfamx/GYdJ/bqo8ttlgtzrbV4id1pTpRb4yhImeT45XTXuOo2zzpsfq73W3sdxv3OqBhX+l3K1joC/5x/+i/7t1kC2YIA5T/4qD8b8CzvAcfkdDYrOgX5lxoUPZwfL5IMwOq5onwYEQ1fqxJMxSJJlG050b05blgOfNezFR8BS2VD51A5JFCg8wFnmouiMzmsgL7DBGsSBi4dk3pOJOaPEgrgMyENMmxD+QJGRMfLq9er7B7Mbv6dkPc0LMylyTMK/vfB+b6wt9AJsjDQEFhPhv3xa6uXnr7rbfef/etzln38PDgte/6bnxOCFSw5cg+gv+miP9/9t60PXIcSdB0SX7rPiIUV2ZlVnc/07O7H2bmn+x/3w9d1dVVldmZGZfuW/JrX8NFgATpdHe65JKcEXLisAtGEDAYjWBC3okhEBYqxNUgts4hkABu0O9z9+nxx6sCOsLDA0iYjQW0WO5SZi+ZBXFnmMcEdvUq4SQ0oGWRxlMO+YTMUnUJMSeOD5FU+6V+OoqGUiOYkaIodgTOcYwgRIoEPIdKVDJH3kNigD89Od1Yj3ymw4NymNmElQuOo9q304eN7uhgl23aXUfKokRKLBVXZZjL3ScjA69o2623y4nlCOUnEqdBLkxGrFzI/IrJaeS0MCxGKYyVE6pZSxmXKO6uyW/X7DVxOSaja5Viz+OwRybG3Rl8mVE1oVBQlQA985TTA+0whlHYIq0EVxXNOkVxl2K+s6MC3vf6zk+N+6+uKqRqcsW1ZVBSFFJZn4KuoiEuQS1p1zSd5TdVorOOckKBGMDVmhjFCpPZgCSB49vtYaehytT8vN2qbbZqlLguurZSbzbqK/U1NccK2sOgftlvXPfVA0pV+jCsf+s1/t7je+mKgeYiuztOcPCwkvFdDiOkP1w4cYRgbLbXOFJrx/I8FAvpBe75M5NUKxBLx8IL7diRmLq5kEZ1IbYrtM1J0F2VukyqXLjkJCDr6kzaQPo1iZhajBAlgVSpAERn3K8VM03Q4lhA2yybl7PFVanYGA5Act00BXsdHB1NxAOzhHMgNaKCx1iXHVRWVvkl6d87nmwKQcFn2iil8pc0RFOXIilTWK5IJQwhXXl3e/fph09H34/+8V9/w/UudvzDw9H3b5jyIZbKGS6GqEdbKsLKENvUUZjF1ZAeBYES1YlmIoZ7lkiIq+npX09EK59fnU5DyMdIV5MfD6GRHBkjm8tHaGqy/JYCMgSErkUYjzcGwtC0J0Vb0XcpW6VKXSZa7WpzmeahJQgOIinSZF2F4+In0tB+nUsLCfrX3t7eWPB8bgb1tlc//fbfh+8/rq3oHZAcl1KJfAFW+Pbf+fn5zs6uIZQvSh6nHIx8no5QCRAHm5MYtzzIES6HWlIsomGEJAVJahzJKJK7hxI6M6fGCGLkGAM1qxTKmCykAAAgAElEQVQp8hIqowZVsc+CaWZCRprIhEiVgWvJnQzRhrhmOq6U+IikHQVg/LRD0YlUVSqbAsb7jsd9cPf99u6WtN6UMAXzVFknuZ/gOX9jdcSvuwmaq7jAxeFdl5fuOFao3W6vdJt8wcvcdSs13rlvjNbqxLFijjBBA3bdb132Gvcj62aorVwNGl/7a/d3rkTRu1W/ZX5S/bcMioWRgAERyjXLVmTOGQMNCHN7ZmBNgYcSQGasS6WuAMRkTMvSDRSnkwW3Z8XTp6y4pzGzolqUcZC+ze1RUehWTEuLer9cZRWMEMlJCIqpEngNphglpByyKrdAIUFdpUgEUK7cq1JlntAORpHUOV8SV+9JR1mMghQn4HjcxYckId2k3J6hCsAD06SSa5sQUNTCS02lcgX6QCFBDT8imraBs+D333779OOP9XqdwF8GnOPj4wMe0wfcFTrjX4akruA3uu4x8IF4HomEnEBYKKXXUY2FBCnZ6CZ6xHE1qKlLQDzqAhFUaBT5tepNqpOUhvJlSdcldGwKiKRdcXBd6pO1yJmzBjJkgkwGVAocv1LEBcFiJAhJKsqCaosTrc8UamihWoxn+GaB4gJl4TKcE45CggHw5OSEnh+B84ri3JTsVJ2eX66sdd+/50On6qWMUlJ41BORgkKVGbF9BYY7bncz9OWIksV0JRmMfPnyaxy1sYmQRoZ5Cn9cfQrcZfG48+ksN1K4chJxkqFYPvyk6dKU4oKk2ZWDSmOl8zlChY+eYJXEuDvTLU1qkrzplOVQ4FgMT22xVK7WJTTnVNYXJ1XlZ/20j1JJmhU8hiNTl3jfGy18zHk9sxJ2EKELdOqjZh3b2rSMXckJONlqDdtmawdh1a2vrrdWWk3VNcxQ36it1gergCsyK7X+cO2q37zs14d2fH4Yrf02WLu5ty5wgZzg0N3TTqsTIM4Cen19PQv6dLieQa8J5N7fpkKfjOpBScFrzQmpKGU3V2tmqanEogQ0QxRF31yYAEwIpg3bRBjLzjsnTVCYXo3kDao9ydkgBEwkkymPFSqCIaTLKdom5/Mh7Q5VTS7OPQOWtMBVKWS5XAOeO0mwnHYw+/W6KbbE6sDmk7MVxpZIPl2m65JSoYaPf3Nzo9frEeO+v78PSKvdYtj55z/+ycjDXjeaTIKU6kRehZBXK3DNx/7aHkQ+BSwQcdcZvYlXTSDHkmatvua9nGpImFOmuymS8uPuAg9EkBIRvAoF79cl6F4qZK1xxhDxsBVnB56I4YNomkGdLsr9BdbRzAUqVaG5mos1XgQfokgCH07k8M2JUnhFQH7DNJywS7P0oBJiAI1G28S4u6ICNI+ChRr1+sPj08ud3b12ndcwLBV79jDGJPMxhBW2+83tbbfjrS6sBGPo2uoQPJ9bfo2lNP7s0QjZOtScYlefl0jhqazHLQ9Nl5cGzCGTMI9TSupzCExVXJpqXCjXJQ3zkWe4TyqPs6qLje8CsmMR8yzpvPICXgtShQ+Mo19rXt9eNuurDTZHK3Hg/95ojtp1NTGqK0t6P4z5RpkbzbVmQ0JOFAgWCInmQ61u3Wqsglav+82Lfv3ac4FfDNfue6u9B+sCL93FjOBR+Jzul2prYCTJeFkOLUWldFZtyFUa+mkBlSZi6oiVKVG1GZhMXUb+XHjXPg+Fa5kLL9c5maj1Vc8FFlgFoiCKyBoxgq6giTsBLYgUR6oUo6LyYJwRRgrYJRRVxz+h5gBcwoeMFipJh7xUQUTI6iqhWfyqMvuTiKnVM67c1rt2OzldjSQsWYLIGV5kgcoQqVSPrUAIwW///RurCAsWsvYJWTquLM5OV2eA7TV32CaBpc4lwIeJDIiH+W4hhAR/ud0oXqflF6Q4ohYsqDOMLN8ENYHSWB6EJE11LroGF6gouqEQ1AUZwbciJDeXlIaHQlKAGfQQkJxAWJqqMsh4giakQghDMak2BSmyaTYWrQRevrocK0kglnmc64ojxBUg3Z290tc3bIx7pkVRRA11cXH5sLrzZm+D7x17g6HjWTKR4RAUCCsmoG/fvsmDX6eAjJzFzCYE18QCORL6OcUJQJIybNMYU0kDVR9PBqrxSk9zNqLlFCeCR1I+8xiZSH2EyqRFHtVJhM6BhZp5OXVSOYB36naJKYjMghLMx+UIPZWoTrqtpuxF2K7fjnbq2jG21j74v/evOk19ZeVCNVbWOs3VtYZsOq4QmfPqD6P6vVllyWRKFPhVv3FsXeAA8vSiP1h96NlYFm+AdtxzE/DJ6SK5KMUVPjWvz45DMmgKozRaMVGvlieVKMoo1ZaPHzQs5IKfZ2mI1UmRzqUuqU9SSi3+9TZ68uQxwJaLr0iLGCzaLPHAbGTZGUjgUzHpED4CYIvsnWXz/llJlPzoKqFsRaXEZU2prVLlDfUZBzzu9wSEYbg7YPCsCtTZMDE0FFkBccK4+hwZVLEHL0HkQ/lMY+0BXWEuM9yxA9321jbCSADPwFBM6DleLhGyjxuTGkYuR8Bd5gRHRypNRhzt8jR8hNe/3e54oTL2ilpIH1vS1uUQltt3VMJSm4t6/RWjOBcplb9AdEvMq8vUh21PVQe8grrcXXQCKCVAQiSpS1JWvwmUltpBpCtS1bQ4gAgywDo6Gi9drUv9Xw0hfSAX1lWkiPtkpkrT1dfXu6kO6FOKMkTWo8thp9nZb/NtPv1434losTMFtkLOXmWGQ6YAeAYEVtfcDrFKn3BI26/xWPrFfjoEyWGVU+zTSaVzMEJuKZzCSmChmSWbQcqCKDY5xSkRxmYtmQxbjZlTPJZsFCA+okZBKbSSpevZx50ez5GueA75WcR2M6huKNky1Npro+ba6Oah1hsS4T066ErMt9adRIEThdJWoeEUMRuz7UmjweNpPqaoLv0K09D9oHk7rPfE4S3zNVPQL3fv/3bxjdhws8mrhNetDO9WBrcT9xdtAZRFKws3c1fwGZXraArDoAUW3Wyy7O7aF4M8OuH061Wo5PO8M9KtGJufvpn2KvkX2bJLl8W4FHaI4NonkN4lC1nkwCNPCmXMzW5M6ZC4bVX6bIB1sUKRG50HX/X7+wcczKrCIyXwXlZXS4EpdEOTkjm/UFFh60n2a2fPeFrEgWVMEC0uvfPzs/6gj8oa9fqXr1/xwePqw1zAbmBEQiqigRmYtNDmNxHK01Yil6QSkADTyR5pGoAY7o0GjnYRTzbbSTzuIZWAuuaV6TIaJtoXDDGLElBTdYKVbYKUGnpZFKnLoihqMnjrRPY3uniwYJ4TOWmHVBpyCdWw2gPRpHS1R86R8Ot1Wv8mpLMQ+XQisD5Rl0Yew8BI5mpcwm9RWhgH5BJAGAQH6xNwcCTo/Dc3t5ubW36hkycotLRub28va+/3u0f+91AjdlUeS0W0sFKztfxUjjno9ORkP/rCSSAlmZz+VYJlCRDHLBBPSjMFDlQnMvWF3AorhSD3q71lHSOLlGHlIPJrHEhxIkbAsk1h5hSnoAqzCbcIsaSykIZXCZEkxt0rf7FJVKQ2D6SrmMe1xHHzvUx2HnRt3mxglNd0iS5trPLG19qovsb2bngpeC9zMFq96TeuBnVmz4FM05jy9WvZgrBun0jLvDp6CEZDxyJM3DbWN5lZTy7OeJTGlxrC2vG5rB2QizN5F8klNV2FL0Ci8iJarnUKvBxODj3eXvrTTz/lVMaL8ybtOLQqNSLOJGkB+TlX+Rcon1U5qFlUUMTBG+fLskijuF4lbczwMguAhLhAJFBJSrAzq4V+X3Yrxz7u93rKSAU+ISUo0SMRKaSfAVZ9UmD40hNm+If37xk0wJYJcDTqPfTwtf+v//N/CMOTPIE7g+G//8//iySRKnj7WE7w6On3335n8xlWF7TL52zS0UJYhuXkjazq5NOxUgsAbFkvIArcQSEQ31CyQMHZUBSQJBlAiBj2yIEBIFG5gVYnryMIB0tGn5WllCpTloWqTleIjZcuM/QUl3hdgU2vJI5YjkLIEEsaZTiZdlpeXr2HFcK6nCWqCzzUMEDF0k7wPN06ybJENJTBLiISqfOpSbUvHNksBt271WwFVzeCJ2TlPuE91Ls2H/J9s37Kngq+4ZhiJAjxIytCTCzBNSQ1AjcCT9qlsASnEiDQz0iSKYi3wGBmmGQKUujj6nPY5xRDLUXQA0zVeILk13hARUlLwOOWAs+vSQGmsjE8yy0FqrOFlVEMeuwzNtyJ/G6sSbu4XfmPPX3QHfLyJducoAq+O857me82Ru0GmqSAIX6lP6q3W3X1khb9XTR8N6hf9Jo3Q0VIld31G38M1i7ujGbCyxDm4kq1HdFdjxJI6+q45Bhtb65elIl9T6bJHDGS4hICJMCPlvKlcrrK567ADY4CL4ETUot63EOQCnJGRL91FVB9bSQmvrgZBRVegGDeHM9LIBKoJOWZjQk7jHXKJZgbW1V2UDHwAmGgEuBE7EQkSz+4wxMUZZ3Ii55w+ennn7rdztpaHYOAwHoNpMZDsdk5ZE9KMdflwGJQgTQr3fUu8fe//POXj58+rq3qoS8RRFKKUMJfUlaAsWkPQOmHEL7+1uoWqkAexHCPFISPgohwlCJ1eNQkn8paGFVjf3RhOuc4uWqbMC3jJJa71b6t1eegQpMSwADY0LGIcVe9unqApIAFSZENKGpSilEEXtdOgWVo5XqVwgorUSJBkkIEW62lybSLagH3oQL0VB3ZompT56g5WLr7Q+8huYcUWVdLzh294crxXXtn7bzFnmiguYp4IkMjLUKCFqotKdcpx2hnd/fs7IxXadMQmZYHABlBdG1koec4BfiRTD5gjFmsLEVUgeRQzSk21ptHKA/QA4mJEivzUJJkCJjPLb8moRVLKbyQSQzMlJUG9GnAYiEM9+bqqFWXXcAH7FoiQ7O8iIkjvN0Y9eXrOStUbbVqOx32apAx4LYvz3ybddn8W7UHixxHztqNfHmH4B9RBnQehmt/ZS/CXvJEWCpK7TyoRkNfVY+SVm9P3l5e3F5dXZG2z9nTvEVDZY5yUGUozR3GF7XEDaPAExyFMR6N4VJNCwli2K7xFEL4ZW6RNTD2auZ1gwka5U37CTsVuKIM94FEsDhykjI5cwpHmYw8KTNEERIgxkcVFfMv//qvvHuqA2CwgKniB9J6LlecpZQ/ZdXjrwB2rd6og8XwghH/9cuXw3fv0uMJhJQvOdeh7JrkEkr25MeVK2lZMcBXlhFDCcFPe9yFvUFNJHGFipRqnE5ZYEU5hFdFCkrarBMZMFNgwDxqtkSf1a/QSOh4FV5pPkyCnA8TUDeZoMwxDUolYwu8HmaLLJIx6tLFrhemKyQvf6Z7WjLmrEoVSlhvqJhTWKdQE0RNyYIYDAqTFBlbrYFz6tLcWCRjE4gV6B1BRpVfPdRvrs4P9ohVledRHmwgglce3AQC5CN5cIU1PtxKs9k6OzvlW6p+qaRzDP90U0O0fHF8uFjrYmUWx1ANQEpwKgESkIQdF8Fch3SNlSU8K6gYn1hZiGpyOYATsI+SdYW5EjoIP5EjjQ+SSct9XaXhjsMHL7iY4DicxFqWL1/yFczd7pAvXzKUUs7NVV9ZZQtwnu7e9VZwmTPGjlYaGOd8H5xYFJrNRIMJftFrPNjNT3qjlT+G9b/frrrtCDONCW9wrzrnguQUe4jTJ2ejvbm1xZhyddsf3t6sd1iepK9RMKGbUS/DMlMwfXMeGdOXvFy3VhgGzcPwkqoJMSeH3zafsV/up9M0/bplepwGymh4HI2Fr1dvhRLV3cDVXSxsYDkUWAQeFel/oxquc4K+eBsPg5jRkiFUH1SiYtVHOVOGD0PXADMUy31AQC+mewPbndvh+7fvR9+/7x8cJBawoh/8aKL85g01VCiWyY8CRhS53iN5ZVaeP+BuVysZtrfX9elf2zvU2WZSQEIzXaWMdK/Qg6HxhoBXqEi4cguhCgxxPw1+iCuKdVJJysvq8gy60Zw7OXTBdhmTSgpcTQBmMhGwdJHk+VPXxadlmZoKg2bWez5glmBgx2pQQ0VOaXhTJMURIVSRQslUGkI5iJacgSKL8SdfTQhtcSl3B4++Tq5GrZXzN7u8w+o+JeBIWBFsgT0HklsgR7U4kdDw4ESDrVbn9vamrbaX0VUCmkM9p9gjaZIZdklBjEbkUqZpxtA0TEI6jTOmXkg6ZDUAUqL45DPzWYyDcrR9JI9lWOzlDOEcfAs4jj1wOSD5hPNrLNfkrGDhEAuVwbxmR0F83tR3GxKOgv3dVr/sC85XeAhBAYYIFBw5TXYfJNBS1ZIdjOqEo9wOmU7k4BL1BmwBLuHg6l6REjYF//VhtXefljedT6RNUvkw+TUJdtWpufFEdZvdBpsxXF9dMR7hHmOGzkpvpplsBSWp7jM3UaPMKytMiZ1qVIyNh+EllT4uL69iGBOVBTQnwlwCvxYNsKPLcFgvt9PrFDohyuXTp4+Y3VjDDAsSJKMOMdETcvLmjTx7lCr5AQypjO2O5T5otDvt3b3db1+/NhrNza3NBDU7zalnmACo+2/MTZgIMUpciIhJ8L1EDqkIHp+XpPVAJoSlBWW4KEDvR6jkHELcU4wPlVeVKveyok19eIWKgSqXHwfhXZAQWICcSOkqRcLwsCdbpnj4hTqdcDR1Bs6iWYzkHCMUlBnCCYZK5ROkmsp01zBFEcoant8IipRqlHQlNfpIV/PGMz1L+peFkLPTy/2wdX702+6b942VTmjcB/CCYwqSriuFHLYN9qxLi39TxC3qqLaxsc430VrtdjH+rLU+f8tc09Q5v35WXuXwU3z1tQivSDGhsBk+rKnJaZMqzkdOCOXgJwBhqpBkUJlLONPVQg5hTlNBY/X/93/0RgPelsDmXtto14cY6fCT5xdrvVH9blTv1VSoibh5V25lF3DvQ5gEqNyvsEeKL1QgbcjVz5UAKwHiU5xr+ilkYa7F+850K6Hvo9rWzv5qTd7uKiULQFyVUqBzVVylxP3m+H2uBBNQeY+gBOASZKmBmTTADYsBwXYuxJbPRCiGTKjJm7cHe/v7WO3OZBfjPBgV5D4RdztjuU5qCGW+i+0+XBPTvTnQu7x/+/oNJ3ynLdtL68PcW8ntFtxsSbGF98/eYwSDhUGFb2cwvNN2VWSeTp4hCoqhH7TI56DSinbyY2VKn4EV4kaSNBUFnfykqzN538jOVFq5OVspFEx4aXSRB+OqBclrs8/Lr/LLBcMnlZPOgAWMlERgqkNx0kn3m1SJPR1RpQYwFT4FU5SgGFKKtF0N+mWOvKAEFSZjTglFLaXY7CxgU11LuuJK7fTsarUxOJDHSj2vc7rmRRMKM1aTWxEDzi+TG5JXT8TlOdPh60gpxS9QlKMaU6BlGGfIGaSIuz4AzWGQKuZ6cUXCqxaQSUSMNiOpzosz8iBc3wrKXCaHL/X5NT7JCFSqtY5VmCiEilAFG6XV/7/ev/HCEMLhnhk+1FSQeUi4RC5OPkEcV59APnVq8SRlesa7xtU5vyO6/3RnZ9cf3uP60q1YvLbEpZ2uNNW6wu6vObD/13SsllhLDZTXAMYXL4USkuJMsvK4xZAM1Ftbm4eH75rscqX3dnTudrHF3C0hN4O1UcREICsndTCeYLpLwMyg0eSZ/aDP23J//Pfvf/r5JwoDAXLuqZziAFUyThz5jizbVw+UAT0e20DYBli6jpwt8M+WqjurNjuIHFwFnfxocGN3x1CiawAHKMaMZHLXCU4cy8ieHYkUhOjQiJOAZoB9mPy0dAFLJElTQq9wbPPQM2AhBVXtiLiE5ZfQz1SlC1xeJxJMSysFYLPEpz3wSMduFqeLidE6Pr/e2dlvrlKl7MNAmxY5fqZfJOxlveLl4hi5pRpTdTQLs7GxcXZ6ygpcFwR1FqbwHKEpEipCvqQTUvZR4W+xbbE9JzVOSAvqCqKJhADVdiXvQcb8z4Izjvq4ekgErA3LpCyXQEwiT2CbjOEn1C1U5lwIYl5SySChDiJb+FwYb3WYyjF0IiSeYVFhIxeqPW6cYXDdbQ+Hze3Ts9N6981mM9icoczFW6h2TSyM7Z8JYuoi6mwWLEEgsrDl5ZbJpQbmogH6IBa2ij5P9dGZ2BExj73OPjDNZlNsbzHZ1dedGBrUP22AyR2gTV6xxygzfndyYLAVjSDyT4z3NbZXHzRbPIliuxk2mfn5zz9TO5OUPrK9GZEOUQlwF2lsoQ9YkJZGmENjJnlbHj8rPbiqSbgauztBUTLk800AmUUDT6QbvZ0cQcIiJoZ+gmDZSZ2FC5C9jIJNUL2aaFL3k1SVX+inBUxIW3m8dAImld6FSiqCRUFAR2FYosLEclC0VIEu8VlruABLQ8rHT4bcHb7v9vrm4XZl+2AHGe6TXpe6QgUUpSrUvH1EkJQmwlk54meHkcjOwrnX7/NsztNWHHmCUnqf4uD4TYAroCCHqE5e26/D6pLkHRXgLQEjJ1dG/icQtt4nHSvz60knBFIVSTZjgAtOCdI5IOM4jqNu8HOoa7HzKlEZLhYojBMiaf1zSD3/1uQNCEzYe7t7g9ra2dkx8a2s2uW2f/7tHd+rXBtdX3aJ8cgGQm2gWxp6CbjUwFQa4Iakp7F9i5qUJNZ8KjIBkrZIfvzxRxafRLZjXnNAWW5+9c9yocTM3hLhLs9RNR1kEGB9SLDMqnzNlH3ceYmWlcDG5gZBOL/99tsPP/xgSQUCzJJBYHjLJu6T37NWfse/HImMyr0CL+moFiaUDOX4QscD1Da38DM8x7HWCCKMoqLAkx8pjx0K1qBaDgVYvnUbkDO4XKvSh2GTwdC9UpPx05R42aCneeU+TMk0FvD19U23K8GQo9rq6dWg1dncXbvW96Btj5YzuUK6qZk+ZsED3oqwq9EJUXrYdqOQFJzLJqwpQlq2j1tf3zDVlpI9O6zHScA2EE9aZgu85KTCZFAp4FCUCXBS6fwroGBzfjQhXanI+QXunnPIti35Ba5GJ0JyqUpfNV5VgpNh50FJMqc+IZCCN1lTH3s5NY6wIKXj2rUgYk4lRtkRc632sLuz0+v3+D4iz7w3NjbLYk4l1mIh+R0gp+/nCYylkle1LF9qoCoNYI4o73iT+JBKaGIesBX6T3/6U8ds2Z68kCqGuDXHLS+5QzDOlFVEWv6b8UGg1aF87pj+3BEY7xjuhOPzOs3x0dGXL1/ev39vSeWc/XswB8Qv1msMs4P7hLg+nVnS3lDhJcdRzAibKRhHgXrhZ3hOwFoIK3D3I7wN/3wxLAeHJXQUZj6OgMhhcJUV5UGLRZ13GDaWqwYDXAXD56PlkBOE2FRGn81i+IUsUIcj1rTypYJe72rth+367/WR+t6XbrwQdiSibHIKI433GWuaHm6gd8cxA6YKeMPk6Og73zA2cEaLgWc41nINHurckAifaKkmqx9TXXiCYAirOGgBoswKqTlSISrFtkCSapjKXUlGGDiy1FlCJHWxLQhzESo5RT5xj36q2HIzRCxTnQ1zKT4ZQql6yaZXghQFaJrBE3yAKZAiIvhrKsq/K/O04GkPVxnBqfjJzmsH9ZvfNzqt8ArnkXhB5Z42klbl3zqTDBAJvWVqcTUQ7QDzEbc8K+7qfq+/tqFXiRV43B/uH3CEb8iHHUxou7cDpDLE1Y8/e1GA/YUk+j+DvwHRZ37le00ScUNcOxvp4XZvtwe8S3P0/Ui9sbpboRbhAjWMKrHgK6T7BKTyB5cphZlAH8Lb8J9QDIU5FodOkTTCg04eAyTVNmUx1NlmFLtEWAsbOYtZbLEMQgnnq2HmIUq09PDi/PxhbXc4WNle/QNi8uaeWwYEsE4QKfUbrW0kAxug5ECGMArIEbe0E5gskRoL57u7O+4+C+2jS5nOh0I6mIS0BfMuG0UqFxYZQEciSciViMDGrkearxAJLiT5CCkBS4plfcJVYwySSHePpJcUjPBI8L3ytOAJUFoqDymSTPBspZIlVRxyK5TWkjHnDKFUPVnIhVCZAsHhlgzfRsoSKiiZROQCMq+sKuceLNZCvqrxljVrRw9rtaPjo06ns9w4xR8CUvfAazHc83tLcS8rUztP2mX4PwMYDCDiuZU7m2iZcBieXHyc9+sbG1vbW8Sji6mtI2Sc/e0n9NfcmVeUHwvT3VkldtAR20xjaFL84r1Xwe6y63yr3WKjut9/+w0LvqrHBbSYyBzUQETv8pFX5vq77vH0N9Y0fdWKr87yI82wf5nGZgpkTWBJKKxSWjDM1MkiYAKudfbq/fNWUzqbPUgqiKTE1LhSqbFELFb0LEAGcAy8ggxg1P2XlCQATNl8FnB3d0+IJwCGVbZkWjDwBNWcAp++LsMYjJSGAlhId8lcAZSTC2lKc3AdjiheG+7S8bwLZJIT2NxgBLqzEigLV2Vi1U6SaEJjeGI5KMXNEYxBONBoIoYalrkc+FH6KIwPI8VbHeW5LJxcA3IV/CtRlsIkOOwOcdBqscHzyWWvvdZLnr6VZVYKLnsrqV4V7VqlCM4dKNQhBgqjSXZA8Qb6uUv0GAzCC5Jt72PI8Lp58K1QsYzV50LHWathH83oDd/47c3N169fZR8YjHdziOudnCqRH8DEpLe7ojEXysHuNnwGgh919FlPED7Q7xNPcI8b/57Hdfc8HLi7vyPNL6+o3t3d6gj66QatjPiyiuC+YwIaKJ1URTbLaFmyCBqQscf+TSaPwtJDlxqyxtwXhrhCuBpu3px/e7j6vv32Lf3N4wuRJOtT9F6H9EE81HRS6KSWAT5BAy5FCjJhmyYU5uV9GAJ85BEYFQnFMJXNjS1JAAxdKUgKTSqcHRRIAuMkjQLrWl0lr9OERxi0Q50CSEHha09tBKSIKKjAmBf0FG7Izle3BfTK9OW2FSFqJAegh+wAnAwp2RxAUcIx9yh7ZS4JDQ8iThGtzeBxj9Nclsol8C/DZBqZHlN2TWHflLu7wcnJSbfDvszJxszlZXCdswyKEjZXYtX/xnbCMnyqgWGAjBLKDDoCFbVvnlwAACAASURBVIz/UbRnUjhpQ6LaWIy25va0xRDPSIFLW3ncV7CnlRkxq9h8HWl/f597G6OdCZ6+iTkOM67s/f3D9fW1GBRcNgmPUYd8u3WIFY7Njj2vLXcp4uuSfLtUfaoGeF6PaclvX/aGrN+DjvHOIoEMVIRgRQfrCwiySph51+qKBFqSWWwNqCGr1MQB5PebVmf0ZX+rc7W7mxnrDBHdlWOZsYpI3QVCwxUFBDUlU1Se7IgdJvhOy9bmlqBqdGGQEErKi6g6oQIgKQ1qTCY8GSDvjrf1GdwyMEIuQcwlhWD4FAiW8STWwNllAPoIHwgUmSmJ6ixlE92SVCg+iYwWzp4TQFuiYJ297mF6SQtb/gwfhc9PlmU+GVkULQ33fP2UrTHXbvpLOD1mRERCVDmYgE8uHzY2dporNxEgr6joFvDApkiqZiVts13TnqegOBuKLGtKH97Qk8YxDXiydqTlqTafmfzGkc/tQMmlH0fipdVjH6MVnOK9XgXfYMLqxfhWx2htbYRrHdtaPPri02djGP7JIU47sd3FOse5jmMd7srBrk6U6qM/UPnesCceeLzv+N3vbm/vbm740AFIEK/weiA8BHGt8dYgclZIeUnqlWvgvtY9+uNvhx9/rte63B24q968fRvVSTBUB5kouC7Uw1cC7YazwFsPbAIylpoDSGiwrL18uOA2kbo0TwcfTTiJqDVC+EVSSj4Qz2TCU4p4iobUJkWZlClIyg14rFyqpNwCM0ylSnRdOPlaShYLAgbMI+WIxnBVc/15Sgq8lYBfJcSzh1GY4eJyTrtlKQRtEDaKlBLHMM1ARIRhkK9yjM5yeIklotgSyh3X9ApIFLHodLv422/6D5dnR5ubm8TS+NDju5kPXVHatticXY+tiPx4MuoDTHC3ghgM7y4cT0MgDH6KTA5uQj1J5YA+02Lnh0jLbx4s+8U5Ossp9jGfVVr2s1hZIbYFj3gFgtNzULL0H7Oq4mT/M4aPhis44PGtA2EMdxUeY4x9POzKyY5tIId43vlODSlFw/6oOqoqEDdFYsRnWRGFRQIKSdUts0sNTKWBldPLu+Hq2sePH2q1e02BrRqmIpWHlL4VknySysN15W5kMzjRPB6l27tb3G2hke2IpBIJDZdyEMmCQBeVEjVFJi0qlKRIQyUETSo8OUF0IkXZ1JpStogd8DmaCEqmEBiBs38axeYSCvESQUxgbC4siRIXNoGpBE7S/oBmfBK0PBIiCboQNzpVSVsj5wReVWV+GKyXhntGK0GBUb29AkHdNJnKCJVi3q33uwcHbBZ7dX29ubEpL7IuzKE0YdThd9r5CZjz5m70kliJZpYmoZ6kZib60ghMpO1noEceAHOJ+CLM5aXaqHjW6yVGtrHaxcBmHpKslMiBIS72Og+dKce3rf/rOn6Vae5ympKUmXJDxABIYaUKRhjsdb2GePabysx6HZf4FWigX2sen11tbu91V659cufn5wcHB37JAqS5zYIjmsfLxuOC2APh6K2YGOeGmg+VZhBwDzMOTXBcxsCEe6dIYSnKKTIRAYWShpKRSQ7D0Zwkmy6TqhRlA52UJilT5VACC9vDE4wASxUEJQYipGAgsoVlSoRiwMJmfGNdDdJG0uBkgFHa0nC3ilMKCjKByqbKVExuUhlMD8Fk50pfXWO9XxNRx7tskxIS+OK2pO6+CRlY2vacjBKz0Q3FIJQwLCjIJZIUANmqKoW0NJfnPA2U1/ZEFzGP3TTlA3kEPJL90WVPupkO4l0aTQk655CJTm4Nk5QiMdgJCZXilRW+wkjYqLLhjfGeuNplMtQIOmWImDJdAxKySvxMrxczI6ZsCLH+0CRoRwXoT0lkibbUABq4uundrO0ebPfXQqudKh4sP18VEerGPcJv2IT4WJcuTedDGuVyKRpeHMm4qT+gr8mkB950qc2bASmgkJdxAgbELaXEYjD4AmVQYsuAKJeEsqSSnAbO5IMChRGUJCSylrpXYnF8w10YFhHk+eXrMdytgtRVCDKqpJqfedEtL5263hlwgl+N+S7u98vNrR0+v5iBUgXTNaEAy91xcX7xUktPzpaAPccxxpeyyd14oGkgrLDT4M7aqGl4VokzS9urlGPRaOFjZoWc6zopJy7G7vp6l0+bXl5ePTxgTDeJF2c9ICHt+susDPCy/6O8rkRSG+faWle/yi5XdxD1uqsZG13tNiMBLCoanrddKW80G9u7u911doT8/aeffyKGvpyYRVD0D2TFKGGFUeELr0Usl3UvUQM8CTq6Wml3375dPY227+b6usLVZpTF/ApZdfDEYLfiaJ+svGWH62BaCjJZmtmSBMHnJ6Vhfpzh7kM7LskDB1OUfThARSKCQ3SJKFnBcRWCzZ/LC2qaYv56IEAD0zPThZA6MjC2wmMqMPYvwMI385IM97QuTLOdRuaRiPCcB5uxNMPrmwMu5vum8r4PN3snv+zs7DzGPg9ZFaVvgRxxbbElIGcP1UtayOLz6enpzz8Xgzx+rW3c43NecpybBjCIleHeZLPFWZhApNlqvn37ttVqKzOamY6PRLJjTK+30sNHwz3ATU0fkpuBOulNmO/idDeWOSkxzyXnnPGkMKYB5u3WRrPVHawDwMKAb8FcnJ+dHh/jcQemEsOdyZDFBttNsjJQ4s2ijyXuK9XA/f3daev/OWj/Rz3Hakcv7In+fLUj91q+JVhduyaeNGOsJ5iz0vxSeXkKGONgypI6n2ViX2uwBCqPlI8NTIKQrkjn8wjq8hS0Axb6fp200c8LYCKDRkvySSpFRgPKL4P68zLc063PbVnSxqpTERGqZjEBPZFmUonYfGJz9Wq0t8enH5jixXyvwrU2odQeeNBTvfJY0mts8DQveydksff25DsXy2OpgflrYAVTmZcyZ7yzcK7/96+/nZ6c7u7tsuTGfCduXpzujZb43PmykWzlSAJG2oDn/pDNIjHE8dbzq08mbQvJYpqPBiPO9+zfzp4yNzfyIszVJUkm0w+ffoRuJVpCLJSAKFq+SmguibwqDZwNdgeDm3e1v7EELGg4nzGZbgfkApqPWcV31rgHiWV9TKZT8Zpkws5nEDNnc6EDlkEmF8WrMAie8WDMpjSldN6jIcmAQMrkSNel8ylSqWwK2tXmSb4oMe55cqdV5Rr0SIlcuR6Jfw6b2LOXHNCkOGwL8ygP5vCrnZ+dMbNubm7pL5Mn8I+W8gUbc+cEMoV4fg6wCCE87j/99HNAYplZamA+Guj1ZV9F7q8ZyXOTsnU7dytP0mu1c1ba2OIY7PIiao3dIdnZXd5M5R8JbGTYASN/Uqr2hhT7XQ7sdQ5c8VSzByQSShF7Qw76Q/aI7PXYExKTf2Nr6/Dd+xnFduh8zgmxYIQ0mZnPQS0Tz08DLP7YSlT2IN/anpO3m+55PHy7Ofjc7Qa7okWVNeW7W1FaT1HIKl2+yfCKDj1HR2bqEjpITfclMFI2wTRsDU7A22bS9NL5lIQWzRQn0ElFkgqtGYbv2nwNd49zSuwFHMKLhM1I/zQFk9vr4xqFyb67t8ekfnFxjjXAcn9GH+GsikkJnPTnMYQzeJmCWm1uMe5jZFtWvzYNYEJjqqrt1SsIE4fO1tYW8buYJnjXxYPNSAAPRlHZR0ZCUPQ/9Kzd7RIpw12tgmTEZifEHItfl5HR5WLG9/ku0sM93126xWjAqu/X5Juv/vVK3Uh+VZn0Cv6A1RVWB0roMhgLB1N6EFo4yasViKUdISus7jCB6EStpnTIN2/epjpMVUxvBp3Li5M3O8erzfFWO0zp1gtoVlhtlOpEzQYbyN4/30h929hHOJfS5zg5phzbAt5BZhzDpF7QEvZJyrPR8ynjmSl69qS4eCQTrklqTHUCuACp5ySrVpdIPJnUk0Gbi4I1wCa4zOIXV7drnb2N+p2yChbykuX3Zl/cjBqkAOcQp1RVOXo+7WV6qYGxGhhh5bTWW9r3PePdpLzn8mBZJ+QVVGO1KxNenO/6EKnE0S52uW+3D1lFiPGO43t1dYAFjf8bMO0HEKLiwtEO+mzDprtB5C5TdxrPBkjimxWRU/deltlCliyo1NNdmEk0zHJLLeeYGXr0j3a7w0MkPuLLUmyuzOF1fN+pPxwd7q2Xl1c62OIepWTrrm/wYI2IuMVth0hW2cV/6ktWWUNyrteYi56wT1I5lMJiRmwMd6gHDIJMiPA8cs+7AUb6yRoxGXT8MjIo72zV+/2bs7NzHsHj5IvDPW2p39IJu/vG+rrp6RCxuD491zJb6QqWiaUGJtIAtrEEtHBPYYXMMj+p10wVazHdTbeFJoO2xMgEZdKXKZKXUFOHQlSwykQHTQDUD7/6rLAwfsTw12+vTtTiENjJhRKoIaqC3dyVtCGczRUaXdF71GK+2nM1WpEL5Q4WdxcXF1x9XibmwQ4vVPBVoM3NDTw7DkYnKmAecE7I39fWj7/94+Dwx2a7rNWuKc1ylyXsnzSlmiBPM7IKf1K5UswruPhQlAEoY3ymOBVmczpQIc7jVlYiYVzbdYrjNY/bxMm4PT+Jx7ZPmjRxsyZGGCsG7/+s7e7u4S88OjpqdTY21xd49e83v8Q9or6carXs4zqtWCLRSqBsvUNYJpYaiGiACZgYcvzhxIlgSNdqQfBJBCGnCBMKq5pfHJ/QVA72FdYByuOuJj4ZM/xeKfa49p3rlLXOXU5b7HrWFERtt8NComuUvd7qdL58+fLhw4fZLSG48vSfXwIAED6nlVJcYNOHDUxoeLZ+3v2aAC9TUQ3Qv3DxqneV+ywygcFfoyOyovBVFsYu2ulDd9i7eX94uLLC91BjEDEJgKOvYu/GKp9Z2cbG5vX1Fa8NPDO5JxeXYaF4TBhHsmz3GEdnunp/1J2OQkmsKKP5xriXlCwG9rQXJSbRHMpMIydr62TQU0rN2M1X6HrDtePjbzhdcr45OiXxuaCl1BLr7Sp2EDgNGoNIEdGCeoCunjKX9pvjwfrFy/Sr04C4zVZXuY+UNTxl8zGm9/f39uV7kLjwiVxQu8EQKaNeTcWQV7Y18x8eOkmL+avNdjHE9SHmOJu1yy/hM4TMSFZFzkjgO5us19lqstFobu/svvv4iZiWs9PT4+/f2YMS4aeU20Nbq68xQxNHX6+CmkfYt/XH3HbJrSqpJOdTeyVpugFbl9zcXKtLz6UZ7uzs8lrk7Iu0GRU4qNW/Hl/t7m93pv26N911lks7pg/N2LzS6CyfuEW5ZZ78ipQWeWpAcUE82+OpRDf9dGyM+xwU+1RNnkNTJidpGj+xDiZGmFy0DEZjFbthH28ZH2Tu7HzsrN5mQBa1IKuuEc/re564WQgqY6N3DDApCzEopyCp9fhlkyFqtn5ZMi8NlLxAs7AnOAQ3IAY1ptLU7ED/5ZdfMNjZs7Xb7WL7shmkstLxVWGp6yTkDQfme9jxJ9a5OcRiF2tdDk66Ukx5vQ5Q76byxuEt+0FenhMocd57uN/e28OAmFpspzfkadRZurCJDd9/bc1O0FGeMiG3XOnbLhA3yEzJ/dHR0D+LPVZNlxeXLPM0f97RX7QwyOu7/tXq/rt9nkyxddKUB/MU98iUyO4Wmhp/EsTiLthpt9mhtdOZvi2TyPJksOoh27O8rZ5MZcLYaKxSw315FeKX1OhlGvVMgxMXYupSHNUc97W1k9PzTrs5p52/phavLOJKDeffOOA8deePtBmMTEGuqZCChEeqZJy0y/on1UD+1cJbhpHML77Mm5ubqa/r6soqj84///EZgxqfOp51Ik848S1VDnabUZY7TnO91YwY82gEQ9kZ8Mp6V552a6mL155t/Ihilu1k1C4hd7eUYLpTisyd9Y2ffv4XTWpG/bJM4GussnIYDlsYjvkam5HR/NHzR4A076dsJIFP6uI+XF9dEabFWo3OgTl7+O7dYnpwR7XV48thfePt4cpxWpET5p/RxFTcRWjI8clxdzbDvXx/nVDNS/CF0MA4w724iy1EExZTCKO4afQ3Dc7cldCqXbV2t/HMHR8fr3e7z/FTF5gz06qp4JKUoFmA7QlUAIXg5qliCW4eyUdPFrTh0WV5WoZ4OhGAFa/af316WXC68+mlt4d8PFU+uqTNLwwy6POL6U2PQOsq9oUS6R+U09XlEB87/+Ssne10JGXTAzLEBUuEjCwGWm1c7pABEnsdt77aCqaCa4m0+tE/iwVWFcuV6fT9IB+TBRifvOUzWlx7LjSRjVzWtxImXsEVzGdbQQ1rx7PG/9hp/bW1cjI7ueub63Yn80bWgg+YOc1mfc4Nrt86yAEZU7yY196/Gosp4Ri1Lkx1XUb9pQpnuh6J+pLUpASnx5yU00zwOAM4bq6vT++7ndEJk8RM5B4XGUtmDgxnvHKlREqm4Bm5zaH9cyD5EhqpbOchfvFZPjxEcBdmGVdfmeEjTG3ldk82gMQ48+0zZZcrg11CYjiU3S5muzl0CptARcv3xfGO6a8Cam1HZAf6AU8JmmqX7hkvLvIow12WFb6cM5J95ej0Ct5f1O8psAzrtDs8gdk4OFBLo2ejm3M+G7C2/bb2j5VmBa9S0GzWt5HGL/hYYu+6lOQ8Zzs9O93bfWmf+g6uxmNtD5uj45TKn1l2nMf9mTXn0cRNemCSmpT5OEz8KHjCCGyVx+LEttogxUn5VA7fXV/vjJjcB6cnJ3yoGfEqZzEPgjgs50F2NprjOsFs1EtjM7gtiCSlRV5sQO3jxvwN36woKzTGNHd/p9PG0c7bhL/+8gt2yebWFq+Jc7vRkxkN9IDgXzlruGs7n1/ZNEad5GzMf1WJU17Z9XwyVWKgGWd6D3dE0Lfa7cP3HxEbphiFbDA1o8HNLpCMYmWbvYSLaYBlFs9tuG4st7gcXB28J3QD3QFiGAtdxiruqLe/Pvptu12l+YGK3rx5s9AtzwqXM+gmN10OQJbS+JJFM2AfS54KVaiV/FiCF13SKu+cIj7Puy649EFm0nZNgsyczbtouO7YBeLq9pbAVGZBXknDf8WcShrmVOGFa7faWPfzMEyVvBGh8QJurG+MuuvXD6vXV73N1ii7B4Xq34vQyc1Fms6EmvQKP0/4yCV+ng1ZIKnxdLMTdjIHlxYNEw1H+A8/fCI6mfsHFzj3/t3tHd83PT05FU/6aEj4Oy9PEMHMDjPymqr44uWXKrHRiZqAynCkXO7y2VSc7/R/5Wtn/MBcZ+sKIYI5z7hx+P4DT8+ImWE3WHCBxDqEV6/37fDwsLTgaUBGCYjDFIGm0EOa3KvJ03P4mAZmOleNC4sOWbQtlO9m6ktxPexenvM91NO19sbURKKIu7u70fJnWsgLxBeXF1W+RvwIY/wCzfZzvOzVKnI6nS0N9+wFTl+XdD6LUVwyLT5THV9wYSLFJi6IKWceZ2ok+pwwR15TYzIWX9pgwEDPTC6z5mjEi+p47pkCZJJXR1bkKZ5cQWmjNaq16gwx/N/e2oapo6zaHWm86qnTdVdHe5oEC6Fp0JY4Sw1MrgGMZ14T5HYQS3qSw1jtP/6wsbEBOkYbjlVz07Lho/jQxXUu1rv88l/scDL6rtflAqH+9JvR+tYWB62khBgScQcqKEx8aMg/MeuVbY+ZDd+NzY2L8/OvX7+yNaRGmaQdAovJiewEdQiz5ZGjAa4CwzVb9fNogjSa57Lu7e1lXSE5BJ5HMU07uus0Hr6+24vFtMzcCLY+o6POTGZRCHDvc0MuijQl5YjM9vmYAE8E/0LHkIl04LSZmFmu6JUl0npL56dTRzVUSvFmPuaxKQczfRRBTwx40YhuYcrGLcdMTZZVAbgy3/cH4t1nH1zx3lEmx0RT9dbmFlzYS47tePl4RH2l6FsYSjeBguwtac/RZsxcaD7ANDOdJYGlBspoALc2sy+GMrdGybsJSAJXfvjxEyExzmrXNyO/MFV04sSUn938yAla8qdP2p6XjE7x646VgdyMAGMy4qTngLWcRiNx+11cfPv2bTrbXUtOoD7DShmNvRIYFMvwy0MVQtXlYQnXc2WFOCg2anypGujV2t/Pbw62h83OXKx29PbCPO60iEmZGLYCn91L7S3xds1pCJmv0RFvyuylr8pwj1/5eOkUqq2M0BS8i1CYFZiJOQp2mMLfxvqezz0ynTPd8stbcfopLXM5jjgi7QmBZZqBE35A8aUp771jDJft7R2297q4uR8+XGLKYwe42uKE1Zw521vJnouRS9c+g89IlW7LEnDBNcDtgHHW7a6TKCkq9tw9VvsnrHbxtXMbqgMCckBE/eqkJukoy50CiCwQ+HGVIw1AgcMySwjyycGdPJJ/HHLLjyTeRjLq2Njc5HHat29fDw/facLlf3leABd2nmQ39/JYLw8SRfIWMPFO19c3XFMuEc8/iVN/9+79y2tspkUrF/3u/e3V+x0WsQ+Z2soKiHHno4GVkVsAQp1u9+zsbGm4z/dSuEG0QjYVWy4RyV6e4R6/DvHSiEJKF1VPsTTr0oD6EXkZcLHEm2sEtuYBM/fwPJdn6fz0MO9lG+gH/HeE2uOroxwznemZeRqvHQH3vBTf2Py4sXZT3nx3rK1q7VlV2HvBnh106QTv28mzOZ/q9MRKc10CvkoNiMH68IAXlRUu9w7ZYjUAI1b7D2K182aLxLxJ2HoSJAMFQ8ScEnpsFqpiUcROVz0aCOFJTrO1tjwoGlnk4VBGJEDsV8OKXOSkZLRWq4v/3bjrKRxtbOJ3//Lly7t3E9ju4GGe8otrmdElEfd1pNgin4O2o2dGTr6nw0NRXhgg+zoUIK0c1BpHZ9cbOztvNmn1ZDFjk2opvqvMpFQWCV7foXSeKebQRWrH65Nl/rf48zXcc3WTWzFj//HpLrDBV95Yn0gfDCLKa5/bYZihTbDsQOJkup3O7fX3Y77rcnvLM2Ce0KMzonGwRNhTmpGIaUy7FUuKYdVvzt4V8JKFtHjBC2RLR0CNRzKFVZZeCm2ZXWog0QD3C++CYgdr4zWpiKWUdXv3ww8/6AgZFRqeNdql50bNPipMn5UUINATNsr9rrF8rtQKGflTKXG4S1asdg6qxfNeX1MJFVozHG1uid/9y+cv795PYLtzg3PfY7zysM6X4EWmWafhGsDM0irjYQuhDqzcopfsRWog1aibu8FlbZvwmLXaaapqHlleEnh5bzGxGuG+297ZmYfGljSfrwYWeTyVKafgGFNdgFmmqph6cW0Z+hXDzNONU66xzE9rsofEWqNmHot312nkFgH0vLeKLbK9xegjkxoH7jzehru6vuIxOu5FXH/yOu1gIHH2K6skoCZh9031Qm2scZ5QkvSMbS8ZKll9CsfDC414B7u05p0qlolZNMAu6djBhIvQnwt8ZtjHeGc//fADu6zy/ApIZT9LkIk7tBhkA3nE8qZA/iRpXezKagcWwgKuqiRhDyHj6vC0Y7RjuWsnPG5+6Ekt/yTifdgYySM1+b+5eXGO7f753ftSAR4QwV5nOS+v07xEjzvtOj8/Q6s8b0SHfP1Hv0/M5bOqfrXnlaOrUaP79nB11u+hltcgoUflgZ8LJH1JR62l7/3n0oAcORkcXliLcho6r+InN9zDqSjWzPEQMayJyx6JzcRyFSAwWxTUTlxVKTHHHdcdrw1hu5yuHq6d/5XX3SiR2lFNvt+UeVWJW1ofoOinzcQLYPfrUHssGx7rY+Vj/bdabdz2ElSgIu/tQCDN8Iz3JGnfXsq2M4EBN1stBKOlVEidPi1/lxpINEBv1OtPws8IL0sqwhRdHYf0J4lrX2fVqm12ZmvQ/QMksgY1SUlnVXEy0gWd7S6lavsZSajeKVVAqI6qwISaJoMAkoKhnOFMAT/EYcsqXO7EIZZ7g18O8bufX3z+/Pl9CdsdeFrEfUpoHW/IGOGf7YnmcEHPz85k6BkO0BTPE3Z2dtGS1uSzbVnFgj/0ese9nf32ZfMRrXbawH30Im139oRlV4nlC1oVd9NnTm7ehrudbArVVAqokMLElXpGewLGE0uaQXATeKZm0oJHbD7T217ta399/fj4GFc6X4aLNMMYFmJI0BRQsMsJ+Y02S8+jOL1ubq6ZSJlE8QqyfwVYyspni0x20W6q7e2xSphnV9kv7Oef/xyjlqcIJZBCKILIq3OcEjKuaJl4+Rqgc9KRccTS8aIP8enDYrVLXPs6ce10UdVP5ewfoindx/SNEWqOGkxu8YmbQxXY5aSq0SVmmQ+gur3EdkcATdKy08Y75Soun9B3bidls9fFd44RT9AM8e7nn2v43d+NdRxgr7PExiFNk6x4z+ZMc9XwcsPzENJsmY/oLP4Zap5NGx5d0LMrvvS79a4ru+U8MnNWiY/M8XHYMXQQgvXCDHduqNhg9jgafQlcZhyD9HwyXhFl4cZTmgoiyj5aOBX5R0HSs+1srJ66ycx5vPhPMOjZFV94aXSb4d2bFc9ZI5l2c9tDjaPAyyLePrbKuX/Q7852ux02xxAPYlPemUMMDAu8/hgnKJeJmUMbTZZbViBdk4iVBxHAFQNZZsE54RAULzOzamCKazEtS/2Mm3kXT62xvD1S9EDla//IlEzHUwtOeh8+d2tFqzMY6s7HSM4dAWgTNxK2u/oReGXGgwATzVJAPOYmKSQVCAkV6S73oyTku0yqhl/ZZgYgXjWVHxpF3C3vqgLw/sM7LVyWsi5hNULbOSCa1UAe1lOVsyyRV+7vH/AFoAc1LKzwhHBn9+kijCMX7anUM4YvV/l48KZb+2Vn/c4uNMegVFuNANUSXBxqTFJ0TPxQiyPSjJIwkjzHxfyMra4QvcBwn2CKmwC0QtmjpBZIlKh8ExUyr858VEBiZhlCAgxADEHsjnZ6eskn3QntDeu9XFb4SWYyhgbFy4x3R0ff3xJ16Tn/jGXPZH1POHKP0YTxEStff/CSNMDassfViHXFjph683tPxGwyETErfhbalSRoFE2E6UgsE4ukARXQNWq123zVKCUXPe3+Dl+7Z7XT1bTNLtcefGYAOgAAIABJREFUu1EOsPSvPWkyYgRTJx3GdBoV3q7KnNXuomWwzaXGwqqkYEMZMTRlEmS17Q4ta72bfWbE7S7edjm4BUgb2702ev/xQ55Jjs2Pn/72Vsw4IamF0C2Y5De4LyZBHAtLQ9gnWz+1020krok9+La2tya8A+cmo7paYxvyxACj2u2geX55fbB5VN/IxD4+lnDmTnksdo/Jh2iZs9PT5t7eYzKdKy894MyVxcsmjuHO2BAZHiJFC6KJxZVsdgWpuXpGMs9EP61mo7W3RxT7yfWIvdI6q7el2h1tXbl5M7tfGKaSb9lnBWB8wZGDKc/sjtXCV+cx8XGUYtmDSzmRAO12C289tThLm62Wqpom7NW2rFxjsrK+3hKruQXTAIs9Oo+2dH2zlU4lVvunj+td/21UbbjjbxdDV1sh/q8qDVoozVZNx+pWhrj43JV9Dp5ymUu1stqlT5mxRXUvfoS2pi9oZPRbqpzYtg86ZnsZHTWjvs2E3MqBDzIJ9nc/P78g/fHjByNHIF2N2wFrmFsHYMMoBCiZU00sCTsGDEnYopF9rliqIx1Xh8cdPBLBrT6LhNHmjxFlPtXq4s6HdA5VVHq68sPo6i/vdvP9L3HcSoV1nTnO63mX0jkZHeiu3FDPuyVL6SvSAN9EiJntFVGfnkyFo/X0QjwOpjR11ubOiv84LY1wwR/J1vG3D1dHF6f4FTgiQGOLUs3PmRHYL2wspRQAI6YE0NTr8h5tziF+SPlcvLy0xth6fo5hcAMK1jxZLH4sA4ZdajF4COzhnVpq8w2FVGNyuC6LF14DeLDVl8vYKCmRVduOYrVvbEh8jNlDRqLbxc8tkGJRq1Pya03shI6fAsGz3V0N6FjMuorCyF1BJ0QeTZwE2eRQe7tLrLu43eVA1OTDTNLnh2yicnbKtiqjDx8+gugY64QyNtZ6D72ZR7cU4cmyuAYuLy+15ErmTYx1QtWzAk9GdyGh09dgzkI+1DrHF7d7m99aO4nVHulncTGqFVbeBY/zeRGlDBfXV1dsrPwiWrNsxKwaKAiVmZV0Kfxqb95SLBcEKDPRTSHXS9Fep7nWOTjg3fmT42MiZwqs5FJKSqnFziQF0fClyOYAiadUtpjwX40Knmlqu0eZOmLZ00yCaCHG21SEDBNWy655ymfPp636PKlHTp4DQDOH4bL4mWhA3pbuqX3cxUWNpcgvdqRY7cS1Ywgnn0flamtLkl9z0EhS7te0WZdYBUjXVn8Ua9tdIQk3qVA/GlYPN8Dam8PEv8MCaGAcL8NenVhPDM03VYEa8V0o9aoqqXoTLBUzc3pyBquPnz5ZocyZJQk0iDejpamquWZ5Q523gVEoa2lajS+AbbALl8pzFefFEj/vbzzcXrzbTn8P1Xawx2i4dFx7vNSXU3X76MB4hmxbl+fXroHQcPcG+soU85j3cWVCz4mQ6GJWfcyKP6emVUBWOdy7N72106v79eZQAuErOazGCHGphN6kRLQl5Axx7PLtHMcJthHhN8jJxh3YfETcIjumD+5YtSU2H05/aLVbRBYxS0GQQxOfVKQl/CNoQMzW+4fNLd5+ljUYF5f9ST4qqx2T3fna3WVUxrO+ntJlSblfkVbnJZUcCsKa7GTEe05WAARb4mXkpJKuTKOb8BWk0oRJ6ArJqsADEhx0MQHBAa987+7DTOpVVTHj8bufnJyCTdOA10T4VYaUvIAre8PP7WA9zC3DJxoIwueTEbIaajQODt6QmBvP1054WFv7eny5tb+7vakCq55OH663kaCn8dGr0rL4Nn9ppCcF5MktLp8pH0o/qeRL5pVrIDTcp+vM7u6pXLqXQFC0U4GGKiDxPLTZbQy6jTobYHFs7hw0VitzMyy+Swa7ByE5MIbyrhY+e0x5wn6YqPQHKTHx+ZqVXucQgY9vRln28voK5iGBxtq+zyO4LJ+TBlA7j1N2JNZbXoHAE/zxo/jajdWurwsrL3ZckRFCrGR9IA8JkcrZwSYflxQosa3FYgeLQRxobYdLVirAc9Z7SEMIK1jNQWOKo31lSJpD3li10TKkZIsZvb07Ifz6UAmelbFMYEN6UIQboTXq26s8YaA/hzxnyqFJ+j/RaPyShjgyLndpnEmnkyDfPgzPBjtv91mNPcb3UMuLNuFzWnV/ladeJeR0ZpY8O+JR0tJwr/JSPFtaoeGebcYTdu+sMM+mRLRWgeYqIPFsVJYSFMuVuf+q37w8+31zY5Pt2FMAU2RfRhAkrkR89hx5GtA+SMwabCZsR6w4LHu+4ollj/uU12wxsJjkxJwa1TDrSWjvbx7BZfnUGpCPp+IDXlslOOqnn34iDExb7Urhxiz2vNtSAi//l3FE/rlD19msssjlR5nf1HHTAC6edjUCWUvewgsl5ZWnwHrYDT8wNZRQAUzEEkk4PNu9hg2P8ADbePfhqEnUjATNHB8dU/XhA++qytGoy9eX+BwPTdYl0/1ChDdK0STvjiAM2uTgQxDVrgemk+1VYTFaHB99X9v91/fNxTLZ9VWge+CweA5XxLudJxSXWDUCwHDNlMYzN3Vp+CXg89BAXYby6TvS82jknKU06qtAixWQmHNbH5E8k/Rm4260u8em0cPrK8x3HoLPwh+Cs6A/F1yMdWx0jjy3PVYWaxgCDLDtBz35Ij0Hm22wqz0mkarqYdkTtQ8kHmG2ymGqoOqVKLCqC426UDJWLwpEw6y15CtLYnbKMxAV5aQinfgxFrL0T61k/1fLE1W+RpDJ2bjcxWpXdjvgas420TICId2fMuWeV4z0pC6l4qtXd4f9lV2Wh1Ko/O7acld+d/Z1l17BSx18kEgzJi+bRY467TY7tmhpsbabrSa/GNztCc0pqBESIH3y9pZ97WkSCuTdD0LVNfHl7+Nr4OGhd/Kwsb2501lbRKsdhfAs6/HV8sgcN7e2jo+OGI35FAkLaoJnGEwKZZAR4qkPPc48tRQvi3/5pdvLavesrVEz5qxELP4i3FxWlkU7Y0kQEY4FwNYQtc679dXLSfwNQWsgpUwkKbTuxQDglWTQAjrkKHi4jMK1ca9/iUzAoSXmWp2oD9ntnm0v1dc0awC22/I2LXE7zCWvRIclm4m/GW1jsl9f3/zX3//+5s3B3t4eOpT9Y9QfpzyrHavZcAHCOlcE3DuwcclR5CJljLc9gZFKgVGRNAqWnJ5KNSnSQtWSEgLkJZtIRoEcdADKV4l31wkWIEPsBlkB3t3drG9037w9dJzls6n9Qa/f6457zRqadLC72ztxz6/J/kt8L5mQAG58mDqCy8RTaeDi+uF+be9t92KuryvM2DomiHYr2PtL9/IZyS4UOrfDwZs33C94BPC9MCYTMIlThZxsO1urMQ6zI/GCjcORW1gNaJHyybX98i5yKR0sDfdSarITnp0/SyIVgFXSaQvov6wqDAUx30d3lxfXOPfYFYuSSZuIDepQsvbAazblnVpcAvVy+NEIu7uuUhJMHhhYHGiVNA7705PbwZD5Q4KaiO3GFQS6mmN6TKgSnNMUj35A5aVnsFzRAK3ndm81WyfHp0ffjwn2WFdf+eFdOnZDlVeQJcxdmc/hryv0E77OtF0LC/BVCIwyt4UIZYJEse7qLuuj+2lDSsJkVMANeTDJqh8ostbQtLji2Nk31zjFJcocML6Q8ObtW7+3wJ6lnHzmYCAAPiOdpsNcXFyqfkRfYmfJTbyJsqSJAWfRlyWPowGu47eHve7w1zfrVzXZ439xj53MG/+RbleR+E9rLXKPcK/p2809VtVDMcPx+dm5uu8ZEHgyJuMta2ANXFHrF4rMnC7y017h8RpeGu55Oko6RJLKgy1ZXhmhkvxeGhj7Wxjve3991L/d7ohrsHwjiwevKKWlNZ+nXjSPFc6htYpTeWsrvccws74+MO951H55coJrFp8QRiA2H1Wk0TD+e4w84lP1k18oc+TxfV7lBIJjltIuGovkGKZafmxWAj/wLl9dXd/dHbENKBYuMDSbOHj8zaCgHNSrV1Di/JbZWFzlTjWKpJBFXYKrfOpac6pKW9zw1zztr8LnJ12ujGjoKAtAPHoEq/CxMUkNB8AjD050lhgqjGoNVx8fhH17eJh3sXieT8QLz2e0eFxlvv6Ir16ialRgw/7+Pq3LQ7fiLs9PpoHbYev0rn3YPl5rPdn3UMs3/vT0lA9jl4efBdLdg7MQ8XFT96hfVTJtBopGAzeBQ1G384Bb9fLygsGEe5khCG9Cm7A2vAnqoZ8DfszE7O2ds7SVX2HkrbLRS8PddYDkUiUpVzl1okpaUwvxohAZobabt8P6kE3oGIz46iElZVrI+KXMnuwlyb2jogakQOdilBHktcBgk2mzTNn3zYIgVIxFDDv2ESKsmamFyGbmGA7xRvNyrWyIScAJlj3b2xtbVtHOXsrF0i3+5p60ouk/7UFEGrK7t8t7AzSQEKPrq+uz87NvX778/usv3Y3NTz/+BAzNJ7SGUJPTk1Mc0uiQQCQWSG0CW9GR2gMeMPSATryOqpzt2N/KBNczN2BY4SwPyCJJnw8GIJaY5Q93txJNzhsPm9s7UFJWe+/9hw//+Zf/3Nza5JbZ3d358U9/YsHMwRVkpcHei5eXV2y7+Ne//AWC+hLDwj+Qhyf4UMYz/+XLZ17QRQl45Uveqj6pZfrxNcBlPV35VLv46wf5slKp0fXxhUxxJAgtVfKMshUOZP7UxL3Jrdft8j94AYD7/1Ked9XEsyBjCJdYPg4oL9Iv32WaV7+p8iK/WsM9UGKQqeSyVU+xErFmIVJVk/yBZXp5sADw2GHc4MZrdHY2uo0VPtVeeDAw5dRHm5Yrp0BHMbQ9n4uXw3xZjGmwuiouIHxFqYgcqxwsCSxNDM3b2yu8tPzD+sTWx9/Phpek+dfpdsQlLIdMV8xApCyBJzsjNjarv9TDcsaERTgaxbMI+jD2MSuWS17CHg4O33/UDzEQHq882yziocae5mBzFb6euH9woF340k51YOTr5imCAqn2fIGY/MN65sVuIsi/ffvGRwD4lCkLIc3x6ury8uyMzswyYGdvH+1Bh29GvXnz5svnL0z2/OeTAvDl3uEXvnI0W/fNB+oODg6Ojr5rNyfrAajyKgQrAsTF6McaoIQIGAR+Mu0vGU+ugV6tdXzZ21n/1va+hzo5mcfGwOP+5s3bx+a6ePxypqZAUEaYnfBVbwYN7tab6yvcADKu1mX7GuLlcSswlvKHdR+QmCWjHi3OQmCJ+0oM93SfS+cr6QhzIVqJZOWJPEIbClhMbPNicOxhvtea5zcPa4NLAv6i/j/dfvY+L6+IXNu80Nkubctvn2nexK2cROoXCstlxazkv4vpTDVU26wquOMewxWblokHW1Nth2O2UW+12pBgZgKXqYvOM2/3EmITIwQv5dMyIiPq3t4uVUyWWO0AyJSJa/ryAre3W14CRnDqL//8h7L7BRebGTN6S97gNDv8aNudKtftlfedvWCGehmr41x5jbgxUBuqQlRWAeJ21y538lDrrG/4n/5FJMQACBVhu3/+/JnHHYiBPsli/Tfv73giz+7pX798/eOP31k71ZvM8HVePgEFeVAtE/3D3T0PSET05fFMNHA12Ly+PHm7016t9Z6JyEbM7Z3tgrG3VFte8cjMSMIeA/z3FcUocXcnwXJXvZ6E1nFLj2rsFiU7ZZV7YSmqUQrdeOWzW6bLa+CFGe5poymdL6+YsZBzJD2W9+wARdIX1c3OOUPBu7eLOXuAIZF67WGni6ewfXZ21tz6tL4WN9B5hS7Emy5XLKSjGZHWYJYk4Cj53lpXGCHv6l5jgpkAAxQTNDX3+LrQFqtEhkisiOyAeXt7h3ZZD2C/aj+TWMliUou3iTRmKLPU1AYB0yFcdnd2sV9xg5PFF87ag/d0uYDacMdsv7u/x/eNG/zTT3/maYKWmbUHi4ubm1viZLQAtBEKXz5/JipAE6HBlLh5UMxyTHUxzfnjE+lioWOik2CNwqoGcKZhDu10J1iGKBuEeff+A+wMX7XrPPSPjo4QgMh29mMlRpb3aK3THcVIwDtbNCIJ3/zaP9j39UwavSEJ7RI7fvIOn6L2MrMLdguPamvfz247O83DHXbdTd7jfy7KZ1eZ/f3WTNI+VUddsJ7gdIivfX09MBEZX2T06PXUhja4RRpkZW/Kmrwvq216h04iqlE9EkarfNyC9KIqrEDkiquCq1Ix7bmTS1/6dL5aAeZLvVpZo9TiDYiXRgnMpzBPgMzNGQVMoBg4cAE+9K9OrvAu3mVds8TmzqcFUapRabOQifzZOlcSoRUpcuDTJErJMQ3hsjhVNyjCF7OV/5icri4VmMO0pI1dZiPsTvY4PLu9JTScCYk5CMMX0xmTlTTmMP2N2B6yYYi5oy0JrOGH+wcJSV+rY7KzBCCcfHdvj8UAvDCqiTXH4c4K4uriooU3e31Dv4QKrhj3I4lNxzR3hbzRAcrZ2fnR9yNpjOz8I88NZIEhbngWHRI/A3E5lEcfCsS8sJ4h6oYvIbBzIy1Ssq2+ff9hfZ04msDdjpJ42WB/b//3336HBsb6/d39r7/+yrqXxpLDGS92e7PBE4Dd/b3/+s//JGgNptJgdYCFcpieWQsxtTvhbf0rPadvsURhYxWSRh2LMCnA7d3g/PruYG+jvnIsuHNnOKmA4+ELXqEZj/y0EBP0hHKCzu3ycZtza3Mgh5thGTMZMPESXJyfa86MALJiHxE332XMSbWP2rhFX65xQKUIlsYzgHNTz6SCTA//XAz3yJWKFE2vhxzMx+CRw7qC4rj08dIK2FVMIitn7H5LQzXrNWaf+/vG96OzTqe1sZ48+8Osr1jECsil5Z+EZEwfk+D7sLPI4dN5onQ14jMtKeMeN7Nx3UW3ymGi0gc28dnp2e3dHe5n+0LtAHMWI5ZQcvH/d7GY29DEzAUFTzS29sbGOjMPdrzyhuP7fri5vuYl0bcfPir3uaiQuW1jfZ34GRK+TcycpeZNs4GPollTdAa927uDg/2//uWve/sSU86qA9MZIxsYooaYRzvd9e3tHVggGACOl/BTB7xwpBGNQxP4ACorinans7OzCzwedyLNyOJflyD3Fi1mZdEgmIfVBs5OVhSWjAgvX18aDHmGgBfOlb/yxAx9dAbUHKX7Y8fR9+9rO//2bu+UBakxiqph6DPJkSNVPDmGI0DXLXja5sBeRaKay6dUVeKKMJIodwb7TSWzrR4hGdx4XoffQg9BjFSMLQwd8vm2pztmUU8JfTxGw55SfbH2xVUaL43hz1r2eJxmlTQHP9KASFEO8oIXZxsSu4sEimjmN602Q/np/Xp7eKTjhk9OTn7+878seBsnES+rj0mwl7BTaUAZ95iyYvsyCTm3U4oYxitTl36mjBccyD9+/wNzls1kcI8LsP1+LZY7hjtbnm9v7zoznRmOXVz4SiI9OUXZz2rjW/8yd8o6odPRrkdt7mNXQ5OVA4Y7u+kjho+OhLylen5+BgXSVBGWc3j4jkKCbXa2d7DNdUt5FPDt61fx9zMVP8jrthxMwBAkWubzH3+kDXe1kQ6guNx8jsv0gmhAjx04Sr/fre/uvGk3zmb2Y2ZblgxQsYE6Cz+DCCsrqb4do74sm1wDyTWcDFd2/2LYadR53GcwieKrSaTN+dmZHgMZmtSzxyGrf4x5/bRwMjaPDj2tPkTQsndBiUY9leGe2/zcihKNmQbksflNI+M4nEgbIkXjqDzH+mwz/XsDe71Tw2tZw2Tvrm/t7qYjcZ9jk5cyPwsNOPseAx6B6YlstMIWK7t7O8SyUCLfJpL4c9lShs8Z8XonLm1c12wwo3zqjfWNjf/861+JosGqxnp2v9pMzyqBcghq4qlaeGG444CHHZY3XHD5A0yIDQ8EsNRDp75EqBMts727c8qNozaSQ4CLiyvuJcwjzHGJlOFtVL683mywdGECVoXq/VcVu49ZT+PYklK/CpySZ5ldBA3wPdTblZ1369epqz8P2bIDdYVc9JjPIrlCmktS1WtAYubkgeHO7i4jjKPP6MHjQRzzjHNcRPYDY2jiqR5PKRl2OIL+6U/wjsQzSUx9F2QbnahvPm3PFTW3Yj5yJFSfjHEiQhWpSDMiRVVwel40skqQp/nd7m2/8eXr75/+pdOusZv78lhq4JE0wCSEqwX7GJc2U5R4oNVERBp7GkuaeBJeYGXrdCLFnUyEtWOCY8rv7e8R1A4Iz5qZw8RHfneHNYyDCnpsR4MljVdemfX8EHyvPuRkI86hz3MnzGgC4YlABR0iB28OmCMdr2yCaZWAmYODN1/VppAagOfgvK6KmU6sf7/fAgYZxOnebvPRU7xo7HGpIWkf5bxWy9IkmHSznJYlT6EBusHR4G179fiwczODl/spRI/xZMznPx07VrksW3QNMETwnJD/vqCMYwxcuANI0F0Z2vCA6IGOsWWma521gn3GC5nOWjWzGO5ZakGjx1QHsHPIPDH7yluUbY98nLxyNtMQzJNiMe4QLV233nu7v3XXG52dfiZwGVN+mpYucZYamFAD6gXTPtYtMd8Y6+zNQockwWzEwbQk30Lq9wlAd4Sp3dzaxudNCeYx/3WslwPwExABHU8VpMRnf3V5dn7OnMeXz+EFX3CJqufZNBlsfELTy9hq7PtOIDsPuxFGG99Y/3y0hecD0CFaBuLMoNp8J07m119+ObCQDEvI3HuQN9WWhrt/sRYhfTeon/W399e+NjovytKloy6CepcyVKIBTPNUFCLXl1GOg+GOUYgoeT7eIUY8Dyvrazz7w7gvxTrPXCmFnA/0uL1vrOE+ppVjqvObWX3NAolSVeMiTfKKvGRVDH06s5OfiML8+z1mzVbjbvvwHZEzV6vvtpo37VXz5Rq/3cv0UgMVagATHbsaqxcLW95SbTSxZZmEjOmO0519Y1bk+1OOKZB8r/TzH58xkV1hQYIIFlYARMLc368wjb07fI+9jrMKFBaocBITnEO5JGWXGLaJub/nBVw89MyDzpWF6c8UCFMtLRSUf11/QEr4E4RKmA00mT5VqIxsaIPbnbUBjeJRgHabCa/VFV5jnckxVtDgZdW0GjitvR9e/f1we7BSe1FWO/qgn0+rlSXeM9AAIwzjEgeyOpueoZKxiF/GJfV8T97VIcZGPeTke3zJBmJzb+FE1k5Wmgk7L4Y7DMfwHFOdFWLeJQsnUIUNpm1cw3QL0/kKGUJqvtTLyZonw4QduoAZX9aUtq6sEJMwql2d8pX5u2terdNjQQHismqpgak1gL2MrYyVTMdTUZviFsL3zgQj/vaBeI/UO6CBD4UXTNm9kZdNs3wxUJiloAkuljHmytbWJusBdnXk+6sEnxMcD5Y23EnAl4fNmNL4yHnHVBFMdoDR9EUYJQ5ysQTgddWb6xtYsJz47bffJRinhZUvn1Rk0cHtI88IJHRHgu+JSeUOYipFKm2444eHLasDUDT95e+Ta4Cv1H2/6O90vne2GQarG1WfvGFWgOUq0WriFZ0ZYRiaaHDyTHJUU28PyRejZUMbHPP9Po8tGXAZZhnQFrSf5Nk/ORfTzBYTYuUQm1PxQgtXVZv9Rpq0X1QVG0NnjqQrljS+qJhq3sFCcsKt1IZ7O5vsuYf3fbD+5736sf+6jANbJpYamFEDTC2EnWCIi+Gu3O1CUAxk+afj11vtjm/jrq/j0pbY9HU+vaTe9cSnfnV1iTWsotjxx+9iKPsomN14v6/7fe1WT8ssQSxFQcCQ4nC3ANLu7e1jyf/44498o4qvo2t3Jlxu7265j5j/WDYQaS+GOyyx3VstPvKq33/94YcfaB92PwRBpOFpeZb5x9XA5XDz5vL0cLu1Wnuxzxgx1B5XqUtuC6kBNdAxLOFNcAGxjEIcuEmIP2RY48khQxkjHr+MdUA+uzGqzpi6QMPqAonyCJ0y0tpIUSWCzItuJcJNTiTbnBKmvDw4A1HjKnhuV/G+j86OL1eGo/7euuynMbk0S4ylBnI1QB9TQSN19oLks656hmDC4MC3jbXBy6n7b8xrnVBhdlnfWOeDRyenp8ORfF2VQoLID9+9K36nRTaS7/Xju8oIEd52TQLWc8X1KghkZ/s2ZaPLe7HU8Is3/frqml0j9cbMTH7QpRyPF++XvX17yATJXIjYbJXDpjQQYZlCeI58g6rOZvaDh16vQ7R9u839KNiybxxSZ29pT5RlcloN8Kmbb0cX3c3Vw21Gtuf3PdTy7dae1/LwS8jXowGGFw7GGfbY9VvNYIUb4vT0BN8DjnnGMQYo3CsMegBzgOXDL0766cyUBVXIvC9NvNnx0hllmQvRGWWaG3q0saE1j0kElAFUJ13PzXmwLft1fP/2bWW1vndwWJcPkSyPpQYq0AC9i7mBOQAfeWe9q2cCcf8oy11eK33o4bPmBQy8QdSSJ8D9j99++9OffprEFhnBQj0UNnsy+qKzRCCLhewXjk2ziCX6BWH4SqsOvwEF4/vm9obpTf0Xphw43dnUnbtJT34Y5XpN8uHjR2bBLCPuRJp5e3tD7I3yyktgDzohbp7IH7bKwaZn7oSXcGQ65buwCzyJZhu4ICV3vdrx2dXhm/16TV54eNkHj4aWHw142Ze48tYx3jLOcPiUGZ142smYqUdpRh7SdC2GOQYrRiMf+KnS8zfcoxbVUzX3afjGVRAvnUXC6inOIs0C4IYK0TaTL5Zfj9/v3bt3/WHt28N2/eIvbMNnbtHQ+vfRl+mlBspoAEc4fY8Pkei3KZgPCCMhRouFIl81Or+4OBQrfvjmzRsmCaaGT58+/eMf/5jozSpogss2C0TdREUCQJnIEwSuaMOdXd4/f/7saMKFdYiSVwL1aRclMqXJDjNrVLHYIJgeC575jwqH6Ce4s/DR+99Z9GtJow0oYM1jxEME4WV9I2/EykNtHRQBOhwx9KGFrpAWQVJ0XnF2hQ422vzp4xt08PKtdhrJcx7nlpnzdV9OCXNW8JNYYuGUAAAgAElEQVSSZzxxL79qQdT4I49G9QNSAIhyxK2g7Xj15PCxR56ZDXff9nlSdS8S87hS4qWzyF09xVmkeRa4qMzXWnoIrq/WPrRPHlblo5Xcn7y66lsDaWjd4njps9DGUsjH0ABGLAbo1eXV97VvI8LAsaHFbTMidpx4ktud7YODA+YARGGGICrm/PyctN/xxkoJTeAh7Van9HLXMdnxHQc5jmsmnonI8oIpgqkQFwkJRQyxmx9YIPRlqSFxohInwwEQTik+AYvhjnlNbIwy3Kecz+AFEQ42kIi2HUXBneZwkGAjfGJy2NaG138x9NWyoa8te9qOzEjF/IqcTj9Rsi+jkOYf3Xa21re7DelIr+Tg6VDJXZhmVog/g8xMbDIC7p6eDG2BoJ9hCxg07HAkO9poZTLs4FdglFP7BOCkkO/f6Wek/DIeznWoKW24P2FfXaBOVyBKXEHx0gIyY6uqpziW5QsBYLLPtCSuTe7Sw8NDbsvv3743D//3Tu13fRNGodlOP/fIMswFXVa8KA0wiLOnAeYyBiVD/N7eDpHrPNLhsSxZzHXsDPaN4TumzVay0QFYPOr55z/+qb9XOoFGRmLo08OjswWBmiwesFzp0hPQFDN9lYAW1hK8HeseKOsZC4OYA6b6EMO92zk/PYM+hn6vd5knzEQC5AHTTLVeWHNSZSGRE31ykCB1wfPv21v1rJtvV8mmE9zmrDioZBHAfjjauI8qMEt8YUuuru9u6odvuoTtxh93LKzkMwqWCl+ekdqiohdMNlOL/KizFK/NPPdbTCuacS87+DDasGxmbLm8vOAxJFle3Aee4RHgCm/J0HCfR6+Yujs9A8RcfeVWTN2o6ilOLcpzRZzkthF1s4UfbwQ+DI+/fP1Cen9/L9rygitTZNNrWo86ZkbFXxZWowHsPyx1POsYivQ0bGW+80UEBw7gXr+3u7fPtjCYzjATg1f8xWJTAo/56GYyxnfMx+Pjo42N9I6NxVLyhijLUgg6UiG8fFEVYeAblo/JIQ8fXaLnn5wcu1mKFuFkogEcGL5mU8g12QD+ofcgAe9rhNOQeGLDUa8otM5pZ/KBK9todSHkSqhDAltpJma8C7WX92nrdQx/5mJ5+7bTUZvfT/kYwbKd4/l776DVOH/bxNH+xMqfYyNzSJ+envJudE7lsrhAAwUzWAFWQVXRrMZNlzNGFRB8NlWM/ByI629ow/DC8MtbTNj0DEqkGVUo6s6woU092Wfj2SjnCQUt6uJFddOJXD3F6eR4IVisg6doSXP14f37D1gqn69arf7J7rbs65ehEx+qsnApxPGWvUOIc3DVy8SjakCb3vihGY5hTMQIJTs7u7j9st2DrVOw2mVAl54jkMrJy9Ddx+O+vbvvRMcrjzOevSD16O/KyySgnddH4CiGO5u6RB46FdGmLbRRNrTx+jymOUEpau0hDMU+VnvDYO8O+vLImEJ2lMe+LyK9AHU0ikMLjDiE1kS9tmiP1ZbEO93fH18csThRu/LLC7Wg8/YtqiBNobLs8eJLQI4+Hq2V98P68f3228YXIm8fjelCMSKscaHkecXCFM173EzcGmHAaoGq8oa0ApTFqqKxDAgcOGh8yQhrZLex05NTqgg95Bf7nqj5dqeL44NhyRtxfTyTfqU3eUQTuUVFvbCoLpdgYUX1FAvZvaZKHX82XYtxN76vPdxcr3z+/Md6d30rbZ+NvWzxAWgsmpZWkEuCjm1eXJCxaC8OYEJ9Yr3xjilD7dXlJbaeYK+sEEPybvPd2EvDIIx9Lza90iJTl7Z6MXMlYKPV1BSwrfFtf//+TXzbE4pnLg/eLCWaZDUF9UshPuNGCxOTadPAljyxxkBIPgWFePqxL5burXz+SfzUEJQ5hkh3+fB4Q3nZKRuxxQeh7pPyKinSI4NpO6PRXCWAfn0zHnPPmkl25X/oXd9cs6rhVV2w0BuXWJ6oqO9hoT0se54AoDHmadY/orji+blUU0dno3e9i39+2GHvoNc7oeNxf+Ntq1pKc0ugp9DAJH1+wtEqtzkLN+1x6zPOy1DvHYyxPPfDMc/Ov4wnDBKU8O4QHnoZMbyX71/vfe6pK5Uc01fGVKeIlczOhWhJ3q8FjFtixqbybIv/bNv35cuXje29zS53XckRocwFziVVBjmvaWmis9DK47Gw5TRet9clyos6GuFbfbh/wOZmxGQAxQIjEpo4deU4L0+ohnVGFA1fDNDSYNeKxx2K/T5+F7Y0h5aIORptbm7+5T/+QszJpFcJE1DWA7a5WjiIaDpiRD702jwxYn+b+K4zuc1hymCjStyZn//4zEIFOExO9njh8S/toC0oR5uhzC6iomYLm57IeFy/xRvP57K0Femua8srOldKXn/2pbPW7gQzsS8q2mJKZu13d3eD0lAOq6l7cdU3uXxUsRBiM3zZQHNFnmNg4qdma5+aTrML1tF1c6tzsrM71fdQK9VBVrzHLNmxC+Opmb4gZUytg0dAfBI1TzqgRvUwd8m536Mb2mC6MIbzWA8ARgnG5KXhrq9Q0XUtqote35KF86Jbkv2rA1P7hVXQam4tjqt+68vRt412+k6bgcF0HWLMaDId0RlasUiorvEuEZNOPb2VCkZGhkj2NNS2tbKiGrymjIs0hle6TMbcHnat8q4KbeVxH/aw1dhhBhesMrDxvojJe3Ozt39gTe6yLKAMTXwyHqJus/xiFxJn3+104e0BlCJOFDsr1Q8fP/z6y68OQS06+lCDqXqqKx53XELsKINPmgUJMe5bWxMuERx1myi8aBZo+vOcyWcEw8eGjc531/l8VaZSFajd/fHTE5ODq55VFmmC9Og5TNXonCzbffKugoJeofT09Oz9xx/X5LsTJZsTDhclkeLiTl4aMp8cvwiDt73391tFENk6mu+JVJUyPJJZlsuS56uB2TvIxF0De51Bg8PXGkPB6zTcx1yAMdW+CidKz4vuREK8XmBG9gobv1G/3zjYuri4/HJ8vbX3vrtyUSHxSUgte9Uk2gphsYR4/R8fBv5OjFAeSjJEbm5u4e/UgLIHQlmTKCQd5ogbwbDVz4jF2sVfrZhSgvkFLIVb21tnZ6cM7RosJJDkgOSAgkIy5cRd3N7csoO6SI6/lg+JYd6NanhtBRj/fn/ACoERn0PwqVPBpuL6VUfCIJNSH5AiRlNIWdlw4eN0l0M9woUMljs7QvKNqXUWJNjutDdDaVlQ2JnoDOptX7a4z9MUCucKEoPz5fhio3W3sd759vlXLop+gUHedW40udCgA0lwIAcdTHqFOcYMFxNbFpZuqfMY5nk0Sgi1ovdxz6OQU16CcA5mUfGUrSwiKXXzEXYc18rrTYx75XSfBcFquga38ysx3Mfoa0z1LF1ijqRnEes14kbfPJtNERLlvFlbOb+9+H56fLDdrsqpP5tUS+xcDWCgn5+fYcnizcRVjJnOxov8epZNgDvOamcy1Xe4SwToLuOMJzGZOZTPHYeq2ndcjCoE29vb/6+//W19PRJFjRHGGgObDXaQIpwcGx1LWm97gsmM+Y2hzMbw8pqoBEOu8VIpid3dXb1gIFYHI/7HH3/413/7N2X5Qa93J1EbdzxkuGUf9PsH7Hs+gJp9vEDJ/f0dT5lA0b4fLEViPJBKlgDq/VQJcxePe7vZbl1fXlGVp1Knk2ViCg2g1fve6Ozm/odPnxorsrnn7m5ARjqXOojEopuxwz0Hznt5lUI9U6JvmM/civ+efsRWObKq1FTmPV9NZX2WEWqFZtKWQBcvK1NGCwUtnkrzBfSWVU+pgRdpuJfq4aWAprs0cyQ9nUBLLNEAby/NQxEEKex0atvt/dPTE4zC/f0DvFzzYLSkOYUGMFK5KOzTgh2DyYvFub29o14QrOQudURcIi6jtp5VnbXbJcS9j+Wtfdi4zLGfTk9OtrZ3fBLYymxdwzf7sPdljSA2mYRRAKMRsaqlXatsb9K4OL/gHQwJn67V+AQSTjrCYwRsdYVvPP37v//Pv//979+/f8f4AxiXPME5+Mj3D/a0OX5zfXN0fHx2eqbdtE4MoXxxube//4+//0ND0hzc8LL6MIa77M0iFiH+XVWlZXMUlomqNHB8fDza/PP7/TPCr6I00TxXh4OrBgCWLNuSpiClC1r7nsAcBi4CcriyOqSKHqavMms5iNCj6A8QhDJHitSk2VnxM/ycPVo06sLVwWUovJKCqTVfreaqpfZKrl22mS/DcC/VJ0sBZTVUsmS+1EsKsQQr0sBc9wtjRtP0Ty7uzlbf7I5+15NfkUDLuqo1gDmCNcybkdfXN8zVxB1gn+zuGsO0am5l6SEVbnBt9BiPO7EroyEed3znuny92yWyGeGxfx1dVh2Y98dHJ2cnfMd3lec57Di4vr652eY7oMm2g8ATH9FqtkbD33btpnh43Cl32wmrTUxkbeC8knwA9e72/Oj7MWsDtISEnXaLHS2J6f+P//iPDx8+OEmQkMj7NwcHCKNlo0reBJB3bJmIJZqIg0IeApDECoSaa8UyUYkGBqOVb1dr21tv1PdQZ5pv5Gqp64VgdInoQx4tM0Y8L37wkgMdQJ4a8bF33qjt8+ClRYgVPYfFJAFm2rKnA3Bo4pU0uQwRrQh+tTC5KAUKW9qSuVqTigLNFeIt10rF6pmp9jka7mU7Ulm4qRU4dwZTSzZvxDm1fL4j6Jw87ild7221R6NvJ8cXuEX3Dg4ba3PSVYrtK81iE8vOLw+Eql/Kq5nKHCF8KetlfEIFqa8siUUr/2vipMbeJcidpwBss4hgmEfEcZ2cnmDH+3Jiixy+O8RZ/vtvvzWIdfAC5X0w0tjlEEkV+lm9JMClil2ly5VbVnYXRoeUsEQgHuaf//wFH/yZ7Kz3xndhIgmGOfHTcNH2GeYbaXDB1v52Hhpg29E01gysInzuy/SMGuB7qNfdfz9c/zvKn5HUROiwky3r2m1irqKIXH66Fqs4bkD6Fn1AdaR7tbBclWdD/T6f1NUfrlIBXemN7aJkpyik702BJSgVjtDzncGmbN9ToUX1SmG03Am5VKFTRUFi2r5eQLLiquKrHDCbADTAmyTzGDwmkWdesE/SzrFMZ7qp5xDjHtc+ttH+wQFmzelgb3D5bXer2VidSfI4m1dZiqGAlYBzmg2zMT0xfwnPwNZ8//69M0kXTTH4KfmyEp2b/9bjrt5PJVRGWRt0FV6WwM+N89IXHtP5119//fHHH//X//nfIKpY9Ns74mbUBuFgAYxCaDivumIwkdCFupxflx2y+4uSAENKs9CI9EuseRXzQkENxX75/beNrS0Meg2mfzHJiIff2NzE/6oNethBXHDUN8yhrhjAccDzDvZO8dGX6ak1gIaPenv11fPDlX+yQpqazpwQueiE03DwCkSUBfLTJeix93f3bE1NPBXPbYjjYplNsBb3L5uuyqsR8p6JfP2XBDeFjsyJEswrpEPmVT1e+dgZrKQor3i6mE6Fr01hi2O4T3a9JoMuebdEwR6PU5T9XAufXdvKCJx7C1e7q8zYC4Ozan/1dLDVOD49Hfbu8JtO7xMay+z/Z+/Ntltnknw/zvOkedr7+6q6uqpP2+3uZZ+1bF+f5Ufwlb38ln6IPsOVe6geTtU37EnDliiREimRIulfZAIgSIIkAAIkSCK3NgjkEBkZSGT+EYiM3OkMaIJR4qJYByUwPWN0i0nGeS26SH3qbgBdbEBEgAyAlwNtEafdQGd01dlM6+mp3pjQa9LeWrX+9cu3L5+/YCeD2xm+JJycngJuUJ3Lp2jlQBBCEES3eXp2RiTafAI4m0joi6PIUQIZ8uLAWsTT01NqpCilBE69vbGra7vVemm3eR/iC8CPv/1tpVrTKNxqCOxhL9Fo1HmR0MAdovCvG8MrgVbDk5/lBHxhqE2+gVh04hNPEngbpHn5P0h8yxejM1N7agEfmbC5F2c5lo3WbHn9LOCnCH9E2iUr1lb0YvX6xye1HktB+OAEKZ4cOj+BsXTq48NUj52tZZti3Ex0Ptozd270QStaRQIR2BaJZ1PDgTc5e8u9Yo9aa2Ur8uq1+C63zZTFvDaOKuWymWd9v+lU4vSogZrz4eE+Wfl4VBqmEobKc31MbFtNTNV4QVHQUDxjgNRRRTfq4jJl25oi/LLoU2nbpWeCobVfGYWu2UpDRmDc/7FBEp1kCouQBBzRropAyd/vvl9/u0FhKVYumTTLSwm8w+TyBfzLqADCocelRyOMbsR4BprvfQHoOJBBnpx8+fIZYA2sJ4UYkBBkAPTnVx/AQxCh0tmAVh59f73RgKaVqilABH08fBJIxRia2zePjlU2Plkqgcd+pd+5P6uzhGBT0/RSHoPJoN/66MzzyKmOKp1ZvSviKofQBeer3XnF1J7+h+Ye1QwZeBR4KtjHl2Uh8wjuafy8udGlOLYI2Lps0WQ2r+LZoDzWMCJ4lYbI0k+ZyXvg7Wrd9XnjboXcwTcseIqqeeE/A/gLQ+m4mQA+Oz09GyRG35vtZL99fHICytkMK5GslSkZ1xawxgkosFDIo1tmIt8NKaGiBkarQY2bDgiR/2BcjmiyOVaqladWa8rAfepGaWBu+SuiFAH1NnZDg8cnKKA1p4dTl8bWqNKhICtWE2xumgbZUDVGO4i0VMKuQXTqOkxVNO+SirgjIHgzAxt/qj2YRH0vLxjAL5hsPjZt2N7MG/96kQArfm+ec7X0baOO6dT4TckLjZ3KS+/S/d9qFeODsjwzIujzNzfXvOLqxwr4/vLyrFX1vFRyiU0OC7j5MoTVPdSw2qczQ5Nzi2Z8skQCq4gq/Al+CfMhJPuQR1BiCBa4+2jI2jG6vn9+OA3hzodCMoC2BUDCY9OW1rh6j5dP/FQzW9PqpN01Np3onx4U39+zN9+bufrHg+zzfk4bIE4wHzp1jhiMMH2C+fD9sqvzqFL+GYuUNWrX6nbkwKJSUAVuPb5++YLa210/klz0HN15NKABiWNKhLW5Xu1KhnR6wqsMeIXqlPHwtI2B60rhesCKAhjWtwxgBE35iGDyQ0tvb27YRuri8tI12TjjhAReRtWnb/96fvkxlZhY8DCRKb5AApPjNt+LtOaeFEA50NxRSPRYAiOPUtu/qj0P+EI1wsUTn554prThGZOF/gClnjPjWXMkGEe6koCadpGz8xS8gMTkXV6QcSuSZtHHArYXNN03cPfEwJg3n8XGBFY422TdK7C9vOhKDVup8HLeAsuxgM8F/dtePV9UITJLh0/RzsElXefCc2PRvp+fHPZ6zZunZHbwdHB4rPxuz82/AwmowRC+rK187TJ0a/sQdgtift2B1rloAjpvraimS40tZZjGlDvIBF8YMHBniZ4LUs5ZUIQjZDTezsnEikm6mNrL3OkrYJ3z2pVtmF5eZFkwLcKxDNQ0QQPfsL4QM/ees4txX9XuUSFAzd1DO1dM4osTNz971HKXTWWgnt95+cSETd1SSrqjAsoJCxbU8nbKkMXSGrq3fmro1TwCfLviaxm4Hz85ls5evzBAeWntcQbPUvIn1Pn9ZItuwYKmLwXuC8oul8BKhZeTX5Zjw9UvY2+ldP9t819yJYZDLOzYotknl/HXkYm5xR0TIDFL2pHuwkimjfMcfkLKX67vy/kk8D25Q9/EmdtwHMHeLoA8baeB8PFJAlhfKJXdTAQqK+BOfxK/GUpHLcbltJZpTOv50P8VS8thh6OAwBbYA0BRb73kmAdLFvTlvDJh8TLvQXAsaEXSBF68yuUSblVRZ8I5yIY26BcBQAsxvD8IIMoXSOLXKhufLJVAb5D8fv90fHKaS27Mom8pkxvOsHDgxbVUUOzJU6nCApq8JwPf+egkH6BSKeze6PMwQDmeR1aW8PVJkH0qzfNOZAzuEY6MVOt5vZk3dy/uIgs72OKia061A3d/bTUYXqlwUI2OBBNBNWaWjs/m+Sw2W/9Wxcy2mlFDKdxnU2jY9CO7KJNjmgON5fJiYvhYLHa7b3edfL73rVavr2lcW86atxzIFm06oJAjUxSSLqBALhYi5U/dW5OCy81UpYC7QVGwu/yJgpzbjaB4yQHHr3LroQ90sBmgO3BPFerWTHd1h6xOUbDHolY2eAKykM4lhsMyFcuzI8wTMMhh/Ubnpfvl06ff/u53TmTiuFkJJB9fMNQ4vDjBZ2eM2mfl4yoGHQH7ALvKGkQmHiV5R53/dsqTAaYH3OPMnkeGS5A9MYB4eVL677zm5jDOkdFSbNgA+qLj2PnPr0EIPywa82b2xfX5HFAXE12SCnCHWbf8us23pNIgkiPEShDNcabhp5F+yjjXvmuxgjHmhqVik7JLMqnkBXWMK5/MVCzmi4luJ5FjfVWpdlorOX8ZGBePxhmmL+xRyvpIgCkzEyvDUMTiLJ9pKRoMRoYLwbRsDgU/9EHB7AQkJrN0MgHawPE507lvdqEGfY4LKFCdXk4w+466oNRUEsgD01/4J54maUSisLuKSGDzI2v+WGf8JjZRK72KTFW9q5eDUequNapWDxqp1tIBZleF4NAuHpZF3dmhRLVadYjdXBQPCA/1gueaB4SgwT2PFG/FjKV8pbRM7cH7vBjIO7na9YwnC2r+PpdtTgwTNdNemjMRtQMXq093Hrs6Mps7W6zOTMB3JHIMBdw+k5zndnouYNa0b7+rDXkLxDzx2LnK55SpxK73pVJnWLq++aVy8ruKTOTRCliRtlpParoRpI6FaKVSZTphiooWoxHjBukgIUtG0l0UcOcXTVyxWLj/fr/ijkXqFUBqmRcEZ6tFpaugQ4CFKAXFzFdAORp33RnAWFRNuzg+PDx0Oy9xl5h3I+zxKIlfSn9zXP73TKptj4/PvaJ2JPb09MQWB1skOp4RAkCWIXQe2zxf8satFPYcsblnEGYi02gefT4onwsgPkNKvoDZvXh0hew8gnF8FCXg9XaxTowiXkuto+VR5Cm8dvtprZ8yIbVgRVYmcG9ILApZbYMYQgWL2z9u3uJ8MMYy2VK6Uzo/aXcfvz58K9eO6lUXCxbHNQTcONQ/4DAMMPCfzGSAYc8O+34JWHY2csyytqnU0LgzIYtNeDKB/4pO52XB5G2jNPd0qcYdBuQzfTbL9D+XiosE1PawChF5DVZNUapRAe3AiRZeLVstjNzlA0OMHhbK865/mM3kT5M/YRa9MGOc6EoCBwcTm5e5KhP5TDxEPGjyrKnAF63ZDwvq5VkOPODAelbLMNFgeUMMUx5KegPlDwZY5eEVSsdEvukxg4skEI0hYymiWdSELU3z02Y/ZVYUzxqqdF/FaghV3EFuILhpntEwK2utmKpdXd0/Pt/0TuuJmyLbX5pBsgpMMq/5tZ/bor2egsb4UttqtQFnWjvLEH9ycsq475XUnuZfdCOMNLGUUdJhWlU3Tr6ndzvdIg4xFhVfIlFAsvF6YBHRJ7ZLAe6ZDMvm2EF1Cbk5ycAITHb5fC+NUPZR9BlpkdK40xF5xFAisydUqVxZpTlz6l9jtPFEhlIjKxrv30+OMtfsBhpKBXtJFBWD7By8f4GnkkC7wfc8m3q/NgcxjBJ8IqPvobbXQwF5QPaMGxTUKJ/iBEYJCOrxRFN2oOYvSoYNa0jyRyIuJRLYBHDf3xvnp+V+yvjr2+uryR9/CxGqi4kWi2y/FYddzln0R43qaHT39NR6vO+gT9J7hotJgnP2RUw6igfUhWElYhGH3MORGL2kko2DBgP3IlpxmkcJyLw6ecuYJgG8GJ2oDZhk26kV346oQAjOZ4z5EswtM7TyZjM/46IUugczPUq7l2eM8rMo1elClsYdHrDXPzs76zw/y2uJj266qPLV0xwfgjlkJ+/XnEx+op96pbfnl/PDx2RyNdTupTV+GA27DBIOtAl8DAyiywXKU9gy9EQ/Kcier6aEeeVEd/POnm5Y2/fJTDYuGatYOsvDriNB9rjF1JoC8sjYNjW6zaMuN9zP5DWf3v6mhD9DhzYCbslN89N+P2W8imMddXjlaYX8C5pjDsVzVRErVBtCUdgdN4YhEReKrPhkU5unxFkj3cpnx6nua9dlGJeB6RjAoOIFhDH+Evj2uqKdxhQbprynoqN+uVCsCxOXtQx7cJXFIiISAkCrW5CW7xssPhvvSLqMnFO6OXdSk1GLOsH2yrwUkC1+6yhtRTpRWhRHQSZydqZpDVpCdyRLHWiMoHQV1CSOBo/P9EOrtUba2n9MpqyKN8wRd/l7t1ga3Z8eVSye/J8sac1M6/3XpEoGTW9FdmaLY+OOR6PZeI8xS8TqkRrZIy84W5NQHyid+9xXSl7I0daD4Pu9vvaHoy7fWTfLs48THYYCxge9oQTUmF84EmyVxKcBSCA44B54hw+gdRsh4UcQfsq4b1y41N3zsaGcZvPbz1FbAcaYbjI3ls1sjJgLo08ajZ4eB4eP918albRLsx/GWeynUahj+Ih+l0rQiZZK5UYjRHtQhwaMW7ePZ2LJbgbuCHObOoqtOX4iWPEZxOSOh/hFXlzoQoBs0ZytEJiAWQ+XzWXB5ZChWUzbGrbLG4P8ScDYXbZx3XQYC33TnFB/d1R9uGY/1B/T3vdD9QX9gm59APRmhjvZrWtZcJFFk5g1/l5Gej3pAQhuGaOuZbSM0NJ0nm5R9vBJdp7anpd5tUEVb/gMOHzR5aSHq5y0fM7FWTDaIpxgamSPaNAZoT5iYIHy0trjDJYEfI3jsYQt+RknfiTip8x0vfOvw6U+v94Ip6C3jhh33m4SQ9tBpjk8KDzc3z8ks8fHZ9nE21SLQG9doDpWjMr6AiVftVoplysg9XhknJLV2i4Fqs+ouWWyTSaZt3it0l+lV+FHQPOy8syjqZy4lluWcW46XQhdW61UF7wu7As1hRp4F6F+YYE8TMOqyXPp7FUCxgH3T91Uengl+6HKHj1ew9I765WgY/7w0d9sO2ZjZlhzkUXKJJMsrsgXfC7emKl1uyJcymhxowK6/yy20buwOXm459pekSEAACAASURBVKM3oJ/BgXGDMUSOw+FbpwO+Z9RgGCSJdVbiKkedk48vAHjc0jY5ixuwV6nLgHsgXWI3JepTND6LLZVhWHSXVrw1GdjxcWt4nc8oqIgvwthZPLxl+8+tiyO27+k+Pj6JwkKZLlRrNaxrGB9jpD5fimtNQdU0g9vFvoQZDht33ECsDNwX6dqtpjJRMjnKYtIVAjOr6NvMIK8BJkg3Ne6yTBatm5llr3/7icJds3XUaOQjv7NSGBPISl3NY8dZYLrtkdJ+Zl/9/ru920xMjHgTg96kD35GFB1ELzAaYXMPsudMRh5c5bz3+XpMYCRjdyuQPcY5YH2Gnf25cxl0I0ZrV79xuy82nzLyWWypPMOiu7TircxweMjqpa0PjGUYGDabj3xcZCXRTz/JQqLf/vYvgrVT33oxRakByopkgiHGXPkbDblruFDUH44ncni5YAgXd5DLiigHlKxtXppxESEQuXohlKGHEwHuBEiqCB3JDMr3cYnf79Bsv77nDi4O3pKJPd0PNcAJammvBd5hBbjfPW6zrXdzt8kzL9v4DjOwEGiMBvdAc8f1aWB68qC0B8q3223mQa2u0oMP52TgnBMsSxlpGZc0WX3crLBWrD2D2icxTGhjM20mqQfhsRRXrGHri8/rZ0sa5rPYEqrzu/3SgnufYUs17mr1Tw/32AxTrP7Bmwfh9HTspZEM35vPmYM/NBJfJ9QYe3/HIyIAh7FUKZTU2mBs3A0HDqtwy2hDN8BaAD/O2N4w1T0/P6OXKpZkhQOXuCQqV0qrD0qyptac9/hl1lSzp540gPIKzosJjUOjV2ngdpUdJlK3LazUzg6Su/CVz7vwjf7gveDcEou7LqmMinMLxwlRkcCCYWHxHXZogB6H0hkWw6fxcO+QQ0WhXAC+s8qr12PvKrWIFgdbwyHrixiwSIIOyB58r4A98F7wPWEewSjEZ7QgxUARvYnJK2p40atow0XF5gJ5R6EZQfPg8575LLaU+7DoLq14pzJEz8bdWbwgMEYZLCiAXCg42TODvSqPjo7ngXLiz47r/feb79/v0N0eHR0x9DiTjmM3IQFzWJ1cg6rWkjLKMoX4niT0d+TmwwOGoE+PTw2WMB8e6X7Cwgbayl68HOlR4uKthwf3ZKfbKb2WF8xziyXEREDvsixhxL+b2SzVTA60ZlW9/mIeIp7a6b4+5f/6tPzv6eRTxFkNnj0DNGxgxqKTB9+cmGLAEgipYywii30gRvIY1CQS1ZnWCLBlFKXz4CTg7fVNxi5U2YNBr9/nVZBLbYyvkb0a3cRMR9D9RifZsbWivUlskyjtMaWhd7jmW6x4F1PHXcTxZmvtgnB37r/kAvqhEF1Q3+4n8TUtmo1kaGA5Ka/++GrkZR+vHXj/qNXr6ADcM5zNJM7Oz8FnX19K+ddPxydn8iktDlGSwOQzLR75QbncfZktvAd06nRpIDiu93/48Qc6Tz5fcJxOmGlA8OjdqYQZiJzN5kO1WqtUPPslZJKDW8vcRpvKSDuEf+OAxsp7a3ahBMK5b/VT+dpF6qfN7JGyKSnKDdfIYGO33t9DtCmB7WO9DB2Ra7ZwRM9B286n7LnLJFBki6sc8YOprG/YSXzQ7/WA8Iy3mCBiap/NysZVun0gfg3uw+uTrpABGiEY0o55NXZniGZpABOORvNbiONX6kIrFZ7Xd0MhOq+yfYz3AVNCEhNwB6sGTNV5xeczH7WUiiXYW33XbmDZB1ar5hrX3x/zqT4a2PDGjpCEsz9kFd4VM3Gv94i9Dx+bzVK5fH5+TlnZDNVAzYuEh1ELmZld6GajUQO/11++fOFca+UXlbSlgU31h2TimLRgXkC8agkxjGFK4y6fbDXEtxXd8VO+j90PThq562Jh864w1ydrbrnuAPq1bX0VT9dE3/Y6hcJ4HNYmAZG251u0Nu4WVgTQTWq1/RIP9wB65S9HdqjVLnGZ3xkJ0cqpXVOY69F6yNImDe6991mDT1fAfapN+r1Ja+VB82qwTjBUWzhev3tPlYrApdfneoLllQpPULJdhELURj8+tUkArGy7Wuspjy61d7uo1dm9UuAO+k7+Dg9Dscss5LMX+Sz2NtfX18X6WaPEeBFPUmu9464qG+HmRYA7H3Nd5ecujkY3Nzfozi8uL6cH/WWTImWpBUTNkbJ6Yy8WfqC2Z8nENLUFDKkxXxHR1GxdCx4EuQufBA80F1S3DUmtfqnbeTurPaZSc7el3IZ2eOfRdvO9Fw6yhA9TmVWm38i0O0gZhkpLDQih1rBJ4ox16EQIYyYmP2eqEVH8YILsGYkF2L/1WLSWlS2oZYdaBk6cjFEckACsZ5BnW6t5U4OtmnGF3s50D7ZwPIUZuLGxJd60lReLeW9Eg8m9aqWrlndsRShEHWuKIyckMPdD2ESuYC54SpWXRiZyHgUBZ/U6MN2wPw6mjmVUMKK4uLjojkrfrn8qFfP1emNZiTh9bRIwRgGGe3H24m5fJKDJl8+fgewM63ZGLcMVe6TDuQbutlc4asfVEt9/P3369erqgzaOdyhoi5oavZSNO8kyCYyTxJZmX4ANLb15KZYTd2eNsk1Ou3tqX/oWpVZOPRRhszbu7R5r2pcHw1ksvsXmTG6LYhlsCQoPaNSdZyfEKf41uFdHsczBGBJ8z8sABRn80bnIiwHb3r0PAgDuU3VzSdfUvVOjeVHvqEwGjsfqZrZMYDGr9oxVyzs2JBSijjXFkXMlgP333LSVE3iueMYwPxAd6gA/TQlMX87PL8y+v3IFfgkUk53i+Rn6/m/NQaVWr6af/VKKywUqAXNMYKh000kwj0E7/vGHH+ZnNinOYVOD7NlMIJ6PH3/4+vXrwUFjdi6ZIkbtYHITryszfSOHAdWhz9+e4PbuqPz9679dXrEf6vQcPCW33bmMoKGyEi7Du6PTwKhJfvYBdMPhfsN9NxLahTyMrgTVElyOZee5EEDXEwpwnxKh1ecMHK9Gdhn+mQM4ZxJQrFrZpoovu/T3IExQDYDEBD3zIiy6Jv3414sEglXJ8G0LpM47MSgFwza+avF1y+6l0QtroedlSiuXE62Xh6/vhVq+Vyms48EPvVXbW4HHEY/lpISrq6v5LZYBf4mem5FWALXDqERhiN/ff0cltOzD1ATr8wbtzXxhnS+dwFOYvR565WS//fHqAx+3A6cfIYLc8Hm3OUJcJpb12yjx6p0Xh4d2GZHI3TRs5zbqiWWZwLYpfQPzt+5PlutJ5hFi9CJXcWK3HMf76MMOtyQYKrOEw6I7W1Mc400CYgS5wt0BqWP9gl7nvf/OyjuWgWJagO9FNwYG3hgNLXetnKslhq1W5+vDc6NxWCrN9X0bGgt7SXg8hQq8pg+qP+mLIGmiFgNu3g9xAnN5uQC1G1JdTMdUk8+9Bbgc/fr1Szp9ov3POOdTXI+fI8cqdx229xP5u++Ph8flQg5xjO+us8S2N1Y3bkva99J5KZZmFhhsCfNh9BF5Uj2GUKUFcR8seWzBvmTfAHCfEq3uK9pljZjQqHurb3CaLdw5UzccDcdUQU+XKxVeXFOIpBdXHKd6kwAIiXtlBxVzxymVANB/eX7G8xOb2lA0h5vGXA5L8YkFKN5YiETuWo11sdX2W+qmW60lbnZbUxUFiRua7klW6IditrhsBSerI+7u7i4vLydLO1yp3u0Qb0Up85Ul4+jFxSU2MyyNULaYVtHxCWzbzWD0y4D1HMlYqB6yHZ6jW93Ba6pwflxOJXbU6kzPaNxU676O7390zxjTHJgLb3beKuE4SMYpakVpLRPJHq1Wd5JukHGbB+6TrTF6ju4BA30lOiqJwK2OWuTKudjYLA4rdsHFxFd7iVhCO04OSQKzyGaqkwCSWu32O24aez3th5U9tEuVciZzOGZpac8bZ43uGaKoFUbVETb5r4+PjwcnV4VMmAtPoiuJ9XOmvK6AblG0Dy1NxVw28At0djbX5YvWd9Nv8QjJawAmWxYh/BWAsPlMZMawsCnNOMrCJl1q9nEgJ5FUR6XzXxXk+4D6bGARNk92/XeUSN8+9UvVw9PUju6sxIDI+LadQxxjGZaK6+uDU5OHp4q3U8JLm7hEJJNKgx2VwVIhBZNh48B9yb3WrdR33DSRN1U6CscrNE8uZS4fjEzmUHHF6ZyycXQEJACumeICCIIvPKyHgT4gdYAU2x5VK5UF1i+LlmZt21AESms0Dur10VPi6OHhy2GtWMhMi2hKYvGlHwlIx5joHIwlejixkLcjWbAI2/2ySolsdFE6MCfownXgyw82LWoTkGS/P/jhx98QQxL+RvnX6bxgUcrXFe4yAfqYsMPE4dHhxeUFr6a4GSaA7HUgj0k2S6VPT4+ObojIAw+ztqoTzXNszJZHsjj4aXR8Uu5ldhK100G4hdt8F1ffBGN9PVQ//D7q2+YbNNVcTzLYoXZPicHn5fqBu6f75dwqfRf1dxftaBKiKXwKJ5KmVj6gISgAZp2bEMeuXwIsTmW7mG7npdVuKTCDl8YR3qwrlara65GbvXx8WNAjFmF6Wruc9vpFIjUiikbirn6Y/97N3t/8cnZ2vu22QJuR4/xaNTqfTlddUINgu/GJlQ2YjhsZlIj44we7oy/HyQC+BgSEmzienQHQrMva6FyWzbxUJxO6nFCKe1uulIHaxHCkOGZR6jV1UKmUc4eHeAomhdTBcIBXYbDpC5bCzy+8D3y/u2OfAUpZ/OgTVTlcT/Zm82r6d6rw1l7et3qp/NFFvsN3361txBzG5Y6qvznp2xL98PBwdna2Ldz65JOb5SOYj6WPohEp4r7d299WVyIPG7i7F7grdsnkSFGNPJKitfKcyYV80sVKXs1jbsnPqcB98ThnlCQA+kGhiDNEIAg7xDO4411lvpdG6TXzw5IxYUFhKbkgWVe5hPx8voJIwRrtpPg6urwCsY3qvz/MtjOpsd1FEDXsMQ2HW8+opFzzKu01W2Zb0kGzzgJozLXoq7xSciK2W8S898VAJZkAxFOWj0K5XB7tOm9ZaOM/fPz46y+/EM0lqVB7f5fbx4Z/wDJeUFHX91Ps150AnX/69Lnf45wgeg9APUg9n8uztg9D4WPWW2cyl1cXf/7Tn/EOiZd31mFrmhAE5zujvOneO31tNXC7Tmj+9/eTg9y3Qr67XZy75XZHblRimzTubu9NQPkcxh8XlLezY3hq63Y2Ue5dsMDdk9BcdB2VxQdR434od5PQ4BIiygElLuRFK29a3Zg8+KjDLBr/zkjALk0te32cyeghwrilS0sA05lrUTcCNfCnjqYSRTtjOpfgj+Pj41kl4lKaZgZ7u8y48e8iDl2VXJxJV7SokjErvs+Q0snp6TDxcv/QHL2/sTtPrH33LUyroFZP62cACQtWVhicM5LQeFuwHQ16hw12+Xl5vr29BkPnC4VSuVKt1XL5vIbsUn4y0Oe1jyMGOcvKS2ez93Zt35JJp8Ho/E3SYKXs8Onx6e72OyY5lEI3/z54//DxCouaL1+/8iix3IMiaOh5DZhYoDpFaEoZP5W6VZftXq7z/HrWeNid/VDpOyGPIZu6w6zVWauN+6baubZ6p4cZFxVvW9dy2cQINmtF4O6y4S5uuS1LGEQRveWA0rSu0UusRCuPm0COEbw9NqlE5NTrzdH5vZaabexcCmjU33poKPuoKlE9gkkw8MU8V1m/TNBB7z5xHfDFXA7Neub2r6UloWAUdpPVrE9+59ZpzzR9nkq8nxzWgYPNl0EinT0sDjCbns4UX3uRwOR9kLsoqmvlbkZha0lXivb3629f0bBj+vI//E9/W6/VLSC+rDZlOThZjVORubAaNtC8sxeBLsWnKl4u/vPf/+erq8uLi/Nff/mEqh7szmsGD9rsy4NTXVscRxu/v5Xz/e9nh7uysxKdbnn32OJbxsKMLeZ+N1hfPD2ROpVhSzrkFNcL7tXaGuQSuLvnfEGjnJNCJG2v0FaNFi4LXtWJmcA1M5Wa15iX8CuvPVTaaezBuSmNCDcVSIFGkpkVf+oMBPhoRK3uaI871Qhnf2FTmUK89CHb8Tjgo7A0Rftj8tUopHrSyIjNxuA08XJ7VE0riLmQ1pjfhdn2LFEswuUPsG60HOBLULh9iFTZy5o7xQldlE8cf/mHP+C4/fDgcHqemyc37rIMX0JwXET3mMl+o4a3menTiWw6k2bhMl+uMLn55edf0ejjauaHH3/gfUMwPbbvUNa1TVVhpzaZZE8J8XzlTviaqDy02ye192xxJ1C7ulMGal9ZOCHeuNVI42aAXroajbj0eiWw4vgQvc7sr0E+2jEL3P1V7fZ+h0t9igsvlemVhZZWnklJPmgz5aoP05xoPf1UDVt+6UVA002ls61SfJrcgmsw+svLsyD193fADQp17AcwgFmOIyeJ4qNjMiL6V54k7Pz4eyIxKxF0sCeJZj85ur25zmSLR8dHSczN5oUVK5tHdtvjDbwszRDtAFLSwF0+EQlwZz8vkujeHz5c8XHD6KjehOl89+2SM+gtz2gWkveBEd8BuP70y6/lipjKCLcAdzUqqnx2Lk3S8qayuWDnyDMXyeZbZfjWvGhkk4m+59K+C4Qqr1CJ+25y0AVla+g4RFoCKz2ZDi3zRC/CT4HLdthbAHCnlMuCDqJbGhUiace6A6pPw/QxjlcTlbG9q0xLye3Ux/uTDh1mtuBsjOP98BOJgHFvgb86Tlh+h7hxgpFJZ7SRrh+Kqsyur14K5I7YBwdD0tBl8SJb8/QGqdunUfb9nv1ifd+FPSyITG1QFnFq6C7DCbbjQGFSwfP4ePnzn/70+7/6w9WHq4eHZrvVZr0GL6rWetN5olNfCsVURtPR2TAh43khaVxKqhVeJiLHycYZbxG8QrCoG95qtTpK97vra14xfjz9DQXhZyCmMtA1FPy2CqAg/Yf/upGLK5qpeW6EEA0/DBLZW3YUPqwU81lV22TLgmfA1qywq5pi3lbzVMr2XrIyJN5LLrK3zzYAbohHT49YJB8QqwVwN6txX1WsFvVVCbksv5b69H3UYN1a2KpdT8r0pXiI9r12KU3HbCGLmLXCwyEwvdvpMuErld6Q1XjlSsWRG9+RuOn43e98l96TgovudS49Oq9jil39+vVb6eBDvWDXvO6JfPw0E726OTiIqhoR8yOgGnu8kQB3TZS1p8VS6o///MdKpXJ5xQ5IslsqluWtVgtXj9rnOjHiv1EAtJTSmm8DsgsuHwf8zWgYPY7iTKxzrAjhSwcU/zpWk2UBKzk7z0/Nh/u319ejk+OT03Os7cmGvVSv34d9nd9WpZqaNXHqkBNbolWnr5PACM2v/em595quXxxi2b/Sfqhj6c6vy0wJqVmwsIyyc7oX3s02TP8GQWOaprvr2E7GnZw2lmtyfNoYG64qdn5AnIpuqMOvCtzdN9Cp0X7jNlPrNLcawWvUTpodx4PmN6SSX6to/FXGrI46sN9/B5Rg+IKBLyCmWCx50uP6eF52XeM+3T9DumaCBFN2R/nr20+lwx9rmWcNH0OqbifIKiN0BWYFVAnwlgDUxV4cQKwwrzSUcyzLb26u3956WOehQWwcsNNAnRNswyjLImwQPM4cWZCtoTwUuOSlV98FDcGx99We3VOprgVo9PsDT59s0SQ8JLA6Q5+vC5IKbRZwt1stHKfigDKfLwDYqWbsHns0Ij9LTGwrvyfGAHkqDQDv4wHdzK0eJlK3j/1y7ews1VydgwlxrE5uGYVJKXNF/b5Z8F3QxqUrGpNc20pPnLrLZRXh0bDO45NYAmuSgKsOb/LisUubxRx+PQN3T3w6VOg7amMVu+V4AsfLtChBI3hmZktP75ach3whiiYo0oyqhNe3V3F+NxyCQlhUirdBDRo8tNXM6oaxqcdkC23czdZG77eY7BbPjp87za83t5VqNXbpsOAWiZrbpuim68ofSuskdu0DngXsUqaKa4NdUPj3u+/X365B1elshkemWFS7KBUKlXK5cHyczaIcRwnOH68B+GsXFTpH0Lw+kZihfNEiDjQPrK7VqgcHDfEN3+t1X7tvT6+dbgeYjnEMOVmTWsgXzs4vxEm8UurzFjHmTT4UpHATaWncx0nmmTx0sjLIzQNqltnc7+vrW/O9cVzpZ4NA7etvhyFl47NvYDKfGjmDbpc7Pt3lMnhTOxsEzWdMLzAJ2Ma/wGhuHyH3XXrZEzgXuLuvInjxbbLuYFpjiV3r47EJ1W3CmhVH8pyjj+doZfNSa/DSCZ5iIsF+MXzfx1hWAYkhy0mBGAcN8afupbEr5Z2qqVatrkQuLjwjgUopVyl9eG63rwcfy91/qVbi9WEzMiLCBtvp/zqAgBkDQNnsiWpLl7y84moq5EHtbbnKAH13Oq/ttizUJpCTwHACkVqtxvcrLolnZOFZKxRLCp2zUMQA9LxcXVxe3FzfoJ6nMMQF8meypZK8eQk/Wg8/2QKYgSw863aQ7b0/NpESp5bjYIxnWzFJ06hm622Yb5yXO6C+cSO28cz67BsQ8/abGhDJCTK+Jr4JCrMX8izEIboS2JqX+UiIcPETyAivuVycLfSWbLj60NtnjSiGVl5VKCoypkPlUg0cj9Hoerx1hCRs9HCdF6CD0vYlEuzwgipRHD+bJryhS3lZBSH7cV9W/e6mg/sqic/t0fDbt6/45cREe3fb6qdlPP4Aakry6AkAVsgdnIxenDdbVOb2N3iQN0p1x2p4lAizW2JRBGuWu7u7bvdVqDGSDAeXF5ft5+fr629UgfvUs7NzsZmRdd7Vg0MPa4ux0oG+Jgs2ymTSvJPrS5NJGVEM1MSPAChaLI00M0TuF5ncvR3UMtflwqu6LZHjcLcZWrFnWPOpXUoxcLdLI2rn3B3Grqhxtb38ZGQe2Qj7m6l1I011rtTwLynQ3QjGvVDzHndFQXozbdGvTJML0helLSi2LAlw8Pz8jOoO9MEzibUs3/E5Rvb5jH0OLLulq6SP8EEOZG+/vF23s/XiqJiZNv9YhfpWl0WzDvYVaCuPokK0yuaEyQzszpNDqtVAIgHH1qXLE8YM6FAWd0xowdmArFwpS5WjBN0eyC4wGt28S3K2bGB0yOoITrjkeWcdrY5RZI3hRwN2yRrtSbr9Xnh5bp/VWqlU/IHIdqe353R2RiNm8mVyexoTcxpLwLsE5prKeCe1rMTs07asxF6lq6lbzaxKUFpaKdFkySo2RGEAfQehOEjWIcqhoLeowXDAjuz4lBBbWNlKMwlQY7iMsmrN3kLtkdoeE58HKwF6Qq1SqCX6T6Pj+2//fHxQKxQKwVaxjdTAzBY0R0Q8m3JE5a52IRVkPH55B3+kWHfqtZmMEEIumcxks9TFOxQePAlUhXaZd2lJg6hgcG/oHWbSdeNFAhKQ4vHXxKBnkLWzSxUyaEmSPToK5zB2l/gx3/nn88a2fRQyrNijIMWI8mAZmEWUv/1mi0cvggPC9t6TMIF75MbtaN4mZzHp2VXsVZltzS6PMayYlyocT6R9BnamskKLedJQBwJ2395emYTF/DWRwEUjn9pXoLrJosovxyYZ2J+668nv9YvT+/vv9y+jk8NGLimb+OxzAOxa5iQK7coDRQBtAIWxLreEw6OG1xjr0u2JUnJjesOqb0zCavU6WnYsZ3iK9SITTQfUDn23NFU+mIFPXUQNA0mM4vJJeR+D9CQpyEsM8YEPR5MV+bl6GxXvH19O6l+yta1C7RqyR1Gifu5CeGWU96Sl/W6qx4bHTkw5lkCIEggOuC99ZEJsxdaRdiksRhkjpx5v8OZAUy0czzds1PBKK0+0SlpBEky34qSx/86WK7LmDB1eKsV39p3ZkY73kBXEExf1LIGjo2NUrw8detXwsJzOZfZ01gTN8rVKo1wF1+WAcRlPM88bRx49goWPFQTxJm2rOG+nEOT1mse21WIT+BzeY7iEHPQ1rvZEeoIZNRqh0bdYZYgQyiZFbrBqynjgMlM2+5ts9quD18fLAz4dbM8gEEN2L72GDZhKpaW2T1ZX9ULaIe+eDmUOkoijNiGBFYB7UI/AJpq9iTp9yMuhiD1KG89orTwtUhswJjWaJ5tMogvbyRQLbiCgVGcCxm8c/uaY+PF3bk3MCwm4TrQz7brQOOPiZozzLTkLuFFLaouTRQKA0qPSANOqZvPxvf9K11L2G/slHB4u9dIo/RhUTT8kEAl2V/GiyLZLBHt4+6Wbc1Glp7Ckl4Ko28VNpNi0sLtTEqfsGriTxCO/ZFCYqUx9KzBi9Wc3C7hDjTZIkyQIbfUjdkFTLZKEDYX3RO7m++PBUbVU5R3Ds2A3wLUeu6k41rJ7kf56FUwTD6wXNq2829AVLWbjk4hJwB1wX72XRqzZa2FnsdR4bhdnEB6X57C1RON4y3GN6MwTguOJ0bby5GU7FZyp4/mFNOZ1nM0xx+P7BSRhozT/1F2u+eV9paxeqRok3bbRF49xoQUSwMn40WF9MKg0X4aD1+FRaTjrGmVB8W1PouOxoJNWqI4MplWonU2Q2IW0h1XMiBhgr9U/wdwYprsXEWXxBw/C5iUcvWO5XKL4U6vFbsRUyoZK7OHEqvFCIW+Aa9cCFft4Wc1iBFmZOhBjPTNCvYRYF4a2XdbhRmSl4MvbqD3IXhxXUonWmM3Inhkq9sjyF2nG2KZgnjumSPI9fogCYk9NcgHRislEXAJOwD3wHhVxGQTJ3gLZCYy2VWU/t0Wr00Vp03nnXmunNG+DwcvLsyja+Fg/HLF/CxusFHAdrb5xz33WA+FgLmtrT1DNwVxh7RXHFY4lAJg7rgnyw/R9MEycXVylEnvheQakK9sbSVDqaa4liMp9AOBW25GOxZRIlMplnlk8w9gj7ecUAVK/vnZfXjqvXY4v6rvZq3bQ/p/+j/+klqawj8Ibr+gP379//fqNl4H/9X//31C9f/n8ufnwQBX8ERYv/ICy3fyAd4kBn0/MDwKwIToBFWSEkdFE/cd9ZMqzYxx71pIRHQAAIABJREFUA1c/x9Dv9ua6cPz78/zT6tTCpcDopAfiWMW+gqD3fg8433P2XBSwwt2Ii4YrgYyASd93PFzetoi6GwkuybMk2bUwAEZ6i0SmWJR72Uy2XJ72/aKdz1Gj/ngsRrbSCWQedl3P9mVcjFG2rz3byTHw/fTsDPh31z8YNf94dnqKz5XtbIpbrgHpCqDr/ErjLhs94pE9Df5GWa4A8UDMTlTgC9i3r4+OwB0rlE7nBT09xTgfDvCwngJ/s2CUV/JKuYKTddalANzRe3PkhQGrmeeXzkAhbKB2q9V6bD5qVTn11hv1k9Oz4+NjmJxtD84lLy4vrXi076xXsHLKK4fJs86j4DvR+gXFKrfuE1bpPPRqJ0cn2XS0UXusYg+uazSbTTYrCI7e/lByePBdND6G+y6EFFoWJ417aJXtFmF/3X1CBquTYI4EqbNpIkfmcfHSmEziUXvRp2qzVp48Q9GjXt3E+FYbyhPP5KxWvk6wu+UX4Jgtb8HusI+dyFnq7v3k7PrmOpvLHzaq2n56d1poawkPEwBaPW3yYKkg6na8oWv8zSslj6+thKwL53G2oLxO4mHvdDtskkq8oGPlL0YfNYjnmxJbK0Cfrs6AgKkMJjrpTPrk9CSfL+byeYxYCsUiuBrEL5rz4bD58PRw32yen/3hD3+wM8C5MKC2c6IKTZAlMNjaWfbr0hCxztcDih5ODK6wrZ+itrbLh6fOMH9yWXsxGVtbza4rQjbGyLsxKbnmdWsyHh4ebg2vu8Coj65r9ftdaP9m2xADd0/yn+qsjL5TMcupeS4wSZKJEaVaX0zVBYYC0AHcqNwWWcS6q1JPJdpEXupkVuZAfbQR/80Kx+s8kxxtzVW8AVPUblUmObg4O+mPMre3X/Knf1sffZuCqlFj2Dc/wGh5kuSpSmAjA96lpZlsBt05Phx5ePt9tvAcB/yuAsHB6OMohaTZIdVwEWMk8IwquvIj1irPz6KPHw7bXL7iDjIxOj2/PDw85t0A6xgi0UpiJCOK/r7+D7ZHXy+v/VPCf3l+ZmChHghS9uH+4Z/+4R8ZejCXh1qxWKJOiliaelW/sEFjp0jZWxHeOW9Hd/2jevalVAC1RzLEKvbQbgsa99PTs9DIx4RXlIBGIe6wyERVWw06JloS4EUM3JcK097V6EP2S/v5Ijpu8znRYCJEc8bcyqJSJkl0XKjVWYWDAYxTdhW3Sn0mUf24WMYzrHwlRjuuERAPpBc8b2iOzEKR/sVgN9L87Stz2eT7+dnZ2+jx9uYGrXDwTo02LVgeWtA5D7JiRD3DANtUCjM2+iS4GU87fDSzswk6//b1K5/OyG2PZyk5YFo+rMmLtSQZGUDo6lmsNxqifceIRozRtVpdkDS1yPVgcHx8VGs0BKsTxKdUHyv5x8emvRbO4ZY3B+xkOGFH5B9+/PHy6uqq+eHL5y8//fnP/+2//rd6vXZ+fnH14UoXVKidvBKohXU0UwTDvnzuZZ6HjbPcQxT3Q5Ubxf3BJnHiboYtk72iv/c27lG+23ro88eh10dmlbr8cbiBUjFwnxX6go6yIGmajoesk0WZ+UDqbFCKogsTUuZenDGzr0qpXFJQeTI3V75rmqG0IEI/DXbHNZJZ2bJqjxj6MsoTEzBIZDUlrr14zBfc2Kgk5ZNv5xcXOEW5fs4VsolGoT91o6LCqC8+xDSFJ1n8J4oZjAqiuEbV3e/184U8D7t9GSiVAMEfHx95jbFXCD6mLDpv+dRmBisD9HkkpRrMYPivoLtUPByC0rvdDmi9xA5q1aoG7SxfpSzvDOzeYBHRJ1QNA5z33t4uP1xhQE+NvDMcHR59+PDhZ8JPP3/69de/+5//Tj1R3KuhgH2pdURbrUWrU2TDuKTO76OPmdd/O6/RCmOdQBgV+aE5VrHvUnf2I4mwy7CyCydpYdcS0/chAYalSf2DDxrui6z4oG0HIIiBu9UhVrzfQsc3CZB69/UVxRhzMWqxYqFUyOdZamao0ywerRPfNVkUgjjRMN3SyuN0EhkYnu2YTpWSPoh6gqHx9OS0Um2pJLfjQQ5GRBungjlTMdHvdHs3w78stv8Rte7GWQqEAXA06JkJTB4QFcDfqM+JxGIFu3OQ/VRFLFF9eHiYAe5kMwzlxUpF29zoWVEfFV7XtQlglzcG9P30cjFrgQMKcYIKQDA2unGU8bLUtT9VO25tqJo8b703HFEpbrN8GWCrB7gF019eXn769ElIqSdIvTJo6C61YgU0RTCky7dE6aH9dlS5ztUw3YlSEJHLvYoST7vMizbr2uUWxm1bhwS8PrCbwQdrGl7XIXDPdXi9Q84V+KPCF+r2c5tJj0mOOY+P0bhn01o052qI9VfTXHLBJ2h9vMbxAi5UDdo4XtvYELGZbq44wZu1nzYvFfsGm+SnPVtQplTMlRK/drLpr1+/VGuH1UpxC5hezKKy/NbdX+A2QbzKZMHODAXZjCD4WQInJyfX377xIcJKAocDpo3HyMDtSb7LaZI6mwBnDcmV+1OyM8zoUmB1HstxupjQyP5r/FhVcHJ9fa3NhQXXs8j17Y3vf2B2zPOo8+X5hQplUQ1LX8ZeZahBAjXxOoAVkJ1gSOfNfu395e68UUxGaj/UsZY9pHbHZB0kIDunFkvxYOwgmkhELZ1HI8GldyY8tSuw7rlvwN2TlOfeRK9UmM+Y//iWx6zJCiqm2Vwu36g3bNPenLq81jSHzEaidSfVaF4faY1s7MryvIRsC6UnuLXxxuolFI2LLfP9PFhL75EfomuTSnQrwnSEv5dB8brVrxYG5dx2yxGAzjiAuAW1C24XUxkg/NvrK5Yv0jFnQp5v/8Ui44ZYeamgl5EKQIaUWjbOMlde+LXxDGTJpcC2mLBzTjb9RiDwHS07xjMKWEseZeaOQR6IByiv6XOkOgxj9Aa3oHM45cFB2S41JhJwUioXcV8DffLwEGvOhSEV5J3hfaDd0Vg0Az8ZJtLXDy+Ng/pBIy9WOhEJBmR3uJURYXCH2eAbNa3zJ/rtHlkif1MZGGSgiIO37rmoV+4DcA+sx7gnpDRe7x1mRKa44SCTZv5LsXpGz6xLOrD7apYQilwyPVF7rRnoHqxANN/sGW+HScH0Y582IfBu8xc2V8QzCdMPz/S1Gz5niE4X8kN0msauXpfT3XIt0Xrufrl7ODz/XTE94X1lW1oNQGfdChMYDDOHMQ4ojbuYqoObZVhQSbPN4TPRt29f8eICmCYVg3ggM47fKcJsiOWLFBGv8GnysGiVRwrjGF4SIKhGIdG6Q5tzsXgX1D7UC1NlaSomeqx573asaRUP6M/P7YuLse92Rq3Hp1av/356evL6+tZuP1MRDQG4o1mHDdW7pWX8J1CFGvFC3IDpuTtodd/OD8uphJPxm0hk7QEpcG9jw5i1C96qUPq837B0hJ4lHI/ZszKZF8OwIENcHLxJYEGvTO4qcF/Q5sCEZyfEtMjAwUyIn2NGcDRVWJ9ipM6Eas829zwwfufWEMEEPfZp0xrYQysvYgCPGLwKmg8woDj0Tm2ag+lroTgew8dnnmpyIupAwCd1B0pbF1WrFGuVq6e394fHt6ODRiHV3a4moF/v9QW4g2+VXQv2LWk2YEJpDVZWUN65E4CEj49Pvn39Um9gcT7ELSOjjBpeDMie5CWY73jqXUCpv0HzgGelWRe0Lmbs2pQdiRGtrGPEnQzLYTGSYcUqJ3DCalQYe2o9XV5eUdyC8pSSfZ36fVajHh4dYnkPcOfVA8oUtGWTxklQ1dG2MG4QS3Nvbq6Lx7+/LALZN70RMneMR1If9/jZDONG+6Dpdqr1QdqpiPPj6pRTx+11B5mjlZgvrThliQRCGV6X1BlWstdHaS4fbggxQ6Gr4qMwi7eY8Jg3UavjpRGfDXPpTiW4qWaqyE5fGkObuaRVi4ePz0yNgBEuV7SuCc1f2PhGjs+m75Q0btWxez51p9qm43bgup5/r+ULj53X5tPXg4NDrYTeinYxQvBOD6AW7bcC2cpWJoPbRNylW9YmGgeDibXXFzA3rUNZdXh0dHf3ndWivZ6YpGuMMspkGIXUNz3gOEX6vAxAAc06BYHa+IpRR3HYjtd2SAHnIQ5oF9iOtr3Lhk4vGL1nMjnKsBHT0fFxu9XS/ADQ87kc9jwwwDk0DxoHf/zjH6vVCl476Ywo8BXD8s1MILu8F0gQh7UhKNjg+P61fHh4kt/4fqg03nqYrZOt6Ii7y6R0wAgH94M3jYh0S7wLmZFBj2zei8YlnCWwvcDd04Pg3Hgr1g0txgWmTFFU9fsyLY0SYoFayOv9UCxSi07cVLOo/H6l6cFLq98trTyekMUyQMZo0ccjUfdjHMa7m5Og3HtP91+1y33jJlvmqabJog5XfrlwILVyFB3goJQYFS9wPd4andRT33kMV6YaOgFGDHCnslUZ4cgRQxkCinMGkM4L0Jk+nWJ6E9uVtzcmORJZDJpM5jnXiAQnjHh2z2TTxyfHGrgrkCy7JqXeUz2OZFWGK+QXNC9G7BIEtb+/o1+AIEWA8uR9w7IeZXunwxGYDa5vtVp4atfzq4JAAsIx9hu0nzHBx9kPJjTlShnjHl6iH5tPKOuPjo4kv3Q26qRWKUKlnFJFsDJ9bL/2c8fnVZYJBdu5vbNJ/fqJiNJz4b0Zu1aCXrczTVqli0dSCqs0aGfuapAN2S7gHvDtX0yOeZRZFC+NzEbMFkxL4quuWKpUZbNSDzfBS14PZPcyq17kauF4pb8UyKOC/OgMjrLZLn9hqtcs7zqTw/TklaMUfEQu58IHUacirtnneUTjPhy1Hl+yzebN0dEhKmEnihGKUyha75+qVe4C3nn3Jx4uAdjtFqtC89pzi51vkYoCJWyHdHPzDZt4CgJTwP0EzgWvC56V/2QkSau+DRsZhd0ZygDuJKCEB18D2187om4fDd/xSpnJ5vBdM4G2lcN46CfyCYx57r9/zxXkBQlmMO8hDAaVfEEWp+reoetlqKQ5+iXC3oRVzqF583ZULbQa2WePL7+rVOtUlqbSfte91IlEHBeWBPTbbFjUt4eu+9E67sjbc1enOY0+cHffD6fb5ni9mBxaMfYLxDydGY4JEJherVT5qO0NqVPx4mocOYsjvUtAI3jTFN7AEGk0fyAZuYWyvasVQDzW+c6c2NpHmyavJhu5bJhelj5JLZSrRew7VMhNPqwmh+WTh06m326fNopqS1GHnFGIQhWN5lt6ZQKluwRgMZ8LOIE9jE9qLF7H35IG6Q53Q4YkltCgJqfhwFneVQzgDnpWunZ+oU9Qq1C12l2gtGjc314r1QqlsJJJ9EY8C53OM8YyGbxSCivCD4BbuJtEpk9Pj3jZKx0fo6q/vbmlYL3ekI6WFASvjWqEM10vJu7v7xgNCp73eDcd79HLe77dL5/m73k9ccyw7kiLC4cbtG5e4vrsEsAuzH+X28u7afVluxhnz/dSNrNiiFZMNIG7yx7lVpTzyDGN4WABt8TMMminwOv53Ao7rs+rxi2bcb5VJaCHGNxtQsiyqhMHlIJoxLRpn2/RsrbPTQ964A6YHnDzuDIclUq3b/XR47+enJ5qKLxqZwqg/IRIwcWYwWjLOngWpJyWxam5Ap4WRVsOahforF2TzAiJePZPwsLl5eVFv5bijlEDd0oJUAZLm8BdrFbE0t1YmirAXW2SCrrGNgZcL1YyLy/YvGOUozG21D0D3FGf12p4rRXiPD4PD02U8021nyvvBmzGpFrIQdzV6AC+x3ekBvSriBBW70cf0p1/P6/1NrYf6gKHtarlfhs4c3fdE1qhqPtKtjHnSiZz/u7mftwLl7LZD2FE5cmICHB32Tc8SM2RIlML8wpffmWOGQ2ZONE0ufXSOK9yx5rmZY7j1ygBPZRoF5Oo3rn1VK5xvN4WCv3kAtOaNXIa6aqC7uCy3jjwAOw9K7YHubPvd7fp6g8HpUE6GRn33rq1yRTqatGFKysUcDzjD85lLsX3orxmauhsaNwpMikmrlLZFNu0sZhVIexRFt22MpXhkuJymyzgrvXf4G4GOqVxZ9ATkgq4g+O7ned+/43Rj1KMhNVKDf2/vtFSrfmfUriRsavhsTf75edfqpUKtNgeVVcLWRlPVV2sAsrmVjVb6o3y3596x5VvG9sPVUN2+wc7xBJYWOGRWqHoDPuTPYzkmYiZItGN4OFi24e18ufjXmyzhBfLdoEwSLJSd1cAi8UTcOoGgbt1KwNr0ixFphKWe4HYcKygZposai0+GeN+YaVaZ2taiVxceB0SACcxahiO5BXOAejoLV3tOJ57Gw8uod6P8J4ekOj5+cV7Innf7ABqGyVxcB5qW9wTz2bSGJYzIoFy8UEFbIdDRHF2ft5sPrJ6RliVzqe75niuQ9dOLfK6k0z+7i9/j7l5p/uCRQoqRrGWyWYY0wS4i+ZcjNx1UDpw1qiK0TlrT3HBTjyp4v8Rzf9rV6rJZPOlyun55cHhEeifWqQS0f9Lhfzn+4ARoy6hwEiK1v/h/p6tj2RdgWJWESZR6mJ9baVWp6zv8NSv9DrNiwbfxzbx6hUuZPctlTAKyi2eCDMRE6meLqTPEowffRHqcTuWMPmQ8PpEGOr9MYi7FMBuNTp4wa4NuLu8X95aOEuUeREtEbMVzteYzpgLmR15EV91/p6tyRunce7NS0CAy2TgWsMkY+MnsI+xpQ2QwdzedZ2TzyR78ZU/CWQSvbODAlsV3d3dZRu/Pci9aFTqj1pQpdCwA5hB0UB3ELulLGdvVBAwOmwWi6JhcOqlqt+qAw0B6OtRjgU5yslVV8Ft8RqpRykB+oYKHCAtAV+QgHXp3aJfT5TK5aPjk0q1hm94THXswpFKJh8TKwbGQP9Pj48Y23z/fndweMDgKm8L8q6g9e0C3GEKN53+xkvZD/X7U62abNTR2a8ZtdNQxbV3LbuIKA5TEjB6gL+OMEXLdjlX1kkWp+GO2ZZ1V05XEeFcca1VOGqU8Faj10ZHo6He2rhK7vCAu1fJu23FFF0mDb6R8fGXtSl66pJtwgtFFpW6pTgv31RN87LF8VsigVlINMW4PPxJNrQxgt7eFShPRyAJG5sB74IAirhjmCKK8m8ui/b9/G3Qu+ng/eT9sCTbAGyWYYYpAjCXDoUqAZwLZAdvsy40l5MloGBxRjOO2LhIp1PTkbHMU/Guo7CwQb9I0F1a7GEI4v5RDHEEtCuLHM4YEjlCYaAsA2n+//g3f4t2X+SANGwYXc98St1uSEnH6NWygP9u95WFqs/tNui8LXup9mFbZ5Uq+RNf8u9o3P3hp87bqPn0eH7SSI8fwbXcLq1i16jdV4Xr6VX6dvhicIcKzZN1MlGtrjzj75CcjKbME5djS0PrYauqTR25nYx009DQ2jfJylquAgTubkTns0120kxd6NT56MxMwbTEDFXC8DO/sk5ds2avySezcbGISsDf8GEZwb9r2KfsCATfKDRPf7EyRLTZ+81WPj24qAzYgOHz52uQ7uHh4WblwdClAHcC1Mt5viCe2gVEi+921qpmsU4RvfVbj/3dBMELGncA8WoSGmmvSabKPGfMTDY4rnspAB1ILctfxTpGrHSMtwLzTYaCuktr0xfhhiT+RgmW74PFeZ1gMStGMgy/GOO/E9vrKTpSjkFYLYMVoxxK8THBk5B5pFiZkGn87uoUBq0XZ080VsjsXcW+QmX+iyKaAIPRVQKkuGlST09Pp6eFTXOxzfW772Hb2XuWtm+LmuVthJ3plUtFMVPCdYRFGnUOTtCeX54x5KQ0qh2A+qorSqfYsCqbio8vd0gCenHqig3Ss7xlhww1+o5oNRVdzsHxW/T8ryiNbSmONfjHjx9f33rf3j+Uuv9er27mkzouWfBhxQaoaMRB293XLiBeo2QNodV5iiXzWJtUR1W6lWHoImYxPcA3OF7U6SBlRE+nUwOXqOFVnzOGMaMH2iLVylddhfiukTPR41NIznRQK1zhiivRoMveq4Bzhl45YJNDHHlJ5g/9faVaVbb1Yigj+YcjOFOPmLfdl3rvibuX1PHBaT7TWlN3MlTsa6otmtUYXWU15iI10PFYrdaauLRrCbjvPZHqIsva56ZZEWmQV+DupmnLxDM/XVOXuer9/aUjXs+YDJge0EsdHhwyo8wv6j0lUGLeq49LrFsC2FAFXqV+jA0TeXCNurYcyVOdhvgRedoDb/52ESzkcxeJz53y0XXrtZRsVauVNY8BqKJx5sjgBhhGhw0K10DZGNgmuZHtI5JJdPCJoiBrjY85MjzqEVKfYKBC0AcB8AQ5GP8FlXOTWOY5HGFDn8lmMWwX+xwhKTp1OQrCV+Y1Ym7Dy4Ec5Ff+C2H1bUlutaYFz9jrs8Rfysrrw7gslvTsfaHjpcCy0Hx6ea/+1WXtk/siy0jOT9cPJ2/eW6Jin9+SqKRMdljPXAU7Kj48PJydnXtmIi4QqgTcd5Fge0NojVraoPW0YylwX8rnShLS1JmBUNVg/gItztFL8e+gcRD8aB5ua1YSRVw4bAkAlcKuQkMCy5E81dHj0MeD7AXNi5ZToPx6nu2wG7ul9EupTqmWeH6rfPt2Uzv7fSW1LkWvAN8kA91PP/2MSXj7uX1+fqb013pUUijYLlN6iUpBxS1qcnEfKQeIoBY30hQ+F+SuTFUEtYuyfIhGXHTgnCsIT6qcD4cnJydWhJTRAV259Q+krsC6EFJZ4Ui6K+c6JFkFm2R8LpcA6BJFNsjIW8T7O+Y97CTlZtymyG3/uFLMHKQ+m20xagj+x4LswZOOKfqXgO73PsqbfXGiaKxxnxDH1l247A2O9z5KjV3ajkBaMAvcl9a7qpCogEEeBwuyoFSWarEDHyu00rVanXlpVeqO5UNvk2OtcWS0JIBZwvoZ4inV+niN5mFAdKcax6P2VOdEBvIwr79121tjJZ+oXJy1R+kv9916fqD3RVpDc/TCTTTc7321mN6A68YIxWAoEfQGHWGdECExAoktBA+A5p9OUPjZ0nwr9C1QWuFpQdQozsV2HLiswLyAbdufQuiid+dPztXB7JJE2ILmAnHhDEcDdA3coc9o/tZ9PTo5tWV3Pu30Uk/Dw5PsHe47nXMEEiu80pZYxR6INCNERD8cdoaIwcb9xEXfs5eKz7dPArP3frYNEyPWbPKGY5a2wA37AHfoLCXlv6maNBMGW/WhBer3+sw96NRxQFwuV/ytF1zCTYitWVJznBxlCbTb7Y2zxzOpjWcsrbw8fiNxWfOOIhP4Y7rT3jir+8BANdmsHBVxcfjtKQF8LxXCxJE2geJH/erqqt1qqxcGY8Cib6gz/atHb9EVq/+CqmeCtnWRaOX6iF/pTUY2CoCpVRD8Or4yY+XXgOj6TCWoGCmgkuVKlVQUNGlqrdVrmP0A3ElmbKfbassaTIDYsMlgwOkHog+ji0T3p4s6hEOT9ljFbkrDiZk4bpckEHuV2aW7uVJbfDz0MsxFJSxlnwyzGvcAuIcuGL3z0kHng5dGjc5x0QhaT1fDG6wD4DwmscMSYEFzBFvHiIFOULumAbVzCRriIUFPnwHNYxeBalSpWyPI/A6wxGDVaDTqiRQau3bmr+uDXwr5VTf+XCwWNOA4ZKzWaqDY29vby0vZOVVBZXXvebFTH2IUAld+YwSGAqQVlOZUfuVoXqtxXmC2GSSHpmgwohNUSZUkReXPulD5zVidJlWoPMaBHxWF/no0PDw6FJcyilGF21Hti6t48mAqY9Q689MfJL93sgfF20KdxcGK2kyeVSOMtxwlk1VpxeW3SQKsHvHnh3SbGhnzGpIE3AwY4YxY/hoUDHCn1QzZ2BBjp84JSJ1PsuVKuZQphWX9opvrRtz+BBOX2jkJNJvNiLdJjwwgN+0STzug5BIDMrAcmAStfIzjw7iJvDo16iiBvzXbzw/33ePjY3YmCqMiaL699a4+XPLJ8fjkpNVq/fLzL0fHR/ipZKhUljKMoILdFbKVH7nz6keNslwaYZgYpfD0wmufgGnB02bK9K8J8ulfE39SSP1xkKB/zEgdodgwpizWqnY7nXKlhK282OkohkTfjsZd7aiK0OZ9RG0N6p27f2Nf27D2QxVRxVYxIfXZLSBr7E6wBZzGLG6nBNygTWOkDL2B/oE743W/9/bS6ViWjpVy5eDwUC3AC5NvN+ILs/6Y9goS2OTN27gPb39y01BLTJmV8ATUqQWvUJM1r2rlqz/KcakpCYD7Dg8ORo3GQyfTaz0d1QCiAcN3jElqtWoul1em6pmjoyP0/TjE+POffyqXS0eHR9iOp9PymqZ4424boBoYjTYkmWTcJUm+wUg/kD4hvxpzi/JbgvIxo0/VslQDkUNiXpBKjIrG2F4jeD779N+fn9svz23Y/v1f/Qc6IJ8LsLCnZ0JeLOnFA00fV+/Fkni3nJLqMJG6+d6qlAYsxqXEVOqql1JbDNlXleIOlGfVHFuk70BD4iZssQSmBz+npgQB7j0Adz6G4tmXfTiYcpgUOPJlCjAUOlKn7W7E4SSiOG6NEoj6TYq+xt3NzdJPvV7wSn7xra1wPFAOZTwxJBETxODghp0dzIP8jsoD9nXDjmWQyJycXWYTb4G0E3TMyImBOO5hxDcMWutUGgNCrGWwKuRb/83tLUYn+XwOZIyxeF7MTiSv0qYLjtaoXTMj0SPoKODKQRE34br8qlLGilVSZ4PC+xZeV+nqCuiP43aU67CEF3eWJOE54MPHH+AYeIT1ozBGxYL1pSK1HZPkP7/8MCWoTi/x2GqfHR+kE/2ppFUvDRU7ZKI+8qza0ri8CwnwHLnIFWeJJbBpCbgZrpbN34uAOy5fGL4ZqWkoSpxsLptJZw4C96c+T4xumjevbBwflgS2+K5E08Z9xRslKlnLcY2p7dTmNBx5/HWGZePAilzsYHFkeXZ2hrb4ezed6L4cHtQyhgWT/8YylmJOD8LAR7vAdqxNVOCctZ6g4cPDAxS9dRmYAAAgAElEQVTY7Kjaemo9Pj6hKyGJzZgUiC+QAQdc2Wwuk8EppHiHTCrNuwBXMLT4YjdgOugf8C0HpX2fhewSY2SmElmJBG9sjMrf69sbRbF4wVr97Pw8ly/wfgE0x68Au0AxKcCMrExVrwqibheNzjulmS3sK1NlP9Sn13Tp+PIYiQWH2qW/xyp2/51wV0vSD3e1aXG79k4CMsrND/h3sSfyvZORm3GcwNDIZ2LcddXD89Jor3sxo/ac8XnoEtjNmxEFrzJh3zoN0LU+3tLKY06j9fFDdr5ESZoA74XNyI7QTyWGp8XOoFC7f2zzafGgmgWz+msbIBqojRuZtIbtAtwdAuQB6GjlFbAeoTwBTMvioW4TH/AAZWqXkvggGocsOBuIT5J6j5NfDdZlKFcDuniEVGcc2QcVkK02Q00SB2O8CaTTGSxhMDbIMuyrNvISQHVAdv5xBkFNHPcdUhdm+PJWwKQBbu/DYgbVjmlZ1B+m71rDw3qjkAzOlZPY5oiaP1axI4I4TEmAZ2IqJr6MJbCrEshguciG1ozjTOnse4Q6p1QMeUWpJcv4QbNEscmTfbkNG/Hjvskba9Ztx/FgdzAd0MdC8+B4WdRnZo5/ZyWQTg5OD0rvw+RD8ynd+MuD5LVGybM5F8Tw7fLw+BBMLPsnmbp2jdwnS0kcSm6cJfKqUCjkwPqAZvKIllwFsLJyzS5QXAB4r8e1Ruqkg7Mlo7qmJm443FqwhhE+XyjCho7UGYQBUcGbCntdjdSq+oXQUCwPUfOnH5uPJyfHuZR4IBCNO8C918eEslgyJo5Wu/ta/v157ddUMqAtzwzIvi8j1WR/iK9cSYAO6ipfnCmWwPZLQLQseqXUmtoSj71rEvSCavb0Hqxh59QFQo9IEkDM0bomAwgDLQLdgHogtlh9NXPDMqnR6VGtP7y7fXxNDd9OjhoWGp7JOx2B3ht9NJYkNm27QsOCicd/dmtthZYlD/9Tsu2SAt9cSXalepYFrBquC+A2zizMbjiZMXNYyeMTq5A6EfuTCbYFrGvW1K9cqncA3hP+6R//CSsaltUqfb7YyTy3269vvY8//uaud1DKZ09lP9QggFQM2SfuSXwxVwJ0xblpcUIsgd2SAMA9h7Ym3EbtKVAMV6heqMc3QKQlK+riMCMBjdZwNEmK1s2Dz0Qfr+xqwPGyNFJhxZmi+xiRTY3OD0v9fvbrSyU/ah9V024+V7DE8/T0FHMUWY+qILigYBE5/8d/ApQl0gqSpCKNWH2uykqM5AaIq+wWILfU5eqE6HEEWYeGWxfiKa0L6xMICqVxFcaF1GIE3h1QuqfSTBm//vLrn//0Z0xsMJXHIr/dfsEA579/av7uYyKY/VCNtadGw1UT40MsgbkSiBenzhVNnLBzEghCKeIoFMZb688xQxwZogQs0euTEGsKivQUx14v3bCBUYGbbHEe4BsIHn2uHBWaQxPPH2ie+8JJLCJQwlXlpVYY3bxfNJsPi0VCx8PJNGtMZTmpHbcrOCzjJEEf9Zlg5+nx08TO5i/pGuNrIpJdXysSxqmRRcgL/XEd5rlVRCVrUlYu45I86k9dygH4nkpqq3p80r88d4DsxWJZrGi+/TuvCcLBKkE6nXwYWoVGXHbfJMBnn31rctzevZVAcMCdYdb+t7cS3UzD7aLf/IQ3xY2byxXF5qaKYBSBKzK6hcXRzKJx58/A8UA3Bd/B8RrN0yZi9jDksunzzFe84l43X586Q1YLOAqBJUT1RgPzcGVpIhYvIkKRovpVP3JtQm9+Jc76k+xCWUWpC11Un6pEyWCLVGxInARVTJ0ZV9a5ujaujMzqSiiN/4SyIq/qM9jnnFcQWWcLXiewPIq/3lvvy5cvE/RdXlj1kT+G7C6FFmezSSDegMkmjPh0xyWwGnC3RltO4rBuCWxS+va6Z8/XLQl39WmzAHd541xzJaB18BrHa0BPVqWSlyNonsu9Gg9ADBeHhWyhct3stUeHU4JDEVirVtDQi6rdNJIx0LSAZf2nC6lzu+xs5wqGy0EhbMHQIufxrz43CpjxKtK8HRYxVVTTUTQMjiVa/vi1/alTiZFg/IjKnabwpz3kCHDPpPFUSezXT5+9LSaR7WK1il2b6hjcxD+xBDxJgD0HPOWPM8cS2F4JeAHuxriuR3dzNtjepm8f51M3IPQGTNVnvwy97hAqiIF7CEI1FjSijMfFpEbz1AKCp7colbzG9AqZhVF9ZGiWUp2LgyzbDH35et0eGDs40uUwMK9Ua0Bbge2CdhUCNtXuhjJbw3FEJsPqGCCbQFkV0QeNqW0RRh5VSoqroKXCqTqRB1fO5UrHCJVxsC4kj5lBZZUyiqBxUGxryC7YXZzfKLW7AHe2acryg5+ZX376aUx8wRlV8UdPkb8F+eKkWALLJYCX0uWZ4hyxBHZCAsuAux5b9XEnGrxVjbBLn/MQw1RNO3nDAU8hSjAmbZOANqqx0LxOwXEN2lWlm99ZmFZKtq8uzxO9p69PyedBke2M1I5L4nsRnKtRu8BoBYf1iYGQVaQpQvNht3418NbA2oakJb+6lNLGmYBhnUVR41Ql6gtVQJ1ykCQdrBOJ1Qm62JhRRUcoadqqKeo1RKC7qNwB7eLInRz33++fnh4N0o4/UofSr9MRdrYvOLY8jgxLAk9PT7rzejyGxU9MN5ZAeBKYAe56SLWO4dUcU54rgXVI36pDn8zlZYcSQveetEOyCrYpaOIhiOMarZXXviYzaOVHhlZe20oEW+kGqVWLmYt64v3l/vb2Go27sicB7+K4Zwx8hT2Rigkz9KmkqzgDMRso2Uw0LpU4TQStLiy6ugaV36BjnUtNukZ71GSMXGnqKg+XOsY4qgYYFYv2XencResutu5Yy2Rkc+0Mril/+eln5w9cmmKM1223ID4NRAIHBwe+6Oge6e/oq8K4UCyBlSXABnjmzKFPVqYYE/AlAftt8EVgWSF7BZzvYRB/YVNS0Jd7KIuNNlnrWN9NxzWyjSt7OCvjeNNxzUb5C6JyjD9ymcR/+Ou/xr30p8csqnfRuI+DQr8KJCswTIJRqwhDzs1rdarLyakE89cA9xI5kV9nM2rQpCSDVYUts1WLmWhGGBSJlhrVEZbMExUnSndlK5MEtovWXUB7Bkt3yuIg8vb2lrqNIOTUbY8huymS+DdYCTw8PARL0AU1/ZT4OLqgHWeJJTBfAj63755PME5xLwEe+BBDuNRDZDws0nP9hfmQlMaeYXG6d3QRJzieZmNdI41Piiaec9A8x210JM/nnVwuiwvIWq2ODrr3ZhrKKPRrQmcaa3U+EvR9V5EKKmt8rE5NzKyko+N1bgXIkZ9ZWKWJtpsI1UsVOSmulw2LNCVeidXMoCIEWnMi+cXsXDGqsurcEiGlCMaPRu6gdwXflcmMYeneY/fWT7/8cnR0lMHsnyBLHviLQyyBsCTgV+MeFj8L6fp4FtSzupBonLg/Epgxldmfpm+mpXoCC2saC5f6ZiQWWK14/1BgREOS1XCEXdCLzwNjf48IMUdpBC9aeUF93DIxjrdr5YmJbAAas+NSvV5HBw2eBdqWK2Xd7VRT9MFkX/cfAbbyT4eJM4mUTBPBdq1STWrq1ySjCkrRidTJC6sqnUlfGgV0UX1U9SsWJ/jQdu5qkaqYuovSnVWq3LXX7uu3r1/UwlOD2mS98VUsgSAloGzcgyQYMVr2Z9HlecRaELMTnARijXtwsnSmFOKkFSJp57Zsd+ykvzAR3ioCnFGAzERoafmrYw6x7b4BfrnXwhB38oqCtphHcYwuF3yvdfOkREdm/X6vWCqyUy8GJGIAbqxLFQSv4begbgOICxQWsKz6iRkpyN6Ax+bvBFomWZGQLizK9XEn09nkVccIlqk5eYhSl5wqYVFOq9SNCPNHKdylvKF3H3NjELWYspTtupkauGczmT47qg7ev37+enJ6xmcHv3c+LhdLwK0EYq8yM5LieV4xRGdMXbEhu1Y81riHdEd5ZvRfwPQtuqs/lAFzFnlywY7s9huhzhW6EQw1/Wf2hJkSCyQ2m3dBzAI6u5jEZMIf8F3r48HuXIJI9Vi2cSt5oDF2MnQ2bL4NzA5il6B6hmBe1SOMW8OFBPkxTtW1eSGZpYgO+kyNLEQIPUm0F+VC/2kq1lHlMq6kwESAkr42U8zfyVjNCHlV1SZP2lRGWbvj1l3ZurMZUybNTXnv9z9/+jRRVXwRSyAcCUzqZcKpY++oMhC4/9s76WywwTFwD1D49i4eINmJRydIuntGa1PfUu3dYvJcASAFg9yNj3Nu2CTRie7imDSHzJZGAxAJ4Hitj9c4nhiaDojXOJ6jyrWOA0spKtUKjhHF1Ypl2Q436sZomC23Rd8aHS18qRT1ozC5gY4lQoJOFirESJwuYSSpGFukzq0yjw9GBcLGOJiR4xjrTDOoKlKsS8WaHYMZqRzYrv4bZu4auWMwozZjQui31zftdtuiGZ/EEghJAvHOqSEJ1jVZa8BYeuKaZJxxjgRi4D5HMG6j7X3UbRk3+cKi66buHc0T5dVL9ts9/3x+yhhZzeaZuZ2zWdzEzJCJcgSeakCNgHiN47U1CfBdOZIXQE+Lwwio24fDQaVcUep2gbTKBlzkOw2+FfqVSPgwuJE8Bjw2bqgupiNJtYKmp8sZhaU5NhhuxeoykmoLtow6llxyYpZSl0TpP6LNc8mgzjkoJjRyN/A7jRXortzLgN6RBvL49ec5riGlwjjEEghGAnN9DwRDPqYSoAQYO9z/BVjv7pCKgbu/e2l1O3/FnUtZRDmJw4QE7KJxcz5R2LjYhL8wJz5WiHPTdJ3HVon7QvNyKmLzEv3F2/hbw6mF45UjeVHPA+stZbze5zUQNrrdbr1Rx525tvnWGncD4BqoW81ZRmWG7PgxIbM6lYMBj1WKUdIAzzpRiipFu/ErVyqzRcmWXVJ0MAC6eSm/uqARo4jYUyWDupYK5cIgq3giRn5F3a7eUbSDGeXTHZ17liQ+dTQfms0NuOqbakR8ueMSEG+/cdhBCeiBZ+lxB1u+oEnx4tQFwplKkmkrjBAW3TB49U1zzY10qi7KGnffcp1X0EkA8/IuXdbpidjcWiYSHEiuz5pF14QyHpY4yg9buirTedC8jiGPV4ZQLwNgi4UiazSVClrQrIK6ug7qkT/zv10eigfhQ8NoI7/KKef6TxdXUNnMCJOkjRlVnxZ0acmi/sapXI8vpLLJoJOtTLq0XMKAlJQI/V//KM6I5ZegoLuC8LIbk1jLZNK9NOb+vMN8/fLl8OhIcTNZ5aKrRawuKhen7aUE2DBhL9sdN1pLgJHJZdiFgSUG7ktvtvsOsZTUOEMoRMfkN3EW+SZtysZ9EzfDW52+b12gQ6BvLrw1dja3tMJEv9qdPDFaDc9xAPDE4EOw6kLYyzvAYABmxR2iaJ+1FtpAtVKBIqArh5S6kl8dozIYmXROdTTK6HwSo/9URrOkMt/nAp7JwcuDUDQKWsSNE6LnW/tDXTlyN/MqrK6qFOSuUqVO9d/iUeKlXoXb5UcHgLsYzGSz7+8DWHp+biMcXEVOM7To2mzgojx+05SQ/BZeZ7mtYXSdQnGsS3dJx6Q4MpaATQIuB5ZIP3qeRlJb23f51OV99SaCUIh6Y2Hl3FvehmC9yqwszV0gsLYesf5BVCP18VGZxdNeNNxo5cH3cj55D3Gigj8VZd0u8NUCs+Q1g0LUhtQE83Kq/mtCkiCXHMyjWVB+FWiWozqXnKBwlVOhdjvqVlknubNfQQTe9VHix2fqwgDwZiy/4z8F0xUL1Cyc6KBV7QZsVz/K0D2DP0wge++t9/z8XG807Exs8pz2RD9I94oao1NdPvpCjDmMJeBPAj4evfU9HTFw1zfVx01y1RvCouuq8hUybSvfi5oc+wtbJJ1op22+PyqzeC0kbUtjOaAE5g5SwiCqZRTN2mBGwVlB1QpYc6WL8iOn5iWR5pXKoDOrrLqIHGf/pJjkJ5XaVEm5mkTuior3A9Ss6YdKhLpY4wh9ozFyqupWEfBAvE5UrypK664177IZUyYDcM/m8jay3pnazxJK6sE33bq9fkiHwdNKDFmN4EmwzuOTWAKbkIDLpyOAjrrnwN2loL31gVCIemPBY+7t49hjA1X22F+YH6nFZeZIADhrDMDm4wOKrtXr19++/fDjjwaGFcgryFZjXVN/qgpIikVa51PZTKQ+UVBBZYPImKAm6zwNtN+rw8GwlH6y6nBxAkPO1ASYW0Y4nGt+5SjcSAvVwbBzNyyFsBoSk5lqraqbOm6uEytzKnbKGsetIoHFt8E95cBuWDAM0dfshALjzr1A4pyxBFxJwN5P5xVY0n/30KsMUrP+5knNc7xF0c098Uw98AJ2dreD4wBEEPsLC0CIMYn5EhiKzn10fnF5c30jmnAD3VJAI1sF1I3HTUHdCVLqmQQAT4xOtoJCxUjTOHiitFxMPMnDtwf+7Hkmku0Js+eqIolWTFm/+so4CjmF1vWPbpBEANsNU/fBYIjGvVypdF6ehdrCMEHcrHmDkQuZjRNXvkMBizDZ7/ftJD31HHvB+DyWQAQksKT/7g9wtwQR5E0JhWiQDJq0LEb1iRm9V7+xv7C9ut3rbywq6X6/B2w9OT358vlzq9USBbYBbg12eP4mAPbk8yipZgaFgfWFgvM6TYN3wcryp4P5a1wakYNuYtCZiDIvrIJmxNTvND255r/8TSTJlcGl+jEOonbnvaXTecbi//DkuHnP+4P2TDNVUaQvjRZbTQ/hJNLtD5s53/Kdw1g+jzmWz+CeF58VxMViCQQqgd0G7vbnMUixWXSDJBo4LYtLTuKgnH7EYoglEKYEROPe779jH/Lxhx/6vf6nT5+7na6qcfw0yuNoPJIK+qpLzhQqVhhd8LBmEyxsxhvYWXIbiSqL/VyXmXdc8vF1iprJgMkr1ypKM2RnS/C6RvVyhqF9t/vy2u2enl+Uq9W76+v3fo+SLFidx9jexmuZBnXcFzHOkRdbKKxBAnMqNx5Oe+oamImr2FsJ7KSNO49P8CEUosGyuQUsBttgb9QEX8QhlkBoEkDjzr6puI7EeTkwF8/l4Pjvd3cPD/c1CXXsRlTlqiManZEfA/dqZKyiVaQ9gxWrmZdL/k9BceMyn81WqtV8Jj38+7/Pn180Dw56RpOn8huxEz+zVM2qSJn+E6BOHA0Qc/9+D9cxbVzrlCvVg4Oj5sM9bQfBK8gu2eIQqgRWF7GL/hFqC1YiXi6XVyofdGGXt2OrZR60zGJ6biWwG8Dd5TPiVig6XyhEvbGwLPcWsLisCWtM1yBjjRXGVe2dBIYqgNeHw8H7ex/P5ZdXl5h6t55anz79irFWo9GoVKqo5B2eXVGuEy0HEw+P+6xKUdh5UqhTE3/14b4yGN4mEjff70c//HiaSv3m48efb25eX18nyy2+gg3lT8aeS7FmA+/SAlqK05jndgt9J409OjkltvPy8v3u9rndxuxYt0U1x04rPo+iBOSOBhememZwhJ0ptdvtQqHonBbhWB8yX7NgIyy8/WVte4G7jw7v6jaHRddV5csyRZq5ZcxvOh2EsWkW4vp3XALo2yXwI2beo+FgwIq5VCp9eHRwdHyEJv7p6fH+/gHgXqlgSFLN5fLs2SSWJvJvMhjXGruPE8dnZnZrIi8Ph+Xnly9XV8O3N6oY5nLPxWLn5ubH4+N/vf2HxFslMVpurwJ9g6A605fjSLxO4v5yMOhiw95+7r/3s5lspVKjeThrZxEq8awC7/V6vLrw+mKq6U1e49+9kQB9xmuwerLXguSv1+s+Sm1jETeCXUWS2yiTfeN5u4C7mx7r8w6GSNonR7ZikWbOxme0T+PFqdG+PzvAHUh1qP+nNHIX9A6Mx3gGJzMpdiM6OTnhBATf7YKob9hYlMWspVIJ2IG7UtluNJ3WYJeHfkZRbR8IxlNz6vvD5/u7zEHjr/7whxukOBwAvaEJMyDsX3755dN//S9Xv6u+FuowI1IeF3Ulc43U4fn1tctmCIBy3MYUisWTs7N0Ks2HBdT57aenXu8Ns36yUa2qWd5i5IVEWqK+I7iqLc60vxKw92+XUrD6crPZPDs7d1lq57MtlaQlt50XxU42cCuA+9JO6PPWhEXXJzuTxSLN3CSrW3IVu4Pckhu1rWxizI19jKF0F8RuBDEokTYpSK/cRKKDR90OWEedDrzudl9RwwOIAfkAd94wAfHlcoVjPp/LZnMs7GTDViiQH1LG2CBE5f/w+PD8oP7Uav3LH//YfHwCO//444+v//L/8sbwUP9fMM4pn52dgd8/GvkVM8Y5P1YQdtV7x/tgwIrS19c31Of99x5vF9RL6/L5wvHxCS8Xg/cB3BI6fUml1cB3sRKiidJKHfjAZTiTgeFsLmdVFJ/EEghKAvpZ4Hh4eBgUzX2gY4whC5tqDhQLM8WJm5BAZIG7m37lU2AhkvbJkVksupyZHG7zb7wB0zbfvS3gHbsXTEc0WtcYFphNkAPsmz+cihnJKIleGk201rhXqxWwMUlg516v33t7e2w2b29vQcTEgeYJmXQmmwMA4/gONI+vRfmnS1Ewn8v95vrb5w8fMfb98uXz4dsz1YJm/vSn/857wsvZ6XOzWZB3gNS7wtYCtoHbBAXBed/gEst80ZJTIJ3O5/KNw0M2UUomUjBs4nP0613AutKpo9BXQF29koDWx7hd0sVORoVEJpeDIEzGIZZASBJA4356ehYS8f0kuxSPxMh+Ux0jUsB9aT/xL6UQSftnSpWMLmcrNixyxfnKHzmeYoZ2SAI4jQHUam1zKgUS5k/QOj8akovhCDpyOZj/BcQD1jEvUdYkwPBkKpfLFYuFw6NDudLYfzhUWnAxROn1+61WG7QNNlZ6bvVSIHRHj5//6b/8y7+goT84PHjM/I1A8D/9CQbqtdpglPj5p5+OE6nLXPFLMYvUsa2XtwEAeqHIelkxtTfeHAydOYp0/vGdCgjOPxXLgWpV0zhIsM41btet58ibydA09UnyyqF8y+zQzY6bEjEJ7I+Ne3QEvxS8xMg+pJu1WeC+9L6v1OpwqftmLaJs+W7P1hTEOGFreI0Z3UIJALLRQOMAEeStAaxWOIPaRxi7gHcFvycTCqMrDTwaaaY2RgTwvZxjCqNcSmKbbiirFQVAr9jHZzJFOUNFr4MC6xQ0EfQg/5cn/+fhX+B+EnzdarfB2OVKmXr/ot3+uVz527/7Ow20rwRzWzgbIK4vieH9QYKJ0a0zwecKqJuJcqmyzjmgiocowB1OYfjjb37DcQtvaczy1kiAD035QsE/uzHG9C+7uSXdg51Y/HOF6JSwEeDu/m46sbwwLkTSC+tdnhhdzpbzvhs5np6edqMhcSsiKwEMyh8fmyzcRJMNcrUCqFfgOyjbHhgTJiNUosLwCtID4wG+ZAIeW9prOwF9DnRX4D3RGZVyo+FBqfj1/kH2oxmNCvncb3/66deDg/aLWM7Yg2BwgvGjUvS5mWDLrHLari0gLwQkXuN6SqrA64v4k6FtAtYvP348PDqeZTuOiSUQoARW9ePudYJ2eHIDbM3ekVos/ljYUx1ibcB98X2Z4srzZbjUPbNjFogoWyZ7e/Z7cHCwZy2Om7sBCdTrjbvbu9PTE5AEinYNd5W6Xc4nGOJKDRHmr8K6xMi16KsVbpcLhdolqwnfDTKKnqYqR/5//fq18dL5ze3Nff89+R//Y61a/fT+/oz+GyhNsuswk1np2KW4kaJ17pAVykJc/4jpu77SbJ9fXl5efYgHwon7Hl+EIAHeVIulUgiE55B036d5guOwmgQWC3sPBRwqcF8s7dXuJNPCqgRCKx9dzkJr8jYQfnh4+N028BnzuNUSAGWzSer19W2xVDg/P9egG0MRwbwYy5gGI4LHJ9ppRKgf60AhMo2Bu5TA/gRsL/E6SB7jH6fs1ZrP33342Gq1Rj/9xJpXYmZQuMonCTMpgsfnFRBwbpRUJ1zIn47jR/0p/C4G7qVy6Yff/BZdu+be5Db+jSUQigRYsh0K3dWJTj7ni+iNH+pFueK0KQksFvBOCjVw4L5YhlMC93MZegV+mJqehP3RiEuFKoFY4x6qeGPilgSAqnQ2/LX8/NMvx8eHB4csMxW3j2LobkF35hPLtaOcKz27IHAB5mBxMXmXOYeixonQJ3GUwAxFckuQHwHuklVn42gF0X3PDSptnG7kHUfMxe86p4nUBbqbAWW7OMQcDi6urq4+/hjvnKDuUXxYhwR43NZRTah1uAc3xuMfKjc7QnyxULdUkEEB98XCWbUHhEt9Fe6iy9kqrdrNsrGN+27e16i2CtiKyXur9fzQbLIwGs+MOCRF4Q5+B3wDxwWGK+htjCJGhIHgla2MpHOimkgZlVuXUVEmWB9jdhVDEfB/3nRpQ6odjktmHcaxGouP85kpWolu5ufXyCg/xp/G7axGfRM3ke+VWu3j2XmuVkuTw+Db4DX+iSUQngRYVRIe8chRNoaMZXxtKSxd1qwA0xcLMrLy8w3cF7c3GMGuow5/nEaXM3/t2YtSsVeZvbjNEWskO5jCEU4Vf/75F7yvA+UbBw0wvfayIljd/C+YGzxvKt0VBBftu9LFq1bZphE5NS6B03JhHdXZKJkpCGAfsqmTEQzAbvzYsbwJ03VGAPlUMBXqFmqXdHJxDV5/e+10OsCm49Nzvi1YWvZhSkZJsdXnXSWZzAxH77jDsbhWDYoPsQSCkgBdMihSu0PHDVCJxTb/fi+Q32bF5h64L2jC/HZ7TFlHHR5ZMrJHlzN/7dnHUrEf932869FoMwa4BLDF4+PT3d0d/iJx1Fir1dDB4/1d+WtnIlBWMTAsv/IDylVA3qENkm5MHRqxjI/qbFRIv3ECuhagbvyYmJ0YiRSALv8lGJjc+pUTM5jJomQn4NOd95BO5+Xt9RU3lfjg+/Dxx3KFDaQmRknNnTRAhYHC8fDMNwdeRSjdPT4AACAASURBVGggOB7Df8tdjkMj46hYAq4lQC91nTfOaJPAxFNri593GotZScaN2MIT1VLg7oa9eXfYVXzoFbjiYk6mSDM3h+c4eo4E4p1T5wgmjl6TBIC2dEIC8Pe1+/r01AIKsxlquVRi+xh8Yoi3dtl0SZvJyLBv2snMcqhTdbwGLdbROplA6nIxjsDa3gqwY5wbv+MIiRe9+mDQ62EL0319fcVtDGr1Wr1RuihzMoXXZxk1WFQ/qN/VZCbfEXQAwafgKpHEuz0eNAcm0DeS459YAu4ksF+mMu5kEkou88ldRDw8xLqo1silLRbVKkKaBe6L6wpMNGuqxge/0eXMR2PiImMJgDk0INFRossU0wRZ+jfvOC48czaviBkvxB1DXK8porHkHQWlI2czT8Zsq5zRVRcLRdpI93t56T48PI4SoNYU5jQ59jItFvAmiWIeOKKNaqTVWgmv7WmM3iU/Go9rUuZRADfnIHKJUT8SZQbKKIyOEt1Suhtqd1GpD4f9Xp/npdvp9Ho9YmAsk4Gx/MlpQ30iMEZJcq7Snwfq68K7fgBhCIa173eOqOSB9+ohpQmzYZV6oTbZi8Zd0Yzf1n41KygdY7ZrtqU6ZrvbS7/ehcWp827e1sUvBVEyLO17WCCkpeKxgPsCIoHJdx11+GY20sz5blVccCyBbC73D//w/wE1QB7sGJ8v5LudLhYLbLlXr9Wbj03cf3DknBjiSSUPCCadyVAKQkAonGagIn17fSuWii/PL9jNP7WeDhoHD80HjpwTQzyp5KFGlutp4BXXG8t5Tr8apdKpQX9g71eVavWx2cSW5vb2Vq1wbdUbdTxVVyoVlN/FQoGRXWN64G4mnQa1YIpDBuC+yLlebzabB/Tn5gNE2q02zhmxRM/l8ijOKQgR6dKjxFvvDQ3/8/NzoVBstVuVcgU/kicnJ+3n9snxCduv1mqb78+5XHbYf0+kxcoGtnke4+c3Hq+mxufCKtumjmeJ+GwtElgKt5ZC17WwualK5onHkkry//q//x+wRaj8zWMi1EpdEY8uZ67YjzPFEoglsMUSUJbs4v9xccDABX0oQXTSo8HgHXctlssWsY9HUS1Ka1GaolglKHoyyIsS21SuKy2zxChaksorJfr8JEdtogOEl8JjO5bFfG0kFXMalPzaOB75mbY3G+ElrjSWQCyBDUnAwrAbqn+D1YoSMKTql81FIVXrjmykmXPXhDhXLIFYAtsrAQxBsOleCtl1AxWUFkStNm/ie9HSdgte18bkklWK7kzQRvCGKTySoaVm6zhHqHGIJRBLYPclsPRJ32lkv3wOcNkDlorRJZ2wskWdv7DaHdONJRBLICoSAFoyEIEuQwaYCqnv/pDH1Gx/9+GcNhMpWvlEkjWvBr6Pyu3//9s72zTXUSSNpp231jC9/1VN9zP9t6ZmC1037XkRTtKfGElIRMBRZeW1ZQkiToTgNYkQdkAAAnsRKG//HEr8VcK9nMxesbqtx7p9t9byDgIQ6JXAZVaHRtl79dCEX5HuZVR+GopXJxDYy7rYHUyza0zYihEQgIAFAnmhaLLFXiLc8262D4R1+9oTwgIIQGAnArNmxexk00DVqNu9HpUPs420K86xmfQ8C8kPlA24CoG5BPJ6spGsLxXueePnoqh/vHX76ntMiRCAgF0CGubVJs248awYuwRMWpaiMan5MKUodh1TuMIdvo06YpOwMAoCEMgTyCvPzVqTnHDPm5R3Z6dPHZi4EwmqgQAETBDQELva6+thXhNmYcQTAqljjTo+HhGnyIdFetLa+U9OZRcEIACBLIG8QE2tT7aMpx/eC/d8RU+LaLDTh5UNwFAlBCDQhgBD7G241681TpGf7iMOheuL2Nd026u+j2mIPnwpq18nJUIAAoMRyOvYbCtzEe75EkzgdGCiCU4YAQEI7EogDLEXr+24q2VUVoGAVLu20I1e9UFxOXl9WQuRr1AJRUAAAhC4InDV2lztvbwMD+/IHvB4yo57onGmTdyRBlVBAAKmCEiya0O7mQrKlsYo3lGma1ReL9KM+c9T2J36qy1NoGwIQGBsApdHepiCkBo/vWCDAAQgYI2ANFtspqJwi1LOmpHYswuBi44PI2A/Q+96sKvealQ+pAnpsUsgqAQC4xC4n+PezHNkejP041S8IMnodcdJjwJPgwqbsoi8KKA14CExL3RXq3yPc+X1Um+0X6Pyp1t9PyAfXIYABNYTaCrcF+io9R5TgjMCbbNkn9qRgeaT8jJwGmc075MV5plgYAGBdG1/TapdZyh7DtNgvJS9XqcDCgrjEAhAAAIfLYQ7vR6JdyFAKkQQVTggADa4rkJkBFb/cBPiBniHLFL5FIfkf3LrfFl6UrNr4jj9kGBwGgIQKCKwl3CvokyKPOIgawSI/T4RqcWZLwBTvBLOMFGZDQJbEbjW8XqMa8q2NCq/VcWUCwEI+CSwpXBPLZBPNFhdTIBIF6NycGCraJr5wnCZyD4t9ecgXpjYD4HrayCNyscLUje86p7o+C3y+rB+nMcTCECgjEBt4d6q0y/zlqNqECDGNShSxj2B1nkVZ7Hrd1grJv7cm8j7jgi4Ub/R0DSFJixZczjot4bnYzjceNJR9uAKBBoSqCTcW/e5DQn2XjWh7T3C+Kccj9onrMhOwg+SEF4DHdcgTTpebsQfpXBQ89+j8oNEETchMCCBFcLda7s3YJRnuUxcZ+HiYOcE9BCly3LsZL7zUI5i/k2iXr5yTr4nNa+bXLVfx8VR+XjMIjwrTl1UHydBAAJvCcwX7jeNxtvyOcA+ASJqP0ZYWJuApsSEWTHHn6df1q6B8iDQikBS8NGAMLtm2jRvXve8pjk2BeZV7x34JlBAnUMgkCVQJtyrX7xZm/hwYwKEc2PAFG+ZQJDs09PqmRVjOUzYVo/AjVJPC9dMa1DuvgBlld4H9V8vOSjJIYGscK9yiTmE0qPJxLLHqOJTOQFdAYdTmMIefspP40gIdEXgJ/ene1ujpg8Xx/eovPbo7c9hFr1f2Z3Zds4icGyyReBWuK+8HGy5NrI1BHLk6OP7LYE0K+Z8vP2AdxAYnUDUsPqdVp8UkbCEvNT7NFFer65XsOmCV8X+ke8AXWSENyd+se6Zt5C9srdiY/SqCvZDwBWBYxpi5+pwFTiMbUrgMrXme7nJaIsuofC3qkmp3sy9aWpq68oXNCxo/dZB81//7Yi7f38G82BBqzEYIdwdk8Dn6eNLN54yxD5m+PG6JoEo08OovKR7+C9s04Szy2A8UnQO7rm9NnTn0B3jWIS7uzjPvezdOYjBEFhKIA2xS7WzQQACGxCIQnK6xfsjLV/z63z6Ouii04ehh0Js1gM/q8cHfD3whktCuBsOzo9psy7dn9N4BYFRCETJzhD7KPHGT1sEfgfVri10Vfo/Tos/apq8nvI6bwHKqRh+LSRQLhWQ+AsRWzgN4W4hCq9sKL8IX5XAfgh0TUA3nuoJSsyK6TrIOOeLgCRhvNVVj3ENlscbXT8O36PyjMdbiGe5ukDiW4jXjQ0I9xscrd+UX0utLaV+CLQloCF2yQKt7fjFVdM2EtQOgRyBSfeFi/R7VD7Iee3UEvLT7Hm9DJ8iD3MQW362rIElnhvGDOG+IdyyopddFWVlcxQE+iOgIXbpdWbF9BdZPBqDQNR03/PjQw+oqTX6Rzr+W82j472nQqGwQd8vCTTCfQm1GucUpnWNqigDAh0QiMuxay32E9dOB+HEBQj8EEjrS0Y1rytcP9J06PgfRn2+KmnMEff3sUe43xPZ8n1Jjm5Zf0dl20dJY1Mt3dJaMWFUjg0CEOicQGo8r3W8RuXVAMTVbNIBnYPAvUCgpNkfKyMQ7jtcGSVpt4MZDqroidRKX8Zqh17lZpgVE+50Cz9sEIDAkARiY/g9tSYgUHOQnueqVWt0LywN5pCpkZwu6SD6yRGEewp89RclmVS9UusFAqUwQstAddIyXXphTWRfhqGQMYdBAAIuCaihSzpeql3NRFx6Mqr5TppBl5GxbPTb3sRN4iDc6+bZ28yoW53d0gDRJDbLsBtqruIQu9gxxN4kgagUAg4JqAW7eyCU/kinPWmKfPTJUEPnEPIAJpf0nyaSCOG+PhtLgr2+FosljOu5xWgst6lKHFe1Z/HGUy0UE3rgKuYsp8GZEICAdwIPOj44pPZFS8tPc+WZWuM9wq3sn9U9reoVMx4i3DNwMh/NCl6mHE8fjeizp/g0tnVZepwPk07X+Fhc3hHV3jiMVA+BDglEARXVvFawiY1V1PFpVH4rkdUhTlwqJJDvFZdnHMK9MADxsHwYZhXl4OCxvHUQkL5MnMa8Qo5JrMcedbV/yxvC1VVTAAQg4IVAbCjig101V16tkPYcZf20nHza48Ud7PRJ4K3CetmhIdxLIv6Wb0khDo4ZxU8HoejaxPRn6trj69sl8MsWtOtA4RwERiDwreO1Ws2lCUk6Xnv0iOa0gs0INPDRDIFXHdoB4Z6J0StqmVOcfdS/h84C0ru5Wrnt8BF0e23JvjW4hlcK3xm2Di7lQ+CRgPT6j45XqzUdEX7rrtfpIa/TDn5BoAEBhPsj9IZ99KMx9fd07l59YJS4msDU3YWuT9NL0aEzcRq8YInhzBhyuG8CaSpfyPzvUfl4YWosQvvivHmuC99h9mM9wj3GymDnWCeJunWsDh5K2ZhA0unTRPaNK6P4nQg4alXQUjvlxHjVxNTSKvLR9e/pNNodWj3tJffGS4qdPB5ZuDvqfeZlQ7eOzcPA0e0IhBQ8a2box1e444sNAq0IbNoYosxahdVgvd8PhEoZF9S7mj/9w2OhDMbLtUkDCvd0XbkO3L3xfXp17yXvzRNQIsZRdj309IusNB8vDFxBoGJ+8x1gRRwsnnpZenIyLS5To5dx6cn4cCgibjFsTmwaR7hXbGOtxLZDl6ygxY4lBM6fp4OG2L3deLrEVc6BQFUC6xtzhGDVgFQuLEYnjsqnGfPS8eF1WA5Xwx1nTZSvXCvFdUqge+He25XQmz+dXldjuaW/BEutn45BtbNBAAItCCzuGlD8LcIV6vyeXTPVfwgPdpWOVxxjRIhLq7jYr7dX4b64ETMast78MYoZs2YSOGrNtENYUiHci8UGAQj4I7Ds0kVW1o20JPudXo9x0ahI2K9W9krT162a0twR6Ey4L2uCjEatK2eMMsasRQT0t93DSUPsH/phgwAExiMwt3tC6M/NkUjselQ+PAZjWlBX8Kchk8vY/NySOd47gQ6E+9wGxHTIunLGNGmMW0LgfJyWddSsmDOSfQlAzoHAmATWdG2I/pgzaRJ8AhLG44OWDzv0Iu0fM8fG8dqvcF/TDtiKbz+e2OKKNfUIaIj9eDqoZ9BaMcyKqceVkiAAgbcECrvI0WSr/FWTLHrxd/g76NQ4a1dEMRqQt4nUzQG+hHvh9esjOl054wM5Vs4noEEcLcfOjafzyXEGBCCwJ4HCLrVXOZsWq4kORhph4Zqrkfhefd8zzSzU5UK4F16PFni+t6ErZ967yxFuCUiva2M5drcBxHAIQOCRQGEX7F3jRvtvpsjrUVAf53iTa8Ti3cfH4A6yx7JwL7y+HESqH08cwH5nop1g2Gw1L0Ps099dbVr4LsJ8DgEIQGAlgZKOwlcDKWvjqHwyWzcq6SbX+Fgo4Ur7V6Lj9K0JGBTuJdfL1ljqlN+PJ3V41C7FO9/q9q9sd3Wjk6axa6EYnnhaO1UpDwIQ6I/A2yZ8ZZO8NTGpdm1pVF7uyGDpeO2Jr+Pvrc2g/LkEjAj3t/k/169mx/fjSROE4FuDfSm9oNenx50G1a7taTnGu6A13DgXAhCAwAYEnjalr+pp3sRGA6KO12sZr/Vq1CVoD6Pyr6LWZH9b4T4rq5vwKa20H09KPV53HLzW8atzdlgWODz2I6r2sCZBZst+mDnv5qPmXdONNbyBAAQgYIVAvondv+1UjXG9GgFKK1HKSO3XX2e1J762gm8kO5oI93x+esLfjyfbUYfRdmwXlzyp9dAqh+Udd9zq1rZ/V7YjKqqCAAQgkAi8bTs3bQ7vCr/W8dNa8jzYNQVqjxd7Cve3ibeHw1Xq6MeTKjiuCwHNNQ2Dr4+ny8h6Bw893SfZ7rosgzHFJAhAYHgCmeZwozYsFpumyOutbNBP3K8/6CZ9P3xwKgPYQbhn0qmyM1sX148ntUhBpBbJHcrRKLs2zYfJT4nZwRJfVZhN8tg9+oKJtRCAwO4E8m1YxYZERf2UNs2lUdXak0bld3e9zwq3E+75VPFEsx9P1lOHxXqGO5cQ9boqRa/vTH7r6nq6GH96+62pUT4EIHBDINOQrLku47nxdxqV162u2qMa9Tu9uLGGNwUE6gr3TAIU2GLskK6cWcwWCovRNT9RT1AKTWN47mlzWzAAAjkCBtsZXTtsEBibQP66XHCJxAUo04maTiPAGppPe9KLscG/8X69cM9H9k311j7uypm5cId2fi4sw8eHOE6tn34xMcZwoDDNNAEL7SEqxnSKjG5cySWST+E4CT4dEwu8G5UfnfIz/xcL95KQPavQ5L6unJlFeFzPZ2FycnActlBMd14rxgkezISAMwJ12+ekj5xRwFzHBDIp/JiPcU8alde54Wcalderu8e+Ooay2vS5wj0ThdW27FtAP57M5Tau53NJ+Tlekj2o9kOQ7I/NoR8/sBQCENiKwMqWn4Zlq8AMWu6rfEyJFl+kteQjpjQeL32vEtLBQ0EsFO6vCLtk1ZUzhREY0edCNM4PCw9Rmp57ykR255HEfAiYJrCmExlTXpkOp13jMommPErj8dGBpOM/NDCvkasg5vvf8sK9KwRdOVOSmcM5XAKlo2Mukv2490OUOkKIKxCAwB4EFnRGaP09AuOsjsc8imkS1Py0+mT493skXj1kr493fSrcH+E4i+61uV05c+3Y09djefsUQe87wzrsJw2y65/wmw0CEIBAfwTmtm0I/f5yoNijlCzxRcwF9ZB6+3k+x8Uo9bqb2TVJuCfHi1EZPrArZ/KcB3I1D2KATzWAcAyi/cCsmAGijYsQgMAMAmu6QkT/DNDODk1LyCvIypGYJvqdYp5eOHJMwj354sjs56bGkDz/rKe9o/jZU8zW+aIbT7Uo+9fx8EXs15HkbAhAAAJ3BMqbVY8q787Zsd/GAKYwxshrVD4uTCk2dwfYpJVG3G2aV2RV+UVXVJzZg0bx02wAdjdMET+cLmuxI9l3x0+FEIAABG4IFPbCSRjenMwbcwRioNKofAzv8awlHy6Rvovk3dtW/jgW7oVXUCuydeodwsk6qLoqJQyxnz6+Pj/OPPS0q8DiDAQg0D+Bwo7biAzsPx6lHsaApNH3eJp0fLzJVW8V2KdBe7qztNb5x/kT7oVXxHwUls4YwklLwO3YcjypCQiz2KXa2SAAAQhAoFcCJR39zpKwV9Qr/Io6PsUhavc0Kh/3v4pkOmtF/U9OdSPcX3F54pPfXUM46Tc8W1r+8wSlI1mwJWjKhgAEIOCHwNv+YCNt6IfQzpZG3mlUXvHRshFajPLpkjVPo7c+YtaF+1O3d47T5tUN4eTmFL1WoG/uYYHHY5jLzgYBCEAAAhAoJ/C231ivE8uNGe9I0b2bEK+A5JeQfxWx8kAZFe6vHOskKzp3r5Mobe3G+XgKiXA+shz71qgpHwIQgMCgBGbpjXLxOCjN924nKR9ZPuJ/xfjxyOvKrs+yJdzzdl/74PJ15+65jEkDo+PajvqSznLsDehTJQQgAAEIvCCQVynX4vFFAey+I3DHTIAvjPWBXk3TbO6OuSshvk2R0QsTwj0Z9NRc9zs7d899fPZzIE5k15SYL9aK2Y86NUEAAhCAQAUCeTFTIj8rGOG7iB9IE8zp6a4fEgRpf3qR8bOlcM/nQMZoHx917p6PIFixUms7xinsjLJbCQl2QAACEIBAPQJ5zVMiSOvZ4qWkSOWOjUBqT1Tzdx9FvxoI93xwveB+aWfn7r30mw+eEFAySLJrKruWY396/T05h10QgAAEIACBvgjkpRH941W0IwwtUxM3kdMe6fi4cI127ifc81H7ttDtv5275zYurQwPs2Kmq41ZMa1CQL0QgAAEIOCCQF5BjS3rr3W8XgvVtsI9HwsX6ZQzsnP3cq7z2UsCR63uOH04rRnz8jA+gAAEIAABCEDgLYESrTWMuJejWwn3Es5vg2X0gJ59M4rcgVnKCo2y65IKP6SIg4hhIgQgAAEIdEIg0+t2p+lrCvcMtx4yo3P3eghRGx/iWjH6+xVD7G0CQK0QgAAEIACBFwQy4s2npq8j3DNYXoD0s7tn3/xEwaalkuxxY60YmwHCKghAAAIQgMArAhmB9929vzq14f5Vwj3jckOX6lTds291CI1bSsgN3XuqiTFaLoZEGTcR8BwCEIAABPok8KpvNyDolwj3V+70ELyefeshPo19CLNiZIIk+5GJMY1jQfUQgAAEIACBnQlkVOJemn6GcM9YuzO3+tX17Ft9WiOWmCR7mBVDuoyYAvgMAQhAAAIQeEnglTSoLejfCPdXZry029cHnbvnKxgmrQ0Z8j0rJtx7SsaYDBNGQQACEIAABGwSeCscZir758L9bS024ZRa1bl7pRg4Lkcg3niqRdn1BCUmsudI8RkEIAABCEAAAksJvBKlLwT9jXB/de5SW4yd17l7xmj7NSfMitHlMi3v+EXS+A0klkMAAhCAAATcEngqQA7TA5iefuTW0QfDO3fvwV92LCYgvX48fWgWO8s7LmbIiRCAAAQgAAEIbEbgV7eytlvHNsuFkQuWXtcmvf71OTIGfIcABCAAAQhAwDKBm6kylg2dYRuSfQassQ8NQ+zTcuwMsY+dCHgPAQhAAAIQcEGgI+GOXneRcUaMjJJdC8Xo3lM2CEAAAhCAAAQg4IGAc+GOWPeQZLZs1KwYpY30Ojee2goM1kAAAhCAAAQg8IaAT+GOXn8TVj5+IKAhdv2EtWIYYn+Aww4IQAACEIAABDwQ8CbckewessqWjdLrn6dw12l4ghIbBCAAAQhAAAIQ8ErAiXBHcXlNsKZ2S69r06yY36wV0zQQVA4BCEAAAhCAQA0CtoU7er1GjIcrI914qlkxLx48NhwTHIYABCAAAQhAwD8Bq8Idye4/txp4oLUdJdXPrBXTgD1VQgACEIAABCCwNQFLwh2xvnW0Oy4/PkHprCF20qjjMOMaBCAAAQhAYGgCBoQ7QmvoDFznfJwVI7EeJPu6ojgbAhCAAAQgAAEI2CbQTrij121nhnXrNCtGqp1ZMdbjhH0QgAAEIAABCFQjsLtwR69Xi92oBWmtmDDE/sGK7KNmAH5DAAIQgAAEBiWwo3BHsg+aY5XcVv5oIrsku5Zj1282CEAAAhCAAAQgMBiB7YU7EmuwlKrvblgrRts5rMjOBgEIQAACEIAABEYlsKVwR7KPmlXV/L5Ids2KUTKRT9W4UhAEIAABCEAAAh4J1BbuiCuPWWDN5qDS9dDTaSI7s2KsRQd7IAABCEAAAhBoRKCScEevN4pfb9VqoRj9aNNDT9kgAAEIQAACEIAABK4IrBPu6PUrlLxcRSAu76gpMUj2VRw5GQIQgAAEIACBbgksFe5I9m5TYl/HwqyYaZQdyb4veGqDAAQgAAEIQMAdgZnCHb3uLsJmDZZe1yg7K7KbDRCGQQACEIAABCBgjECZcEevGwubb3Mukv2D5R19xxHrIQABCEAAAhDYl0BWuKPX9w1G57WFdJpG2TUrhhXZOw827kEAAhCAAAQgUJ/AM+GOXq/PeewS40Ixmhgjvf41ZnqN6bWRtI8P8DJiDGZAAAIQgAAElhO4Eu5Ii+UYOfMFgTQrRmvFdCLZuU5exNrubkJmNzZYtiUBvrJuSZeyIdCIwCTc6dca0e+52iDZT2FtRzezYrgMes5HfIPAeAQstGl8eRgv7/B4YwK/eJD8xoTHK15TYjSXPUj2T2POW+jGjCHBHAhAAAIbEtiu1eUrwYZho2jLBK6mylg2E9vsE7jMitETlNRSb9dYl4BoW3uJhRwDAQhAAAJrCFRs5/kOsCYQnLs3AYT73sQ7rC8+9FQrsu89K6Ziw91hWHAJAhCAAAQKCKzpShD9BYA5pCoBhHtVnKMVFmfFnI/TKPtGzq9pUjcyiWIhAAEIQAACIlCxh+I7ABlVRADhXoSJg24IbDsrpmI7eGM1byAAAQhAAAJWCSzo+9D6VoO5pV0I9y3p9le2hti1VkxYjv1YybkFTVWlmikGAhCAAAQg4JjA3A4Uoe842Ml0hHtCwYssgc9T+FhrxfxeuVbM3IYmaxUfQgACEIAABCBQRKC8/0XiFwFtchDCvQl2P5XGWTFaKEaSfeGFXN5S+MGCpRCAAAQgAIFuCZR33AuVQbfktncM4b49Y6c1xLViJNmXzIopv+ad0sFsCEAAAhCAAAQKu3v0fbVUQbhXQ9lPQZrFrisxjrIXeVV43RaVxUEQgAAEIAABCPRFYIFOQOs/TwGE+3MuI+4Ns2LCM08/wvKObwEsuAjflskBEIAABCAAAQhAQARKZMaI4h7hzuWhq0OS/fwRnqCUv07yn0ISAhCAAAQgAAEI7EagRJb0Ju4R7rull8mK0qyYlxPZS64Kk65hFAQgAAEIQAACoxN4K2OcKXuE+5AZrTSWZA+zYrRczGNOP+4ZkhJOQwACEIAABCDQOYG3mseWske4d56P9+5pSkzYzg9rxbxN3PuSeA8BCEAAAhCAAAR6J/BWIO2q7BHuvedb8i9Idv3EIfaUhelFOo4XEIAABCAAAQhAAAKFBPJSqrKsR7gXRsXtYUqnw/fyjloupug2bbfOYjgEIAABCEAAAhAwRCAv668NLZL4CPdrZH291loxQbXHWTHledMXBLyBAAQgAAEIQAACDgjkpdpF1iPcHURytonpoacnDbGzQQACEIAABCAAAQi4JhBl/QHh7jqKD8ZfJLueNj2JnwAAAdBJREFUoIRkf4DDDghAAAIQgAAEIOCZAMLdc/SS7ekJSmF5RyR74sILCEAAAhCAAAQg0A8BhLvzWOovJxpl18Snr0/nnmA+BCAAAQhAAAIQgECOAMI9R8fqZ4dwy2m891SPT3r50FOr5mMXBCAAAQhAAAIQgMB8Agj3+cyanTHdlxD0upZ31FoxDLE3iwQVQwACEIAABCAAgf0JINz3Zz63xu/lgSTWj6cPZrHP5cfxEIAABCAAAQhAoAsCCHfLYfyW7NLrUu2665RRdsvhwjYIQAACEIAABCCwJQGE+5Z0F5b9rdd1+udXeNapJrKzVsxCmJwGAQhAAAIQgAAEOiGAcLcTyCu9nmbFMMRuJz5YAgEIQAACEIAABJoSQLg3xR8qv9LresesmOYBwQAIQAACEIAABCBgksCvv//++3C41Y4mDe3LqAfgGmIPPx/Miukr0HgDAQhAAAIQgAAE6hD4/fv38a+//qpTGKUUEZA2v1Xt0uuf6d7TY1g0hg0CEIAABCAAAQhAAAK3BP7vr//99a9//rek4n/94x9//PHH7ae8q0jgmRyPkv30GUbZ9fmxYnUUBQEIQAACEIAABCDQCYG///OfP//889///p//BwrQFgi2vvtrAAAAAElFTkSuQmCC"}}]},{"role":"assistant","content":" eyeint","tool_calls":[{"id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","content":[{"type":"text","text":"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. 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"}
{"ts": "2026-07-15T04:47:32.386538+00:00", "type": "image", "tool": "get_viewport_screenshot"}
{"ts": "2026-07-15T04:47:42.241147+00:00", "type": "llm_call", "call_index": 32, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:47:32.388048+00:00", "finished_at": "2026-07-15T04:47:42.241147+00:00", "duration_s": 9.85899999999674, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "content": "Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": " eyeint", "tool_calls": [{"id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "content": "Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Check backdrop coverage\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:47:32.388048+00:00", "finished_at": "2026-07-15T04:47:42.237637+00:00", "duration_s": 9.85899999999674, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "788921", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect camera composition framing\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\\\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\\\n# Currently, the height of the lantern goes up to Z = 0.50.\\\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\\\n\\\\ncam_obj = bpy.data.objects.get(\\\\\\\"Camera\\\\\\\")\\\\nif cam_obj:\\\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\\\n    cam_obj.location = (0.75, -1.15, 0.40)\\\\n    \\\\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\\\n    cam_obj.data.lens = 60\\\\n    \\\\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\\\n    # Alternatively, let's create a Focus_Target empty!\\\\n    \\\\n    focus_empty = bpy.data.objects.get(\\\\\\\"Focus_Target\\\\\\\")\\\\n    if not focus_empty:\\\\n        focus_empty = bpy.data.objects.new(\\\\\\\"Focus_Target\\\\\\\", None)\\\\n        bpy.context.collection.objects.link(focus_empty)\\\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\\\n    \\\\n    # Update camera track constraint target\\\\n    for c in cam_obj.constraints:\\\\n        if c.type == 'TRACK_TO':\\\\n            c.target = focus_empty\\\\n            \\\\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\\\n    cam_obj.data.dof.focus_object = focus_empty\\\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\\\n\\\\nprint(\\\\\\\"Camera composition refined for perfect product-to-hero prop framing!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Reposition camera to cleanly frame the entire lantern and background floor pool\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"content\":\"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Verify updated camera composition\\\",\\\"max_size\\\":1000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\" eyeint\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Scale up studio backdrop to cover entire camera FOV\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\\\n# Scale it up by 4x to be safe!\\\\n\\\\nground_obj = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground_obj:\\\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\\\n    print(\\\\\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"content\":\"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Check backdrop coverage\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"},\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":"Code executed successfully: Aligned all viewport spaces to active camera!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","content":"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":" eyeint","tool_calls":[{"id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","content":"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Check backdrop coverage\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"},"cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. 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jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. 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"error": null}], "response": {"text": "_", "tool_calls": [{"id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\n# This means we should rotate the ground plane to block the background correctly.\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\n    ground_obj.location = (-0.3, 0.3, -0.0001)\n    print(\"Rotated and shifted Ground Studio!\")\n", "user_prompt": "Align backdrop orientation directly with the camera perspective to eliminate corner gap"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\n# This means we should rotate the ground plane to block the background correctly.\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\n    print(\\\"Rotated and shifted Ground Studio!\\\")\\n\",\"user_prompt\":\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\"}", "parse_error": null}], "tokens_in": 25773, "tokens_out": 488, "cost_usd": 0.01953315, "cached_tokens": 17421}, "error": null}
{"ts": "2026-07-15T04:47:42.263729+00:00", "type": "assistant", "text": "_", "tool_calls": [{"id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\n# This means we should rotate the ground plane to block the background correctly.\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\n    ground_obj.location = (-0.3, 0.3, -0.0001)\n    print(\"Rotated and shifted Ground Studio!\")\n", "user_prompt": "Align backdrop orientation directly with the camera perspective to eliminate corner gap"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\n# This means we should rotate the ground plane to block the background correctly.\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\n    print(\\\"Rotated and shifted Ground Studio!\\\")\\n\",\"user_prompt\":\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\"}", "parse_error": null}]}
{"ts": "2026-07-15T04:47:42.263729+00:00", "type": "tool_call", "id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "name": "execute_blender_code", "arguments": {"code": "import bpy\nimport math\n\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\n# This means we should rotate the ground plane to block the background correctly.\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\n    ground_obj.location = (-0.3, 0.3, -0.0001)\n    print(\"Rotated and shifted Ground Studio!\")\n", "user_prompt": "Align backdrop orientation directly with the camera perspective to eliminate corner gap"}, "raw_arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\n# This means we should rotate the ground plane to block the background correctly.\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\n    print(\\\"Rotated and shifted Ground Studio!\\\")\\n\",\"user_prompt\":\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\"}"}
{"ts": "2026-07-15T04:47:42.297628+00:00", "type": "tool_result", "tool_call_id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "name": "execute_blender_code", "text": "Code executed successfully: Rotated and shifted Ground Studio!\n", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-15T04:47:49.459671+00:00", "type": "llm_call", "call_index": 33, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:47:42.298628+00:00", "finished_at": "2026-07-15T04:47:49.458664+00:00", "duration_s": 7.1560000000172295, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "content": "Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": " eyeint", "tool_calls": [{"id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "content": "Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Check backdrop coverage\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "_", "tool_calls": [{"id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\n# This means we should rotate the ground plane to block the background correctly.\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\n    print(\\\"Rotated and shifted Ground Studio!\\\")\\n\",\"user_prompt\":\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "content": "Code executed successfully: Rotated and shifted Ground Studio!\n"}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:47:42.298628+00:00", "finished_at": "2026-07-15T04:47:49.453120+00:00", "duration_s": 7.1560000000172295, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "790204", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect camera composition framing\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\\\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\\\n# Currently, the height of the lantern goes up to Z = 0.50.\\\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\\\n\\\\ncam_obj = bpy.data.objects.get(\\\\\\\"Camera\\\\\\\")\\\\nif cam_obj:\\\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\\\n    cam_obj.location = (0.75, -1.15, 0.40)\\\\n    \\\\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\\\n    cam_obj.data.lens = 60\\\\n    \\\\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\\\n    # Alternatively, let's create a Focus_Target empty!\\\\n    \\\\n    focus_empty = bpy.data.objects.get(\\\\\\\"Focus_Target\\\\\\\")\\\\n    if not focus_empty:\\\\n        focus_empty = bpy.data.objects.new(\\\\\\\"Focus_Target\\\\\\\", None)\\\\n        bpy.context.collection.objects.link(focus_empty)\\\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\\\n    \\\\n    # Update camera track constraint target\\\\n    for c in cam_obj.constraints:\\\\n        if c.type == 'TRACK_TO':\\\\n            c.target = focus_empty\\\\n            \\\\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\\\n    cam_obj.data.dof.focus_object = focus_empty\\\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\\\n\\\\nprint(\\\\\\\"Camera composition refined for perfect product-to-hero prop framing!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Reposition camera to cleanly frame the entire lantern and background floor pool\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"content\":\"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Verify updated camera composition\\\",\\\"max_size\\\":1000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\" eyeint\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Scale up studio backdrop to cover entire camera FOV\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\\\n# Scale it up by 4x to be safe!\\\\n\\\\nground_obj = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground_obj:\\\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\\\n    print(\\\\\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"content\":\"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Check backdrop coverage\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJFCAIAAADBNEo3AAAAAXNSR0IDN8dNUwAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAADZlWElmTU0AKgAAABgAAABIAAAAAQAAAEgAAAABAAIBGgAFAAAAAQAAAAgBGwAFAAAAAQAAABAAAAAAkHjvvAAAAAlvRkZzAAAAAAAAAAAA2iq2zgAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeAHsvXmQJFd1N1pVve/dM92zj2YfaaSRNNpGC0KgBSEEBond7DYY2w+DTfAZ/+FnOxzxHA4TH47P8QJjO8As5rETAhsMRmhBEhISQvtoRhpp9n3pnunpfal6v8xTeerkXXKrrOqemUyNus49y++ce6rq5slbN2/mlyxZ0tfX19raWigUctmRZSDLwNmZgQVtpUJ7XzHXcHaGX++o8/V2WG9/s7OzcNnQkH0e6p352vvzf3j9rVp7D/U2OTnZ0tKSKAwNW2Mkgg0xqosTGYPn0HuVsrh0GhjsU4AJksU1IuroKmIPIkcUWTGi41C1mZmZE8ePHzx4sHHBggXNzc2lUgkDfd49QAfbQws6pIZyn4hgk/pLEWSxWKy/38xjloE5yUCpWMo5X8WQL++cxHYuOUVdMjExUeseNTY2tre3YxCDI4zMY2NjoaMZhvG2tjYKDB+EpqYm0ITA0WLcP336NDcDCAUN8ShQsEVg49FSgeuHpsbGAHcmUdhZMUxuwqwtr/YRaR40Ru16GNEV3mt8zPCBiRmJH97figkVQ71efhCS58p7jRGlppoGhgiJ8VPCHXcPRjUSGE86OzowzkCK8WqmaWGubZGuOXny5amREzofH7Ouri6abi6W8rPtq3KNrYpaqTg7dvA3xpETU9UYYEkfCv5pa+ccipBwNi3jF4sYifUBECfbyeYlhZZexa9jPjvdOLLTD1vWgjtAGdDcEdU4PkO5o7MTucLXaumyZQBqxFhPczNoQIwzRBnefUFq1q5du3v37uHhYclHn51u5XIIAqexxYsX9/b27tu3D+8XQNasWQPRnj17jCmTOLWj8cbUDjxDzjKQZeCszgAGrwQnKWfC48QJDGsd7nHs2LGaJgFOgH/q1Ck5LGM688ILLzxz5gwGWBqEKQYMvAOLFrW4szAYn+mMiOEdYziGaOhDGYQykkPt+uuvHxwcfP7552VfHLSBAfiCFaGBQz/MTk1NTU9PQxnXMKf8lwE4UdnG/I729vjFnIxIo5O9hRpMKowEn6U4fjV4jREHLYludId4l/Exi/xe+4H9rSSBRrOpvR/hQZDRojNr1QomJVwOemZ6+tjRozQOdHZ2rl+/HiPY3r175WBFymOjo8uXL8fAMtPQWVj79lzBmWvA0drccPVFiwqF/G+2Hz0zePjUqVeIr/yFC6wWAXOmZ3PDshtJuqivbenC9n1HR4bOTJZmJ8/suJ8GK8UWI9WyZcsw+gEEwxo+rhgqV6xYwWpHjx49fOQINREJVfmocnt6ejCWDg0NUXemui4p9G0s5PMrFnX097QdHRw7dHLUmT+bOtOyfx9mT+Bo4cKFAIcJDAGIeHDVAT7+9vf3IwYMv6OjoxDhi4NccQySgMKy5cthBb/9AwMgKitklDEX/XnHO97x5je/+fOf/zzN3zAQBY2MkwmKe5zPcFYjBYSIgOTvZegtqnmECCbeS6jBLyb7GVAnkESKbenSpcooAL/0hulWzFH6Aj660N3dDQIBIJVKj9jQSOBtW7VqlbwYQF/ovTTqG5noDq5wWIQPAdGIR+kg67Bf2FLwEAXnjW0z4jzLgHMhnR01zQDmODCw4Nv60Y9+9DOf+QyGEaM7fFtXrlyJVYiQSh0Mg8o3HV9q20DEwykTQMOw8773vQ8EhqM77rhDeseYXHSXx4AgLxdccAFGUQSMoWbjxo00nEoT0IsWLfqrv/qrj33sYwofIDShAwInGACijsdfNBEzj13Sqqe7e6C/3zYq6gOytDXR8/3zjIKH/5nir56nwWuM6n0EILA3ELEOfNIC324NmBmx3ERWZngmIptGVGRgl3CmZ6v9cEgAoiOGYlKTYK5cY5isEvKcIdJd7IAx56//+q8vuugi1JB//Md/rAx9QKfh1HHT1LWkv+fqiwb6e5z51pnZ0sTU7N03rVne79SKtqMyMLY45TuO6y5Z8s+fuekf/vj6L/6vmzatLjNJpPxFhDS+gaDAMEuC+RHU0HRs2LABxTVZ4fOMmvOqq67CqItRHSPhNddcw+N2S1PDH999yf/94av/8G2X/M3vX/N7d14EdTJExXjnnXe++93vhhUK4EsvvRQ4JII7zJhgCgbekSgcSoRKE9Fi7CUmTCpVu6KHEhOnGcC9/vWvh0vjSM0mWHODavLrX//6t7/97W9961tvectb9u/fz2nFqQuho/7GKQcnkptvvhkXYcgFgkb0mzZtQjWPLICAlz/6oz/CuQQhfupTnyLR3/zN36BMhzkuD9BtoOGj8IY3vIFtOYxgAu8Qnepgi9MqcgpMXPDBO/Dp0gdviRHkbW97GyaubrrpJgSwevVqdBa2CAMBIyr0BacrvNNofvCDH6QrEx0Hb96HPvQh4CDvUH7Tm94EHeQWsMb04ioNfvEuvPOd74TVrbfeum7dOgSAv0gXEOAUrhEA4n/ve9+LvuhOM06WgSwD6WYAI8mPf/zje++9F7PXGMTe//7340sqXdx+++0Yu2688UaM1DhpYYS5+OKLMSwQB0MrvrYYWjHaXHfddaFzEBIZI8/Pf/7z55577sEHH8TXX04lSDXQGKYwAuOAF4wkmMiRswasjC784Ac/uP/++5ljJOD35MmTmDHCuQ0EdICpaOKi4uixYxiWFX7KzVrWHBFDpRAiKsdX4x56phrDE9TqtcoO4vSEn2VMwfmBa9YvBvb7M0WUnCecCNIp2as4GKkKDDJlJH9AxK4aPQrAu971LhTBr3vd6/DLJIZBlDoBVqdHp06NTF29adG7b1nX0da4fKDje/e/Oov1n5GPxobChhXdzY3OXT29nS3vvXV9RFMMjG9/+9sxVmNMvuyyy974xjfS1Lg0xziPEQ/VF0ZUKqAvv/xyUnjLa1ZtXrsQVxqT07P4u/XixTdfWa7EMLyj1xjtMZeNQXtkZASVJ1nBHEMlqllMeT/99NP4yqAElR6DaUPhjuBuueUWFIIoEA8dOoTVLz/5yU8wWL/2ta9FBFdffbU+NGORDE4h6D9qbhwoQw8fPsy/UOCkdd999z366KOIHrM1qF+RIMSNPiBfSMdb3/pWVKgIHUzM7sAjgn755ZdxWQN3r776Krp32223wWrLli34NOAUCHdsyz2EFGdQwBo/IrhkwWiCChj66AJObFdeeeUVV1yBgHFxggsj0HDHaEzgfTpw4AAuNrZv3w4XKNkRDAJAomCF8y7eHhBo4oIMfy1jVg62yMPmzZtx6YLOwi8+JUCzvWH4KQce8X7v3LkTuYUafG3duhUXP/hs4b2AU7hGtum6jX5t4bAz4jzLgH+APs86n6C7Mc4JfnQU7vhN062KZzAWvfLKK/gyShV8W1EKHzlyBGoYPXDhDQX8xRU7BiiMdfgK4zL7Ax/4AJ0D2BaVvW2MhSNMCmBkAw4QMM6gUMbf17zmNfiLi38GAUE/wsIEpwqMIai2sayFhj7g08kD5hiKUbj/x3/8x8MPP0xWRjSYYPDEgXHys5/9LHBwYlNm1rFEBwtonBhKJcTjEF5xTyc84sT5q32ewdB4cQCr1SX/tQxB66HGqLYPYfapOOQPgPCmAYOR3kHo/Dc9YAWJPaQwp87QApR51RCE50dgH352jVqobTCd/IUvfAED4FNPPYW66O/+7u9QxsAdRjD8EogxBOWW9D4+OfPKgdM/+/W+Xzx54K4b19z35IHHXzy694iztoQOFE6YPsCQhWrH41Ver7pw4IqN/ZesWfDinsGndzpr4lubfXdZ0NCKvxgbK2YuhZHwxRdfRJ357LPPPvDAAyh69Q8wxu3f/OY3WIC+a9curFF85plngIOBEZPrV24cmJ6p3E45NV3cetEijFSo8VBkSl+YzqapYVSPGPlR0f3nf/4nXSQACGlhZcoPzhcImJmS8J1vSADExx57bNu2bY8//jhS/+lPf/qf/umfcK6CV8yCo/KOWyDieguzwsgOAsUpBDh4C1GLIzs4e+EchpMHTgMvvfQSkoKZIeoJ3QSGiSuceCAFCE6QeOeAgJoeQZKtvDYCH9cbuMJDnS07yfQTTzyBuX+8SeCgdkfRjHyhzsZ7gCVNuGzAm8fKTEAHb9iPfvQjzHkjAFghNgSAtIJGRxASlBE8LlcQD1+xMAIIuEBPYY7LD3QH7z0uRpFMEOia1GQaToEPQ5xokRZcmUEZJ2MooDIAGnyhF2BiARloHGybEVkGsgwEZwDnsmoOfDHx4ymGGowqGMEkFAYHjLw4aeEcg1EOM+47duyAAkZRDGL4Ll9yySX47mNgAVOeJIBmG2MxmfLTn/4UI/Mvf/lLDM6o3TGOYY4DYxGGaAwmMgAU2Rg2MWIcP34cceKsA180IQIC51GcJHDBjyEUszMgaO4AVhjKgKZ0hyPEiIdhnMKW7kC3tbfTMDg6NsY1PX6NxlwDsoFD0bc0ub7wvznEttjUms0x1dKR1kONUUvvDna6DvE58Saw/MDUwt/qDobxo1cHarD2+5Etg3JUloQBnd5BwH48E8+vkX4LQw2GkS9+8YuYq0Vt+o//+I+YtyU3qFVw/Y/FF1THK74Hettuv2bljx/be/vWlZhuL4pxA/XVXXfdhXUKGEIVKzQ3ruz9zfZjDz59aPPaBe0tjZj5vuehXVINI+EnP/lJZ/rBXU8oRURjjoNKQTR5xJNqmKnBAE4IEsSbpqjoEgcgCBUXAyjMQKNyQ9mGcwHhY7T8zne+Qzio44EsB0mY4OIH64twf2kFV1ANGLthwxwEhzEdM8ooGXGgyrz22msBgeoQk+64gQnFMZRxMYG/iAbO4JtmmnFxgLMIDqihGMV5i5BhiNITk9l483CCQRGPDjzyyCMgcBICDUychHBmgi1qX1S3OOWgM/gFASAwx3uGKy2Eiush8LFGBRxMRZMtFCh+VMxw/cILL4DA6UrPPkphgODkh/iREfxF/LhggF98nmCFs518SwgWOkgFpucBjgNOgYO3BGnBhw+lM87K+IDi9wH0GsrAoXMYmdNfnMjx0cFFGzKMywMo44Pyq1/9CulFlhAMrKQ+aFwgobPIG6LC7B0m4FGs42oK51rM3CMknOZxCgcCOgtMSG3XAApy1jz3MtDelMs3t+M++HOva7XoUbLzJQYHmrbAzDRmgHC1j+kZjMWYDsDIxvUuvs5Yj4eR+mc/+xkGrieffBKzCRjNMG7g50eMFRhGMGZiQMBXG8Mdf21R00NEYyxg8fVH3+ERfjGtgGkejBUYG4F2zz33wDukCAAjIRYrgkbFTGMaDDHO4PSJkQGjJXQw7tEgAxcYyjB6oKTGX/AxNIFJoyjGGaBhbkWioQjDZQbQMMg89NBDGC3RxMiMg7KBUh6dpYkD3JrW29eH286AgLBxLqSRDbvKIBgwLYdWXxBDY1vM02ez//ShK4gmJ3Xvci0c5vOF6WnsLVO+3dDpcXVuOFPVwVRSb6ekKy/sNLwyrt11MokdGJK6H/jWnzxxAmUSpi1QjKFKwbjHUaBAwgiDeg9DB8YNmr8oNnYuuGDLm69fddn6hd+9/9UzY9MXXtC7/9gIZrInjz0/MezcJIphBIUW5u+BT2gYIVF9gZ7tWHXZ5ou27R7EnP2TO449s/PkDx/a/eKeIeztMrL7l1yFolhC3ciRYNzDaAkpCjkMcRiWMRQjbAyMiA3jIcZzunBAJYn5WdSrmLkAgWGWKl6MuuPNKxYtWbF2WTcv6mluLNz/1MFX9h0vDD2HARaFO6o+FG+gUbkhAJwp0AWs4EBmKAMYhDF1ghobieIpGAy2yBIXt46mO/9C8edRQcqSEWMxBKgFcfJAT0BjbQaKVIYjM/z6iTcA/aGhGQs26J2gnGIQx9kCaaI5HjIhfdAg8BeXBBjTUS5Lvl5qky1bkUIUE9vMENsqmNQMDoC9Qxk0dYRoNpf4YOoHKZAtg0T3izoAHzi8I+xIQgXg6JFknHMpA/3tpULHwmwf94jvabIzGmpWKrJvuOEGrHNDDU1nBVxdYzqcFpxQAMq30vhthSaNAMavLU5pmCmHDgp0DJUYUVH04xoAv0HjL4Z1vacY/TEyIySaYocCTXPQZAFGYwzvOD3gGuC3v/0tznn4JRejNH69xYiNsl4BpFMU0BAJcHBWw/kMUHABTZwdkA0sagdNpzoESQg4seEMx5cxxGxrbcWYTLT4a3kfLGxhWCuyXp41PxqjVj10cWvprYw9PTONDzY+CYk7UssglaCEK0EqSgmaqYIZ/Vs8WNhGiHSZGBYOHTyIAu/uu+/+h3/4hwBwXMljthF/p1uW3PneT2E4nJiawaw5TLZuWnTs1Piew2dOP//NU/ufNoKgEKKVJ1MDN12x9aY1S7uf3nn84tULDp8ce/YVZ7UMdpU5fP/fyvpW4qCSxlCGAQ2/f6Ks4hEV1SzuX4IVRsuiW4/TrjL4fRVDKK4WUF5jpMUQCp1TXdd3LN70f929+aJVfVDOF/JPvXT8S//14szEcNv+72DkxIogTEYjVAy2WFKOOh7jJLqMIRqr6hEP0oXfPDFjgqEYHlHaySCZdnK1ciX+EsdXuKOoR2Q4x2DIRqVO5x6M7yjHqaAnG6ihw1Sj45sJES5ZEBkuIECDgzl76KAop7Ma+64bgRhwpqH46+Y0c5RlYA4z0Ndaau5aMJtLfpqcw+Dr7zrZeQ2DHqbJ9TMBflHE6JfigIPxE4MwBjFkBlsujribOeLsglkfjOw8Qy/zhgoJpzH8RRgokXm9CgiMwzSA46/txCChQAMHJyeavyc0DKoY9nHmAAhlgAt3xVZpItWwxXkEnfJEpvSbeJ5+nV7rEoLmRGPUtLc18+YBe6/oBT4q9BmO3iNhHd0ogabfj7+VAE43qQFkBCd18arHITkYJI8cPoyCGCMYRkspUmiMCSgdMTjMlBpbNr7rsks2PvfqyalpZ0K9s81ZDTE6MWMr3GmERNkJ5anWFQ0X3LG4v2ft0q59x0YOHnd+7sMRULhjZEPhjlEOYyOmLTALgzDIChzU0OiF+5uAUyjPFmexmy15RJGNnxZ5ygaFewO2gyzkMemOdT6HT45iXT6mprEdJAp3eIEVZvExcqKixg+bQMPgiVEUfHiHCFcCQKNpjoDx2cnV4sUcpK9wBygLqA+2v/IUFdHEBlUjvoywRi4y2CwD8ycDXc2lru7eqbw+qTl/YpxfkSQ7x2HYxVHrnmBkx4BOXrD4Ec98Ch3QcJLAUTbxNjtTBmeck3BCjRK8ggbvegDgyEWoOixnmM5SOL3oOkaeQa1mLFNMqTszOTHxUnfMgKl688C8V/aiEFOTk41u7aLwqRlmbTSqhikcCrIaRKNtLbHhMBA+UGiMthZMrJfDVb0+YkhfGJpoIoCY08X8bM7wwN3ZyeHizKQ0JBo71OJHPBrfMApNl5pL2lPDMT7NjpcnoBUEjK78AyBqdByKApCx0Ivw0ZGZmWljd2bzrfkGwzkXrvPTZ3hfSAmOaAFrfKMwPk+bxmeoy1wBTS3cpYOMzjKQZeBsyUBLQ2nRgq7xnLpJ39kSf/3jNA6d9Q+jRh7pVMQ/rdbISzRYU6ZNvGhoqWnVJQSTExMvtV5pQGl48zC8V82JmYFaB7U77tVgcUwAtktMCIeCTAxnM6wltvRp8WNhS8s60/MvosQJqGVXgI2V4/E9nAU/rBsvdBK/CZlhloFzMgNNhdJAT+vpnLMqOjuiZCD+aMmoMU1jqrObaohpd8V5k2FZeTWoZltL/+KxzdC14foj87fIo4mXKBYNSGMwrF3CKkmIRLDCSJAJ3GMPBjzZ3p1h9AP5WwmQycQE4+f5W4kdBRhaPJjYJl4Asl9kMbaw/bblVhxdI0BUZt0cRQ2oWj10SC2xsVqd7shPju3c7+kC2yAsefQV7rgTvLEZF8e6bjFfUn9KsDmqhu/+ZOFzhN8OqgHMbLMMnCcZaG8qXrqqb39+Ffqrf4HjJyENDMVrDSAVD3GbSSOKaRdTPW4vjPq0sRWWZgqpJQ4LWxhaSZOpiQcAC1uHNimaeHEwyYsJReNpDD1CO5pFEgFTUwlnRAhMgAgyJEpNE/qCZyZlMEIjh7vmcFM07myuKEixD7mi4lIhen4xLARDkAqo0vQUvVcWawyWKIRJ0cQjsyQSWNrNwoRVRRsWswIum4ERJ8KNgCgDMNJpYPiAjx8/8ZK71S9zsZAy9blmLKzhdTvkqFK445q4d/3re9bdOjlTwkNccYtAU2MBz55tKOTHjr/UO/wrGECHFgZRPY06G19KWouJVYy0whJB48BKSiy0JxF+rsUyfJjrIro5AAqAAgJMsMMOLyGFPqcjI7IMZBk4KzOQ+mA5l1lI1Bl1pqbmHUCUXqDeq+LTwla0bE2LtcbWGDZA8E26Jp5FNSayCcXiTUe2KGpsjaFDaXGYbEw8CSXkZpKUhUxjBMn8MWqafjEhV3htbe3Y0m7BQlG4m99rMhTogmTQCq7DMmmYeNKcaE/Le5UaJp6Ugw5UsQstEgubfVrkFjabKYRF3cJm4zA5K8YnqLqL7ICLwcgWekiMAVEVMBXg/v6FBzs78LMSsTABj0976tPNKLxxB21//4D7y5Xjyle4L1p3/Ztfv/7Hj+65YkM/dsHEfbIHT4xefeGi7//nSxQWdqO84447YEy1NfbUxMZkJMK2lDe5T+9DCY64sTMatigmEe7YxU46oMGH9H/+53+w4QyJ6JlHtJMx9g/CjgfoNhfupJP9zTKQZSBaBuS4FM0i04qXAWQ4lQE/nteI2iIykPhn+jwIpYiwrGYyjcpjEJ2ICmHS09EUjmakMWBg4ik41DQpRuUpgJqZxrAH5lf1Wt6rdKTxPIb3alH2i/0tNtHYgiFIbIiBff3bsWCGLX2En+1vkaLG0xjQM/F8ftyGp+W9Sg0TT8qJDtQyCU08hk0uZIhoRJ1Hrhq6o1EtMHFRUmIaHCN+iCrwKIZx+zW3UcGnXrUDHFPY+AUVyHzPUqVwhxj73v/o4d0j49O0leaxoXEwR8acLRQQH/Z1wjeQdhnDLa5g8oQ64DDdTkzwcdDEPCbUweS9DoDAs+80B4+HHwEZewYhLOwEH3HHA3KR/c0ykGXAmIE0Bs00MCi49JCMnU3GrHrYj+m2ZkkQHRFkOTqXo7PjxK5ZawxGs0tYhQmTrokHAwuboXTCZBGVp6NZQkgO6LcULUHKMATbTLKyEPuj9gs0fb/Y39KUmRHigmFom2oUD54lS1yGv+XpOK8micbTGBJB0p6i98oyjcEShbAr2iUKhGgmsWHzmMYWdQubvVRHRBvzoIUjfiRkRxHGtyY7/a9EldIEHvDkDTwQCQtJ8OxYfo4S9vPF5o94eB8/SlZ6CaMRXTlAX+He1d60YXUfnhwr7a/ZtOin+7fhERvveMc7EIQUEY3dhVGR4+F8mGVHFY7np7LOhg0b8JA/7FT/jW98A8ybb74ZE/Mk3bRpE3Y+xmQ8CHB++MMfopSXT9hikIzIMpBlIDQDzsiCpWUJBhgDdDooBuCMlUYG/G+Pv6XgBwoVXdk02UXlSRydjopi0tPRFI7JSONpDAWEmnYtk8TEU2CFikd6r+Ga0DAp+3lBLa1T4comn+FWfg3HLcoGHHiQKk/hUTDGHpHID+JvlY3NCWEhE56x98oCIixsRQtNu6JFYmHryCZOVcY6IMq9hIhUKCY09srMcO/letSNPL6z6qz1dOkc6UGXGjl4Ut7XvvY1XK/eeeedmLDGrvAoj7G3PepeTH9DiucdYak5NnfHjvL4ajzwwAO33XYbHreH520TIB7SBAWsQEH5rdTGvkL8zPj0S/ucLeLlgUfIookvHqpz3vlSKuBpIHgaHx6khydy47lQUkQz8QgXi9dxYJ0MS3fu3AkThEsH8GnenRUyIstAloG6ZwCDZvxxMzTKBMNeKGZ1ClVElMg0paR6bw+9ei2ZCikBHeeQpsKO2SE8ITaQJhSoEduvb+L5NQwtzcjkUFMyAJljgqIdEBLL4dng7jIDyUaejJRES5CkLBmgRVSGliL2a6imhE9/2Yvb5JYfwG0p+oLHeI75ggUL6UHszHW6qh2OqvtPSOx6QslGesbeq9TTPEmhQpvsoRIHQiAmNGMESzQsj0wkGsoio0tFeIrsjHQjq0s3oNlaEopO7Zt0fyb+YhUJymBUzvfccw+KeFTtzz33HAhMWH/rW9/Cj1EogH/5y1+iOqdD3tgJq/e///133XUXJseVkH2FO55WtWGFup3cFRsHFBs0sbs+LhRo4wJ6bCrr4EcBfEVRpjMHy+rhGM+IghVE9GTsdevWQQEguMLAASYePMtP+2PbjMgykGUgQQaSDnsJXGUmlgzQ+bnas3S5OvBgvFfdp12i6yockynx8Nd/mFT9Gr5WIEq14PBkwveHyBqaN1+gbsNvyXKNzZis4hGexKnUnWLde9UD9TQdpUo/RIXvsN1DiP0MRdnTl0oVZMePzxWp8V+/F38LSsxgAzNPiMs2DgcliPMIG1RVJhyygUQ7NB6Za3o6w1P0XhUNDViRc9Niz3ITEWYTODiHGcNhoL0pIIeXyMgDg3FV9mUcgomMFFPdC9bwykiSMOilxsJsNWaxP/axj/3e7/0eptKpBAf64cOHccMnVqvjfs4PfvCDmFCnNfGY1EadjFUnK1as4Nodc9k/+clPMMeNZeRKZL6lMmMT07sODSsaL+waVDho7tix46GHHiI+0Hk9PggseqGCnq1QkeNAE7el4tqC+Lt37waBe1tx4JoDNG51JVH2N8tAloHEGcDAX8WBga06gCp8nyOmKeVPwAhSyZFdoigamyZrEw/GFrYRlpgmCxMvHXATsolnC9ii67G9V8VcsD3Se1U0qekJvVdwBSlN/Oyglh/ZhOi3tnvRbGMYBqK6wHg6/fDp4Z5edWYwqld7MNK3QLMb2CUWqGhsRcvSDHMeYQQOg7B4dmpvzRQ8HBrbCBFH1wjATEKK6ZisEaqpHwwdneCVzWutAAAgAElEQVQgQk2ipUeBwRLxxx9/HBU8ynFMWH/zm990VowVCih0wQRBk/GwQvO73/0u/uKeT/AxQ89QWM+Cg5tMVAp3lPnNpdGbr7z8vt8e2LKhfzvtKnN89KJVvYd2OVPsmOQHNFkODlaqeewJgwl/cHAfKkLBrwAvv/wyqQ0PD+/atQtXG2TIm+ZAumrVKnQGU+xYu4PNanAhgjX7iEEup+EoMyLLQJaBCBkoJRphIgBXr5LSYFt9IIwQPyJ7du0SdheR8CP5WwRh4kUEtwNYQC3sAHcWCxPbxAtAhshkEZVnRNaMBUOQ0tZje6+WsPxsoewXMLJfw6+kycjKzxYtQTI+E5pQY/idsyERJm1WsQhddld394H9+2XhbtKOymOXFsKEA1UL2wgSR1cAhJmFDTth9nAVBiGiUUmLqYWtWrtt6OKIEKarF/KHwFgpAipZ6HbgRLBmT7EIxZtiK6S05wrJUfTSlDSa3/nOd5hmHeZAgSpkyVGcoKDn8tvRv+aaa7C2hpSa2xe0Lr0iX6jU+8Qvjp9sndynAFETdba8PpA6CUS4PMBGmPxLAQimJXJGZxnIMqBkYElH8Y7rNhxscFagVXfUbPirGXDi/kaIKFAlUBg9Kj+Mv0UoJl4wPv3sKRcfmjBMvKQeTVhRecF9gVQD0hhxwhbGZlLG42l4r6ZoWN+sJLisaYLx6/lbbOixvVdNwAwmNFWSmNgmHrQtbPZgUtFsjh871tPbi4m8SNqErYFIl5L2FL1XKWM6UMhaRNh17RJYBgojyKOpRNaCovEwhWniGY0VZlI7BcbWrBq+agBbZC5/+4svDg6eJBXMXGP5d+qzzyjZ8YOV3LaxMuMOx1Njg5Ov/MIYpLqAxqhUNVNeUmRVe9XpzADOowxMzOYKszOlhtATR2hO0p66qO2wGdodg0JgRMmFBk92lnAjSKlvYUuVUNqEYeIRkF1ic2SyMPES4ZuAovI4YGFgJlnTH2MsZZjG1febaC0KRmMLL5qMTZjwa9vZFj27QZjEAtg/MHDo4EGs4mV7l7BoW9h+W6VlsbGwFWNuBqonFxJ+2sMrRx2P4Gli0R/mAUqwQ4GT2oUCk4KEN5pQrFCzBB0KYERVmMAmD6qf5StWjIycoVs3MZuOfWNSn25GYSxrY0TWiFXpOJQY56qZeofnqiOZ3ywDdc7A2FQ+P1u5I7zO3ue5O8uALqMOVAkUSpRg2g/jb0lLu0RqBdAA0DA0BtvbJayiEyajqDwdTeFEAjIpAUewzaTJl9D0Q5iUmRdu5dfQoDWxBm3SMPHYkAiTiomnRcQ4Fu0weaCZ82N/Po+zvKhCTAYmHjvWCVc9po2OIjhqaVYRhXmxW1Yw0qLgC0dYRKHeCEZDYrYmCUaUdqRZdYTBDiFln0wEmCQNh7Dl37KT7u6uK6+6KvVZ9oAuYGWKb8Y9QDUTZRnIMjCfMzBVxCkxK9zVtyhwmE4uVN0EtoUbQeomgUJd3cgxYZh4ZGyXGMHBNFmYeBZVGyzz/VhBLZOJ0BckaxLhSbxXP1dR9gsjmfiVACAYglQc+SX+Fqta2GFyk5mJF4ZDcoulha3YDAwM4Ga2gYFF7KvmRGBguvdAddRtgXIdzsQJQ6HqMJqjOLqmWCo8OxJJoBktpAqkS7G15CdCkgCJaWM4CdDQA4Zy7idtaOAm0EjKvdRFukc2kcq6GjizxWJWuBszkzGzDJzPGcDQwWNO1XlIFSw0mghx21XsklC/uoIAE6SiZ5coigFNCwbYltRbDOK4sEPYJTZ8v4VoCVLaCrYgoeFvsYnH9l5J4G+xsiYUeoJU9NEUQkFqcLphiK1fbDS3q2iR2FUZ2WRDwiQSxSEWuE9MTLIvH2GH96mJhmsR30wg6KTlO0OKEXwF2rO7CFoRVCQc6AjRsYWNgFd5+CEVIRT9cmkZTOtIpE94cboe7Kd2UqUHxqbCpGCMTIiIb5MqHVELd/EblqJpbWbrW6ypyQRZBuqZAfdLjz9JR9OkdvXso8VXWOiB8kChxaGZLZAEqesGCnV1I8eCEY9tRGamCcvEg4GFzVCSELpmUir7sSPpC5O4+sKUgxAYzAvXs1hJBAFi0jbxFHOBoEgsxha2HUeB1Zp2QE217ATPgxwePt3dbdgX0mRSHS9eeKG+og2uqWm5A3r0r1ZM9dDeQoEgWVNLpyInRU2L7UMJxmMi2KQKV8HA81zqK9w7l1zcvuqmfF7dVSagD8Wp4dM7fjw1NhSgk4myDGQZyDKQegbCBu1AeaAwbqgCTJA6SqBQV7dxTDAmHtnbJSngRwMXWmZSj8TT817D+uLq+ZVhojEUR57ce43kRWL4DSN41FQ0hLAY2H0MS4tqGFRYKIGwNiH2xzh0+JCvcLepcnwmwjVKZGlCkzwUjTXBlT5cmqrTMF/RtBhcKXnD0NkuCqFgk4nmwagFXU0xistgHZurYCuWckTAYRrS4CabxyHw7LGpUkshV2rKu784SRfUCyUAgBOHpNJXKVcp3LFGp2PtGwvt/VIhlC7klnWsPDb10v8YNbH35PLly/F0KDykKe5cfnd3N3aIx6ZmeK5qXFvcbovfAXB7Lx7jin4ZY8uYWQbOuQxUuY+7HEtSzU1KwHJk0+ILFEI7TK4BhjA8PO/Vph4mt9kpfBOMiUdmkOBfnLRbsOKx4VwYCNIvkF0TSoK065Otp+q9MqLGYImA1JQ0BltpEo1BqhY24wjvdoMIIBZju6VdooVE2Mpfi72FrRiLZsUAZ/PmpibcYNfS0lJWiPNBFZg1JKuNCPZ0VPrtcbTXaLqsBfsIoOxF2sU0ZYxgQvFgVy4rhkVPciiHKdo9RZXI0CUN++BmVAdlvRPF/m0zm4ZLXYVcsb8weGnjtvb8aLgLJQbhtAEPP6JdZVBkd6y6Md/YCmkhX3zD4t9e1rvryMSC25c8ubh1qCFfvG3xU6MzbVf3vXxx997dY0uLpQLhzI4cHj9RfuKSQM4B9hOf+ATq71tuuQUPf8KDmVCIj46OrlmzBhtSLlmyhB4ctXbtWjyYqa+vD5Hgy7xu3To0IfrQhz7U2tr6pje9Cc9YXbx4MfbZwYY7kOIvEFCXY7P6lStX4suPRziBD3x6ltOiRYvwdKc77rgDP8lhT03oo4LHXvUbNmzAnb8TE9kNfPJdyuhzJwNXXtB8unlldf1Jb6wEkvyXNKwwDJabHLAQRBqHH49aJly/nkkjKo+R/AYm16wKIpejp3M0uXtm+23VViCWqyyRNW0hxPP5ZMv8ARAa0BYtQTps7XDljouKlWKiWQm5sCJkIdNcOQySC5GFobGFSZkUKkTir/8QGn6Br2U39qmVG2GYWhASxOKKVAItoWLy7LPBSnc8sRHPeHfwTNrkJ/ivi+iDNeiHyQ0mHivQNFDoIVRea6geE7oSk0vBGtVhdRgKZFpNCopq17AAqRvkWaHTCic5zlBxwcNT142W2rFj80yuYbjUeaS45ILGAw25WQuo8pbIppMPlMeVGXcJ0VyYQdX+b7veMjbT8uDxLbOlwkyx4cRUz8Rs88mp7qbC7JaeV58c2ihNdBpXAnj8xw9+8ANcYaPI/sAHPnDxxRd///vfv+6661Bb33vvvW9729ug8+KLL956660gnnvuORTcKMG3bNny7W9/G4Ao+vHdvummm/A9X7NmzQ9/+MPbb78dT5F9zWte88wzz6Bkx4Ha/Yknnti8eTOe3vqjH/3orW996/bt29Ex1PEXXXQR6ntU87hyOHToEB7j+uEPf/izn/0sP7lKjznjZBnIMpAwA2GDa3TYMKRAeaAwegxS04P0XqVM0mFyqRtMa0gag+3tkuATsmYnGIJkP0wIoZlkTSasekLAyky4Qk1DY7A+EX55UEsxNJmD50ewKEWAMuFY4COghVlavBFyoNDo3GNGsNRUNEYuh3qgWMLMXhHnfQ863qsLakBWUWTNo8pC2oGmTv1k/mwYUROpM1JgPwk6mi5rMUHWOgY4gV4ZoHaEDErSJo9SbqNNdmE8ygEjosm0YhogenHmotlcw8SJV8cOvYi/i6778Eh73+6ZNRsbdyggoqn4UZo589cGZfojJy4dnm7HA13wd3SmdbLYBGKq2Dgy0zY223J576vCh5mk6hkF9BVXXIHie82aNaihsXDl5ZdfxgT8Qw89hLr8ggsuwFoaTIpD+Ve/+tXrXvc6LG5BOU6IO3fu/Pu//3t8t6FDX/Lnn38e5uDjga+YPkcTe0tdeOGFS5cuhUl/f//+/fuBfPjw4X37nEe9PvDAA3ffffeBAwegAF/8jFhzxBk3y8BZnYGS+vWueW8wXPG/6pwxDAjTESgPFJrQIvEY1eukxUroWTSisjUkjcFIdgmrKARbuIRo+WexlRO20INEzFsLUjGBX89Kg/YEZQ1/iJ5QWrkankB3RABCLkipTWy/O9liM4+pMaQnTyngFfbi8LdIQB6Eko00KZoAo8HaLaPZ26K08s2DUk+383u41ShMANCStYgKM44sd70EaIfJFdOY6mxNdtGspW40C3bDBOxw6EjMYamreA7/UTJIiTH21y7KD5ac+7Bb+9e1DqztWHlFY3sfmsQ0QkVh+pfKrLyBlso0FIobOg/tHnWr4e7mpQtah0amGW621PDSmQumS+XZ+tkzB4xLZVBw79q1653vfCeWrPziF7/A1DgWu//2t7/FahkshoEIBTqKdayZeeqpp/DD2d69ezGzjn1eX3jhBUyQg3766aexsgV8zMQfPHgQZTrW0pw5cwZXAjt27AD+1VdfjUL/O9/5Dip7jAI/+9nPMGF/+eWX/+Y3v4EO5vJR5WPOHuX7tm3bAPLSSy+98sorcVfMc8czIsvAfM7AlSvrslSG6ghTNRErORLGVEyEyeGMVWI5DlRmyMp6DFn/sa3QY14ywoTEPBMkCU0SyXMmKfI5LCZ21f1Vtr8lrUCzb1+ZDnYSKxdbICq+hDuJ7gvCmH7CYWBh4PcgNPyCcsskJ55f3aTn19AB2QaE/zB58GuUWxZFDZDULdqMHCZnFLbwExa3UklTsfrE+f3kyRPOLaqajQQMoF27aMbRtAJ8kcgOY5dUARpqGhaVEaCKUI14RiY5QTHL3iRtNDl3mbtm107nGmfHTw/vfKjnolsKhSaMpb2F08sbDsXodCmHH6mwhATH7MxM/qqrrqJ5aMyFD9zwmXyLc3HQ0jB99/JHvr3vZtDL+9v+1zs3fPHHu14+MEJumhtm3r3iwW/svY2akwd/Pbj9v4iu51+shKEFMPV0mvnKMjBvM/CxGzr3dt4QPzweXO2mEVTsxj5JGFKgPFDocxOz4QF7rwHmEVQCrFlkgjHxAg1YSISwz+O2fkyatbd3OCIhUEz8Qr+ev6UY+oWiJUjFhJqu3KRk4hkRwBS6gmRtE4+FRGgqGsOip+Bw02/vb8WEgrrJ3s6Ohm/BJOMw8AhyUxRBPjFb19bW2tbezhHEJVz0IBc+wMiKPit/IwJGBBU/ZrmV1E4BqwKmClMliOqbiEVOYsvmfAgz+DqEIqeY87lt05tfml03cWL3+NEd+YamrtVbG1u7b2h6YnHhcPQ8YcIaB+lj6tm8xn1qtum/Dl3PoB2tDZ9827r/90evUu1eLOb3jw2wdK4ITLrPlevMb5aBszkDEUa+CCoRMxCIFCiUDiIrSqNgWkAK0mgTJjca6UwLjIUNe7vEL/TrUQt/LWcXoS1IP6IevJD7rYTAaCXkfkN/y2bLfKEuSBYLN5Kn0CZLEy8aGoN7EN4rC5iwS1iFCLtiEkkYpuLc0rR7lgbRtCoW2JECP6SvqKJwdwskbKUVzTOXU5UQYlOEATO7ywgqRrdsx1K7D1bRCR2GdCKA2UwBAGuSRoDRY0rAUWKRTUknQE7LJDQMUijlLmx8aajUe6x/TWv/GjjP50rrG3YvKhyJFQgWm3hvgWNnXuPe0jD1zhUPSdzmxvzv37F69RLn4riYK+wfq+Mji2UcJhq/HWAFvEmS8bIMnFcZMI4lGGqVf6acRFAxmak8BQZN/6HI/UJuKVoaCivGJTxguTDDgu6pBpyiI3pnJKHPPNe7bEnaMxA8J/LKshVBVt5kzwrve1LDChi7c8sjF0+AlvUqHh3Kk/tDddgVGbUcTtjhoUFPkNLKwpYqbCyYFjMLWxiWSVYE4cWmabGSJjEyXChFYoewSxQI47ggdAhIMOpCYuIQ611nZ2x7a0QNAovdY6x3Ry7C0hHFMcHIv5qVFCZyqQAoTc1fMEOxjtIUgFCnI4pdqA6gSMdDTeUtYbB5RDjdbMxN39D06DWNTy0qnFzdsP/Gpl9vbnwujzq6nITQdLFCDt8XXO6if+bCHfekPn96DXf/0MmJv/n69v/n/9tx+KSzl2JTYQZbQ7I0gEA9Le8HBV2LChuw73rXuwLCyERZBs6zDPApHYTlkCp2LYuxjy2RfIJKg1UqLB/FciJ8smobHrZTR1oLLHLCchBVHwzmInHLJfwtxZVfKMIWZLkriqXXvXJlDyDush9Us2M9IqR2BcPm1HNTrtQ9A+GE8QQvmBQWglRsSKIw/U3NWGOwfgQ01vUIO5qnEfHV4hxs+4ETeoTDgsyWgS5YKwJB9UUExYoK9pc7ceJ4KmVbjNod/iu1UCWYKimGtLwtUk50dQ51vDRQZUw2DwF8MlcCQZMPl2YGKVKT6XOEoA+k2zdsnT67omHfksKhNQ2v9heO4vMXt5NIIKp27LAyhZuXcuIBTJxZh3CuYcvf5+Gx6f9zzyunxM2pU7ON9xy80advaeBWUdwkiv0ZsYHjv/3bv2GDF9yTintGsSEMSm3s9IK9HXEjKd0tunr1asyd4x7WRx55ROIRE/eerlmzBvenShHT2E8GNHB+93d/F1ck2GsSt6WyNIDA5vHYLR5RQQf3xb7jHe/AFjdf/epXwcdeNNIQgQEff3FDreRndJaB+ZQB+2nYLokbfzSkCFoRVOLGxvoetvfKAoUIkyvqAU2BJEgY+FsKgl8Y1FIMNWC/LblVeRUMIREky008FjLhaXmvLCDCwla0ZFNYCFJqgLZLpKKmpTFI28KWUJIW6oKUGqDtEkWRmhZ1CzvQRsEPhFB0a9FEfRIUgvN4FrcQcSrpIMVIsbmlEq4e4xxuYVW9a8UloTLTEpKiReoWXcYKJYyoulXVfnRIcKTzKLQR5JxhUgYqeXB+GcITVJP0DyCoVFGC4uYl0OZdZXD7KZ679PSpDfAwNV2cmPItRME+7ncufWLb8Gryb9tVBtLf+Z3fefTRR7FpK/ZZf+yxx7CT+mtf+1rs137ZZZdhM5kvfelL2BYGhTKqbVxJoEDH/jOXXHIJdn+/8847sZ8MHpyETWlQiH/qU5/C81OxZSRupb3tttuwreTrX//666+/Hg9ywv6PuAy49tprsV/NLbfcgvX73/ve93CdgMsAKOCO27vuugsdXrFiBZ7eCliA4JoB8WCJ/Ec/+lFsQIkNJWnvSCDjbldcV6Bq/8u//EtEixiw7ySuGT7ykY/AI6SIDTvYAAe9Q5MykP3NMjAfMuDuKnNBJRIMEPJfRZCQYjCLPcuJMGlFUDGZReUJeCZNtiwEkfSQGOUZbkq3M+8clHkh9Kv6W7bxXZr73AgBbKdnnIkZ+QAmIVc8uSkQYptrTpWn672ygAgLW9FSmmQkeqTIgySKqgdF7MBoIIxzeOp+DwqCp6SwbU2TeiA+AZnMdBdhWmFyIEZQUfz6LfwtRRVzhw0N2GUOp3JHEqKrGQcx8CFPeiS3jOERTlCBxXQVUz1GOHFU50cUcSKeI12q2b10DRX72vPjrTmsVo99oF5HOTo9PTMw0H9m+Ix5qczkbNN/Hrrhrcseu6xnF57E9Nblj/Y0jb556a9v7H9+Xeeh96x8EE3y3NoUFAE2fERZTE9Egh7W16MmRoGOCXJMcmPmnx7DhP0cUbgTEKp8FOsozd/ylre8+93v/uIXv4hZdjxuCXu3b9q0CWX9F77wBTwVdfXq1d/4xjewPTzQOAI8g2nPnj2YNUfhDs1vfvObqNqhv3XrVuhj/yk82gkXDJjjxzCBZzmhXscOkmyOPStx5fCe97wHd8zAIy5u0MTeNSjZsY8kpLgMQAw333zzP//zP9944421WPbDwWREloGEGcAwQf8S2vvMGMwbfHxSz1OYP0ZRrNNoetimYlTB91QVdpQmm1KNXqnNHbeVln4ethpCVch0Q47K09I8eYIyDhsIQqgIUihUYpBMEy3smdT0SKKxAxgelvdqVI0Ga4IAz3SYVE16ZZ5f3YIJXdILANJEdixNlRnxvbCpRlTOnprIY0Algpanrb+G2Hd0dI6PYwck96jKkeKapjed+iA2KtnENlMCCGkSPLtiItCMtSRBFuDU65DOa0GjH7WArT8mvSHst+r3B5e4Bw4cBIx5VxnM5p+a6nrs5MV4VCpSeHh8IZ7B9PjgJqx9B+dnR64Zm2mF8YZF2Foyd9oeDTZuR42OYn3t2rVYM4NZ8yNHjqAgRln8mc98BrPg69evx5oWFOKYVsdSdSytwSw7dDCx/eqrr2ILdjxdFY9wggeIsAE8iu+3v/3tuBjA2jjUzUrpjH3f3/e+96HIRuVNUsyyY30OaNi++c1vxlbugMIMPebdoYMpdtT9WK6DKXnw8WsASn9c2SDmBQsWoEzHFD4KdyiAwBXFgw8+iCCRO2Di6gcdsXc9k2QZqHcGnHNUdR/JaNZpaiXIkefeew2GiKalYAgjQZKSxpC2fqG/BT2NIW01eJO2iSdBWA7C/VGWGVIrUiSR4oGSxYPfn9oSRoJUtOwSRRFNv66/pWgHChVdfzO5pR+n3LLAWdgMUfXXnJFqRCBAHP5uBAXd0tKKUqE86U7Gmr0LmeyPg+ihxjxne2YVx/5eVfjpUbpPxoZzkvqjYAsm2MJI+K2NKnPLjNiPuQ0yTe/oML0p1p4XCr53zbyPe5SQWhpzK/pyg6O5Izut+7ijZMfMN9bGoODGN/Piiy/Gypbdu3ejXO7q6sJ8Nh6hihoai92xLgUK4KBcBo3SGSJU2BBht1eUyKibsagGBEp8rFGBOVawoM6GMvif/OQn//Vf/xWwkKLQR+mPv5i2h1/39pcTKLgBhWob+ugd+Cjl4QXuAIKaHr6+8pWv4FIBmnCNwh0FOla9AxzVP0Dw+wAe9oTBBb8bgA8daEZJVKaTZaA+GXj/lW0nBm7Epk/R3fkGA7NZBBUYRtMyewjjCmxBBlhF0/JHrdloDMWhkAuSlDSGYqtpaQYaIwwBcp+NsxSyVKJZD7/EiORjukA+NJ9YdaUKg9spgptCNPEoILvEFrCwEKRRO0yuG1ksLGxpH0GF1SPoRlAhuMiK7N1kZ0DByRdzesuWLVcMy02DhVkxJhe45YIpHQ/poMTsRHz1OQ2TnHOdStEHN2UPqa7lHgQ0SSRtiYYtubMp6CZRORKX+ymN8SSmhYWTPXnfXHcpPztdGCrlZxqL3Q1F6zMN8B0Zdx7N4RygfYV754qtpQZMscc7SqNHzhx9yWiDMwcOlLwkxcw3Km8cRuXETBTQl156aeJt3REhuU49sMQ9ygyzDCTIwN0XN0ysvHkm1xBgG+G7F0GFHERWDIgnQOTBe68RVANUIBJAgiQbjaFDeSreq9Qw8aRcc2IyMPECQTRUoe3ew+QV7oIfQLr+A4MIFIYhk9wOYZfoyH5df0vXBieCimLnWXivipibYXJWlITdyC5h+wgqMXUjI0ZW5ACI0OxUxuHDhzE7hnk0xdDXVI18wjQacEDVlwOWpjcfcBqRnlUY1HsOmZucYS6iiSOb9H7wu8IieoNkk/HrSRSKpd7DxxtGnFmSXFvr6aUD083mZSyIateMW7gXKoX7dMOpobZfzhSG8MHLl5q7pq7snNwUGj/mjn0+Bnc9glo+1ExRoHUmCpOabpXO746znY1RrUomJuMTV+1wndXrVeY/M58nGcCtK+7usIaTX+VLaI41TM5WkRXZIi7hefBedXu7ROoKLUGShsaQhpqKpq0xdHNwhJYgWdXEY6FO+NX9LUU7UKjoUtO1sJvZJUY0yfRMvVcpYzpQyFpEaLoaQxoECqWipBMZSYBk9By5jRUsFUrxI9XsVAZ+KsdP6/gpOygcWabFjyEIuSwjB2U37gvcUNPxl9ynD1gLJDmuBjUvGeWEerFxkwlPUs610rQlTzdnw7oRA6/sP9zaONXt3FrdUCwte2Xf4MZVs42GU7Abki/kUq441PbA6MTg4OGp/pUtx/acXr7x8cbZnrbpZd6ejqIf0rRkWeMu1HPYsAl7IOZGRo4NDo7NzkpzqZbRWQayDMyfDEQ4F0RQQX+iaVXTcc+D96pgWdh2Lc1AYyi2aAoVQZKextDNTYqamcaw4ZjQwAu014XghA3W7u2xgVHosIHqLPTsvFcWKESYPFA9zDhMroCj6bfwt8K0dbmNY8HFu2WR2IAi8KOBRtNid/S5ih+sZkcMBxfTf1iSyh7CiYqpqxs/mHAXjga7cQhuKO9Utc4FbnhUcAb9al2G+8k0QjPQfXL4eFN+qgnz0c5bOFvIHRpo7u9+8vTyxvxke+nIutykaQGL93ZPNR6bKZxqbisMHZ06fXx60QUtV+7re+22A72nj001N7y8cuGT65dMWya7fTPuSqCN+fw1i5e86Q8/vv/QoW0PPrhqtrh68yXbJyefeOIJXl6imGTNLANZBuYwAxjP7UO6XaJHHEdXtw7lePDeKxtoDJZIQmgJkjQ0hjQ0qWgGGkNHYI6n672ywORJERqbGpDGkGaBQqnItGfhvbKACbuEVQIIz9p7NaoGCnULk7qJx5aBQtYKJFKACMSvQhizboumTtVEnF5Hw9X7SZ6Y77nEZtDdZ84M4+61oDGMzRRCAYXUw1UUU2r6/PkajmNi+CLwNaoJgrD9LqvBc2wpZA4R4ExXCz0X9kr6Za6opykF1XZm5FhLEyn6PukAACAASURBVF/PFZacGl789MsvNs3saGhoKS66+NlF3TfkjosNmr1PRqkwU2wYn2o+4OQ+n7tgU/uuZ0fvOrPytqeWuGjFtoni5TuPLBwe/++r1s5qa8vRId8+7lgnwxV5S1PTrf0Dh0bO/ODee3/71FNHR0Zexb/BQWy22NvZufPVV7nvmJJnK2ZmRJaBLAN1zsCGhaV839qib407Bir+FxgOaxERqBtX6GFjBCr/c6JySBGd3a/QEuZkLGRlNFNwQkv3GikGQvVwymGIDvi9enp+blDLb8EtEKaD5SYh8XDnD7T8+7jLvpssCdYkicJzrSNEBpU4h189fXyORTgSJIsVIoKKYiGbJmsTT9owHVmRLYiIbBdZUTpIZCQBWpqbMene3d0tme6X08+oslV1nFX6F+bWUKwCYZyRc56B9jNjw3ksTncCybdOn1n5m4NPtxannQXhpdn8yJHGfP+ezsKa0nR53r1nePKCiZ3t7dsKzXuKuTPN0z3ThcOwLuZLLYMNf7hzQ8Ns/tHhE5PF2cfOnFzW3NY/Nn2yt32oo0XpKbZFtBbuv//7v79/5Myz+/aRzRve8IZXXnkFW6xgb8SLisXB0VEsmyFRVrgrac2aWQbmJANe4Y6f0TAa0D97IKwSpmiHMEtcvEppW65uZdEY6FHEVQbx+iLqZFYyhcBCGNC/CgL0hThOkmQHQryaxAaeDMQVM8Og7AvbJFd4tOcVts9yQSl4RcVrBrr1lKyvwhqk/RB6diWfhCwEKxCf9CKoCMAKKewEWZFrVDQtzazMMFmbeDZ78GOqM1I0u2haDCqJxKb5PHaWw6Um3aIKGG3mNzG0jM9P1wDS7yDdFoUrp5S1JAU6PMu6G9gXKeQsUGpIhM7KptSPSZthZlqauo8OjroPM2pcc+jgntOzk77V7eOnCgvXl/LDi8lda2G8darQNryg9/jCJUfaLzjeuPH4wvVHChcf6Xn9zOJFg86m5L2Nzf9xbM/KlvaL2p0r2KmWxj0DyqWss6uMeakMdkVcu27dl//937l7uAGUaNg8vG/fZV3dDw6eZGlGZBnIMjCvM5D2gO3iaaAaw5YToShI0tYYSUBgExmH8YWFIFksiTC51GVaM9IYrEpEmFxR5ybs8C+VfdwZUxIiLkFKDUlHUAlUj2YfTUs6MtHRUKJpmfADeFx7BOgIUUx1tqQaJKwHSqUSps7oIKowxQbNmHTHQxIZxg9WblmisbBlbDrtd6DLK5xE8BXzdCgZLtNMhPuIoRoONr809K7pnHQjnmxtbl3Yt/jkECbFZ/LD0xNqOT07VSg2j/CWLJPNjccWdPpjKE00dUw0vbr+WHHDXkcyVZrFGnW6mxSfOGc6X+9GybLl84Jc7udf/ne5eyOeNgpQ2oBlcHr6pg9/CBPt/giyVpaBLAPzJgP4wst/VcTlwZRnrytz2J6g4sjiRShGmAJPBgIr4cahIx+enfdqMw6T2xyynavALRCmI0xusnF4rp1Mr0mRwU3CiDwvbsIKNIqgEmgPoectWC+alg2jOmsbagK+fooOBIF6TAuGI9PI1ml4ioCBufbi7CwKD47TSHDwknBzgT35mGc0rYLJwKFEFU4y07MrA6cHesfXrFjY2NKT72loUbdkLDQWC9PtlY+k4ZOTb51a2Tv6+rHmGyfxaKRc7tmRU+8bWN1cKAzNTKG5p1+dbqf8mIvv1lJp8OjRlpaWrVu34ru0bds2PNIIj0/CTqvYswkPVGpzt1zFs5POrixn0WYZOMczUHUN4gJoKBojII1CV5BsYOKxkAmhJUgWg7CwpYqRFnaCNKpW4UUzraEvFzoMP0xuSwDzPQDvlQU6EUElmlE0oGhaukfiCGtB2rSJjxNwZF0jkh3ALjECgQkLHEnjIetoAKwbTd0JSzskBgtF8L19fUODgwv7+1kYkWBgj/BeDbmBP5IKxxHdRFGreI6ibdLhACGsTYwmr+cKD/nnpPF7QRxuptfXqeamqSULc5MdAxt2H3rah7tw/VTDyXVRfI41NT5x4bIbn993S6+zruaO5qX4u2t53/6FuFfbcJgL99Gmpku3bHn2wQcffvhhMtqyZcszzzzz4osvoomZ/0NPPplV7YZ0ZqwsA3OXgcZCoVgZscLjcIcyHuE8fY3hCQyvQleQrGjisVASQlGQFg3Jjk4LXEEa7cPkRiNi+k39LZtZNC3d2l3yr7MFB8hJwQWKQ3ow3qsils0IKlLdQkdGiaxoccTs1IAYMZiQ1YVf0y7x6/laVB1U0QlZXkSAianui1VrCLD2tvahQTyPJs1DwBMsM5jgT7j0GyENUj1FuhKXd4mRIvj5ACUTSP3VOenmYaJ1oPOG0pZHjm5rLM0U8g2lhRsnl3ZeWzrkmzLnKNzPlvfsTzeS51csGGltuuLVowuGx8famnesWPDsqv5ZR4+NyhHjZyVz4X58bOy6N7/5J7/+9cTEBOliK3cU7kQvb2174r77sNi9DJO9ZBnIMjAPMtCBJ7dZFr+5w4TpPGTi2boidAXJ2iYeCxXC0/VeLWKFnaDpOfBeAyAiqBitNTuNoZtFUNGNiOOaVmFvwzXxPTfeq0mnwoumVdEXlN/U3xJqKhlZUTWcH206IZs6YZcER852IEy4wdYsJRhqRoCJqc5uLERbW9vY6Fh7u//x74ijuk5ZvJXZsg+eponnyignXjgUGQTMZtpDyl7PqgzwG+++o77y2n2XWe7rVenE0oG2twxc98rk1FBzc3f+2PrSIaxotyj7TMuN3f1d+GeSqDzzrjIoyjGtvqGvb+fBg2SBB5sddOmenp63XHHFA6++Ou11J9tVRk1q1s4yMBcZuHpVy5m21fCM4YY3VAlakk4nGkuoLogPpwxM5ydPTM6IZ0FilcoqeQ9KWEhAwY5FehiBjiSiZxAcv7Qg2mTHPF3d40RQ8VTVV35D3dSp0krbc8G7ylREMSkguYf3GmweTUvHoHgFPzJQZEUBbiA9GO/VoGJhxbewADlsO5hdEgDHouqsGSYwQqml04jAK3F1oYmDwh1Lc1FpmIQWXmwnFpy6sClYdqXE7iULbBxOmRX+2xpjnVOE03X8j8PNhUNS0+OJlsua+z8zTblTixvOrMqdWpLzdoGMHRW6xZ8JUxdnZyy7ysDTz++995qe3psW9j83PTU0PIxdIAG1aePG3/uDP/jqV786mk23x343MoMsA7XNQFNjoXz/eGQ/dHJw1QXJ5iYeCxXC0/VeFbFsRlCR6gG0h+S9BqhCFE3LhuG39reMNhFUjHbMdAGioUTTYuQAIoZTQknqWrPTGAFRzgcRnV9TioRO0KYU8KnbJAz1ztakmQiDTBUk6TkQleyM1la7xsYGzB7SvpDSkZUOcGK18QussfjV0mgpudBjdzkVLfeO2zQcn30Y5SRUclHpgolXkdaAqqdD8mX3aF4qg07D5DenT63s7f2Dj3+8vbNz94O/PPLCC8WGxs997nPxHk1cgwRmkFkGsgyYMsAbT5mElcLVfo6ySxREv6K/paiiGSbXLQI4Hpj3GqBatWvNh8Yweo+mZTR1Q45sH1nR5kvyXbA4iHF0pSMTHRMrprrJo8LDGS8+aCIjxbFs8pnaFEqgUKIE0IzBOiZPLIxIJEXV7coOBwYWHT9+bMkS5xa9Oh3WWCz+kTc2kTkEUzYt1hk7tQzwuwBE+aak5mA+AlkLdwSLhOw7depz//t/Y3uZfKk0NT1dPHF8PnYiiynLwHmfgQLGrELl6Q/i3CFIzpKJx0IjISwEaVQlZjStAAAWeUjeKwuMRDQtoykz/Rj+FispRDQtxcjfxAqfyEe4KjTkOS0E2cULB62gxNGtWHlUddYeShqvlKPk8VRrb+4Dv3OmwFJ1yZ4QiMmZOb4wrkQ16ga7woy7u9YrFCbVoI2B2pgyNElDX2naEOrAR5ZlMNRkJhMUidJUwguWKspz1ZSdnasYwv0mjFKYiZtTsXkqfpky3nI6OTkZEAxt7h6gkImyDGQZqHUGOppKuYZO82pIjLnxD7+Rv2VEi6BitDMyPTDv1ajEzGharG4k/Bj+ltGAmXF02UgQ6dbrAjgaaf7MBNtW12XNWmMEe6+BFCdFPLw8eelKJ9Ua9EOcrZ1uCw92SeL8KJB+f4lRjYa6K6mGbi5YsGBoaAg310m+iQ5GYguROead84SSG2oykwnKg9JUkhMsVZTPhSZ3GJ8cpuN1LKFZBCeVGfdSqVQoFDC5HsGqooKqPfRxCRXtjMoykGWgNhloacQj15rliT2uH3FmE2QASjStAABF5OF5r4pYaUbTUoz0poARpK4nOZEVpZGJnsuS3e1E/J7Et1A6rgFoDMXA2MQpMZGdEYyYLmR1uHSiTjswGTN5II7fj5RA7hdKiFi0gkrAYKYEb4sFHto7OoZOncK27jYd5keLRe8JA8Qi4I36X/ssxIrrvFCWbyK9Eel3W/pw0TVGfJ/Tpea9U4tHxyfz+cKC9vyypqOFXMhTxoKdVAp36CUowVHuBzvIpFkGsgzUIQNNuDG1obJUJtSjONsJMtgssmIwDEs9PO+VBTYisqINAHwNQ2PEMQ7QtYjiFOumcC2wCdhxOk7w8S30sHDCEDCC1FUDOEntAiAhcmPzBxhsYJTSKbE2EUqH5Ic4mjcptKhIrOg0AevwCoIWkCKP0sQ04vj4ODaZCVYOjUWaIy7W5xjBYVoqm2iyln9NWvOLJzs9vyKLHg2/azBx+yMZ0WHmQHOq1PLM0EBpcHu+5DyxdH9Dx6nF6y/p2J/n2p26Ij+CwZ0r5XyFO0Ax6R63ZwnK/bguMv0sA1kGgjOAehBrZUJ0KmI5SFS4BiqyosHWxPLwvFeTjo8XWdFnpTX8MP6WpuxjxNH1GWqNeItS0vOrBBIvDDZOKZ50YPRTHcdZHeECl9wgq4uUT73VwUTsDXtjfc2tVNGEbJYWIb0ZMSOEgHUyx48dCy3cjfA2pozLRttsq+Gju9IdNSkH4LOUOeRLNokmPpkwLZVBa1LHM5s7Da3p8nx/oE+aPu78aMx9YByBklZTfnZPLCoNvpgvlR981DA7Onxs9/GVSxY1HPOpM6aPa274CnescccdIbEm0VHoYx9348p4s8OMm2Ugy0ANMrCgtTSR72BgHk/EEM1CCyFsLBrx2AJPkMEYkRWDYSD1I/lbocaafRQLo06SKjl+sEbXOjNJMISSXkg4PaUGliaWL1vuObR8Ik2eNIIsw3j46HzNwvZ8OK+KWymCMCyKMLkfLkkrMD4CbHDvtseu1eq+kPQBqksak3TNZKN0l5rM1AnCYD6aktabCkdRVqR6Exzl0BEUhblvcoj0eUBAzKlbcBE8nhorctVOcTXODJ+YWr0o5JekoD5UCnd8N6ampuKW4PE2Ww2KJJNlGcgykDwD3S2lqXxb1CKDR7rkDs2WLnBM9JjqZscuV0PSGAHGEMVUt4M5QLHBYhvY/RskMZfoSIRUA0sVzD1Vp4woe+7Q3i7aVSRQQtKZns/3NQ5eepa0EgWLvHBscih6KmxTS6K3t29wcHBgYMDnhLPHhE8cs4EOAYe7RZjcjAl2PqorGeM3hXLIzdqlpg4u0g7ef6aurDinD2OwN4xIkRbG4OHD73znO4OxMmmWgSwDc5iBtqbCTNhSGefkRP/SC9SDRFnj/BMnwEAfnlnlfBmoHixkMFeNWyCiHWwRTT1Qy8EivEA1IUzTu4D1k3CS8EhuaXboP8n6W2aLMC4w0oAJdYPzpfsvTDGWnIKXf2OZp60sAzHSbq6NEmYiJqJTCK61tXVqeirWKoDYXhEsDhm+bDI/I2wZcPOnJpBzSNJz7a8zFNCHxhsWaHAw/O3rKJTyvlWsM009C5tHhWElOwCNcoQX7tg35hOf+MTHP/7xq666KgpippNlIMtA/TPQ1dpczDX5/HJFyIRPnLDBYKJSj1DcCbN0i3XPNzuI1i9W9+yjmdm0ytcthGpTMvDT8W4Aliz/7I6UnEM0nUbr0aHKubkm3pTyCD4insxrEo0KqkRnaiI/VNR4r16JI6pjFlH3qEm+QPuOzo7O0dFRHytrZBkIyQB/wOjbCu3K11ZUzImZjnv5qQ0IZ03L4UL/JaVCM+nMNnb1LFrd33AiwCRUVFkqY1PFEvb7779/7969+MXKppPxswxkGZjbDLS1Np7GZX1tCkEXNT50fIvQHApIQYaaSYWkdhJD0M78epIjoVksV/BRDzexYoIylWZeZP5WXCxFH2AeriKpRdOddZPAju+U/VN66K90RXTKznQHVXFsUQtQqcI03Rxc1uru6T6wf39nZ+UGHmHOJBLB5pwUcJhmzYyoTwbo7ZD5l2+QQ7MGC2oWmXMxOVdHY276ip6D+9s3jEyU8JzEvrbikoa97lMj4kTkT1Z44Y7bVXft2vXe9773u9/9bhw/mW6WgSwDdcoARse2lqZT+fAf0KIHlGSaVo7S0T2FafpR/a0w27I8kVEgtoOYBDWJTWAgNiHev1KiCG2AafNxJqpJMvgUXRP04Cw4vj3/jvuah+A5M4dVc/dmt9VzlW41Nzfjwe1NTf5fFH1upIWN9hlwg5MEM6KJUJqsH0YwBinKpnSlS5nD8UvbKFJHRwlbNiEFIqMTYo3/Gr35mL5GjaOZK/iG0szqxgO5Ts9/4j57htYzPdaWvfGNb6S7uYeHhz//+c+TzyVLllx22WXZ01K9dyB7zTIw9xlobcAPcTwqxI6HFr0ofyOh4Dwg/0WyCVGSeC6tMELMK2JpV+FWQ5UR3UTFr8nIuhr/55wtTkPemUiQaXWTIGsAHC1Ax7HpR3mny3UKij1FJKJ1rP5a/QMDJ05UtbQgIGbODXT4fQGhNFktjKA3nd9l2eTPg1FKTOgkljqGStiySbSjkR3IgEi0kw/ZTJ1OO+HmGfeurq6//du/xa3cCxcuxMXu1772tSNHjvT09PzJn/zJyy+//JGPfOTLX/7yPffcU9tbRtLuaoaXZeBczUBTYy7f3Bqrd6g+Y+mXlRMZRXEkgAUZxVLRqc5aAXObZcSqgKsyNgUVwqu3v5BwAsVUbbgRCzLQJLaQgNlsLtNTCaVCOYGVY5ofoXGmLASiRPT1ihW7Tsfd784Sd8Y+ezKgfMD835aUu1FT8MBYpWd8n/A5xwEmfl+yzY9D2rBs2TJ6ghJ9N6gWx/IY3A6ydu3ar3zlKy+99NLY2Bhcz8zM7NixA6LFixf/4Ac/wDQ8xYNF8FkFH/jWZMIsA7XNwPKu0oqVq0cL3TY3GBHcf3wPZZxTrmec+nlaAyaGrROBfMYK1IovBC6lLr4pWVTRpypc5jBQwxzTLklBhJ2Tg/odnjfvtdae6+Wnun6Uozw7go3eVdkf1COy6QfBJ/n0qVPY4M7Pzlo1ywBVlPyOUJO8gSmbNQthHgIrH9Iq0kCmnN/cxPj4yZMncSvp8BnnvzPDw9hPqbm5qaFBXReDyj6PvWJoiNf3ce/o6EDJjqK8s7PzXe96F4p45BHfHxT6KOI5p1hUkz08lbOREVkG6p+BLYtnN11546nCAnbtjgeVQYH5kYikdpHAfWfn6jxVZx0YbRm6Kg9VGQdGFyJ0HI+NjeLkijE8RDeieC764vn0XiOGmoraXPisIvBKuBWqCrj5aXro0MFly5bPo9iQ64DCLVg6j7pxLoeC90d+IwLerphZSA/JdXxq6BQOrKzDL+Eoud25did2HAMD/e3+YRwPXDIvlaE+8B5MqOlRnQMCiLCJ2cNMPctAloHaZqCvrTCZa0m4+oVCk8NbDYL1w/tbCdxVDWDyWQGtUCa9SLwUICL5qZOScgKsi1c6N4rtF+qYU+W8XEfPiVJbCbdCGYBCuhEiNgDWldXa2oaaJLVr0epjD8x1UE1fvetzBQEplJ+64IzG6XQF2MVMDzhOEBF1Mb+Oqh1XgS3NLZdu3tzd3b1v/75du3aj3sZx/PiJxW4FLtHMS2WkBkr2P//zP1+1ahXW0mCBuxQRnS2V0XOScbIM1DMDly9tKvVtKPqf8hAeAIZM/heuHVuDsd2h2d+KDSZCdeESAISZOLgUZZhmoDwFiED8cKHTkTSXykiPDvYcH+4FauXEPGfRzINU1L7v6GSkVEfVqyJilCLHTxzv7rIuCKwCOzN1MsDlLX+0mQMpvcVpZKo2qGlEVn8MrF45fuxYsVjCMrD3v/99KNyxFv2SSy5pb2vbvXs3vScYzLs6uzg2LJVRV8+wjImJiQns447vzAMPPMDMjMgykGVgnmQAW8N2tLfM5IN+PSuHiqFX/qtBB/zw/lYyd4yRzDzcquyAXsLVAzRSgAhAnx8inHPlaXcugnK3akEUzh4dcxkNOTf+nYu01MYnvdnGTvqY9F7Qm2L5W37LhJncQaX8XgqpysHCgYaGxumZmQAdKaKEKBxqylxJjqIMtWApKTCaYh7QlKLa03qeJcf5HtE/r7sGDkTQYYXqCE6Ykj/Jn8c00sBH1SkZHxtDIY58Xn/dtQP9/aC3bduG5S1btmxZsWKF4yefwzqXickJ9gkiwsk+l3v44Ye3b9/OK2ekfUZnGcgyMLcZaC6UOjq6TjkluTj8LSFImfT78beqcZUekiWKsoOa+7G4rwObzilwlH4f+dSVPnS8xLiBcDQVWzeuuQvOEFElNjM1d8Ga40mfy0lhAj4kHe5ywYK+kydPYEoyXNUEzc6YYJxgTlyp3jEdgV3XhdD965y6BFIjJ9QbfInq0630vIyPoyJHoZ5bunTpyMjIc88/f/jQYVwkXXbZpdjUcf/+/egTpJh0b22pbBwXPuOOPLvrbI7XKOEZbJaBLAPVZKCjuVRq7vJNpde+CHBGk3JFyGQaXhmsmowE2focpBGx64xQg/zWU5beWSU0ariif6Ga9VVwg6pMHLqThfWNIK43zmQwAdh5mfC43U2mjz3yZmdmUZAkM8+s5jID/MFGEEynRVDHzsLPhfthxp8SnlSAbWCu3bp1zZo1W7Zcjlp+aGgIf/P5Av5hLY1MlW/GnXaMifvW4tbVbFeZuEnL9LMMpJWBhW2lqcaar/vUalyNkbg/6SEFhqC6UduBxmeb0Fep0umstv2Vp8zaekr4Vvgy4mFUdT+3B1LXV84zE9Hd431hK6bn5ZsV0Keenu7h08M9vT0BOplIzQDed3qjJQEl/hioBrVs84ewlk7qhq1kNK7fApa64n3I5x997Nfr1q3DM5Q2b74EHCyPwQQ8wEulIoYpV40TV/LdnApVTMhjkU2sA4V7dgUc993K9LMMpJWBi/rzbYs3TBda0gJkHIwo/E+QxGOtpISATgoRxc7gJqUOeN4BN/+OgJtT6xdv/TzV4g3AD9TOwSfmWvg47zCRUy4/0Hlu8kdFSbfSxIbUTc2Ym+zuqc1Uheau/AYxnwkScBOEfnDvdFHGmdMMGN+upBFVBQZjWoU+OTX14ovbUXufHh5++umnDx0+/PrXvW7Xq7twlykKd1ypotKmCKHjm3FPGndml2Ugy8CcZWBhR+NUoS1d9+5plM+l6WK7Z+u0IU14hvgNLJPlucLDSQH/1E7zeUYVpN5t9iSRa+5VOquGrizIMPWDulFO71nTp2rykYqtkkpuMgEvktabuVxbW9v42Dj+phKRCqJ4ZzHzmSCR0mR9EAEiqZbR9gykmsJUwewxx5LgY9zU2OTMs5RKmGJ/6KGHYY6fB1GsYyXL7be/4bvf/35LS4vyKL3wwh02K1euxAT+0aNHjxw5kq2KifWuZMpZBmqaAfzO1tPZfjLfVKUXf+Xhb1UJzea1QWX4UKIm/msCGtqVWAp0ujIEyucxgyyWh1jK7BVWcIxmXd3HijVAmbvhENywGJR7eFZ21NKlOWT39fWhGqlV4T6HHTurXPMXN+zDH71XDAmT9FCj+58LTWyzvmDBgmPHjjl9dtauOCU7jYpPPvnbF154Ac2+BX3lcZKyUsr5lspgBl4uesGym+uvv/7mm2/G1QAuBS688MLXvva1hw8fBi07mO3jLrOR0VkG6pkBPA75ug19p5sTPlAQlYT7z3ulVrodYOx0YYPQyKVPA6yaHLXCrTZYy1KZoHCDZNWGE8d+vsQRJ+Ya6prTAS6VObLYqWEU8wkalQmKEOx7jaJnPsVV11iohKMPB38S0o6AnRABePrcpe3nHMPjbEXrF265RhU9Pj6O7zTKdBTh+ISTKQr5RYsWYfZcIoUslbnhhhv6+/u//vWvQw9m2F2yBzeGdNdmbZmMK6OzDGQZiJaB/rbibMvCaLqOVnk84NfolrE0PTexjKpTtrq0CqrzV7amc2YqUPUAUU4pvtwoMoTjE9cjPNeHHofiem7CUoKoW9OcDuLKv+kFhPwSMCeaY2BOet4SIWEOcmhocGH/QCLrWhtxumrtqPw+wU0tXUpsSde8dyk7mMexYz8ZVOfDw8Mo37GqBbU7qnlcmmKpCy9tl9mwLpXBE5c2b978pS99CVU7LJcvX75v377T7nHBBReAligZnWUgy8CcZGBxZ2m0IeRa2j3Z1uWMWxcnpjxbHVsFJpSEPJwP6uEmYXSBZvJUZuiDIp4vHZVhGbuHrpCOoU9Gg4wpM8D5ZYKlOodFdSVQ5QyePOm9zXV1nTmrbQbmy0estr3U0VGpY+N2p2Z3b67Br0k8eikpQdO8VAbfio0bN05OTuL2bRTu73nPe+68806sKsNCnNWrV3/uc5978sknT506Rb6zpTL6e5BxsgzUJwNbV+RnFm4u5sv3m5NTfOHFPyJrGQ47q6UTC3ZQ7yCr01E/T1E7ZFkqE8U8RmdiqEbxPCc650If5iRxc+y0OFucmZ5u9q8imOOYzmH3XDzy94U553Cvo3VNyQQypHCiwGCi/czw8KmhIUyPY+p9cnICBbxyTyrhoCY3LxFDsf/pT3/6T//0T3t7ezHXfskll+Bq4HWvex1q9Ouuu27Pnj1Y+86rcKLElOlkGcgyUIsMDPR2Tbt3H903FwAAIABJREFUpmKw8P55r2VGLdy6mOynZh7OGuAE4/T87Rs6w/9ComQ9SYTYzDexDD0ijS6QJvVF0vOtd+duPNggDyXOuds/S8+UD5vyiYWRwkmryeEwIHPOWoK7UiWhJABocQ+skDl4YP/g4ODExPjMzEyxODs+Nnbi+PFDBw/QFIwHWI7UPOMOy0Pu8cgjj+C7gYl2VPBY7I4dJbdv345l7t/73vdoPh9w2Yy7l9PsNctAXTPQXChtvWjx6calzn3olX+1j4G81d5PoIfwIKBR16Pe/oI6V8WMexCsK0veT7LEySc5RGh0Z6kCMsInfE4T9SVLluU9nRifaGltqdUtqsrH1PbuWGLL2GllgBOfFmB9cfTw5VfdiWViYvL4saPeo1GdKfGFC/tRwUOEafjx8THlPmxn+fqyZctoh0d8+tHmchy7x+BuVGqicMeG8KjaAXTTTTehlN+/f7/j0D2ywt3LRPaaZaCuGVjWVVy1au1oobd+XjHmzJcyIiSOEHHtUjZnjn1dqmXh7nMkGmn2PE0sEWJGnksZwI6QWMGLG/jOpU6dFX1RLmoQs16fnhUd8YKcsw6h/D5+7BjmyhEJanZs3I5V6G+/+y7cR3rja16ze88elOXF2dn2jg4v1Jx1qQw0ULLjePvb375ixQo0gYvKHlU79nR//PHHGSIjsgxkGZirDFzQnRspLKi5d9RQ/K/mzqI4oGhCNOfsRALH8l9ImOeSWHbbRlN/IQ05bPbBfICSQgh6Jj4nMoCahAqVc6I3QZ3gjz0rMWdOCISh+OXA0iAU7Do0a9whe1ImxsenpqYcubsFJDZyxC2q+GBjc3dsBImPNyRjY+P+BTOWm1PZy4EDB2688catW7du2rTp6quvxgOYfv7zn6PeZwUQ2Yy7zEZGZxmoWwauv6BhrG9zDefAI1XIdesuO0JYkY6oepHAqlNCKBiB6xjQXMy4V5ci1bqOyRKu6Y0iBkdQ37dORJORYRnA3XvDZ4bb29rDFJPI9ffdKaO8gz4ekuNJzpPX87jr6b3DuBHVKdxp4/ZSacXKlfhcLVm8GMU6yndsBrNr9254a2luaW5qJreowK3bQZLGmTNnfvjDH8Iec/iY0qfyP72YM6QsA1kGEmYAX0Zcmo/WrhjksiVhgDUyixEWTiwxtGsUL8HSOY4Cmkdh1bTPVYJTyuKCVPuGS682Om5M6epTDyk27q3eZBG861IwgxWkNBSBE8VWRo+UB9JhE2YqHOKH/m1pbT1x4nhpQYxnWYRiSoWAqAJEEqFmtPRvTGrNPGfASgborcCbIN8TRcfSRBXuGJVKGzdswCr0ocHBDevXYzf29evX5QuF3bt34wa2fMFZHiMBQgp3UqVF8NIso7MMZBmY2wws7ypNty6qSQx8+q0Jel1BMSbOr97QyC7H9/kVX13fnRo4k5mtEl6+MfPocyR7KGn0VjYlTYlQOEoz1FxR0Js6R3eh64BjY5Io9G9nVxcepIpH2IRqxlFA7PIDAFOlN5AqnDjwKevOn0hS7lit4GqRsESYTl3udDK/aGDg7rvueuqpp2l1Om5Ixdzcf//3fxdLxULOmTqXqYhUuEuDjM4ykGVgPmRgdV9upCHtBwf6Bof50Es9BoyO8aKMbaD7rClHH+6pf9XGrePWtBvnHriSQKVZ//7iYyFj0JsIiRX4M8RxSn2SBugbzUP1pQJo6ZGaigKaKRzY5u7okSOdnZW791IAdSA4mUa8YKnRJGNGzgBlV36CJB0ZZv4rYnJ9dHQUH7ZfPfpYe3vH1ddczTEfPnLk+PHj+Biidm9samI+CF/hjuViCSbX8VzVBFYyiIzOMpBlIG4G1g20niqkt50CnanjBjEH+kkC5XNsEuP695HDZYJioFPX2dGH+mftnPeofBpiNZEcqS9pypvCUZqKeWgzIiapVfsXk5GFhgbc1IGnzVSLldlHzAA+IDwQMa1/aiKi2dQkoKRt+mcVnzrU1t5eGBpyV6GXfnHffajRr7ziCtw4OjY29sADD2JHGeQZq9vxUFROAAhf4Y418spKmih5UObwo5hkOlkGsgxUk4HWxlJ7z8LBnPkBarGReQiObVl/Axq+EkbMY19C+/p3V3qk6LkPUgT6rOyS0oesmWUgSQbwnJmhoSHswpHE+Fy10QcKDBE6M3H3JZSkEwPOb0PZRTnWSn6CHqBG7+npOXVqCLYo3++77/7nn3++r7cPG7IPnzkDJtbS9PT2KmW2r3BP4DUzyTKQZaD+GVjaWZxq6a/Wrxx+qsWqsz1Gy6qiV0bbqrDq3HWbO6VLUANHZxrNz4X+GzuWMc+LDGCxgHOTX6mk1Df167zyRcMXSuHULxS7p3kYkj3YWJI69yxdd909PdhvHTvB4EODD/DRo3hy0nHufl9fH9a7c5OIrHBXEpI1swycBRnYNFAYLpzn00s0eKZTcsqBOB3Es+BDJEKU/RfscJIKlPMxZeG5yTRqmAHt29/V2TV8+nRPj/c0OvpMJv5gVxn6XPmtMuzamGfJCM4r7jtd2L8Qi92xqwyWvaB2p0tQPBUYPyWBr10FloIK99tvv11f9d7R0fHMM8/s3LkzOJRMmmUgy0DtMrBkYeexQlvt8M8eZD4ppFY8MiIngWuA1Hww9NlOULL0lKXeL6ReeuF3gpjcVPxCahMpmud8U0mU0kT3iXN25UHEjF1lDh48UCnchejs6lP9o5Wpoq+L5NQ/nnnvsSbp6ejsaO9ox9Q7DmQAtTduH7WlIqhwh+W99947OTlJxhs2bLjmmmtQsrtXADbAjJ9lIMtAbTPQ315s6lpSqqYkOQerGTmYptw9hmZCvsEpO5PQGc0ZUFIf3GQrEIqmFJ2HtJINpXmWJ6S1tQ3lCm7jO8v74Qtff4sw4OhMn00VjdohVxFURFPErg/GZ1OHcIP1+NgYCnfMuM+0tLS1tTc2aSU6OlTy35wakB7M3l9//fXXXnstptsDrgMCEDJRloEsA6lkYFN/brhpSVVQxiGuKsR5ZSwHa30oTzlU6UyB5lNszYNQHGfNLAPnXwbwlHjc0rds2bLadV1+2flLLZm1c83IdXbHftMmatGPWmAm7XecWHCHxsmTJ8fHx2iRDP5ij8h8fgi/I/X29uERqEoQWjkv5Nu3b0e9jnl38DDdjh0l/+Iv/gLfiqxwF0nKyCwD9c7ABf1tw/meens9W/0pwyfV0nzOrW2v2DcTof7qFFloHJlCloH5lwH6HsnviO+bhcXBuRI2p8YjJ+sQu891HfzVz8W52zNbDue0x5hiP3r06PT0FKJzF7jnsNUMDvx8hDtWZ2ZmBwYGwJexWwv3tra2P/uzP8MNraT9la985ac//am0zOgsA1kG6p+BjqZSZ0//qbx19VvUkGio8o0GUU3PZj3qtm2cpnRAOmd5sUUWN+e2fsqOzWU/4/Yn0686A/KjxR90iQqm1JGieUUHBDkwsOjEiRMD831fSOoB59v4bsyrlNc+mIA3tfbO59ADJtcHB0+6VXseq9CLxdmB/v477rgDd5O+8MK2Xz70S0zD46ZV3KUqg7QW7ijwsStqf395y7lsXbvMWkZnGZirDKxfUJpoqW6djAydzyCSef7SfPZgQs8FnW7pXAspNJnWlecdR+mY0px34WYB1SYDxvfdyKyN/1qh4hlMU+7MZWQHstN1LqDZNRORo66bIoWmj3DzOOS65SYVR5NTk+Pj4ziJbNy44Q233TYzO4tkY7r94KFD1167ddfuXXv37j0zcqarq0sudanHL0qpdC8DyTKQZQAZuHBR8+mGqndwV1KJUZj+KfysacgAnbJkypgOIABEhgbEjJVl4FzPgP7hD/iyVCXq7urGvpDeiBYKJRNPypJzttGh3Y2rQAnQrc62xMzbeMdGx4qYdc+VrrziymeffW7f3r24VePMyMjJEycRM+63dhbPFEu8SQx1xDrjrvQTxrgzFZezWG2T7QWpJCdrZhmoTwbaGkuLBvp3553bTmpyYIDmQ59lYVFGxM4AZVbmNzaEMOD3BoBMC3lGnkcZkB8q+jBIDhIBpsKZk+zUKYau7q7Dhw5193TPSSeDnNYpAUEhZLL5kAH5QZiemXa+tPn8nr17rtiyhda2LMe9pEuXHj58ZM+e3c6y91x+ZnaGrUBYC3fsA/8v//IvvLPStm3bsM7GQcjlxsbG5kPnsxiyDJxvGdi4sDTcsqJOveZxAv7o1O98+7NjPmRAvjeS9sWGWZz5UbH5okqjQR/HNJDONQzjh8HIPNd6LvvjLJiZmqJ9NSS/TCMf+lB23iXJkJiMJTNQ50/Erx9/fN++fRs3bGhtc57QcvDgwR07duDOVLduVwdya+GOG10feOAB2Q2dxhcDd6+ioNdFGSfLQJaB1DNw0aKm04W018lEiZLGMONIpp8CowBmOlkGkmfA+EFMDpdZzlkG8E7qA0jVb29f3wJsgrdkif1eoKpdzFnGMsf+DJzt72RTU/NYaSyHxTK53JrVqy/fsqWjvR30yJmRBQsWbrn8snvvu69YKmLVu+x38jXuALr11lt52xkJmtFZBrIMpJ6BBa2lhQNLZvJNqSNXBYgBJ/hfVeiZcZaBLAO1yYBe8sgvMvmUnFrQ8KLDVt1d9zY+Z1/IqpEygNgZwPtpPPT3ORWO0dd8Yob0sqOjHXPqdIyMjs66j01F/NjX8a2/85b1GzaAbig0tLTisWIVqAaspaHPN/Z4xybwzir5aAf0MZmPrZdARLeKhp1pZRnIMqBm4KplpZall08WnN/Rzq+D5uRoZOL5OTSZPr/SEam3eAgf9KyrBSJhZEqmDOgfPP5kKoTJOuPVJwMNDY0jIyOofurjbp57oQ/mPA8yvfDOpu7iInMaGyFNTeF0dvTosdH/n703DZYluevFeu8+vfdZ777M3NGM7p3RSBpJFoIHg3gSiwA/IAgvYRMvIMJBBLaR7W/wCb6jb0TwAX+CDwbhF49wOCBwEMZh0BOSQSBpRtKMZu7cZe56lt5P9+nNv6zsys6qylo7q7q6T9bcOZ3Lf6+szH9lZf6z38c+VIR1/+EP3/36P34DYSIfPXpUqVT5lgxH3TD9jl7e10sqltPAawdjX1jybo+ipCxwjizw8sHWk/T8XIVzpDZU5fthu7REi7D3AcqLvTawcioSn5XIXRYp3lCyaAamA2HszBUrOT0qKJSZFbKER2oKLAQLwNE5Pj4OgbAckqY2wh4OU7kcZiumwnRiWlKBWPmK5Vs5++2d7fF4NByeYVcSdpO+9dZ36fw5puG/8Y1vFovFWs183qLBcYfXD1/erxqg7hdFwSsLKAv4ssCN2iTZuGnv/vgipoAdLWAaUFiWJSi2KetIcjWVkDA+QsZHktXcDMU1agvgCJtev1cqljwyRgu1ujLRNNtouDjaIQIRImDhqGI8K2dkJczBwQW8Z/Z6Paxe0TxqYitMi5fL5fnRS7zxZvZRZUw6IkjNYDAwFaqssoCyQDQW+MTF1Ekmqngy0aikuCgLKAsoC7hZAB5LMH+6Wqs9efK46NlxhyC8d+QmV9j1cZIlbF1jSz+qmwAfHaedwkfvn55imTvcd0R0hNfNH7rEG8kw485X0HS1Wv2Jn/iJx48f//zP//xf//VfYz7+W9/6FlbIWCFVibKAskBIFkD49ksXL3xwDle3h2RQRVZZQFlgaQtQrwZetSmxNGEzgWDuE2YuU8kUFiFkMrI29AcTxKyOykuzwGbdELjp+VwOC9CxZgYL1+HN2xnKyXG/du3a5z73ub/5m7/5xV/8xYODg5OTEzwJv/qrv/of/sN/MB3jZEddlSsLKAssb4Fb27Nu/vLydBQFZQFlgQ2zAFyXYBPSsuzAfCeWkEXZGx0ntlg9fHJ8sre/542UgpJtAaebI5vXOtPDFHuHXO3RaIy151jjjr+5XB6r27HG3apZ8o033qDxB+Ds82vcMUv/K7/yK1/96ldRyFaxg/rVq1dfffXVv/qrv2K0AKk2pzJrqISygHQL/PtP5Y8vfH7ieWGbdAEUwbWzAI7JQ3eNZb5rJ/nKBYazYXKFTe4Hm2NeuaihCWDSODQ+4RNGUI6LFy8yHyZ8hivlsDn3baVmjJY5/Oejo6Nerwu2aKjot7W/xHfHVa834L7zNxY+ue2MO7xzrIoBxV/7tV/72Z/9WarI7//+77///vtYfIOXAHV+KrWJ+qssEKoFLpRm+fpl5bWHamRFPCYWoOMT7zfzIxaEjMZpNjE1Gce51gTsLWvVG3gh8PEmzSZBwenBGZHzHX5xUEzd1TjcBUkySLmZaJ+a1066vf39/ZdffjmZSH5w7wMcpIqSZvMkncmYpmBs19DAqf/N3/xNzK9XKhXQohdOEv7lX/5lLKFBQpLiioyygLKAkwU+dz19mLuJDoL1ESzhhKbqlAVEFqANiW9CrCQOCSoyL4lJCV5yU9VyWcoTNBhzPs0KQ0pQ2U3El1NIYWsWgMfjNMloMnkEWXVfwrdABLeRslheFSx4wQoZbao9USxu3bl95+tf//o/fO0fLl+6tLNDTknH7Hu71cI0PM9LPOMOUMypIxIqEjw00lhRgycBk+6mcpVVFlAWkG6B7cJ0/+LV+6n5Kjf27LKEdI6uBGmPAAFYgqKwnoJV0XJTlqcfuApE7Njx9JEGC1w8MMvSKq3e/MekGkVxgDfjxyBPpXWQ2aEqZPF5zlZLh8zchTwvG592QVPVcizATM6eWEqXlQdik8/lz4Zn6jCyQMaTjLTcnZQsTBzI4a2SLjWHZ47jUN95950z7ey8b3zzm5hoJ+aaJeDcDweDAneamNhxh3eP0DF/8Ad/8ODBg3a7jVViVENMw//FX/zFF77wBZxJFgedlQzKApttgdcvJNu5q7r/adWV93usaSu8hBLW87IEJcpn+TRqTVleiGBVJpoORKyymXB5YVjaStBawoCjTVBBeL+GlZhkRJb+i1ZAf9xMMvtDVtChW4DeH765UZZh3zep9Hd2dj788OHlyyqcrlN7kWpyJ0aqjrfAcKiFWdemyLEZA7Eg6bIurJYZjcfvvvuOtuQdwZFYLEfcqJnYcQddHN+EVTHvvffen/zJn/zpn/6ptlgeFGYgjfcDp29PvFAqrSygLBDUAuXs7OVrew+cTkvlO1u7dFD2Ci++FuDvNZXSWhJf6ZVkS1mA3Wren2aFS5G2QQ6VuA1PucVYKQC/xSG+nlx20qmt/x2QbpIoCfLmx1OHrMxnj1DXVsLgo9D/9+3vpNMprI+ZjCef+PjH7959fzyewKufzqYa37nWtmvcT09Pv/a1r/3SL/0S9nb8xm/8xh/90R/duXPn1q1bn//85//2b/82SpspXsoC59MCd/ZnneILCAx1PtVXWisLrJMFMPxG848ZhWfHClVCZAEsF0bgDlGNhDL+PoSUliDlppEIydJCsrztAICLB+NrfadxbCqIYX6dTLGPRs1mc3t75/rVa9ixOjwbghxeOKfTGd48edK2M+4Aunv3Lqi8+eab//zP/wyXHYcx4fqzP/szbtKeJ6XSygLKAtIskE4m7lypPM/sSKOoCCkLrKkF6FgJ4dlsFytZU42U2EtYIMDNz2SzWOgbAHEJMdcdVVnL5g5KNQx2k3Irz8kk3dHhYb/fo7wxEY//sOAF+zTIy8K81C0yNA5d+su//EsA/+AHP+j15rR0bPWrLKAsEJYFPro7TTRenCQM79lhMVN0lQW8WICNHF6Aw4ChAqxcjDBUW0OauA/Wr4GxvTmVahXhOyqVauwtzUy4RtZdnVGZtVYnwjKccQ4SVsjQI023tgo/8iOfpdQQvLGO8O1YITNLbBW2MhnDJLsh48Beee0OxlFVygJyLYAO+0deLD/JqN1Ucu26JtToUMSP2gEGJzJVw6+KXBPdlZjyLBCg1fhlHgELvyLZwZfL5adPniznuEesbsTs7Czns3w9pfappDRwzKZvb28/ffoUPvrf/M3/lUqh36dnMBEWk8k0m80AwMRv4bhPJhPq+GMHqgnIIYvdHtaQkQ7wqkpZQFnA1QKv7k0n9ZcmCdstKK4UFIDAAujY0Cv66N4ENKIrWhc5o7PIGnNSN1PSzQtuSCwiTmfSZ8NB7rwFsw5uM0k3TZFxtACiq1+4cOH58+fjMQLJkMDtDBw++d7+nnVT9cJxByj13RmOlwS8fGB5gVQwygLKAl4skErOPvtC+XH6shfguRvKnnT00SzN49Ny1oMz/5UmWJZH2cg0s8BGamdS6lwpa9Jdz1IbmJ4JZRjdPAF+19h4mLk8PDw8ODgIoLZMlDU2oUwznCtazvc8m8tdvHSx1+uf9vvYRIrPpVjUvoX170VyhIsJF1mD4w4I7F89V9ZUyioLxM0Cd/ZmmG6fYrrd9Lw6CMpD8mkehS9naZpgWR5epdfEApg9QV+vBR9A+AGyJhIvbzhxBhM343EeszXWCZsVamZtaxG8NlqZrtACklhvok6STGNHBhE88LBMJ9MUIu6ZLmVOk0FWkcXdQWQVdGVY+R0G/zjf5GQyhdVcuLwobnDcA3Tx5DHAYOFndY0XsRSMssD5tEApO/v0zdqzjLfp9vNpI6W1NgeDozoGw+F0OkklUwgWhv+zuWwqhXBE5MK514gEjC+v+PyKhZLpVApfXbPZbDJlcVlWbc/4jabxk2jV98grf1jO9HWDYcbDqLVqTYu4Z140zMRUCSkWCHC3T46PsZYJ/iR1RNFTmXZkShFsM4gsHHcsrIH/7fdkJWDh3UitltmM1qC0WLkFPnN5NmjcIdPt6oqZBehQxNySACOTFIXQSw9OT0fjUSadyedy6IHH8NAnk9HgDEd1YMad7DqCK59J5/D9NZspbhXS6QwABoMBjsHGCkr49HDipQgjm8iqjCpbj7jRi9KuUfLyb2c0fsTK84+3rhjxvhsLq+JtCv56NpvLYB/C2ajX72NiYmd3dwGxySm/d4k7ORVWQ6BTv8bBKILLL5aCVxZQFrBaoFGYvXTt4ofp8xW7nXUf1CdmWat94lCyQvHQ056e9rF/CZuZECAMZ2UfHx8hBnCr2cRHT1gP7jiZo9L2NuFLKKbbMRGfyWQbjUa5UiluFcuVMpz8TqfT63Zz+VyxWIKPr1l1hWrF4a6GL4MycPg29sgB64ZxviQ8eI/wssBUE7Cz5GQyxvzvdDLBTcGsRKlUQl8HdxRBEtHX2WF5LucNb5p4iVPvx4vprNvMssbdDh7jwZe+9CWcmcrFireDVeXKAsoCQSzw+RfTx6VXprYfm4PQXCMc7x3X+igFnfihAumAWg4HQ0yZw9vG1e12MGuIczowpY6TrW+++CKGOqyWIVPtekSCfq+PwQ/DHga/drv18P597F5CYODG9jZQME2D2ffj4+Mihspi1B7Mym6f1fbBb8jKlFCMl7RArV579OGHly6rYLtLGtIjuvWpMyNiPR86xmKpeHh4dNKb7lT62EaMXg5PZz4v99ugSRguyyXN8sUvv1gq4yAbvPZf+IVf+K3f+q0rV6784R/+oQOkqlIWUBYIZoGL5enehev3UpVg6OcMC70sdYjj393yEvJpr3cMiz4xXYKF7PDCkXj+7GmrRY7F/ujtO5VqhXnqZnI4QTuRzGQx4Z4plUsXL13C6Hh8dHT/3j24+Pv7B41GvVqrtpqt58+eIyFjZsssgjgfxAZiShJKYyWMBH1iTYI3Nn16mbh8FSsMLZFMZ8jiMTwIobGImHC09pOtXLfTJevas6Wt6n67e69a2Z9NZ/hOSPqw9dZMtqV0esk33ngD23iRRSPGtwnranVM8Hz5y1++c+fO17/+9a9+9auIpqTjzn/JNw4Vi8ZkFJVVFvBjgUwq8SuvZs+u/Pgg6Xf6Ex2b1YVFierw/NyAuMJivvy0f5ov5DHf/uzp06Ojo929vavXrrlGXSC7lWaYxCrNNdObA6bhEXIMzjoiju3v7zfqDdQ8f/YM5XQmPq6WUHJJtoDeIiSTXQty8HMOD58fHFzwKS1sZnrjYATW3JyrFn94NjnpjA6283BH4XMiqPnZ6Gx/bx/76ZmJVYJaAI66y4w7LPjbv/3bX/ziF997770///M/x7ChbKcsoCwg3QIf2Z6WL7z0KIkYWAF6UCuKtUS6yIqgyALU8PzgvsSt6PV72KGF5TGIt/Dow4eYPH/tY6/P44W5kqUAFjDM0Jfgzt8o4TBsuO+Yg7906dLFiwgh3Ds6PsaSGyyeESmmyqRZgN0TvpmAOiuXxmkzCUmwE/Z+YLNHIJMH5c7w2F03lbDsZt41W62wPWc4SR03W1vZ6WiU6p+ewjFFR1TL1JTXTq1mbRpp9Np0vhxfKvAainkXZmDqtf/0T//0+++//zu/8zt2XjsW0vBYDF0llAWUBbxYYCsz+89frz8qvTaznc7xQib2MLRrYeMW5F10NprwtMpUGHu1whAQPSq2kFLKjx99+PjRo0uXL9+69ZLrRDsThn5HRZQGVmJKoHvHFDv6+0ePPkRcGqyWqWHlTKs9GJwW8gXbFTgmKvHLquYjuifnwyp+tEQod+wD8f5AiayqyiRYoHVWKOdGlRJuRUHr9GZYFohryVvDtwV+zIHEfJUEBSIngV28to473nWwQgZeO+baf/d3f9fOa4fMynGP/MYphhtlgS+9lDy78OkhFsnQHoXvZkwlfJcDMD67USY518rAa2932pl0ejQaY1MpptvvvPoaVqVjMPNuFzjuaB2ICOmAAu8co2OtVsdK98OjQ0Tb2NnewVL4o+OjwlbBlR3o802VMlJNUjf45lpiIzTDo4Ed3pWK2lOkN9ilf1m78NgtdM5ygCxmyVJtciWT+UIBu3IKW1tqrp2aRPjX1nFHhw6X/fbt24hjAK+drmvP5LMvv/mJl9/8ZDafaz4+wgJKSlQ57kLjqkJlAS8WuFiafvrVm8/SV70AK5iNtwC8doQ0zmYz+GT8wd33kX39E5/A2hbrWAhTsJHt1VtuAAAgAElEQVTSahbXGXeGguktuC/Y4/TwwQNMQ27vbGPG/enTpxhEcagTA4tZgqputYqDSWKmgUdxoBCvJZ/dOF09mkQWGJZk4NCDzJqvoja1AlNjkWUruXQgc3NQqOWH6ZRJfPiVM9cpA7nChEON6cVuCCtZlqGt437z5s3f+73f+/u///uvfOUrGEXAp3qw/eZv/tIH3/z+u//w7ep+4/Vf+NEH//pDhApGlXLcl70PCv+8WqCQTvy7j209K39ymoyth7Th9wa9KetZmarWLhYw1kIGLyuBQQsRY7CIBVtL4bUjcee1jzksd3HgSxz3WQLxIh1g+KpcLl+tVh8/fowQ1wg4U6lWnz59guNYLVNfEZiBlys26fOqd2xugHxB8LkJSwlwxIFf0s5tIZq+wq/M8YFvDfNYFFrJCU4NQgc4wxFyKYfR0Nn28dGSk0S2yLaOOyZgDsed//1/+2q/2wP/3RsXPv1f/Nu//1//j+OHz6bjSfPD55iSr13cRQK1ynHnbpFKKgv4sMBP3JgWrrzR36wQkKybYg4xK/FhmnMEujBPp91Gd9rv9+59cDefL8BrDxyxbj7jnvMRkwETXfV6HfHdMVmDFe8Ix4btqhgksDs23LvBDEBbDLIsES5jRX39LMAay/Kiw43BGzIWZuCj0/LU1oqCRCv60HsyS9KJ9lwaO4MFF6JAItpV8Bn31aglUCTUIvTJtuEg91+68sqbn+w3uzg6G836W//x/xn2Brw0yVQSVkYJXltVOEjeMiqtLODFAteqs59549qDrY9R4PPR53gxTDAYZj/2vgA6rDAYzUixsDELngTCPt69+z6iqt++82pgrx1ya+EgEzjThNiANwnTyd42WO9+2u+ToJP5wqPHj3PZ7DyUDcNViXNpAfsms67mwGoZHE+2u7u3nALMMOxJYyXLEY4ttn/9OqNcNjXJpabwHO3UouHIl+n37ChvUvnIIRzks3cfPn/vUb5UQNSk0enQqja8dhy6t7u7i4ib1lpVoiygLOBggUJm9pMfKT0uvGzsw4w54nCZShxIqipqAZ8WAzgbbZkJfdJgeIETp4NTTH9g8eHDhw/gKH/0o3ewTGWpm09V4P96Fm53Z7eT63xw9+716zcQKhLrZ3Dwk/LdPdrP2nZY+7JWeaS5WWAxMkMul4UbhOUZeGeWYWRONS4pg/Ia04AlTkfZTHJWsJloZ7phoQy22bBsDBNxuKuQwTaqDDHZbDY+G2FtjNB8mBNCfHcsiMS52TC3EEYVKgsoCwgt8PmbifzVT/RSVWHt+hXSDoAf+1SX4PkuIpALZrjxgfjDhw/PhmeIIWNZWe6Zlg6ob071sVRGRyW/6N5xwWXfKha3G9vNVms8meQ9r5jnSYWXNjUx1vpM5eEJEDPKm643rx+72ewe8LWs0EMCHvtoeIY9Hh5gFYhvCwwmGbjsQCtkxq7I6GTIhIWclyhXbusKYLvG3YtCcNbv3r377NkzfNdQjrsXiykYZQFqgZe3px+7/ZFn6WvKIMoC6Dxb7VYun8fBqMeHz1/92OtLBjCmJtUc91mwja2UAl4e4M080X33k+MTDKuQc3Nvmcn1g29oKtlc1a2anRvV0cyxRXWz40Ku5GYiJiEOfp6mtrCi3Ro9xtriUEJc0ozLqaBCxHNVCCsZbIQgBn5Xq2MrFRx3v1jnyspKWWUB3gKNwvTfvNJ4nH2RL1TpzbYAGzhNE4Uox2ZQzG23mk0ctPTRO69ihpsBL2MTSmRJUti3t7e/jzCR165dv3T54v1799HbI9y7m2A8W6oxSljCDTsu9bwWkmQKgaQkyc4vGSySgecDFxOP4TJWUPeWt95kMr336Biv/i9cKfHlzum1tWEkgi+YzAyOOzZq0M0BzsY11UpaHGaiqrLKAhtoAazf+6lbmZPq65OEQ8SrDVTcr0q0j7K6uX7pxA1+0fdqkuGYUnwWxoLDhw/uX7l6bXt7WySwCUkEIiijWMFwF+Qw/b+9s3P/wf2bN25evnLl/r17mKuBo7OAcEkxAVjCBcGpmtIwNQsgyKDtxFfVRWuBiO9nY7tx+Pxw/+AgWi03lluv222Nyy/duGCMD+N+V7Un2x3M3XBCGqZ+QwjjTjoWEAbHnZcIXfOXvvQl/H3w4MHXvvY1vkqllQWUBYJZ4EevTguXPnGYLAdA5/sZ1gXxhQFoxhzFXjvUMBvwSthj8FDxSE8n08HpAGG7niJySy6HKC6aXPJUEFKihVbjCYE1gUrFEkIUwHe/cePG5SuXHz54eHBwgG+tK7OivagrEymWjJmdrHcb8rLaWMoeqVAIHI4Va1g4YHQ0I5XByCw2N8enINjteNgaYRnGpRowk+yYTqN2tjnCzSdHW1rWivAoW3mFXGLb+dKp9x/7sR/7u7/7u5BlUOSVBc6FBV7bn7z84ov30xc9aMv6GH7MZYXo3FCOLP3rgd4GgiyssRrlluaPUC0IaoH1tZ1267XXP5FKpmQOWlQ8OyHtym1MibOZRuMR9s5evXptf38fZ2nDdz9v31phM/5pNJnKp0VN2OFm4yybPM2X0rJaq2K5GqbeJcjDBLFrLgxAArMYkcByo+PR9vbWEc6gCCiWCnOiGc65gaDWyXFHSAF8ul3lzErAm6/QlAViZ4GdrdmPvLz3IP+yzykFu0eYltvVitRnsPxwwgpFGKosPAtgkENLOD0dYPfntes3PSwcD08WT5R3tneePHmMaARw2bHt7PDwaG9vV9h8aPsSVnniFGOgaJVi3PgnllmH1bKSmCWcBYROzgDRarNV2GqeNCWLFCcFwzbn8WlmMjy7UO8lEz68dquFrCVhS76O9G0dd6oMPuDGf0RZR7srmc+VBTKp2c99NP+0+Pp0FoMgtaprjLDx8cZm/he+y+OApGwu+/zDD/P5HBbX8mBSpKME5ZLd3z949OjR1lZhd28XC2Y6NsHd5TJ1swblxkzLg0crCM95mbRAakHRMhxigRs/nbCdA0/lejk8cbDiZJp81knVcr1inezujYNIsWjhEoWY21Q37cx+xp0yRcSD407ztZ/73GwyufdP3+8ctiQKo0gpC0RpAaxfxOGU+OiPE91xnPvJyQk94B0ny5CDrwsFbM7G9yUaIgnAiK6NiHgopB06wPA4AAWIQAcRkMJqB3T0mIDEKy7g6RJJUAAdxONDIaoA8GKp/Sh56957Dxvb2ycnx/Vavd1pl0sa360CQndnshmseEb8WlxYpQa+mJHFAWf9Xq9cKbdb7Xqjfnx8sg2+rWa1Uu32NL6nAxxHPx6NcWgFOSp6NkPQD/BFeATsdyyVit1OF5+AMZMUQ74IO6Cpa9C31+tVKuVWq93Q9IWdW61mpVLtafpifhpu7mg0Tov0xSmhTF98KjzW7NzptEsWO5v4InYKb2fEPST318bOGJdMdhbytbu/Ke3+Yj1t//Q0nUrd++DuKx+988EHH+D+yuWLk63RrkrlEm1XGl/SntGuNH37rF2dHB83SKR2cbvS9J2ydtU/7f/w3R+CLP57+7vfvfXSrZNm09Keh5lsFjHL0J6pvhxftOdKu9Wq4wEkfGHnVrVS6fZ65Dk6PUW4yfFolErj8ClyMb5oz8VSqYvnt1ZrnpyQ+3uM57fW7nTKpRJ5fre2zoaOfMsVnJFZr+PB1/g2W9VqpdvV+A5OEfhyPB5hrTMcjznfM+05GpwWi6VuF/1GrdnEA4h2pfFtd8pljW9h6+xsmMlkp1MceGLRt98rM77Qdxv9hg1fwtair+Lr2c6XLl1GfyscUHR/x1BpetUDDNrkY7yXugdNMtA555neKNs6fn6w30gnl4rJ482M/J003UCegAbGw/KVG5FOvvHGG/SoDnSRpqgyn/3sZ3/913/9uz/83g8mTx5+570kXJnh6Pt/98/o2Xjd4daocJC8QVQ6nhaAk3358mU4bVGKB2b/1ccSxY/+/NnWkqdqRym14hWiBfCCd3x8hFe7737n241647XXPx4GM/iyIBvG9CHc7qPjozt37uD9Cq8ct27dIs8UxgS0dfqX6kNHCVbIA3BpDCbkiaTAnCHm2Nr2Njca5OWCY0/SGsosmcDRL+YqXUphOScBSfKcTVUqGyMLYBbg6//pazu7u0vK9OzZ052dXfhCS9KJBB0N2HqhxZou7QkwlcnIgu5ha4x+rFGyMg3IAL4oXvIDIp8bNFjJZakMpu56w9OX3nz95Tc/+eitu9/7v//p5mdu3/3Ht86NiZSim2MBTO/RqdbIVEJ/9nMvjV977dPvFC6sxVAQmWXOMyN8q8HXm4cPHw76/Zd/5EcFXgIbkfkBkRV6s91sShDIMYQCr3hBAhyMhGmOZ0wJ0BJSi4lJfBjBDqjr16/Dd8fmWmxXXVBUKWWBVVhgNDozt2UvYvCtP5nY2dk5Ojyct2f2EPAwXmiuEiYiWc8m6WdP7u9fvJZL4UOTnAtfnOQQih8VuYqBmovjjmkbfJcsJG7AFJfu3Oy3ukcfPH74r++OBnhI1KUssE4WaLVapZKPwyCW1+0zlydf+Mydb+dDmVJdXrwVU0D3w4ZGKkqg7s1KhullpccY6lX67xwH9aYSRkxOAtPtWFMxGEwe3vvgI6/cxudKp+FqKVnIMOg622bDwVpsKLl46dJ7772HZWPXrl1966238B4SxtS+HIsrKswChnvISs0JQFkeEzOMjDwvDWNICfNVXlmRF+AgeBz9GXnRxROKR3L+EYmrVElqAdi41e6eTXLk83VCmtcO4qBM/ylTu1rAxXGHo4PVseypuv7Jlx/867s71y8++cE9V9IKQFkgVhaI+FDrFxuTX/nM5W/nPjvBF/tlh5RYGVKeMEsOtLog3sm4QbrV6xwD/2KdCZz1B/fvwzO4dv16YDquiKFqgr0cGLbff//9V199DfPud9+/i8XuxNeReElSICgZEx6vmqlKos5xIRW5hhIYOr0A+7Grtq8GeyHqfpDmsBLUCMA1QhQsSXp+3C7XtvdyU7CVri//mEWolixWgexBkRw0t1KdGU9OtYr/5MmTk+dHZ//Yn5J9M2QDXP+wnctmsIRGC2dmxVAlygIxtYC28bESjXA36tMvv7n9vfpPqhNSozH4WnDBJqLpbIp9lh8+uI89qVgeahbb2kebIZzyDJuMAlqG//iMQh2A/nIFBqp25QYg7JzGtsuHDx9cv36jUq3g4EmEmjFAbFRGt9xGKbU5ysBrh4si5SZhlzPCD9QCOe6bY1CiCW9O0qN0u/3epLjbKGcykibaeQ7gNyPhGUxbKDfLpPbaGE1hDzevsYwcHAamVV555RUMMwhz8f3vf/+ll15CSIELr38O68DQsv/4j/+YHtLEYaikskB8LQBXIxrhbtQn/8tPNt6u/Ww/ERFHF73QKZCOV3Tx/YUJhq/iUT35dTyCbRoLOXSe81+dp/5rQNVhDSOKASLmGWyPxpSHNt2eunDpkiRPQ6C0Zj7qxywsuUjNMSwFLuVmRnt7e++/997u7u6VK1fefuttBMYRvIqYkVR+1Rawu+3s8YKARhhjLmz5vXBjsi6ABXtFxJIuUMT1iQRGitN+f6soqfd2Z2gnSHzKZ8+aZ7l85aA0xsYZU/OQJSXevRCESha1zaZj67ij7SKqzDe/+U1E0MP6RYQOgCGw4Rch8PCEwGUPb9TZbIsr7VZlgWi+Ed2sT/7nn2y8VdW8djlzQDIM5mXw8AIDWTyCeZNaJ6b/OmF5gXHCX20dWd0+mQyn00cPH75y+7ZnP2O1Ujtxx9qYS5cuIbDM7du3L166+PDBgxs3bzohhFpHWwfGfedmYgQQwjLfQVhrVIJxNRaLcx7oiREjKY23dEYTCGQdnY1kTdbCyXn+/HlkEz1G1WKXQwyTZ8PtveIRIh6HKhxcShpPOVQuIG5qPex5t1aFLUkw+pDf1nHHRPtXvvKVYHQVlrJADC0QQZypF+qTL1OvPVmMoQWUSBFZgI4M/ICQSGClFqbbHz9+hOC5BxcumkcPXjKRc2kkNodmfExDEapJCUbC+aujEJsSsaLOies/Rmn0UvqLdQWI1Q4vB4E48BezPNioagSJNueqjSuAZVz3oIAHoh6oKJDAFkAzx8MVGN2ECPcxlUqOJ+NM2tZBMqHEKmvXHFkvYAdg1aI9yAwGZ5fqnUQi5x3LSsdLCVmPnUT81qiv6Dkur+GiXWISHecXYBWm3td7Ik4auPq64clUCmjFFgh7ZRe89t9+s/525Wf6zGtHl8A6S5P2rLewArAqisIDmKoYTcBoVbSex9D9EFLGY+swpEwv18vmZEkxivTaeamukqWY1asEtQBnITjrOKYHr46PH334wq1bmG53ivfCITJbisrmleIqw9ceMQgj7phwwb1w4cL9+/cQkP7qlat3P7j74osvqhHB0Z6RVLrcNLEMgZDEpCIvnWGqUaL8FXLeVgurgiNXJESGvuyD8wCPetlCorlfDz8UG5UMXRYSplGIt4cvBRaIAdEWBOKWMm1OhWcTYJFi2P5Q3Iym5FlTC4TqT7zQmHz5J7bfrv50D1477zO5dhrLA+B+GIkYc/R2mcvMeQIlKBMUicAoD/XXzgKIq4sZwePj4+FgcPnKVTswueWieyeXA6GGIaNWrT9+8vjK5Sv5XJ4eS2xmA1H48VgoGQUQVulNk6cBFjawZuYR5h0k4mV3AItQ2M1iJTcgKZ5WBMLGJKbjqMFt0hEbE6eLaRXrdsP7ZymcSby7XcmYhjOxmhJLnSY0JLJZd1IZTLHzTdPXdPu6K6/kP1cW4Nu5XMVv1ib/05vb36389GKufXkGtK9nw70wS7kAhh8YwhkqwIHyYRJR5kwuXgShXMubhNPTJAVPWygID4C0FYYHkG/BwWBQKOSfPn588fIVfNiMoJslGoIN/w7Jqyg1jSOZ7t/7AKtlrl69+oN3flCr1QTPmrPJibTuMnkAcSeyIoh1ll2uyUSWEJX54Ip2jmVawWI42rHBm0Cv23Vb+uUmuFu9HfcVlj9vjjLJ4cFOOZkgMR8ju2hntYYGi8xCc0YwUQYfbRHrALMmGE6CrXvBV+BohoeozaP4bZYFQvo09Nre5L/7NzvfKX+RxJCR7ieZujFTlt4gU6EpK+8mUsJC8t4LJYkjZGhH2xcwiPiFt+M7L8fuLsTS7ff6rWbzlY/edoGWVS29KdoLhvWS8N21s1RvYGPf4eEhAs7Ygy9dY70/pvc4I4Axt+DOI9nBLKDXOAXleF1NmmyI6nhdNCnmmnXQHPYqVypPHj/mHXcHeFdeawEwmqafP36we+FaLp2FwNHrGz3HVdwXq5YIgukoCH18WZeOpTJw3PFmiYgH2pxQARPwjgTMlZhZwXkiIblEZmYqryywhAXC2Jz6pZdGn3/j5e/kPt1PFJYQba1QaRfj4AlAG1M3xAPrVfjlixcmmHdS8wIdnGSt8HztggKXMhLjKiJMYi4QESqIE1Apl8pl1v3KEIEZwGwbWhHZlAr8dax0HwxOEWcGoSGxOFgw6S5DYTENZgZxtbg0EJKYVOxLN1xXPFMIUV2pVmXdCGqvTDZzNhqFMXDYyMluk+lxZuU2eKzYBGgiw8CQMEEmEs1WZ5QoIDxUMoH4PDxoRGnCU2rnGJHcEti4mdtSn0Y/iylzzLVjURc97JeKsfvCpasfu3V074mrVJitj2x4cBVGASgL2FkA4SAxfSLLn0gnEz/z4vjffvr2v2Q/d2YfnclOGFV+TiyAvvH0tI/x6O5779184QWJvoWzAckUzGwmMdSGMzs8VhhEWq0WFsx0O118ZJAWBtuZ8apqMZQ6eEWQyjLWEnhroTf5HfCEUjjAe2O4ZlA4u6d5coxVA3Llxvs2Ph+VS+HvzpQrt09q01nyyWG7WNmpF10atU/C/sAn0+kGBMn1p3MgaHjsi6gyPIX9F6+88Nnb+7eu9I7bH771PgLj87UqrSywjhbApyFZYhcys//xs8na9c98K3Ubp73JIruZdGjn4cVIfDdjgmdVtJxleZM5VPFg8tJeNBucDnK5fKvVRFSZnd1904ySnZbLy0gnU0zslifrQEE7SPX+cHh28eLFd955B0fHk3MQXS52IwHJ0kIcRsoZTIgbTqFfQfzCe5M6HKreeMcJ6uT4BC1QrkR4HdVWE8DGrPnJ5bB6aqeDs+ZZcW+nlkmNViIN34D59EqEWRemYsf94CNXL7x8vbxTu/r6S0f3n+zevET04bpWzK48+Jd3lEO/LrdZyQkLIF5YgHWQVtNdqUz/m4+nk9d+/K3kNc3ZUL2N1UiWEr9GsoO3KwdDhyqLOBILnNn2+r1KpXx0eNho7GQz6G8N4IaMRJkWpMLnoPOCl16r1p49fXrl6hXEd2+329Wa96ULrnK6AuhybNKvg9J0OGb+pANkDAwilI7JDgGFAN4Fx2tqtVZbkoiQ3c7u7tHR8fZmxYWkmuKt/qQzSmZKF6rEZQ/DeoQR6Hq80xAoLCGoxvzf6DjxXOWkTeEgGdHv/NV/aj4+vHzn5rf+4//7wmfvPPjWO/iKwYx/+bVbNz71yoff/uFkOmEoKqEsEHMLlGR88bzVmPz3P179QfmnmomaYy9j6q5ibhsp4vlS2S8wJGQ9EJOWdr6mclrIYPhaViUsdEZhAjAiDJ4lKNkFAL5pplOpwWB4fHR0+86rZFxaVDKsUBL6jHtU/DQlsJ/v4cMHFy5euHTx4t27dyuViuCmhaLu+SNKb2ykt1eykWXKPkt0Om1stJAsIo4dymbJkdtRfrqSroOIIJbSnYx3arnDQh5r6kQQEst80PcBKlHAtSMlnnGHGg+//d7h+49e+9nPvv+PbyfTaWxipbptXzs46/Ufvf3+2qmqBD7nFsAeQeJJBL1y6cRP3Rx94ZMvvpX9VDeBg1FduxhXgKCixBfPl8q+gKGzHbxdOTWTsFZYyMxqV2tXbotIwrdvFY4PD+G+N7a3GdymJrC0ANtvtYNUD0ggndO+tOjavO351y6PpuTRHVB4yh5RHKjFtYppBnVpmtdbKDVDEdauvBAvxFLmZYSKgDKOPQ6PvpBpqIWtYeZsMN6rdlJJp+NmA9901q6YFrSBORPEXIMzAKN2zhOwkq3jPptOaxd3ju4/TaVTuVKB3Ynahe1v/59fu/PFz5xz2yn1184Cy6yT2dma/revJ+o3P/tPyZcmM7zEqh5m7e7/CgQenZ3l87mjw6PdvX3s3YxyYFrJjDtMXKvXHz16dOHgAiJCQvGtq1vy7R7ewxceZflWkECRqcsSEoiuhMQs0cdKyHojDOb4jvT82dOijA+2YYgnosnfT/ZSRgoRu/t5L1NKdXZr2PHl6igzXA12nqM/PAtdBL1M/9XLvY+XrhItSJ7n1MzWcadWOX7wrPX4kLfQ7o0LfFallQXWxQKIFxZs1uT27vjff6b6qPaj30/s8x47uieuY1uFGRh7a09pEodBsnJnFCs8Q6QJE7orvBXLIwoQGS/vKDwW0oERl8CdzqapdBqbNRHv4vVPfBKUXC6mJg/nLDlDARhLU3SaZYV2dBgAz9Q5bSJlpJBMJBFnrNPtbO9sf/jho8l4sr6RIoyazY1i0t7ZVHytkBoPYE3zvBg6K2QlVsTzVoIZd5za6+6IBrILDJ5OpcejERp2IAKrRVo0k8E4fXx8dLBTTacL5u5CLOMCl9TPc8ZCMaLvUkw0yAr45pv3GiKsY0NcQzMrkWNggW3/axXy6dnnb06++MkXvpt5ozvD3KG5zzLnI1bTO3vvkFSFsOHBxS+LYCjB1KFYy+FiaSwiQGPpLQJTbBWLAafbPVrJAkZX0y/W1FsAeBX9pd1IIZ7Ms2fPXnzxFr5xnTRPENPdH/14Q7tpL1N6IS9hoUyu60hrlkAE0vAEx87X4+NjhDoNj4XptrLXM8rRVOtbjNnsuDebnTUv7teBuyw13+zdEXCKZzwFcxc9cghYSjnukVtdMVyRBTDj7mvF7at7k//6k8V2/RPfTF4bT9XyGHbb2Jjisf9n8IwCEq64QiyeAk2b6HjEMtEBkWCIJjqGLDk3oFJunpzgC762TsZQG1IGalCL0G/Ofr88M3SIxyxiMrGr5JiYxHcGhL/cP9h/9513thvbLC4kpemXoCvHkMi68lUAMbEAXlDxYUd6u2K9VCaTRah4rDMRzAqz58S7Lchj6YBG9LDXxQGRSmBGxXmoh88eNfYuF0oIqGCu9S51qJCwLrFtXMULVXffxGeBHHd8/8XsEYsFieaMQHsIki1o074lUgjKAmFZwPsa93Ju9u9enr360s3v5z7RmxUdOlGTrN4dCNfel1FGR+sdmGGFmfDc9c/N4RmeCj3X1gbLppigEkSHakpd/2sCTJryOljQXzq/PhmPEQjy1ksva2QksxCKxvGgSa5AiGAs5KH5tBHKPYctqgiihxlKeDx4gckX5nvglqHpwDUksg4cVZUcC/B3bplubobV29NQ3b5ypdputwSDCK+CD6MERPPRxWnCtE8Tg0H/4OAglRz7kC5yUMy4Z1KpyNmuK0PfM+7w2l/4z26///W3yXOiXfTU1cFgkNpq5Ganyn1f17aw6XIjqgxOTnXV8s7e5L/8+FZ7+1P/NLs6I2183s6Z/6yNL+KZAa0zXow/i5SFq69u2xewhVXIBVRJiGjV1io3K7ECMzEZDCvxmAiMCPrL4IrEw4f7bDbT75/CbUU484DrZESUPZZRjtHzpeIVS8Wjo6MLFw6wbObk5PjggtoZ5fG+nWOwJZ5BtHPt8mM9YRdkL8NWodBqNqvVmh8eq4TFyYCHJ91iZXuPrI4J9aVGgpqYcU+kM+JhVQL5jSIBKzk57sP+4MJHrjau7EFp+hEDf4uNylvfeHvQ6aMQW/0QOuDJkyfw3XE4MGbeB8MBHHeJR1RulL2VMiu1gOvO1CvV6S98JHH9+gtvpT5+OrUeszrv1O37dqreon6RkqI4G2lMdGk5X8ggpfB1JsL4shti4DUAACAASURBVIQzPK31BeyFYMxgsFIEXeLJ8TFWZ5GP+NGPSBrLFfDVbgRGAYzEmM1pbDeePn26vz8TvNfJv2Vo9+E1LOeHShZfZy7eTeZRHlnsXAWj8oTFTlsqg/laj1pr0vqBperBsRkMyOICV21XDoAZg84YR2iWMqnByoXxLID/W+KZ9GYBOkaVOXnwDP9MCt/9xvdoCYalL37xi++++y4DSKWSmvs+RfRijFX5vFOIUIalEsoC0VgAC7rsTugoZmdfuDn58Y/ufy/7qW+Sk5UwwCzRiYQ0PNlJZC23lkRj4nPNhd51mJ4kxuMx+kMscK83Gh6cdnbDpDUdytTKmknp8V7xAjEpGS5fywppYmuriF0l+/sHmQxC6wwjGQ6sApqEWiYbKnEmWDRcLOxMNzIsKTzQNUnCJHVOkM2pYw/Unam41GL4ODw8zMfecT9qDdP56n55gAnXsG3iYjLP1ZBzXUT1rFNYgDDUYsYdoQ9yuRzmSDxyA+Rf/uVfYq4dkRN4FJRgkgnUlPvOm0WlV24BoeuQSiY+eWH8y68Wntd+9B9mlzSXHZL670PoeAM8JPxjr9w48RbANJjz9iVVRpPztbTSrJyJnLk6YJ7xxfafKbx2rJZpNVuv3L7j5ys1IxJQCII2V2/uupMSjiqXJDWuF4G3t5cDNYwCxydHFy9e3EVA96PDi5cuufJSACuzgMONjFimQJJgxp34Idb3VKnC4zsSHm1sXIlthFPMFxyOdhr5w3xec+RCNohM60LUNZJWpub+aWE/AI8Eb1vo3PAwpjQ+xQLLVIgsWjY6bjQj5b5bjaNKVmIBU7ywTCrxiYPxlz5amDTu/Gvi1tmUvn/q44a9syIWXsfjnSQx5CaXUqsxW1hVZWa1wrAqK5YVmDmTpEpQvaAhqBQULeAlpNDScrk8vvCge9wqbkWxXoU3nqYeZSqHdTB7QaQZNsYN6vX640ePSDADXkgJZlYk4mOBYLc2WMMSaI12jpbmyfELJqnOs1FvNJvN7RhGOJ0luqdn/XFmv9xMprLSLKsrHvYvBF47mcO2iR19GMrguAOObTm1w/FVjrhguDCMwX1HAjP6vtAVsLKARAuwaRLisl+Y/Nwrebjs35veHE7pmi5jv2HMBRFjuREiCMfV47haTQCg2Ql/BFUOCvmDdiAkvSqZwIGp5WoF8WTwVR1Ht2AqSToTM0ELB82JgT9jqTBjhpjP5fPNVmt3ZxeT9mejM9O32RAZR0H6PD7e9nb10czCMByaOZbpeupDfEgqUBdtGP7MPHahoH41RXDbjvq5/PR0v1olEqz0qQ9oAsi8jmIH1HY5NNOM+3LEbLHR1mlzh/tO07agqkJZIDQLwIlJJxOfujT+mZfhst+Gyz6Y0DdJFjdGKm+bEcIyblkKpEoRnFhUcml2sjFWcOm9YxI9ZeqqRaZLp1KddrtWqxPXeRXKaS77Imivd3MIIINap5AvdNsdhKJrNOrtVhtnqQqIr2vRKm6q2FZBb4+FmjRCFsp8QRiGQ2vvdbt2W5h47suni6VSt9crl0rLk5JCYTBJt1rd7eo0m6loT70UqiASTXOYS6v8du+3DbYyz7h7R/YLSV12Ovuu3He/1lPwS1qgkJlVthL/w48VtvZeeBuz7HOXPYxBxEVSC0tLAUeA6ztJUgMlO2e1S/+dZ2X/OMgVMmfZmjjTI3o66GqPLLACiuCnJ1MpLCDsdjpXr1+XOpTay2KpoXwDcjdpFsg6kCiJhf7jMdYEY4fu+++/jwgzFjHXtMBkoIBayKESrPGKRA56n0W0oi1DO69UKtHIj+hkT588cY1RFo0BjjvT5LS7v10JYZrdqznlNWOvHKOxbYy5GNe4RyAoddkRK03NvkdgbcUCFtgpTD91eXptt/addu2HtTenYzrLztzfMI20dJfG9WSLpJ7Sf5fVgEjpT1JZnD1K7k84j0SXBxNYAUVw2bEsEEeHIpQKIqtgNxvHyUUTl2qOkGtyKcddoBnH0I+UWJ/W7/VxfsJ0MoVlELqAI7S+SWcDedXLAxU/ttbY+kbwKmy84Wazk1az5OGYDilqwI0Zj0Z4zKVQC0ZkPEsfnvQa1VI+U0oYOplg9IJjeWjG7sTRX9Euyx10oyCCPK+Rzrjz1sZid1wY2PrYDZsgsSP5WpVWFljeAnggbtanr1+YNbb3780uf3tafDZ8fnOWSul9nJTuxipnkveBQ+Jh4hrk2edJEClDkNRJLKc6XjSaDiycPzZWxkFKxqNxqVxst9uZbDadMUVwd9FEr/YntxCajoKhjIW6lF6sk89hmXvzypWriH6NyZpSscQ/H14orAOM8A5IEZzY2hd1PzdHioTRErGxBdo5to2G0tpF+mG/9bNnz7AGTFSJMhspbaADFPdGqd7pYL+xlUxM4MbJuUKX2klMKCFLDyc2sasLojQ+6q7ylVGLYAPvfYz/0a1vymRM7JrGuRIIG08vVyYv7yZu7BY7mf1744OHY/paOMO7IjbxOPZOjpXe7LhY0SyBmFeWFE7EUFTmjaoPKDETpy7Jqc4HY4CKec9L5bHxLhSWhmBCrtftFbBbDq0B46pYRgeS/uQWQtOZfuN8v5CjQDhBkRDVQ2Emm8GSIcTRw1nxODG+uFX0PT5LlMaDwIFAhHeAp7SUDibqS9HihVqjNNPZZAtdBTxlx8fH+MClF2i/DMtQKiODBo3QkFMsihPysJFSBufZLHl43N6qHexVEc0PoZpcLqF8YhxXWmI0SaXoJ6W9gkgSKbZkotmc6qw+DUAJ3x1OFdLKfXc2l6q1WgCdZzY1296avX4wu7RT7Gd2n062/3m6lTijsPMO6bTf14ImzbsyUY+2dNfFE12CGE/Gqq+dtypiKCoTUfTvXHJUPDHBscthXGLe5tJweAv1IS+HyVSv29kqbE3pUKRJE6EIRC46++hhDtJsKoIrVCxoIYIC42WmXCk/f/5Me5FwsoSgTq40AbQQyOSXikwdPNNaSu6lkP2axxXeTWe0c8yCLyZNaBfphuXK1gEAL6LN5kmjEem2jeFo0hzkd+rVTKrn8SkN0wYO5vFXBSFxB3H5QxNAx6vZCgSUUhTl5lRngeGy47bBfcdf5b4720rVMgvsbM0ulqfX6smDenGQaTweN/5lUkzMzxXg1xYTjK1i0c1xZ4SDJiT1G/ZkaM3yHZxFQXuWFlBhgRnfnJc7nWKhLpRpUehuMELRL9UFfS6VSaexnLvfPz24UEPYOFYTyaaKuRJQhMRN1/8yGVaSwNzk4PSUHOsxGrsuc3e/Ub51cL+rLhAhyORbCSC4SGkluZTcHpB9C2QVkZYsTwhuQ6vZRBtjLDzIz2CDJPBVDVv1ZPiaHrknm73xLFPeL58CQYKL68J2+XviwoCvRvQrcsirhHvmSCJSnXj9JKfxgrpYKoPulZKnbZFmI2yXuHFJLJgBR+q+F4qV5GwsWWNFbv0tUMrO9orTG/XphVo+U6i0k/XDceXxOKc3loWrZNIVX+qxQ87jRAXB9fOca7B+EEzCWbM+iRnBjTkrcb8lQegRnCB4HmWTTFqOtJhRxtJ2bFw7Oxsiirn2oqjrI1lgnaz5d64ImdvWAlOa60PNi3TMZnOdTmdvbx/LZkZno2zOcNL2cuKI+JkpkrHcGc5xtDeT85d3ZuyLlg8pfXD1AWqW1odAZlRj3gchG3HR2kuV8vwDl9vtNjIPnoO70uv1+LeF4LQcMfHJ6mSQq2QnhVxfhnfryGxe6eOeeCEnglncS7LoSJt0F4HJK5Oo00J2CeL5J8Y57iYf3ZSVIJ03Egv3fTybnZ3i2WBvFN4IKKiNsgAir6eSs3qBzKxfqc4apUIyV+4mSnDWfzDNT4YsSIWtv87MUalUMeHnqWG7PUnGemOO8fOY8IDNgXBJj/RNYJ4J6ID6r4mOr6xXGvrkgS/iVmCv7EyYC7RFygTilsUQi4F8OBgCMJVMLRx3UAw+bAjEERTNZSNs8D+d7F8I4Ca5uN6ejRhepGMqnep2uxcuXETAPiRq9RrvWfnlYOQr4meEoDknuOUkEHHjysyM58xC5empqekSmAXkZA8nqTN2oy6GsxEX35ewq6RULFOqCygxFQJlX+MmmV6PiJBHR0fawU96UQi/3f7ZYJrfLo5SqYynwSsEGexJLmPFxV1CN5XCMB/gpWQZ/vZasRpb8gvZGWzwhF9isNNixj042xAwifuenswKBZxjDPJhPxshaLA2JGFqPDD0LxPaSyFgKDxFZ2kkTOh8lqatMLQEf1OJmbYAZrJfSmxlk/VSbpzMnyULzUnpybh4f5yZjtnTBGfdzl/XhWOwiQQ62fEY3Z9bCCNDXBgOH8KZLlGlViaqMOGyrAdYDoRLMgreE/bYlhpLgXcugHTE5iq5pC/6VmBvlAz31kokQImR72g8yudzOHoJjPBBE2tmTCQlC2DkzvOaaKt0rAJoMPrTwSNISdvIg0UF8CYRFPLRo8eVKWJOmy4BmqDIhLRMlqfud9g08OUJeamYM1uKp4GPlrGVwgqql3iVwCtpr3Ce31/dBeRYYsadfq7X1dN/7akIajiCOr5zT0ag0LaxbIbBS0xgYD0abhUmnZ0y6AfyayVKIyYFK4qsJga2LYXjnkxiXBbcEzOOiZsHDDMFx3zI5B15e65EwyCOu8mX8oweOiAEg8sOKU/Ppql0Npcao2R5rkxfISla6wwjRFx5ITWN35YM80Jy+peqADr59BQ7PsfTRCZFvmHB6oBCNpeenWL5EnwScml/8YfMes9KmcR+aVrIzvh99hoe2XlfzSdqhVmevidSbI0E/mAnXz6TTWRw1iTRYDpLIUjoyQj/Cvcm2Ul7zkwDnySZp0611YkIfxdOkgZcKZex8liw+3kBR8m4kzY2Qwu8XqD96hmTiAumIgC9zBsFkNYReC4LZFGtRVcjkDHHyC5oWvAZDJ8wwIto6mUGQJ6Ca1qnQAFd6OjALmAOTHUKVhDUIFZ5JpPFdEMWIYywLivwZc+FkjTWG3MaBOXuWwYBJWcd5gjOeBNyCtMEYXaGg4FXkZwpGoRagC5SBgBJGR/UxaDiUknSCfsAG9piQUSlxs7ahhyKvcIxCiJmrNI+waFxLLHWAg8d3gztEVHD4QrhOIKsXlDGkcFZra12e3vb05HAHB4jb5s4m6SPm53d2iSTL/Gjsy3CyipMFvKl5VxoKOhVRxM3k9ZBmBuahTN5E7dVZWEr4kkxk7HEqgQS8oUPvZVD6KXRYDCE14WgfigRQrJCCgB1uATpWige23tOsiyjI1Mj2JkCGLk0nExtSRZQcJ95WbjbzpIakzkkhSVVcISTyUYei0Dg7BLeKEizdR+kABejwaVojfZ3gtdU4viSTDGTPKgQz3gk9hbmUmh4VAotufhDCwlHLE7JY51uMp3E7LcmghbQjq5CgxdOzAqBCAL86FmSij2cZNqjbH+SWQitAQAIMO1x9sNRZnyms6ZAem4hhTkFOJ0eQ9ELDJY3Iwryh0eHl69cFTjuAliXImpzFyCf8rnRdDcWk0dEyhO6CBFUnXBFKDbwerH5l8lNE3q1DWOuWoSoVbvA6JS9gLnD8NSwywrvn6enp3gdpV8LiYwcjcX7GqkwXgYwVHF5IyCr0SDEYDijGs8OlpTrhMRgC8J6vQPNObAOqVHmMgta5hSWqA2Hg3wuB8nxUcKhA3fnrnE1MbAIYSlwQzDV69kFnUVKrwv464nQHMgTbEA5NDRPDARAgqJlxLDD9coG7sEUxyHZjdo6eTZy6AXBfjkyGE2m4zFe1x2aNGOi4XlRadaZ1Eadx/uNEnDxTsIozBMiGqIyM14keYu0Hrii24RXYezxAinkylxE1RXJpIGIhgkk3Czaue1SGcTDhj7w2/APM6FUN6TZOI3mxOZWqXuXTs2wIrmQIYuScQ/I/0R+6vU5aQKagMS/iUaTksUZ9duFWTlPOVP0DAaAw3anM8q8sJdZDIGL1IILNS7+An8rk6oVEvicRYQnYsH3XEAiBV2oOign9SiiemoZgqSVax4qjjPHrHJa/wROYA2X/iZAhQIizgokjx7Nc2SB1R9lO+PsKeaRyWUhxRWwJK8rXfNBq7qTzLNBZqCHKSdgDEej7uWPfwxC1WhLL3x0GAPmIrNI6YCLX76OTy8gbFO1KonyIX6vsUWSUEHvuU9CwW6FTyYA1/nov0IKjpVCDEuhjRGCU7YQDE5qIaxGQ0RIVLZAW6S2trTt9Vjgl0zYOO4LYD1lQ5wr1pJcXsdc/BoedwJJubsvDNNI+DOmLoj5l0mjV9ACiDYajfEyQyZEZwns58ukjeOODq/96hlGTafCCgy6slIuwaljQw3AhhoOg6NjSBrggW3MU1BzmW+ymlxmKgYx7DIa0uKPHZjGgFWKtWDVrgknSUmdUz0lbobwhmURDP06doRH1bsbhEawMuy9LpctC8DExnUZtCazzEl3WNrqlWtYa2AzWIloiMosZjIUGLQw1HjJiLBFZV5oaSGw0vCyeCX4tCcinoDMVIOIbKbhytiGiU2xKzliqMwXXpgUUnhbnZ8kgJ4GYoEivGf8nTuyNI0C4rjjzYj8j1ekFEoIkA6WSI9mmXEyozc3TbA5CJFGy8/FWqQFnRupPB1nOqNsb0JnoRfg1VJi3Js8GnYK2WyxVNTIz2nOvW09x35b09S9QXrYny+68PJyzHDjmViYg8i3yM1TiwKv4vvHWFCmuP5aM+O3QFsUsRR4LOoXDAOm0L0CkycekJBftCA6BMHxKxeB1/nMf8XW0YGCMJjjGPpkdzpiOdzxwoEQdFFmRkRg9KcImTKZTOGkIqQMZhnMUMK8pqsHhT2AcPSxTRYSafPupNH7QfYDyzhqSBbMeQFMQbal1ur5QuG0f0rP7mCoXhKEkIW6sMgLNQKjU9N/hXiOlSaMuZHdULR69xuik3GH1MTgmqiOaRKPZef15McNlJKeY3qSZEHRG/0FPOHimQWnriYdXHZceD/UiHhUTMMkf4xCIG8uYJCCBBa4t5otbRueGc0XHZDuYxav32tU8qnkwLHzMDOyikUg3KGseJ7RdOL6LyEVeJzAFgVcQmlsCnm2NiBcsf2NCCwyR901uWBiEHtR7ErBCICPMJmHieungwnWkBurwsgZhF6KAT7/Zkut4fDx036hdmkrRSKbernmEgQ2mAMPecr5YWLkasw50GFVNhg2xQxtkViYkuEsihZg9imGBpA5Jl+EzsdMz5y3p22qQRAA0pcZyJtAVDYmFgh8k0OR38NbBxEY/SkW8sFJhdOM9dz6hJlbg9N09aCwBxBOe20gjPoLk90ACQP2+32sK4B/0263saqBkzRmSYHz6HYHfWmgERNRFJX5osyAxSzk0QejuZXcaOr1AqMyaX0lbPiiteOMAARhnRPT+XqjvYAWpZxpEAzMeiKmDZ59Arog4YxoAk22usNsabdSmE3GAw9v/GI2dg+gSBQBBT/oPMllSWmzDAIiBh4u9TwsTdsjcDVc0krBS4kbAUu9pcALFwMMRpZMb5zBpsN1vDBtg2s4OTtpnhTyefNxx7JUWt7MsiRhnq1OkHNqNSn5kd2b2AzDCM6KdU7k1wjC1xjTFE5IwghoyTEOBmRWSuHJUjjrZcCwVmslmAe1qVHFy1tAeFuWJ7teFEgzxBp3XJqvf+5s4vCGgwjuME42myEfJhzgVn7D9bWOnCBeehcOPMqktybGQXFJKXJa6HEFXDIEXjx1vDNjqYzXb1y8MDwVvtxPGmckH58cW7aoCkgLihCXZjzpzeqV/DQzOfJ8co2QkkBoJzinOgEprWiOY0A1ZOwQUW4LB8edHBhnW+9A01olpjIvFVdaiZillYNO+XiSQQDEZMBraoxnPoTG5As1PfKZSb7eOB2cnpwcYzoHE108yEamjbfUmIPClgJXI3jD8DR6eQIyCWSLM6+wrTfRccviNc8yfe+GI63em42lsVOEVmMBuKTYlroBi/Gkm0+zCdmDJp3yuSbozZwcFJeUYjgLPa6AS0rhZU8Erkw6kyGHjvm9pMk4w2uD44NvGAJYptNpJ8pXq6lmcpoUSM/g/OoVBH7OzB9Pf9BisTTT8VX+iAqgBUU8fSlpdx4GCEMmsACECplx90FADmMfDL2CJkmgd1z9Xu/46KhULhMXbbMui+3dC7wYwEKFItkUCyiauz2KaS4VIHJFjJsZbU6M1VMcMxRHyTlJ1vuaaDkj2NUGkUCEI0UYOyHlqGpLXVVYLYD7iZk/DN6O47cV71yUYFM/mWn3t5L1XFhGKSnFAjj1DPN2UkgFIFKt1Nqtdq2Gw8XsLsMQgAxeNlo4ri1fyieamFESfOkBJQOSDWVp48icmReeC1H8QS/w+JS2xN1ZDXMtn3cSgYfjWUpIu5BGtZNgVgFc6C0Q0I9myPjuGWGBGstUsVTCP0QtwHpKLGierzmLpahehBLdFq9lzvRFVIDhodjcEnmURR0tXeSdpWG1jJgBk5VSOPNSGQMsIyVKSFtZa5JIxCsGZd4NE72w62FBv3bRQqWSEK3KcbeajtxyEsYgzs3SKrUqWRsLYOZuhbKSuJB+XkoHZ5NBolzL9CSEJ17/R8rutYW7oWYlzXkO1JC0g5MwBNmRnvN3qTZIqWU8I2iOuxV/zUvgsuNi7ntIp5qFZCRRcxKVgb1NsbNgIiRLmaVgQdNatWhttM4+v6DiluK5LOhRLL4OJWY/ngKZkbRSbQUk0M1yUhT112IBk6kt9arAaAE4pfinTbhHsNffyDv2OfJWo80yxl5SJeBaWgAj/mq/tJNv/qf94lbR2XzwujrTamr0uF5CPGfVUWjWEg7YznZcsjZ6jksKzKPPEn6WyvCYsU/Dd8cTgocZF9IxdN9t3CKbYhjcvsb1bohQuTIu6UrKDMDjkofBLW/Gd8vz9ABrft5M1XNqeqkB2jgPqoO48Vf1ygIeLYAGpkWSwaoQ1brMNtOePnTJsragGXsaM7el8mHfPEOvtJSkClm3QDJRqVYMg49eE9lvYavQPDkpFp0c99Es2zsdlvKtrMshr5FJHQ9GeOTCfuqWVDRmD+3GOu64TRgqcOQHhgqEEMZVqVSkLZbw2Qgc26RjJVHDJzMjuAXbWGDMGVH95xi1eSsX55d6BBhJKp0LLQM0dg36V0lhKAv4sAB6G3Q7Ej5/++C5HqDaWw1Z17tsj8bUdXn2GZzvRGiExZIEjcEnpnY+S9HRt1qt3d3d1aqfTqcRIMXOzegld6b9J7VSjgsHt1p5Y8Gd9pmxEMVBCIMr4QAXUdUmO+7UhBgw4LKjcXTabSyWqNXrIQ2rHu6sB5C50BJuv4WZscCYk8CPJ8GIz8dAmjdkAC5hgGSMeO42dLWlMjycSisLSLYAwqaQNe5kI6YgRIRkZmtFDv0wnnhc6nOE6b6t67J/Yd+r6WZfY1LdM6AJT5BNNhoNQXG0RQiMgWMK6vW6ie00kW71Z8VCO1/EGTQ245MJR2WVBewtsPmOO9Udw0a1VsOw0e50k1v75bTXTSGBuhb/SP4x7O+pdY6eo84lHShIq6LsbDx2Q6U0lrYzejjVUiITRUpZwGoBOqOM3iYeb4kRP+1WeyxKUskUugHYR9pZPAvaKrUKC9j7n/Y1Jjk9A5rwLFmM7MdHx3t7e4uaVbR9OicIYbTX1Lksw1mhfzqsF7F+jhxloC6TBUzmMtWqrNACm+y4W59cPE71amU6G+C1GAMJFtL4n323UhUa1rFQBg0rAwtVY4ExZ0UPsYSxnnfUhrwhE6IQieFwqEX8IDyIINJGjTCFVrSdLcBajzNYOLVW5ohTjk4GvcpkjKDO4XD1RlWb4PZ3eLs3wkGg4K+nM2k8ceTFBgZaqWWoAurpD3IjA+BEZejGdsM6ZRVA3iVRSGCMbq9cKYMO9nO0u8NMZadRHCQSHo5DXZL32qKrl3m/t25ljvsKu26EN6jX6pPJtN3p4KyUcrni3333Y+eQVRWRN5YZc35Elw1LJVl05UwyUmTIyOYMeny8MMKL8ZPKa6HcEmTDEW0JgRSqNwuQ2SNyeCqJWe4N45xAad46CeNOwmXG4YqHFHGwRMgySDK0y+OUJGvcd3Z2QlbGnTzCYHQ7HcCNJ7N2b1StljKzI3e0cwxB+ky1m99nAyCOu6Qnyydnf+DyZcQ+Erjv4/EIs+8YVarV6lITQfIFdDeQhaexwJhzJxcZBBNs0RkbigwZeVIhvpA8YraUmPC2EKpicy1A/PVkIp2KbBu0Q3PDNBYeMAeAKG/DTFsqA/PgiwSMExOporSA4rWUBZxbDNo6drItxUAeMo5vbzab6crVRqWtlsfIs6uitLBAxv55sK9ZoK99KpPJYisJztRstk6yW/VyPq2NdvHVS3RXjGXGXHw1gWS8qHMn3lDEZ6giC1ffv2Jazw6SVqomWsswMZFau6yrcdZOo0gF1k4GTabSGeLBu7e0SGVbLTPtdCpyLPxqxVDcN9UCmJeJw5GL+KY0TNXPJk/305h3V93ppja3FeuFGXcvrsyKpfTH3v/Dgm2Ljdz2aJY7aR/nMolSiSxQi9Ul0slYZswtI7xfSnL8XMZ1QY4VQZt5qaDIs6r4luoNlmfiDUNBKQtoFiBruMlSmSQWuyuT8BZIp9J4mYF9wl2XyLNU6fNkAX4l5Kr0HgxHg2Stmu12s9iePk6nV7YUeVUWCMZ3qcUOwViuOVZkn3RFdoKDFMY/ESsvZdnkWaNWzmZzJycn0SyrcJaKtw0HyYr1Mlqg54L9MqJI+L2WwRXwYuQMdax0ISBftCg1YBkycYgXZhBIZTbOAnPHPZ3Wpt43Tr2gCsFlT6Wx7j8Bs6hBOqgVFZ6TBQYDbABd5dWe1ifjs3quj1dTbJzDFtVVSrM+vLWPk+sjbjwkFTnuJocovGw8TGCSAp/b4OHhDIWT3qTf75tqw87yxuZ4iYpZGQfnN+lIg1U6JMwMGai5IkDe1y+WbgAAIABJREFUlharoIk5aVMpsqbr+PjYVKKyygLSLYDNl1jjrkYjk2G1DbtThAQwlaussoAUC+TzeSl0AhAZzzIn43pxeoSQMhQdrR3f3FQn4MWY1pHaC9Y5h0kJ5rzPuUk09dELNEppTA7B2zs9PQ3VJLzHaWTEarhiURlX7TVJyVigGXUkvFw8vAGFVXih4gLDaBk4MCS+mqbnVaaK7Ric0MGEVomNtACdcceyEDWvbLi/sxmCAWirhwwhrg0wKqMssIQFsFFtCezgqKfJRq9/Ws+0Mgh4yl1bhcJp5BN/HP81SorH9TVSIHpRRTPu0UsRV45YNre9vY335pPueDiU9iXO5FAatecruRpWzJUFSIrIsDIklrwEpFjRkqTn6IwcTYiJmoDmoFjjzleIUVWpssASFphOydJ2bVmIWuO+sCNsQWbcNevEJBzkQjiV2ggL4M0wYj2miUxrkE5OerVSNqnvwmIy5AuFs7MzllUJZQGJFlCbJ9yNWcSVSPT7Z1j7jqSvT3LwFB0v+3r7GkeC4koLMUuBEM8Vysk54ZEJnDkv5Oi3kCfKcMVSafHCAK+j6L86mhhLr3X8XQLVkW4IlWatQ2CxNiRDsAVm3PGej4V2k+nqD2DCDHdsvORZKpWekr2pWOOOwzPWpo1slKCR9FORMBHdFnKEgsyG5azI2Wyr3+9VS7kUOQ9VzBcx68ajcSarTuwW3S9DmdiABpB5xvm2iDA2sUw57l7vKlx2TMBj1fvpcFyq7mQT5gl4b03PEcqx0qugHJyFnqWAAyZJt3oTuBne9pni6RIglrfFMHPyk2fUGZLGxmXDsRWLobslFqih6OPGnq9fiMKXqnR0FoBvmop88i869QJxwsIhrPsfTyba5tRAJBTS8haIpHOIhInZFngVlL6g3EGRVneQyacbJSxYGJtF4fIIQHxyctxobHNlK0uufGiSpLnDbZHEYR3IKMfdx13C8FMulfAA9Ae9br9VqVazGQcDemth3qB8SKmBWqhaChhF+xoG4jXBk7LtJxgQgTBkvLLxD6ex2SpuaQwZT0bHVlYG4TVhpe0VU8GFZIGobwkcCHy117hGzdpoQTqvvVoZ5hKh50xiu95oRBfLcM+9UWSVUxYIaIFkNEtlxjhw/XRSLVcyZKLd/UK7R4eA5u8OGjLE6iWwV9BZNnnDs70E61bj4Heumyoy5HVuQJQDYEqFdKmwjSNX26MRzm/yF67VC4+gulhoWwoYZfsaBrJUgtG3fewMEDRjC7uUKHPk4XBoQ4ZJYlMvLg5VWDFLGaXBlJXB+RzQwCCNGXfMLtPF7udAY48qwm+HTVRIGY/mUmC+LRDB5lR8sx1vXWyUsFfKk9cOHTDp3ul0cC67b33OD4LbiORWHyNLReYTbLjjLvWWm4lVqzWM0+1OG2N1pVK1feM348lvZxYOlgLG076GgUhO8BzF7ZpBYJHi4hLDLup9p7KS1xrywvoWRiFspAXgr+Of2pxqublY456E445I7nGYfbSIpwrW3gL5Qn7xAVe2Nmi6rWGukC/W0m1f34vSmfR4PJIvmPTRUbbFPNIjnyPkW8cjc/lgkfkEcXfcozJEQD4YhGrVGgbrbqeDrWDlSiWdjjRQj1FuY87ULB0rTbBhZZkM4n7HUG3IyBBIHbcuw4qKhpMF0CFMp5N0KoMdmE5w56wOZtFm3CdYiXzOVFfqRmQBRG0uFLAYUv51djbuJ+vVXMt2bs6RZ7FUwtY4LcKFI5yvyk3qXaALVUf1DZ7bgHzHPR4tKnwpOA6pZKpaq8Ev7M8q0/aTSrmEUcrzLQgCyDEHujFnoudYaYKNLsukEj+rrBoSSZuGV1N90d3fc8wJZwxhxj0WkVP4x2ildwRuO54+YplU1DH7Vqq3Yh6dBdjhRxJZYj64O6slE716tptIBGy6hULh5PhYsuMuUcmVkiIz7mwDQGz6q+AmEfszwenZYWZgq1Wba9X8TbYJKg5exyuJ3qRU7LTbuH1Y1ibXfbfIZSngFXGs5AFXnOblFDd6HgJt1QBkyLhpsugg3CBVvbJAMAugjeHLOMLAYWbZMCYFIxcUC6wnkzH+4opDs6evMTg3FfIE1UnhKQs4WaDT6e7syDw8dTxLdyflUuIki5CPy12ICIkl+LLXai4nk8IOwwIGbyUMBnOawVokpJP4L0T1DKQ9imzA8Z2B+16r18vlcqvZbLdayw9UTGpNFJZDQnSxelFl3Ms8Cc+AiAX4DMk7Xvgkgnd7VzBHGqpSWcDJAprjPsEbu9yXdieWljq0c5w1ceXK1a2t4rOnT+NwCgxZY4BXmsnEIqwqUBaQY4FarSaHkEYF56H2+8NappPNBPORDLLga0C/1zcUqYyywBIWQKM0OT9eskswlIXqRUwTjCzWHujgBJbG9jYWtzVPTrCp3AOGAYQXXKtgBQawRcatfgG5FimmDhJOFw9HQM15I+58wgO+u90/jYIRSeWUBfxZAGvcsTAEe12Wf2n3x1iDBtNqtfLiiy8iAsb9D95rnRw/fPAAm3DMz4bpUQk5S5bKJBKT8Zgs/Q+Z15x+ANsplHW2AI5HlCI+Ti07Oc0kx91qCesR5MRBIq/xoKWiKlnvEDkkDj2CuvxZQP4ad0/8z8edou77eDxGn5KrXy8mWs5t1GIVS4HJuG71JvC1zDId3b+xM1CqqGFxPIrObMNB6obRCJio6HWWX00ed6EseOexQLep/ruxNsAYNBlPsOlFWxKCZSpRawrH/datW0dHxz/4/tvw3fGyms2ksXgPUWujFoXjB8cFTwo+BcAyEZkkIjackqtOrkdfFJqU29sSzjk6SxQ6R48bO3vaeagy7yg9vRGf4mUSXX9aJMwUedVWlz8L+HHclXn92XYOTdz3RuNs1Gx2W7lcDptUePfdYlRLgZWpBxArUoASj3xC64qNIvPSeGLJIxBS6Dr1SXkjZZLzRNGAppE38zBAqMy5swBZKoMVWWSlDP+FPaJmAq8dDnqpXP6Xf/nXarXe2N7J5bLYG/f82fN6vbHCm5HBUplZAlMY2hgdkTVWqO9KWK+HWQVS+ut7xdDJBGbHdnd3A1seM7/d3iCZzezs4ElxOg81GAsM/S5Hdweju+5YasY90B3MqA+LgezmGymXTeUaDZwBhP6lUCiWigUjCUF/ZgSIYhmHLoT+a5ZAnOeg552quG8VYwcq5VgSfG/8ek6rDE0U7aTyxskO+1yXe7TwetsIU+zYgom9mJlMWpt0j1RrOO5Y6YttcN1OG10MuowaotXWa82TJj7TG98lIrWzxhr7ZREoM2BojkjFVcwitYC/Z0QIjVnbZda4j6eJdndAjkJP2B3SJ8Ei+VwODgD2n0igtSkkMOO+KapEqoefGfdIBdsQZqZeppDHMRGFwShxdHJSLOSxe8xFTxO+C7Tvao28RB5zUkaK4QeSMPIjVhD1BjjEzreBzAhWTmYIlT/fFkiOyEruFLZjwk/F17aIrZHLk4k9wjqbA2vM82EJAXwF+PQRS8LYgTWsMZ3N6LcIVq4SygISLYC9ZNvbOwEIIsj6OLvXqGAO8yzU6THsecO0HVyAAEKGgLKyDiEEXc4dyajHlY03sI1nZyguZBOFRmMwOD05Od6q7hXSxmALBlj5BtPIh8zDIDW/0Dcq98Gq3yzRarUMcqmMskAIFsDmVCyYgct+NpL/wd1ZXkxfYbodXjseMzwBGJmxPAxz7niN4JfnORMJozaTzSA65mQ0WuGsfxh6KZrxsUC5VLL2+s7i4SDf9lkmny3Vsj3vLvsSDi9ZQ7fab1+cQfxai0OVl9T3qsujaKa0xO0yk4pRXjnuS90Mm7ZvU2xkhWPe8O80UTxpPy/mk+TE5jAvTSZPgoUqBZMg6m9kyQR2GoSpmqKtLEAsMJ1M4SWnM5npEBN44V6z2fTsbARXAC/HcNYRu/3k+AS7aE5P+5jhBm8cJ/nk6dPBYDCbzkhoF+0lGt4D5uAjc+XnM+7TGda4K8c93AZxjqmjqfuazMaqlX7hZjXzAJ+DfJmNDWG+sChwtVLtdNq1Wlg7xdfPS13Gmp5uQOgMPEkhGWimHHdPFnW8+Y6VzuSTia1EZ6tWwNft7lG3Wqlkc+QDt8RLEy6ohN7x/HcY/F7yaJz44+Pja9dvSLStIqUsYLUAJrxRSBaHhBy2HIyw9/Ta9etYD4OnCQ8RrhH8+Nn0jU9/GuFkSH6KleXT23deRRLwCMg4HJ61Ws0njx/vHxz4dVmsynopAetMOgNR4LhntQU8XrAUjLKALwv4WjvemdUSyV5j9ggPqi8uSwLjxTXU0wy8j9hLKuKK7tUj8ArnyvBcASSV4264327t3q3eQMwmI6KBAxpwYZUeOpRKqj2POG5DwEuxxkTEyQHZJ7iBkhXXzwPJnPhQPXg14264ZSoTjgUw1Y157bBXt2OWHYEeX3jxxWJxK53OwCEgE+qaRphqJy77DCB4d8A/uOvkwioa7TCmJNbawn1/+uTJxUsXk0k++k04FkkksFRmilcIyMyvmwuLm6J7Hi2A5k3Chrldk1mqPa2Vps9zudU4P5VypdvtbnxcSKtHYHdnvEPaUbAr9+OD2NGIaXkUvXZ8VEcTcf6ni2oHpdf7+jURs8fF7snt/OnglKx9x0BrD+hUAydY84PB1cPFy+YB3AcIT9mbLCBOhddV8MHNC2iz2SSmMf7zgqhglAW8WwCeMlxnOO7k2KNwLuqVX79xA6EeMWtOvHZt4Qv5o6Xwq13zAs2rTyGiCy740MBCV5MvbCFMZDgCGqjiFQJs8RYBsTUxDbUqoywgxQLk85Fp0LFkT6eFdn9YzyAusx5Pz/PYJEVIEMH3sbNR6IvoZEm71nQs99+1gUgDCNtuq3npDEMrnw+gT3BfEi9HG0GpMMJ1T8fT035pK+dx6g4jtlcZPQN6JegFjmfq7UWYaaSBe8NxlEQYL0x3cQSY2iJhQbkqUhZwsIDmoWI6PIuTmBzAlqnC7PmNmzdLpSKcFUyZE19du0ATz5n2qOAXReRwE+0CzJR47hOsvYfrToK743GA4350dLSzEyQWhx/5SXBMbfqfnCnrB1HBKgt4tQAGTQdQVLbPstlpu1HGYlTjG3XkTRKrevDtCwGgHAS2rXLS0hYpvhWRGz8aU4StVowc9+VUXQ472M0MjSfG2koxO52me90uvi9jeoxMJ1gu5tpaaiwFoYlq4eShwCSMW0+kgS9wNHA3HJEUWIgkKrYtc/DpbXG0CiJcEAGdqa5t7eLWuajgGdCFzmqr4SLASc1ksSoEl3xZsKnu8pUrcLvhgqNbmE+3g5OBG5nbxgS3PgGvh7Ogvjs890kWe+Mb241nT59iCrBaXT5Sqq2mEA1yYvE92SBLLltIVeFggfXrUaKV2MFxP0sUuycf1rYP0olAvrLDXQlUhe3jrWYzoOO+MY+PtqLPhw8TyNSbiiTfcZfXruRRWv7urUIWDMmYfYcTQJa+zxLV+k6KnOjmZ+Bbhdi+jc2E9NbRa+BzHB1D/3XkjV0E2owk4wdoT4iOVAWVhAHPRACiijbWAnhgsSokm8lgbbl0JTFRt7e/hzlyeO3MZde8Yb7BaQ0Q0+3Eb4cItJ78BQqZd5+mieuem2DeHVHenz17huXy2rnC0uWdEwTXyXSANTOQISwem053/QznKLHknld7ORQ2AZyslMymtrcRUiwuC1Too4huAo+jUOZzUUibB/46tpMoTCG5LUYhssBxD8GMIZAMyTjxkxTPNmbXMFfVHswSpyfk6HLXwS9+Wni6XbzY3p4lHWP+qyPpv0auiBdmLEBOJ2CuEFMwQ6m8soDAAklsCsWSFOlOKva94E3+4OACgsnQuXZ0DsQJ0N1zXRbSevWFA4taDZD47nDdyYKZSTaXz29NxvVG48OHj27cvI4ynYLMX/DFhwEsHNLmRO2eOJkcFa2QpiQkGlZuOwA1bE41iTeZzlrdYaVazyYGpqqVZ2ksCuHqzZXLdu4EkNsWg5nPp8eRgcyexfYMGEz0yLDWRw/2ISmNMOSF6TRXO2meZIp7ldyYjtYGm62PXgaxhRleF89tWkcivxzSPOknXphOSSjbvJDj4ASm6qRbwMvdkc7UM8FkAh62trYNckoTVQv+mLty5TJWthDfm7jsKZAn/YD2H+0QSKOkPjvx5lE2n3dHDhgI+E4Q8R9x3tOYaJ/k8vAhEG7m3gf3b75wE7We9fQKqK3CJ1HwiMDqisgC0hpeRPIuyyZp6t5Ph+NhutGotJPx89qhK96cyYe5871d+5yrb2jyPp9X9KSkf/f2z8AovhlXbeIr+lwyMhxr/0ySYsDebmyX8ulmq6Wtn9F8R6avCXpjskxBJDxfHNLcmCPJJ1lyHLw+QR5RPCvpBOiR1zqCOam98jo8uZj80xx3EpZRijwandm1a9fgnWBlO9xrXPDKNY+d+O0kSS7yQzK4aGbOnuZoRRJOO9AhIcKrYxMt3gTKlTISDx88lCXwnK32A4EhDWJSGt6meQiVVhZY2gLsgypODW6ephK5ej3T0Z6DpUmHQwCL03AyWji0FdUNt8C6TYGQAcjt37reMoxu83/OGqQTZ416HXvLcJBKp0uWv5+ji7/7PtWmvpRPpJWA80oGTq9EcsWU+M2YHSe7RtNyelc40+PJGF771jxkO51un3vh1Gs3ZXAbNJdl3niIF0+LKJw25059dzjvcNzxPoBFOFhA/+TJYw1U5h9txj1J4tvO5ZBJXNFSFqAW0LYwkQUzreTFSvZsK9mNuWWwwwShn2MuZNjiad1U2Ew2kL6cocWHYQL7IRTRB6f4gy48dc1l9yAwZz3MkGFxKkbcVmK32x/yS0M8ENoIEM4a89c5R7XCmE10ZKgqz6kF8G0Hb4lonlKaHM5Nunz5crlSwRp0kCVz7fOV7bq7rrvjfDl13WkXo6XJELlAAA3iuafwyR4Xpt1pgMjmcQuHSMi9bRAZBOFRgadcyoqasgCzAD5BdyflwaxYnz3GHg5WHucEHjycixZnCUOVTUr3GKqEsSW+RE9qdZu8lMTWEhEJxjvrnlkyw1owMFtWTx7m0onDo8Ner2epP2cFzFA0YdRedRNGe6hcKBaAc4wZd7oUhW0RDcwJpErlUq1WxXp04mrTFTILB3yRSiUQI51kwUsrJT+U7+JX990pKfwlHwbIYneyYCZfyJfLpQ8ffgimgQW2IuJlA18FR1j3vybulFUFVRJzC6Bvz5Z2c9MWdn9poppGgphmS6Vyr9fHgxpz84YkHu4a7a9Cor/BZPXzwzZYxRWrRp7JIM+lHxx86t7N5xHg+bgzKqRHCBMbhtIY9E1ktUU6MV6pY5QXDgp9bTJrEWMNTKKq7FpYALNoxDNGKBVEfBOdwOBdCwxsp/3+06dPtXlxBJmkF5l6hysPh5tMl2cRwQb8SOumbRkjIrkQ3QYS4I92jbE/FCvNcXrT2Wh4Rv47OxuOR+PBcIA0/mKL6mBwSqb05W1RhRR47vDtYUJsYnwgvVtBQSoL2FugM62ctp8Nu8+re/v2UHGswYO2tltU1ai5yha1Hl+UVmkh37zJ4BR8gAqOmcCymXw+MRhMjo+Pi1tbha0t37ITyX1IoIHawmtPdoweb3ysFxpEn480VJ6vnQMG1VVmKQvA1caSdDipcKjhti5FS0PG6nMEbsezDZ9dC8wygzuOGtAeDs/IdzZsx9NeSYmzjmtK/oMXDp8A/jz13GkhPHckcAEe89958pdMhGcyQ5CA846XBOwl1XaR2j7XfjXCqwU44i0BrzJ+cRW8soCDBfAIPD8tbE2f7tYKve1tB8jYVmEJXLfbxRmLsZXQRjAp/QOISKFjlVFCx2slGp8S5bgvfy/mLS94AwyOKRAea1VxYQA+7pyVy/VcEl/inC5fnroTIUudptZCN/1J0n8t8GEXkNcaz5fQm6fYcwVWpodnHRTgiiwAxxqPFSbF7d4VfclFXPH5NcOqE8zSwdEms+JwhDGbTf4jF90DClhtXh1T62BO5tfxS/5qk+7kz3ii5UfTESnD7Dvm3bFJbtDvIy4HgEGciLd4cH0JawHGGgYS+W52RinLImvh41KgnlYXA61fNc5Dff7ohweXb2YSRTwfmK7a9z/jvvJ2gdf7rs8jvdfvVq1A4lV1NA6qymxrynF3MLSwat4glm0Xy+ILZVsUbhWLmG/vj886zUO8zWMtzaLO57Q6j7hMWtd4/qu1YplN2VU2LV4YuOuCzBF8yzDHN5GxYb+gvkjZgKpiWMCbVQOYKjTCAllI6MNkEjPNWL3m8AYowLQUEXS0HHI8OOrmE/j40f9p8+tJTMBjbh0QSeq4a8tjmLdPJ9nJRDsuMh0/mwKf0tAIkZx2TfVGuqTUCzVAMZMlIavxwpApkGNDVnNFwli33mpUlM81vvokm93BJJW+fPlSIjGkijcaOBvV9xVJu7CViho4XygMh4N8vmALt6EVqzV+5EaVqa5y3J1v39zW0kwujZCz2PPaYmZc3N3Fl7hur1cul/M5g/vuiURoQJol5ubQBwj9NxymNF6YhbbwlkiTZEF9kbKIoAo2ywJ0KQvON+10yJqWpS/iZM+9duJf46WAZEkJueCIE38dy+lRjrlt+o/W4a/mmrMcpUTK5uVzInMAUii1oYIuXmDoO4S2zmdpY8SYgFTLxUBPTR+0lFhd40T+6KRTqW8Xk1wwhmSi1Wrt7uzGSlRXYWiDKRWL+FxQiMRxj9fd3LQHxvWGSwNQjruh7RgyyxtZMjm/As2nzSrlCobPbq/b7/XhvuPbnF9CBN5Zl+X6A522/qt5JZqQy9E16ol4YcYCh9xCEgcgvUqmkDpN9RuZBXzda09SYT0KmjDitGCa2e3JcSGIdS7Z7IS61cTHJo8G+aEl+As3nfrxyeQUDzyyxIefO+/aenYNlHjkFIemSGZRRmuABGnI+pnRyNe6MmcdsHdWW6GDdf8IYibf2s7cVe2SFgj5hvnrPLv9UT+9vVcfpXmvXWtVZJk4k9Uf1SUtJAEdS90m0wlORpNAy5EEs5AjVESVpDtTVyALnB/H3dBiDZlAhhMjhUVXzM2mVPCZG3vR5u47mX7vVKp1HL8oRg+mggNWoIdTp0d+dQL6r1hu99JareYOFARCFzYI7rJKBeEpE2cZ3WXKETdacJ3J5lTWeAPJh6XppVIRkRoRovrsDM50DgvQ8T6grWnPEM8bLksS/jdm3MGA/IULTr117S/Joob9A9DcR9eizZAVNdhIOx7hEwHKs7lsrdEolkhEyBs3b2h+diC5jUjEKSGbYvFeoVqL0TQqt/C1XWyBL0HPu8mt4vZ+6kQDNbWlJHZpL942TZUutMOvJo+n04W3Dhyn2Gis5f5aJ8Uc63CXwr5RboZ3lC/GlZvkuAvagKBI7s0InYFHcYkcrrIQ972izb5PK6Pje/V6fclYdZ6Es4rl82HSCZBfDpVLepIjcXJycvOmN9DooHTlouOoOIVuAW3KG457DsEWl2EGOrl8bn9/H+tfNTeauOHwgLGjdJQc0eCPeKjRhsjDgDrSmuC+k0n3uWeuTb/Db9ZEmk/GIw1nGsDY3ZrN5YuTEgDwYoAD2Nut5snREWbcASPLccfLBsJN4s1gGVMo3PNsAbTMZuHV3cL3M3OvXWCMrUBR1ASEwihy6+bnpyy7gQUULbZPXkj6cmYKlcMK7bpejrvgLgiKuNsmPxk1P2cNiDR+JcLxipVUd7a93Ww2McQT911e2GZncee1Jon9NH8OdR6yWufoTmV7PeOF6Qqq3zWyAM5gmmJT5pJPFibXH9x/eHJ80thu4JUb7jvWzZNJ92xem3QnUWUQvRF/6blMWmdAgkWSpSk0kIz2w4LK0AT+wjWfTWb4HSJ+O2LK9PtkI0y3gyReDS5duQa6UsyN1woYAfFrwoteJUVORSS2FmhOGpNp/0LihwnHNomN4LH23d3sWyqX8QxiLasboP96btT0jxwUw21AJpMQa36FZ1dX08jpnZe2v60FbCuWZumJwIrZ28kY6JOzURcMqNiGj3m1VrOJkbVSqc5f+u14hlfOC+baYDkxjHh8DkACQphxv3HjJkdAJZUFwrLAaEziKuL5WpIBHtJkCuthkth7l0i0MNrBJYfDTjaiJhAdEpHdyc5U/IcEfGSwIyMi/tfWzOiHL8GTJ648LkzFoxoxICEhKUJsyMl4ihiRoxFiQgK+XK0eXLi4pNgMXZM0BUaQBwKycpVYdwvg5Q+x+TvdTrVaC8ljRvM8mu5XJo+LRfewCgH3bsXmNuAtvdflttvGRrCAgnh41kmH4AHMRQDBUO+CEf9qV6uE6rg7cXeqW4ldYyeQ1Qr+xz03peCyN7a3Mai32y14A3jdX3KO0Cq0vxKTwJ6fSQuepSCRCG2Nuz8VFfTGWwADElxVLby6zU4SPyYAnWq1ivW7cE0wu05msMmAR3oD/IHLjsGP/geqdLqdrJTBU03WsM8v+M3zKlZO3PgxfK+zIc5dOsUSYXj14wQ589WPdC6woAZx8XaAD30uoKo63hbAqx2iFmpBdckOZsQoQ4Pc29uX22CYDfr/P3tv1t1Ikuz5Yd8JgODOzKzq5XbPHR1pjkYPkp7n6MNrniXde7t7uqqrujJJJheAIEGAAAHoZ+5AIABEBGLFGlFZYISHu7m5hS9/Nzc3Hxbb7afz+mPKnTM0WDLS7ugNm0xYN5hZ6u9oMdbM9tr6FddoZA0CWAncV0hlxes1lMB9FrvEqy6VcOyNa2+xJ7IDDdSPjxnF26/ddLFRyfQUKnAv2chiLhTHdctZSKeliFcZwpdeWSnnIytQTPgwJDAG5eTLea37DtiaRHnOpVTp/JUtqBPULpg4Kcp3fYloiYCa34DvCrbjQ12dv4TiO5Uakkbpv8UHjU6ABl6lI6mEhHrpXTQY5YehWwuVs5jYKgkwIqjpHH8H1CvO9eOwr5OT04iQusEOeT2F4usfAAAgAElEQVT2ipnBw2WjbASuvFFtYmWsrY6Aw2IW1g4FuGuFw1Z/kHnmlqHD/Pt1PE0hEMB9EcxsA3uBRLDbBZhw760Q3mJbS5eV/Xo18/Hx1mo9swSPks863gZDF4o5rcQuOaqUy7PKbkq7QBVqppcuacfRYgmYJQA2FoMW2pRAbe9LZQYttc1UPQl0n9RMaNJvi43MXJhUZILEI+TCpRKquBMrGmVLo34mqF0Z1girsvmVi8m8wUOQG00HqwqcQgahE6ddgwSY5r202yzUsAzD/I7d1YB1lmHDqgxuivCeKD/d/Xx6+UO26AG1Q1mWoXb80h0FX2GdAt+UzKTj2fkvtnbhTSUmR9lN79fOhO8Md4/jlUWVInkulucEK9lg/08ap1ToCx8eHvLFylF5i49zMxdfUMmKSy/yTiKZ0xrppkQsXxJr+t5IEN/EErCQAAMwNuQoJtk3otTYPo1PANAMb/yi+ISmUrAnBVzLCA82J2uqqrlWasAOcjfAu0bnUyCvkD0JVGJJqHG7QHXlhwbEkC8Wb29vr6+vNYywKJ7rIHLJ4RNzPMYAAFm4ThdHXJME+Ort52cMmTBYl+3IyQSbnbRF1po4mM+m2S+PBm+XF+fJRG/+zeqnPTCVoZBMk2js26g1W/0FvMWgW4h66cYbQzsVe1u1IHbQaaeEu4rZSSG9ldVb7FUs2Lyn7z49PR2M0o+P31G62Jw5apN4I8ELYjGDmSk/agmSeDqqVYwFIjqhKaLl+yl5+WuKaw6O7w9OAqI2S6VoR2Aj38prwPTJSePklPMgUeFrfIXRQgJ7cbamAuQVtmb8A2/LvUB5hdkFh08u5RbyQ/1iQYPJDES15Qy+1ccJVgVwNYlutVY/vvz0GZuWVrP5eH+PD0qYD/7Z0pk0IzR29JkwqAXn58ApUCk6bIHsvFFRqDRMDPEqVsvlgk/SAgp2lMjcPb7WT2rFnM9ONJTqGrAUwZMzfaLroMls/IsEL8sKCvRgu79IsqKMkb3eBHBfCX8iK+0WEJ4U3rMMPCcIoazZFLjhBG0ZBzIX65+KqW4IRNdDYllc4wTLCKbMl2Pw0mrMsIxoEJpP4RzXSKRv5pMuvIwfI5SAp8/kjw+MQxiXFDYa+c6O5L/88guAHXRVKpXAvjiDVChdtPmo4AWwy6bPSQ6M9yAz/hd0PrkI4E6jeP7ol2D3oZ4HqL2psuMQf5Avz202qQ/677VGAwDhm21DYvCTzTB1wYnNAIfxwQkalLfkZsubMPLHZRBO9F9e2oZ2Eycwl5fbZQbZ6Q1fUycXJ4lUAtdJPi9xB1nyZl0zzWm7PiOaMvzWR+SoZ1rkzf+Vtb748iuBUIH7/nXMfsU6n24iFz/i8ZNmPvPATyiqud4T6afmc7GQ29UOJZlA+bdKGHbitu9i7FKYc7JJ7SapmUx8v10SsP9+aJLAzvzi4u3t7W2Kqz2zn0qmKpWjm283AGqB6Kk0lif84SxVLrzNKOSO0ly7mhEwTx4AZQPAK/SuNO1TpC5WEX0c+b0ryC5eQvDjTgjQnVB4LpYrv/v9HzUpzxzPJ2CagKMMmTmMRnmc1dhLbD7dzjy5KpBN84+ikGQlPhr7fXzyU1uYrmH6WCqWLi+v7FhdI3cWJR4nUo+v42z57CL5aPHaS1CxWPIS3RzXTjbmOOu7pyBPTw+7Os6uT04HndMq4L5dVXqHPtVEcH7k5ydN5JLJJ17zxzU0c4+Pj+VSqbDNx9TZCAM4Y/NmZbDDJ3FB0yH1ypwXIrjIbSHFWh9DLOla+Q4/M8yGIcqMV/lf908fnTpHL51fcHiqHLqkF5cBZNDnF0+O1AikrrzGECL1g3Cqulyieuc/+auV7bQAhemJMsLSBgsZmQzkC8B3yBATvI5anxmBocX3zzokkkm19D9isqA0vgdZP4IWekWbB6ajoOWSLzgaoq/lC15cXOiqsvLzeeZuBTsrM5xFYO7YzP3rce4v+eSTDg1CG9PwfGGLd2TNyr36Tg5d/hjKroMNX0E+yIZZ3+/sZT/K/ilC1vvNZl3f7M4rB/5Tes0pUHzUAFxvnU7zvVQcPzFIBCK33sQgmQgyDPjlPLIUMLcIyh8ByX0opMLOI/Ti4mfdbw+LcRewTG9EpfYCtZXafeYAEnBmxmcKlyvALiYxXAq3C2yfXPoO0C96d05gEh/u70JZbOb1hQf6IasEOeWlO+DHhR8F3GVaYeYzINkDS77YHPhy2KnrfQpMw4qFIiswp6dHamoUvWwW2fGZ43PnY5CuXyR+TuZzBgnftKldzG8NOrt+wzpbq9XES8SmC+L7g7hhHOLB6U/7LTcZ7lGcjU/pdlSWswo3u/NalFUpRYvS7WLYKsvi2LaGejCKV2bN8UvlcnHM4D5sPj1xUDPsmd9u7T0Ky+3jbVUl2D6OY47cSEDruIG/8zsr3CSVOIBpWn+xWEDRzkHov/7yC7jkqFplmzjNTZm20x+IsxrztGAK3EmtL97KDT/63yRYtO6C7FHDiw10XwxmBv0eFvToLC+uPsE2mRIL6BAQcGcyWXoxt8WO41lJgGkW6zZ8O6ZbfA6+DtoTqsH2jAhWXNuGMYt76DfK46+1QpjwAxGdnZ3Z5mp6sf1Yb9rozI3bVID4diaBNQ+g21J3wmw5M2Hu291c5Zh78FpSL4kZs9mLhuoOLxCv3S6GqYyCbElDf8WYyj2Z8wotXCFfAN1HAUwVvxZMowWslCvjUrnTT3VeB0f58fKmflXHt6WiIyt/EMrrF47jxxLQEgAY53LZ6RjsQSpANBThX758ZkMq7QcVOG2/1+1xvmnzqSma9PEI83flGCSDhxnZpiq6ePnllWB0DGagMhorlbs4qQCki45dXeJcBj8zigiInn7j4uqa1TNsZjCJJi0xQYfkNRh8x+jCA+vzUeklIE6eMORDDvPEDuiJmsNhGsB0vhofFhkyadsq3Y3vj9EZldrPnIfaSqcqvolYJjw+PrYMXw60GM+WI4UREmTw44u32y+H4BcyDEmvjcba6g4lcqo+MXBf/uSL32bxeTmFc4jf9Ax1nODCQAomdrApZxxnaMT6/L3XY5saw7eEDIeAfkZyGTXH4yKH3mG6mmITG1TlWmbZx9GG0Knkx4l8ps25HS/tWrVmNstTeVhkpOqjU6Vc5i2UECZCodCJicQSWCkBwDM7PmkOgqS9XBPU/sMXPDqTHNCGYlU1WfkBZ2stuqB34DjU0e0LClcadOU+hnDRs6v/de+vm7YoaOVO6MARLVDFAuKL7p3/BNUrbA/MJt/KUQUn33d3d7iG1Em8lEPiAjnhHaMOxzHIK9V9i89XQAmNiTZLE9wjeT5ro9FYVoXsdMkp2kOvmO3fXTUisWnB9RkVdatEZDH4ueZPdHbDD2cKGxhEXfMfRwwsAaePHwP3ReksPvsTfzhUXOXNeMyyKRcjvWUCPTCgRcO6hSEbtRwjNY/MCkgLAmDgF+1+Nqu0d4TJ5Wmorh5VyQVfcsMER67WMsmhJSc6UMlmTkDTDmj61yFxgFdzBzAFoBMnjSXgRgKotUHeAGWahsvWREwMV7788BmTGAO168bIL5kqOtbElJ598iN/oCX/6z8a58vDFPHzd3Ilh9IYiQxk1Ac3kbVEHY9R+OHQ5vv37/6wu+YcQ306FjcSO5A4CJbul0UVTNVlsYTviYk2/Wattq8SGCQK989vp7VRrhgJakduxw23GvddETKDMsts7GGwYziiRhXtMDwtTETMT8nv+d+DAu7WVcU61Md3D42Qj7ydkjAqMBJz4RfMLh76NhRuHPfIYM5wyy+74vQqLWM5ijgs7TGBZZiBAnpA0aUp7b1BkFxqtTruvdpv76P+C1AeHGC8db6ZSm7yd9pxTP86J3b9dgeOkXJdljjilkuA5gA4K5XK3LhkFTz3Dmr/DGoXXTvNUF0QkAsi6lffapIGZWkpRJEJAj/Gy7GOQICRajKF4Hl20ZLH8h+XNPmx2NvIg7oqR0csp33/fndxcakJu/9lvYBc8DyJN3f3qfYvJoJkFzD2Tpx8xDflE7H+iZ06Xhr3r7BLJUq2P0rv3derOpPY/tLb0AKwcefQwNDI2REKeVCyy0bC8QvZarUcgLtT4gDvjG4lAI3FpMtiWw5ZTBM/20tg/4C7da2zDrWXy+o34VNcnafXGHqJ3E0qQeK5NIatdpEZe1jPZS2dnwHwXtxA99HfYWqPro5wYDrDM+M0WjsM7putZvboUyX95h6+G1lPRTv9q15M2/n0rxHb9Q377WQXu5mqf2Kuc40jHqQEBLD2+2hRmeHSdnh0FgNxBLV/EdTOKrnYvInZ+sxIBgoTIpM/M3ocQahsUQSnqxpNDMmTJ53tFMuTRCcWfrgUiCQS/mqYkQufhIzTiYzo3yfqegLHlSP07re3t5eXHrA76YCn/KJapneZsXsYd+9YLvZ6lB0503MCwlgUde+lcT+ENExkH1qdSr1+dkRt9GYz5lUCa/Iqs6Ide+XaKb5uoVQe2xNIdmf8WhYbIcuBTuJw9253ROKuPDaxdhe423502xc2InAbHBVdt/m7jOcerLskqKPRiSitvW2FYYSeGMsOxU6mVCx2O/ePnOvS7bIGzAo9LQprHJAIhrigeYYxrVZ0ycZU/JO/pvZpunWkxXYfEk/pSNSJRnIhlVt6C8nix1gCMwnQXtgLCg7W4HX2wupOodvely9ftIWMMg1fBu1ScyG7TICgSZ2VO6JAT2Ip9btOZU7EWyEj/6s7UbjLo6B2Ll6L5j2TVjfKtGY0PqqK3v325vbyygN2p4HT7gGvLNaZOdjLe+ZpqAaAWVpkLLZg6sDMzfKT7aUEFgr11hu+JGqYx6QTzYVXUTyKH/e928XEbITNYxycbC0xi87AOqLP0EiHwmiYD5FqpKX3+UWmyba5P13xCVa8npbQ/9/IM/DP2lJKq/F8KZLPAHdyYHxKiw+JdDYxWRZX509XMaCn6wGL1Kr0PjKocaHOYzccx/uxjI56EdWf9jAtdvbJFIMf1MTsPqc21FoVzsSU3JramOl2vsDqKBxTunkQb8SN0bwhivgmiATwkg4OxlxEdGb2KmfwMdrZz1++4GWV9StiKvwsRibGpdngcY4fQd4EyP9yO1WxK9ROXAhLdPVKbqaXkDHeoWkHtIPctRIeNT/05C3/icX7KDuWJTX5B4Z4BrvfXF65MvCACHid6bxsp7Ev/pSr3ftLuZ6fW/DNeiMyLBQLej8xn2/3ChMyx8kHzkMtnV+kgp6H6p4vTI/cR96VmKy60Y64Ftv+egow39+ElqfqXWgyoRGMhlCI/NmCEr+cbxy4rxbO6hh+Cz+Xbk3ZzOUZ8CHkqh+NBFDd4agL7NJMXaSf/8p2t8nC3zgh5zctbVXS/RS/JNGrzdgLgPu1qT3IhmV95SR7lM8XUNtDTdb5BaDoAsivqZ3Mbqf+wpbLOYsjdKy+ijWa1zHnUlsljsMOTwLURj3/xPwM8zI7AVDPUUh/Frv2MrNWjdlBfiQ3XyTncUJkdieVVdnJSBVUcJw43NMxyNgoN/rPDOFPcLwmTgyNCWS7imADciaAH+ywZRbOA+1OsLsyehe9+3P75ubmygV2JyklYpaOaR07ZCbM7+wfisMHfW61cH7Pfn4kxXpCvX6MlMwfZGfLFxrj/cHgcVA/Kbzk1oja4Z52tJfYHZ+weJXYqw1a017Keqz1XRO3eCCe9t2+yyYJzeWLGri7YthVpEBFXkq8gSyXePAZYAzgPtPPkq1RCAxvjcTdR7n8+PiIKp2T4SyKoSqmASlIAi7H5HfGsOlOj6Movd7eOgykDKJoBfFfQSqF8nGRiRftnHJvDyphnE3hL+z3v/+DiYZxayeIWUtximH3ziA/I2MExTf7LwEqJ5UcRSwVz3IRnzosqF3s2svYtVNFVT2Vv+ZLJKXrmBVC5A2QW3Tik0sFTHt59UaHTKb5RNRND2IwoElOs9PgnXDB72L6TnNSmD0junNAPEYz2Ls/3yTQu1+uVByA17GLQyFNkabs7cxfiqu6lzfWQ7hPp2WsZPJPV7MzZVg7o61XTvqtXpbEW86aM2eWuOYc15MdXQcmWHsF3Kf6gpAFqDvJEIkafWqINAOQMpcvYB9kJuXEkdt4TjQCvNtw9gE4nyXVo+3s2c/dpuXAmMfGf4xBW6+c8JIt5Sa4YVKWZfbsWw5QA2pcDloW0fbhKue9r/fOlkpFnGOIBjEne+ZgA2CB1h9wgnAZmLk0aJrKdpkh/WbGll2MuXjOkaaZzf2d5TAXHD8ElYCPb+E3S+2ZhXEXTe2ybokaqHTtnxiSqXhqwkntQ+c+RdHqL5mrlg9Itu0BKJPG7sTQuFzBeBKQieZeoiyXQ0iqKNwoS3cF3JWPGaLLG/4TNzPcstVUfiiU2N2227y6ur6cTAKWSasQZiOUnQvqyxKwSbSxYKYlsuX+vY8uAIGobiHJCmH92MbCeA2cWny0NeTqJwu+8uPwrJT4pV7uTSeafuj4TgMDvtNueUIGKSomeqgt59M9e/QkOzCZD3ewCLUtOwB3D1x7iOr+2/qLuUWs+CuAOZXtUG2OtOJ++wRCB0QXhHe0ZvOFI90x7bUtwjLzXmo/XYPKa9LfPTzcn2N1aVL+TZA9g/U75sgDehP6R1C+PvCSeyJrZI+qEXSFR0zt/N6WYXkxY3GZfYeEs2RE8pTSgWj8anMSUAZd43yhwKlGC1xQ09576NpNqJ2qpjG7fHtwo1yk0r/TP5qMgGDeSYWZVBpl3q7CNHaXCjS1lhEzGVWddFx1K6mhDBuaMjc8auwOLX2rPUTClajdRdsuF02A+wl2T4yvPl0rXhbKJ49gfvT03a7AOCGpmbCIuCJorl2siOvtNQXpdbt61U6XEbumYqlUrVVFgB6uyHj0xIUHhkONOk50h7nnl87p0UOmsmT7GGpWDsQmLcUhxs6+wlqm1WzmGo2dLcEi49LheGtiixR27znUtgxwh54FSYugLRHV9nIWXEBqrA5IZkfkk89l840GVuxPnTG+0oqprqtyW5bO3bi57C8MUGJG9ssM0L+gyAHKM7qDWjgOA4iPohRkT1rCsQQoFPJo63mLsjSXz6tXfsxepyVzV5hlXg83ZCq5LZMAkz0qj0a65lGKSiWo/fOncsm8G5UKJTZd0gWAQVRPYP5VoXMllGKrossgONW5KyxPOqUsl9cKtUudmvQtqnrxI7Q1fVTrcqN3qfIHt30QnLiX0VYz6mwm+FYKfBJzg3/35+c2958+XU/4mOMuQXMADdN0iDzJaD6CyydVRJdxV0SDE1w04ueKqTrc8XVY7mBJBLV6EA4ti7+ClWheq48bDWkbqoi0mfwyfv3L5bG9/sU6bajMGpXZOq/dDqVy0jlQXWlQu10SM/chNmwz2cO450wEK9i+8cIf0EeVogYtbtD0G/ve6CNxHd/tvz60m+gVuPywslB8mxEBf2FeidNjigFNJiP7aG0u0UPKcfGyaY2+9fkZYPBGEtA8jyB+kAEdLu8AUxj2sKeWt/ZAYaEwNrnGwVsvAUC4OrkMR0kzXjV2FNReqYh9zMSHjFi3T1C7IGxJoGvIpJ7YVxcVU7D3FLsbeRkhOnuLVgFV+NE5ccPj7FK+3cXWfeLWQrD77GAmVelxotJq4lZlfH39iYRGxvpGgY30oD8I3LstEPb2iGrg5eVFcw7X7K4BrGOqvsywN7pbGXvxG0TMZD9RfGx3G0ff8/UZareoZ9ZshMus7AW3zmcvQukuOq+vOFbei9LEhQgqAQdTmaCkXaUPt/G6ynJLIi0NdD742hfpFXPp4ukpe+efHh+xnHFAya6EtCCW6UjiYA3viqxNJNGUiosJ89aouTVNIBGXgjqC7CkmRrQQYzcVJsOY1eI1T+nsOdrqg5V6+GQdAJo2GcbBOyIB2S09UH7cRUUNUuQXHCmoHbt2gPDseFS+tkaS/E4uCsmd8Tspsw6ZCkCqtvqfYI3dVSLJTV6oHx1XdzfEnTaOif07WRCbOEZek+zVH+YTo8mZqsQacy6U2qrKXSZHKmUz03xqkdWnz5+nTE3+MiWBBvZmlHThVaSP7FBnNzACZS5NqdEF1Op1x6lypOzsLfHnj0q/276sLZ6HOq1g6yi4VNzpta+bU3X5qMBohqZljf8eugSiB+7rbMfb/jVFFkHlETT99spIKdxLb4N08/W9nBuJIXwo11RimLiEQs8rEY2EDCAOLq/ZKE7ARpjfwCeOO8B8WNzCO9AHdaxyic3B6f18IY9lEaMUBLk0ca8sxfHXIAGBre/9oyq7n2UOxsfFP8knhdqB7Iau3fiMCjzr7ylVljvjV7jVz3I3u1SMKWTnQbTnPEoESS32MvJH3RphOvnEfAWuNGFu9At5VIYH3HBRxSQKCnilezcOZlJbVQXGo3d/emqSmqIRXxPhVwEp2YCLUYoRGPoN82GaDEc0MDXmyAiZDWWzp6dn3ISeV0xQS2CUSN89vlRPjmtHyrBqc3Ixahs31DQOvXLNixnzu0600Yis3KLy8bkovVHO48xDl0AYwN1oPaFztw8ERTohSCgEErshzVJ2WMpmcIDFdVQ/zaZCUzNsv0oG3AOTXIAhu6+Fzh4oj9kPA5U+kBKIz2lWep6DBT66GYXsZfsK8BBDY43v7QjG4RFJALGznFIXW2/ZAoEm+NMn0bVPULv+LspARvUQgpL1BT/cCFcGDp48W3NKLMHWgthJBSIhtsbh8igvSGeg93kaQljF1TnolKJoT46455Idq1NrGe7ExYx2744Jv77UDWtlTBNwSE8SyQ3/iersVVYYqM/zeQZ6QpLUf6zR+OUe4vAYe2kMJFMvibv9UWtYPz9hNraO81Dds+ZxnVa1L/fUw4zpc84AZGcpKQbuYX6KnaW1CrhvsHrvrEzVMByD9UDfD+TK2P/6kXtpfT2qHOGOPRA5lXg/jCBRJaKz57ITiNZBAmvATGBHUBzInlM8QfaoT9lmC8BikBM4NU4A67nR2l87gnG4bwnI4anogNMpjKN+97vfYQamUbsS+AQWm7TbEkJe5l9Rfpv7Ev1uypBC5PKj4DfvaDREF0276oWmSH4aXygprTwBUw37JD9S6lhChWjClnDCZcLuCTA8zBN5au8+GuewmhGjmceHR15dX7NXVa5sRk5f4jgeiqxD/P1ChB2lSJK9IzCDNLkwVQ93PuCPt4NKRW/x+HCfPv6Xq9x2QXb9FageKCx24Yv4B1XYqmEAhmrGdTEnjdp1/DjibkggI125/4q0G4WMmMuJ+EKQYggkIi7rGskzSB9le+PjBk6jR51X4DuL4EHyh2CQ5LuSFrAORueyU9uDspjDYGAAth8O5ER6Lpxt4NUeSKReDUD2WO0TE40wrnIYKnh1IAIM60MjLoQM6kWASJi5lpyyJLBT1kCUlZOydOJngpClfmohm381P5bC1wlkcJ6o3AW1K9xOdDVmT6xlJIZUf8KUel5lpAd1CRVdvWod01/xsjySQKV318hd6d3x6y61gk0dHEikM+ZZtnCMi4UCHls0t6DtXD7HL4C74BFOQQ2TAKmT3S5+7SkSAmTvB6bqmnj8u34J9PuDp36ldlQvprcRtSMQ1rLWL5Y153hUrT4+PNAbcxQJE2rt9sCRB+khNn3pfmbTXOxX/u6nbvtV7qClUSNmUCLT9NvQuKa8bNtfkAQW4SAAXEMkipfl1IsXfcNcaSClIJIETtWLcxEO5AEpIEMuh8VlBK7Bvf7FMgGFlsC1DFYf4u0et5fqNM0EEQsF2U2L3Q5jyYHI0GUx0TcjbSB7p/P2959+Ojs7bTQayFC8Pqr/+WOH2kHNk1yIMVWuSHTTBcbliSDDUmaibZ/FkZcSR1nSqLg86aFUk+JeqE5JCQGe5XHGGQFyUQEIT2Hvrm+YgIwwJZcZYK/3Vq6Uzs4vjJzl2NSP4eBjUFq1zRqaVLBetyfq+XSKusd5yZgE0PDJ1CAY32xKAu1O/z3dOC+1I92uELB0DBCF/JzvL13LA5LdquQ0h9OzM9oLGgF0L/TJGEyiVOFJ3M4mEvTDeCTesn7YogmrDs0i3Lu09+8ju5JBDNxdiWk64E3HT5eJHKKFUmkd6O/XK4CCwPdx76XdQbmHVyxCvBYRDGokWcYDhwzlDbEYN4iXy2yNcHxsvJQbBg8AFhdS5R6FffOpOxwxfohRE7bd6JVJrsaYAQOqGOfkRKM/R2XfH0CuSIDS09zzufzTY/Ph/hFjj7I65Ye9dHhDlS3IqJU1fJ7/NQLNN2aZaVxLFqRXJjAKbgsRwiQRwbqqG4/m5Ob7CSkxk1EGNzyTkkf1A0XmGpoWXxyc/dZBKS5W5kTjhISz83NzbSF7pnJyzMFQIpgz0vdUmHb7RdUj6pJ4aUSbKFMaq8jLyeOQ9UiA7/i93yiNfj0rvybEx//2XvWlHf8W1S4k9jeLFmkjtDXd3IxlVd0V0x0/t55Vu6dDYGVM+lvmwDpySKXfKjIRfeTNfuHVEo6Bu52MZhVidmcX12V4aIRc5rdv0fBvMdG+f5THH91aUVSD7gvp3HlZUorRvJ14kbzYfCh0ThyUytXqoo9hRn19Ae9Zan95ekI1i04IEAjm4xX3SBj9PSAP+1S98gtlLrt8dyscQ3BgKeWisHAOMNX8g1kx/EC7/Pra6fUecAMKwiUOxcYOHn0zSRAO4tUzKFF+y2gsqnJDNIqkkEVcklbp1LXk1CuNuMlf5zn9Ven5WQxXIBo6CgGIRg9jFQ4bk7vRkPjwgxKdKYYyo0qj6uNA2POLC7uPxXo+Fi+sz2j2+Mqc/oiuXqxqlGHDyckJpbNLPmU3/rsxCXRH+WavcFF4TOc3dh6q+8I3m00OxnYfP0hMow0GIWJOu9BGza9c3k86imwWNYGRRDXnIU315aVNZ0JbpgtCm1DArA1tglr0MyKv8yZ4eSPmNvQvDL9hFjoG7kYFmH2q2d9DlI0AACAASURBVJ3x0vdNmLR8M7FXCemharnuKDPCCR2dEaceEuKmhPRfCvYsfxLbFmUJICW2bQo3jBxKHDCZhmUK3+ccjFABiwA7/Ahh1szQgmUzYwyXaKPZXCsOMTE4Adnj3n6CZRXt5U+5XbJF3zyQUuTMqz2wSEGOG8fsG6CAmBh1Xjut59b329uvv/5Sqhx9/uF3xKH4mNZgatJ8aqKQRoYYIjFBKnCeFzJSPuCJhhyQiamiKmU7+FtBcD1yEw0UzvSARzj54MAA2BJY3u91xZqcHQ9HtTqUFGofXF1f/+0vfzuqHtFkjo/rP/z4IxNmLr4gMw18L768vOJ28a9/+QsE9ScmC/MFP6zgQxnN/O3tDRt0EQJaeZdN1Uwqvl+/BPiszeTnRPuv13Kykqvedf1MLuSIEdpCyA49htiRmYcm2iZNr1Ti39wGANr/i6x3JUSzIH0In1gOB5SN9PFepqjqTZgf+WCB+5wQ5x5C+WzhUwyFrSBEwiqSuWPxzw8IAI0d4AY1XrZYr5SySY5qd7zomGzeWxbNlk+JbZlC43nbdDaZx8FAg1RKVEDoihYscqbCAUmANAGa3e4rWlr+A32C9VlFweEl9/xXLBVFJSyXDFeMQNxNCWzsL2yDWc1TPZAzEBbmKBRrEdRh8DEzlhc2YY+GF1ef9NIQzKOVx80iGmrwNBfOVTg98eT0VKvwpZzqAuTr4imCElP5fIGY/Ad6ZmM3FuTfv3/nEACOMmUipHN8fX15abWozEwD6o0TpAcdzow6Ozu7vbllsOcfRwqQL22HX/KVK5d/z/V5d3p6+vBwr9WczAegylYIZgSwC+gHDRCCBQwMb0z6ccbeJTBI5B9fBvXy94LpPFTvZNadAo372dn5unPdvvxshqY5Rulh6vNbvek0aK1vnVfUANKvZsR9DfbyqBXoS/kfdD9HIsiDWloMQiBOeyDAfbHOLT6HUhEiIRoKZ+6JrKEMDll4xrwAjgbwPZF7fuunhy8Y/Fnq/3T58X3uXhC22NxR2S5lsy/fpHieS+mF6z2Ny2cFVvLPsOlcKKjGrMq44x3gCqZl4AFrKnc4Ezfq+XwBEoxMpGXoovJErV6CbWyEyEvptCYsw2qjccwrBktQOxFkyEQ1/dJG7W1ML4mGceov//hZ4X5JC2YGRldlB+fEw4/G7rwyqr3SvuMLZqSnsdrOlW3E2aFyqApRmQWI2l2r3HmGWrFcMR/9C0uwQSREBHa/ublhuQM2kCePoP/ce48Vebyn393effv2lblTJscIn2HzCUngB9Ey0Pd77yyQCOvxtSMSeB0edV6ezuuFVGKwIyxP2KzVaw59r6uyHHDPTE+CjwH+mQVFL9HribHc62AgpnU06XECb1HiKcvdhiVLiRJo9Ffm7OJ79xLYM+C+CJoWn90LZmXMCEmvzDt4BCfund4Fz3mJgqltO+dsijhPJJPo10toCgutVitX/VxOWwN0ttDNp/P35MykQdOC20lKlwQMSmZtrRFoQd54d4g3jAQAUCDowthjloVGrGIZIrYi4gGz2+0hXeYD4FetZxKULJBatE3cA0MZpXwDAoZDcjmuH4NfUYPziC6cuQf7dPmAGrgD23vv7+i+UYN//t0fWE3QPDP3YHLx9tbFTkYzQBmhcHtzg1WAJkKBCTHGQYHlQHWB5vzPEemC0IHo3DBHYVZDdIZhLq10x1gGKxuYuby6JrtJvsrrPPQfHh5gAMt2/LFiI8s+2qnSHcGIwTsuGuGEM79OTk/McuYeucEJ5RIc773CL1Dbz8cta8LjRPq+1S3Wcxd1vO7O9vHvivDxKnNykg/E7aYq6pbVBEOG6NrL5TmISP8ivcdgoBzaoBbJ8ii+KROyX1ZjeiM5N5YS1T2h5StzWof7bRWYA8shv5r7KiHTjpzc4qdffA6XgWiph8urJTXrAliHWhKIJtCOgaXGaRlxFouOAxVg/+P16RXtYm9ZNYttbjQlsKRqye1yzBn/y++MEAtaFkFGdD83rvjwQ9htmrALZJEvsJV/QE7j3YJhDsOSBruMRuBOfBy2ul1MwxmQGIMAvkBnICv3wGHqG7Y9PM6bmBu05QY03H/vi0l6OgNkZwqAOflxo8FkgLwA1diao3BnBvHabufRZpcrehMqaQXcj8U2HWhuBLKjgySt1vPD/YMURjz/yLqBTDBEDc+kQ+xnIC6X0uhDAZsX5jNY3XASAp4bKZHiLXV+dV0uY0czp25HSGw2OGmcfP3tKzQA6++9919//ZV5L4XlCWW84PZclhWA45PG3//2N4zWyFQKrC5SIRyGZ+ZCDO0G89P3B/p3sYnNBLZSIItJVybwGqHbGz53eqeNSib5KGkjz9Arg6vjO2yhWZ14szE81AR3jEb2+WjmNG0u+DBGWPpMOky0BO3nZ50zPYDM2MfYzZfocxbKx1trRO+ucMRaIOg63SRiZOLxyoj/+LsC3C2+lEWQfznYpFxHHjZZhxBszb11aAjZhUximU+r9rYYK5dJMPq8v2fvH1rFYr5Snq39AetDZjEEcov8eyFpJQ8v6c1xg/BhprOh+3DYZ1hS4B4180R1Z+kqh4FKX2DiVrPV7fVQP0831A6Bs4BYTMlF/18CMRegCcwlCZposHalUmbkAccrbTi67/5bp8Mm0fPrT0p9LiJkbKuUy9jPcGPGxIxZatycOPBRNBOKznDQ7Z2envz1L39tnIhNObMOoDMgmzhYDTGOFkvlWq1OFjBGBCMvyU9d5IUiDWscisABqMwoCsVivX5MfDTuWJrxiH5djNzzlJiZRRZjHmYbKDuZUUzJCPNy+tJwxBoCWjgj/MBvAtTRAElthG7uOx7u79P1P102mkxIJ6AonAzNmdjwsRDsPYVBgKrrsNpmRDuIm3A+nxKViy9CT6LUGfibmo22uoekc2O9Dr2F7oLoqehb6Drk+LbNXUHE40Ie6yjYJsVnVT5rkVqHWqUPGra+nIJyapPeogAWQTaJtzx4uSBWrUhiYc18li/QlTffy4XRg7Ybfnp6+v0f/rjlZfTC3rI8vKSO4/qSgAL3QFnBvgxChtppgRjglaFLrymjBSfmt6/fgLM4k0E9LpGn59eC3AHuuDyv1Y4NmM4IhxcXTkmkJi9QNj9q8K1/GTtlnlAsatWjhvvgamgycwC4400fNszJ4ZBdqs/PLShwzyvMci4uLgnE2KZeq4PNdUlZCvh+dyf6fobivmy35WIAhiDWMjffvi0Cd+VIh6io3Mw5xvdbIgHdd6Aove+Vj+tnhWwrsB5zuWSzDsqqo16OH4CFZHKhbltRj8O8S2D2Db2lFe9fdDvZDMt9k5RY8SXE0ua51dJ9IF2TWnscMfsHzOvVQm/ZrD22X3kIo25bgYtCbQq42xbf9oWLwviJsu78/PC4Ko1FGSyCVlHZxffLxTS3DfB6MYHWMgFkL5Wrx8eLlri7WOSY552QgIHvAfAwTE3E0QouVo4bdWxZCJGzicT+XFzKcJwR2ztRaaO6xsGM0qlny5XK3/76V6xoQNWgZ+NXw/RlIRAOQU184S15AdxRwJMdyJtcUPkTGRMbFgRA6vNKfbFQx1qmdlxv0nCUIzkYaLdfaUvAI+C4WMqwG5WT13NZpi4MwCpQ7X9VtvvAegqHS0q9FXiBn/hxGyTAeajdZP2y3Fn4+lHwttxRh5iL7vOZJIdIMyYVvgTEZk4WDOvHx/QwBn16D5YHUczTz/ER8QdG18SqHquUdDtcc/XTPMAbJHbkxncrWC70THzRlN2WVdsX0fAxo7qxjGcshHFnUQyLoDBy2i0ay0KQ1fxSqfuRvb37+vmPxUICb+7xFUtgTRJgEELVAj5Gpc0QJRpoNRBxD54GSWNPwgZWXKdjKW7whFk7EBwo3zhpYNROFNaaGcNER97rgYZRUEEPdzQgabTyCtbzg/G9OshpanEOfdadgNEYwmOBSnKInJ6dMkYaeS3fMKxiMHN6enannELqCKyDs10VmI6t/8dHnjjwIEr3QoFDT9Gi4eNSx6R8hLOtlqnJ3KC7nFMcsgkJUA0ehueF1ONF8S2AlnsTrFvlSZ/PPyq21cs4bNslQBfBOiH/zIzSj9FxoQ7ghupK14YGRHd09C2BvvUyCjZnvJX3y6gmCHBfpjZX6BWv5+JG8LDh7EMvkUV5LIJCz9YNQTs+tqOFaO5KmcH5SbU3GLeaNxguA+XdlCyOE0sgoATUBtMP0C0234B1fLNQIblhNOJiWJKzkD4+MEA3MuLtUbWGzpsQ4DH/tK2XEcF8AxGSo6mClOjsX19az8+MeZx8Tl7kS1qs6lmb5gGMj2m6G6yG33cM2VnshhkNvkH/HNrC+gB0sJaBOCOohu/Yyfz6yy+n05jAKHge9GWnWgzczR9rG+57w0zro3aSvssW9wrpUlG3QbwxD6FIAGi+YIXI96WX46K7oxfCSp7DOwTEs1iZSbP2B7h3lbUdXHGV2D7SemvfSuC+opQrXtsXM/w3W8RKWIWzKJJFUFi5LdAJnpMnCtHXe2BNNdurXVxiOfOauqzm3gqpyck1C0WPH2MJhCUBIDq4GtQLwpZdqtkcWJZBaALdUbrjNyYp508ZORKT80pvvt0AkY1AhxssWJgBYAnz/p5kGLu8uAKvo6wiCRNUchIIzqVUkuIlBjcx7+9swEVDzzhoqLKA/gyBZKq5hYLSr+sDpCR/jFAxs4Emw6cylRGHNqjdmRtQKJYCtNpM8kol2cYaSDHmUOD4lV8JNBNXo9efLmrDZGKvUDvyoJ77lUqcbgckQA9Dv8QFrwamp6ukL+KXfkmt78leHWxs1CIn5/HNHIhFXkJPaGeZG4+VF+BOhivyXPF6mYmoQ7aOoRALbF0269Cwso2Wujsu7XjwWKEdMuNkTd7S/rFJGCdem5wy3+uwtU73BQ4J41exBHxLALwMVgYlU/GU1aaohdC9M8CIvn0o2iO1B3ROh8IGU7w3stl0OV8ACqMUNEkLMgauVKtHzAfw6sj5qxifYxxPKg3cuSFfFpuB0ujI2WOqCM48wGj6woxiB76YArBd9a3zRhZMJ3777asY4+RB+XKkIpMOmo+sEYjpjhjfY5NKC2IohSsN3NHDky2zA5Jo+vHvxiXAKXX37Y968b5YoxsMr1fdeMGmDMSzxKkkDugvPQxdEwWerUmOE2r3kJwYLQ5tUMx/fLBsSYdLN0uHtqX1xA7/2HzMyWjhMZUNsYiCt5q5sMpsUUiLoAhzC4t02HQspeBr3AEhGcwlE6NG/Qife2jfh+U/NDKP5u0yRrT4JpZAQAkwtGB2AhAX4K7U7UJQALL8p+3X84WiGeOWy6i0xTa9zNFLaq8nOvXX1xfQsLJiRx9/DFA2JwF2o/3ufHxotfoiz2LE4mQEDCkuownAbaNxApL/4YcfOKOK09G1OpNcur0u7Yjxj2kDlvYC3MkS7J7Pc8ir3v/65csXygfuhyAJKfgiP/HzeiXwMjp6e2le1PKpxN6uMQLU1ivUOLetlIDq6OiW0CYYBrH0QlyoSbA/pFtj5ZCujB6PX/o6Yu5cH5WhT92ibnWLWFlDpbQorUVQKIxERTcU5rwTWS6OCygvC2ck1GlVfJqraN/HrceX5Gj80SiLPw3v3MQpYgnYSoA6poxGMviC5FhXPUIwYHCh2wZtsDn15GyyrRMqjC7lSpkDj56azdFYTlclECPyi8tL55OMxJH84MPaq4wQYbfrzGDdll3TCwzZcd+mMLrsi+UNv2jTO68dvEZqx8wMftAlHI0X+8vOzy8YIBkLYRtXOTilgQjTFMxz5AyqDM7sh/3BoIi1faFAe5TU4jcOrpebtImV+NavBDjq5vtDu3SUuqjRs+3eeajuy601r+7jxzEPRwJ0L1z0M/jYNZeazgo1RLP5hO4BxTz9GB0U6hU6PSJzkcocf3vuNwdTtlQgUX8a62JbhwbkJRKiAXmKLLllYefRPJCIWJOI6o9+T+M8rYm/jvvv35OpTOP0IiMHkcRXLIEQJEDtYmxgDEBHXiyX9Egg6h+F3GVbaX+AzpoNGGiDeMszBu7ffvvtxx9/5wWLjMlCLQpPfDKaWWeKwCMI2Ry48p5JLNYvMMMprdr8hiSA77fuG8Ob+ieZcqF0x6k7rUkPfoByPSe5/vSJUXA5I1oixex237C9UVp5MexBJtjNY/mDqxwwPWMneUmODKecC7vFg+hyAbckpDdIPLZeL85OMgnZ8LDfF0tD8aEB+/2JQy8d/S39DJeZMr0Tq530mbqXpufhnqpFN0dnRW9kjryp++iBuyWi2lRxN5OvtQisQ4NwGD7FINxsQdp5gWjMZGbL/B693+Xl5cco8b1fy7T/ghu+SROdR//m5PF9LAE3EkARTt3jIBK9m4LxADMSbLSYKHKq0XO7fSEofnR2dsYgwdDw+fPnn3/+2dPOKmiSFjcLWN1YskQEBZE9GK5o4I6X95ubG4MmuTAPUfyKoT7lIkSGNPEwk+YVkw2M6UHwjH+8MBKab2hZ6OjN5yya33KPNKAAmgfEQwTmZX4jO2JlUVsbRZCcHAH60EJWcAsjC3QO+DFJBRsf/e7TGTLYf9ROIVnnMdQyEX/3eEiIWMAbJU9/Ymx+1Yyo/keWRvUCKRGwckStoHG8Wjlcd88TGLibsc9Gxb1NmVsLxTo0CN/hUwzCzU6kRWRmqS12wZlU4rrw1E/JoZW0T7aumtHAYmxdYuvQnZBGzOQ6JACIBYC+vrzep7+PMQMHQ4vaZoztOPYk3Xrt9PSUMQBWGCGwinl+fubeXPFWcglN4kPamJ1Sy42Kicd3FOQorhl4PJFlgymMKRMXMQmFDcHNfSYIHzLVEDtRsZPhIhJKKY6ABbgDr7GNUcDd53hGXhDhwoGEZdkRFLlTHC5ucISPTQ5ubdj+C9BX04YPjewpOzzDFeMrfBrysSS7H4EU/6FbrJZrpaxUpAO5WB1y6YUpsEDMI0hgYt4IGG3aW7Itir2DJaDTmHZH4tFGC5NuB70CvZzyE4CSQs6/02uk/NIfRtrVuAbuG6yrW1TpHFixFpB1qAOZla/Cp7gyyz2JwGC/VBJradJKLy4uaJb33+9zF/+1nviqG6Fl7LFlqM5pOcMlDuKAvZQAnTg+DYDLAEq6+EajjuU6Szosy/IIXAdn4DeGc0xz+ZmjA1Kx1POPn/+hzyv1IJmxAH1quOVogaEmkweQK1XaA02B6SkMWphLsDvWWFDWIxaAmItM9SXAvVR8bragD9AfDF7smPHEgF1kiqnmC2mDq+WY8Ik8ubjhrs36d7er1ro5u0qcTtDMmXHwkkkA/nA0uLcU4DLxrQ157fTeMhdnJcx2rZc7tpbzgIwtmC8HpLatyR0GG98sr3WUYtvMrjcxLWj6veXOh96GaTN9y8tLm2VIHtm4T3y6RyKH2CTngXsUtcJ3ddqBhLbysn3hu1DhU/TNyq4m9NJsRNy48GNHYH/0eHt3y/3JScOy5A5fxgnTa1pr7TMt2Y8Dw5EA+A+kjmYdoEhNAytzzhcWHCiABx+D48YJbmGAzmQmgFf0xYIpiQ98NEYy+nfg4+PjQ6Wy6LHRmUt2iDIthaBBaj6+nKgKM+Q7H77iCX44dIma//T0aIxSlAglEwXgAvhOnEKmxQF8f9AXg/c05jTcbBg46hmFljnlnB1wNS20+hDyJdQlhq0UExhvmNrLftpMBuDPWCy7b4tF5fze5zLCNNsI/94PTvPZ5/McivYNCz/CQtqQbjab7I22eRkHO0jAYQRzSOXwymlUo9HZ9FEOBHfmFT0/F+yaHdrQvdD9sosJTE+nxD29CkGlAA5tMjM/GzsjnA0y6lTFnd75Yzl8iv742JNUzIN9lCSX6l9dXYNUbl7z+Y+n45r49VuiY91VLcdbSLga2RsJrHMwXsc3a5WAht7ooemOyRiLEULq9WPUfsvVA9cpoHbp0KXmSEyl5KXr/kDjXjs+MVhHK48yHl+Quvc3wt3cQNuujpCjAHeculgsOjnRpiyUURzamOo80ByjFDX3kAwFHyvfMODd4YcsGROIR3nwvRPpLXhHobg0w7CDaY2l1hbpMdsSe6f398f2A5MT5ZVfNtSSnN23iIJ7AhWyR4svBjn6Wlsp30eZx/faefYWy9u1ZbpVGWHWuFX8HDAzTuMejYmmMW+w6iAquy7NIcl2vaKwdAhcKGjMnGHWiLex5lOTV5ge8gu+x2q+UCyh+KBbMvW45nST+wNt5BaSsA1yqoVO72wJOr4In6Jjdof0Utuf+Ssx6sarRP+tk7y5+VYulauL+GzlZ7PugFYm09xKYpdRVxbPmpGVyfYugkd5gt7YY0pX+/ryAtaT1MkkNiSXR5crPw2dMPheML2SIkOXRr3AXDHYyOc0BbA1uu37+++i2/bI3uTzoM1SrMmjpqB+CURnnM0DMRk2J3Fd/mGOAZMcBQV7etkXpNuV459ETw1BGWOwdJeDx7NKy07YGBcfmLp7zcslS2uOpnFGNpfCgL58ZG1zz5xJvPL3B523DrMatuqSCrnxiWVFRZ2HhfRA9qwAIDHGaeY/Ijjn8dlVUcet8eWg/Y/rOr6DDndAR+N+ZnKr6kpycaRNSMBLnffYW9kWZ+uGPZo+/bx09aaLPpZ1PxTzeP6lP6GTIIS9Q2jopccwbb4/3HZuEtfC7Yq6suL1AjGXj5EQdZn3oUSjSQQsKmtb/MNt3+3tbaXWOCrR6lz2CG4+sC0pN4ntirZINAgtuzz2Mnw8Rrfaf++Duekx6UBBYFhCY6euFOceygw6w4qGEwM0mgbXisYdih8f6F1waQ4t+Szj8dHR0V/+4y/YnHj9SkBAmQ9M4bpmDiKajoDI/qDAihH+bay9ztgWhyEDR5WoM2++3TBRIR6QEx8vLP9SDsqCcDQMZXQREeXyYHos41H9Ojuet81y+mKx6k7DQ/obKnl97EsxXSjOjcRmVpEWQzJzv17vDaEhHGZT76Kqz/H5eMVECGf44kAzKesYQPyF0dpMTd/jBeuhk6sWn+rHvs5DDVUGy+ytM6Q+nRj7znSPhOFbBmtIuBExe+1QLeUQOee0d0uHNkAX+nCW9YhAL0GfHAN3/YWcvqvTO8vv6zIwKrousz+4aMpfWAilpmlxvX7kbx++VwqLLS1ABv4qxIrexB/RAKXY4aT0jHSR+DQEBE9RVJZtyqhIA5VK+twBuFZpVwW3K437aABWw8MMKlgFsNG+COR9e2ucnE4ht9tsoQxNdDKmhPrLyy+4EDv7UrFE3qYIrohjxc5M9frT9a+//GokUJOOD6iRqVrVFY07KiE8yqCTZkKCjXu16nGKYFCf3kRcdSMmPy2F8RcdGxidc9c5vsoInLtR3v3R02OTg6qeWRb3GOlRcxiqkTmPuPtkr4JKlSS02WxdffohLedOuCzOfHfhMtEclwEe5jMPQMgiKbu9T07yFi9cB4UljChL6bowccTwJRC8gniuGuB1Og0uc2noCg4TuK/4ACtem0Xo6T4qup6YONzI9OwhFr6Sea+cVtvtl9vHTrVxVUq2QyTuhVRcq7xIaz4uSIjt/2B09J2AUBYl6SKPjqroO+cjBn3CbgRgq9eIBe2ir1aZEgL8gjqB1Vq11WrStetodlkSkwsKKtEkFnYX3bcuHtSFc/S1HCQGvBsn0NpKZPT7H0NmCPT4XJKed8rYVFS/6rLLjnB1gBQ2mkJqyhsqfJTucqklXMiA3PEIyRlTZSYkYHfK60DzYF85NVcqg9rti4t7O/kgcL4gNji3j+1KvlcpF7/f/MpH0RsYZK9zNseHJjkxMQ7kooJJrZhcTvkTxTOymNJ19XdF5nY0XDCV1H7c7SisNdxnKVfx6EIKq0hsw/uJjfs2sLJ+HsKpGjTnAwHuK+S14nWQ7xsh6SBsHWJay51nwQQhVs5HieRzt33ffDytFcJS6gfjKk5tKwEA+vNzCySLNhNVMTAdx4v8mpCNbdogLwzwJJCZS+ncUagqv+MCqmCs0Tj5+//4H+WyhRU1IIw5BpgNVA8pzMnB6CBp7fYEyAz8BijjGF62iYoxZJpNpdwcHx/rCQO2OoD4H3748i9/+pNCftAb9MRqo8ciQxc/6O998D0HoC4vLxDy/t5jlYkkWvcDUsTGA65kCqD2p4qZu2jcC7lCvvPyyquoRRrkc+xuWqT6Phi33t6/fP6cTYpzz+PjudJI5VIXllhUMzzcc6G8l60UytSeujE55lb099QjXOXIrFJTiXq88oU+3TCVpJiUZU4W+/XgRgoOJfYleQd68atNSmAvgburGu4qkr9PEyFpfwzFqUQC7F6KQhAYKdSLiVrhpNl8AhSenJyi5Yoio5imDwkAUvko+GkBxwB5QZy1Wl1tEFxrK9XoWfE/xe1i4v4B8tY6bFTm4Kfm01O1VjcXE6yM6xrO7APvizWEYDIxoyCOTgiqlnKlcG+SbT+32YMh5tOJBEcgoT/FPEaipZKc8fSv//qff/rpp/v7e8AfkVHJY5yDjvzktKHh+Fvn7eHxsdVsaTWtwYZQbr80Tk5+/ulnHZPioIaX2ccEuItvFkGE6HfVK82bQSG+CUsCj4+P46M/XJ20ML+ypInk+TpcfDUigGRxS7oQU6rgFN9jmEPHhUEOX1abVFHD9FdmLgcRahT1AYJQ5log5fUxaPql/Aw8Gve6S7KZC/AteUPCc+T8PoRLzS8XO59uP4C7qzrpKpLvDxotdd9sxQlnEojUXxgjmqb/1O61UmfH46968JtlH99FLwHgCGiYnZGdzhuwFbsD8Mnx8QSYRp+/dQ5whRpcg56Jxh3blfEIjTu6cx1eLpWwbIZ58K9BhVkH8P7x4an1xDm+KdZz8DhYLh8dFTgHdOZ2kPjYR+Rz+fHot+OpUzw07oQb7oSVExOZGxhaSQ5A7XWfH+4fmRsgJTgsFvJ4tMSm/z/+4z+ur68NTuAQy/uz01OY0bzxSnYCmtaE/wAAIABJREFUyB5bBmKcmhJFsDuLANyCAqFmlCK+CUUCw3Hy+2u6Vj1T56EGGm/ka6nvBWNUCctFHs0zIJ6NH2xyoALIqhGHvbOj9oOFlzwmVtQcJpMYmGlkTwXg0sRDKbIbIloQ/Gpm3CRZjBNjyUWJzD37rmqxXOfkGOrDLgJ3txXJbTzfAo08A9+cRZ0wopJH29Ij0rgvyLpRLYzH358e26hFG6cX2XREslrI9kAfwcTi+aWPqfqLbM1UcATzpWUt4wYFpE5ZEkQr/xKipAbvYuTOKgBuFmEMeIQd11PzCRxv5hMscnF5gbL862+/ZbF1MBnKm6NxDy6HyEKg+VFPCVCpgqt0uFLLindhZEgIUwTsYf7xj1/QwbfEs96ZWYUJJwBz7KfJReMz4Bv3pCW11rezaAC2o2jMGZhFmHOP7wNKgPNQO6V/vSj/hPADkvKUnOzEZV2hgM2VZUI+P1WLWRwNkLpFHVAV6V1NLFOyNvTxwZG6+uAqZdC16NjOkqyPQOqej1SSJMQeOtoRzGf5NpXMUq4EWoYbTMYiNEThcOO3rjuQDPmV81eey8xD1Ll0Xh7WkYcXfqKKu5Fyrsw0UKOOwMbdWvpgo5PTU2BNc9gYvnw/ruayqUCcW2dzkKEABVACymkcZgM9gb+YZ4A1r66uDEi6bYJBT8nJSlRu/k017mp/KqYyCm1QVdgsgZ4b5aWZeaDzr7/++sMPP/yv/9t/JaGyRe/2sJtRDsJJRWQEQsHZ6gpg4kYH6nB+jccR3l8UBwApnYVOSL0EzSubFwISCPb262+VahVAr6PpXyAZ9vCVoyP0rxrQkx3EJY06wxzqKgNyHLLege8Uc/L43rcEkPDDoJFJPV8k/8EMyTediBLy0TGn4WILhGUW8E+VoMa+995xTY09Fes22HExzcZYi/aL01XZGiH7TOT0X25oFNoyx5KgXSAV0u7V+sJXjmAuWTng4cKfCA9NYNsD3L19L2+xXbYWy2jry8ky+0gDd65sbhi2bcLhepVZ+WFQVp2kmsNq9rHZHA166E3964RWZrbXEdAEo8RFsQ5KYHjG6BaTjMvq9iL1ha8BdDEBEQEyAF5+KIs47QY6o6vOZtrPz7X6nF6T8laPat++3nz97St2MridYSXh7PwccIPqXDyAKAeCEIIgus3ziwsC0eZzgbMJhL44ihzjGabHxIG9iOfn5+RIUlIJnHp/51TXl3a78/LCfIgVgB9///vKUVWjcKMgsIe9RL1eYyKhgTtE4V8XhimBVsMTn+0ErDBU52cgBp34xpME3odpJv/HiZt8cXtGak8lYJEJm3txlmPYaC2n120BP0X4I9IuWbG2ohar6R9Lan22grDgBClaDpWfi750YfFhocYu57JLIW4GOh/lsR0bfdDariShCGyHxLOp7sCbnL3FDlij1ppZQF69Jt/nsk1lYVfGcaVcnsZZ3990KnF+UkfN+fT0mKx8OSmNUomJynN9TOxaTgzVeEFR0FA8Y4DUUUXXa+IyZdeKIvyy6VNp26VmgqG1XxmFrjlKQ3pg3P9xQBKVZAGL8Ao4ol0VgZIf7h9ub+5QWIqVSybN9lIu5jC5fAH/MuoC4VDj0uMxRjdiPAPNj4EAdBzIIE9uvn79DWANrOcNISAhyADoLz99Bg9BhEyXL7Ty6Ptr9To0jbeaAkTQx8MnF28xhubz2dEx0sY3KyXQGlQGb48XNbYQbGqYXsljOBH0rI/KbEdOVVSpzGquiKscri44X53OK6b21D8096hmiEBToFVwji/bQuwIHmi43djoUhw7BGxdlmg+mlfxbFAea+gRvEpDZOknzfw38Pa07vy8cRcgdvgFC5+iKl70bQB/YSgdN3OBz87PL4aJ8UPzJTl4OT07A+VshpWtzJUhGdcWsMYNKLBQyKNbZiDfDymhogZGq06Njw4Ikf/BuPyiyea3clR5brcXDNwXPpQG5oa/IlJxod7GbmjYeoYCWnNqOHlpbI0qHQqyYzXB4aZpkA1ZY7SDSEsl7BpEp66vhYzsHsmILwKCn0bg4E91BpOo72WCAfyCyWaracL207jxXy8SYMfv3Wuumv5er2E6NZspeaGxV3GpXbr+G6WifzDuuaHO393dMsXVzQr43um8alU9k0oesclhAzcrQ1jdQw2rfSozNLk304nvnSQQRFTRD/BOnEfzzoc8whJDuMDdR0HWjtH1J/TDaTQfP3yqIZQtBBIey7Uyx+A1Xpb4yWY5p+Ck3RU2nRicHxc/PrJ3D81c7ctx9vUwhw0QJ5gPnTq/GIwwfIL58P2yr+OoUv5NNilr1K7V7ciBTaWgCtx6fPv6FbW3u3oksag5uvJoQAMSx5QIa3O925UI6fScVxnwCtkp4+FFGwPXmcL1kB0FMKw/GcAImrKIMOWHkn6/u+MYqavra9dk44hzEuiMj55v/nJ5/SWVmNvwMBcpfliSAOtFWnPPG0A50HwpigRQY7noeZTavqfOPGCFaoyLJ5aeaFPa8IzBQi9AqXY2aWuWBONAVxJQwy5yth6CHUisa3R2YCHEV8vow4G4Q9F9A3dPDMx485lsRiDA3SbzDsD26qSBChYo8WreQovhwKdD/TZnz4oqRJbpsBRtfbmka53YNhTt++VZo99v3j0ns8Pn48ap8rttG38PXqAGQ/iyt7LXpevW9iGcFsT4ugelc1EEdN5aUU2VmlnKMIwpd5AJVhgwcGeLngtS1lFQhCNkNN7WrwkVk3QxtZex09eFdU6vK8cwdTqyLZgS4VgGaprgBN+wvxAz9761i3Ff2R5QIkDN/dNLrpjEFydufg6o5GEUlSUmbOpWUtIVFVDO5bChltkpXRZba6jeutVQq2kCrF2xWgbux0+OobPXEwYor8w9juBZSv6E6rOT267v41D0lcDdIe3qQgZKvJr8qhgbzn4Ve4He+y+b/5SBGI4wsWWJllsu/a8lE7bJLV9AYpm0JV3HQIaNyxx+Qspfbx/L+STwPblHa+KMbTiO4GwXQJ6200D4+CQBrDtKZT9fApUVcKc+id8MpaMW43JKyzCm9Xzo/4ql1bDDUkBgC+wBoKiPXrKMgyUL+nKmTFi82DUEy4RGIEVg4lUul3CrijoTzkE2lEFPBAAthDB/EECUL/CKv0ba+GalBPrD5MPj8+nZeS65MYu+lUxucwRcS4XFnrRKdTnQZJ4MfGfRSRagUins3qjzMEA62iM7S1h9EmSfStPeCYzBPcKRnmo90xu7sdu5ioQxsjvnENZbM3D3V9YJJ4ESh1WarWAirMIs0/FZPJ/JlvPfqZDlUtNrKIX78hsKtthknSJZvrOgsVpeDAxfisVu9/3+LZ/v31RrtTX1a6tZ8xYD2aJNBxTyyxCFpAsokIuFrfKn7q1I4cVmqFLAfUJRsLv8EwU5nxtBMckBxwf59NAHOpgM0C24Jwv1aRarukVUqyDYY1MrBzwBWXjPI4bDMhRL2xHmuTDIYf/GW6f79Z///P0f/2hFJg5blkCy1cFQo3F1hs/OGLUvy8dVCDoCzgF2FTWMSDQlmaPaz05pGWB6wD3O7GkyPILsCQHES0sZfDDNzWGcI72l2LAB9EXHsffLr2EIPyoadiO7c34+O1RnoiveAtxh1i2/buOtyDSM11vEShjFsabhp5B+0ljnvm+hgjFsr5Vik7QrIqnXDnnMMp+PVCzmi4nuWyLH/qpS9bxasl4ZmCXfjjtMXzijlP2RAFNGJnaGoYjFWT7D0nYwuDVcCKblcCj4oQ4KZudCYjJKJxOgDRyfM5z7Zhdq0OfXgQLZ6e0Ey3NUh1QLr0AemP7CP+EUSSMShd1VQAKbH9nzxz7jd7GJCjQVWch6Xx+H49R9e3x0dFxPtVd2MPsqhFDKdXR0FAqdsIjQQGjUDu2aBsKlwT1NilkxfSmrlIapPXifiYHMydWpZ7QsqPlbLgurUAHpUF6KE5DI1iUPPtw59dzWxbUdLYIzY52h79CtY8h3SZwTei6n5wTO+e/v22BdnoOY55qdq3hWkUqcel8qvY1Kt3e/VM7+WJGBfLsurEjb7Wc13AhSx0K0UjliOGGI2i5Gt4wbpIOEDBlJdVHAnb9o4orFwuPDY8ATi9QUQHKxuwRnq02lQdAhwEKUgmLmK6AcjbuuDOjcyZpy8fv09NR968RVwu5DmMNREndK//Np+W+Z1Is5PL73IYHn52eOOPCRcFNJaCNcAFm6UDseaF8y41YKe36xuacTZiDTaB59PiifByA+XUq+gNm9eHSFrB3BOHwbJeD1c7FPjCReU62j5NvIU3Tl9lNaP2kiKkFAVuZwb0QsClltgxhBBs7lnxXPOR6MsU22lH4rXZ69dFvfnm7K1ZPakbsNi7NMwiwf6h9wGAYY+E9mMMCwZ499v4QpuHlajLKmoXSicWdAFpvwZAL/FW9vHYfBe56Y9dNKjTsMyDJ9Nsvwb03CXShqe1iFiEyDVVGUCl9AO3CijVfLdhsjd1lgiNGDo0jvB41sJn+e/BmzaMeI8UtXEjg+nju8zFWarY9EI6KhSVtTFytaywsLavIsPzRwYD27ZRhosLwhhCEPJf0E5Q+HWOXhFUqHbH3RYwadJLAdXcZKRONUhB1956fMftIEFM8asnSfRTB4Ku4gN3C5Kd6kYEbUajFV/fTpsfV61z+vJe6KHH85vaxlMHs/jef9L2iMldp2+wVwprWzdPFnZ+f0+96JHWQKp68weSeWMko2DKtKZSLr6d23bhGHGE7JV8gTkDyZHhhE9I3pUYB7JsO2OU5QXUHO5jUwApNdlu+lEMo+ijojJVIad7A7TQwlMmdClcqVIMWxyX+NwdbNLBwG2NH4+HF2krnlNNBwKMZUErLUIycHH95Fq+Si3OB72qY+r81CDOMES2TUPdT2uisgDsiefoOEGuWTnIteAoK6P9GULaj5C5Juw+iS/JGIU4kENgHcD/fD+Sm5nzT+6vb6cvLHn+PakIuBFotsvxlHnc5a9Cd1rDYfWq126/ENfZI+M1yZJHjmx1I8oC4MKxGLOOQejcXoJZWsH9fpuD1nECewl4CMq/NfmGESwIvRiTqASY6dCjg7IgMh6MBDQjC3jNDKm419RKc3VA9GepR2nVeM8rMo1alChsYdHrDXv7i4eHt9lWnJ1o3Qlo3Aprzz38smkp/g537p/bVz2Wglk8FQu5fS+GF019KwGBhGldtfsSYF2bNqymX3bUV388GZbljbD4hMNB7pq9g6S2PXgSB73GJqTQFxpG9b6N3sqEtPobUV9jHiN+4kEP0IHVkP6K6AG4/lp/x+0ngt6Dry8MpTgPgOxZl2xbaqiADZRp90jAtFdnxyqM1z4qKebuezDkW1ZUenoV8GpmMAg4oXEEb/y8Xaa0A7jYVcp/JeCN72R0exOr5cVTLswVUUg4hICACtPkFa1jfYfDY7kXQVOav307GTnCa5qBtsr6aPArLFbx2pjUArSk5hJGQg52Sa9rAtdMey1YHCCEpXlxrE0eCxTD8ySjt5t/Y/U6aMjDfMEV/5oVssjR/PTyoGT/5vVpRmqfT+c1Ipw6YXkJ3l5Ni449FoOdxjyAqxeqRG9K0XnKlIqA+Uzt12SsmEHG09CH7QH2h/OOrxg32ztH2c6NAV0D/oAyWgxvjCL5cpk/g2BAmEB9xDr/AhlG4jJPwIwk8a94WLlrp7PjYUc1r8l9dd3QFGn4g+aTx+bg0brcev9UrapdkP/Sz20yjUMXxEv8sHQCdaKpXr9QjtQafy3tDn3r5sxZJ9evFFGNvUr9ia4yeCHZ9hDO54iHfy4kIVAmSL5izAxQDMfrhsLgsuhwzFYtjWsF1mDPJPLozd5RjXTV8zoW+aE/Lvjo+at3+9uP4h7f08VF/QL+zSh01PfRMXJXMRRX/eZePvLfjssBCJ4OaL5lpG88l8PNG6RdnDkqyd2p7JvDqgihk+HQ4rutz0cZWTluVcnAWjLcIJpkb2iAadEeojOhYo++DnYJP46sdjCS/WFz8S8ZNmMV/752ip2+e7xW/QW28xd6tZo2s7zjRHx4Wnx8enZPb09CKbeF9IBnrrAtWxYlTWFyj5jo4q5XIFpB73jAuyWtujQPUlNbcMtskk4xbTKr0qHYQfAc2r0jOOpnLiWm5VRNv3VCF0bdVSTfC6sC/UFGpgLkL+wgJxGIZVkW3pHNQLjAMen7up9Oj6+gqjYh9lX/llfdBcTrI+9DfL20XJXEQReskkmyvyBZ+bN2Yc7eSdSxk5ly2k789mG30Km5WHexa9Af10DvQb9CHyOxq9v72B7+k16AZ5xT4rcZWj7onHCgAet7RNjnMBDurtKuAeSpXYT4n6FI3PZCtlGBXdlRnvTAROfNwZXu0ZBRWxIoydxdN7dvDavjrh+J5uq/UsCgtlunBUrWJdQ/8YI3V7Ka71DaqmJdwu9iWMcNi44wYiMHB30rUbRWWgZHCUzaQBLkZW0bdNL5kGTEH6VOMu22TRuk2jHPTfQaJw32yf1Ov5rT9ZKYoBJFBV81hxHEy3PVI6zOjBv7/br83ARI831+nN++CnR9GX6AXGY2zuQfbcSc+Dq5yPAavHXPRknG4Fssc4B6xPt3M4X27WBa/W2ByOVGxL6rNy+0xmy8b0RVR0p/T362+jwe6lnb/oyzAwbDZbLC6yk+jnn2Uj0e9//4dw7dR3XkzbVABlRTLHEEOc/BuP+Gq4UNQLx3MxvDwwgIk7yFVJlANK9javjOhECESuJoTS9XAjwJ0LkipABzKCsj4u4Yd9NV96H7njq+P3ZOJAz0MNcYBaWWuBd1gBHnaN22zp3Xxt4thFm31hOhYuCqPBPdDccn8amJ44KO2B8i8vL4yDWl2lOx/uicA9N1iW0tPSL2my+nezwgqYO5uF7SUZkPaeJLerZyuK5zPZCqrxx1opINsIO6pxV7t/+rjHppti9w/ePLjOz2deGonw0HzNHP+5nvg2p8awlUT8Yq0SmI1IRrZKoaT2BmPjPnHgYLz0cUNvQzXAWgA/ztjeMNS9vr6ilyqWZIcDj7gkKldKwTsl2VM7Hff4y6ipRk+KKLRltJXXmNBYFNpHuXY0ySiR+t7GSu3iOLkPq3zb8BWcqy5v6RW3gc+YB0cJOHQLzl/Ygqruh9IZNsOn8XBvEUMFoVwAvrPLq9/n7Cq1iRYHW6MR+4vorHgFHZA9+F4Be+C94HsuO4LbEG7SuG8DO9vCg89v5jPZylJHRXdlxnsVYVds3EFg9DJYUAC5UHByZgZnVZ6cnNqBcsIvTmuDj7uHh3t0tycnJ3Q9e/XldrwwxhAwN2qpvaQMDwwhvgcJvY7cfHrCEPS59VxnC3PjRNcTNjYgNs7i5ZcaJS7e+nhwT75130q9ssM45yxsikDtMixhxL+b/JNLFZMfNeYF0+trgjv6+9btPef/83n5b+nk844WYRfZppLvItsHxnNEUMaJLPaBGMljUJNI4F554ZK+i16UyoOTgPfeu3TFnFAxHPYHA6aCPGpjfI3sVe8mZjqC7jc6yMbA3fiQTt/eiGR54z+lJTkdGAlRh/z2/xWradtZSLoGtpMy9cdXI5N9vHbg/aNaq6EDcM9wNpO4uLwEn33rlPK9f56eXdD/uE8ex1yDBObbtPLIrwYGf8AdnTpVGgiO6/0ffvyBypPPFyyHE0YaEDx6d8rICETMZvPp6KhaqXj2S8ggB7cGLNemMox+qmiTHzRWaxDmFmaBcB7bg1S+epX6eTNnpGyhUNbFkr9GtC7u4nwEIG9fvyAcUXPQtrOUbbtNAqNGcZUjfjCV9Q0niQ8H/T4Qnv4WE0RM7bNZObhKf2YQvwb30dVJD8hgv6peoCoUKLGdHCMhapfZIYb7gCkRiQm4g1UDpupM8VnmI5dSsQR7wU/tBpZ9Zrdqrn770MqnBmhgo+s7IhLO4ZBVeFfMxL1+I84+bDWbpXL58vKStHIY6gQ6OwkPoxYiM7pQzcbjOn6vv379yr3WyjulNL0Dm+qFZMIYtGBeQLwJuSuNO15lCHe1ZdZEe7dvWR97HJ7Vc7fFwuZdYe62KH1xT932OoRSbeNrbRIQaXv+RGvjzjEjjP+SWm2/wsM9gF75y5ETarVLXMZ3ekK0curUFMZ6tB6ytUmDe+91dsLnQQF3r+167lsGSjxHyfQQCVET/fjWJAGwsulprbc0XXLvdlGrc3qlwB30nfxrNCKxyyzks1f5LPY2t7e3xdpFvUR/EQ9Sa/3irjIb4+ZFgDuLua7iq1Xdu7s7dOdX19eLnf6qQZFKSC4gan5Jqw/2YuMHanu2TCxSc2BIFpPlIkNFzVS1CBLkLqvPXB5oOmS3C6/ag1L37f2i2kqlbI+l3IVy7DCPPkxlggy/pkq/w0JbJ+uqQ1hnhmvNi74OnQjXLNf55UzVI4ofTJA9PbEA+/c+m9aycgS1nFBLx4mTMZIDEoD1dPIca2U3NJiymWW4N3dBGqYIIWh6S0FGQtQypzhwTgK2C2FzscJ5oJUqL40M5JibCzir1YDpE/vjcPJYRQUjiqurq+64dHP7c6mYr9Xqq1LE79cmgUkvQHcvzl7cnYsENPn6229Adrp1M6OG4Yo50OJeA3fTFI7ccbXE+u8///nrp0+ftXG8RUJT0ELvpWzcea0mA0Y0saU5FGBDSe86xXLi/qJeNgQQ36xfAguNImoGFhqC++wOpWFYS8S32KzJ7VAonS2XwgMadec5CXGBfw3u1a9Y5mAMCb5nMkBCOn+0JDIx4Ni7j+H+AfegNSNo+oVPoR8jIWqZUxxoKwHsv23fBX5Bu6KNYX4gOtThiNaF6cvl5RVNLjDtQASKybfi5QX6/pvmsFKtHaVfA5GLE4clgWm9YCB3U0kwj0E7/uWHH+wjTynacKhB9nIkEM+XLz98+/bt+Li+PJYsECN3MPkUrysz/UmMCVSHPv8OBLd3x+WHb3+9/vRjOrE4Bi/ILX6MWgJ075ZOA6PO1yv95QbohsJhw303EtqHOPSuXKokuBzL2rkQQNezB8DdX0OY+8whkJijN32Iiu6UfvzXiwTCVcmwtgVSZ04MSsGwjVUtVrfMXhq9sBZ5XIa0cjnR7jx9+yhU8/1KYQ8afuRCizAD1TO4H4zZTsr16dMne5akw1+h5wZuC6C26JVIDPHHxwdUQqsWpuZYtyvCpqer9nIK6Q2u85/65eTg5cunzyxuh0Q1JuNfAqvqrX/K25DSotGuYsuuba5KF9l7bOc26oklsoJtgPAujt8+6rCFZMOhskw4KrrLOcUh3iQgRpABvg5IHesX9Dofgw923rENFNMCfC+6MTDwxmhksavlXDUxarffvj291uuNUsnW921kLBwk4dkQKvCaOqj+SV0ESRPkDLiZH+IE5vraAbVPpOpMZ6omt/0EuBz99u1rOn2m/c9Yx1Ncz9qRZZb7DtsHifz9Q6txWi7kEMfs61pLLA5diwQ6b51iaWmDwQF/HGmpHq9IpQVxHyx5LMGhRN9y4B7ahw6N0HLFiJD0cmZxiH8JgJD4VmZQYdtPqRcA/c7rK56fONSGpDncNOZyWIrPbUDxz87GUlar7Is9enlP3XWPqom7/dZUbUzKpownmm5TCLfUQzFbXLWDk90R9/f319fX86ktnlTttgg3gpT5CppiI8Di5urqGpsZtkYoW0yLCLBtNoPRkwGjHQlt1cj2eIxud4e9VOHytJxKxFZnFjVkU0H0aRZZO9Z2i/jug4xK7z7J1scMKK1VIjmg3epRf+ptA+4Ba85MXKERmpE03UVL3ZRRfBueBJaRzcJnBCS1X14+cNPY72s/rJyhXaqUM5nGjItVndMs5hbfIYpqYXw0xia/12q1js8+FTKx0/f1fDDldQV0q7alkuVCJVxgAr9AFxe2Ll+0vpt6i0dIpgGYbBnJ8VcAwmaZaBrCxqY02mE2NulUy81BmEkmyY5M7acKsj6glg0MwtObff87TqS/Pw9KR43zVHyy0tZ9bPoyLBXXx5Zzu3XmYy8GkeUirhDJvNJgT2WwLJVIQjYO3Fd8a0+FDpPWcsbRUl/OLw4JWQLgmgWKQBB84WE9DPQBqbPng2OPjioVB+sXpzMkdq0rAqXV68e12vg5cfL09LVRLRYyiyJakFj86EcCUjHmKgd9ie5ODORtSRYswnG/7FIiGlWUCswNunB9sfKDTYs6BCQ5GAx/+PF3hPAKf6P89/bWwaKU1RW+Mhf0MWGHicZJ4+r6iqkpboa5QPb6Is6UbJZMn59blm6IiAMPy7aqc8WzLMyOB7I5+Hl8elbuZ2LUvpWfMvghGOsrlm8ssUfNzJMM9qjc4dSy9QN3T99rRSHDpGWZVeQZWOYaB0YiATanclxM963TfmkrMIOXxjHerCuVI3XWIx97df/gUCOcMD0FWk07klKvJIoo6on7WiP/0M0+3v1ycXG567ZAK4u85gganS9mqqqgBsFm4xMjGjAdNzIoEfHHD3ZHX46TAXwNCAif4nhOBkCzLnujc1kO81KVTOhyQypmB+VKGahNCL8kxyxKTVOHlUo512jgKZg3vB2OhngVBpt2sBR+7TAfeLi/55wBUhn86BuVOVzP1+bp0+LfhcQ7+/jY7qfyJ1f5t0TCrcf9nS3rrjL+9PR0cXGxq9y75Nth+HGgMG2WDlG2/JX7cu9+WV19iqiBu3uBu2KXSOFTXMg58gwW8osfI5QA6AeFIs4QgSCcEE/njncVey+Nzt9+RZ/gkFhSOrzWAlhBPkIpKe7GZ8Xe+PoTiG1c+1Mj+5JJzewuos1776lbfHqB11qDDqbmyGxDBmjW2QCNuRZ1lSklN2K7RcjHQAxUkglAPGlZFMrl8mjXmWWhjf/85cuvv/xCMI+8hdrHh3w+DvxjxsgEFXX9IMV53QnQ+T//+dugzz0Xh7YmYACkns/l2duHofAp+60zmetPVz/9/Se8Q+LlnX3YmiYEwfnWc9DF2rv4bBSjt0rqAAAgAElEQVRwt24o/sPH2XHuppDv7hbnh8btLmnc1/xtLPofFxzsZgv2VNbdLKJ8u3CBuyehuag6KkokRBcyX0ceC1nu8WMU0nTbxIDpjLWoG4Ea+FNHU4minT6dR/DH6enpshLR9ZdwLpcTh65SOkfSXDpl4roc9hGR0tn5+SjReXxqjj/eOZ0n1r7bS8vtG62e5tPxhZGwYGWFwbnjFRpvA7ajQX/jgF3+dF6/f78FQ+cLhVK5clSt5vJ5Ddkl/fxFndc+jiBvWHnpaObaru1bMuk0GJ1/8zTYKTt6bj3ff3/AJIdU6OY/hh+fv3zCoubrt280JbZ7kAQNPdOAuQ2qC4QWlPELb3fq8aWfe3vtXdSf4vNQt/+7sVdnrTbu2y+RgBwudjMuyEU8PLngwFsUl0XcwmIFBO4uCx6JNL0RXY4dCe/L2exZyKakZpsvGvX3PhrKAapKVI9gEgx8Mc9V1i9zwkfvPvcc8oMth9N8bJv/ypRQmCR2E3Wan/y1zdMcafE+lfg4a9SAg83OMJHONopDzKYXI8XPXiQw/x3kK4rqWrmbUdha3itF+8ftzTc07Ji+/E//y3+pVWsGEF+Vm/LYMJ+NVRJbWA0baN45i0CnYjWAycV//7//+6dP11dXl7/+8k9U9WB3phk0tOXJg1VeOxxGGR/ey/nBw0UjPllpN74jGzN2g9E95tJ5eOLtQoTV/dVWCGuBawee1lYgl8DdPecOhbJ+FSFpc4Zrysac5S7e74CYgBRoJBlZ8adOR4CPRtTqlva4Cx/A2l/YQqQIH33IdtYP+EgsRTEfbemxaEj1rJ4Rm43heaLz/eQorSCmI5UZv47RDuylWITLP8D6pOQAXy6F20dIlbOs+VLcUEVZ4viXP/8Zx+2N48biOGcnN+WiUROcJdE1Zr7eTGD7fKAl1XQmzcZlVq4wufnlH7+i0cfVzA8//sB8QzA9tu8Q0W4i5qnNVYH5V5YZhR84x4Ef8r1E5enl5az6kS3GqN2PADeSBjcD1NKNZB1n6lMCAfuHwC3dJ9v2yfwVyEc5loG7v6ztizL/Jlrq83nNxrCF8PhxIoG1fg3fUgejdzqvgtQ/PgA3KNSxH8AAZjWOnM8SHx3zAdv/5OkDWTd/TySWJYIO9izRHCTH3+9uM9niyelJMmFv+x4ws+Xs9yPEpObWdjLaYga8Tq2mGnOeFwWlen/+/InFjUlF9SZM669vlt+E3uqI00QyHxizDsDzP3/5tVwRUxnhFuA+M9cxczklLTOVzV1mjjxzkWy+V0bvzat6NpkYeE7tO8Em5eWb6e1KKEdDx9dWSyBQy7QomSd6W9zEXJbDXAKAO6lcJrQQ3cqgCElb5r3u/CyZ2NrA3ZAO4z7uLfBXxw3b70ABOMHIpDPaSNe3cPd991IoH9fcOUwkDV02L3I0T3+Y+v48zn48cl6s769wgAmRqQnKIk6BvRiLo3HHdhwozFvsUvDx8tPf//6n//TnT58/PT01X9ov7NdgomrsN7UTHWmFgjqBVYzn1YUJGe3FeJQwyVZ4mQvUsU2/zCKYQrCpG96q1RpK9/vbW6YYP57/joTwMxRTGbKRFQP5Y0qr6EseqoBLL+dieniwqJQeUruNOkxkv3OicKNSzGdVmvmSuSXjPp6pWFFntcCUKeeFN7v7yM6Q+Cy5rf18pg5wQzx6amJb2UCMEsDdssY9qFgN6kEJuUy/7vxcsrUl0XZBOuMxUACY3n3rMtQrld6I3XjlSiVcIeKm449/DJfk/lFzqjC59Piyhin20bdvN6Xjz7WCWfO6f6IIrUTo1acDgaiqETF/BFTj72UswF3nxN7TYin17//275VK5foTJyDJaalYlrfbbVw9ap/rhIj/RgHQkkprvieQXXD57MLfjIbRsyDuxDrHCBC+9IXiX4dqsmxgJebb63Pz6fG91zs5Oz07v8TanmjYS/UHA9jX8U1ZqqFZEycPuTG9NPL0dRMaIfvcn1/7vXTtqoFlf6DzUGfStc9r+mYNxZpmtfDXOmcvvC8QNB7DoGEQ83QT28l4Etf6I8/3T+vP30uO1g3EisKGKnxQ4O6+gFaF9hu2mVz9crvWdGsVjb/MGNVRBw4GH4ASDF8wGADEFIslT3pcH+1l3zXua6pnDJBgyu44f/v9n6XGj9XMq4aPa8p+J7NRRugKzAqcFeAtF1AXe3EAscK8UjDusSy/u7t9f+/jqxENYv2YkwZq3GAbRlo2YYPgcebIhmwN5aHAI5Ne/RU0BMfeV3t2T6W6BqDR8wdanxzRJDwksDpDn68T8hbabOB+abdxnIoDyny+AGAnm5l77PGY+GwxMe38nusDpFVOALyPBrqZTztKpL63BuXqxUWqGZyDOXEEJ7eKQqhSDoN3VzTcce0uliEhmoZxH9/EEliTBFxV+CkvHqv0NJnFX8/A3ROfFhn6DtpYxr45XlvCCEUTFml6Va7ee0+c341GoBA2leJtUIMGH5Jyw9hCM9lBG3cfgllTkmKyW7w4fX1rfrv7Xjk6il06OMhd1NwmRTdVV/6htE5i1z6kLWCXspBcG+yCwh/uH25vbkHV6WyGJlMsqlOUCoVKuVw4Pc1mUY6jBOefeJYkPhnxC5rXNxIykhUtwkDzwOpq9ej4uC6+4fv9bq/7/tx7674B0zGOISZ7Ugv5wsXllTiJV0p9ZhEz3mShIIWbSEPjPns1vZNGp+x2pgFb/bfXe29+1E8rg2wYqH39RXXTDfrgaqHn9EHBMYk7rt3FmmSkTjZwzDR+uUkJmPq/TbKx4bzdV+lVLdAWuLvPInxZbDLv8EsTNsXwpRM+xUSC82JY38dYVgGJEdtJgRjHdfGnHrZAbOkt5FQ9OrKNGr/wJYFKKVcpfX59ebkdfil3/+OoEu8Ps5KjCbZT//UFAkbpDcrmTFTTe8H4THE1FeKg9jZcZYC+3956Ly+yUZuLmFzMACBSrVZZv+KRcOYDtLVCsaTQORtFJoCeydXV9dXd7R3qeRJDXCB/JlsqycxL+NF6+PkSwAxk4Zlgboj2MZiZSIlTy9k1GW12YpCmLM32+yhfvyy/gfpmhYjv1MQyUjGsgiV+MueD+kkWp1mTBCZ9yJpy2/VszN3qclno4XWgc7TlhCGHbDj7kEsTAbkwBRQmLVNR0cO9dYAOStuXSHDCC6pEcfw8NeE1xd3MbcR+3DdTqG3IFdxXSfz2Mh7d3HzDLycm2tvA1fbwAKAAUMMPTU8AsELu4GT04sxsUZlPjGgUxyBvlOqWzNOUuJaPxCIJ1iz39/fdbk+oJUDqw+ur65fX19vbG7LAferFxaXYzMg+76Pjhoe9xVjpQF+TBRtlMmnm5PpxyqT0KBPUxB8BUJRYCjmNsHV/kcn9+3E1c1su9NRn2ToO95uhgDXDEqHHwH2b6wxfh75rmzncLd4yMo5shOXN5LqRovrLNDQBhUZovhyAg9fXV1R3oA/aJNayrOPzu7XtM/Y5MP8Bw30a44McyP7Seb99ydaK42Jm0fwj3Px2iBqadbCvQFtpigrRKpsTBjOwOy2Ht0ZxCAQcG48ub7TlOmlxx4QWnAPIypWyZDlOUO2B7AKjxXO85wuMDlmdjBseae/so9UhiuxkCNGAXaJu9yD98lHovL5cVNupVLxA5Lk+bEOC5RGNkPnJ5DawGfMQSyAqCdiayoSf4XJrCz+PXacYmoxCI2SS6HA05ER2fEqILawcpZkEqNFdbrNqzcS+nExpfozvQ5cANaFaKVQTg+fx6ePNv50eVwuFQui57BxBMLMBzRERbVN+UbmrU0gFGZtU7rQs9p16LSOgWcglk5lslryYQ+HBk4us0C4zl5Z3EBUM7g29w0y6NplIQAJSNH9NDHoTsmZ2yYJMZJoQRSdkzsnzPYzdJ37Mv/3bZT1eFPIsvS1PYBiYbTmfh8keTW8LO4Td/RZRAvet67e38zOFI6ZwqJgkREtDHQjYfX/vMQiL+WsigYtGltpNsXbpVvnl2CWGd5fXWvKhdnX++Pjw2BmfNeq55KFPmQC7hjmJQrvSoLhAG0BhrMuNb01Tw2uM8ej2Rim5Mb1h1zcmYdVaDS07ljO0Yr3JRNMBtUPfLU0VD2aMEVd1A0mM4vJJmY9Bep4U5LXCHVfxW3e9j4uPrc5Z7Wu2GqP2rfs6wRlS3pNW1ruFGhs825hCLIENSCA84L6yyWygdFubZTjCCofKVEgMw+KkcfDBkSuy5wwdXirFOvvenEjHPGRa1vjvOiRwcnKK6vXpjVo1apTTucyBjpqgWVarNMpVcF1+MC5jLkx741fQtUkjpSCItw9kJGd2CkGm1zTbdptD4HN4j+ERcmSqcbUn0nPMqB4HjT6kNBG6CKE8pcgHVkXhrxE2fbfJv8nm4GjYa10fs3QQdwKb/BLR5c0BTKXSStunsKrlgXZl0X2+mLInCQQA7mE1AU/87nDkcOQVDhUlRoZYcAMXSnUGYPzG4W+OgR9/58bAHI68AzIdUicZcqHCEc2eUwGUnpSGmFY1m62PQY+qpew39rzUC8WjcalJo9RjEC/1kItAsLsKF0W2OQn28OZHN/eiSk9hSS8JUbeLm0ixaeF0pyRO2TVw5xVN3qOlDNtqZ8zoZTcDuEONMkiR5BLa6o/YBS2USF5s6PpI5O4eWscnR6Uj5hizsmyInTjbqCSwXgXTXIP1VaS4KvoSW5xIScAdcA9eSw9R3CFILQQS85IHpuO8Gc8vdBuM6zibY4zH9wtIYj6izZO7WDaJ/QYHz1R1km7L6JfNOJ2dBHAyftKoDYeVZmc07I1OSqNl1yh2afcgnIrHhk4KoioymFahdg5B4hTSPlYxY0KAvUb9BHNjmO5eRKTFHzwIW1p3t1sul0j+3G5zGjGZcqASZzixa7xQyE/AtWuZin287GaZXLIzdTjzBUkoPJtaJ+S5ZB/uluwU7LyPX4bZq9NKKtGeFiL+u58S4JgCO3dMW1lgU7sJh794JhCOHHeCihVwD71G7YQkwmEyHNmFQ0WViKG303lFGYZCHaUYSJ0rm2E93TETx5fhiGqdVFRxMFdYZ55xXgsSAMydVgX5Yfo+HCUurj6lEgfheYa2JscbyaXU04J2uUTlPgRwq+NIzbIqlcu0WTzDmAPN9yShXfd63U7nrdflt6PWzXraQft/+7/+m9qawjkK70zRnx4evn27YTLwv/+f/weq96+//dZ8eiIL/nE5b/yAstn8gLnEkOWTqQ4eNiiFZkxU+YIc1P+4j0x5doxjLmDwewz9vt/dFk7/dJl/Dk4tprD9Ejj4M+B8j9kx4t/+2r3IYUa0QL6/+CK1g30OQYIhkFDiBxjpIxIZYlHugdHL5VW+X8LKe7u/vzNG2W7e94c74Pv5xQXw735wPG7++8X5OT5X9qd4ViUBpCuArt8pjbsc9IhH9jT4G2W5AsRDMTtRFytgN99alsAdK5S3tw56epJxPxriYT0F/mbDKBPySrmCk3X2pQDc0Xvzy4QBq5nXzttQIWygdrvdbjVbWlVOvrV67ez84vT0FCaXece55NX1tRGO9p39CkZMmXJMedZxFHwnWE9QjHTrvmGXzlO/enZylk3HqH3dwt9Ufs1mk8MKNpX7Ludr0fBdFCeG+y6EFFkUK417ZJntF2F/1X1OBsFJMEaC1Dk0kV/GcfHSmEziUdtpqTp4rnOF2JkHcMzO8LrvjGIncpG6/zi7uL27zebyjfqRtp/ey3KDdAHQShst9jDqEnU73tA1/mZKSfM1l523NGcDyutXNPa37huHpBIu6Fj5i9G/GsSzpsTRCtCnqtMhYCqDiU46kz47P8vni7l8HiOWQrEIrgbxi+Z8NGo+PT89NpuXF3/+85/NDHAvDKjjnMhCE2QLzHu/b9ivS0HEOl93KAzk8k/zg239ArW1PT49v43yZ9fVzpSxteUcZ7RJCTQajU1mf3B5+2jgJInhfjgVJQbunuToo7Iu0g9IgnERpdqg32cdHNIAdHa7oXJzsogNmOViCXb1OT6Aadu+XCY5vLo4G4wz379/zZ//l9r4ZgGqbhvDvvkBRtNySY5eGxsZ8C4lzWQz6M7x4UjjHQw4wnN24XcVCA5GnwUpJM0JqRMXMZMXMhbqwVDoj8evr6KPH41eeOzhDjIxPr+8bjROmRtgHUMgWkmMZETRP9D/g+3R18u0f0H4nddXOhbygSBpnx6f/r//5/+l68FcHmrFYomsSWJo6lX+gtwp7AIpcymiu2d2dD84qWU7pQKoPb4OSwJo3M/PLw6rzLtUWo1CfGCRGOtbfOYYuFsIZT7IR1WbJxBM88NAiOaMsbX33mOQRMeFWp1dOBjALGZjPIfAskFrf24w2N2fwuxRSbLJj8uLi/dx6/vdHVrh8J0abVpWNFrQOQ1ZMaLaMMA2lcKMjToJbsbTDotmZjZB5zffvrF0RmxzOFvJAdOysKaNXdQfiSDeYoR+rV4X7TtGNGKMrtXqgqTJ5f9v70y3G0fSM02Km0SRWigppcys6mp3d7ntMz72mfGtzK+ZM3c5F+Fj+9fYPt1eurOqclNu2ihREimR834RAAguALEjALxIJQkEYvniiUDEi2AgIMfPz8fHR3sHB6LVsWFqPO4Y7u+vri7dqUh8cg9wi3ky2MEbkX/1ww+vXr9+ffnd+3fv3/z5z//8T/+8v793dvby9XevdUCl2uFXNqSCR2mWIkz78HbcvJ0enLYv+D7UtFGbGX/l57ibWSzaqjjie6EBDJDJOGkFiN4MLxTuq+UQtqKsxiAukWNBzweljheUYqALU0jR92IxZrxXpbvbxWjdmsTWua3xVnknyCDht4SrEpd5Acq+U388e/kSi6Kc37a3W7WD7clSQRUgD94mytQUXMmyfqK8HkFtMnCNoe7JeNLZ7uBidz8Gipggwa+urnAb444V+hhhMeYtP7XZm+MB8UO9SzKYBqOWd5FU1QaVfn8/glrv4g1q/b4W7Xh8FWFxz4C3NziR6B0kDQOwP358fPXda0ygR4q4ZzgaHH333Xc/YXvz09tffvm7//536opCWcmz77JNZ8ir89DqUrRpHCLNr7Pvmw//cbaHXMzXwEkjLcZpLAE82YWlF4w1r8qGoVlaHH9IFUbMfqMYgoDCXdehmIVtVcTIsUCp3z88YGAMfTGGxXa2u9udDh41Wxpvm9f3yCnNo6jc3vX1uifVNpIsxoVcktLEdKad2mR0P/40/d3O8F8xrFuOjEFHQz+jA1ND5KK4ob8xfA5HzFjBvHMo+6Wc4hHVi4uLFeEOb9ZEeZmloufc6F5Rfyq9rlNTil1UNNoVPa0FFiAQksYQgGhsjI1jMF4edZ0spY5lbZA0/DyOH7GOpLK2hV8G8KoHWAtN/+rVq7dv30pU6gpStwxaukuymAW0FGFKh4+17sXw8ah33t7D1B1u1SWgp3VVN//MeTIENgqCpWTy0QcZNa9LeTXjMGwJrbc6Wiz4hXp4O0Snh04OfR5+jMbybHoUbX0ycI2Wkmd0lTuB1ayj5Hkj9nyu3ChZKUqY7k67W/tl1Gp8+PC+vzfo93aKYrmnnWrmt8xoUcodehcrtODhVGhnNAWtpij41bAnJyfnHz/ihwjnFPQ9xLSORybLyFz5On6XU2rcGmwW4awluVr+FN7RzOhQ0OrQ2fPzMoVG3r+GLycJ7Jyfn+vpwqLr8ZDr4yN+/4Nmx/Q8pHl3e4cE5aGaugz/2wGRgmxICbcDmAVku6f4fTnZe7r7cnawU+f7UFPEXIyo5c2pO102xqaW1sZ+1FTDN9gVKl+JVc+qCfdQlD1LLGws6M/Q/+G3PPSaeIIK3Wy73TnYP3B1ex5phU3JIxo6gwCeXsJAo5pu5Hn9eJ7wIbixjKJE6pNeVU5h6gj+7p53zm8m/e3n3XaxOUKgox1A4YlqF90uU2Wgux8fHjDzZe1vyR389r+zg3ZDZnmpTT9GKgIZUUGR40lXeegFm0yeQbTwpcS2TGHHPrzpOwKR7xhlxzx3JazFj5rmjgl5UDyQ8jp+fCI5TIzRL7iFOoeluHAw2C4p1mqwpLu7g+VrED/8QMdry8Ugtck9w9OzXo7GiTPxnWmtcX5xd3C4f3jQkVk63CpPAL9Rg8HGxngtp2K3LGuzZJIjGgZpKLiFq55+tbIKwj2xGhM8IjXi9TRCj4gubvrcbKD/28LTM7pn3VCBgyezISKeXiDgWi/ME/HKieWLZ/l4IQWPg5VIl/1FiXQ5jrIe7zbud/dqN7f3779cDM5+u9NYWH2lKLmGQMdzK+jAYLAMlGOTEXdR29DN0iyoU6vZwc9EHz9+wCouENM4iwnxkMxY+B1B0Bti5osEkVXhG/AD6Q4djckxuElAhKoVklF3xI19eThVVPtUP5gqj6Ziih6eeb8fOd0qVkC/vR2+fDlfux2t1tX1zXjy9OLFycPD43B4i4SQEQh3jKzDDFW7JWf4jw1JqBYvxRcw3d4/39w/ng12t2rrJr8JEW6VIyB1Puq2sYVejZht9ioTLxc0C9LEcQtHwKdW1ssq3H3ynBg8d0ToFtFwoCfEOscYCUOXjKEwTFJHh+r25rmfmL2eKfAEBg7DQ1gumOVjiXHehs/3QqW0LtI1EUSMfU1MhXPa6+3s9V5fPz5dXD0eHR5sb90XKwsYXx9PRLhD36p5LZjf0oBux6A1tLKS8usrAZTw8fHJxw/v9w8w43yKZRnRyqjmxZLs9WcZ8n5S9wJq+BtqHuJZjayLWpdp7HoqO4jBWc2OkeVk8DgsJsngiVXswBI8jQrDrm+uX716jeCOlEcoea/TZIKnUQdHA8y8h3DHrQdiRkCXN8mcbCo55C2NAsKjuZ8+ne8c//hqB5KdL0JOg3FR4wza1SaUv/WXq3fkFW681ciBNxmeiUAgleY1gh1JBAl7KXmmGSQi9FAYq8KPwnh4Cx0e+k0Mq2OVRqzZ4Bnv0okgySwF4WEMAqmtFzYvyPnesp3Sbsdtu71jX5faslsJjvc7T3ud7avRw+X1h8PDgR6ELkS+0ELgnh6CWka/lchWc2WaWDYRy6U7s020DoYm1qu+QHMjdxisGhwdffnyFU+LjscyJV1rlFmziVZI/aYHOY4gE9wMIAaMrCMgpDbWilGfsmA7Vm1HVJDziByiXWQ7Rtvv8UKnO0x6bzbbCIMXMR0dHw9vbrQ9EOiddhvzeWAA9hHn4cHhH/7wh36/h1U7URkxgK8Mxq2CGm6X+wLZZMHaFAbYYPG3h93B4KTD96EWotJnaySuhWwTDJda8MYb8Rqdk3D5Ft9oHnTLFj4oQ6wnUFzhHupCWJ95xzVIXGgX0GXKQNVkIt3SrCYzULc7+n0oTlR+O0GS8QvPc7EIYPJurPCxAkvZhyp/1XZHbcBDpbQxX1Gt2BhxBA/12uywW5vtvMTS4zezk/2tr7gMI8STcRC0GNCdaq7KDAs5YqIMNgycowEZ3UE6QypvoXuTuSuPj+jkcBIPg9brHexrRYJFGLGye7PVOD451sJdiWR5a9LW09YYn/CqJq5Yal4mscsmqv3pCeMLiBBBIOXh9xEz6zHYPhrhEzIbuv7m5gYrtev+VUkgEeGY7Pc8vMX8eSz2gyk0u71dTO7BcPzV5TUG64+OjsS/VDakKYP8CIJEsYskkiV8NXyYtI/P+nhMKNnKnayZjC03Aqh1uaWddMJxqriRFOJkKGm4pYivWMI94eL3jw79KHpRrNKI3gi9BbolWatup9vry8tKQ5R+GL8hoqXXkASKtV6YqjWbq85iM714FJKPp/fNVngGDXcisPm4HjHiPp3dXN21Li8/HR0NMCQcLq3MfSsVrd+fqofcRbzj3h/usAUCe3iDp0I7euUWt3VCRYkSvA7p06ePmBOPgJAp0P3YsC96XfSs/IdHnNJD39YcGaXd0ZRBuOMEBuGhryHbH0Yy3D6bPmFVymarjbVrFtS2WjAe8dc6NUzm+fb1a3tbbpBgDKb3YHt+7nW25eFUXTt0umgqkR19E+HOQpx9xPnp8ai/fXPQug158xsnWYYtGAF9N1swo1MwN3hrHbi5TcFKRhmPgPnCPXg9DETCPzqMiuF9gZiejh4OHSBker/Xx4/a4ZQ6DPFPJpCl9JQwASiehGM0ILrFirZ4tGjepmZ60/nF2FI58jN/TYKQq4N+fbp7cjFqTobDFwc7GDxe488MJwxFY+RbGpYaBt1lgyzGzwXYgYGYfLKHh9frsF98rPuxXJokPEKDYXJkHHIW9yqWcId6VmPt+Eb82NRTqDKJBtodHzLi/vjQ6/cQCrNkauMZroXR6BaTZZp4RlZMEXsguFfTvr6+wip73eNjDNV//vQZAff3D6R9q4uCtyf56LsFmViPxDBpUPR8yNJcW0p3T53hZPdF5xtuT9Z6yM3RgMslt7wbmTDmhUWvcpUszYBXVCXZGFnFXUaZKdwD1ihXPnx3vaJDN4YFFrAsMXoZ9HDQ6512jDeueyXjaxtPZkagEHMq0qOxqXp6nk+64U44PsjN49501u1+ftyfXf37yYsXWgqnRzJwzAtIoYsxDUbPrIPNopQb8nBqexsrLcpouazJDumM+aBIYAUS3LemM8xwubu7g3CHOsdyjFq4I5QIZZlvYwl3mbUiM92tR1NFuKuXpCIU5sZA18ssmbs7zHnHpBytsSXtFeEO4b+3h1VrJXJcPhcXlxicv1Tvc8W9AV7GpHKIDyh2tKayQd9j7Ugt6AODWuMRpn6bfdcY/efZ3tjE96GqnK+xO5DTSukGCqU8xQgaPJEi+ozVvEcrzWqURUA21YBhypVhiHAPWDdCUFsbo+5X8Muv9DCzKTpOjDQFXaXRK/G1KXl5pnt+BCAp8ku8wCknXcHraTTxkL2nO8Pn9unXL58b/V8ddp8bdcOW965vYbhaxsLVLBToeLQ/WFzmlay9KA9vaelsjbijvixiwtFWawuvacPDrEphz1oY2ycn7f4AACAASURBVFZTZXCI4FJMjnBHIpDSasBdj7ij0ZMolXCHjr8f3U4mj2j9EAotYb+3h/F/XdCSrP0fobCMjHsYHvPNfv7p536vh7jwelSdrKSFWLDJQ7HjVjvutKXxrPP1enzc+1jS96HGuKRiBEXBLm6LNQznVhwW/Rt9hIsLr33I1MQIZVFkwv5sfWDglHO2vAD88SR8Nkfh7hRlYllajRFdCR73gmLDwgqqp2lhWAs/GWP5hViprqYUKzoGzoIAdFIWyTCNTQTSu3qgRM/OXj7V6t8uRxC1B11Z4HyTORmdbzUbmFguIwYzPFHahGyHhUBxenZ2eXmFp2fEVBxDw2uLbEwYa4eD3O7U67/93Y+Ybj66v8OMFAwxymyZVhNtmgh3GTmXSe56U2PgmCwj6h3PnmIJdrjjrKz/iJH/h3tJptnqdHsvzl4dDo6g/pGKJCLj/5Ig/uP3ActFHSIGtKQY9b/49g2vPpLnCpSxKmKclLTwfG1vbx9hI2/Xk954dPnyAAvXGHbrFTlLhga0a5hj3oqDcyb0jtRZbNaXPkj1sxiPMEUgnB3CVMvHijwggHJlOnmwmQn3gOUVLoerkaJfxCgReissvobuDH0hekfciMftv1dTCmcpfedPQIQLtwoQaNbGp4fbeFXRly9fWgd/cdi+06o036xjhB2CGSoa0h2K3Rksx7tRoYAxho2HRTHCsFpLtXDXEggZgdDXrRweyFGLXN0ruS2rRupWSvxbQ+AQ0rJhLUiIdSh7XADTWa27u3t0fNLr72FteEzVccORK2TxMnFcYBjU//XVFSbbfP365XBwiMZV7hbkXkGPt4twh1FYpjNaeynvQ/16vdevH+xjzL4wqp3Nypory6oB0SrCmvgsJ0/WdTychuWYvUMW9kwchJ64MqURQX2FzbQZGc2OanrCPSz5oHleihedBn4jw4+/eDZFd13ymvDtHTxUGjRGL39LKXl5o3tBCKxKooIYTjOjEGi3MPp+9vg8/jTC6idPg+6zUplRokoqDJopbJC5ELvozKBzIdmht/FcaLstj4BCi6M1wyemp8topeqOrMc8VXOknTDDBuOL2HSVxlR22WT5R5mII6JdzcjBHppEfCKGZzUzEBn5b3/ztxjdlxyp4Xkna7rnU8PtVsOnXfTTshD/9/cPeFD1djiEOh/Ku1QnMFt7lSTxJ2vJP2HEPZp+Gj3OLq+vzk4OGninVKG2bDqKqkmT9VXAi3W91u/H7vHXJ1lkVy9ca/OUWg3LoOENktHU8reWZrqOCQr3IOgiZsYdNboujKnjR2f0FOiW0EN1MfGzE3tMXZvmTimisQxmKIEI9/2G5oRmBSbQaTy/7D3jBQzv3p1D6Q4Gg8BBU/GIpksJ7hpUL/Y727JSu4hoWbsdz6q2MDtFxq0fx3i/myh4UeNrRLzqhGZ19SCrPWTetnqmdUPmkNTy+KvMjpFZOtZdgZrognwioEh2bMpFrMEO/mY1PL4PLY7bCTzMikkyaH4xGf8JruOxikfCoRFWj8HKpByEwo8Jodhhag6eTGge/Pb1CxhYMNUeKqdxPCfbNVlVJY5BhoW9vr5+8WLbMKMKZU7wGlbM2rMxfwXKVrgWdqUabkSxEiKwgxM1hnOwCNrt3S0mciI0hnYg1OM+UbpkhpPYkjsPS0SAD6eWqDDDZQWzwb///vuHx/HHp++69/+538/nJ3UsyYI1rPACVIyIQ23fP9xDxGuVrNo2pd/rW3hkHrNN+rM+mj5rootMixlDfEPHy3A6lDIAQDKrhkuG4VWfYzVjakqMa8Be5DiS00nI2jWyJ+P4CCR7ehNn7Q1aXKarYzo7HhBCyvjAnBy4wS884Q/j971+X82tl4ky4n86g2XqEgv39qXxU+3L3dbx4YtO8yZcodJ3DAJWVYkRA4IaJXRwWcXLDUMHJhC89hhVRTblL0i2DMlQWOEeJGub8Hif17FLX/X0dDeSVc/QGaB7wLjU4HCAfsU7aPgziUYWPnmGyJoA5lBlnSTTM4nAdqf9svZutHt0fvPQrd/0+72M2wAMRWMxRzRuEMMYw4YKV3rabtgWrZHXR9TrGIOv7eBbxLTe0DzqFlLvYIIKNv0hAh6bfFj/RZWjCPCY53SGOfTNVgsT22V+jkQpY+ryKbJfTa+R6Ta4OZAP+Zb/ErGj0HRcsBnz9fGIv4SV24d5WMykx7svtHuQkr+8vnvq//7V3tvgQYJESz/ZEFissKHTTFYDXVxcnJ6ehTaCAVIlELyKJFsbUsvUxgxlk4+Nwn2jnbEI6djRA2GoBtNfEBf2MS6Ff4cHh8m35unmJhYKBk6bAKRS2kkwfvMJdLdG3b3a7WPv48dPe6c/9rayG+hFg4aG7s2bnzAlfHg7PDs7VePXulVSKtiNDz2AOoMhbhkml+Uj5QORYHDcOqf0uSh3NVVFVLsMlk8xIi5j4NhXEh5nZX86PTk5cRwkjN4wVu78g1JXYl0iUl5hkXRF2NdbHU/B1tE+73Yh0MUJ3hCN3EU8PWF6D94kFaTdRpDPk+PeTvNw652dFysFflWEgK73ETJr18WFoBxxX8BRuIOAtWFt2ZuU2Y35SCQHq8J9Y7pxISEBNPJYYEEeKJVHtfAGPjyh1djb20e/FDf2teFTz9PaVOloFgFMSzDLIFqTH4Fep9Z7eTqcNd5/u9/vPOv3ImVgjn5wEyPcTxP1ML0l160WCo2hOKBp1w7ODhzERSSxo+AhoPFPn1D62Rn5VupbpLTS06KoMXAuc8chl5WYF7Ht+lMKXcbdZehdvpWDRQP7rk1bAVxYDEcLdC3cET9a88f7h6OTFy7v63dH463r6eCk9QXLd673QVcS8CagLw73ebhgjvtJgLrnDsX94hFYLfvVPCy0WKunc3bZmIMg5kO4I56NUUXPqo4aHQZe1YdRoMl4gr4HY+pYgHh3t5fK84Ip5iY6B4bMncBwOMzdBhpgFIF+/bJ3tIMlDj9e1yDfu9sZ6Uiso/769evhzVDdMFgNFtprtae/deutVm9XDvp4kZ6e6yJusnKMbPi0YlMyHKJaNnUrIEPjK5sl0eFun1YuWsGrgJKuOq1jV5/1vf09TPuBcIcXtO1Q7XpmDaYA4YVN8OO1IaaL2cva/ZuX+4g4I9pextC9TAS4qkyZSjNWXuwmMEQk65rXEMET9brRfHhYHXFPwATEC40+uhthzAerNGp1jiUaodYb/dQa643ZTSBnjKLABPBAc4Gtp+npEECzcXBwsF/bwojdsPnX+88/b3fivvjT31KMgGNBxv7eHlTs58+fX72SN6cqqQzVDTelh2UJR0Sj1o0R9a7HwUVBY19/aqltq3042pv40DFahugTKqQ65Qypiz/5U/5tV8tRwmLX/hBf4iSveJoOjgaypIyaea90O4b2Zal4+MFUGfG2bps817+OWoc7n7f38XCwim2dN7qRQAQCeHok2jqkEdJikLIRCKIeTWqxkhHuyDWabMwhxjx17ECp4yfZ3d5ut9lNa/aLrjhBcJetijE/EQlcXl5GDMlgZSeApzcP9jEI/PFyeHvx7f74+BhvJkop04+P49ffvcJPjscnJzc3Nz//9PPR8RHWqURTqWbKoAUV7a6UrXzhUGlp9BvYn2/T2mwLK70oia88yET2tZvIfrUpuSzx6D9HqtuntScVmXLChzLD6rLwrOr9aLTb62KuvMzTUQbJeDtG3NUbVQHN60fUm+f90Zf/wHtt+T7UlOpVxaO13k5QcQrMfnoEgqhNq6VMzwgr5ujCHe31ZPx4Nxo5Mx17u73DwUCPFKVoeBB8KSbPqOMQyLPwcl/DOw44hs2AAEaTB4eHs4ODi1FzfHN9tAchmrB8x2SSvb1+u91RU9WbR0dHGO/Hghh//vOb3d3u0eAIc8cbDSh1faVAwluiGpoaoyH1OtpdnJrCHX9quF2+tTKXwW/Z1Bozelc9lmrrdqXL135IIlZCtqpH5JYbRPnt7fDudgizf/z9X6HBx88FmGGPTgrRy0x6WYFmgqXed7qyuuVSSU1rW5++3vS6z3gYFyGWzvKQBBIhgKfm8Ir0RKJiJCQQkcBy47cumiTEfQjhjh9DsbIv3sOBLgedAj7xyxTEUOpKHXkPgmMdIrplSMD0QuKIe4aVocBJQXoe7T7jvW6Yx/Jca56cvmrVHhPJjxLfU0wQx/Iwes10PJSPCYSYLYNZhfit/9Pnz5h00um0oYwxWbwj007Er3qQVHS0Vu3aGHGeIR4l2+3Bb1uuy7cKZT2x6iHXbW3uku3KSCzzNcbgOkzCKu54JAkrB3z3/a/Q5kMeYfajGIaERetLQup1TOL/7NV3S6BG49rVzfD0+LBRmyyd4iEJJEgA11GCsTEqEkiLQBChtEnc+wl3LPmC5hstNTKAQZxWu9VsNA8TX0/dC0+Q7HmFpXtaBApcKpzjnlalKGO8kO+np6cYLf5636jd3w0O95r1uC/1RFuK6fRQGFijHZteCxIJYR/PekINDwaHGMDGG1Vvrm+urq4xVoJTeBmTEvHb8IAFuFqtdrOJRSFldci6GnmXcQ1oaFmL3ZLpUP8Yd5cPNfq+VrWrFSPFA8bLocVhG16Mir+Hx0e4YcYLZqufnp21O9uYFQNpjnUF8BYodAowRp5MVbcKMtwuIzpPCI3ewv1kqrwP9fqh0T1+dYz6QdVexovEpDyhHppkDm0hgRgE/HUW1ndxx43fO9Fyox3Hhm4APxNjua799FZpdKftb6jbJ/dTJ1DOwuCqMqlXnNIlsFWbvtgZPW/vfbsa4qfFw34LmjVaLqGRIbWxjExDy3YR7ms2RA+BjlF5vRQ7Bk8gpuXhoftLrAEPoYzUJSQWU59vLehsSHycsieqyGg8NmnKVYMuK0KqPXziPagQ2eplqHW4wTDcCTQaTcyEwWSDFpp9lUfcBCA5SHb8wx5i05Fj+Q5JC9Pw5a4AnQZ0+wQmNjG0Y88smkwbX26mg/2D7TqXcopWXxgqHAFcE+EC0DcJFJZAEzMX8UJrtONYERjvPcJwTncn5SdKHVi80BwUee5UpRi4jnuetazIaTfqzy8Ou0/T+sXldePgd4f1c62SQ+UJv10OjgfQxPL+JOhu17YYj5yQBdqxakutvr3dhtaHaIYffEJkY4NWVkuzixQXAT4e4xhntQfobPGoDpEMFDasRZw6FbTwne0dmKEdtQc5pSbW6PitT8QgE95lpqK2qT7FMH/j6vLq5OS4vSUrEMiIO4T7eIIplDtdq+O4Gd4/7P54tvfLVp2vPBN+3DIggGqeQSpMggRMICCjLPpJqYysqYpKzAhnpGQqWgZ8c2qk2sJAFoHm1uzF0d5k+uXz1cPW9PHk6MBRwxsZYdwb49GYSeIabVdqWDTx/M/9MI9Sy+IH/7fktUtKfONIvIuYVg+wKnmuBL2152h2ecUSNpH7Hhv0uT6jdtRsG3dGRKxr09S3HKp7ANwn/Nu//htm0eCxWjWeL/NkbofDh8fx9z/8+sv4sNtpvZD3oVJIuXFyP10CqIrpJsDYScAYAhDubYzWpGtPRYViulDDxM4CEFryRB03EohHoLU1Oxt0J5PWh7teZzY86jfkIc1NGx7xfPHiBaajYChdS3AlgxEM1+b8T4TywsUqp5Sj5ar3VVhxEd/Q3ip1R53r8XIcqh37WzQ6/uPxVm0rDhEanzoaOZSZ8mKNjl4ZIgfOMWQ7VrXBrUcDXcYvP//y5z/9GVNsMK8eM/KHwztMwPmvt5e//b7G96FqxPzMkgAfTs2SNtPKl0BqgyLS49h/+Waxiqk76PVOARAsWRz2MEgOMakgiDf6IYGNBKASXvfu9rZnn55eXl5eWNrZIxgqHhaZxjOm8jipW7crhSztJDb9qfeUXF5qP201bX8jhJLWWlarCPSxisLatbzIWYl/noa97wRRp1Xcc1/WIfyoP3UoH5DvW3U9qx5r0t/djiDZd3Z2ZRbNx//E7YJYwI0EsiWAn32yTZCpkUBuBJIT7mj53X+55aiaCbvRSx+c77ZkTZDDmAYHSYIDgTEhM/gSgXarcdb8gFVxzy8frkdTrKOy5EEf4hGi/YMDTA9XM01kxgs2JaXVt6hqrYy1vEYsUp0tuaxOynnLSfzKkf62PuVIB9IOKl1xk01O6T3rSLnaH/YZlxcVE8JZfxKzxCGJKNWuvlVe5Dlb6HVseDwKf+PH8fv37+2o+U0C2RHgC5iyY82U8iYQT7jbTbu08dyyJpAnfXfaq/tZkwiWnp4WEMwvfZFAUAJQDC8H263t3vnleDgbLAXDQOBev4cRehlqV/PUoXstNa12lDzWgdSVhA9nc+1boZSGluBKaMuX+JFPtW8FUPsSi+zYCTiRqSA4pZOxv5WDeIaD60/tOrGriNWQO7KCP71Cjgj3ZgMrVSKDH96+48Mkmiw/sySAdw5kmRzTIoEcCYQR7roHcH/maHgVk3ajx37q21J67sPU004hAQr3FKAySotAd2v08rCF1wy9/3A+fLbe4Igqh4nmvf4epK3S7VC7Svvaw+7WYLaW4+qaVhpZ1LNs9rc+slyWHPWhfIol2qe2yXKSYRUdVM7bp/S3+tQh5aS2QxwljPwtmqDM1pJdRtyRGeRKDbrL0pStFvQ71pn5+c0bFS8/SCA7AlilNLvEmBIJ5Epgk3C3229pxbllTcBNP90CWEpJH2ad3ZTTg3hKOQVGX3UC3frw9auz2vj6w3X99nkHrzNSb1yStRehc7VqtwSyyGVRypZCtvY1QDiqzfnWwlsLa63N1ad40js6Nuzr6Jyzclqi1pv6tg/UKcvdcbODSxj5E084qePTTmpfZUXdhoh0lyF3iHZZyB2+v339dn19ZSXJLxLIhMD19bWuvCE/MzGOiZBAogRWhLs00q6/RBNjZMEIOAUQzHskX04aeidSHAULlPrqSQXjQXPTItDfab7crz3dffv8+Rwj7mo+CeTullv4Stq49vSn3oGD0sriZilmLZItf9aBfKlw9vHcvwomge3NtSu+rBTts+I09yuucuRywqF2sT5VBuBBNhl9V2Pu6nlb5BGzZZrycu0mVpn8+c1P/IFLgHLLisDh4WGkpHQdj/YZKUEGIoHYBPACPNU2O5+xY2QEkQg4BYCdVDZ3AmmlkYrhiUUq64UtUdCHiaXAiEjAIoBlGtvN2l/99V9jeem3Vy0MvcuI+3xT6leJZFvNOwG1cnZdo1rEKz2tIoBPHZETyXI9ltPWSfGs/xYFuU7OScU+aTtYMVrRqMgQjz60UhfprubK1GVxemvQvYmZ7giLBSI/f/5sZYlfJJA+gYuLi/QTWUpBXyURPpfi4SEJhCMQ8fXd4RKh7/UEcMGnuKUbe4qGpxW153phEUjppbPTspTxFp4Aft5pt1tYAnJvbx9j0ONHe6KMUr9KSSOPusvXmcUJe0cksq2OLQlua2bxolW7XWvFA6qjHVidlNFuOKhaim/5kwXfdVg9FA5/1pi45REpqhelWp6VRtdh7CQdo2wLrDypSe8yF0ieUVUz3cd4e+vbn38+OjqCg5jMjQRSJhB1xD1ls9ZHj4ss7MYuJyyxMvtfmSpT5syakDfpFu2/5O1xoo7QMCRvjWExYvUPkUPWnxRD9M0N2n8/ehoMWVQCUMR449L+/j4mjui57bu9XbvmSXXB/rz+2fuomY7jwh48yAklyZ0P17ErLouYhHB27fhtl6VvJykdSB9aEeigVgSStDLRsUF29Dx39ZCqTHUX6Q7tDpXxcP/w4cOHpcR4SAIpEVBz3FOK24Ro3ddiwH0TzKYNqRDgcEgqWF2R4hpLa0sx6rRMzjPexfXCBF4cgCsDICsOOq/R0vCILE98TDswgclkvNPdwZt6MQYtI9HWc6kyQK3lt6huS4iLFNZ6GNHbjkoia31sedMH80/xrKuwDK7PK5n2IS9FtTZrWF1VdjipQ3hXFQzh9KujLAf7C846NL6UdS671AnHKGewXWdTC/dWsznBG1Wfnz6+e4/3xeJnh8Dk6JEEIhLgqjIr4ObNwsqpgA7shwKCytobR9xTIo5rRv8lHL8Tb/yLMmHLjI8u2ZbdXRBq36VxHLGjduyasBLCh9iqXx8Xn3h4KnMCkMaYJ4PKhoVWLM0OxS6brg2y47plxIFs8mXtqmP7QDw7MtnSz3LOjk/23EHllPrTsTifypd1JAEWNsSrj+0z9veiq52qyocYYG2i3tVsdyzrrua642VMzQb6/KfJ5N3btwtJ8YAE0iGwOC6TThqVixUNQfC/ytHJMcMU7gnCd1fxBKNduHSSjLdiceX1W6q7WizuQ01Zf8HaR48CW4x0obqsPeURDZ0TIYBHKXr9HhZGhIi1htpF4CJuKQy9o/aUg3aWhNUZ9SWerJqhgygHKwYVwIkGO+JT/Xc52pFJvM5mJSBmzDfbce7i7Ena2kYxx9nXqcEXdmSTuxKt23GbopU7JsyolzFBu38+/zQcDp04uUMCKRHgm1NTAhs4WqeR2LgTOEp69CBA4e4BJqizu44GDRPEX1rxBkm7pH5MfnrJXdze+95n5spq1c9Kca56CeKyEg0dVglguH06fe7t9tRwu0haNQdc+Cp1jRDY15+imcXRcVN+tAdbKutgtn/EYW06Ph2XjlAilejszdnTIWxn69vlUbvAl+zYodShGKf+4Gzviwe1jw9lhFbuln5HZkW6y5qQTah30ACPX37i0pDClluqBDzXHkg1VUYehQDajuB/URIofRgK92hF7FS7aMHXh3IixQ63BQJuNEH2FwJbB3msF7bOjhhuQbKu/bgSCR7Iy6eKzOtkNHeXfWXavb+/3z/Yx3Lmes63HnG3BO5cczs5ttjhy5bMalc+LHmszlghLfGsT0pQ0dP2txwpz05MLu9yRm8qgH1gO7mOVSSuY9nVbpKQHFjRKpvgIt/Q6zJ/X/+XYXe9ukwLpzCH/vLi8jKHpfqWMsHDkhOQ1X65lZCAbng2fpYw5z5Z4sOpPnCWTkm3lcaWVrxp2Bo5zowzuS45k0fcI3P1CrgOgJdf/aSi51lbufl4CH9qjX3GPAgV1RAML0O87mzv4BlNNQQtSlZJXShYIMJ/+bP/u6HZ5/VppY5tnxJW/+ngSipLbBIlnjLF99xg9aCqTk28qL/5WRzPD1TohQ992vGkQ8shDJCQ4qD/6y9lGVzxjU3JdiXhRbnLbJlmY9zAdH/cw3x4/35wdKSsWUjR98DPVN+APFlFAnhhQhWzzTxbBNAyBdzK0LBQuG8s7OAVYmNUcw+pRDqPPo8947OU1xz3PAojXJqRiy7RJjCyFeEyu9l3VEMgHaBZMUtERp/1KLSlapEmpK2TMvbUkXzPHZWr/aH9W2G0P4lB/6nY7JB6eXYlyeEDNw8SoxXQidzagbM+v3xCjhG7Wsjd9qu0ukpSlLs6K2mq/zo3+BR3sVXpdvnSG4S7TJhptZ6enmHS7e0QcDAOb8Ud6MvOYCDPIT0lWnFDph3Ke2EMDZWrNDxLVeRGApsJBKwnRl96oVrSzUhK4SNguYbLayqRhjMhtu+C5yHZVWVi0yxDBJnVCKMbUbsksYgK1lNRs9tFvjpiVnSttSlFbVETzYtd9V9HISeU/IBvtas1sRNYRLMKYZ0WFa52ZSB9QXUrf7Zdq9+IREKoTzk731MHVlS2K77nf8okpCqb9SW7eqjdku3qS010x0z3LUj28eP49vZ2/+Bg1ZR8XJAf8zep9KYZWogL0fyipYXmE4hw6WV3dVC46woUoZAC1by04g2UeAxPRbXbL8tcL8yPjtnnzK+PaLMxtIyBZj1hRsSsEuaWxrYygC9xUI6auH2kPGjP6oQEF492JO4ddUKfRWoqpBy5x8txHG1DbE73gzQldpmNg01boGLFseTPVu6Ql9Y+7lbUqLseeZeXMTWbEO6tdscVbTTDqhdKUU8+207xRok6DZtiGeRkAleCs88dEsiDQMCrI4GKWvGHU6UDUn9JlrITacBiTDLtaHG5LU6eRzSbkg/F9cKSZ8oYbQK4biCc9/b3zz9+hJsablfaViSuOMif7Oh9y0Efi3f9X/lRIli8zQPq4Pan7W5HZcfi/h4+9W8nfbdLgH3LvjU+VZLiro2TQ5079aUc8WHNc7dmCmHWkEyZ6e/19aUnYbz/1iRKpzQI+JRBqFOJ2RYqVS/PNdQ197nErGNEJJAwAXc99drfkGQFhbub1AY6wU+nEmnw5MP6dJuL/WpsXC+sGuWcXy4x6Debnb189en8E3aU7rbEtyV3YZp1uWkHt6nqmhRt71yc4nseUA6sc04s7vBLjtPHC/y5PYS40N23GCqYY9N8R9yXtbtW8Wp+v0yXeX6eYsR9t9cb3d26LVm7P49Z5ST3w7VG0nFOIGYJzSNKZK8+mUzcEYWyzh2Q+yRgAIEN9bc6wt0BkWShpBJpkgbacTmG6h3buVLfXC+sUsWdfWbxW/1kMoZsPXlx8v7du5ubGxmFt8StZQ6uP1u7K5fF61HOYlNfEs7yreS8PqfFu0hm+dOb/W0dWo7P97Xn0YKTfeAEtB2Wvpfjk2P8l7+FU3JkWam+rA8Zdsd9y2h0ixn/g5Pjy2+4f9Ar0ywlZPShlWMn6ynsGJ3/tI2LzNfDsE4H07EibsFtiZgAg5FAogTKLdzd12OS2Jx4k4w08bgcK7HDrVbDXFtiIIE0CciI+2TyhPkh3//qV5Px5O3bd/eje5Xi/GqUy9G6JJX0VYfYU6pYaXTRw9pM0e6Wu6Wdxbd1Unlx7+swXp9BZlbOY7MNsG3FsXLSBrnNEr2uVb3sYb7Q/f3dw/39i7OXu/3+l/Pzp8kYITEA72VYZd0106Q+q4LRgxdeoZABAY/ErYvTfTYDY5hEZQmU8uFUXD7Jb6lEmqyZBTAx2QyHi030BTcSSI0ARtzx3lS8KxSLl0PmYuVy6PivX75cXHzbk20f80ZU4qoiWpURX5buQB8dFwAAIABJREFU1cpYOStHtwfHVRsvh/i/JMWtw06r1ev3O83G9B/+oXP28vLwcGxlecm/5brwtRqrnRTOLP+JUIcbMgC9XpuMsXTMEEvr7Pb6h4dHlxffkHcoeCXZxRu3VAnERxygfqSag1iR7+7uxgqfdOCAxVFo5kkzY3xBCZRDuAe8RoJC0f5SiTScCZt8F8DETVnI8LwWGRkmyKQqR2CqNuj16fT56WmClctfvX6Fqd431zdv3/6CyVoHBwe9Xh9D8muuXRlch7N82Hp4XmfVGaWdF6Eudfz9i2+95+nnWu3T12+zX/3wYmvr199//9OnTw8PD4vh/I9ghlpPxu1LmeYS75ID5BQ/ZN0ObzDeicwenbyA6+ju7uuXz7fDIaYd67yo7Ljj4r6JBKREk9uWamZyEa+PaTgcbm/vrD9nsGsE5hmDNRhedU0rrnCPUOEDFXNa8QZKfJMno43bZHze5zlVJu8SKH/6GG+XDV8yzXs2fX7GE3NbW43B0eHR8RFG4q+vr759u4Bw7/UwkaTfbnfwziaZaSL/FjfrWGv3+cn5nu3d6ch3p9Pd27v3r19PHx+RxLTdvt3ZGX369MPx8b9//pfaY6822zxfBfFbEao9fTh3xKqTM5l1do857MPbydOk1Wz1envIHhZrx0OocMdT4OPxGLcuuH2xh+ltW/ldGQKoM2E3pyaHDQj/+/v7EUIVMUgQsHFIFpFJ1WwulnAPUmMjlmCKUUe0yBXMaONcdpq9y4dTzS6fElgHpTrV/7e0chf1DhmPyTNYZGYLbyM6OTnBDhT8/T0U9Se8WBQPs3a7XcgOrJkorxttNLTYxUW/MlDtbgjmXfPW14t33740Dw9+/5d/+QkUp8+Q3ogTxkBh//zzz2//6R9f/7b/sL0PY4TyPGgg5lqpw+aHh3u8DAGiHK9b2t7ZOTk9bWw18MMChvOH19fj8SOm9cMbklUpy12M3JBITtTvCIFSo6fqEnDX74AUnLp8eXl5enoWMFTpvW0k6XArPYpSZrAQwn1jJYxYNGnFG9GcxWBGG7doakGOuBxkQQqqqGbKAoiQraLVFzaZUCJ5UpJeLROJMXgMt0OsYzgd8vr+/gHD8BDEEPkQ7rjDhIjf3e3hs9Npt1ptPNhZr0PQyxKTiMpqGyRS+T89Hpwd7l/f3PzxD3+4vLqGdv7hhx8e/vh/ccdwsf8/MDln9/T0FPr9e8u/Msbax5ezidHqvuPp+RlPlD48PGL4fPI0xt0F0kXuOp3t4+MT3Fw8Pz3DWmyjiZxFriHfZZYQsii51BueBbcWk4HBrXbbSYg7JJAUAX0t4HMwGCQVZxXisdoQ36zaDYWvJ57Mg4Cxwj1IvYoILMWoI1pkBzPXMtvCIn/rt8AUOQe03WgCmPeCqSNas2sNC5mNTT5guP2FXZlGMqtjXBoj0XrEvd/vQRvjFLTzeDwZPz5eXV5+/vwZihhuUPPYmo1mqw0BjIXvoOax1qL806EQsNNu//r847vvvsdk3/fv3w0eb5Es1Myf/vRfuE+4O31xe3m5LfcAW09KW4vYhtzGpiQ47jdwiJn5MkqOAI1Gp905GAzwEiW8WAkG2/oc4+v3EOtqTB0D+kqoq1sSqPW5bpfzMk9GbbVmu40IYSQ3EkiJAEbcX7w4TSnyaka7UY9Q2edVMYwS7hvrSXRKKUYd3SgV0lzLYmbMuOD4ld84m2hQiQhg0ZjR/Z0ebd7aghLGn6h1fGlJLhNHMEYuH/Z/EfEQ65heomaTQIbXt9rt9s7O9uBoIEda+0+nahRcJqKMJ5ObmyHUNrSxGudWNwUS7+zq3b/94x//iBH6w8HhVfNvRIL/6U8wYH9v73lW++nNm+Pa1qv2zvudFqhjbr3cDUCgb+/geVmZam/dOVhj5hhIxz/8TgUJjn/KFR9IVmUNH7I5+1q369zjE3cmU3uqTx1PDSKhEhU1s2IcgerMcTcH/UbxQmWfUmHlK9w3lnusXKcbe2TTDDUrcn4KExCTEwpjKw0tIAGI7Harg/cuQXlrAasHnKHaZ5jsAr0r+r1eUxpdjcBjRBpdG1oE6HvZx1QYtaQk5qZbg9UqBoySy/z4ZnNH9jBErzcl1hHQVtDPnd+d/M/Bb7D8JPT1zXAIjb3b20W6vxkOf9rt/e3f/Z0W2q9Fczs6G0JcH8IF9w+y2Rrd2RN9roS6fVIOlVePDwzFI1IId/x1trd/+5e/h8kFLFKaXBgC+KEJNS26udSY0dl5hgx+zRO/J8R1J3IR7sFLc53Jvm4pRu2b7uaT5lq22fZy+Li+vi5HRpgLYwlgOhYWRry9vcX4H5Srs0H1inyHynZvaBMWHdRJpeGVpIeMV3pXNLKte9e0I5DuSrzXRrNuezY97O58+HYh76OZzbY77b948+aXw8PhncyccW+iwbFZX+qM3rdPuDwrn65jR8hLBOKudT1Cqg0TaGQ9GeQNvx80fvPjjztxFJWbGPdJwINA3HXc11xYHilp5zVXrq9/nvQl4I+fsJfgZSbc/ctlyarQh+nGHtocO4ChZtnmVez78PCwYjlmdnMgAO2Otw5h6vjx8TFGx7XcVcPtsr9gEI5UE2F/K60LFzmW8Wql2+VAqXbxast3KxoVn45VPvH/w4cPB3ejX3/+9G3yVP/7v9/r998+Pd1i/BtSGqcDbyue1Ri7BLfO6DF3RCsxS+T6S6a+6yPYs3948Ovf/AbzcBZyzQMSSIEA7lR3ut0UIvaIMnjnjiuYWzwC/rArCDhV4e5PO15JWl1e3EhSCZ9uvlMxuQqRXlxc/LYK+WQe8yaAKd0Qr+cfz/t7eIeo3C5qBQ95iykzehM9vmCn5aC+nA8IZXiaC3cJgfkn0PbirjfxY/0TYT372ul8+e57zNiZvXmDZ17hsqLClT85sXJG9LhXABHnVki1gwP50274Un+i35+fMa3nx9/97uTFKTJu28lvEkiRAB7ZTjH2OFEvXud+Mc0vaj9fPLdEwB9wKaEmLtz9GS4Bj3KYegJRjFruhKPFwVCpEuCIe6p4GbmbAATrzk73fvQwunu/f7CP5yvw6KpMdHekO/oTZ2lH2Vfj7KLARZhDi8uUd+lzZOBd70j8ODmrYRqK+JZNvkS4iw/tDZ/OJmPfnps6Nz9v+Z07eOp37dNW6iLd1YaRdjxBi8n4x6dnp2dn5gopBY4fJSOA37gKn6Pg4sa6/Auf4wwy4A+1oCCTEu7+cOKWTrqxx7HOXMvi5KqcYTnHvZzlanCu9Du/hje311c3ne02Jr7jvUi1RkMtF4MuQ5oP68u1p1ygt/VAu+yoLOrlaKxd9SVhlKafa3blIo6zesde0gZn3XJcPOtt7qq1+NyffUYPotv+8W15lC/7D/tqJXc8kjs4Otrb28ctim0ev0kgIwLVWrYooPAoqCzNqMpIMv4gjeUXuYX1z28y4LNII5ql5loWLT+VCMVVZSpRzOZlUgtZLJD45fNXLPAI7Y4ZLHiZkSyRjiViRKcrrQ5FLZPYrUF3JcFl9F2NxatcuboR2bUOIaflwPlUe7N6c1sE+xQvdbI2S7BbX24tb8t07RGCfGmDNLc3OaM8wOFJVqfEMvM1vCzq+MUphtj1rYaylR8kkCkBVMxM0ytEYkGECrF5F6UPv3yxBRfuPlnwznfIM1mkEdIky7u5lkXLTxVDcR33Kpa6MXmGqNUD8A8PmD8zgl2tdnMbi6h38UxdV4l7NSsGJ+RbviDHlZBfkwc5b3UdWrHMP9XebLvxiB1ocBHq1pet2eEijiLQbZEOHW45Wi62Utcn1Kc4YeV4vB8KO5gL1N3F7UcHI52cyL6mhOiULQFU32wTLEtqYbUNMauSD4ItPVQbhXsQ82LV4NQTiGOd0cbFyVgVw/LNqVUsdfPyjCUSt5ryyOZsWhuNsHbkHbRyu9WECNavRcWLURsYjVePddrzZFazIZ2CrVW0aHE+nZ0FpS4HcwfMtnc2JcvV0Vy1q0Msx453weKftS473qnaaPT7O7jN4OD6apHQJUcC1ZoqkyPoIKIoPcWaY8bDJ+2PKg6kVeHun1Z42z1CZJSMR+p+zuZa5mc1z20kgJFOrVC0TxnLlKkJ8uif16dPnF5BbHeJfO3GdG1Ec/JrQWnHVc+LLmXgjDcqPeEtpU+jGUS8GvrGDJpGo9lqY4y+tb2zjZecNltNUfz2y5fs2mXJd9Rh4HJ9iu6GCyITd/UlTvYGr0qjyyIySFFWcFSj6djD7Be1/4QIgBp6qAlD2gvTYOBBlw4+WZ8XK6T1KLHDZ2ln1fOiSxnqszvLi7mbX/K2ezL5Rb0uw8OpbnCF3t8ooqRZqvrmA2kjHke4+0SSGN8s0ohsrNHGRc4VA84JQH38y7/8P2gOjBfi9/7Odud+dI9XS+KVe/t7+5dXl4PDAT6xDxe44yz8TMYTKBetVKCbIK8gpx4fHjHD4e72DvPmr2+uDw8OLy4v8Il9uMAdZ+EHKUIG6YkETJeco9er6+tev4/3OuFNRrj/hJrHGHgTD7lihvxspurzBBNusJR1r9cbDm/wFOzl5eVgMMDn3t4elobsdndHd3d4fnQ8HjeaDSy2DsUE8YT7Bcy2f3x83O1270YjrPvO+szrF41m4dqrbb7ka97XGb+3UW5tlK7GZzGOgV54HCr1//W//49+wChOMv5hvYzwD5XFWXMtyyL3TIMESKDQBNzj7vJQqz3tfV2moNK1s3y5dtf5pRsJkAAJmEzA0bAmG5mObXJTnU7MdseQUuwxo6VejwmQwUmABAwg4NbiaHPlHzcSIAESKD2BjU1dqZV9YsJ9I8acK5Lp9uWMh8mTAAmQAAmQAAmQQBkIBJd8BZT4sYR7cDL51APT7cuHClMlARIgARIgARIgARLY8DulkbI+inA3XQ+bbh+vFBIgARIgARIgARIgAbMJ+OvJnGR9UOHub3z+4E23L39CtIAESIAESIAESIAESCAZAv7KMzVZ7yfc/U1KJtsxYymAiTFzyOAkQAIkQAIkQAIkQAKFIuAvUGPI+mXh7p+QKcyKYaUptGgHCZAACZAACZAACZCAKQT8dayvrLeEu38MRuSzACYawYlGkAAJkAAJkAAJkAAJFJWAr+Jt4qyvh1xzba5luWJh4iRAAiRAAiRAAiRAAlUjUK8tT5XJnwDFev5lQAtIgARIgARIgARIgASMI2CMcKdeN65ulM+gCJXMd6JZ+QgxRyRAAiRAAiRAAgYTyFW4R9BRBqOkaekQyLeWZJM6bw/SqTuMlQRIgARIgATKRSAP4Z6NFipXOZU0N6wKumAT4UD1X9KrhNkiARIgARIgAZtAVsI9EWViG83vQhFg2WdTXElx5g1ANuXFVEiABEiABEggNIE0hXtSQiJ0phggYwIs6YyBp5pcXqXJG4ZUi5WRkwAJkAAJlIFA0sI9r06/DGVRlDywjItSUsWyk/WqWOVVdGt5o1j0EqT9JFBRAgkJd/a5pa0/LNrSFi0zRgIVJlDclo23HBWutsw6CdTirONe3HaPBe9HgOXqR4fnSIAESCBXAlk20bxJyLWomTgJrCMQfsQ9y0ZjncV0S5oASzRpooyPBEiABMpAIPHegXcCZagWzEO+BIIJ98Qv3nwzXfXUWZxVrwHMPwmQAAnkQSCR3ofqP4+iY5rGEPAV7olcYsZktdqGsCyrXf7MPQmQAAmUhEDM7oy6vyT1oLLZWBTuMS+HylI0LuMsSOOKhAaRAAmQAAkYQCDB/pH3AAaUZ/VMaNZQhxOsxtUjaEyOWYrGFAUNIQESIAESKD+BCN0utX75q0XaOVwccU87NcafMIEIrUbCFjA6EiABEiABEiCBYATC9toU+sG4VskXhXvhSjvsZV+4DNJgEiABEiABEiABEAjV41PlV6LOULgXophDXbqFyBGNJAESIAESIAESSJBAcKlAiZ8g9qyjonDPmniY9IJfhGFipV8SIAESIAESIIHqEgiuLijxjaslFO5GFUnwa8kos2kMCZAACZAACZBA+QhEkyWU+ynWBAr3FOEGizraVREsbvoiARIgARIgARIggUwJBBQ21PdRSoXCPQq1JMIErNZJJMU4SIAESIAESIAESMAsAkGEEMX9cplRuC8TSfM4SB1NM/0SxW0+SjY2JapuzAoJkAAJkEAuBIL09tXqbyncM6iIQapdBmYUIIkykYqZl2q1QwWomzSRBEiABEjATAJB+tvydKoU7unVwiA1Kb3UDY2ZUAIWTDRQ5WmZAmKiNxIgARIgARLYTGBjp1qY/pPCfXNph/GxsWaEiazIfgkil9KLhr0wzVUuTJkoCZAACZBA+QkE6T+N6C0p3ONXxiCFHT8VE2Oobs5NLI3oNiVSjka0Z9EZMCQJkAAJkAAJ+BMI1Vum1StSuPsXktfZUIXnFUnB3KuY54IVUZ7mxqweabVweSJh2iRAAiRAApUl4N8rRu/0KNxDVSn/YggVVQE8Vyu3BSiQMpuYSGWL3hCWGS3zRgIkQAIkYBqBjZ2eZ4dG4R6kLDfyDRJJAfxUJZ8FKAqaGIVAehXYswWNYibDkAAJkAAJkIA/Aa8OrU7h7gPOi5pPkIKdKn8OC1YgNNdQAjleKbxnMLRO0CwSIAESyIMAhfsq9Rz76FVjkncpefaSB8YYSSBPAgZesLyXyLNCMG0SIIFqE6Bw1+VvYOeYTMUsbcaSwcNYSIAEQhMoUKvCe4zQpcsAJEACZhOosnAvUO8TrhKVNmPhMNA3CZBA1Qmk2hjyrqDq1Yv5J4E8CFRQuKfakudRhirNcuYqN5xMmARIgAQ2EEiw1eU9wAbWPE0CJGATqI5wT7CNteHl/V3CLOWNlOmTAAmQQPYE4jfmlP7ZlxpTJIFcCJReuMdvD3MpF89Ey5Yfz4zyBAmQAAmQQFACkbsGKv6giOmPBMwgUFbhHrkRM6NYVqwoW35WMkgHEiABEiCB7AlE61wo97MvKaZIAppAyYR7tCbI0MpQqswYyphmkQAJkAAJhCYQtnui0A+NmAFIwINACYR72AbEg4QZzqXKjBlIaQUJkAAJkEC+BOJ0bRT9+ZYdUzeNQHGFe5x2wKxSKE9OzOJKa0iABEiABApPIGAXSX1f+JJmBoIRKJZwD3j9Bst63r5KlZm8YTJ9EiABEiCBKhMI2KVS31e5kpQj74UQ7gGvx2KUSKkyUwzktJIESIAESIAEhEDALpj6ntXFWAImC/eA15exbOeGlScn8zwVds+cwmDPUNhKRMNJgATKTSBIR8EmvNx1wNjcGSjcg1wvxvJcMKw8OVnIljEHReebuP3sRoypmzSEBEig9AQ2NuFskktfB3LJoCHCfWP9zwVOlETLk5MouY8dhvjiIEyVHrugOEXDsCRAAtUjEKpJZhNbvQoSMcf5CvdQtTpiDrMJVp6ckFc2BAqXSiJVnF1T4cqdBpMACWRCwL+JZduZSSEUI5FchLt//SwGOG1leXKSHnUySo9t4WJOtjKwKytcBaDBJEACkQhsbDvZHEbiWshAWQr3jRWvMATLk5PEkRNN4kgZoReBbCob+0Mv/nQnARIwhoBPc8g2zJhSSsaQDIS7T3VKJg+ZxVKenCSFjESSIsl4jCVgbCVnb2xsnaFhJGASAf82jA2JSWUVyJb0hLt/VQlknCGeypOT+EDJIj5DxkACiRAo08VI7ZBIlWAkJBCegE9DwusyPM4sQiQr3H0qQBaZSTaNUmUmMhpSiIyOAUmABAISMLCdoWYJWHb0Vl4C/tclL5G8Sj6+cPcv2bzyFTHdUmUmLINKZz4sLPonARIoNQET2kMqo1JXsaJnLsglwiqcRilHFu5BiiwNg1OJs1SZCUWoujkPhYmeSYAESCBzAsm2z9RQmRcgE/SpwqyPkatHWOHuUwqRbcgnYHlyEpZfdXMelhT9kwAJkEBZCMRs+amzylIRDMmHV31kRdtYQAGFuxfhjfGb6KFUmQkIuIp5DoiG3kiABEiABDYRiNOJUIttosvzDgGfisZ6pCn5C3cfgA7kwuyUKjNBqFcuw0Gg0A8JkAAJkEC2BCJ0RtRo2RZRIVLzqkdVqyxrhbsXnEKU7LKRpcrMcuZWjquV25Xs04EESIAESKAEBML2ZVXTbiUo4uSy4FNZSlkvHOHuk/Hk6GYVU6ky4w+tQln1B8GzJEACJEACVSUQpysspbirakVYyrd/vShoyUO4I1/+WVviYO5hSbKxEXBV8rkRBD2QAAmQAAmQQDwCwbvUggq9eHhKHNqr5A0vZ2fEvcBF44W+wFlaa3pV8rk283QkARIgARIggVwJBOyFDdd9uSIsROJe5WxIwRZYuHuRLUS1CGpkJTIZFAb9kQAJkAAJkIDpBAJ23IbIQNNpGmSfV8FmXJLFE+5e4Awq2/imVCKT8TExBhIgARIgARIoJoEgHX3GkrCYIHO32qskUyq9wgh3Ly65F1iSBlQik0kCY1wkQAIkQAIkUFoCG1VBStqwtEAzzdja0otfYqYL97XZzhR8BolVIpMZcGQSJEACJEACJFAlAhv1Q3ydWCWcGeTVq8SCF5Shwt0rYxkwzSKJkmcvC4RMgwRIgARIgARIYAOBUHojuHjckCpPhybgX1DukjFLuPvbHRqDaQFKnj3TcNMeEiABEiABEiCBwAT8VYpbPAaOkh4TIeCUDHaMEO6OQYlkz7hISp4943jTIBIgARIgARIggYQJ+IsZyvqEcXtGl6dw968DniYX5UTJs1eUYqCdJEACJEACJEACKRPw1zyU9cnhz0G4+xduclnLKaaSZy8nqkyWBEiABEiABEigoAT8pRFlfZhizU64+5daGJuN9Fvy7BnJnEaRAAmQAAmQAAkUnYC/gqKsXyzfdIW7f1ksWlLAo5Jnr4AlQpNJgARIgARIgATKRCCI1qqSuE9LuAfhXNR6Vea8FbVMaDcJkAAJkAAJkEBFCfgIs9Jp+iSFuw+3MtSkkmevDEXEPJAACZAACZAACZDAnICPeCumpk9GuPtgmbMr6F6Z81bQIqHZJEACJEACJEACJBCPgI/AM1jTxxLuPlmOx9KA0GXOmwF4aQIJkAAJkAAJkAAJmEnASwQaIOijCHev7JgJP5xVZc5bOBL0TQIkQAIkQAIkQAIkMCfgoxKz0vQhhLuPtfMsFXSvzHkraJHQbBIgARIgARIgARIoCAEvJZm0oN8g3L3MKAjFTWaWPHubss/zJEACJEACJEACJEAC6RHYKDVDKvv1wn1jKullMIuYS569LBAyDRIgARIgARIgARIggbgEvESph6BfEO5eYePaZEj4kmfPEMo0gwRIgARIgARIgARIIB6Btaq1XhPhvvZUvNRMCl3y7JmEmraQAAmQAAmQAAmQAAmkRqBZWllb2oylVhcYMQmQAAmQAAmQAAmQgMEEFqbKGGxnGNMo2cPQol8SIAESIAESIAESIIFCECiRcKdeL0SNo5EkQAIkQAIkQAIkQAKRCBRcuFOsRyp1BiIBEiABEiABEiABEigcgWIKd+r1wlU0GkwCJEACJEACJEACJBCPQNGEOyV7vPJmaBIgARIgARIgARIggYISKIhwp14vaP2i2SRAAiRAAiRAAiRAAgkRMFu4U68nVMyMhgRIgARIgARIgARIoOgETBXulOxFr1m0nwRIgARIgARIgARIIFECJgl3ivVEi5aRkQAJkAAJkAAJkAAJlImAAcKder1MFYp5IQESIAESIAESIAESSIdAfsKdej2dEmWsJEACJEACJEACJEACpSSQuXCnXi9lPWKmSIAESIAESIAESIAEUiaQoXCnZE+5LBk9CZAACZAACZAACZBAiQmkL9yp10tcfZg1EiABEiABEiABEiCBrAikKdwp2bMqRaZDAiRAAiRAAiRAAiRQegJJC3eK9dJXGWaQBEiABEiABEiABEggDwIJCXfq9TwKj2mSAAmQAAmQAAmQAAlUh0A84U69Xp2awpySAAmQAAmQAAmQAAnkSiCqcKdkz7XYmDgJkAAJkAAJkAAJkEDVCIQU7tTrVasgzC8JkAAJkAAJkAAJkIAZBIIJd+p1M0qLVpAACZAACZAACZAACVSWgK9wp16vbL1gxkmABEiABEiABEiABAwjsE64U68bVkg0p/gEeFHlWIazHNNm0iRAAiRAAiSQIAGXcKe0SJAroyotAV4nhStaFlnhiowGJ0KAt6yJYGQkJGAWASXc2a+ZVSi0JhcCvAxywc5ESYAEUiJgQpvGm4eUCpfRVpdAs2bCpV1d/sx5lgRY17OkzbRIgARIIL1Wl7cErF0VJeCaKlNRAsx2+Qik11WUjxVzRAIkQAJFJJBgO897gCJWgOraTOFe3bIvfs4TbLiLD4M5IAESIAESiEIgTldC0R+FOMPEIUDhHocew2ZAIE6TmoF5TIIESIAESKCyBBLsoXgPUNlaFC7jFO7heNF3+gQSbAfTN5YpkAAJkAAJkEACBCL0fdT6CXAvXBQU7oUrspIZHKGpKhkBZocESIAESIAEIhAI24FS6EeAbFwQCnfjiqTsBoVtaMrOg/kjARIgARIggSwIBO9/KfGzKI9oaVC4R+PGUMEJBG8pgsdJnyRAAiRAAiRAAikRCN5xU+KnVASe0VK4e6LhiRgEgl/zMRJhUBIgARIgARIggTwJBOzuqe8TKyQK98RQVjiigNdthQkx6yRAAiRAAiRQXQIRdAK1/vrqQuG+ngtdNxGIcBFuipLnSYAESIAESIAESEAIBJEZVRT3FO68PoITCHIVBY+NPkmABEiABEiABEggMoEgsqRs4p7CPXJ1qUjAIFdFRVAwmyRAAiRAAiRAAsUisFHGFEzZU7gXq/5lY+3GWp6NGUyFBEiABEiABEiABFIlsFHzmKXsKdxTrQ0FinxjxS1QXmgqCZAACZAACZAACSRCYKNAylTZU7gnUqjFjWRjdSxu1mg5CZAACZAACZAACaRNwF9KJSzrKdzTLk7T4vf1iz3wAAABzElEQVSvXqZZS3tIgARIgARIgARIoLgEguuuQBKfwr24VSGU5cHrTaho6ZkESIAESIAESIAESCA+AX+pZsl6Cvf4oE2Owb8SmGw5bSMBEiABEiABEiABEtAEtKKrU7iXskJQr5eyWJkpEiABEiABEiCBShOgcC9N8VOsl6YomRESIAESIAESIAESWEOAwn0NlEI5Ua8XqrhoLAmQAAmQAAmQAAlEJUDhHpVcnuEo1vOkz7RJgARIgARIgARIIBcCFO65YI+WKPV6NG4MRQIkQAIkQAIkQAJlIEDhbn4pUq+bX0a0kARIgARIgARIgARSJ0DhnjriGAlQsseAx6AkQAIkQAIkQAIkUC4CFO4Glif1uoGFQpNIgARIgARIgARIIGcCFO45F4Areep1FwzukgAJkAAJkAAJkAAJLBKgcF/kkcMR9XoO0JkkCZAACZAACZAACRSOwNZkMimc0cU3GGLd+St+bpgDEiABEiABEiABEiCBlAk8PT1tnZ+fp5wKo3cT0Hrd7cJ9EiABEiABEiABEiABEthA4NP5x+Z//PsfoSVPz85ardYG7zwdnQAYcyMBEiABEiABEiABEiCB0AQm4/GHDx/+9Kf/+v9va4+Sz9Xw6wAAAABJRU5ErkJggg==\"}}]},{\"role\":\"assistant\",\"content\":\"_\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\\\n# This means we should rotate the ground plane to block the background correctly.\\\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\\\n\\\\nground_obj = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground_obj:\\\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\\\n    print(\\\\\\\"Rotated and shifted Ground Studio!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF\",\"content\":[{\"type\":\"text\",\"text\":\"Code executed successfully: Rotated and shifted Ground Studio!\\n\",\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":"Code executed successfully: Aligned all viewport spaces to active camera!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","content":"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":" eyeint","tool_calls":[{"id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","content":"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Check backdrop coverage\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"_","tool_calls":[{"id":"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\n# This means we should rotate the ground plane to block the background correctly.\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\n    print(\\\"Rotated and shifted Ground Studio!\\\")\\n\",\"user_prompt\":\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF","content":[{"type":"text","text":"Code executed successfully: Rotated and shifted Ground Studio!\n","cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; 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"}
{"ts": "2026-07-15T04:47:50.698627+00:00", "type": "image", "tool": "get_viewport_screenshot"}
{"ts": "2026-07-15T04:47:59.894091+00:00", "type": "llm_call", "call_index": 34, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-15T04:47:50.699628+00:00", "finished_at": "2026-07-15T04:47:59.893086+00:00", "duration_s": 9.203999999997905, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "type": "function", "function": {"name": "get_scene_info", "arguments": "{\"user_prompt\":\"Get initial scene info\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_scene_info_El7NNW4G2qFUh4s67C60", "content": "{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "type": "function", "function": {"name": "get_sketchfab_status", "arguments": "{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj", "content": "Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "type": "function", "function": {"name": "get_polyhaven_status", "arguments": "{\"user_prompt\":\"Check Polyhaven status\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU", "content": "PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU", "content": "Code executed successfully: Recreated robust basic materials successfully!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h", "content": "Code executed successfully: Base test model created: Test_Base\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W", "content": "Code executed successfully: Lantern Base parts created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty", "content": "Code executed successfully: Burner, Flame Core, and Light source created!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu", "content": "Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH", "content": "Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_1NsobuZ1llbupAIinQUi", "content": "Code executed successfully: Normals consistent and mode returned to OBJECT!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_FY5NJmURZh0FUyjupB9u", "content": "Code executed successfully: Upper Chimney structures and venting details finished!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc", "content": "Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_a0BDgzG7kL50JD8u1caU", "content": "Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H", "content": "Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1", "content": "Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz", "content": "Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR", "content": "Code executed successfully: Removed duplicate glass globe\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_GzVY244Mha4VP7H0yhOC", "content": "Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E", "content": "Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2", "content": "Code executed successfully: Dark studio world environment set up!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj", "content": "Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_cdClUbHwspRU8x0v1m7m", "content": "Code executed successfully: "}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr", "content": "Code executed successfully: Hero camera built and configured!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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FrMAC5C7Ol56C7deG+EnGsb4d1xxx0YZyBJMI899hgv/3ALQSTY5LrFSzhcb0gQrzSxsb7vvvsSFZocISJEiwsto+AQ6hLFgWFvzS0NRvDF3Qt3LMTD5Tx74HHB0FCk5ukXZGpuY8gttfhJwDhlR85nK4x03bp13Jnxohh7fWjETx4gcFEkb1w1SR2uOZrZ+KeKhPfd+VvV3nIpPWwnYgDZI6gZlcg5FlGuZhKvDqsWa7Ca4kScKxEhW0dy1kgSwHg0o1PlhHpKTz9nyUNjRBy1mGpzACwLb37zm1nNWJpMM5mVl4sW1xiMcHqy7rEdJz/s8jlVL774Yq4W0lL2pMjyEtdYInEwPEzhxRgWAZ5rsB1n787FBsw6+Xu/93s8ho+xsclm2cQvT7yIkyWLVVEPRHDKKopTwmNhYQHBAoshTLRYVbCWPIB3hCxiDju6A2OBqABYwKYwBlmpcO1RJFqjbMpsYpxoFbBq9xpknYqZlSOhC8XElISxN9d4Ikya0V3sijhy5E51NqoCrcTICWwmQcYmI6I4NnDsjdic0YPoMVpLvA/ZRXgFEUYvolFHFwW60fUE42aiYhQUAqM2H8xSwzLC6X9XvbBB8qWBPQz7KB7Xfutb38qOaMn8mTe++cwfPPTau64667v3rY0Edsw8TcYLb3ZEufB5Z83/p5+8vGjujP/lo5e8unkfT77vvP84dWL4nd/5nUceeYSopOKA1WTJYnvGaknTg4oLO/s6RoG6LUgxTFsJUK8BjOiZjl4V4TEK6zBrsrpYJ9mmyhT7eCzHeNjLcZn44he/+N3vfnfQ6PH/HHdToi42/twt4UMfhq5atYrN5d133039ta99jVX4eAu1FlcCxsxOVAMmBby2Qab00J2dJVvkD33oQ9zNwGHPzc0T1wYeG9955508j2dXyr3O+973PgEIXHXQwiOWucAA2BPDJAwuljz75zk0xwAJh9PxkIu/+7u/Y/z45QJDMJSYDu6ldAPAMeASCIdCSPxhFptgksVlGBUbFNCcI1ruZJh2PJQiCZgiJEbBLRRa3H7wHB07YO4LMZ4YIZncFXDTScx4xAi3CtzDoUUwTPQYp3RJINZ4KQizqJBVblKJ4cYbb+QSzm0Gl3kIfByDOz5tYZfAtTzxO7Waze/ds4eJkUpIJnN7JY+1kpPNfEyaewXcNCeRZwkwLXRgltjO1AXOgEZHjUTCcRqU05i1L++xjhwpEpsIsSvB9REcdwuJCkU0zj6eKbAEsTpx483ixlLAyaje733veywXnNc89eCjQnbtfKj4i1/8gqsCpzBLEOsbKwAPGlgQWKBZCqRYsMayuOsvpbiTZ9fO+y1S4WEE1rLLMhIKHllO+cCQPTo0qeCRVYvljhWMKxNrC37VxTJCVCiqqZqrC3Zg/qReEHJ1YLE1hy6eYmi1R459LJBAksN1wdds80cPfCASU8g18QREU02Q7kJLWLWNJM0hablGbG3MgcNL/DJAFRPsOitx16QCSZyxqaERLaBRzObDMW5EzsoTlaQZ+QVdkRZxroqFcXRRaIypiKPlE44dmIFCYm25//77WfpYA9lixRUAzEd8bIdYKFhV4OuwLpjTecPlZ86b3fGtH7/MOyfsY7tmtB08Mvhnk9Dg8xo260ncN8cpwey46+ENa7v3tre17jvYu3F77TmCC/tJlkE9ULCQsB05ltm5ERiPJ3jFg50eXY6cEfGyA/s0OGDWfLrqpfrUK7veedkZvJcvsx1tLY+/uIOnjxhkAWRLRip43sr1goWRjSs07PB0A/tkgA09j615mYKF14EB2Ov+/Oc/TwI2oXzNNddowUUEwBPgLW95C1b015+8jMF9Q/LXkDxfUdJ5WkN87B2Jj8VaOSUUrhaESHz2RJTKsjLFey8oKjuWx8NgRQFpJUzbTMg0mS66uUm6pOKaXmy6WRyAvVtLxi23qcRpbNpXNNK8X26BOOTcrsgORgA2VWAnxjDeuDgP7nUqHLYk7dUq5yvTRUMrjrYZTrGFie+N45147xPgkQE2M0Y4cQ4YNx/hBB99AuYOn/BYNtm7c8PM2k2Tp+ncS+uFEwWvwJIB0hXlNE2QVqyTNZYVlXt+tuk8VaHm48dIFmbhxSA7VLbp1AjZ7qPI5RM+qzHLO3t03vfjz1tZRlauXMkqzSYeRR6aJAb50EDW+FwRO1yxWOExpQc6XB1Y/FnA0VIXTVkgcvbxus2QBDssyNSJC/cCdPQFLC8+vsW9MlJQx3jAsjZKmwXuJr7LAzTw6AwmOCpHkvUbu0YcHkasa5D1NYUkMS25YU+2YRIwiyQbPP4270//9E9zY5aQPSSLDPF3LFj9kf/6ByyHR3r7732im96rLly6fc9h/lZ17zPf3rPxiVwj2mTTNffCD1/+lutWnT73iZd3XLRy4ZZdh556pfbklG+V2fKf/93728QIm29cEwMrKtsqr6jsZvn7JbRY7vQElo8NiZNHxiyh7CpZ2ZCwhMLZM+fqrmUXfunDl1ywYgE3G+WW8i9f3PE3//Z8/5F9MzfegQue1PDeOOostrxnwRNq9r045dkHu3lCYtnkAQrvVbIUsz6z2U7iVBMVVmldbsC1VyRxrz6GwQafmhsOduoA5KzvbMe1oReNSwImhFnHGQx3EkTGA2BoHDae2cNhTZcbMSeyJgYuHrp0TaTfCfDF0Mgq9Qh8Na8lJhMgq6JZgVygmTCaZ9ra1Pp+d4c9LODcjiA/w3LUiOwAGhESufhEq6OvOuFkm/aiYZ6owWYDG4GEsbDcecG0Bd4aZ/Ubw6GxdrHwyj7LL04xztWFT11Z2Xlmb9cGrOaoQCNIFnCrsxKyYmgBL7gw2I6ArIGxxtMg3RJwpUSOEWWASw4fMjQzaoywcZflZvhJMBPZJNRcd5M87NyYGwkbjVF8j7SYFo3DREt1lBdgeyngjKYrG3w2vPGOYTTxN6nbzDAxNVYjzbrLxgmH9YqdIasQbw1kCZbwOIBlgdhaO7qWvPk333jJ+U+/uqu3r/YqC98Mg5wn7o027lohFc+MpRfPXHPrssXzzjl9zobtBzbvGPzAsGDjzmN1FkZcYIHVm2cZHhqrJXtoVl32wHrgzVrKswk8MiI22Txx9yMbNu61r4NsKZ9zxlze89my6yDv5bNxrn8d5B08v0CLB+qsnFjjs02GxuLJKoqcGOjiTgBreh5UsD5jQbnCArrHPXFHxEgogILCCD1IaM2oFFgbfRfBJDHXA8xff0fvblwtxMTmOoqDFS6uc40kwiR7Se+JavIuGDeUteeZ073ooBcfBXGaz4RtxunRjHpxGM1YaMQhEgq9uBg/L428j6G8yecRGu/IBitdx5xrRMk0h9XcmC6uCm4KyIj23EPmX9awAzPXGjbJg6w5EpnNGq9daRpsiJMgJ2fTwQPi6CKebJE75oLAxGEUuWQP1sM0KLA5Jbpyxxsjn5YjzR21jn52vD76MS0RZ1VibzOL5HFLVtuMlvbj/t5d1gZ69lf6Bz/Ki/YJj2JJa+c8fofJTR448B9Xmv5DOV/XS+THuT7+p6BkBA73HjaorbabBgPlznLr4CMJCwG8ZVPu2x+9uJfMJMHHLq2olhjIlIYMTjfu5k0VoJEUz6ExHEucK8VmYRKVaphZRfXSlRt8rlB2GnUVxzMVe6fcc/fsUc5Nu2eFe5FQaHJwi49vca8NTnKgwSrI6TGigoTXj+3gwS2gucszAUByklxJaLLAJMmhQlXtkAySmKd608clOSKjGa9tNkqOc5swGzltJG9kv5Hc7gxs2aCR7pSTe4y5kZ/Y8RbHpoDFIU6DZCAaQtYUkuzospLE2hRqZoecG/wkHzJ3EelTmdxhnFghUeYGoFuQeGOno0KtIi1woq6jEmsINBOmJK4TI2rKSG4XQhlMzJpsuYG7GgGGjMdip410p6Kcz8zm1L9nJv/7NUc6JB8XAyzFo+AMC7iGA451cQi2k6VFd7GXQ6yjHCd2JEwzrDxwjpMr/YXM5BmgYqNWITCAw/OskMTNyKELuSQGsiMhQ5aizRqIkFjLFVplUoEYqjATG8CQp9/c1tAYHQXMgQD4cDBeig90nAkSQnavtbISd0XjEmYlJo8TkEf7dVPDT4Y8TjFMvNk4Xh1HJBoyTY26ICrILtDAIqNLAcdavZJkbTaSm2njBu4qAAnZTQAe1aRWQaKJPWQwBR4nVRfjcjxgjWtwtEe7PGQN3/wxBzGYaBz5cRt32i1tncymSKphHvtXB79DJ+0a0zaJoMgkgPOBt4uS6NXUbj5+llELM0x9NRPJmAZ7AozxyhRDdopOQATj7zI53Ey7s6rl7S2lATrqMzMhEJElSWYshxO7kllBM5GM/yiH8MB7Dkx+Fohkhg+hNpW7OaMZMoeMYzGsSR6PshKARIUmwHVuenAnTuzVfEDiiZGASB4xZry85Snv2NdV365HbHYyKzJY3tRk4AqSwVIK7l7GaSzxiAtTG+BUOHonTjXpAqs2Qb3UKsgB5utVV9mUnNqXfxuZNoCRasiMyOkCa9QMXHgKjVfHTgELU6tYyKyOExs5BPUmA6fpIsIUrRkvY/Sok1EwRk1y5OCkd4o2NV4dWWrebOGF/jgWdUVJLm6S5rzprJEphMc27hiavfLtc8+5oae/yo+48icC7W0tfMdNa0v50I4X5+/7OTpw9DdGssKCS9D68hYOHucqHIxSuPTyor26uCTrhctsF3JMQcAUFlDhm9G8WUFi7META474u6g4EvdOe0AOJ8MYk2nnpkDSjAGrq9Eo6HWXwdZqdWml+jp/f10dfGrlrmh5OmFNe84LTjfmefYsmLqD9dzQEGKTw0rhrNd2FszYqT3YiK0ehcKqtT5Eie1MEkCECpJ4yAMDZz1UQtTl3kkS8OjD4HAkk5lRM88ZtQYOQWPHl46dnYqgpnFNLXNvJokVs3YsEVCN39i0+igBNrm6UaIdIvSoIdCkpigGQCRPOUz82SEzCobpw61BaeBgD3w8xo4X51A4WzseQBKDg5TcNUDFxnMB6rkFXfkap4mXG8zYCokcg8nctgtGzeGmACw0OJq82r8IVbt3koAYuTC1gCJkL8renQx4FPzp6piPBY9cE7WLlt/adYJCg/wuOeett1y35gcPrn/TuYufX/86fye7eefBK85f+v99/0Wx+XZ2/USo3qDnN7H4YjJ1rVmzRr/exxg4nHwzmr82mO9N45t0oCGnl++n1JeIIeGPefkWHv5il+9J4PuD+MIEvmXMK7sCk33VurDxfTpReAoXZMA5NIBsLOBmYkfy4lkYe411PqtpYWJ8WM0tpdIZA6Ud5drPM508hZOFwompPyfnvBiTZA43gdlJ4gkDMI5mo7BRzFEu7MsAywsSjHhzo6UjqkR30wAzNB1ujaV2uTu6o6WL4VOm6PA9GRKgpoUMnAEyai3yYLooGr5qJQccAZmRrmv1TuaaIfgy5zgZO+NlziMBUENTmbpH36PTcJjkUSLMYGvTPezwdNzp9cABCCkIVQsYy5SEJA05tYUCkrgGqCS0sW0Wu9Coc8d+NLqJCHJsh+xs5x5u9XKAVOLYHYbHjqQ4gVYpBviCoFog4mJdAoDgOsaGnJlJkQVduYqtjaAXj2x69RRP6m0KggZg/6G+//nAugOH+/RVmttfr30EcOBQ7csiGSRbfl5c4Tt0aPLdNNR+oM5aw52HhMgpejDPEoxQT+IRYgGm7htU8634WOY7g/h2fb4JPruW1Y3VKmiokKACjslTEWSnUSJRBhoNzWSOY5ZjoQEcsJqqJcnqTgYJQ9rSUjq9UtpZLuX8hflkCHHcYmDOc9Lq6FMz/71MNPKpyRBrMbMS5AiZAOrKGvTcoCs7W6IkMrN2RiDBoE92FndOf4XKxWDIDIzA3aRSYYAeI6P28AmStExYBjQrYk0ASVN5kzCbwzgrhC3xABN5YkTXdWq5Nhl1m0pUpmhTCYl7HbKqosnPuDx8wNTKgAaig6gDhCQ2sxiCbmMiH+yxkwGla+rOBB10DTCpPed1CtBLQuIEcCoSxSnRZCAaS260cbY4D2Jq2oATC5bnGkz41jVopDViOT+gwQ8iEfz3v//9XbsGv9yG7/Plyx/5BdIHH3xwuJYZINYIGHDsVRmszJnVfu7KBY++cNyXBF954dJ/3/gcX+X70Y9+VBlM/PHtwuzIu7u7ecqOxWuvvdaEc889l18G5Zvq//Ef/xHh9ddfz4N59fI7UnzzsX6AEAm/NcjaFH+zw0YmL05DAAAgAElEQVQA+KV3nO5moqMmsaZIrFFUM4JG1szkGCQcSwRiTRLIwFRfp5LxNt/klnZw794yDffucS5lccwS5xpNlvtGK352CmUlMmh5tD85scdLcnzzj9A7+8kZ9phExWHirNeJj8GYAZokga7sofQsMpCuQpJQuKCW2aSOTXSzrgsMjqBL89zDxwIrIfF7L4tE00PMEbiYtCrkVumNw2fsKjEDYlJH5tiOS06xCVAtYEmuO8WvLmHXAGF6DXKNJEJFQs1M8M5eFmoWp9otTTI6NwvmczYDSBi4dOs5GMyDrU1CQMxE5bEoQgkLotUwmedWjCrZJCgbBQbHqYvfdfqHf/gHHna/733vY5byc07EyTfBs+/l8Te9/N4Rr5rz5e78wh1Pt3/605/yW7O8K37XXXcpJH6kCQK7Prbf7I0ZiKYE4LiN+/7DfS9uqH1FfCyP/aq2j+cM0Y4hdgnzlgvmyCNvuUCLG3fCJSDC5eV1yHFN4be+EdJLkX2eu+dehkUY8esxOqjUBgpbTeHiGqZng5mWAIRzTzN1WWs0gNySwDE0OJpgToiu9u5nVEo7JsHe3dPJgJzESRVxcbp0TH1kAS6JInKdCFyzObmYD7mnTKI1nZpkwEMmA/pkT0Lq6TFSZo4Kw4kgd3TMBCVBZC3FTgVA2Ct+NKKuKJkSWGPx1YQUETaTgVQ4/iQPlk8DUD+ktanuDICVBK4RXC6FNVLyAF9MyalVrBWBtLK1poprgI6Cmepyc1wBvuQuZgCPjIuaJGgmqKlIpAJfiuMa3gQY13ByHWnU1OSBkwIOOI4azLFLDl+uqeaFuHCRR+kijK5pSm4hQCWJB2Hz3rNMOVJI5MF+I9N+R+kr2jSWTWquVrpO8fD6s5/9LLv2p59++sYbb+TJ9d/8zd987nOfY0N/33338YCbDTolBsPC/pGPfISdPW+mJA+1j610uOTXqtYsn/f4i8f92NWbzlvy4w2OZxDwt6HcCvB+C21+NhXf+lEomnwoQKa0UxebX93jqTwBocXmXjuP1atX8/NUaHGHAQ3hxRdfzG4e7CwzYG5NONk4qNq+uzcOT15Uo5t0uQlwgQyOipMfZ4c2+WMe8wjZu29uKS2vlLa1lIb7hw6aV9QJIEgkzAfJHbMkWbkImj+umaLGtjB+gOVAKwKvq+GaE378fE1ay6wMFMLjwHGpZtnhEJCW5BowYfHHeRWxA0CoSWJJLvBEAgxrXmGfNdMXKtTJTzMec8OYKkINkOMeAyYJTAkdBclJhZMZmVMLM6JYCJ5moyHQpa0bOwBpwVQSSJfOnek0PTQWxqWhxbQw/DglYCpvABKinET+1MVKgsaVOwryQGFiOAMG8FFU9kyQEXMAcmEgAkKKMwnO9T4xQnkvjoH4Yx6ygWksGCm2k1VEgvGnnnrqtttuY+N655133nrrrZ/+9KeRb9myhT/4xDJ/z8k+nl00GDk5Z5/MkrV8+XLcoY6QZ9k//OEP2WDzGrlOZIQqx36AiTEsufp3Z89b/Pr+494iXrZwVvfap9d0vPjrv/7rR7VKxHT//ffTZC8+Z84cfHvdZKutDT1dhMjrMdbiz1JfeuklafHwXzSa2pRr88ELQJbrffoRZM0epxNg8R3xxw5TJQ+arzFaSagN6GVSn8b77i21n1aVPKokOM4fYdcGicrUanLm6glTcr8+tUYxVtFqB481FiVWw3j0o4vstKnNsPpyGUGi0siaaHE6gV2ikYnEyWWJbJAToprIGCaJL46pNiI6xERFHjQ9JiAhdopfYWoDC4fMlUJ1rUv+COJnYqjYI0Yomh4Ay6c94CiQivrRGDwiGrKyMeIMn6i8aVLZexyXuwx0oNU0BmiPKEmsycaUS4hTMVyg1GluRF3kzgmAhPCQmpVEHC7BfmZNL7ti+FySPv7xj3/729/WSQcZIZhepVo2qZEgT/bosoyQnb3yD649k4CqUDqrB6+//NJ7Ht902bmLX9C3yuw4eMGK+d1raxt0HvJDlpXdu3d7MHwnzPz585Hwd6g45p0e7c4h7Nu3b+3atbrdp3ngwAFrrVixgqfvbM35CIAvq+FGhC27hkqtkMiCdiTWOmmBDu0kHL6nBLEZC8RavZpmpjUajmiq4bgJMOZDoLP6SttOsu+Zyc0YJzN3dGRVN8Ccgzq9c8nTQOj5kwA1qZkk1CwdfBxRnzK1KjcnyJ0QMWmK6aYJUw4wEI1FkbPc+3NLRqd9/JQb1MgCZrycF1GXGUJCqCmSK125x92cCIRjHe0nGLOSCMiL64Q83k2NNPHCNZfCcDQiehUeZIGEPz2aDM27rjgikkA2NEOQKyeQwdQTkBN59LEACMc6BqzA6NXBMkA+3Gj1CDUxjsFsQsRJEqIsJepTrqk0krpGketkYYYAoOm4gM1H4vX2jjvuMPae1hJUpB4ltiOA5ZjYY0/c6W6bMb9j2aWllvSXSquHd83oybwuU7fHaug4Ek8j6GL/ocftCpEdfyPjia9p39QsyV1ihjt2TRHV6BoYZyWJC44OnDiNIKgZhVES5Ym1ETc5Rc6slPhtpuM+IRqxuWmhyHHhlGG2sPgWLDonZKyaV55dxU1HKFoyf9zMzrEokRGyoeWVLu1WrW4vJxsgJyqkl2wwVcjMNE5LnGxZzNFH6IR4MigzJMeZiUA41lacHoCcKC0C1BqsEhLTMj3G28wolBCYzBZqmtIiMy5kJppS9iLZEglRtB1biwACTdcCIkyGmuCVDdWEpOHEgMEqkyHgsY2Bh9remDHwcXotIu7pa/eT11xzTbzBIrljO6phWdOBVwwE50f1wzIy/cjMBnZjWih1SjDGLPDA3WUJKZUwHl+wmwaoCEeJ7Uw2wNqp991P7d2TQ6PtO3MmedCY0JpvMn80hWKNel08ePVKrNGVzKI4tdyVADcTa6NvEg8rDAsLpvBCZsbP1+ijnTALSguZoZAQCtPGl6IJC2NIR5pp1DBj7Wa0IAJjsVC4NryjQoFYmyybGFFaADW1o49ak51Z1Jr2mFRQSIuAxqvkOD/TNQkecgQMVk3XSEiFOUqLzymaIrgGTNfinBgwUmVAAOwyRZPAkWXvzigUv0EzwyEtBbRoKjJrG/cbb7wxbtwLrIxrF2FxS8Gl1GtiDHRcXY+HcQXfqJbHJgeog0dydCdHUxIZaYTHY1CT0ya7MJ67bx3+36pOzuGMbVRs3znJmSScWZ6NnngGuU7pzc4uSagFUMyCXGuTTcjOo7b81dfNuOxMtjgnPh5yQmbIj1xzfMfqDgfLKlg2EM4OE4IDiKA28+olCsHIJBm/mpC8j7eXU5/hkAodzWQ3j5yDovw4XZMEKGDVil+BIWkUoSdYffbVKphRmKtoR0pO5KDLbkd1lJ8kWKmmzp02JIHMKDnUkzwno4nQM0RAI3VyssJaUvg6yNhxorLDdYLC/QMBKeITEolTEQGR0MwNLFcIH7niF6B2ieNCGJvFmBi4fBZzTs5e/ipkU0vprIHSltZhf8/MJM8YB50IWdeos3MSYXYKSSXKwQj5+xMuEtz76VIRBx7JUT69MXmgMEaSw+LDHQ6ATcaps4z5EJPA9NOnN+SKLpJGlnyt1XxTGkWgVnFXnGBgFSUffJSeM5ndNUkA0TJ2iuNhjKTCn2LTVH40OtOmPYjH1IMlGxTNHwlpwiQ5KmaOBtSdHFc1Y00B54aNsBkLTXLkAnKcNjSJmJrkePGJfskP/Chp0t0Uoml01Aw2G7bzo5U5IUgLxUmSIkXrIGl6hUSY9IoWIwerOBU0bS0LaudUVjrBElY9omz+xaAkC2pSGxA/GJuSNDMcp0mA2nPCXc3YGSdO8wMZpwAmuVnO+42tpbMHSt0ndO+eHCaaKmQP4FrJlCSZXYnQs3H0E5LP8jCuHYbukCf5MZ2w8Mit96lcRPVr0AinU5Y0r5xSmrEgp+leAc29rJwU8QYjHAjsLUidLzaJhZOhqSTEPRkZ01aM4YMhkB8RlNKTIS0aI+OlZKdH3K1GpraqZE+p89xTGp03yzGOsO6kBnTJltDkSQsUJ0OmZIPUFPJIRdBIxZ8qw8wOrUlJcX7ITG3zevz3vWJZWp4Jo8+SDwEgFo9CLuiSRM0kDJPHCmB/fDfuHo8ijk0wqWcnwUQk0YDY6xESInLVEiopEUuiBQKspi1MA0AGNLppMJZxGgJ79w3au/POTLoJGcKnJ14EwrG2FYQFc8xdAtQqqANsZOIB3rk9Jnida2xMT02q5CjUrqL166izBEE7+BN77DQPCSYCsIpGAU6GQzMJ200AhQkgkFVsRqIdmO4GsUPqZLAZ3enKIQ++D9QYvcPQAVLaSRRlOiXB00+AOgHZwZIKCyFrXZIWyXGiABQzTwZQX4fSDT2Z0RkHoDgPyo8ydpIkimHmpoickBmyRKkn6bitI70oKlFiiiNMHQtMepVP15Mnycdt3FvmrGg9/a08MogDKMbVvv2V7vtaK7UfRlWJAz4qq/2rwRtwtwTmOy+Ru6vGO1UyGeD+O7kYZCgno4DzimGrFtjcWjqrUupuKdVe6D6+1wlCnp1vlkQgTC1gC1MaMBZ9usVlksK80lZ1Sg9qzIN3lrBMluqzqfYiDWVkkyHOxgSrqSFELEniTk3XJ/ZCgneKI2eZUq4Ij6JegAgnba08ePgcYnYV8ZGqE+VkmnxCQO78jEJFJUlyfGsH/ugRF1Y9goEoUfU92OC7gjJFlmx5BGanrgqjZv3Jxk+ilCsBE5wlwCSZWo5trEAcsjA1xlVHYI+celErZili8ycnGPy5bEbCoW096/ryzMXDDbRyeGfvloeyWhjkG+P5UVU+WgV7zuEr7hjoWrJkCZ9Q803w/u76rLVGEn1Wy8854agRZ6rLyRhTaqqPIsYfzxwwXbF2M1HJJkESy1nRN7SUzhio7mip8j0zSW+0dgrzIJkkcP98Mv/2ajPTQIlifrLikyt2EixZupEectLKvuenJyRAGFMCqpuJZ3JyiD8+XNB+y/t4hqkyOYOfyKhIVO3+L+zAyJVmF+kiEggUpQswmtgwS8FCBGrKrHrtwu4EXBvANMda4wRwFLMkLwSsqaURUUNTIWMC4xTPpDXr4ScRKkVKl56TmuBcASycJMBHlniEBbLhMXAJfdw1HDezKo0kdqqTkaaZ8iKbypu7TiBo4/GbrkwE0dpSWzgoLeXKTUt/2dV25KfbL3vHkqf39M3edGjJmxe89MjuC9bM3jyjtffubVf2VwYfzJePaknXNYPkV16/+tWvXnrppfz00p49e5Bs3bqVn15iid+wYcPq1asRsl//zd/8zQceeADyV77ylXPOOWfz5s3s9fldJ36ViR+AXb9+PZjfZ0WXV3VXrlwJYJsO4Meb3vOe96AL5leftm/fvmjRIr4Jnl90eu2113QUHc8pMCYZiOeS5nec5RHLnSSa/Q7ATZ0PyOPBUq851moSbOY7Ivtr3zNz6jsih8wYZyJFW1ISrtdChtSaogSmogtDAGsgAtSacpZ7mJ6K7CRIF009MQVYAtk0K560gNPZZzT55HJIxrwxJWmUkzY5ycCVqJgQbbZ02yMyHGYXhWRSJDTIbUJGXlc6DkjoWrpTpWY4MVEKmzwoY9RIaGrstSlYL1NldGMbpw49CUjMkh8VLWKx1ypZrUgbFj7qrfYviqojiNZ04BQGcoVhYWSOLbbHrFnFjxyQ+1a9dKkLklZtm9V75nUD884rVXradz7dvu0XpeqoFsDjXpVx0B0t/W+cv/ara99/qL/z3h2XDVRb2Kbv7J13ZKBjV+/c9paBy+a9+tjr55k/JGAN+uIXv/jggw+uXbv27W9/Ow+uvve9733uc5/7q7/6K3RJx7x58xg2Egg333wzW3B+V/Wd73znjh07ZsyY8b73ve8nP/nJu971LoTs2j/ykY+wWV+6dCn7DH60lfl3/vnnc3vwL//yL+9+97uRPP/881dccQXNIQM7eQg+bSIQLk4CHJ05ogmrjudVNBL5UT4xmBNC3zPDOzPDfd99YiKcbF5q26j6V4Xo7w65mT+xR7BRfjRddXn2RRpyM9PY8xaggiLAviK2sBEgXXrjSBc/nSPT+7Ynm4pkZcgSJCGx9fk1eKFCi6RxCXDvSZU3hp8U8oCkIHv0Mtul5aSx9uoecljztpGXKS3XBItDUD6VN2224NCkpjAbC/ZY0c60xEoCQyMPcYBOmtc0eiGLQ8ZUaCqxAE1LEWKtVEtid6gjsUGDqDgJseMntiRjSBipipKWxC/d/rY5B970vw/MPkO9vadf07H0ilnP/NVo9u75G3e26T/b+YZ9fbPwpBrQU6k9j++ttHW29l06v6mNO6/KMCp20ps2bWKr/eY3v5kn5aeddhoP2jmd1q1bx+N2bPKM/MUXX/zRj370x3/8x1wOKSTol7/8Jbv28847b9WqVbxC8+STT1577bUrV65kb8Fzd3btSKCx4+/u7sbLz3/+c+4Nvv3tb99+++2YPXz4MJanQeHc4PAPzo6j/zAuoEYHyJ4D6k3kalJTOIuoZcFgGqRLQ2BB4m9VVwyUeOu9d9qMapwHwpTwl88wf9hOaakdQ7ealtQUL/rgJl1ooioqzn3PW4Mm7YwhjTAoGGQUPJ5gUIRn4Rg6moSmRpZ2tGqf8hz9clsyFh8tI1c+J+F4k5Bqk7heCmYyg4USFZG4eKRIoFFHZjHGafL8z7NuWHaKvUzRXmWAOruCkTf2WEw5D000snfS5k3TONZKjhNCF5ONZJI60eiiIKEodTSd0pMH1NNQG7jP5Th2csV8O3L+f2HX3rd/e98e3gaozlh2Qe+yKzv2vmvG5v8ge7YQFYfE+Rt31pCu1sG/N108t2NeV/urW469Qd4z0P5PG24Y0jTH+J//+Z8/85nP7N+/n335RRdd9Ktf/YqH7osXL+a04X2Y5557DiOMDSGbePjf+MY3rrzyyqeeeooNhN53Z5v+xBNPsH3/tV/7tfvvv58n8ddddx1b9scff/zWW2/lAfxDDz102WWXPfPMMwh59wYXd9xxB3wkQ0Y4HgSGE82qSW2gIYsjYfGMJ1dsoThtdICpNUWKtWIMJyfm7v611tLKgdrT91PP3ZufA8wr7pzhc+oxP7WR0kSVEbCa1KxKFjbjQpOWmuI1C9yM7iTnMArljbRwtlIImFOVMj0GOE75Zxoob7JP3vwwXnsC6vFwrTksy+BYENIc0imHVYUIKeAhVQoIw1XHY8wblr0fVfAYZO7lBgZhuO4KIp9aXbVDlZlRSeoYEfkRE0CZKmP0vAXEQvw0NYrscOpDrFUMWbj58ZI6io1bcSpmz8GPFSCZA6WOnoUXY7ClrbPcPmP/i/fMOO1Cmj1LrujYeDcXUF9Do1MdCGqEGFGXAc3yTTfdxL5QHZ2X3F7unAfmmfqHz/zZdzZcDz5z8cz/9rFz/58frH1p0wHROlr7P7H83n987SY1+7c+0r/5fuGk5nBy+R/lQxSiv/zyy9mpMz8S+2Pb9ORLAM1Egl9JYipjMJYLuDaI5GLMZYzbmGLOqd5GGWBfWdu7n/TP3TVd4zQGq6maBBrEZLKRQs689SrhOQxIVpaoeAqTgfpFrXZVI1csg0rXqcw0mQEuaSTQFzZtQ5McetIKuAYYN3KnmUwvQAWMfXAjlSkkZ/jKnvJA5LqHnDYDHNdjQdJqW6q8N5SUwImZJHEOg1UYuIGSkASjps4UsMC4pitrPMkeATsqB5aEnTUyDSTVzkX73vZn1dYOVpk9z/7bzNMv6Vx8DuNqO7B59kP/W6MBJtmDxl2694Gc1/lP3HsH2v+t+2ob7ZrR+jsfXP2X//NV7d0rlfLGQ0vc2wjUrLfV/vh1lIeHMfA0HWuNHDEn6FKdgKxQRiTPBiaJ5QKeZ+i6S3YmoJ54jxMwqAlzwR3rej13n+J7d0/yOKUtVD7dlU2vZxFAWEA1fAmzilosOJE5ATkR2H02YmZ1x1bC6E6U6xEPhK0SBXUWMRKoD+glnHJjGXESsoqNDiVyFVQAUmTWScgzINIIloRauXUmAcLU9BpnAzgZJAyfszWOlBlI9piEyq2z5FxFcgFGXbkt4Ez1Lgao8zQOhIHrRAZQ1KXsUVMiuRGWrtRd25oB6k4yQJgaL/XWZL+3rOcjTQijo+gsBjBGag3HdZNpbJTeySYv9+4u9+ypzlp6ZPuLPTvXtnUtbpuztLVzdsu+dQWhZrPHGyha7pTA/I17Z2vvx5bf/w/r32XTHW3l33zPyv/3B2vXbz1UKbVsPLTUXbmARZYj4VuEXA5CgqAIuDZQF00XSbBsCYAm42Q2SO5ez4BEHnUnP84mYfLHPKkiZPHgnZmz69/vfgI/ufA8BxiTKGFlLOIkh3EOg2OBqWaiMoZNdgAUTjGd1zx9x+MY2m/G1Bh6JM+jtDakhYTghRg5+yfSyJARshZ7mWomCZONkwzT4SF3kVDNSDAW8BEBqCBXcmiCVYvMVKSQSZpYPoGZxHsMTOFNzloXfsdG5E4jQDlXJhuNSINt1GvL0xUw8OxuXvOQ2yHdFzF2ZVLTMubKWAQzI5iuqdO4NPDsikeuVMghD4kg01S6rJLVmhq5qlZnrv3ewUtu71hw9sIrPkXMLW0zyv1HOl/74QjiJzlnnnkm75nnb9z7q63P7F1lu927jrBlP3BkoK+/dgvV3tJ/07JffnXtLSKQYucaCU0uS1zmSTSHAeB3Fnn6DpOJbss6NjR1eFSrN3aZnwu4F/nQhz5011135fZOaSEJTFbbKT2cExV87W9VW2p798287z6KILS4uMYS2PYittBA89k1wCtR81Pd1k4IIGBuxRmm9p1s3z2EExIPkTh1wwpgWFq5Xoa00IiAvHYPVH8UykrIeij72Q1BHFFuDJEwSlxgny6XuHTjEXmuX43dtYCmioW5ihJiVrQCDtYoSiM0Xe8VD7p0kc8hjeTaH9J7QhiZl1zXwxUmkTSvLkUiT2Ydcg6xPhcCQKDEZNJs3svUZZIHFZIAYCCqAWTAWE0Nk0wyG2N+oKFOEUF1bdbWSxSewuRNqSONMRvkUMUnuGhw6hq1yUmJKpMQt295YFZrx5HVH22ZU3ve3Xqge+av/r71wMbhhsrF4qqrrnr55ZdRBB93fzNoq0peBhflfYf6/u87X9lzYPA9eAi9A213br7WXpVB1boO6beQkPB96m94wxv4hnX88X0vvE/P16uvXbuWKc43PC5btoyvjuGvSDW5IXPYuJ9AaOMA7MDkK9vPPvtsBR17hfn6djHZwfM9M/fddx+OsrSsRL/9tGnTJrpQfP/738/dBX/eike+0yby+TIcpgjh8ceyUT6umPnqd4vH1dG0N669+4oK77uXB+qLKbOORYGBqx4yA8xDOJrnACaDsXRFGNLOVCcwTH2SxvadHDI/k9V2WAMk+SPOW5OKo3HBWORFk6RJj40ykETCFGK1gYycM50HHADWRkpiwX4TCwltyF4IFHxJEZxYcJMuOaV2ITCwORFbaGALljQJis3mGmEGehJqgPGFEMLOvborwiTOIb1HQqKbG9v4CWMk8tIoHuQQzDdIYkOeZBICs8XJlJHISSxM2iYZ0ILvZb8gVPKgwrSJMwdhgVa2y3NSXYpB+TQZm7iAOVzjtjCNgY4CA8xmEiH5JJnac8YkKJ/xwMXeCcALqi2rq63tpTK7jn3lyovl/o5NP2H73rPyg217X23b/VSpkq7wzUTFBpXx8pWJDLz2FT9ejge/WZftfMvAmxa8/MSeNZjbfyj1Qe8NS5+4Y+N1dkZascWFHBBfj3nrW9/6s5/9jC9i53shIfOq+nXXXcfG/YILLmAt+Nu//Vu+mp0NMV/czi5527ZtGOEb2Tm1qB9++GGsXXPNNa+88sonPvGJb37zmzxZR52tM18vc+GFFzIMuuI2GjLf8v7d734Xg/xyE19f88ILL/A1NdQM89lnn+Ur5Nn6UxMkXyTPN9Xgne+p1MYdJvcYWFizZs2Xv/zlb33rW4TKN9WgyP3A6aeffvfddzNG/lIW74wFOQE7CeMBsH/qfCaxSZ5pxpIlNDoW3dXqmQPVLS0lNiBMCcqUTi9JGH38IzaiTScnMoU1hLO1IJhGXgpUkoPYyEJCyzbHxEXzRrIBWGIjyViQk0AKTJZm7eARJp9poAWBOlsktyMDjECWX2oVHAmYVgxsoZiW9OJCitR00TTBBg3clQuyFrI0c3CkTIqDnPmpXiTMUk9UheQ4szaHlMRBQcbLaKzJnQfSyLu85PYm8ZgT5QXq5gtIKyYTOeqaoiaziiql0Yt7xxwoP4pEu3AkFLxTy13ESBSYVnvOqYmJMztw/NZn33EPlaGRzzhFFTDMqX55ymZgrCQ6gtSkKGuTWUFKkw09ZMoETNQFpfKaauvzLYPb5vnVlksr7U+29JUHesq9e8o9u0a2a2eYu3bt4lsT3/jGN/Kwu7ZxZzzJ4PnCx+93X/OBMx5af3BZqVxa2bX1vu2XXrv4mb19XVuOLLp60fOzWgffFp7RXuK7ZpQmLuSJKba5fGMjcvKFC84ZNspweEDOE3EmK1vw3/iN3+BbIz/5yU/yO6myw/c8PvLII+zpIfz5n/85D8UJlF9afdvb3sau/S/+4i9+67d+C8tf+9rXvvSlL/FtMw5+7ty5MHmWz96aOwF+tPX3f//3/8f/+B/QaLIpZ8w8uWfPDYFfd2K/zk814UUW7r33Xm4Mrr766q9//etPP/00cu4N+GZJzL766quoEx7NBQsW/Nmf/RnfGY8vrxSO4RTIzYATJeAaYJyraKGnlgA1RbMXYNqQYEOptKKvtJG9+5DUSU9ofuAkuREZeUHvkDngpKZwanBKYgqc6yhXOKTxSGjewoiHE12M2EiMORdjGTlXF/XSjMUqELq6upjh5FbXe4XnGuD5HyO3heYBARRYoAuCrOXS1CtaJAhHSWIkdjkGA8cfaRYmQMJFXMkAACAASURBVBzrOiTkusOEj1ATVbp0FdzD2FTiqKCpGFSLVmwktzeq5/qKBCzAiZJclShsRG7SFOokjWKb2id5PiP3prORL+vKKU0BauwkQpMNZJaa4q0YWjTNmUJAZ3cMmLEwUdmBOBX0crKP1fkefU0/rERlx6WJSkpjVpkzyio1KqOfQudU2l44umvH4J5yZXG1patUPlhicg4xPxWYa51TajJJAGynKUyM/Hfcq6Xynt45D+26iJ9KxdmWw4sO9s94ePeFvPuO5K6tVx7qn0FM5y4tVaqllzbVzrb4oN0p4xk521w266tWreJ73Llj4IvY6eVZNRtfnmEj56n5DTfcwI80sRbwmHzhwoU8ep81a9a6det4Tv+e97xn5syZqKxYsYLfaWJzcMstt/BhAbt5hMq13bGT5kdVMchTcx0MPL73ve8Fo4sXXraRFr64T2CbznHizRzMIueJOzt1frmJGAgbCbcK7NQfe+yxd7zjHSSLTw9IHy8CcW/A0Ox32gNNHYbpKRVxFGZTQYYh+HwQcK3DhJYJWQtjK+EO8rWW2ndEbpyQ75mJY88OpLg3yx+xpDi9xb3NONW1h4WGp8Uc00bbd0w1M+RRckY/HOLEyJBhQKBoUKrdFHAXQMWxAYQFYn2UO/gvpnh6pA2N8kyGE46bkGXWkiyIHMVpFTVRsUTqsRnVk15o2d5sALkSuxBwJJDdBS62b2Y0EoW1LefRTacu5EosHHJLYp1ba8lpEgZNlRhPxOqNRo5qHPs36fWQE/kxhQzKMmUkK8+opoKokhjJjsvKdMWkIUfCdOVyqcQiwbKMq7auQb2/tqcBRGuN+FaMYFjkqDgJMWPxLHV45FOFDFMkV7rgq5h8CmQzEKeWe5VMEsuMRejEkk/4R/Na+9cqxYA3ZBLq7nJlSan1cEulXOJ+rHZ2yKnsyKPtl1urc846NGvxkYHe0p7XZh7a2a6u+rp17G45/3vciyNTb2dbafmC0u6DpR1rG36POy+rsPHlTRUu52yIzz333O56IRS24zzJ3rlzJ8slP4PKjzSxFWanzqjAPJJnz60u7jAYG3JNZbb47Ox5FgUNPjty7hk+//nP82YLiZ4zZw5dPF/HPgQOBjGw0WcngReYBw4ckDXiYXfOfS1GeLqPr+985ztgmPCJDSa3BzytoUk8HFoi4bUfJDSJHHfNJGrEHGIj1OwJPDKDmh/UBthRk1qTQ1259kUoqNWVqzsJhbzku7JSe+4+mr9VHcNx+RAU2GyGU6A++i5NDw60ATazUXkR5FQqPkeyusMNshkLQ3IgaCBZIHkyzGhQ0z4OExxPE49Ixmk6gcImAMSRepQbsyZQyDAcliyKyIli0rR6BFkOEsdmIBV5iepDYtlPaCOwIwuKLbGWNB1/Iy8KKbeXLrLq3GI5pjdxpGZBSHIELddXtDakEVkooGGtoNeRyKnjiSoRx9iEZYHkCIjsZpYvF9QqNoIiWmpyjug0oUYim2YKoC5wqi7IACmlkEAVMZ1bpbdA/VRXowyQTxJLr9ML1pzUxFaSwWbqEFze3/pim3aGbNTRKZ1RKb9eruwsVw+fcX3b3lda978mC7IWA2hpq666efvc5YO/mFQZaNn8yOKdz86Ra28FayHFH2CqzL2g9kXxwyzlw9tbjmzNVSIyhsdqqF6WQmUhlzxiITtp3onnCf3ILDh95H1kFsZJi7sOMhbPPc0M1ThVwA7bIBuPxwgQztZoSZhVH0MJQU6Al2YCbnLv3kzAY8VpJuzRcwqijV0R5zotJtCrp25s35nGuRaaFGIKZpPTBnIsKKqZ64uu7ImgMy4rhxyNiGBhbIIllzBqGds1EpMNLATkGmEhpWhpJWDW9IRmUwLyKw6SSDYhCsVXXWAKgq2ZFnUT3CSHSGw2sdBk046K7TTqdXpFIMPJUtxkGKIpGOFGSR7SoIxIPQnbzcgpMAiNwhjhCAtIBUnUxWNSIFgSmRHLCLRojSYcJBRlGCAtusgwJRoRWUZsSkYS2lg1iWcM7Y+ttSbHiFMVMiwgRcalaTyGA2wypClKI3vOIZhRuBYgkyIAVMjwiv5S/0Ble+0PU2uFbcZFlfaHW3qZ+j1n3tC2j437BnVl69Mu33Pa5bs2/6rntNUd29f3LTqrraOz9YV/Patnz+Dr3FLB+3GvyuxeW3sVJGuuWMJrLTznzuXgwLt2CBHn8kcm5EWXEe/a8ahjMDLXw9XCVyz2jjBrin1Pcj0+Oj1q/8JnllALR5A1NXkkjvaEh8RE552ZFQO1b3k/9hHU8WHpuFA77IjNda8lWQAnVzdhDsmBgIo8RnKCzUnsF8iTrqyLxFTxqOnljpqo9Lk5M5mSWHAzDioKkVO8eqoLiTl4oZlEwnkhiUFCUFN2IsZsblPuEiMIo+tcaxIm6tKK1owNYiTRiE3BrO9zass1+WENlFl/ymFTAjKiGoltgmNT9mVKmBqCsIB1ozDSTLCimJFjSeJLclmwHQmj92hZtCiJjpDbTuQgpGlJYoSZQ/GMJcNMY2rxlXnreiwA+0piKOZYC5vGUpGEmmIjwo6ZJpjiMwUMmdpaEdOFnAECqMWhFrAXAZmKQikip1glYsiWGyAUBwlFRuyCLoLXn2VLHQJ5Fg1+tCmtghprw+LblBy5mQzK8iZBYq1JrdHTlLS4l1NCyDCbCmF50YGAeaJCHf1gR2CBDJAKCrpgStYICXEhn2A4qrNk2VG9nr+ErLZc3Nqxt1xtr5TmVEovzqi0VWsPYeuTMld7ULhgzUFiaesov/zo4YHe6unnsqpX5608vP3J4zbusI/buOeaZME666yzSgcObN+9+xAHOJd00gh1jOvHunbsGbdwkwnwVICv86p4QuT+5UCTvk7RnAEdtXjWIaHJRpJd+/L697sf+7pTq4UTVXx6bMQS0bMugplBaN3YZTsCcKIEZqIVm83g6GtkuJmhOebEBRGylWQDxAWD99CY87qoyKbJ0CyxKYQU+BRh8wFI1JRibtM21WvLKEYcrcUuOBR5wULEorkGOIAEmyN1RxJptowwyhMMzWHYHULyo4WCFckXZnLeaIWRnbqrwcrWLExCciSSm580pZ702qaNAKwIOfqiSYkqxlaRhKaYqqMRCNFIxHQldmQt4SQ0MsxdqJio8wSKLaaadDHDrQ5QYNQUcaglFzBZvdCiRLiuXas4psI2FQFd1gUQDAddEssj39iKAIQiC5tTbCexnzRt39bsJUqE0aUQvO+UUKeQZ/35mXRFgJm1kEiyHKzJCHXEappPl7H5gBGXxOCI7TSvGOO3loTMDYqFAMIjyV401AUZGtnONRXVJxtmOBROGZ01ueFpUDpTINCUJJfcvFBGqLH8XKnU2d+3qNpyqFzZy7syfbWoyDO/P97e0d52dOnAeC2MCpvqSue8/pmLejq6aovMslUdm17oOf/qWVo/Wtn+Z0rRxp1X6K9cdtp7f/u3NnZ3P3fvvSsGKisvufiFnh6+8mVMhpoJZhwFJM7WhTm0SGoZrXepNqcR0MBJNwRqgXHKxjiZbTS0KSHnMMW0JM1GQ4gq4ljCn2pvaiktH6hubi2xd9c0cK8NDinJEhxb1qa7sG/FCKTi3uKmgywGSRixmbWfDQaOaAKxLvYrU9T6e24uEmA2mnaRVZcj5OLkNiWEII6NJE0ZsQUD8y2xYhYkHJpwKJIDsJblyLVreYwqkohgOUDYikkzuqsFEfYxLEfawaPCAxf+IIfe+sW6dhmWnbrGcXtlhRHtREyvFKktj9gE2aE2zUBd0U7sEpZNcWJv1qwlESQqtiOzjZii0ZuoR0mWQ0q9xeQ6oo875Ci5X8KO1OPlxrsKW47hGXPIiIpaQKYcp3TdtCNJ6I2EiGXfNJo2YiCObUaOhFmmVQRyCQojsRYVHSfqFAbOTZEJcYsJE4L3l2DrqktN+4JgOxGboN7YZYN0RWw7zYBoUHaiRKE2Y6d5zrBsEoxnsl1gIaZacpjOtpkTAJR51TpxwGpmvRMkRdMGkCWMuaS9qzJn+ZHKQPnAps7+I7UtpUpPqdp99G2Z2glWj6q/v4+PoSu9PS3t1dpOfXHP7KX9HbNr5/eRPa29u7sOdM+de/aecktp9sLWjlmDW9YD3bVvgknKce+4609Fxehsb79+4aK1B/a/fPAgCWBt4pEDr8R84QtfeP7pp3949902VPCqjDmjBzpUPmC1Q3d0wy2AC4Ncdz6QArVchpKrcgKFnDlM07hsncBg5Jr0krAxCaMZU8PlJHyahKqAk67sEHgRbVX9nZljV4ksqT7BmslA4i42HZWEbuItYjkXJxtIZEacGHFXAtR0DXDJ+mK89FJT3KudhIRRLgJ8gOTCkiNRk1oP0pjeMmXLwwWYklkbt18BDIpTYFlRJQQblNxmadpyokIzmrKFqCt10wpMybiYMhXtRCymOLIvier6k57B32f1tpIuWzYWP2sHptyJUFyLHI1n+epFXmw20iJG0c1ovMBa5ItmSdJUVOpNDCJEQq0S8VFZbcdDYYujnbr3OjJFTYlznmYcgjDWskKYUZ5tZlUSCeq57qBFyzRFI4gkuJZyaUZrqaOt2tFandFWHqhUe8IDXLYty7qqS7qqszt5sFWusKLua31ld/lwf85wFFvzIXksHgK6yrbjV7Zjeq0ljnWjXFgZgFAQkrUKOLJjJqDAaaQl2PEk8pE1FXBB2EOaZT6rxMAYWrwWDGkkErAjUwKqIUhoprJH7YJH955IUC6dfsUevv6lZ08bf4Q6Y0H/3g1dO/hD0uScCSF2Ln3LqjVP9c1/rVRuObK79dCOzsO7Onv2tlWPDqhjzsB5H9jUPuvYO6V71s3rfuBM2VAGNPyGG/fbb7vt2ccff+jor4TytYx33XUX+pwq71+58sGNG3cc/eiwyY17PEiKI3uokmMWhnxs9it6ugCqDSJ/qmMeTGoZmlQD4QAp2+MXVXTh+TBKp9Fmo8hre/dKaUPh98woHoIxkLWkibAZj1FLGEWN1E1L5Mg1BHEEqFFkYYUgeQRgmwWoSJhg6SKkN5Yot33TYq+1EJpgYQQQmOTEPOT2Pdd+NAUWR0L5TbSyhMRCbEKOwdtUNBL5kRzliZ2kKzaFcyOnywFkVSxJYsOUtQSovdFhbWETnxt2tBONxDASjmPIBZCjI+lKEu1YN5ItNDO3VzRzaEJzM1GRXEKwmAAKigKqc81KyOUTDrpJUa9q5jYJp5Zlck6RogjoAtQbFSWPEtGQy2nS5WbsndlWmtNZ7Wyt1L7aghLO6faW0hlzSktmVdrbgnSQV+KcXNTV1jWzpaOtzNc9UzDBF5wBaiNpmVVt76y0olnrbeWbM+pp4x+i2z/QtW9gRk+19kl+e0tlWcvr8/o2Pfna/ke6W9fvZYk9thBF7HEpFbnDhx9LHKnlpFqFXhHItkquTXGsHoG6qC3MteDeMQEF8QzX/piYGtLI0XwPznCCJEsuyh61SjIEaAlfiglt0jaXXLK/c27P4Z3+2dLSnOWH96ybs2dd7evLc8vi1raPldvurPRua3zv0TGnf9lle7uWHar0t77+yuydL8yp1t+Nx6DSSM4B+a/K8DbqOatX/+3Xv273XGKFWXoe2LDhjXPm3rt7lyRY0WeFJucCHyd6fWg5qUSeWscsd4BjK+TwTKrH7T5MzQyTKaHD3Qw54UTFiBPasJq2Q2BSlCQ2eXK0vqV8RqW6tfHe3XYwYiwjWYNwYlc2YKtAU5EK2LoR2IIUqQ04j9Di3JRQcvMNZJmmCGoa0zQ2R7rRoDlWj732ZaHNuss2G/31qi3DFI4AbOMy1YwEFZu1TSu6SwZFtpcsEM12AOIkdqDFLtuxX9kRLZIjjl7Mj6YkjBJUpCUhNU1qlhQKmBdptGgzc7SDt7qBzcoUTXcJSO5ex2bFRJKV2yBdtmOAMDqK2F2yqdrWsEDRFY4auSSiga1lFUBugSk+vQZWjxKwmu7VllFNuojEf+GKNc7WMlWlNLe90tVRndlebWthZ1zqai/t6yXCwS316V2lpbMHZnXQrIdd+3cw/rYyO+zO9k7+sKEmqUv1YX0NVsozK20z+1pqW+t6DLUttpaWgWrrvv6uvZXO/ir8eq8oNV65p9SyrtLeU21la15XPL4igMLv0D2mM1DaXFrYVl61bNWe21e/unHL9gc3lp7aVttyMHwbFY61uhQrcoFcLRsRiAlHgiI5V9rBciEOdWLQBJnKxiO5zCa67hoZsGs5dXNk1qQlU6OxgG5ihMCSAgdJ9EKThKNoJqmm6bQnNqPu1MJzzz6455WuGPP+TTPnrzlQsHHfOdB/Z7Xy8YEZd7Qd2cHtcP1vTElf/Jy/d3/bxgcWlUr8nxZSRyGT5Lb2QwlKPSITF5ZKP/7br4uBkK5HH30UABPMl6K/47/8xs/+8i/1dhQSP7yJRmztFBhBBnRQRqA4jVU0/TTAOGmdK02/2JVkI5mfsYkWe/dufpup/tyd3U2iS9N8AHw1LUSSLWIiF0hs2gJAhZNOHJrRIxbcFM5tqkvuZCfWskmvCG4mpiRHCM1d0Y6FdUuDeUgINK0uIEJ0KjtsJVlAeDbJby+wm2E37wBEkKJqmbJlhInxpBl1bTbGAN9ym7URA+yYZpuWiEZtiTkAGwFQIsfYwIqWGNgUEuwkTElENsHCaARMzqXOVdaXAI4C+Ucusl3YWuIx2nQXQIpWtyRyjKORiEXAiIWESlO1bUYv0SZaKgwqGpHQTIOsHZj2IhoSaBR1ITSIeE5H5f3n9S/t4hWRml5LmR15+7yujo4ZPH7nqNUOXKXUxu65d6BtRltvqdzRW555hIdovNxas1musSg1R7Vn2HsGZuyt1h5XD5baTrzGYGO9tdrWV2WfJIWjBP1L+IU77OPZ49Xi3mBz/8Lu0oI5S4584MzXfv3Ixntf6blnXVsfP0cTCllVyynNAnOCXg2amSUwnzWloamX+cNS4weR6IpjIzYuPnIDdWWZUSW3NxuYzIqcqCRNG0+AowI0qZJYUFN2qHVmASiR6SbXJhypCEOjGcm5uG6yZt9LjRWxQ/6bMZJr+UQJWzpKldrddVpaOzh7S371Je0ulbaVK99t7fmN/pn3tPR+sH/G+ZXafeyrLf3fbD/8UvnYGzJZxURS+1UjL+Lu42+adm/bxh/OX3XVVczy5557jt8i5adP+Q0jfo3o6aefnrl0KXJ0peLdhi2cDIDpGCdc0hxNBsbQFGGMrbVmxhXTkstXSNTqNV8SNY1FhmlaFpsjLQiSqBkNJro0VcRhQq+v7d3LG1vL/S3H1jI4tgyGTJOShESTwumgMwIsDmDQTYN/oMms+tWMXFnItRaNyyOK0aBxNGthop7rFKFpVjQzmjUtqkiY0GgiVw3Q5ZPFne072dOXeERFYdVybV1HYhAVHYkCiF3RWi62EF1iczOxKb/0yoV7I98cgGkSJs2s0HayQLrU7rJ9SeiiyKZrkwH+e1aWdH1ZivJvDlo2JQukwkLbzAXRSEIgKuyoQKuHmcYZVeAQGHW80kf7HqasSVcEdyGM2OoG0jInkdOkiyI5IEYIntlW/a0rqjsXXPtK/+z67rp+XtfeKmkZYKs6eFcelIZxsT6qNcRacpQ2mf7lHmNfZebDRy5oK51//kXdN128/rFXX7/vtdatB1o0Guc5m1KNAwLFmUcopoQ06/1parLWmELMH6sDmIHxlQHsQGBvA6CXYqAmtc2qy5HQdJfJCCluGkhoXcsFsnYkt6ksSCzYjk4xDAKsRW90jdyZEScycy3bQqNeyzFFwb4l1tVNVBwsTF0LEjJySSAbR85E4kpfqbUz55giPxpmw3D4o9Wft/T+cc9cv+5yxUAH3/X+f3Tse6Gl2eWgjRRks3Cwvf0Nl1321L33PvDAA/J/2WWXPfnkk88//zxN1uzuxx7zrr1hgNO9I8lb0hzN6JmaySwfjbWJ0U1Op9gEE0PMj3CUKEhLpIJQILEQm2C0rCgQtcCsDtQqtimPufXmavWM/srWcqn36Md8uTSZsmtx8BIljicKs9boVXiiWQthQpZEfLrMFLAkauV2yU5iLWrZlNXVG5sR2yBC4ci3xEC6tiDAks1PGnOB0Q6S7btPBCkmxhNrjjlxbV3JRUNIcQBgawm7S3w1TTNZIKl9jUy0aEYLWUeJHZpwRMt2SaJec3BBMTliWVONHBX1Slf7FTDnC7dP0OjlsY4OAZgiswY2KDm6DFy1u2iKb2AjWKYLF9EgvUnTxnNtqjfWOIpNcCODkQknNmUhK4mmIobPyHnWfmjhFd19C2rqxw6CjJ2q+cWMlhd6l79QWr5s5d7bz9lY3b/x0U0D96xr768MHqMkpU6ZDkTsFbY8OVL0Ion8RqaSbSJa+kYm85mlcDRXJYxms36taKBIzHTkAtRJF4rRhe0AzARQdLoJW4WmVJCoEHyMPxocEmMNI6JFjMTyIY2YIAuOMLuNpIv8U6JHgucQDA7maDC22QxIIm9GpYhTLR1+vbNt5kD/4WMfhXXO6zu4fUYzZ/2t/TMY3pa+Iy8d3r+orYPdyYUz5/52/6z/tWPfcTc3jSPwpv84yo5Dh956yy0//MUv9A1u9PFV7mzcRTpzxsxH7rmHPB6nMyGNMc7+hMQ8AidcOJmpI1DMVRnB2ZVrR8JGhyDxEpsRR8uYopnbK6EI1BQkcZGSMFoztkEBaoqWLWEzC8DGUu17ZtbXvyPSNIXhJgCDiRBJJAhHGnwpqstNcRijD32uqWjcZsWUKRunSaHLvQKyEDE0a6lXkixGK5IjtpfElIwkvShK12Q1RSMDegysJ2Fs7OJqI47MUkvR1rLu5AKCOFY3EEGKpqlJbS0BydEVU0ZsKgusboPSpWkQLZtmYJtIstZME1BUidCOJFdTpqhpUqwCJtsUmeIOijmJUGeQaRFYHUCBibpwpIHlTq5pwjGOTHmMEpFdqyvqYirhq9ccgEKypJG1aCpi+OhmYxOHrrec2X/+mtW/OLIsieRUM5uBbf3ztpXntc246OKLN7xjzdoXug/+dF1b94Fy8mK9c4sFcDx2kvgAGURfkR/VRY691qKLBYdiCUdcD4ZlgZrprdMBTJGdXGsyAgeg2mal6C6p4wsCGEDJtWkVYlBJLNtFBDYlMKQKNDuynSG1zATYI1p2mrUZVcAQYvLVS/4pJERNxcbYORBIAJIX1I0i9zALdHO7tjw6f+VNO3r3tPbs5e8/qzMX9ZbbWrbePzeXHIVdpfK59TdkTmuf8aPXtzxd6f/tZashnDfQPqNUPtTMxr/RDzDxoOsnP/7xTZde+sOHH9ZyuH37dvmeN2/ezW944x0PPRhDmTDcKPvjF8CIj2vzIXlOS4UmEzQ7d5s3mDDHfAhZg1kJMUQhmCKhajXjqZiE7WMt4JqzFKySqMgyXZbLCxIBE9yMZGsB+MhqbWtp9UBpXdi7R7ItyKZ07Sgy3SVm0mUVesFJM3qJdoTphS8sdePYNM3W7MUAvmlRN8GyIC1qAZuFTEmaSKDVe1KQMEWLQr0ww/adwrMZnRQiZG3adW6Xw0A9WoCspiN0M9KsLpqa1rWKXMemOJZYUXbEt81IE5M6EWYl0UjEVgQIu64LjlUKUrq2IEDOAVwgeQbGtRPvnIASgpNi+8iFE0IiF8ceTY4S2wEgd9NkgSiXejQCR80sjS4LZd9Nu7ApgLH9wkd4/sL+Wy9deH/PedY6BYbMAA/gn+lZWSqtWH727i+ds2Hvri3fe766fk9LT/gmDRlR2pPkqyt7vOw3y0cCnwKgmJkACJboiqNnw6jQVd9GHvu+IITayufuIOF7Cy6zakYXMRIwBVOy6TCyAAsws3IkNm5CFuQqWmi+JFmDZjYC0ULE5hfED0cepUgqKFYU0FGAJiZCyDpYAgk/t5kbWC4zEVb6yuvuXsZvnfKNkHTt3Thn72tNPW5nDT36NypV7sw4fjqETLjae2PH5l3i8Ljm4NdBMpNYl7u7u50d1K+cN39mW9vTfb2v79uHEpILzzvvv95++9///d/z1rvNNPl1kOaPK+AQNnMkmqQVhxqNxKlToBVVCmj8coo/6xCtScVcmwW6SVfSbGQNmgoEgGoBS6KujojrRiCqCNumu6QrubFAVIkS60ZQbBmmLPCBFN8RyVvvxx6/RCsBO6TEsoMM3GN7iII4seNe42g8sRybVoxObcTCaE1CcVB3l81Gm+qNEts0EEcGLRwSFNjkD5tYqVmguIJCc4SymZVYLpCNRBYSj4lZdLOKUsmqZyVy7drGE5sOPlq2VhKh5FigeAfA5Uq7aoRWBKNLEUAO1t6iLh6szAdYnb7YjBzvWuBwLHzJEEeK5icGs3JJREt0C2JImLlmbROyw4CZNCWxhQRExaQrNud1Vn/nbR3PdlxzoHLs6+Ei4eTBx+Zf/phDf4Dizm85dG77plmHNjy+8dCDG9u3HTr2pxS5B6LRNMAa/Nxe2cnOAQWQq6IuBxA5CCk6I6g5HyHrFEMOoIgPUJfkCCWX8dwapuWQaSYqUSJyQrC6gPkGCcE0QLEpM4tpikpkmM0EKXJBXWCELi2JXhgVXv04HDsWWePRJrh4UFn14Ur+r955Fwy0vXrkwLqeg/PbOpgiV81euKml/wude9O7kzzTRFjbuNPFOPlsOv4AE0JmzVnz53/ytttmzZ697t77tj77bGXJkid37qBEa964e/ATMPIYgPHI/DrsrJ2RGbSdBDRpDRofTycb98RUtllgvFEX8liwqXUnGofgpmczQIWuCNQ0PwtkDRV1uWkv7srqDinJWkNFBukaseX2UvmsSmljS1V7d3vBeMTZZkHAiieqC8eAZVDLfYEp0RLFRnz5VS8Jic2okpU7vIRGs1FinXm7i7oJjvYVWEKgKYM8X2AHT1p4GN/ItXSZzCLYoJr2pabmPFjNrF9LpJjQojVh6siJ2KYAVgSLQyQI2ho2pQAAIABJREFUYxENCYRYi0/tol43o6MEw7QEvptguXOvgbrcNEDXj+F1Q+WuxLK8UMtUI4NST0JCKEXL7cVdMmj7JkSVXKeRIJxLw6BdCEdffAnMH7xtYMu8q7v766+22/1JCY5Nr/zhh/4AI7ejPHB6665V1Vdf2bzzF5tant9Z+5wnt8TDJwKHKQpjU1jHMdeahVhICl1IXJuJNQrLkQByAc5lFws5R8S0uoAsRyFGYtM0y1ExzmUmQshZFUnsvcCgAxBIrNmyDQ5pKgkv21RUshNxljmkBHUKh4M7KwB8maVudETgwGxyFM0zHerllfb/s2fu4Des16Xs1/+0Y/8DrU19DxQey7fccgt7RH0MlGzc7YZ9JIPo5a+U8uaTN+7mNwNwDa3J1DRpMLHWKKGN5ImXJmloNc+MLtByM0auGTasV2UwpYIuQCHJuJp2ZCCP1LHQS1McA6tgSkLbTJpSV29W3XayIKpEDDM2I84aKZZIF042Zis2irn23D3zvru1bBkJWEYSL27ahZm2k4BoNulq1LTxRgTkTZoVTQapC6KNTNuXisOgycykCaA4BrA5BonBRK4m1nh5BnVt3xOb0UKxr2gcpuJJrMGJRiK2ei7QyQifouEDIpOmPEqui3qyCYAfOW7KjhQTAl1R7uGIZsVcuXoTjq1ZBULENLkosomHSfysXdTRVMRwKLKgkNSUxJgmWAR1UeeWqJJEFfkJjS4bjzRhkyPH8ajXnFvP7Vtx3hte6D0ra+cklBw3xXPGH/oDzBL5Vr15LQfPb1vfeXjzD57vf3gLX6SRsuLRUZ8OSlZuTQicjCoSQqZpgoXIk5OxwCxaWddIpAKg6JE8QC4wzsaRkrh2U8zEqYW2Y75Awk96i5u5NjFop4l6I3mkZW06QrpycVQfEkcjWXKjCJFTOO7azTsMAJvh7PIFGeOm0TTOOs2XlFuqMxaXeMOm53UIV1Xab+/rWl6p3ZRuKw/8Xfuh+1t7MxO8gSXc33zzzXqRFMqePXv0N2H59AZS3nrn1Q46k7E1oA9PPOY2E4NJc3jB1dm2ICALOqjuat4sKvqKWSyAKeiqzhoRR76ovcpIAt8gq5uYhSkvUrHHRhYgNOqSL1tQGMMynhttYjY3Tnux9+IgE0eN1JHX9u79vO9ee+5uWqLuCBs5dVRmAhqR3VXsDpoJuvxkDSJJXNs4oKBgMGtNfMmjWXlpxJcWfBOibjaGyHSvdW2t9kyhXGbVyi61cKILG1ScBTZFSHzZIzlxsRGA+PYiCUIVnZumRUXhGKokKAIsd1MSNVVnrWUVoyThRxcYjPYTpo008osuhRWMFEHmEkhJjFgXprsstCQCMamLaaiYU8yMrtES2UKaxgIi5Nq8aHH/p65dfv/hS5u86MZxTUt87KDmDy/0B5jPrUt5AH9h+4bl5fUPvHTgkc2tG/cztY7T9OHjYOncBFDQpk6OGk0K56NPSbtGLi2AhRGoN0oaMcUptkao7BopIhMPgPPFNgWG6xQjjfxG+QjMKs7cOrFG5EhcJypJGB5vQlMzsSxhVqXAXa7ZXCFGKBwRLV/mcGi4v9IBsnBYYGDemoE5q8r9B2rvsbd3te/4ZflI7YOkhdwzlkq7ypV0lSy0XouSV2X8cJfIhvuGBiPUtRNHWMsmNBtAk7SsYpRgRE15VNPeR+YiGokYR7EZsbqiXwcJLVvcC4iRSy4JjxI5ChwLmipRK2LsQ0ACoI64oClatFOMs8axINdJV65lMXNduEt2sjFLEr0gsVauTQtt0xIDxe8mIDfySACzdz+n/tydT7Mckr3IgpuyaVpiKjbhZL1HOyIj0cLhLnuM6uqNEvuyoiScuU2uRHZtU1kAh5LrN5eMEHIxH4PSFU32LZQFNTllWG1Zx7xNbGQZPsW9alKTDeMkYJPlUSemT0+0YpCJrpsyLgsWJsDDlDz6TZhq2nVuL0IIic0hj7htJoqJC9FihBGbzA6ex/A0uezpKmOaHf3/7L1psGXXWaZ5bw6SZc22rNlpzYONZdouecDyQHlgiKa6IIKho5oGoqGb6IAGIiAM9BBFdP+h+cEf/mHCgSFMQICBKLAb45LtojxJNsYIy0JDZio1WEpbsqyUlFLmHfo557n3vV+utfe+J1PGZbvuCsXSu771fu/7rbWHs+/Jc8+VnCEEcGiRMhKa8Z7WqFUplWvigmqpoeETv+CMtf/97Wd++NgbnlufLXOnsQMb18PoXpT5AkfpZeKyPY/fsPfgvYcOf/ZLp939xC6+QbJpHGLOcNvguVGP4LaEiMus51KmIjioJi2cZFUQcWlcMuEzNXtDfvzvE4WpIPxEKo6dXhk2wNwkVnKmSAHT6izBZtgoTw9RGySMacofm1Wq15zmDxaQIK8OPs27cHtOMw/NtsprZ162es6Vu4/cH8GV82887Ut/t7RyNJEJoJ2EFHDCg/tE8rZTVX1bciWQyNDFRyT1bbsp5CYrsokAIi6zGY7pm9j0DG2RAqRVKTF900IeA+j7AXeAHBQA08MxtcRJV8eIaolEPHxAZmswuAoGD+pEKrPTyrGooOaCaYioA5ZZh8GCKjWII9LMRsf47vX1y1eXHuRvM233nU2NIDo10gyZSp0BTSUMK81hzzHSMCut6ltSjVSms4lMk6UhNWEdKUHVN7EhZKjmWJ3QlOLxncdEnt25nxKhccNtvCLCLDiNuzCYPqZjidpBFoSPINh4gn1EWh9PSggBvWbIAZChTTPh0AatiddcaTJrPHYBlUkwwxAEvOz5BM/QA2S8F68KzSxTRuSEWWlg4zUYJqY1bg3pJ2TDqWDPrqVfuWXprhe+6cja7Dvvd5o7sHE7Ht2OMl/gKL2b4BdYr9778BnPHLzz4Wf+4p/3PrvSqtTDTXZOiU6pDYydGzmjApIZL3MzDAEwKBvm4KzpcLhq8p4CQe5s9ceSJncRTUSkkVv5FYdjGfaNV52KYBIl9/Gata1sTx6MpPI6O1Yt5LGpmi6WPJhCkONigywH5Ryd6nL8kjfNn9q3/hlnffcL1ve8cM9jn+9Nozbom8K+Pg/uejROzXCwxIlgk84QstsRHDCtI41dlsbQSO0bBY3SA3rcpGSIrGQiWjhFsA4TTKKg/0qZhjA4rKYQpoeDCgaTWIFTLoo4Q9eSZU4LypfTy07kjulHpM+1PB3BtEERg3W24l42Ed5Yu3ptmW+K5N+2ECHLqQhmyBStfxaMVIDMwTrDEcAEyJzmVyYpDhu1XrMSev1GM2TikKvFGNOUqjzGJJ5GFpjrl16FAIZRA9CY4gmeVzg/P0PElL6HSasE0weZGkmoKQ0ZQSNyHA7Kzrzn7oNqCcKJRYKJACoheJDZF0Zkghk1gcyKaxmpBJrMZtYhBzHPIrwHz2EiLj92Sey9JpR7MjqNZjNs1OqwMms8tfHU/j/etLLn8psPHL8wwR0w2/NtdqHMF7hNUjfNJ+Cv2vvoi5677/Ennvj/7l5+4MjuvAHP8crJkONYg53YRiDkEDz0iUckBIG0JphhiklEcAqyXD5561cRXl+4iLyOxvSb+ES1lDQxi06tuQ7B2ZwoNOSmjDoMUx2nDEa28g3GqE4tglE2N76LSG1LhsABsqUMlJ+5+Lt3f/WLjcXqOVftffhjpBC3T8q2AP4J/8DHJzSefvrpxmBahdPonHPOoYfWJDZDCPiplqmJisNJCmQ2hZ6mmiDKGSalAgRrY8qhnPSIEM+wBxBoiffkGqmYlGZIJFJOscDKYbYOY9qDhuYw4vIzZLZiyxhUYKqPk2u6fYqpphVDi0gPkt6UMfMo+1xpYDbKZ2I5g7IEaRDom/QMq0Wz+XBMrAo8r9+3a/2q2efdZ98R2SiHn6p6zVhXQKIujWDlWA80mfTNbD/UXc0xZeOqVTymj6aVNHZVv+okjiC59DRyexBBU+jT8k6G1sRDbgCy/NIOdyT+5Yosng5znvRZRkjJVECVTdC116kGR6rZpSiEn4j7wNAIQzj2koMDZPZSicOkzRS7jSKexDlrw26QLJMNTNYEjSlp2aXenYifmUGZJ3h/OaoeptQGqOkptRLETIUZ66RnKpFkCexDy0ozG+twmLpl3/GLX3bdZ57deWrPPn1DwdrS8r3HL17adfEFLzny45ccWn/ywb9/6NjH7t975LmNa8eDVQ8ZOIfSc8a+1i0/swBmTZRWBZMYfiIhR4GIOFMVjMnCqSVxpdCqFCc89zoupYhD4DmeflCz5vaEeFlb0zMrIXGHsWYYDKch16koCBodgpKjEJDERGCCI268pg9OVd9o9kDxMXJcYjH/GeqE3zaeTa0cpeeVjx9q59/gvr626wXLq09x4DyaWUstgBTXlf6E2foZ97FvlakJPT7zzDPPOuusPj4RoaamQSaSvs91bfRpcMBN3ycaQVxyQ+jjfaRJybAywcZ1cQiWk6Ecg9GpwDcLmx+gky6o/ODB1WV2DEwIjqXURS1iGgsTe1lFpNEzNNIza0Q+zKoPIcNKBvfMhuDQAurUoCA/7+5bXTo0f9+9ksVN/b1mTan64ia9kitekMadfYzZFFbdx1IsIDUDaFgImAU0HIcI9o0pgzWRiCn2EUywiTR8aIlQGBcU/LHfXoUcNQDNegR1NpoJymQ40eaSG6/9DS0WxMH2VbM61lzJRiqnxp0lQgunghpvyKHFFHIwwDOKYPMMURPBTVadrWpgnzx8GePW5y8q1Aorhq8yghOaoTVezVBlehpTak7ISrvyvLWfvuWCjx17DY+PjeDOcLsdKfMFPs99O335+L49X9m3evddDz3xwftOe/Tp2ZsLHCwOpX30m4PrAc0soCFkqjLFDdOhdpU8oclUz0enT28iY5rQuDxpXE1WjhrXKdeURib2aiELxvokWqRLbsjh1PggE4JkZyuuueJBWaaS24PM9mqJIJvEBE3MlPGU18RrVphqOlw78/LVs1+2+6lDm8zl1fNvOO3RT+xZf44jpSxTpND6R//NrK3/k3LCR2XGHtz5wsfv//7v/9M//dOt1IKYPe+886wgRQRQSrBJRACzGuet/5mDcJEfhcguyByV6CZSqsqDw+dpmrIVj5rD+gH3THVlzgJNuhyD4KZ+4mNqY3EFB3tdJjRrASerz4nEKTG2kL6eRfT7UqPfCBqvmhVXMvdF/q6qn5mpcXF0BPTq0BupKdUiiZXQ4ChEthLGgl5oldljctl/GiDNmi2SoFmpGVlbIhAqZqgUtDhGx4hDs3AnWMmNWhUhsTLrFJhZcul9fPehMGrOJsU4QSOhhVCBNHoazJAdhpkhgGBlhtMAly+TrCRC67GR5uf8RtDhTOjEvWKoZi2+z81sQDiUarCfimmAdjAlMxRISJAHDr9Wi0Xx3nzlxLfRrArgGIUGQCfxCc2kW16yIkXkxWes/+Ib996+55Zn1k4zvtPXHdi4fmroBFzmCzyBcqoDPj/z4t1PXr/7wPJTD73/C+ufe3TrAwUe/Qg350CdrbihNcPKRLmZjRdAZuU35AwrR4VMVcGKx1JiCuAteXRyb/HpcEw5gg3BuMFwaiUNv06Jq0I/S+QUZJMy7R5afOtC+txtS7VaEqtydAgGw1w997rVc65YPvbk0vLu9T1n7nnsH3YdPZxKBKRwgGxVkxc4fu6il8AdEjB7cM87HIcPH+5fA+D88i//Mt8a+Wu/9muf/exnGzOGvON+/vnno0uhtp4zGEE5cRIZ0icyDRYhy2lckE1EO4eLW1tYFbf4FFxlCTq7oP7gB9xTcNTiFTCtb7UhB9S1x8WC5Shbp5ILGJMNJ4kTzMZughnZgOg3ItYmzSVwPQjso9ADNRtajKJJxPfd79+1tPn3ube/dzeyqjXiDGkwK9lIqs0UcYIZhmCcIYC10/seTOJm1aGRXMgAWgTHQKMgLcEURsRgNDM1qOzxqhzTIQMSn4nOdyCyDJ0NJ/o8vrMJ3O7yaY1MCZTSouoEOxVlAbO0FNBo1qFMIxFpNOE4lR2ozKoWrGylRaRyIKSAVFuzQhZQAABCw0EkTHEiPbnPldwzowmAw5HieIEp1Sf4KgXBoT3DJlLVKoamPj25DqNch2JzIcjZvbz0c69bfexFtzy6ek6V3cHZga2TI6ETQJkv8ATK8x6csXz8FacdvGD1/o/effTzj+w5NPsGya3WHFknPL5bpBNRUgZpzoZTBQmaUhPD76dOtJ2NIhtQpRqvJr1nKsjFRauCPho26YPDQU2ZCDIbWYKD5EqoFoPkKAsW4VTNiidyK20QZ2mIpH4F61DaoMIS3+N++vx73I89wcZUThS85/vBpxoUO8v9kDb7A0y+jPHI/sgjj/QP7rz/cdNNN1199dW33377gQMHqp+Yd9z5mDu4LqPiwaleZ5FIFlO3rybqWyOLY8Rr2RUjUofgkLfVD7MWT9bgkAf3o0dnn4iK47b6SlnSImQ5Y0tItY2U8dTczE4Ms2+CCWamPEEzDKhrjCyzCyojyxkPP6toBBN3sdU3uAE8u/u+u39XtYrLrFLqD1bbB1NtdCKODg2CwF4FeyIZAmyuHZEAlcd69JkiN2oym2ENQh5TS9xqmwPBLHE5ijjUK7IBUQuQ3xMiGwtesbgzUsDgG7qNAuk0gk288VVcJhgyWBBmALPsrYJjsiHP7IekCPa5yhLvpyIoUJO+PxAQIh6agpENGJQ12HNUawqAFmZ84SQIzjcO83jBK1SdUq3RjI5MffusGu9nB5X/22uPX3ndTXceu7zO7uC6A9s9jZf5AqvC1wvzBvzlex67ann//oe+/OH9yw89uev4yBsRngn4Dp4GmQ1IheEHZKoh94SGOc1POjRwyIlHLSCcRCqoIjDnT/Ibn66BxlXmhTaobw3QYiGtj1dH8KBaOKpty4EQ3+QKBnOrbCpsEh32shGsIoO500HT6W157U6WRt6N6QVxD00wE1nkM+68of7rv/7rv/Irv9LkOxz7y6mITxiTyyycaEo2MpYY8hiopuAM4xWX52Ohe6QGhwmmhrGaE+dw8mOSfwOLrMQHQSM7vZyGrOC2Fo3vtEVDjkV2abCGmhV9C8uwcipWMOQGyIx7EreVlRk1huIoGInOxvvuu5fq76RktmaJUWNWTfoKHNonMVIC+jQub7D10IuTWIEuRoIrn2AdyiTCOelNpKoNYstuRCIbYC6ygNQ/KJggZGRVRidxQZQBNIIhSzCxyeKFijd0meJyawiKhD9X3ZBVPFMBVcGlEanBMAFV36U5Cz/1G2FYRRzWSJUNhkZjWJlGDNbZGXXuArnyo1ZBszQS62zwXHJjqpEdtAi/P9OqBZifuGgY+WBR+SpX/Zpb46kzwALoKw3cRL7r8pU3v/Lyz6zelMQd0O/A8DmxxSvzBW7N/wugF+8+ct2eh0575sFPHjz60fv3Hl1ZXpt8hyFnTs6HgL66kJlqaHXKxBoJOaCKy3QKPMiBLy2yY/yaXpWrY8XcHrnYaZBpXGh5jq9SNaXibTlowh+kOVXVKh5MkUBiZiuu6eBe36xtUyLeCMYdBZv7Vr3Mpaf5qpd7FykwiQ/KRhyCzI1I/cup9TPuvPX7lre85cMf/jAHjKP4kpe8hPfjybn44osvvPDCO+64I055cK8VVDxR0CJTjVQznFCAmSLHaKoxG3LdoJoeQiWPySYe/UQmAE8S+Reraj2YknoWsQi5kdrWpeEzjF3dKOJ1OCg7VkMsoqzLoEjIDViQXC0ahd60KXjCwvfdD/D97uVNcAS5gJVNDxB73aI52KTJpIeTSII1IsZONYdN/QYj5VZk2KtlrwC0htkMk24NuSsl3gNliQ9KNY6SYYYcMKY8527sG+kNTX2q5aID80U03OgaTmPR11D5jYX7QJCtcKpR63NrwXW2YqRQJiK5Ue6HRGROuKsPk6ZyIhlmSpBhZAPMTQ+T5mw4REIAOGyCIfeAFDaB9zjcZO6ZtCoojiAgmKkINrgqyIcZ8kVnrv1vbznn42uvP76+9cZkTdnB7sAJh3ZgU8p8gQPEr3do79Lqtac9fMHxQ/c/+sSH9+869ORu7g45vgG9bT15mA2zieecCVAq/Ea5Se9pDcGzPSLy6Ru7JquXjQKgIWfKrCgDeAi0lwOBGyY3N1qyelAV+tkUH00AKdM1y6kp4vSaZhig3bR4Ck5WAFM0joI9cYA9mk1zWwiGFiyIbAPUbIIMk7Vh6jvuDJjIg/vZZ5/9G7/xGzysf/CDH+SF7fd///eZPffcc3/u537u7rvv/smf/Mnf+73f+/M//3O18uAeM9UyFKQgszK0JoeZEjQKDAeVKy2yKFRsrswx8aozgcdka0p1J76gI/8i7NvtpOhiYhyrBUFn9Zq2CDkKjWZPqGUPzio1NtXox3cCZLGuqBYwkcVUc0ebJlPY9L2mpnO3ohivVVdEL4AWAIZ38fG1h+d/m4mU2phlWGXNbYJVDZxZMK2vGQLxXpxqe7Lu8ismEiPj6Zu4zCYYcgUwFzwiqlkV2GGV6rHKMM2qhOgYZEibq25t/qAFNO5yHGse35tHQKZ6i5nupvKgYILQkp5gIgGh9ftGlrN9uvGxYx1xALIZRjCRHqAMzdbP1kiULSMLgVNxM2yUGVZNyaY3TGlVmQJ4D97rFHL9+FOVDSaX5jDBxp0hsjQfVhievnv9p1+9dOjs1z6x+sKevBOpO7B1xtfoFi7zBW7N/wuj5aX1i/d87erd9z/3xEN/fdfaXY/t5k842cbOh5wwlsYQEHLAYOGSVeiZzlbZiEjuUybIya0gBRBUzWIqp7rUkuTU2WR5gdgr6B3ACzAicUxiwKBsZlWAE5ApQIIxAkCmOSuoKcHJTSSAqawITMuUgDXS5j4bXUNYcNgrm1iVufkYhOxyzAIDZv/sSHNCTP/UU0+9//3v/+Ef/uEPfehDahl897vffemll37hC1/49Kc/XVOUVrdXU7bye06dFUfNdPqTcpGc3N5RQePVq3FnOC1VK+/tqnKwmiFnyElTg4M4QYDWyka8EghWjlNG7EOuQ8rg7MwUgNkx/dDilYiJdTjIqQRT9LKMMesqTrV9zY2sQ5X92gqwjSmAOwBIIhGDs4t180MpRuzDFNy/vnTVc0v37Zn9bSZb1ARxYRZMzTOD8tQSHABTPLhAptChbfht/s/nmORuhjf+r6C96ayu4TRDaHpZczPbDGEiaFVgWkNohhAso4kzVARQRQjW3chUgDoOVQY7dCqyWjDFIztBHt/54Zlnd4YyqxS4ilADKTPdkQXGBRCcrSbSJDL0XX+U9ZLQ0FIYcURSRuKDQBH54JQxSDYYcjhEghuQlxmVsaA1nAytmWEtI+IBELI6giFHGcCOuWmQqYEjCCDOEQyNSBo6aKYNcgjSsENZ0x955epXzrn5iZWdp/Zs5LcqWF9a/tLKefx31pk3vPP1D/7AUwfve/SZvz2w97GjW+e2ZyDngIvMiecwcYeQaTUoJpg9SmTO3SAbTGJAsgC9iLMhS1CWKeMGxbVPbiWYG8EAyc7SE7ePyPzK2/gDatKY4gLk4oop91JojaYK9GQFD4IQAiwDcixMrBaVUxNhMqQ8bhQAmrkCsmxe+OC4SEsPn9lZ/onHPQQBs02E4YQse8X7tr5zpD4PKnzyhYLVMXdDfPAdd+f4uphnnnkGNl/TzkP8e97zHuIcGBamurT+HXfj9lavZVbSDyutpgdDqHUn3rjEgjj8wWGvMy1evapgjfc4a+zterKRM844g99MBTcu25a3rUWvUC2a2WaYagddxsjJaoyID+rIr1Mm1kg0K4BG41L0/HZY1YjUIYK2ekMhUjUbjIIEpeyJNMpk7V1avmJ1/eD8bzMpsqBy71gjFpAbIlNVtuJkpchEGiCh9oM6ZjllbxkT5GrkQZHsKpxtynMqwUXEIdNgSgajbERMvA6phBuXZJgBtVox7+Byl+MW1zy+O6t4cIYRDIBTMUPI8ptHAdVqL9PdmybHQmWGiVTBihWHBgjZdGhGMiSSMqYr0UIyuJJ1lNCbEpGsdUqSn96STO85xLH2JdkneDRVzquVWcS99mMHjYYREYMxBbz9yuOvfuXLP/fcVTW4g8d2YOBppaVuUjb/385/A8f8Ausle7768l13P/jo4b++e/d9T/BPp629p0dODIEnjFRwZkM2kngrOh9XkUFmJVRM9rR+Qw5/rIzwASgrXsmJhOls4pUshum/hiWFK5Grj9aTByMT4rEQyMSIxn2AJm5kodEogDsAU+CG4JBcZwMGaQQlNLNjsuFLIDdM/vgpTQJBp+hpPGDzBG9QI4LD77g7zV9RFXAf5MEfNsnbvpiZkoICiFfcDJspZrGTA6BBsM9UFBLvI41sMyRRNQ6kWIXgZmh6IwJnrEV/jFDjnGopo7FohmYh3sRr2VUZPDiV9IBk9ZFM9WpUXvl9YZkFNORG1jrpvfA48SSk/kglkQjNS7E+i4cwCBQkMY6NUZNl2fCJ4+Wsw8o8trR+cHnp6pWlu0/8XVU4WULFKLBGelrVAdeImNXVMhq+w8YFcatFoU5JJmhjyCwtSxsUNwgHpmVHR0AcweRmCCBYxSuNKYf2rJEGmaGJ9pGtAKYukGlaAOQI7J2Cz9BIdBp9mNxDWSA/SMPMHa9miRNBQREiCUa/AmZh+huWc+7WdjU0Zn3ipGamJsgmQgAgHr7xFFaHYvpsiPuTuGoOnULHlkoYShAQrxE20Fn5yYpFBdZMOrTEVaOnQaAFSwtZcXpK9SbAW07q8JrnbhOvH6SJC8B0APpN2TdesPbOV11667NXVP4OntgBjt/GaTFB+qaZ4k9oPbTyooeWXn/2i4/+u7ccPPPZB/7j3c99/MG9Tx/feDbwHKPn3KBqAOeh5WdKUINZn2dsCAEhK8swsmCCMCXbEwww18SkN7PN0Gsnicw2hHr5R1N+Y90kRhNg2ckCcAerL8qUwcVY9X2Un5ezdeFHoTLVd4qaFJFJAAAgAElEQVSL1BU1jtbGWpSNTg9MjH6zqGaIVyJxNJJ4tYgswYZQh8Hcl3ivnSzeK3/Tm9704he/+Itf/OLnP//53Mp4dmff4Ks8A9t+qwyP7L/6q7+K1t/8zd/81V/9Va1P7DvuWU9DiBNxcUNg6AIqs+dMR+I+ZtGkuwXpm9mJYa8f6z7LKVfXzzYR3uTjONGM90aJZ7t65T7SZDWmYy4NrR9Wo7EdIE4j194rjV41g+AqJaancfkJ6PsC+giCiDc3oOQyG5wCEgnoZUMGSFMq9WfIrEGuMP6u6v08u3emg0ZmDU5hGiMrcQOzTN2dSh8pZiUkEk4FcBwqXslgZ2swufBzxiY4BrjbIhKdmA7yYyo/WWNk+DPpzVOlildMOkOaGxi+ss2QIAvkwzMAbqzZc8n2SFWQDeyloCUomBdyQtlVucGQq7i+6ADS15QQYlpnGwy54SdSmRoRsRI2RPFpC8lkQaNppL49hNgBwskx0sie2ZQEGWwvOVMNgMPpx0M8AOb8Rrv1L/gEq6yYqs7au/Zr3/2C/7z0xmPrG+9wNbI7w8Ed2LgkBudmwc35zf+PEr/hE3uX1166+/C+9QP//NBXP3lo+d6v7uJsq+dGU5HnXj1/QnZKfsVEwoccfqMsrQanmdUCZiyiUNMrWUKdTUpA5Yun+UmcFkeKq5KmJj2yXJtcoQaJ0CKiKT0NmqDxqsPkJkhKMCCEAGcrreKam/QJQviNfuLk0o4cOcKS+SVS/lbSJZdcwk8dLP9jH/vYe9/7Xm9rPLXzTJ8s1La/H/Hm06233vrjP/7jH/nIR5LZAO5x2Bu0xAwDmBU3hEhVJsHBpVYFE4nArLkVR7wHlRYv1UKuw+CaCLMZJleQ2RQZr4Ypoc4mtzJrsOJwqgLBXjZMwZhIH0eKpqY4vVLO1kSwDQKAs5CTEuzpaNZ0n1f90HBBymHFRJAlEvHQmKrYXCI1HWycPtisDMNPBIKc2vP5aP4q01UrS/fuXu//UTC5sQN4BSliAcHSMuSGpUJ1rFLi6uIeRqEnE3HWDWSYPczUYBY0bjHkml5Ne74c+GSBq0VPNkLlaMqs4j1GsJJJ1w4waISCIqHVGpxSxPdC/Cpxzl5P4EpWgZ6WUlUYtE4ufDF7opdD4rWA8KPm2eJQkUiFbAQdj37VD6e6aCoZnBaygDggywQrYjx2AnqaHAE0AOnRB/hGuAq60Neym6lwBMyiyR7Sg22xC5kLx2PHFE/w/jw2P56zf74PLYBj+bP/au0fd7362Or2r5LJ2gHf0jtwfH3X/pWLDyxd9JLLnvwf9t2/8tTD/2n/6ice2LOyvvWiwwnGGj3BXGyNGKf3PIQQ0O8MNFqNK9XLEuGKCFlayL0FkZCrflVOfFbE5nUaApHoh6mR5AQBPbPOhg+wMetyMlSZOJF8UJ4L0+d4H9OrZsWk1GGKCVA2vSCzsa4i4pSXqWQRqbipgVkiUa7MSJmSX+f9gR/4AZ7aWemnPvWpW2655c1vfvNtt9125513wuc2Rdx/OVT5hH+2iGID/u7v/o637vPJmWaWYb3xNSX264EPp8aT4lLVN1gj8Q2/MsGQaQkGJxFQZSE4rIIVhx+FOht9ghVDZkir5OCAaAo4j+s2KtJwMkQ8uALFq4W4KSYpxOv1w5DGLMEkSo4yII2CwRCMRHYCaKFLaA6VIsgwGMCw7kymYFYcNUFjoWxSMsvrPZdEchVsZDM0K8MxI+L8dHJgz9JVq8t83p2/q9qkmFiD1FDF61RqS81EwDlGITRAEXoBKXUbG3KGkGGyLfO8rRM7hAZIsxhxQ2iG1OApB5mpLCqg4RP3XwmreMU9n5SxlVYXRBhSDAoWE6k6VMpfPuEVxd9e5W4b8iBQ3KdJFYxIrvpEJFAMDcxsQ2gsPFvcRnMrARGGKAgy5UrVN9i41KFkFAjSANoBxCgAIu4QJvpaUOQ8ddZVmkzUxmYjFSZGijRSGQIwlZ+qCNIINo2gH3+Cyanlr3/50qiI/H97/crRF33nV46d3aTvDL/td4BfYD28eu7h1ZtOP/3Gm1/10NuuP/C5+5++/Uu7Hzoy+wHbU9dNmJ9iG+fY4MnGOdbw6+6RktOVuOQoA9SsfU1PrqApoA4h2Gp6JTRelSau5IqdRVzAYmnBxu3N8rZAhGstGH6vqQi3UAS58caCRK5WeiKDWTLtJdAL1By0q/wxGhxpITBUvNGPRcOvNHEe3Pkjp08++eTHP/7x/fv3k/5d3/VdPMfz4K0X6625s8/NpIgxAOfLX/7y2CzxpjiZKld9aX08yiFHkMgiOAosLzhqiQTUqehnFiAhUww5e+iJJDcg/Cg4VQlMRS20CiBr0QTrMBhyrSRxgY8LqOkY3wBpKRLAvtESgcmwkR0cNhaVw5SCNQiOS+INrRmm7ArCSVBlhiy8Fh9mQGpISuVbVZU1Yo84oOdXcZm8735w1/rVx5fuLd8zU6XEGqUH9OLJigsA2lgx4TfAx/EmODhEnErcyZgOMg1yC7YYh1nOWAoE3z/ASP3a91nwacT7YownxSHF0LKTTVaGAZBRCD9qDeAmy2sG79fy+/pgWkPIEGUbkVnp82thWh++ZIuJVAOQIiLZPsXXYDh9unzItHlpswNNE8PXQgCHKSpPI84mzLPb144oDM5GGRoEhpyQ9DTEBfbOggGZinjPT258AfM1zRaVYGgBnLeshSE0TkiOLGQir7no2Zdfc+Wnj10S5g74r3AHnlvf+/nnrti1/LLLr3nsf7n+0FcPP/yh+5bu/PKe/P0mThtOM84ZNyfAIbMAg2J6gcHwm2HSITMVi/B7O5jQFI8p/GqXS8n0yDpMlsPGSx17HzDADu1NJwsXc+tdolGLY0wjkilTUKMlCI2yuVTpCTLUEa9Ki6yJiitYcWTDd5ZhgFm177N6fjgmZliZWDBLL3jooYeuuuqqd77znbxRzjvuTB0+fJjelcqMzsY/ApJJiB9rmsWHNwEWfBqYl7dR6ISa9dUqweGPYQguQWbFyW0AHBdbNRtOnQLXIcxpl7HZRkRHL6GxzWeWhqB9tVZNLzE9DSl/nAXLHxO3gNqrlvoBishphkmsHIN9JOQqHllAqm0UOMdSPxxpDSdDxSvHqZjWITR2NbmC9L2IT6h9PCmAGPEu+oG9S1etLfG+e74jsjLBStELyKVlsQ25GUKj+AUvQHLlY7SIvvW4OWIULK8pI0PPT8i1ZbYHqJEiuZllioh9psI3QmKmAA4TdLHEt10ssmTR52eJKguWoAVMPjxDY9t5sxZxcEybRIfOIuKR0muQWYPwGULetn7OSZjsJA1yijFir1rVNwW+rd4uohA+6Sow1c9KC4chKzXY840ognVyqbNOGa99pDBCnz6RSjNOJGooJxjHmiI5b8Pf8JLlt33HhZ85fkPD2Rn+17kD/ALroZUL+O/882/4gTc88GNPH/zcQ8/96RdnXzxq4+wSeA47zOnnFMMN9ub/oMncDGzcwerQLHsvZGYZkkhPk5xhPb1n6ie+dkdZUMlEIBvXyKvGOEbOzj03fmneoCKpRIXBPvom1pRM1USCiUOWT9+XTan8yE1vOhzuZv7YkIgggpJhJtIsRLvUE1oiDaEZwqdFHxCMAji9gpD/8i//8qabbuKT7m94wxsIcjt6/PHHK82sufDy1i+nMuZtdV8D1Fqw55dTzznnnFpWk5gpjZvZsWFd9hjHuPryF7eQ3yhPmA7yx+wGyXgR15HzhsbQxhMDUpw6ChJMYURoTDnrEAIgnG2BLjVFi0QaQYbYbSsbAmuRryzxgHAqUJ/eAlJG5VQcZg2C4xKCkdQDp4pXXKV4DljkzI8d+mNSyjprv3dp6ZqVpXsm33evxYCpJ/uJSHwbmvrM0jw9GsLYsO4PudICmqyZ+ub5UNfV0DJEHDx2/iDFrL1AjLLi0XHYBJmd1k+64vQ+RM7UN1/wKkfMFMAePmCs/iaXYnh8h899lpSsTqlKZoogvWDB4yXf2kwHE8TXBjbCLI2gEfRrY6oWE6w+/dj5r5oW9OiLEY+I4rXPlPUwZUt8DHiwMEU/1uQ2fCP0J6tf+WrGxeEZe9Z/5U27//G0Nx5Ze0FjujNccAe2Xr2GEzbnN/8/zPpmjfLJxyv2fPmapXsOHP7qf7hr98NPLa9uXQpbRXte0edc3ZobQjBtTuacDyBeccgJBgzJz24aNKcAXAi22DmLCM1bhzhqDIMHgQr0TSJkp2pWIsouKB6FMb6yPFDRJBNhOT7KExlMTDEQwHAGaXFv+MqSkniTnngUQuCfbfMp9PPPP5933Pn7p/v37+e5mm+Y+e3f/m3edyeLf+PNCxkHbuPBHYTQKT+445SCBoF7kal+GXXK9WdhAeH0oNGvBL3UrPFg9UNLvIJBfVPSA2YbOm9oglVo6ndIz8mUntf+bd+3Sz2xMMIwFsGCZhhapMYAiUzBF8RoW75Z6RfhV07sAjJLpK8/kYBpfmYDNKKncUSIA+jr2htxhxxf+ZGaALw5w/fMHNi9vDa/c2IxqB9rpNRvrCcs4EOWr47kims6dwHPQIJm2VdOxVW/xsGxqAA8q2boXm+wmVrwcTkW3sW2PQRxMXFbfpbGekkJP74hVMCsb/nw+M47PXVqDEefChWntzHlLLlEXAJAKY8avS0LbIxUgDNGiKBAPmT59tGch0942Q4fi9DGABbhqxz3xgiFEODo23N6o0Zfgi7xijiAIE39LGHX8tIvf9fq4fNez1cE9hY7kQV3YONMHWVvzm/+f5T4zT3x4t1PvXzvwV1PPfhXX1z7/OFdz4x+bm7jZGM1nHJZUz2rgwMq05RMRaGC2dk8F6+9D69Garq3jpz2TlVCVRYrUuOVH1ODIQMiXvlVZxH9yo9XDYKjHxACKWyFLxmm8xxPYwd6slnSwILQAJlKsKE1KSmjgohQA3/w1EoSlPm93/u9r3rVq37rt36LyptvlVl+xzvewSO/FXzlK19hMVUdzC9jvfSlL+XR/NFHH33kkUdysEPzHfcMe0A1WWFAT0uk8hOsIMtDDUxT1qHMCSP4cAYJc7FZByE4LwnGa25EADR3nx4mQytZpOcbo/36fckaRaEZwqGk/khNGDUKDiufiHaZSiRlBNTEYErqz42oSavD6Eeh0W+Gg/rJ7fXhE0xJWqOpb+NunGD4jXI/hFxLiiBAsvU7BJ/G31VdXbpn9pmZRU8M9EmkKRhlhhWnNi5v6pefrIDQApotShzQ6Dukpw1uUVwCFIFPpAarSzA0+XXJBslVJGRABOUPllT5wf3jvi4QAiJOMFtEsMYj2ADulljMPvw+//g7mjalNkdbwaTnwAnoaZkNiNrgrDQ44VM/zeLpmWpW4TDB6BNJMGo9gI8+fa2HocxGwaEWM/V56zWbiPo9OS4NX32ClNRnNWSG6BuE/G+uO37Fda+889i+nrYTWXwHts6/4ZzN+c3/D7O+RaJnLB+/es+DF609+PcHv/bZL+098LWt0ykr4NQSB+QsJW4wU8mqwLPdLHrI9Jy6NKcaHdV8NlVnEf3qGMHYEdFL95AlEKQl2ANoMjM1xocGJ31oPYhU+DUSfg2KuUvTsnswuV3Q2DGzdG8UDKLQxHt9I+E77LMk8GKRN90bKd5oZ4qndqrNFFmzB3c/20e0eXBn9vWvf/0NN9xw77338mmbffv28VuuH/jAB3h8jwTA73GvkYoR6cuFQLzSgsf4EAZ1lJqYQtAmc3O04c5Q6ygA0pjiWMKhJyhz216LyifCsPEyqBrvuPPgXlOmXTjhKElOZPuUWMhp9Jthk14t6lRvZ2SQ31jUYWqr4oM4jtXCdKeqbFWAf1I/3sCvUvGtmg2GkwNRcxuaw8HPzPQuNZIlN+LNMHbk0lJS4gKmEhHTa9EL1kjwBD/KAVqgTyQKme2BHLJoi19x8lEjBaxsAEMwyvbOWlIstq0tS6hMgmjagrXzJQHMrZWU2jQlYiX2ioBl1qmKoYWfkkKomsEBVsiQApIyAeRDiMIEGU6WkF2d4DMln55cW11dciWEbzwWJIbZAy2Iq98QeruXnbf+k2+86BPHbuIzzQ15Z3hSO7Dd9pX5Ak/K4puNvHtp/cLdT1y7674vPfrobQ8ufe7Rvf4Cq6fogidqFuWpm7tKzlV1cgkzBDvc1iLigsqPvlMZAqDRb2uRlOpSLWocDL+mgCXXlIpNaUQYNpxKiL7ita+0YG/aue/N93XWTTxCxCIiAEvSjmHPMZLKBbxS8MVlYBOTBeCpPR/7UZ/gxjvujpsH9ze+8Y0XXHABf3QppfPBeT7O/sADD8BPy4N7zDJlJCUmHtBMbcsnEY7NLaZ3tWoy1YgzWxsEhhwP+jCngSlVOXynmmENLuLCEvh3dj4qEx1AtauCckhhCZUPnvBSbVtCBOHTsKBPUNCIZCgzwyarGUrmUvHUSm6166WYJdjHG/EMB2WrRZgBbmwsAkJoAGq0/lgQhGlfU05bWr56ZX32d1Xn+9roN0MTPcMHp6Jc7cC0WhLDmh4csIhFvADwG80620zhQsRgraqm9BgLguGTztC+kpU1Tgr8pEDLAmuKmBSzKj9TSNHk2DMlH6AsPbn0TVOEnrutv+6/7afgVI6dgtEJIB4MYEgKdQL6VVRmcGOhb52tFmJTTtbC8qKmUWOnr/rys4okprYKPDRwbHUqOLJEwO4SuKaAw3/RC1b/j7efeevxNzy7zs/XO+157UD7mtGKlfkCW9a34JjVnL3r6HV7Hzjr2UO3HXzmb+7be3TFM3G2Ts83xpzn3Bxyh2GKYJYLzRsLEUGui3AaYDp9TumAMKsFOEOZ9ILwGzCY0nAyrGSCDBWvFhXLSXpAw0m858fCqSaRIYSaLm5oleDR4RGFRhwmjQPhP6US6QVTg1PwTayyPY4O4rxSoD+3mv1JdR4L8z2YoaEwq23sL6fyHvBP/MRPvPvd7+bRConLLrvs0KFDuvLWezARPypjlbWsLKMGBzFMSrFBqCXKT0SXnM3ExdL6Gno7UqRFc5oj35RF9FFjIVQ1KFuDqcQfp9jnRr8Z1twxi8qpCwTTEIwmQ8gZmliHi1g0dlqoXKd6rBEW+Zmw5wxGtHBKo2k7LPCymQUeVE5wbOFjdvDJbY54dakYJp+ZuWZ16e4Fflc1joLoZAhIsALiriLBrG4MuAr56jfMmCaue7PwzPag3yg1J+ycqtc4shOLgk+DMMhhihpsc+JGhyYWJqqvY3yroCKZ6pfZRLi6+fwM57lfWEZ6Q7DUWrBlEcGlIQ8O5TMVkbgACGYYTiILLgQ++0Z6z1cqglbI0JaU1BYgM/1mxqzg6YWHCTCdFFqkABkGEExJ4Mbi33/32h0veMuTay+sIjv41HagPcVblTJfYMv6Vh7vXlq7eu+jly8duPvBr37yoRcePDK7dmicgdwKuJQ4/WyzE3feJPS9J/nEqW5KCJUP3pSfeoiERov1dD2xq1mmEAFUneDoh1OZoVWQlBoURyRTqIUvUH/aJSlVJ7jOgmkcOB6GAXI4gt7Yk9KDkDM1UVLILqcOSU8i8a133JnIL6fySnPdddddeOGFn/nMZ/iEzY/8yI98x3d8x/ve975/+qd/uuKKK37zN3/zXe9618GDBy0l77g7pEeWFc7UN4HBECzLITjNUznx8McA+uRmlmEwoM46RY9FODU3wQZUkWaKobI1biTKTXriNeUUfjMVWRfS26FcXcQNrbqPYW8uJtqPMROHRlXVPVODAL4pdZYIQ/saF1MV+rEQZNjzibiQwSmDsyI2zxwAaklxmNwxu6aq8MfA6cvLl68uHdrD76punbGIW4ageoGxQK2evWPiiXOLyeFAWfHMChLUjhTuRGCHDbkfUhUKpDRTytY+BPcqC9Fowg4RUrKQ6FQAh0bxMG3MztYwvzkwlSERpGwqEIFAllNVdgInRY4W4MYxEQA3VVx4T6XfLkWaPhZU6xQRWrDA+hOE4OqsxPhYP9ebdaQM8iWQDtAoEasyq/a9F6s2PQvpOYmoz6FsNHWHBqAJkqUFw7GFhCnQhd6UH3v58XOu/Ff7j1/U0HaGp7YDWze14fwyX+Aw91s5yuJevPvIlbsfPO3IwdsfPP6h/acdO/FO6XmYJebcTqQB8HM1ZapmeWlkCnCyFn0KCr0sNIP2uoABTg2mMJWWwszy5mC6HHDE5UTTqQyj2QDV7CX3Kc7GMWQAZFsjmyHHgjsVLSI+x0/f6CCHrxQuAHo+MEMzHdn6jntMBbNvIaQ1UQr6pV/6JT4n8/M///PnnXfeK17xCiTe8pa33HXXXXzqnUd2vmny/vvvN5Ei+N3KYAARvG312zQblzokpS9DQl2kWCZF6lUTp/GES4rp7aoptF6kmkKow4pjUQFqLIRIlpNZcvVqHNWsfU0JbkBEAE4FNMzMcurgQuNQDnKaoEtoggxjFBCOa89QMOFIJaQsUo9e9DTe9ZyvY+M8J8KwmtbZ1NBwKr/B1MNeWRXizgoUqZhZvn7gS7vWrzu29s8LvO+uGuKINGtX1j6mtWyqIm5h/RpNqb0LqQp1Ftw7EuTmQCJZzCZXuwyjw60NmgvpZ0OLkWT+xdAp430iBdD4d0Z6mZ5aGUa5B6hZVU54OCkAHDuCwQIiM+NN0148EX/7nJ/SeYLn7tx8+Qw6tPgmiyBnLz0WONoyOwggZ+3wBzkEodGc9Txh6EJq1pgjFqZvu3YJkHGhr+K4M2yCRPz3aOKLLASR1JCqLBsFV5q+OlrJW69Yv+7aaz5xdOepnb3ZaV/PHeDa+8rq2V9ZvXHX6ddff+PD/8+199z1pSP/4Z/3PHZ0Vz4B70moK2evJ7DBOhXc/NgP39zmIsoySExumAFOeZkkJWCeOrs8JTAEGAxHkCCACLQUxjCzTplSewg9XynT05vF1KymeWtojayJEAEbCZs3PRPpBTJrelIMkl7JBLnt0PLhGSKsgldD16Isvc/AcTFRTXuU4fAWeTgAGsq80cNn3OvmkDJbyPd93/f5ogWPP92U0+Lmm2++5ppr/uiP/ggeb7fz1P7e9773yJEj3Hx/8Ad/8M/+7M/g69q84+7ynOp7ZpvgrIhuRxpOvBI3JcMxII0egn2VmhCRNkEYc2SLOZZ6aRrHBBNR5GR/M5UsXUyPrHYG+76uJQUE9Hwi1aUhDJoSpLH8hswwRgFy4NfZPjERmQypqhEJpwHS6E1ZMEsRzvO6kLjHgkgjiIuXTxNPSgNOX1+6enXpi3tO+JaZahQsQBYQF9X0mnAkhVbX0pTBbCJiLIhEmWDVTzxZAFMmXEJGzUYKwFwjcuIlQBNgD00ctWkAHxdT9JJf7RpTU7DTcVo/arj0/MYRciLe3Enh1uwymQUE975uV+/SMGMB4BxWMLIBZjGsEVJs27rEFH4tjCFT9rFgGBcBEdriLlogmBTS1RdkSBALh2Qx5JIkQpPf9Fecu/rTt1zwdys37/xCarMzz2e4dUMZVinzBQ5zv72il+x+4rq9B798+Evvv3P3o08vHT/xDXjWWs9VTmPPZOPpx7Yk5ICc9okM5kKTID9Zg2SZEexTMmV65YPhcxUztYgLNFMW4Vdfs6pFxRameLVYxMXceCmbIp1Nzyw3Yd95MYW1c0eip4VGhG+EjBRMvsjxwQcfJELjPsZ3y8QRMGvf8z3fw9s/Muovpzobdbx9i4gneF5sPvGJT8TVz7g7JAuAWobB4TeRJiW0QSCZKUB0GsxsphqRsXhDyzDK8XUqQwTDYcp4XAIi2AOOCq/i/FzVT8UlU4mQlUPDbDWqOImAJqVOBUffCOecT6JNHAsiMQpY0CV2AHRSWONSLUyJESkWVqWm8eBaqmNwdWGTMwyYMIpLw4k48YpPX1q+YmX93j1L9QYeo4BGLTvWxJthjACkNGpjQ+Om1OU34v0QC5ZPHAXTLSB9HAFpzHIoFz+aKtfC1Me3BxZJnIZjssB9/TUC3x0jpcaDIYDtDcLXRfFq0UdM4cKnYcHlX/lxaYBVaQqfVgtgGL7YnsKMj60lWQEaMUxKjAIkx5Q4jSEpCUZwEKQw+DUFHfljgLguZtXc3kiXpFTC2aet/9Ite//x9FueXju9xnfw89yBBZ7GNymb/3+ejt9a6WfvevaKXQ8vPXX47kee+ewjS19+evYvfrnWBtfCCUzL1OyC2bzeEw+QVofykxKdBpDSZEHos8KZ02cdHIPVqE9MYaZU9zFyUgAzp5EHD2YbBZjJdaohOFt7U6pFZg0OTsUFgMW0Cwq8StIANMgc9yeeeMJ3cIj81E/91Ic+9KFf+IVf+J3f+Z23ve1tf/AHf8C5wctE/R53jPaMnTHa/9AP/dBtt93Gsz9P7dwBb7nlFn4U8G34LMlyM6x1VxxCBfPit456plwSQ0CCUaOS+kqfOMyKkwggpV4YkdUow5riVI1EPIDZikmpLjUXPOjCIUTBVxelFvSdMKq+MQWI6TESy6xLqBEsnBojVKPgqlwxhFhHMIDZBVcEkyylYqq4w8Y0U/zwGQt96QVVJ5ip3iWzgngF6GJiTY/RzHLznnt0aem+XUvXHlviMzP12b1xqUM161qaSiJOXEzP9eIlUGerbIOTkoWQDq4tvtHM9mLnvw8m0ujXIcpcBfWKjnIDGFIAvTc+xGMNENubWHsSMZqzth5wK0GsPhgmmH0Whxn9SBlxpXFZZOHcrGmshY8aZsdilEpqAZH1cNQdSGIDamFoUm0iMqtRxcxSG31csvbGog4pjBaXOiXWovbwHTZGiNB6BSJJGSMkC03EoZkCdvn8raV/d9PaA2e8+umVnaf27NYO+EbsAH+U9461q/ae8bKLr/nqL1y3/9Cjj3/64b13Pjb7A8ycnNw5m5uhVweznsn0NM9ngjbq9sQeW4ApzMIHJ6vya3DmMW8NwSFMwKCjWbpUcnSqC0H49oLQKtCOXsekyHG28sEJBiSLCJieBlfikz4AACAASURBVLPxTTAgykbsExQkGBfiCVbA8aXFlGPNong0h88x5WtgaIBLL7308ssvZ4pc3lv3V6T0Inf4l1OdpufvLvHUz1dAksmry5133smvqzZnVT4qg1nd1ojELNUTwZqhvYQ62+TWYU1p4gyZrUGxwegHONsMk57lJNKDagcmJZvTmCYXO6ZielK/maoI6VUhQZWrUTCAFI9ODU7jugOkK9Kn1GJMyeogV9znqjlR25hvHvXiXo0qjmlSEhkDMWU5SPVqE6ZM0U5qq/3MzF17lteXR09gakA2lQBYDi4nZcRyWHJNIWKbV90eYoLw6b25kIgvjWBAv4fw0ZTczxqJcoakVE1dmO1BNNkBZutyMjUILIy+GlmJ/N6LiFmnYGSK+tUFrwzjyPK5dxNf/LdX0SHLyvtNQIrmbIDWDLOcFBBgSu3h5+hM0CIuMAVsSk0MDkhurY3gtg0XjaoUWXXJTlUCKf/m2mP7rvmOO4/v29Zih3CyO7Bx2k2lbVI2/z/F/baeW15aP3fX0Rv3Hjj9KH+BdeXvHz19adfsmyI5XTmHua7zgYiJbfBszzlPLm2CzxRkW2g1ZRDDlyyYeWznEiOYyTIY34Coxcipmc12RqZUiyYlBOONBUbGm6zUFkAirdIqDk0AEzDPGEgx8ZlnnuEQyyTyMz/zM3zyhbfIb7/99osuuogvd+cz6pwGPLjzehfZE35pr3FlyIfa/+Iv/oLTCF2Sx0pk1lPNXp2G3A/V7E1rxPUkUocVQ1CfXpAUAFX1L2yV0GCUXQiAxmw0GQbXLIM832Q2oNLEdSqCGtn3KYmQm02uOuIaSYpgTHnCF6NGBP0JC8jMIjjNgdaYekaa6JRSus8s581h+sFgZnvAOeDDbizgaBpyhorzk7FPhwzDmQYy6ymnXTXVNzorS0v7d6/fsDL/u6pz40xNAGrDJQU3TOLMUrwHsdaQ2lwj22JDweLtI6gUnCYeQgUaweS9anqyjFSOOvQ2p7h2TsqILFNygGIEQDlD9YkAqIeeKRrpTk33JMao2QREVLNvTJnlWZygWRYw5gXHt2G4QdO4lTMcI8+qLy+iYDYBsqsD6FX7RooUToMJQuVDyw6QyJBWCWKD9lTi9sZoljOUFZ0Qshwi6DCkSQuoWXBgzlkz2iKH9frzn3vVNZd94vhLo7MDdnbgv8gOrC8tP7H2wk8+94q9u2943X/z0A+tH/z8/V/7z4d2HXhi9mkKLtJ8kzcnOfeEwdsCUxRv74Vgb2RsXczaIMOxD5mpYEEiAvi+uGQ2BN1reiziIpleUMlNRCOD4FnCeG3o9F4EvUskMSC+NSuzAaERqUFriykghJRa+WFqp6zM1772tXyv4+HDh7/zO7+Td8n5ShheQ++44w7+VZZ9pvEmr0yyZt9XYGvUVbRnzXXYYxQWuV0mUUeHYKztayRkQGqDlmISrMweRxmQWXEUmqGOGoVjbjOM4MQs4uo3ZIZOcUiSjv60BUwI2YRe04iO8dWoJ8drTHBWTXeR9Do1kuWkEhQoIL3xme68ZThWQxUPhszDSh4OiGex4VSQAgAnZYRFjKpgHPVt3BmyD1ngxlLHd5JHyPuWl64/vnTX+PfMxAJlGlXpwnIAWIQQO4rnRt+s16zFL1jUIOMYlxg1GwKTiO7cbp6PUWrWi17QODpkymOUrEFagtZJCmAsRbuZa7lvuOcuEDV1Mox+gJts1iIbjhTvuOPIszu3afZw7HW6NyWLY4Q1K7KwlNEDCNYzO5M2T1RXWtdbE5NCkBT6UzNqNhw7W/USE1/8J58smdpIZGjrZYm845rdf7/yCp6ZBmd3gjs78I3fgePru79wbN+dS/su2vfE/3TVoae+cujW/Uuf/dKeZ5/deH/By4EPOHBig7kzcH8QUy2RpmammgjDwYtCZuV7EalpSp3tZYmYkiluL9umJAsmXhpFIaDGwRjR6qyYKUAlhwMgpW5RTzNdBRPha8SU5VWFiJvobW2QEKZAfmOHER+M+dmf/dmPfvSjV111FUw+1A7nd3/3d8GIc7h9aWCIy8Y77lFR+mR70vuKiYzJOuVs7fUdy2Jt/U3flN49S2CKLAlRDhh0tLwo9CB2AXAGs7K6xlFNUqiN1lsMRrSj93wa5BjsfXl6WNwoIjo6DBYMrogg7WSN4LMiEuuKGrs6lXp89mI4t511Pa1GMCKlRnrc+DrkAYLcXn/C1xX1KYOOuPAdkQf2LN+4unzf6bu5OUFLJYiAldKR2yJvyrrPboK4F+8jKJDu+aCyHO1ialDTEFjU4KNk70JEI8/VvrwYAYJj5KJQoCnVK1RTVoQRWYAa73G8UAb7x011qeSZ8bz1vhiRSJz5mjKGKQk+tZkFDdCTowbguyPpOet4fGfD/efyPqVG4Lt2yqOlvHgBgpOoKXFSCKLQrzfkgGqEwmBKvGaum+sFsCh0albFsRBUIyJZVEOrQ9RoGOX3ExhKEOzetXzu2Wce3fkLqXXXdvA3xw5wpj6ych7/nX3OdW+9+aF/++zBzz3wzF/ds/fZ+V9g5VbAFbFxGu/ezY/3XLac6lwXPMRnamIpkG1yBq9cphKX7N24ylJDWo0Hb/ps3OgkZzYgRhAIehdyFgWzAEbkGBQTd1a7ShOnj5ER+PGKLFPxanDsIghoNIlUmjiFZSpAKW5THDswn23hQ+l8NN04PX/nlG96BJBCtfQ0hmjOfvXKxsDXsM3Aov/P/bFJ0KMJMnRr+lnj4Q8Om72WPFvN5q056QL43vqbeDOsXmCyOCQ1WPnxmttuvCRAMKsyt8VsXYzG7CJSffszJrQxkHQI1Ss4AELIBh0uaAqZrCjUeqpFxXDYvT5r2pd6uKEsWFXs0HTb6z401TqkF7ghdVgXNYYpDCMVcKe5RrFZjT7vn9yzvHbN0ZW7dq+tjJzSvR1nuOJ1K4iEGRzAFNaWZ5arSz3J7YF7PsGMi4Ce1hyppOtbK4+j2zU4FU4FSrEogmbhWwng3je7x9SCXtIoj1ZTYhegu6aWRwrBRV5ioXFDp7F1/MspRvlRTVn6GAkyZIoshlmU7rXaiFQwX9PWQ3+dqlgj1wXOs7gRmBWAq683ZIwsj1laFe9x0rOopFhJUhIH8JRDD8EGVmfv8vreM05fGvger8jsgJ0d+C+8A/wC6z88d/Xy8lX7rvrK/3nV/sOPP/bXdy3d/7Vd+RNOnMzcHKySM5xrkFsfgAhTnPz0uRyyGCP2kL0MMwuoKWJ0aOF4NTHUy3jNqpHE9YpI4o0Ow0wJqkudbaSSZXnJMp5Zs5wlCLAnbqIW8uuqTex1IjVIqGpgyb4EJJGfvvxWMcT/8A//kNvjW9/6Vg4lXxD5x3/8x9zzSeS+5+uaWTOpd7zjHRx+tKgyfzmVicVbfjl1MCVOzKbufkeYbTalGVrhoMVgsHrVGgbJ1V3yYIVjucTNsuZqV3GfzttpvrXmVLPknm9kbCuqV8WDWdWr4sZ0zKuhVTuwWYBB5QQDUFOhRhqLwSFGxMmqBUTNqZqovuWdrBcXD2eFWdjR0BHUXruIM+UDBBFaFGpVDX7B+tI18+93H3vrGE1S7JPL0HURwShxcY1kCmCWz1I1PojjyFZAYC2JhF+NGmsP1kldWZaHjlK46DjtC82tOCkvj2ZzgGLUA5dMnGaWRabU7EkPsoETZGRjASCFN1bgc7t2XTUXXIdxRIR1OTtICLMCUmhENEol4URKYE8KTLAt5GlQs8LUsfFFthKYpTwrTHwCwHdRLzt75cfecu3tz149Qd6Zej470P64PKC1Sdn8/wBlJ1R24PxdT1+794HTnz50+6FnP7R/9gZ8mWwh9wqe87yyuEB8iK8kryymAF5W9GBaaAtexRNZUQuIOMBEXRbxUqRKTWeF36RgTWL6WlJwUgKmvUyEHH4i2WSmBn2J8xBIk0C/b9++iy+++N57733sscfU4RnbN0ciu/z2t7/dt38Qrd/jLmORHlHe4ccPspUBHNaIUpxMCz4lyE9WbtBRroQ+SCV4JSuF1axBXLMqIRYBdbbJqnYV1xQwW8fvFDfBfqhjfONlpOqPYTST1etPRLgFeOZVTioZs8PrFI4yXslqlhz3WBPRvfGqJSWrB9WrzqJPQ5MmZhZgT5DdwCLNIbMBVU085tUzY/SCpaUrV5bunn9HpEHJ+DaAYYIwqTB72OsPRsyifmbjFVBTNLKPV9wrcwwnqxL0imNAOERorCteAeH0wCM4cVxIUTm5DJssjWIXkBRBk9XMDg7x4sSgt0JApcWoAdD893HeieH1uKZM41ohIjT5AUnXkX7OmtGm9zCJEcQLjIInVQh6Vcfg6pWsrD0KDTCLvvfqmUS0e92la/z6173Hd/5UarNJX8/hCWfzgPDm/Ob/Byg7oW4HTl9euXTPVy4+tv/gI0988oHle7669flSz2178rwoHHKf8VZDnIuL+8Yitw5ybV6G9IKuqI3LCrK+ArApZo3lmpIeGukmEpxounCribKJpCTSYIapLWBxr5qSwmIa8epeaeAJL749hvfdIxIvIry9y1s2kQKw6tmPZfkHlzp3UrgvaKz63JSrfqqsuwAhcXBeDJJYLSqWQC5efTzpoQFiJNCrYTJUjV7QEAaX1nDqcNqrMsExBbi0hrDtMGuEKa4R02ukrlFcI9N2E8wJa6a4v6Acu+gE9L5MkbjI5nNY00ihRRYcX6TSEpQ5T1rIq9aJGuui14WpgErTCyMa3+9+967164/N/q7q2PvuTa7p8UIkBMuuw+AA3qHJk3HNDaEBcAa9GlqsAaS4cN88IB2yXvQBjUKGnhtmJTgBFGRdc+2t3UhK4+jQH3s4TygVL4NJGQNW5dmVCrP2Pqt6SVvQi0Tfm+FWzhM8t27/LTUW7vCYNXtIkXjlp7tUG4WArB01Nz9Z4VQQaxLBGDWHTMHa13SxieCJwpKFlGpk+U/JDkMImBM3yFect/LV1bMytQN2duBbZQeeW99z4PjF9y9fdP7lT/3YvgP8CaeP7l/55MPc4mY3Uu8hgGY59bLlquRK4aN3XDIwmeIe0qegQDBxyDQuZ3qD88nZZS6AP3jBztI2H+gbzRQZQSMMSaHOEBrAbFJcsoSZ02Je8FUAkFK9iGfK2fQAmy6bo43/N4n9LJEJL44Ix4V7O0cEGuui54WSp3biOtLHZfmd73xnjlzzURmmeHlojge/6/oP//AP99xzTyob+6iMZpYbssCtkZBtCmjIbGtTQ0MYHNaDMUgwGFPBqXk1C2FoZMLXKV56OVSpYVu+BK6feinWLH1rzyz6s4LK6W4wfVVoMBtCOq2J90P0CaaPXWWqU/s6C55YWsOsQ85sNMmlWnxpdVYMgcaJxNZ5OnmsCfbkiUi/IdrNbQd8lZKD9UnZ8ZmZa1eX7px8dtdX/ZTNVrBGvfo+tAaQBXnxa01rstAZy0KQBsE+juSyk2OncWgNMEsvl2zf0BzOnTcOrhsyVuRgOkGX1heJqW0wkXUR91g3qx7kGyQLTSqcTtl03jjTuLObyG1Enax6QgeRFDlRUjNFFhtCH4uGkHi1jhfBk9p/14WF+4+OrTHNUHfXRXBiJ//Xm48deNH3ryzNztud9i+0A6O3wg2/Ml/gv1Ax366yvAH/8tPuv2j1wP/9H48/8ezsBpKVLnKtwed68R7iNcujoIlKEQyQYE/QJmHiWks9AFOMgMmtRdbZmtVjEml9vIk0gtNZIQeo5tK2tWuyyK1LawrLkCywuVgA6LndmZsbYPgBTM1+OVJeogE8tf/t3/6tb+ATvPbaa2+++WYe2fkhIBxBvDMEuFp6gWXVROM10mNeKvLCiUJDMIJOncoQQGtSpodVp2E6NUiwyEzpu601j5v8i9W2NMqIsiAvTuYajA7A1tRP1tiBbph1iJT6tYeQuNiUDeP5ntcDVwUXwXrBBFC2rRaAkbMCMEtjM/Mj6CIunFfTRVbHlIQyWdlJC3DhCQ66swpEUvAgpwYhH9u1PPu7qqtL+/csr5547lcdcGPt0ghWWhUfxGRRZE7muvyerzI9ew6TxJOyI9EiAU3x8RX01lTIlP/a2+T2ZCOQSfHAWfkYExqNWXtA/fcBhqb3ZUeQdYEtcvE9cSFksZOI0yJYgVP2xOPFPRmFBd8FID2JiFhkVp2FV18wWWHCcdhw+mG8PLUgKNIwEaQRtK+z7D9BsqiZvk712HURx442WORLzj7znp2n9n7vvqERTu8T72jfUPdvEzPegP/cc1efs+uS//k1t/6/H+ca2bppcPJz1XD+s1R6gcsmThMT55/sGErgmcTLjVnumbRcUPKT1QhqxyxxW+UnS6AdPXahkTV4ZwghIF7qTNgxVbPqnhCvs7qHHIAFmEQIsctsALO0DE2pQ2dVUNPZed7WKyA7wJ57l+O1wJeDqpOsje9x7+eaCGZveMMbXve61/F2O3LNrPYJNmsgTsrgGWAK4rSszSBDg+5axAHR70EEt73FIx5NsT12xLUOAaAXvaBOgbe1a/joc4X4xZz6QgjoLYzEJaCRXXAYo2qaYOPOhnDsCDbxBb0qTQt6G8qAbLjMuMwNZx2Lzdlc1SqePsEqE8c67E9mZ5syagq7sbhdElkFWSxWZctoiumt+czMweX1607yMzPoUKTbW0+V2I25WxtnpkccnZqetTSALPcEMMEfdKdItiVZFoD+tlIkJqupZ2wI38Mdl4Zp3D7iLI3KPXAJNon9kCyC2iVrbNthxppTHRqNSBJ7/RrB6+mnn6ZCHt9J5PG9SSQIv/Y1nSJjt4ippZKS1RmpmhXrSwQa2FdrME2awL6pHII7yepoDCEk0fTBXp1qF9ppZ7xw6SR+OyB5O2BnB74Zd+DJtRcun3/Nay669zOPbDwHc9rnGgF74aR0r6AQEgfUz91xdfPWLek0UnwJ9rKqKWCkoiYfRwBB+8yGXCOqmWjPrK0xctjU4OpIdJZEQIZVgSnXLgEOkUFmslRLD7nZTKacNUW1XpOIwfBrFrkUxsfceRfM2pg1hQ9leFcnbor9CT/x6J3+i1/8IskcPCK83c4Had71rnddeuml3q9Da4D1GRSzVMyyYI2ZEsgEO7QnaG5zkBqvfkiWdgBaT2giMQWwLl8kGs70sHHJMIB0MPr2UcuQKVrizwfENEB3+zhqoencfBv3Rcpj29FPy0lCpLFj6PVDL6j6p3YU0CGR3pWm1zp9jAQc7udjV8+WLFOQYbXG1JOzX3VoPXh2eem+3Us3rmzzeffG1yF2Ombh6INtvZcR1sXTFT20MU4Tx85DoF0zyzBSG94nKnMUiHv4+tw+Apny5ovbenzPngckMe4eMos8BTvPsaipHztBhnH3UTX7ua2v+hS5yLZUO94IIIuPS2LNCy0NkGoFY+7okFv5qX8QoMaKzKJnSKvFmEWwAtw9BLEjEs6gkcGUTZE0UozoGN8AspRNolNnc17vOWvnwX1iq3emvuV24AsrV3//Kx78p8fW8mWRnPy2Zi1cBTYvokGOKVynXqoMSeEmlud44txbuLKIO4tONfKiI6hXONIasokEa5xEKlTfqWbWrBQQd7IawZoYmpoMndWun01EkCx98dKOuBEWDs49p0nPELJ8IgD2k3de3G3jPK/z9hlB3oVhq/mAukx6HOGM/uYWd/9f/MVfPP/88zV7z3ve88EPflDc91afuHvB0PVQUCI9J5EGoOlKjNd1VibxMXHi/VTNrRidie0edNeaOnGpO6DpzPvEs7naibclJMUC7PHS2iGcqiOmF0TBOptgZieAdngFxDdg7rbVsZOcbczWIzhhUadQIbHWGZcA+BUzhJ8FmjtxNKsdNBKnyXrFMUPuZalTQC8YEyTO05gF1zImMHY8u+/ftXzD6ux7ZrhZ9mRNI5sh+8/qiI/V00sZ4fDlqTHrdSr7kNxqxyyOg5d8+D2wThz7E2Zbd48CC7SMFNO7JIILsllg4oMAJi1TPoXjEiPBPDB79eq32kVhh06/wCg3QB02kzs44gxrGZWstWVw4GjMcuun8RaO7+JU/iAmPdsCIfs5W/zm8gOqgu5MUSo4iZXTY+2Ik5XEnqZjfANIcT+xiyOavUITueCctafWZ6+CO21nB75tduC59b2Pv/CGN+37x1sPzu4VtsHVNVNetvTE4fezBL3ouDnQHHL1cRvkAjSFOPccLkOGTesFdRy0q7kmak2vFyCJ2g2aRsd0qk2kqWcsTiJZpldOg92QBMPXMaWGEOCUQzDvtbMQss4++2x28rLLLnvta1/L9w2CP/CBD+DCOzK+HWMK5NGPylDEhRdeeMEFF0jtP9eeIgBj2+dKapU1S5zVBhBnDWS5fmkRAQT3aqRQzAShSdGU3sQMUQBXHXHtIzW2/BAGgVK40BpCjaQGAI0p7E7NsXdh1bRZBZtNDiOBjpw6ms5L2OgatX7oxdbHa0Sj2AlIrAvEjxRdBVUhGALpkhNcBLgDpvd8BekFEFKbdSbe5w5G2EwSc81Ts22QTFD9o/xtpl1LNx5b+sKetcW/Z4Z0qkW/OlYjphjWvs6SZeJs8ZtHoRJ6DA1HsgQ9oYk0a/chVa8wGSaSzc8sO4kIB7HSMjsGIJNIFo5oMnQTer7W9jA9cJ4zOYh9Vh+xTnaGqZroDoy5h4wj2IfyXryP+LtJrI53a8hy2NN0J14LAFOnW2qpWX6vUCMkWif8/khVZrDKZPkTkcciswAIcsC9po70/VQVCb7s7NXH187OcAfs7MC3xw7ct3LpW6+97xOHeNybvaDnMqyXT79SZ+HbzAJ7xXkx9grN7Yt7BW/GkyuTuw2t9yLSSGFEltb9FJGkVMFaauX0jpl1ysTGDg4RCPZVhClacjNV4wkCQg5A0y2VNpjI0nxv5dWvfvWVV17JkzY37Y9+9KNgnt1vvPHGO++8k48w+c8d6iC78VGZONU6ThlnCziiHPtaejRxhKZv7UNY8EYcPkBflTMMoc7q6BSYht0pO8YiQC97ggAsMiSSnZmbz7rkTgOlpjnMsuc0XOCnsUCwufElyHXokKm+EnQyu61vCBqRS0SMSNyl6RVxh07VW0M0pwHp2knTq/Z9uo54kXgKR59Ednis1Fg3q7aMPC8ydAfq8vtSifAn1O7ZvfTylW2+Zya5KYAImCu/L1VT+34H4JN4CptDIlk8iqkwuAPW6drFnookwu9Lzbp6gIiO5NZEfekFfSIREyEA3IdBWhN0rzj6KjSzGeo7s9+87pgCcywAiLj89EnsAWQS2VIX2xMGI7wqsC5ONt7LAfAC0K8x7v0U+0kj7noHLWoQJi5EyLJUE2frLzvQpJjl/pMoU53KHMTVkcORYcioEaepfPG5zx1Z3XnHPduzA75NdmB1fdeBPTf+dzd8+n13zN50z6o8/zPsAWRbc42TSPPyBzSz0SGXmwzNiBd+3h4mK/ecud7G4weYRFMA9S5hPMEYBTBFy1BZfIkYHys1KdJMId3Emei8hVYBMxmSQm5dwphjI0gKNyIdVSPRGx3M66+//vHHH7/77rv5jPoNN9zw0pe+9LbbbjvrrLPgkELPbTw1jH5UJgwBmfxmKpkveclL6ndBOusWJMVFkpLDmakK6l7UOGsbvGtnwYIMzUWNRJaXQ0iEVpXH8JjjIB/fWFdHyUxpOjcfLYBX7gW/AiI1aOoZYz+rY/PlEKeK2Qca+09vPaRYUgQXBKZX8tx2+IfU0OJFAQkuCEih2olEV5oyqqwPxEQsIGVUTo/xOtlzABELiGMj677RDy5k2zU2ag6PLi/du3vphtmz+zLfyjC4A0lMAdYAmV0dvLKS0gNzBzdHd/o+ywi5bA4n4UmZsjNo+tDvEsb0+wKIcGVZc2qow0EpHb2WBw/WYBZBE3EEjJVKnNbLUiqHQ5Ex/SaOjnfU+kwMx31oyBmSxWun77jz7jvD/rdXQ24AZdPQZ3PoOY6kNxzd6Zu4Q0oFkEgbJDRB7IigljX2jknRlJ7yaOwn2FsHQKnZAsqd8MIzVu5ZOz0KO2BnB75tduDQyoVvvfKifYcee+iprd9dzOq4CsDecwwSmbi4mMqslxX9tolo8v4xtynJ2PEQD6ZlKrKpTVDj8Gups1K6O0+y6hSJ3ApmfvMnsXne1i2rGdYCTInpxOY0IiSSpV1KAhCp+uJGlkQiWvPU7g851113HRZ33HEHj9x/8id/og73T9alyMyOv5zqiwGh+j3u3Dff9KY38SlJqV/4whe+9rWvKcEb+PVzk4Pf4450szx1Bntl7UmEw2JkEkQnWcEBmYJJS2Li2wKy3D6YYPtePzpO2bOVC74gJV3A2cwHm/Syn1U/r2R2VOZNJkaMYgrmeLFMmkFp2/bbHhGUFZmbn3DOWUC1A9fhmPtJ7U8tgEQW2MjiaCXEde9rOCnHqu/+GLGS1FNpYny1podG66vts5oIWZg25w9BaYIM40vk9LX1q1eX7tqzNPsy0XJ1NPqDQzd2OivW1Z1SEazLnJtvXwCOkGriYGE1qC+JgCSC+7IHa6BUGhvb86tLj2ti1k6wZyZiAZBp4FQbwjQgS/3mNCCLKXNn0pu4qpFInLuBNdSpCUwK/xpLLo/yufOP8fW1J4UGUzt6s+KeSK/GoTTxpPYHXxJjyksSEZpSgFTC7tGq+LzYjX8rqIX9X9992qdOe+v6zncR9gfp6xo54fVjWHmTsvn/YdZO9GR24EW7njrn8H/63c9y5m+fxjXi1QSVa6deJhPJsytws0FLVgXBVYck3sfxVkA81+wguSaCNUwwKcYzlODQPokAIrboTIBFEsNpdIgTwct4vS81TIb8BiqP06Zcc801r3nNa3x7Zf/+/Z/61Ke4S+vCo7j/TksKkY2/ycQgNkpzT//IRz4iHusR4rdXjxw50hC81RK0GvuGU4da27PI6XXWxIox5VSokfgGMAvGyF4yiXEMqDpjuIr0HGZtTHmF2DMkztbRZ+FZO0vw7LXUlQAAIABJREFUubwXNFKrHePUOC4ZkqtRghkKYAJozT5wsTV7G81FQOx6UNPxdQjAblbHZqTSpjGJg1vUWGdY1bJMfZtNqMyKIXtkt602pgJ6WvOGfUQAwdUO/MzS0n3LSzccW/QzMzWdRfEA5NHEvU5VrDXk1ECK91xzK3kae0SytzEVZNiI4Ov+s7copIyGNjgkkYZjRBpaTAHBckjxATGmSG3rLoFS3dtt+XjFFzKJ3BAAtlrtZmz2/xoHeyBYJlKLFGk6OrxlQAqvBDzB59331NO46Eufp3yqpRHBtCGPDS0VC0+hVEvEpia9Q3TiqxFTFIDOgqbQZLo/iuxdXlt+wZnra6On/Vj9O/GdHfiW2IHH1866/vIrrjt46J7HZ1cobaJsLxAJXHdefQxNpCfIMJekUwwlkA4gK5wkEukbs7yjnDgXJndLTZHi0u6vbgsgpdaQIYLgbU2l6YuOBTs0V9z0mbIG6wRHTUJoTXqGvSNTEQGzDzy4G3nlK1/JE/Wtt97KJ2T4FdNLLrnkzW9+8/ve9z5o3K55VLAMhot+VAZq09j0t73tbQcOHODB3b1wVfS0ek5su7YoswYOXoaDAPHEg2eWm68KdVNgxr2JV5FabeJjQC/t0GQ3jcAHmKWX1vasixceMTSA77iPuSwSj11AX0NTz0mt9BRqqJVYDMeUxTZlbKtMCidVEqf5msYax+AkRkowuA8ESRycis4g4OD6xMZsU0z4KYBIrcFr52RNn1mefUfkK1b4XdWlXDCNdb8JWlMtW4QjrVaVUscAiVQ7fZGO1UDcZ9NqijvDMTvjEHAE4z7NzKw1IA7g8gTQMitIpNbjlEaeRYubkosUjS2iAYg0u6F+emugx4WbA2QSGZob2jRItdBIzLrMQpMGtq9SvFrQOG95fMedt3aa3EquGBcagh4X8EQitPl+zJ7IBUiRaIq5M7n5Kzf1AKpXg1FYxLRmuT8mXnbOruN7zpv9sshO29mBb9Md+Pyxa/77m+7/97fObkRcdDQWyuVm6xctIbRZwvxSpd9M2vjFlT6XSL1g56kbHbnxHUzkwvTaZJZSvRGRrKCzVaQaVcGTMq2bkMSqFsys7kZwr8PkSquyURA0U0kMjZfF3HuffPLJc88998CBAzy+X3311T/6oz/K+/G8hLEbbBE1cKMmcSbCR2W4XQIY14/KRHcMsNH8EMCPUOjym0+hcWPN8UhwEUBl0FinxUynZBMFz8e07qznuv1sd+bNSoAC+fqy0gyNTJftLDsGYNvHyHrFUZpDjx8R7appxb0y6bSxs7/nEzEFkO0l0jCbMpoaVDgp0+hjamI1rTjMpgbi7NKpnYSa9qtAM9YBKQBACrleVw7r7DQmkfRtdwlfW9ReuL700pX1e/cu55EWHZqEgPArWNC0poAtID+lZJZ47AS1Dw1T8CkcGvTJ9XIDW0n66AdUd04G4qdsynEZPDRuxVgN3kM4H2olKW8CILt4wanBMtxeqtWUYHVPsHfnjsTrB1uUl5CeMxbhZNAXgqsGeIxwtHngwLW2kGtwzKXGs0UKOpWt0LryxZBfccHKv37dK+48tq+f3Yl8fXegfZ0YUN+kbP5/gLITOqUdePlph/7pjs9/+MDW7zJyUXCR0nMVKCno+8bQq8k7kuTBm2GT5ZBcG0NyTSfy/7P3psG2XdW93z63EeolrgSoQ7oS6htwF0zZ+CEnAWJj84KbykuVy8m3uMqusj/b31zJy5fYrucq54PLVamUnZfCGGNX8mLjBGMeDjYmIIOQkEG6alED6q+6q3vPOfmt/d/7f8aZc6655lp7XyGkPQumxhxzjP/4zzGbNc86++xrY8nSx5pwOlUwQM/pwUHaGNcRFQV3iiNKSGxib0Ip943GlgVILfsuZBbUxghYMh2aEQb7wgsv6HcReL373e8+evQoNvwp6UMPPcQ/e/rXf/3XHM5cto3QhfvQhz6kGyQ+fIq9MTuGQACUj7lLA5X8AUkUelVHRymJqy4GMO3egyMglAQ8NgmBmQoyRb1o7DjHWDxa/CyRMkJZxndsuhSXOSDnjitAU5LevRZkVsywKVWEbszLizsClrEmippGiDQ00oSJLetChTARHdSC0ead+/4erp0AQT2DBiwKihvrfFpjKgDpo4EjvWOXhFhxTgFLyfNg2jJQE1nCWbuz67dnXw/v3W0/KECYcHHD0qTgqDpHUFxq3d0nDBZkLQkPIYliAgkHmiq+BcqxDyfC4qgl0U54Ga07N5Dz5eRUEKiPA0HpjRmOrCoyEYkLLEVM+oxlQy+CbBQ0J9yHYD2Oejz48zPqggld1BTI2B5Z0Rkg4eil1uzYpkVoJOw8mAMCQQlBXAqCkuBUJNF/6l0nL7nxxx85tfjHSZLeTXONGdhbJb2gwSSIveabjuYMbM12f2Lns//2s6dOLD8VluwLbyXt3779kgeUI7UBi77RDNk4khVUG9ZdfQIunA88brzTuSX6otjnJb3OK2oigqO4qjFAqLhjT4kGjYRxka/cqSFAcVMEQPODDMH/ABOWMSjGvFXxBVtdQB3Cn6I2t34goluLrDMdS0ISRqewHZUdcZXsrijgVXkDHS1zWRG73Cyzgw2xlCnJIsDokhszNh5+jlzRECsZDlAUuViICLKnxpegiXu0XEWes+ho5BzcRWiXllgYt5hFG8WiZrBRH2VxQNOHz8xOmx0mGl9q04hxo6zQZkIXEfGaEBcXguKbD6eFBoniPBobl++Z4Tsibz3VdHfPaRBUiVJOnIdBGuwjHLWSYz4HZUIAzmFEnScq0ihyYAjErfgWCRCI3MKWB4AO/TwViaO4EUhHIkExkJxYVppagfqprM8XVpDJQcSQXhz7fHMvNDLGkdKYKCwZoMaIfOGFF2rgcicVYHpx9s0aoTU7si9yKypNWAQq+HRRYJXg4Kgc5l3R8sjZO8/udP+s7Kac7gzwwCis6dMddYM/zwB/e/3wW2754DVf/Mt/6b52Ni9sIiu7Y2L5jNZ+d1cuzPffwpcdZ9+oFzh1ZTPqoBC+fOVVjMjJQ1EXjhyn+qYaNOh5EmnvqzYmgjj48MTAxb2yz2uxsh5HBkuNnjpPFEpOIf+AIUcTEAea6BNkNCDzoXY+O80NHGRFkZ7PMXqwchRI4WuDFDKp9WdMidJNBYOcQK1vFOAd5xjectSyUL5QWh+j0Mvk4c78RZCW0CTak1qxV9xYY0wz0pBMLaGCpq5GswSHoBQ93pIuN+cUyjSSPNtlUACOuOY8Z1H+2cBQosGMSLCvDVoEvGLcxKVOg8FibxqJb6WJi3wrNrFLNKilLF5JW2iwFH2tjPiDMp93v/cQn3ffurv747vCD2xGyGkQFOasKJ+MNh4U8CVRySZSHpSTPgRosFuV5JYNGHHwhSpBEer7XRzExwh48RtCmsQVArV7K4ITNRg3gig6PBksL7MRKNEAeZAGviyMom8CFZsKRKJwt0xTadGokSVgwAD12DAIZz72zBTFyroAmhYSjlgCK3x5KRsiUMTBmAJh6sS3aG+lVhHIxbg2u/S8s+7e2fv8gPUb4XuRAfbd5m5/uhL/wMm333792/7hkee+8+JACB0OMmLrUSTHDag9q/0b4din7HdqlDjKgDr6RnvLuGjPohF4Ma7tJcgScH2wBCVenKvSg8lJBaz4JL400ccuvDgu0CDQW+c8d11YYk9QHTV2Jy6l5aOGuGuwCIIVCK/VOXIZAkzgw5lPCCzV1HBo4kXpfgFBkTavzz///A984AOPPfbYz/zMz/BRG5jdcccd+TNeWDFADoVGZtTIiisNMtRRqrZAE0zCUbuLXpfGuLaPgmgIQXppJCfhaKoYAc6D47VxFIgClDUOKsFNG0iwCwKzkPQ2NvvA5e7eIhPGiz7SwKslA9iw/to5JzR0ZckHKCaqizSISNxiV46WaPAirjgXsxHtzcECuWofb4QS58Q3JsRy9EJ+hY/EHdi96dTsGwd32S1Jb70JbSIy3nxfJ44xFWaCoI9Hy1hJoB7MPEHxVZ7llYTra+IFOEnWz0hFswoN4uJLAWRUXIzla85KArUEmCAI0wJNCo6k13EHkxMHhTEFX+JKjr1d7HkROLWaBJWZBPQ8EniugBDdKzIfmKGXRxRfT4YvxZhFL+JKL4GaCZKGoPZFiM0cSgwhzP7FmNTlNkWNjImrJGADlFnRPOecM7dnvQ+7IuZGucnA92MGeOl+187NP3/z5//wK4vPRrM7VIrD8TbBRjK7z0r2kUrRV0oM3Isj7mouw+7deWwWDSTjmPjaxkIMxOHAuWRfTkjuvjQB0ZGLMQRkkNTo49mCi88NuYgJSgq+UlKDSVAdj8KUgRAwUEnCuZn04osjvdQUesWZBBrWvQZZPAnkY62EK6+88sd+7Mf+5m/+5qMf/eg73vGOZ555BrNf/MVf/PM//3MeANGYGATTTGMjKgjYqIaN7EVatW7kGMf0RdhBueiriA4dBQNCAF84Yywy7moR8MVr0DJmAGM1nSu5G0cClCqwHlrFpthlJjDHIMcxDXqRY9OAk6fJCI5rPu6y4NASSMgqcQlkQIdAiATMKjHQTKEEoT4v0VHG+DbSTpjQjPdgIWsI1CoxnGX+UYBjW7ObT+77nhn31gVgYat7oSyLOcFMvQhJQnyXrQdKesHhECBXETBJCC5oMFBtAvLVem5MtaNDnkJcNBXfIhNcCM146cUXGQ1oEhyiKMzDLq7g9i1aRiWBGKZq1kY8fBSUmgI4mDxREKK7ZRwpNPsMbBkFjnoKV3BeCIHPuy5iQYYiMwko1ZRACGtiXCtjiKIMAgVwUo1Bki4FdY2QIMvX46V5zuHdA2ectXNqQbsYdKPcZOANk4HvbF9w9SVX3fDWh+5+sjvo2CNsB22ZZLO4iWA55gEvinaTbIpmdqGXDatmn6/0tpEgWPki2waBpoqjJAK9nH4U6TneOTo0ZLp40ADLOZB4yYBahUAYOBD2AKpJnfi6GWHFWbVcjGb7KNiXo5WT1l3IDIGfQxiFEGzZgetbZbBgVE8++aTTjcPP//zPf+ITnwAOM8Hhzz/Eeuutt/7VX/2VA/A+hn9OFVChJ7XNigLJkn2xN1GahgRqCu7RzM1ciGbIGnKibGziy3iJLnvRcFPKnIA0JJaUeg4aIzoQUaKvgsbQUU6YkG1viVFxMR5MV4UJccVZGcgzUyEDLCUOuWKsLuwRVLPovaQTx0jGlKINvuz2qGmXtUJkLybIFiKOQ5sP6cKyj3b0zeVzdmfXbs/uPDRLzieFNgELEQECok22zSoa1GV88RqcqcjENPAF3IvEqUA5yIR0UUjXoGXCn+gUZjmOF03EqTDBUkOeNlP4giDaGgI1GtVQRdbw4UARSWqKzCQngxpsKlafr4ImNdHRUMNZHzTi45ggoBkMZwNxpu4LbUsJCQetELocV9FjnSC4qSFfes72L/+rK//p1Rut3winNQOLp2MtxtJk+d+a7aZvfAbO3Xrlmuc/9+++eGBnvlOTDasthlKlER4vFeyXrrVzwMbY63DDC6V8vZ1boi8CL4/odto4cs5zdlETiKCqFZ3apUjDceltGXIEkS8aBHyFIAM0tuRfNdU7EWswli+fcefXntIrOvrFYGj7ZJQFt3M+FcN4fvmXf/mnfuqnpPzt3/7tY8eOgcLbFyIZa9pXkou3BiNZNbAI4q0Q1DKTYJfJ9yoWEEMzeBRyJrFXMu5wEA00fVC5I5pK6NweMuJDl2VrUIqDmVjIoYRQNyh6WQlzyZGAexEAN74F9OQHl1FZMiw4jitlMQ+2l9DxmG8SglIkJzaDTS5VbIrKtUx5MJ8EkGOC9TknsjjXYJLYFJuYMWRKo71AROOVra37D85u3P8dkTJoYUJEHW1FYnWl0oU7Qp4T5UoT4dqU5KJsq7ceK/bCmYIvjgixyzQUPelSdOZIZnAYFRpj+TLkOYV9oWMsZGYThvNZXbz0QilWKMERgjiIWIIQmxhgj6OyPYo2jthDBl8FooaJQSS4K8aFHk8X4vIIwIV3QmiiQUUGUMairfEq88pD5CCcyMS+uNOrUVTCxS7FuujM7Rdm50b9Rt5k4I2dgRd2zzx80dHb3vatrzzW+9eM2oPaVsVdn6RINlJGX2no1Xa2l+0R2InWI0R3mVFHg0Q2VN2XsfiwlSU1vhyzHFkKgQFFVKl1RCTh3IxxBUgIvASV9NorETDTeCXQK0dC86kbffCGM/niiy8WKwyeeuop3vfrps3vPKVX6G469X4elBiJA/pXfuVXfu/3fo/vaOffcFIXzj/3cz/H+3V/eDG6FGUFc60oZgBp0ielhpHQKGKihH2yCPoso564Ck1Q3ImlZrSJNIpkCM0KKHZFnLqsuLHO7RUi1oSmKU1uP1kDjUEmGCj0hOi4wLyFnmlIkIs2mOOKBl0WKsjMFJuBumJT6RqkLQ5FJlw1dK+q4Pd1aV8QXYu8mJbE10ye3919cGt204nCe/fEpdgkV9qSffsLMiq4I1ATWrIEfmLhuJG7NMVAiRJLTRaO8k0Mis0ll44GtEk4vjEosksRQUpwdCa0h5Yj4MqYXkUzZRS6AKRLNU1k1gOWORmFloEwG+suTcsPKAIOgh3BdLEejWTF0lWYUeMFMfsOCgTlTQ1oXN/5/SGPj4q7aQBrAshkiclCwBdAk6xH7wY8f/aLNl5q1r3Ue8k5O8d3zmmx3NhsMvCGycCdJ6/9hXc/fNd3Tp3o+RGbTUTxeOcH2OJhzeaKXbKJO9peCLKkV0qag3sTG0qE1eEpvbtiFMvqxZ6jgOK4CMTlAUQRjl2ioNMPDfaAcHpTCxPHyoGGSwKLI0VQ6qVWkWUlD0THhohC+OEf/uFLLrnkmmuuQfPggw9yZf/bv/1boLjWc+UWPWq8DhU/tkEH79T9NTRLGt1/yRGfjfGre3WhjDZRFnXVUQ9RsiMqUd8owzCmgyaOsS7iiAZmTFt0Lxr3KZPQuZlpSIgG0pCuvrRE47XIkYwWRw5rMnmXNdgARW3NWCEyqfiKTJwdJouMeadVfItdQOXMW8jAhCVK6Po2LgaVspF5kQxKQkNeOc/TUon7It8zc3D27lNbdx7a7Tmue72VKy5VRIypk15u6hKxHIh06SeWPoPcxRp8WaW4K+cEVbFBIhCCotWiFWLmiWW9CQgRiVVkjh53iDEjFFPCS13iQA0Hv9SpR3RvDM1ANBb39gniYCZ6kyIauCBY7kOQ3qPGftQWw15viXR95znS5y4m1DkT+JNVCr5Fg9xFGsXCUdPt0E5L7vjOt259d3vx743kvRvNJgNvyAy8unvw8TNu/MBVX/t/HuheagyO0TZsJZ11cmF7aodKsFkfoNyjr9z77AXr3vmpsPeyj16OOJTA2gYlG587LodPBPchIGOoxl67SxBIPEAIxO1XvcJPBmt8bHBXFASZqVcRqSUkQd2kN56cN910E+68K2dQ/FkpTD772c9iAzIHNWedHfckqxAwxe53fud3Hn74Yf4V1kcffVS9xPizP/uzD37wg/xTT9Hew47KiuyhVmzUJUtkCbFmVcEzQtGkDGLqOTFolhs4erKAoqUIzIksJjL2knoSOzZdEcGyyNBEsOxeCTmZyaGBYtQJvpsm0GeDAfOlLaTk2HftQk6GDUDak0B9c5SYkTGYT8ub8k9OEGBlYkkImn1kWDBKWu5S18zv7rt8v3v8vLsIzIksjj9kkQTNMhoODpnleavHVS9eYq6zrOgCvoJKkGwyvALxahmVAUKTcKIzZUYrErBSI6WJI3PNW2Rk+RoBgQI4mUGwbyIApXVeGXjioqbAxdzjNbHcpWOzfFS4l9C4tA88hsaRpIGZLHX0Musjo5zgS97wHfVzi8OJOQOneDhFQXxMBgRkVgu1Bq60UCfuF5598Nuz8vMusdw0Nxl4I2XgnpNX3H7TA19+/IVnTyxesLLLvEGSDeWBYxBPEo4mW9rXxrkgd+lxpIBAE71K7iIzjgKKjGWDPQJkdEHvczdgYgCU0dQlQNtHgS6iUFDCByYca7jLhbMOGspe30kVwYUgfPQqMRwymIqFMQ+XT33qU/zd6bPPPnvs2LH3vve9vCXnvTtdtkGAT/dXt5CjASi1y1133cWnYu67774//uM//pM/+RM8FfXSSy+FsT/gbvtRAkEBMRv5qik5IaPQVkKY9I2KaGMNxE0JDp1QimaKLnfkvmmLLrkMc81T3hU1CQ3TU96wdCoQKNPIxIgVOUavmEGDXphIyC0Z+2SeOGrSTSZJURIuIUNTJTFraeKIGeEkJC6RhrklNmKO+4Ths1q4ErWsGZGMNd8z8yCfd9+e3XdoFn9qEZPicCJzDHQ4TNtreDHwuNMjuGRx6FszuGM2IWm4UHDXSAFhamCiWgIyxUmQJTW0dWrjLpycdkUDApNFiPpPDoQWK9cRE3cKTMYS0Ew5bx5dBC/K82R0v8MkLpdgHFWcIjUxKLqjnD/sXmXUvKzCjOt7xTgHEXOig0AvTVHKLdH0kWHgIoxB4njowIwH8Mnd3t8MJ/ab5muaAaZr8ePhaxr2zRPsntktH73+7//Xry0+0aEjgs1CBtin7JfB3RoNuuNp+RavxVcbVtkmqNypJcc9SxQKhwl1cXYwpii6YKmLllZGqD536XFBkKNgOYh816WL04mXgDKji6Me8AoBunSyCVNDBkcu1BLoRbj88ssvu+yyX/qlX+Lfv+OXmT/wAz+A/dGjR7/xjW/IQKOW3L3Pi9AKQM2nGL/whS987GMf+/SnP81XQHL3//3f/33gfvRHf/STn/ykzXLBI7eATZQJT1Bp5sy7vHsAOWCiYc0VCSdmbjq0BNyJroUrmxhaMgZ2T4Sx0XN3FiVMRIZeC7Y0HwluymDU2I3ZJyi6OVhI7M2EzCR8EssVmxAwBwvC9JSZDPrKTEUmmvGxqRMBaiKyXYvuzgZCHxkc2fCNl+9IW7LdYUJBGWvbx7QgqzzLD/Sz2Q0nun9XtXdNGyITGBGx+sh3bAIfeaMhtGS5M3GM3cosSK+CgXNW9EVP3LCkEN0CBloz0CA6tQTeakhIEJJmy/07cXFT+ESHD7L1UVBCRCzqLePOWFqo2kWCFmoce5wmyYmLZwdfBq40YkPTXYlLsYkvBdrckgHheSEyiXGFj8Ix6Yrel70EUE3FAlyZj+TP7b4L8tx9P78WITbK1zQDmwv7a5Tux7ff+v4rr7jygccffG7xd1MOrM2u44Ldp+LeohB3ZTxv8Y1dQtZ+xAxBaDLDkoIc9YPRowGOLiCri7rI2UoZyJFR00SmFhlqWyYCNjw7KNLjS9Hv+ujitOHoq7jjFXtFgLRwtZCez8nwipw/KAWK716/9tpr+XqYn/zJn+QduiLqfEPGt/arw/vvv5839rfffvtXvvIVUPjHmCgf//jH4Scg1wxAMgOwINka6Z0j+44S5G4Xmsiqo2ADBNOQQI48/mi2FhkmdTIygInIqF5L6CKIwqkLmVWSm8257N20cgNrQFiRsPgIh9rIFmJaIjEM1BWXvr1aBNwZfuIuPrgXyTgovqx5bCavHBzZI3X3nIxZIXBA4B6TIHlw7C9szY4dnN22/zMzg17KiQkQndQpIkoJrhEkF2Fx1NgrNkVHlArKNU4TBw7RmUeKXMRwHn9xNSdLnK3SYIMBXrgLoS9QUY8LOH3uIFMUwu5oPExFlHt96u0eBbloyPmRGy0lz+ksKFlD6siVVo5y4szkCNZgE6PL3b2DAgPnNRXMdX3X+zOnRe6mYSHCOjogGIyaO+zlzsCVfNzPO2PnxIGzYoiNfLozsLmVn+4Mj8K/4+RN//Wt3/4f/z597Hq/gMbx4aO1cd9pb2qvsd0o4OCLhi4Km1E3WpR1wjrBRABjlYpLYhDdxUG+6A2yRF0cKToobObh28yOuYCv3XFk4DwlRd6jBseOokfTfBQFR+zRX3XVVbjz3v348eO88uCL1zm9+ceUhKDE7qHpe9wF993vftdUbCGBj9q8+GL5H8/ld6N8mj6x72sSvi9E4uJxSq/RRnclxamxkODEJnlRsqKyURYfEMiyUx99B/ngxZNs2ldnKlAcvvigL5KRfRwscnSPzBtl3Bm7hpm7iIZZ9Rl49vtwckdrRvE3H9wl5+5JfhyoKNSHX3SJStxpElFkYpflCh8WHo7Onl0ahXN3Z+/a7v5WNb5PAFAFEATNiAWUkY+yJ5vGoNEMd3x1QkW9ZeIyRsw0UprqkiBW3Mi1AkWsnQyYlJbrr/kgEFRF7oSmoJFN5FBnggvjknvEb5TlTghFFwHTMEiFD9GxZ/HUeRoqEfrcwVSRPTL41DQVSDWvlCj6LE2C3NJU8oFqWfwLQnMOYqL6hy/d+aHbbv3WqUtaIm5s1pWBvRtTL+LSZPnfXsNNx8oZeN8Zd/3FP97/te/UXtc6CBuHracmu4/DB1lKvQ5ARq/9pcNNJ4yOKRmrFzMVgw8KOKpgufTeuwRHd5uhRI5dkuvuub1wIiwIGn7ROFHiyIFJiij24vTT8QVUYo+GPxml99xzz8VFvY4OAvd49HxVjB3pXXzGXWNzRy703dpzy4qG8Yi9aFFXjE3JQ8Ue2bmo+PZ1gVB0FxNqCUV3QrOOtTSLBoNKuQ+ayUBkEj4k0O5Ky4oJMdqgICYQUNyiPV2UYoZlX3cvYlaUxRRFe/GhllJChV70zWWmnv1TufyZj3LluIZi+LhrC1jZKEBbN5hR/HERGT7v/sChrRv4fvdDM85acaN2GaQBc4Y/ef0re/AhCRoIEYlO7XQxNMw446RXbWLyquTflrkAMmjwR6DYIJky6yXgQsFeUwZz9BMIAIIX7i0JhBIjJZDSYko0uf6KDIDWtwjwxx0COGosLV6ywREvat4nIajYvctm/w/zMuPz7hTceTKRB14m2b1FUAgIkD3sxZ8UoZE7lExSZKgT5CNnvPr8bO/Jl/RumpsmFeXKAAAgAElEQVQMvBky8OVXb/jord++87PdJh4cL3uKE4MS9xpebGG9QEHuw5HevUDpTJNLvj1zMvhGdyFQ50FlaeMcCg3RRxEQpqGUCjVjOPQqtkTAgDMqPiYIzelHkRnZo9gFBN59c3enSIlGw0FAgzu3diYCpboQ9n3GHX8u6PIxbl0AlLfI0QZoChrV7lKT8GgIQWmZP7sjEGvsUwcvk8GdpghEWMktlIDCLPdt1PDc1YSBQ8FLddG9j8+EDBTxE+Wc0YJV0uWmxn6aCDiKBOcnpgiZ1W8CfSlKoNxkvenqZs0oQUxAiGsAZWwim14OTtcqBOCvY9Qbh+gqeSxp4IOxGD7HpWfWfb/7tM+7A6jbpzCTiNBAQw1DCoK2m0KrV7TRcHWTPgGpN3GnKIEIdWP3ElpFGtwR7A4NCk1qu/QJTB9QYwkYTe5KjmS6ALQBgmhU+JBVEDCgRMdBGWSmT/wxjs8VcQA5ISNMHOUrF/GfQEBv3HHnl7e419cATFySoZF/qEJGfJLevuY7Lzj8xPa+51Sf5Ua/ycAbNQMnZ4eOn3PzB6/52mce6P7CksJOZKOxKyk6AdjsaKjp4pjK96nMqHGX2RypVmEGlCwUTjJ6lcS5b+/r2BEBuaABIXHPm5hZ2ULAxhIIAYJZkSsbMC4PzcpEwDe+reAe6G9ax5dzDGReavDxQkOh0biIxbUcwNhFBvb90gS45AvazQBTJwhQ65FjHmWjOiYLexg4tt3bBaIkgKYhwc2Iac4IlFUIgA9CBM9lc0CwbDMywMWdWjjUyYhsuUbBNBDIYRHZfBAkF81QQr6vq0UPeLKQTC9xFw3qJEU8tleZRNDyhURo0XCNUMyD3FchgG/jRohkYnL02lVpEUnkItvoJZnPu/Pvqt5yanb3oe4SP6rARwUCmkQ1AXF0mFBY5NRWJlHwUgb6DBL7pMkxh7sQ6AJNtckk9kShRD4i0BENp3ni1dcESgRYh30XxwqlOZeu0jKesJBEm+ELAag+qlEfKeHC9On1jzLQEdqfouibyOIsAiKTGNSbuPNuCHc9jbjNAyJ6doQMcp0SQyAD7QTOOevgid3FZcWBNkJ7BthmTUutHXFj+b3IwDdPXfYT199/x5O7Ly7f+bL72EecZvxaTFuvzgtjG7AN2ctqom9xx8bnHqFBoAiBLgqyhBjIERGiPncXQrRPZAwiAXHARkFljJISHSMlu2MAAWdANtTRMZd5PlKkx52jWBngjQafo+aKrycLsDxqKWIyZ7RHqbu4m6JIuCloeAhX+RK5yCbmMeqjDIk42thVlM1BAjUcYqCYnZxSjglCdMkNBjUioFRgbIZ2jPjIka1tRiXBXhLyiNYgWE68aIqYKMEq8syNV9SYhgQ3DctK8I2nL0s2zgX4gzl2CJGMbjxCjlDCHKQEARA8hJxhXQM+hVVEjWUkVnTELJkylpCuLEIoelWUx7dmDxyc3Xxydtfhfd8zAxMKxFRLQBaUYokMBCjI05Igx8ZblwgUayOIW5KlSgZEgHVIYSAVy74uLUIOXKAk21JkBikRlwx7CHZvFEQb/mRGc+EUWYhQZqUsyR1LEOhSM9oPynKpDyEyiTLgBNUrKB5LvCrys2owrg0YCAVYcggassZogyiccXD3vLPPOTUrv7aIlhu5LwOcVr41DFxM+iA2+tdBBrZ3Dxw7eNN/ceU//PGd+36OZSuxnUWQraQyyJd9ZxvcI0LswgZ8igS7KArPEQQZyMYGg0KMIgRxWAygeodOYtHkPFRE3HWkINQ55AREo4UAyLj7ZTyOHIa80YCGolNz0iqEKIkMlt2vHfkPbXUoiQlXbOSc6Gl2EMsP1Oe90uAex69wqoWQO8qeWgLGRWK5Y58GDpXnUwslj0KU+hLSR4AQcuwzyPW4UKSX7KaNhUmtYn1RwKaoH6UUE7nkfNAriuo8S7jkynYC+LLekiujKYkPdT5Ss8IXg8piGCQDVDFEdDQTlJJjr3am8gCaSjSoywwhT0LdhV5osIYpJ7a2Hj/zwE3buw+ccRAtXRBQDSUKyeGVQJ0VOIxichoZAu4UwolbzBgaCgRQmgaW4qleunBHIwQp22uYg0AaESKswc1HQkSWvRYSeUDIEaJ9UYY2hSH0cci9YKJCF4JqvZLRKJyNFj7YaAgQQJ4wlfDHUUNAzgnTG0tuoFngcUVhyfECPrepaAD3LJCQLqFhOTlF55yxOzt8zua7ICuZHNW1eCaxQ0e5lY3BMF7ZYqNdbwYe3b7o/UcvufyB7377+N6PsmwlnwBsHI41BUWvPTXIIW493HWqACUEagogfWg2wAYvijngIt86hwqC3YE1iOypTckZSDjY0r5FQWbqmo9gUaHJEcQk1phxmlEwRqaLHOp4RwM3FeHv+20j/0jq2O88Af2CCy7g2BVcwkNNJoDUcL5jI04ahlycNTX7ajn29db1EICJ14FYJS51SnJppJogq0l08h67wBSs6thl2awQZLYKB8NWhI7TvBRtdFXyXEwgY98iflEJHfRmRSZ1KERjwYpPPQS9uEffsTLzCAK1iRURRINaJdoIYUL2DCIENrk1CPAhOcyRBNGTnhoaRFR5enf7le2da1/h31Xd9z0zEa0ug6OJSDgUvWCiQi8CTKglk0kdVQCioauIkCuxxFG3xhYOOYKYcDLSpei2AZxivWT3RkETgWYaB9zx1XpGVpacnBhIHKjzRGGv0zUZRXTvkwGEOQjmkFv2sRIlgsKcxYAZGo0oB+nT4KJ3TvwGg18W4+5XUH0u0psVgjQkgdkUB2on6tyztl89dPbm4l7P54ReX4JaN20lBhCGq5htulbOwO5s687tm//NrZ/93X/kjWoBLu5iNpfOFuy8p+TjDZhAyN3noU4GGdNFSezzZjSTIyAyo6t+yhVZOToguK/IoQUhHwJphAZ61QKJZkke6PKLDFx0uOmmTde+G4ztEohKEwjelOgDi5iJB7VkauL5Az0VnEoXCJWHAUPCN9Y5FL3OtbjlNnUN66bCoehLUBX16pUSY7Gxs6SkWd8ngDaNfARckuo9I0XG3KIvMmlckQYI+YSalcDzEJEVCBOmIw6Eqcw5RAPJZuUu00PDFYGzKRKzWYvQyCGBggDMKWSAwh+lgINSZpAhOWgog8SOz7rvd7/15IxfmQ4fpQmPeZNYRNHPMCjgQy0y6M0KpchgL5naBTPf3a1sFxgpCJ6LxJEul6TLlMRqFQ6++CrtSaC8CaWYKxmggYOmr5ioHMcaxmIOyNBwV6MgL9YVxxTRaUZHmpQ6KwXVKDCewIGnD4UkxL9e9fQhRErIc1IFVnCgYO/puOy83eM75ybum+YaMzCfm67anXiWrJHLBmo4A8/unHPz245e+9YHv/X03oWk6Ka9rCOLbcX2REOT04CiPUhd9JUSM/caAQ1escs2iaAQVkYEK6Mg+woy5Dno5DKNQx2BXhWzEiUfR+h1QGGG3MIBG054ijC7JEgShORizYR95CMf+cxnPuPvrLEZoH3HNAHoteUEQSlwoosIhFAp9qLEvTKRfV5Rn+RHrBJltEcWJcfFeMUfYBL8vCk+4pb3omE64KNSNBhUMihABs2iQcKKJghSgoagRLmOvn2y3Pt6B/WKC4gK9gjU6KOvKSV62bCoZBBd2mVhakbsJT4gU2KWMKBLLuw1ClccKdHTNMIogc+7339odtvJ2df3f969CCJuxdnnXSlnCuuqmKgimpW4sC/6bt426xPEitBwSAiIjJSDxOCgZ9K0ZIJPBpg1T4eIUefMMYYV+pwV6aXAIe/KcRKNOODOQMBXiMQGPhhUWBGaXgaC+4RUzMPuffS8EYGILhDmj+QgyfWdmt8AAwJse0LMgVGAhvvl5289dWpzcU/WwmlpMo/CbZ+v08JjAzqUgX8+ed0v3Pro//SFrVPbe5srbsMIwGwme1DHiKY76YqOiay9KSUI2qE06wgJK8ig0QITAWQKIEm4YhNLn0u4m8MEBNwZhX7LJyYayCBUpCoQuQ+mQiMCv/uMO4U2jeI4UfIk+Nmf/dlf/dVfveKKK/7gD/6gzyzXw8Y5SnpNFMFyYgMl0qqfMyr0Eq+kyejafc1kTmpxBkn2BAsfzPpqS2i0c0gc1YQDAyFiJJZbEoUSl0W0kTsGUTlWhoBdLJuVNURBdizlSk0y2bcqjFwXEvDcWDSS2mSk12VRxHKEQQ1DYF/4h+BBewyIq0IGul03P7lgpXnBAJkCMvfIFmIYC2RyPnV3v5nvmTm8xbIATTzz4RALSsXxoiSZ9OZejZq+ezPpEh/lLUGDEhoR47ZHVjErMkwci00ccWdOSaaQi2ZSVoiRCmxAa5nBJAQulMkDgZWywSiQgUoGot5EmXCQjVIxpzN6WkFwMoXmpAFLySNilqxhfjTFkt8p8SMZkzt2WsWBQCTz8gvPeHB3812QSdZPb3PfLK/2xDm9RN+s6C/tvuXEBdf+yKXf/PIT3R/ZM1/aqo1bHjNnrnuSLe+QgFDcVRFyBK2ZojtK7ItdCoEvO10yZiqV6Lb0sQMCozCHiCAltQT7IsAqIshGZnXC5hYDoZzncvFudDGMUj57380Yl3PzN37jN2655ZZPfvKTn/jEJ6yvCOJNHqHubEZ7CGFDjZIas9hrGZuWh6jti4JA1CVisS66OF9iyBDWQqMYq52YaFfSVcGf1qWI+CJYZmElc6osNS5Tj1de04hpXTEpkRhygkmT0re6GIUMpnHAyzQ8KPGhBlynADK9BJKN+FBzVdWG556HrN4JtTgQq2+YwhQN04uB+LeZHjq4y3dE3nNol8umqUabQVk37wnbxMSIS9IocSAiQ41ykJjWQ+PNuzgiQnBBBEERsSlmDL3IUGsSEzQdvsWuxLLY9EAYNSWxccbELemFEoXpoGYg9IKGnJgNNnEhFQwEkCKNHKFITAgYgwYmUO1ksOS7jYHlAzz8ayFc3yfslN2d7bPOOPzK5rsg8wl7zTT+UW38OnzNOL4JA91z8ui/vvWBLz964uT24v2IDi5SMWqf+oxiq4KgTLJ5VYqJjWeFDbC3LKGdBr4+b0WDesFg/p8EOW/KWMQYBU8ibNSEhphQ547WCMFNJxONmbi3T4ghiE6JKcVLyn2fcc+xuLX/+q//+oc+9KH77rvvT//0T5966qncBo0GqS4oIhDMXKUpOtaVgHgy6pb0Mp6kpukx211kqGOC3NsnTB4CgEo94URGPPNALcRGcc5DiIz1xYxJGccrmdBWtk+KY0VBqaiDQENMEs5qwoRkKhuRWIwyKLfQSECgRFzVElj5SotrBJAZHZcMMUxAkqZuitSJvr1JLF3R5NIlzk/KJUpCT81l5+z52ezBrdmNJ7rviEzPThsNCRoIQ05GLTKuE24mZkdSyogShkPBF/14iQZCfYHhkFCKxJhcUgqCWI0lIy8QWmjkQxMTQEiFQCIBZDUxkJAjSEMqgNLhPC2lGoiyIRlAirInwdHnvLrlkxPDEhrUg5NiNAvA6s9Vub7zz5SwrXgZnw8ccApeFoRw+MDumWedO3lVm8ZGaMoAie4moaf4XMquaD0OG/VpzABfkHrvwdt+4aYv/Puvd18uwp7y9mTLUxQ73859nCIC21AICPluVTj0gPehoZ+zWA8NRzQZHxQIkYNYORUys/EgZ0PFocldCQFBxZa54KDuwgWlm7WLu27tH/7wh48dO/abv/mbfbd2sDxI4xIjV7q3RWCQ+chxjOwTHMaGZpGVuYzxKleiejhHJwqlYswjh9dFohcHZYTTJCTEaJJVajFRUKcrKut8EoS6cd6rQFrE9IImmwRWxOgqZgwlCKusMfCJmASlCawuGcqVGYo2cSnIeqmJgPEqawwEAAEZHMuc7GKZiTa1UketT/4IjSaCu1qE57dmDx7ib1W37uRrOVoc9tuIG0H1IwQjUr9ouEaQvN97X4s8AEJKBy33uYUGvswdIEqpsiSGwaoT53QWD488HAh4eSyJ72DTNEAogkASkAoxrSutQ8aSMxzkgAsghGCBIQ+uMQGKErXoSQmCPhAlJqBRBgnIAEvRWGUsHKFc2XkqnXfeeaBpA5rAnM5eZf0Zh3cPveXw7IQVG+F1kIFwcE1/VfA6GMf3O4WHT118+1Vve+u9zz3zyt6jhEH5vNJRoJNK+squj+eGM6M9KQTJBrdNnxAtQRAIxuDErj53WerQwxd6HGLUuFODQBmEEmeHEIiag772iiAgUOJYZIYysYeeNVHA8hAjUTfQsY8zmnft3Np51/5bv/VblVt79JJszLyrT9MNJXtOJI8ZDV48E7ZFWABbzIq+VgpEs25lIogYSguJASDJWBKDaU1gKfiqLoKIkokxNSsyAYpw1MVwVnbM5sUaBBRyFA0vvEG0CCJZNHL9oAYORKcgMK0IJAQZRzBV0FCWrYGRMgpw+vbYIB8M8NUdERkmKokjZNBEStLYjKuM7+5WjhK4u99/aPe2nrv7klf33whrGggMhKIjiQRGs3YZHO5kJERoLY7enpEbMpc8QNqnMolFSkHWDwAeZmJTbyonmtwkIQKUASAVfDkCgk0CUo/uXhx1b44g3UQuiy0lYE8hdTQRYi8J0ZZRb+walIHyWDBGTsCNAC+tIgQrEbDXG3f0fPydJi/jB5kcPHRgc3GPaXx9yftm+PVF7Q3Phq+GvHt28y/e8vk//PK+f4/JA2eLUdTUrtSWtNKWCHPbRRX1yN6kuHOAqBdT6xP7vGlLEOogMqCOIIrl89M2MgMcg2jfJ2MWQeJY6KL0OUqfH2uRJ+4eZh2neyllHjYF64Mf/ODFF1987Ngxbu1PPvkkXby3uO797z5y5SWP3f3A/V/6BhFsHwWYzfnvG4DTFC2jbBfxJh3JC5Vo3CjDJB9a7lvnBhOeeaJHnbu3aCY7wk0zTRQvkRjRxCaHiGgtMoFEiRp6fS7i07cf1LsiZ9whkIOg5IJCusxQllBFoMCKhSFumCGj7BvIoB4cwlEPWmIANxfZExoNMrdMsRLUBEosVJiwcVqY5DbQePHA1kMHt95zanbXGd3fqrqIDHULN0ZB5mECHyOMFfBlajQ7+DppylWCViEGAi6Ns5PA0sSRwlgIwbhyAzSD3DQjMBFaEWRQSUIIxM9mWiSD9jLIuUHDIMobdSMaZhoCIFrzNNt9bamFAQLrpIhQmVCBYPDiiy/izmffGSPX977Z6ey3Fr9tN4GNsMnAJgPKwBPbF/745e+87ti3v/XM4j6dZIb95ScpXWw9nSo2G3Ws4e6tCg4niXAAocuYFQEzWwqBAw13ZIHIALkRBDMGSJH9KCZxLBCIIIJNOMCtQkwI1INMyh+VOXr06K/92q/x16gf//jH9f2P57/jyI//tz/9z3/5+Xu/8PUrbnvXB/67f/35P/o/tk/ufa8kkRSPmWAwng8xaEllHCFQnpuoHyWLD7VKn+8gt0qi+zATPQQSjZpLavt6UUJJNYKXERqvkiLaepUJN/FRCE1u5DY2NINKVkgjAjSwnO+y7s2fblRoIEOtXsBJFLellnRxhwBEN4lGDrlZBIl5E59oD0+K5zR2adfQFZWjZJAjk9zX3HJiGM+p7T63u3P31u6NL3efd59MRaPgPG3/KUKUmNnIDdkXxL685cOMGrIKJkzIDAixq10WCB94K64o5a04pzEEBGDCcACZxgQv8qllH0HIEgXwGE5yHzdAcBnknANKozzkTHL7ObVFlfTCTTlBDxNKYjDYxIW/XsWMt++QKf716iVn7x7ffBfkYCrXYMDm2vcgWwPkBuI1ycDXTt34Mzc98QdfnJ3c2fuzQDYtwXVSsdEkFOnoWGuxTNxx8YkKCEUGxXA+SSKIEDhGpATBZkWQ6BtljN2cE6kxsaUFB7VGByz0KO1MZG+QCpO9izuR7EAi/pdP/fv/8+N//vIL3bF48dFLfujnfvI//uFfvvgM/2zL7P5/upv5vPIHr++EZRE/Hkv6humlesp/YeLprPhrYWGgrCWWjBkQoJSLODGJZaUJCO4Vg76uyE2JJTNAoXeNr7ip7oOSXo51m5ZeEXMtAcccH1bo+9ZcywTV+Th0NEPpQt7Iv5rYiI8EZAiw0liolIgwQQaNKMavI2CJgUnaGI1emaMBqi9vts8FeTUu/twdjbiBIyZqRkuNcZDbq/PPu3ffEXlopbs74eLdXXyoVSIx5D5uGOvuLoPEq6Wp8erGjFx0IQr6BbO5nJgRXZdmECYvfnwpOgpGgUR6IsYUI4AGMRUAERLafU1FZ7GBPMoxAgqE4QhE2YsGyOImnkmXmnQhwESzrGbRsqLUP/hd/OvVI2fvPL+9+S7ISvI2XW/2DBzfOfPAkWvfe8W3/umx7qshdSC0HybsWW1bTgAe2WQTAY32fmNyhSBfQIRD0yASbFaEjb0RRGSKLrmyAmJj05Omzi0ykSW1oSpCHxNcuo/EcPXh3MToiSee0FlMx9uvu+LG23/opWdfOHjoILNxx1987sSLr8QYWwe2dne68Pyzqfy1EIIGE4NF+0ZZc28QmjiqzhE0fuUi6V3lAmSoPhDzQbBsLzQmhpLHCVdMJ9ZmowQwSW8jiCjF2rHiiJQ3UbVBixBBWuyjjQZCDQi1CzZOGgILwCW6JzLu7WlJfGOTS4N/ABAlehFcy9gMaSKrqS7qCGLlWIG0MPAE2SCmZMFdEuakFp9l8ogSm8bmBbuzd3bf7z7i7m5WCBTIMDtaLRqR6KlupCGzVW51DgQIMukVPeslmBUGSVdsMhwG1f6bhOhr2WmJsczKgu0R8tRhprQ0HgsRzTIgjIjm4GoRK2qMVRsEbowIJWlBtn6sABNAyElMSwuIWSHwIw0/LpITTl3I3H7ViXfe+N5HTl3cgrOxqWegm/tamfcPGdUANn3fowy8ZevUf3Lqs//D3546sd09UrWh4MIOGrsZNQIQjAMCOJRkcHHbxi4ZU2OgIibSR8tG2UywF5kWR0JjZgKAqAkNjpdpZMwE2L60DHIDpHuSAQGPZHq+861Hvnvfo28558ztU9snXy78TT63dn5Byefg9XerGl7jIwRjxRVFNSVzdsMEZuRFGiXITSnrNe7JcOr2Sa/pgeMulHBQjdJ8lH2b5QIuq5AxoCOiAZMiwQZoog2yi20QGucouoySoUHeXCJP8QGNhFCw4eGKMAo/GgMo/KgclHGRl2vdGEDDN5KUZhBQBiR29bs72QBEczSnufcY1GBFz3WRGyCMiPSuktvniHxwdvPJrbv2f8+MWVmIHEyM0MgMhMucRkQzWo6S9XKBQQ2uXrOykATSLwFEL+lqaUIAX92YJ6TXrPCFCRHNRPmhtqbOB0tyyyyDQ36mpRcvfGHFiBQambjmaQ70xmK9BM2L9hEylolBS1MgjKgvvWZlwbDiRur4vDsv4GHCx99RHth6ZfMJDmdpI2wyUMzAid1Dj555609f96VP3dP9Qb9stMt0MqBpPJfky9bTdqbJjtbJgKyTQbWayCpyjHXUiwxQ0SsaV+RkRALBPo5I+NQRRwTk7uFggJlBFuzbTjwziSCgARLJRA6SIz3kxUdlIpx9QHrlePdRmWLhRTJv648dO6aLOxMT39koDI4IRXeIolft6BhrAEWXdiU4xix6tdATSD2bRfBEqUElynozp6dVQh3zhqxSRzsdvdwVICOe1IRQvWTUrUKth0oC9XhekR4RYZJMd05MUTC2YKpooAqZuIAnsBJgTiaH6qMnS8YCGb28NOEcpK7hlGFLtpAp4mhyXzg4e/Tw1o2ndu+d/7uqsnTeJBTdE6XSy7goSVd7kxHBSpdUvGIOI8ggPZOZnFscAYFM35qJ3JD76PGZbHqZplUyI189F0lR46A0v0qj6UGG19UAMjql0V2Ngp5tY8kk4FokmmtoqCkbsXKdOMYmTPTXq9tbZ25tbu4xNRt5k4FSBu4/ecl/+q63fubYC8/zWcl50UbTBuRw8KGBRsoSTKfDWEUGyNrI1MjgSA8IGsmDtcgY0CBCHnSXgUAW5OZvK5DVBRmdny2UsNFZh28c0VgyEUQ41EUCKClKOzbd3/Xz1rNx2NGMi8WnP/1pjnhu8Hp1xHltA4WhacFdFQEEACsGLV2MCjPNq+Sil4hV6EHGaS0irKKEmEoEQQMfaRJ6bkb70yeTPYZPQYAVgSBgQU0WgJaRuJ0+MkVkZU8196dkpkTJ9IoIiVIDpE70o5rQgAxxI70EQSGoK/ToJfPJoBKcwabIAEXJjSsMMcZF9J6ZzU7O+FvVra/vf++eA9Y17Gsyg41g68bqhaGXX7TnwAENHEEVRxftcxl31nbftTu3L2q6tO7uQgYaCNFm3lObXxtjCRmGqTWc4NhsUNBSAQQEL5u+BFborZcMtCFTGZTmF57JADUKfOlipsiwB5VY1ps4nsvzbaf8VXd1303vJgNvqgzw1ZD3zG752Rv+3//tzsJ+SQ4WHeY6SYr7V13USQ7R6Nz26SSb/BRNHGMTFx8IOiLUa/BonD9EMFOJIOIjx3WRUZRIJpeJq6IuXCSgRBCCMhZ999149Loldg/KzB8FXKdg0KXPgPzm/IrGGlIcbTTjoBcZxtwIGN0tK4qbowRxo4YMNKgTd82H6qSr2ARqlbEkmKLHxEFMMgaQQZaATA7j1TxBwJdnfKIc22RElUkXMdNLwJU6EBDoWp0M4x07KLjlhwJKLnOgQcz0EvKDTRwhU0nOIAIGMAGHTa3tkLgkCUx6Y/N497equ/zbTKvf3VlvFPPR5KqOESX3JZBlSXKw0dTnji0aOBB38EMmFYZKIG89tAZWWYFME4VBgenktIwCGzGEg+wRGJQAxXAsoHyZpsHk1BmSELiBQw0mdW5vnnmXNAxBvtOSA8jbztl6emfzx6l9CV6vnqN4q/sHlQpTvd5AG7TTkoFvbx/5yXddduWDTzz0/N6HhB1JR402MhszNrHRVqW2fV3A0kcTUD+pBpsAACAASURBVD7BhFP3jb0RBxCed6ChVMFy8JDBxsZCBociGXd6JQ/WWBYHhaNw4kiNJoZuJgIunKJSgo8x8t4/wESDh5CjJs6VZnwSV8wGu8iUHn4QpWCvuuioRGsMucGEUSQghK7MlumZYW6vFPPw4xfiq/NJ6FWa5kY+mRpqNFJCCY0WEDWs4NaXw0qItXRBCT7kBxqil8DSS2lJHUNYyyIEh3Q5IWJFrSJ6UKJpGWM0CXMuGdDO9YlZvcle0GWlgiMmC36BVWTINZfkUNfD1Xu7f1f14O4tJ7vviExHW/XMGZJh5Qc/hkYp5rCK2v2cxqAoLcujD4rQpEXXXGSlMTHu+A0x1ALWSvbiSXBamrrpgoNAULvkOXQXwpxgt03sohnHa0JijQwghZlCg2BwG0RhkCGTDsIgTsSMMr5KjnYEOLG3Lp9/5uxB3rhvrpL1NG16NxmYZ+Arr978czc99u/+ae+NXkwMRwolaixzCOiywW6l9JnZPgrYe1ODw8Gu3goOZi6YIVPjxeknATKU2BUj1uVIXjiyRy/wurt7scedJvQ4u6iRGekoHCImyenQUFnreAj84PKRj3yE+uGHH/7CF74Qu1aRRR0EBMs0leI4YdCN6WsMCsIErwQcYsKBVSQp2po5aspgLBAGbZLog80ucfMCODyXrW5NQEnuBEVmZlnHkQCrh0fgYIjVDWAFSF4bOaFq/QSBAeqGMdbX9JRD3Mmnt4MYUiuZ7eBkmOsXmW93KVrCRPNleiJs40aGmK3Oh1cwD8/4W9XZN7LvmYn0EoZQJTol5pB1S1lxHZIcQBrXs1iJZ8IQYnM63ScG4encjhJw1HW5kU8Obobw4dTFIGasy+D+HOYIUaM1rE2xSp7lKxxhiicZi+GQBxniyObCcjIfQuALjvmgSWg4jQgL+cDmFXCSpE1zk4HeDDy3c9bRt19/6yUP3P2dcf9uDJtRRwTQnA/a7MjxHOuNGjpyHG1k9Ahxy0fLALAQiUtRQ9ckZFxQRpDcMdEYBL2eFDJIcLrjZl6ie5GhzHyEJjjRPZcFCMLij1NzC83B+9///r/7u7/Le/s0INIV69xSWaNWsYFn3ZppArB1x0hPMvYI0ZG0KqFx2uqwxd4EtmiTK/HSEpFAzcoTPdeix5MMgZKDnG4NrAjhWoKDmica6MXc2sbuUTNNBh8CeRSUFAWSIPxoj2yGyuTq+WQxM2WNS3rOcY9nzADc/Ap2MitAYDL5TqnsUR8/tPXYwa0btmf38t59nlUnkxDITqP0fbXMVv9ZwjjsAggojQS1YAKiR11kSHJwUX5kacdRgnAYFwKBir7iZoYIjohAwVF/dAQfZEoRp0Wpay58AIFSiws2UFIdSaLRjxNKNWhQbQSUGfbio2s38ih3GwuH45FxIVPc5UAoGbK6Dm5NT2CCvGluMvBmyMA3Tl3zi7fc/2+f5CjoPrxHGXsK2Z4zhK2qI0VbErR6DnXsyEWWcwqL5zt68RnEiVF8+gkcSur1KRGNK7LGJRDOMZNETxGxiru6NBzJk/nULu6PPfbYkSNHdM5W2BDbP11hpoSKnOSKr7pwZ9iDZoMGwlE2Y5071knKdy2U+jKge7lqwimiaqeuWwvzJy41Q5v8qMuH36iZ89p7ikcv7UaImW3fSKNXLrOjgPK+yg0GNSapC0piL3ooRTXpzZuY6cKUd43SCMcuIqn5Ve0uhJjDPI3agABGl7Gy3FlFlVQ7kxYUxZQQnpntnNzavf7E7K7ek6OJGlCsZ6aM2vgtnuKWMOROCYiGhsBgR2ESF3temZNq47SQyW2Ew7iAgg88ExsMVAZJ6hX+hBTFiMQChy0GDnziKsoziQZ73PsYgqMzNuLEcIMyyMw4geAjue6Sk7Q9wwEBPtSS3WWB7B8+2H3m1ZqNsEoGWBybVK6SwO8L31d3Dz1x5i3/2VVf+Q/3dr/6067X4TB4aiUD1N6UEgSghIMeKGSV6EWXeqMyyriAI03dMnrZHhfJwqHu4i1L7iKGqiOIn1yJwagUKaz5ANU4tIHHL09E/n2lfDBRQ2zOzagZJcMVe0DavZypxIWHJdk02nIuRiALkNytMiKDkD0o8W33IgwfDxaZCSYKZTLPZPgTmjGT4mYQWCGrhqplGWgHQt720wTwk7hFHIWjKzdW9mCoWRPhIkijEqj6BbcPx8k0Sb5JOl+QuI8iCYIulKO8cpIaly46olq0UT4rsfhb1YcPzG49Oft64bsHcsheDSE0NOokXJ5Jo4heviBJETYrLkiYkB8fIw5aFIokUZohUACKahFhUMlwcIcP9eShae/AirsyEYFytpEpKK2pU5I9gPmPAXXHpJdw/DhBruKsAYsZymiMJaVCki5cSDVepAjj6A7Y2WeeNe7PMqL/Rt5k4E2ZgftOXf4T1x37/MMvPn9i71sx2Gg6TEgJG23UiYQvRY6Stfd1pGiDN2Y6hoaP9r6QJ+NASUND0BnikwTBpchQverS0HSUoYEPvUWvXJnj9A2NKHt/nFoM8Pzzzz99/NnbfvrHmKUHv3zP8Sefy+OtqNHjLQHR+KmlRxA9CzSTpJAsHgajZi4J6qbjWlMUiMjigz/2coGVBWS6+NdAYIW7+BdxTrfSK5JAXgoKCiuKkobQzgSXBKrdt2g5T+Giyg1aSHJJYqSjTpM8EBqGpjOl2BuTmRh0qdy/LGliszolhsZVSQspCVpsVpLJrMFHlESviFBX8nn3h7Zmt53cuvPwiDWTYIokHPwlPBigFCtmgYKmkeSoO3fCJDbJDBzINoAKTVOTHs0kY1AhCRQLCV+gct9GDSGYd0AipaJv38oEgQJP0YASTS2AIs6gspuY+fYfpJRD5clEwwKAEsPsiI45hYSPi4cGmieOXp4fW285PNv3T37npDaaTQY2GdiXgZ3Z1kMHb/zItf/4v9/VvXRXYaP53GCj6SmJktMg2bbxwMQSd5lhuQRb/JdejHkqIXTHSmaQ2CfNaG+cIqXEkSYRKXiZvBzdhTBnlHLOoawBgeKmKNFECVTssk1R6FCWOIBQdG5DGD1QvX+cKjjm6aprjr6yfeYjd9531Y/cdOrEyXv+7iuwKAZrV84ztsga/xEn3EULYU674zcK06uq3Su3FIeO3HJJkTU1RQxZAvSIWPliFn5ZoadRHmWNGnEzwwRZ0+9FQ3MtWSKK8pCEqzRznpCRkhuAGK7CTWgVAo1dGheUlLroJY2TGbuKMsMBZ5VBGRYQ3bmtgadWpjUW4EkpxuVaAyUsMbD9BIHvmXnoYPcdkcm/q5pAJZNOk7iqxQGSokRWKYn7qCZQJITRxatbI4J44i57SOoj3WAiU4rJHASHCcjTKEVwJYdzEjQNk16n0Zwxg2p0zGVTwnjaoISJL0WUkoQrmaoTAtDLSXJIYgzUipSgQThwqOHWTduMtyeLOU2YbJqnJwMsv803Qp6e1L62qPefevv7r77k8oee/Pbxwg5ib2m7sXM53zg5u+02393QZCNTaA5SxoatKjOgdIOnqf076B4NiOgmOJaFD7iOdwQTw0WjsHEiiJKUHl1iU2l2WViy0tAUGqU5JO6QJGhUYhlJitLAR2V4F/LiiZevu/09N9z+Q4/edf83Pvvlq9978/1fvCviJrKjIkimFksLZo+GUztBmNBUoHZH7FVIkxKqJggI0FMhv8w6DJ399hCGGuWSG8NHSq/pxMZUEZKu17IpnkqjOUcCfTwxZmjevdFlrEwI0Op5EDdqlTwE7kw3lOJuyc0aNYBwk5gABT1CLGl2staqlqLGiFwfbJEk63n1qyTIzx2A3u7Np7b4nhkTTSJCjzLIE0q6Aq64DBRICSduJJPnM/bOaXaPEHsxZSScsiIlADU6hBaoQZ7M3eQTSUMWJVb4KstAPEk4KdIr8zjLhIjNmOqiLEpAQQnHlkQVcVBqrzE60HZ3uh+c2Dz7lkKf50a/ycAmAyEDd+/c/LGbPvcHX1q8qu220rLYSjuXGg07btk/8BS2exQA8cYHii1Mr44R4UfjigwH7KmxMQ4y4MavuMeuIiUMRh1uAnRoiClRSSDRjhGjgWUZDFzcX3rppWefeebM2VHcLrvl6peee+GpBx575KvfOvnKvn9sVSk2NALoFIYXlYkM+7pBYl9pQiC5Gyk1yhEyBXfNJcSQRY8aR3IqJXqgnOJKxMEuTcOgGQZiRW0heokYXZFkNBgrA+XBjvI1PQmedNCMiTx2TU8jU2ROiri0afqgRMFMdbQnImWQ51pWgmKx/IpLfc5xBE+NDsw4nAmy7u7Ujb7iibEFOb4wmz1ycPfGU7NvHtrl49irEGMbknDduRtZ5WbQQ8kC0J+rknM0ZqW5GJx3w8qSqySAxemz5aDA6FgDcfqUySSfwqnzVKJWuXMrCoOiQIkmmH1DGOQJCO6YAQVzmn1Qg3qSTCFRE67vzqQFwp1x+NCr293vl7plsf/6jmbVXTTH3FSbDLxRM/D0zrnXX3rVe97xyNef7LY2w6Rmh0rOR02XlOxB37hk3+eSg0iDFwVZUNQgUKS0F3rZSLAeSx1E8TiCEo+YPij79gkxNFAUWaInXJ+X9cRVQYNA3Y1nfndysxEK34GL+znnnPPWI0d06mF91Q/d8PBXv3XRVZc+/i8P0nSJ2bFyUGDklQdGizsIjJnJoOhX204B7mRBidALKnI0iCn3QbMWA7gRXZbACtlCRJhPX8dN9jlP+eb6CLIW2fTMNsKKgNhSM3dKb7SZLBNx1AAjVbFVaFYCgklKmMCKoelOM8E3cYGDrlmRp2zG8iTnQLVfuBMmsZlDKaUi2Uc1n3H+6oXz4YYTW3ev8Hl3EeMYYdeQ9sFjwVT7eJIi2ayYKyYIBBZVC6uYXsnigGwh+QQO+HlKc5xEQ6LwghXu085eA5JquOmiDJp52gABPaXOEwMSpelDXoUVgSh9UGZowVQ7lvt5XnLm9iuzs8h+Z8Pouv90FR+hWUidZu8GPzfYa847N9UmA2/eDPzzqzd89KZvf+3vTu0M3572ssQ29AnARqawW5O9uWddlSKUr3kocaJ24cSownSdttHRoRcN0oMz6B4NEigGqF70glII6uhFlw2iXrJcfIGpWGK/98epORCaxx9//JnvPvXqF1+CDr97ZHQvPfk8rzH43Sif7S66NCrh56ntc8FmPundrDs1yoUmjLGp8MxAGJv9PC5RwMn1gxolXdwwRtC1z4tDhMVwLE/Qxrr0EQaHMVKLcG5GF0VJQMgNrKF3XcQIB6t8PYhkkaq4edJNFZAilGm3C0C1rFIBEjTh6eTAjesRBtTm2U4jsQRk2iVSOElK+d4bpV3EqFUwRkhC9zWf7f6Nm+7fVV3xe2bA18LTABOqMbpIDq5Scr5KrhxRi0o/fRXT0kJVjloJWldFKAetC/gKqsKqiABV0kIXgg1AQ0kBk8FOJsaMUMAh7ZIdYqyAOzTAoSDQNAJNaaitLApHzto9vnPWomtxfe9ai7Ev3bfCbb7rnTsMQM9tvl+q9hENWpKWvXXz/TL+Dc+pGXhl9/BL513/o0cf/OoT3QN6wr9S552rQ5KjDy4th4NOKh9T2uwcUBLcS1Oa9iHKRcTEiiMLAT3KUWiCkjsInMaigZ50jUITlFmBCVQfq9ofp+J54403vvzyyzC45557rrvuupdefumS9/zYRRdd9Mwzz/zRH/0R+vZkJZYQQsMsUghEkQb2FhgDRVOlUSUganKyr8IkYipNUWOZLhcrLcSkI6Pn1Rqpk2yzaQJxyUO7r3kiJF7SaEnRtQo9KDF3Cf7Ypqgy+0DlbPOsDuLjkuMMehUNEihRVW17msoh2UBQ7d4orJEVgchYZUnUqYon3BBYCUCxxSLVCTKfd+d95rTviNTej/lB1jdpMkYNE6oTWOGL49irbTGQocQnt0GvrkGqstR9dMWDy1DwSSaRHCqxCVXoqaBHSHq743j+SiXvSiwrTbECpzHzFarkh3HJQLCVuHnXuW/ZPTFbPEoXvfFEXFzlF0kgSmezzMm8saV38zny96NGQ0+nvDSSdsuS90b3hsrAXa9e9bM33PfPj5zamR3Wl4OzEyvfydE3eB07+GLAXuN80I5jj9OVPwW834tnEcocyi59HHK9WEmvc0asKlAyMxRNGUeehkJAP/acjy6GEr5G3ftRGb59/H3ve9+XvvSl48ePM1vXXnstRDmIzz///PaTnZAYqyBrqIRHQ63RMiT/GCdmzkiLwHxrJC3GdRsYUuBGnVvCjUIs6rw31wDSaJn7JpocCo2LjImFxnIfVWzWm7GEarGpoKYXbeBJ0bJObh7RbJQMILGoR3nJGEcKKXKTXy5512nFTkNmdGyfFT+5IVbwAcoMxTlh1UgVM9xZ8x6jYCfUfM/MIwdnP/jq7I69LxDbg1FWqfdUSwnm+bYiUYyRLsrScMp/cQeKW7IS0gKhfHoNRBeypCGsvlZBIARjRFhljOIJK05s6DFMEQYzz2ocS1HWMiBd9K44RqAoQMFNY0SArRIYow9SlQGrlHSB6TFGkKJ83hmzE7tnKmJvkpM1qeZy1Q34FqO+vpXagX2bCr23qIU+49f3QDfs1pOB7dmBhw69+8PXfPEv/mWm5xe7mMeiTh4ubxMeavEQ0BEBV7b2hDOHfY2jhgolnV1C693yPYmJUDBMroJCQ69HSR08h1JM9O3Hl108QKLrCO29uPO2+Hd/93d7BrhPDRCvx4ToSDShKJZE9dUcA1mayj6s8Q2iN042cRUdQTJNGEpGAKplSho5AthomZuJUsdyXlhAiQ3gFNhSJ12DTSAHbUYZCFBUi+Bzsnu7qwiODb4ThpOjsbTYvX2rwmyZ7tzXVM0EKADdzF3aNeCMuiKbKoJlM+FXOpyY09ZA5Kx0afFH/VgZhs8dnD14cOvana0HD/Oqcl9xYvdpqw1Gt/q9VhFYDGQ+rgrlk1FLSIiQ1cqk47IWYjptgFK4hIObYkitFYtAMqV0VhFIF2PEhsHStPsEAQTwx96SFUjEnFg1eczTC2wlq4M88aUwxnZil5w7e3HnbDjEjA0G6gwW1/eFrUYRs8oZulKKm0iszQiqyaHvZssoZNxiucc4D7nXt5G+zzJw7NTb//Mbjnz+oeeeerl7aHJecT/UGNja/CUkMrs7XvPUm5wGUrKPdPSp6RrjVa7d4BgWKB9BVjpQn4CXigyQoUpBsAtHEBo3WwTs8TImJ5hk9HBrQbCNoOCz7+LOBLz44oujaEFCv9RuHw8uzLGprCIos0JQcl1LMLgHpWS5aQMEXJzfqJ8gs2iKIQyV8EzYYiZ3alFaV8bqrEwvESLbSDUuQZBZhdPwCYcvSVtL/mFI0b5FSMYihtSNKxazda1YxsjBBKWYJTGkVimyLSaWJQGxCJX4tjeB4jIE1CAaJIFdki3k9ll+AN7duf7l3bsPr+Fmw2WUjDF8SvtwbGm2LAaUpIuiNaaRUk9YcnjphwpYTXA3PQm6cJN/7fFk3ZonxsRSM0FwEwMoKWMrEtMYSZeI5XGVW7GFAE3bIFBgQi1ujBEDiGG/IjHcKSKGUF8Ybz1r9sRu9wsgsYWPBSetJuxf4ImvOsc9w2vBvjd9yef7KyTm42357NDmzl7J4vdr1x2nbvuvbvnc//z/pW+7+MwMhVFxUPDRDDY451g8GJOjoDJ+LHUGstF8sLS7R2S8OBykAYqTRzJKuiQTxQUlMjUlHlwRExkbEZOl6sSm3sQlEuMck73iVnxNFQEzRtQNCTYUftl64YUX0hQhBqA5oKvvfNRIlO5KVHcBBbibowQx1gDkCHmyoCEIVnUl9aMiTjaGEjkxVTEHzYLZIqhMjrUux8jWPA2+pLlvWaNcV6qByoM6ei4MstUVbXV6ECM64STkTFo0ZgsIb8q9deWLkjKWqrwqe7OFmG10E6VGo4kQZ8uEa2T75IEZP5ffdGr2jUNruLuzj9hN0EiSZuYibLYijFKElSV8EbQltTc9nIgzStaFW2ijHCNVswWBd1esAUiOXQkxOuOCGKui78IdjQdl8gYZoLBEEFunDoHSyBZLE8OlMpuDrDDAnZJf3xk4Bb2Et5x1cOvAQR50c6Z7t/ZkIC0RbbOYsm43zE+Gecdib7B39jft9ToX2hOii343zuWB8Dof2obeWjLw9M7511165XVHHv7W092NkQWjNcNGEz5bjH8nnho9JwanGTKnGbseYRQH7NndctFGBhNl41GTxMKLAgJQEBNtoamrnV4kJkCPHagk7mAzusCNU8vDxBeZYhBCU+SCvvtWGZJLmpRf+mwqZ+4ZWEeIaDBWhh/hKl4d2XlR9Ggpbh39eaEL2nW06F6XIRbzWDd275Jsl19k61m1/C4JTJiiBFmCDb4ngthqiUAgEqbprLazxQWQNQ4tQRNh1UnGBtmyRSdMaBJFTV0RBlea2eKFLF8E5UeEoSQcH0zFiI1KQLg4OhWNXjYzYbEFhx/doSdAaiUQwS6NAt8zw/jX8j0zRIQSxxQjhY84i4Zp06RLhOts6eWgY22QOo2ucURFM0DYTaApaYkN9OgVSdUYiF6RLcQYJkUrJEEb1QSfQsbwakSDoQteyNRiq7UKMQTJo8hEYxFT0oCiGXvbZejNU9VVLNqYVcEy6q3d7QvOv/jgrPutlEt33VzuzUU4+toD27JLz3yPz72T6+y8ozONyFJGjcFeJ4Imfc67jyb65eCcxkn5e50MeUOjMQNM/NdO3fgLtz7+e/94kD9TpbB52TrF40WnBMuJI4jtiYCGD9JM2O92AcTHKXEVoo88lnThQkHQFqeGg1zQYyMzQjhKH2CujxzA4SCSDfqxaCJpYjq3ac55la+Oh/g1Bw8Mh9T1XU3VcKrwoJfnVrSvyILqcjkvWPJfaoZqQU1FRF9BI1P1yav45l1wKy5BURU9vBDMliYyJWeLpoiWx127xoTN2SGgKs45YduMEgBkFoQ2yjEaw5Pko0FIbttdckvpje59Mqy03voM2vVwwBh6CNQuCYLMqC0kBmqyaDVMmRVt2pUsM9C8hXNHs0VIeudM961euHFqrGXpPtN9R+SU75mBJxMntuYMVZQsNkbKzK6YOkAYJuCrHyBa/PygDpRYibNySy2DRsKAYA83Ccl8jW0yj5BheUQ0NLFAjKbo1dkCwhQkaGMpyZ4xUphN0CBZT46CUlPkDmEh4EvRx21lQNNoV52//fKhtx3Y6d6exNLd3Wkvd/TeQ2jaYLStuhNiXhbNRXu+5RYfL+kizv+Hnc0XXq+3/+wfxQA7Hyzzc3LAeNP9fZuBl3bfsnvh1Tcf+eZXHl988qQ+FDYZJy0FMzY7hySbFCW3Z/Zp3TfvxZEjSHpOAACpaXIU0AWymjKQkjrHkSai4eu7MiHo6vPq0zsQHCigCUQ0Ei/ZUEe9LJPQ2MANMwS6Itq+CXj++ef5ZUeEG5RB5Htm+Jh7tESp4pASIEFe6CqyjAgtMjjJOFu8ijYQo3jyog0hKBBGiRC7vocybJ3hhMacb43wGvPWnpBBwkCxNpiCCVs6yYCaoHEt0MQVDerKhDBJ49wRN6iuuB7A4dxZ10jBIW9Q6lsSdFEY7+B8rXcWeO9Oue3k7M7Sj/ZkmIKBF6QEOFCKhLEnb3oSdNArFE0BK6QdjejKsHiaNmx5FKm3Ha2PO2iAMMxR3IpoUKLQxW9NYcuOcFYtFB37lHhRWGxavVDts2zRw4cCGty0F5ApcI4ZJiJmvGAiXCXiHGwPDctrjuw8vXURUJAR4BxgUXXXdxd0smvYI3baJ+x7Cnfxut4uQvf/Bfay2Wn4v/rmwve0glbCfknHd4uYq2Vn4b979oXOjeoNkIG7Tl79X972wNe/u/3qds+a6Rkk21M/YLMXOI54mGLIrtfn43ucyur5qTZ/tMx3lprsYB0UZZ+qtjvU5s9HuOnwwRxAOFf9Cp06XBI0bX91yQeD2CwAzVWRg7jZct/FPT51Lr7msove+Y5/+dwdNi0KQOPFX7WKHDZoqLtM7P9xR09c9RahRinJb4Jfd4ceBa/cDEp06dzPeydoQFt9mB3dZeFhltAAn+GvHiWBXaW5JNv9N8ERz9ee8GB+oOqNmnNOCIPGIhnETHCKTUAoY9cwUH2EgaKwE4EtRmxXalux5HSXancsWj53kFcjB67bmT1wRrf1oKfl0Y1/fD41I6vfaEWV2QQQNEYKGSmhRyGZ1DnhZEnIRTVduPBMWsssmJuEGCiRO7rLkw2BgVBj0+V3mWEooWROpUkQxja1MMgbaGMXibiRXtx5IogwBHj1g5LXRjzIyeRYSrYXHx2Y11906qnZkXlEEtLNr8J1eVkWlIiLm3UnLWwMuJLQzcNWF7nDnf9PV2Qvtg695c89V2LR5CyGfaY60pe0+6z29N3AN+UNmIETu4cfP/OWD73rzr+6r/tN+3zHjBsmLuxxCm7sU/6elV0JFO8+OOtyLB3F81286MS4eOxgo42PHVDTuJkDaDqgQCMiJedW1OAozhBAppYvMkVoK3IDP70Risrb33XFNe+7+e3XXvHi089/+65ju0P/3K1HWxyJlJCeQLcPEOpx8pSRLjHzknsRmtI3ncx3yxBy2KKm5WIBTXwXdHUs7scSYaYcgZ442P2Gr12ryJnFLQYJ4cm0iKIhT0awI9ljZqmVZ+stEMgZtrJPYIUw2HVNBGisE6In4ZxkVnjSRbOSZJ9ZuddYjTLGYFs2hQkryWoqoubxGfK/u3v9Szt3c9MbSyWzB5MpIHXUws9MhhUiqeMVa26NjFRo1BQ3h7GCBY48fpgIIYSeKSIgXLiZBQ0WCGc4SXIL4QSN5hROwQduJJAfVJiIfBljqPTCn4RoMSsoNfZ48fCONBgUlnFoIdo4EXD+IvWyt1/81IxfNeC771pKCOKqmDlNXd8RFhw61TpKfovVgb+EV7h1RetjvC8FfUZ1/YJ23WjT+wbPwP0nL/tX1x/7LX6yRAAAIABJREFUjw+++NL24vY47bQkTexTCgLnA+/gOSuQOVjYldoO1JSWxxCOWAoNGShvK6Ohby8xLvT0eEUZ0RRCB52QMUhs8ohCw4wuGec2dQ1e3S89oQIDAcnhHde/85Ibrjr3ogve+Z7rnnro8YuvvqzTcwAtTzJ+pnj4n785eKF3eIbtnFo5SoAhpWOx/IFGqRRIzEIcyKgQ6zLmQcu3aoqqOUtwCJNEqK9LHG1s99Mk5GwjbdGgjmsXJivObBwLqWBBjgKMnCNbwYIGYUo9yZFDnywcdh3D77Np1MOTAiD3nhxNgSCM0AiIGUlj4XHqtbtULInO/oqDFWdcUFLTpDZDcy5i8j0zvEu5+dTs7nV8zwyxGGbLu21xNvOcs5IMGoMFdvVFwvCZCFbd5B8txDYmGQ1PNdaJ2K5CEl+KTs5Ru6w4rVDiBxVGqmXMqKEdk0wsbHiPni/yHFDTCgKAGmxu06656oLtrfMu5dda8zfe4dG1hIAnEVUjuOzx35OWPuv97/57sKLt7aj1xlojWrfvN+XNm4Ht2dY3dm/9N7f8/R9+pUsC69a7nm1LaUwNjvKVPYcwhwmyDk+6OKBQtgPGuJw8asJN9LTBp6F1o1q+/uN0ghvgAKJ0HaPX5TnYIksMVqfxWJzufaQH6Xh3/tU/PPvYk5ffcvUdf/H5a953y8N3fJMD2Bv28tuuPfojN377a/du7xR+tWEQC4wTWm72CZhR6I21jYWg4WHAjLprRQG0FnrFKPhS6LKAzELxZAhZE1xEGFQKf9BslAEMsY+c7Q7hWKzvEzAGjQH2GYzSg1a0F1WlIk+ICItDgkBzleQnZHTpaRyseDrVCZSIYQNaI2CCkDc56Vh4q1zI4EMBWbUuZEovSgkkASGPXtfwt6qsuZtPbd3NDblu2tarKyNkyJ6TPKe/OKhEUpyxqXMmaTpDV8meiYsVcyHB+iiIqmu6zBBBhK2hFzSMAYwg02TGSMZ4AlXo5chEx0u3cwQxhyFFU4Dw0ksvUee+7RqgKHp4j6KXhHj3O3ae2LocltBhRc9rnfMsDxgu6vnZ3w0hlgglUzQYRP3a5MVqXeKRVokhnjSnJ/wy7ua/mww0Z+Dx7bd+4IpL33nvdx5+vvtjUx9KHAu++bCLwxJe/P7Nq5tQ9FLwpY6R0egGD5Rew6PhvjftZIaGwHVe9dGLBCzjooMODSRpggYTEfYZiIDGgezeIjh1MZZSl6QlQeveMyUqNR/52n1PHnv0tp9637Ev3s2HfXgzL/2RK9/x6osvPXr3saJXUckp7KQzSGyUxMQYJi50IScGp6kZ6RVDiC01vaplJoY5ZzQebxFwlJKIE9aEOUfCxIUb4xWgao1iFKVoDIgBo36azPKgaHcJAf5iSK2CHqERn40B2hqng9CmZIbOdsJqwXjOtsiZ7On9YuI4rUkITYePgz4cE0aQjQQT1togb9CjLpLvA+/TP9V9z8zuLSe3vn64dfoSKNaGmatLP1owXo0d5WSqgIDPeNfyUgAa4LD2wBQlZdh5hipJVp5bODML+I69bScJdFOhocfmzccLZ/QquJgzXtCgyN1oEjADkK7B5Zc45k0QKBAYtfy8PAC87Z1n37d14Wz5sRc07MJusfM/Vl+s5wNkCmIRJTQeu4Wc7WnRzONBwODaqHttd2yETQZe8wx86eRt/80P/t///ee665yLjgVWLjuXk1nbB6V2FgKWcUnbsSjoqKFLx7L/npVrfTuIkSMHGOqsyJXYa6cj0FvhrC7Z48LRZ5dp9DjxQKAU6anLdfkz7nRD+YJLL3rqoScOHDxwxjln6qBDf8ElR772H75wy4fea4hEIKoLXcjUHpUypYFNGJ6gmNEk6IpNExbbBE2cK1MY7UHwjEb9KnJfoiq0K5zXy5BAxaRVxqs9g0HuCBrZc10Bae/Ko7T72hIQF06QuAIrqbZ7RWC7sjsiYMV4sIvscddRDjGGc8y2JouaLiVZQgWWW9267rJEebo753dvO7l1Z/XuLs5KuLkpz6KtIdClyzHCWjYdIJRpl+Mk1aaNwGMMniCbduxtl3HXeMnPWhaM1t55550HN/iDrxqBLmLpGdlIW/TgNi2BeR7gQGF3wIrxmoZTjWAvelUYy9UXbJ995OrtrcOLy66u7DLVrT2X55tlibH478JqecShVUQExz2twt4AlxEXI1pz1JiUNUNv4N6QGei+GvKCa9/z9nu/+p3F10DtrdX5aczO1X7xYU4epm0cdvT8z1lPEIKzlL9nFVTjZ/Dy/EMDTBEDEFgKGg4Z6ty+rgFNgJiBI1hkoCag4Qga2VNQ0HR5FiBdaDqluvvqpx/+znOPPRl7Lz56SWwiw9XQNLtBLK9fNNd4KUnijm12kzNPa3SUEo2yDPPYO0HmB039omeCb9EFhlpbee8804ubbt77mmmKSVOqlV4ZIEugJttFL3FmzVR6x46L3UgCvRPq7qadm3mFwI0ybU8WYTW/YOa9LRolmc2MMTI1f22pi86c6d4p0IKW2wC1xlWtu/t1p2b3ch87sHfMQVXkxbm+QiJJze8azxkurFw9IVNcM+hJNTXFNDR3XiHWS8ASenqSJV0TmowXQBhqihsR4IwLK03rRNmmhpW/B52BayCNmEUzJYHxEmsUwyIaIxVVvW/TjKChVPJ5+9W7Dx66odsNi7frGbZuqkxgtucUUSFUyxmZLuQoZLinTaHFtjwi5itv7+tovBCz0Zw2PhvgN30G7jx57U/f/MhdT9fuTmwW7Vmypcccgo6ICfkDjSOFgi/nmD5Ig5JrvZQVTOwpNsArecSwu308Vs4WIxSFOF7CcQzKbDKgzhyBzEfQfSyZKAMX9yK5RAlKX9ZIBGES+1WaAHodJDgaIbVK0ksTnjlVMsvjKjeerAFQX3XUiABbLJesfQLveTNkGPaNes/uNZfEnMUEwzy4sv16oA0TSJqJaUtImGOc7OfEgCZQ3IHWuLB1U6yvQ7GlZiySoWoBGVbUYst+ZB327cp8RHUNsEAx5DrDIkikjQFN0I5zkd3efff27C7+cctAu4jQogSEtKxxyIyXVa3P4TjJFph6irM9yBBLUgc9SK5lUgTIjCBEQBhSYK5YyBhQw5AUYVn/LXMOODi0PgNiERcaCcM+e4wpXts2w51Ukz0KBi2A579l96ajl96xdf7WbvfvmbInSANLb35Jdz2PIPWiyzE7gViE3is8xbJbOzZYYrPP87Q25hG7Ic1LN7xugHsEOkLzcc7/O7JaZGOk18b8zZqBV3YPvXT+9R86+tX/697Sv9ORpYUjSDqd1cgs3VEHaYTEUW8cUHKDp7AfdVAAi6xiF+zjUWm9BbxswBGKO10rMiQoIED5ZKsALhjP/6MhUFOcN1PtEghF0DHGwh3rEoDNo04GBw3GsFUWaAoK5pI1BAxOx1gm08ZR9KhVEqhB2pr+xOs1aMJW5E0bIeYWmQI9r/i1sAIzCTQWNmHOgtGlBBzAVa+yTvDVUhxLrM9egOKmbHsIkTMGbCiZ9UHJHkC29rp2H2jAcm3qm2hzlmBuoop7Qvs7XCV3Zze+Mrur6cw3Xq8ghmN/ulCSk/NEMWAOJl3r+gUaqdM6RBicwd5xzjugDRQg/GjBL44RVOiEMwXOE6aex54Y4gtIncNgL5QMCJr49CVc/Dvq/XGLgDmN97xj57mzr92d+Q9TOxM2PYdZuJoGcaHmrEufgLAVMc4jCQqHrIFEIWdyujR68C3Jikk3wmXRc3GvvdSjWTwyl5rNfzcZWCUD33z1ip+47tjnH3r5+KsjVpa3OacNDykIsI9QUk8gw/rnnOFQReAJxXkILGh8kKbvaTUYxYenGeKCchpDvIqA6CmEMB9lBqU1RQGD7he4AlUGi3aJkmR3AYe+3H3avYHsU4howdE1H+qdPM1GU4jBHEX7uizCmmmRtL2idDmbujoNtXZBVEXetBHMGYFSYY6xpmZd3IgFoJZlBVOcMbBgYwgjmzZQrMbJ29iwFgDkjghPa0YJImzaCLhrvyjPMB8cfj0i7pxia5wXTQqYCAlzZ9sJr3NTL3+ryrDX9R2RYCp1xU/1FBPeQhtHJnr1q7aGDEOKVk7j/LIqmEQKgkYBbRWguKDz2oku8NeyvOcE1/Nvo4otzGHLkJkXhgy+ySMoLaNqMQQWTAFG98MHdz980/n3bl05v0foqk4YXXXnN/PFO3g5QWBu2LUk00xZMRBxdr1w/h5f38ViWS+H6Bu8BpYOZmm++e8mA6tnYHt24JsHbvvY/8/emzRLlh13fhFvynz5phxqnlEFFAoDCbJJAkSDsm6CaFI0a2uT1JuWmRYy004LmfQBtNVCH0GmlTb6AFrKJJmoVhubLVHWxEgCRKEwFIDCUKjKOV8M+rn/7/U4cae4U2QliDhVecKPHz/uf/czedwXL95b//f/9DdHPbRpLzOQLcaOpoYOZqVCnSqqQ0BD2J5xBqKNA4fH8MjEY/iQb08EmBQhhuBTt9eDZCBPR3F061TpqjD7qIyO11RjHc0F8voXPv3dv/wGlupk4AtlnW+pD9CpHg2hLnvS8MAv1dCexutmL8qqmpHTi0JKneNlhY+HI9jYgtA1n9oFrUo/5IwikqnCgTRgUBiZDTSwBb6guSVsxKShMHxIE6+JZIAsq1LMVdMbAABDs4ycQ4cV3qCwbKKZg0KSGxlqliz0psihFT3B1lwoYRKnMLZTk+93p3xmNv36SN8RiTaw6SP+EXCYCgJ11xXOEM79Tqm2udRYUMhEoxMiBEELk/CyqIRckQcwBacQhgj5lGCiGchFhczwSUEzCoVHplNblbRWBUMoEhAMg+4FhfBxkHqUFS61xKqQvv/hC/PLpz57OT3y/DzPytlw2adlHB0YsztXlNcgtwzfu0TnDUnjGk6lJTzNvE7fIHS81KVqnDqfAmXw+jgNmkHeqN9i1Ciw69xFoCICP57f+tLLzz//vfd/cts2R4VECxYDdSywlThMdFBoj8fZEmrEb7aFDE/cGYJCtB0fH0PoFO13/gRC6RQqmBRsBbaUKCAPYYgQS73WMUJvG4R2kipYOltDI8TDew+ee/PlGy897TJ+rE0n126cff2vvvHg9j2YJycnTz/99Hvv8XPvYuFIxR9qOgQoJISbWg6nboRMmUBJS8ny2DpOpU6YKoyCKIwFA6UOOe/wmj9FWtA2erOwVuQCgCEEW3V5rocgQWc5UF0VogHwAkzNyhFsaAgV0dQ9CpsBnW22REvlqFLSoNUSNVAVjRx17WopGEKedUUQtLoKvf2aTDTHVpogpnoCs4iWyPFarqWqetOeuy9JtZq/Z6asP5a6oi0BAWMPKi0eK5IEkDBSRtk4rEMK2vipbixIoEJTMAGNI2WXGzgMwWt0IjMKSADgNTiJJAqFh1BH2AOMYk69ETZ60IBCORsaehOKGF6DE+V83eiff+b82/tvWPrNLkRv9q7TLBBR7hBYerYuavUJGm9nj91Fm5YiNPCHvyIkAa3bwoj84hBdVPHY2rq8gOPuZNvE0Nm/XdlFYJQIsJj+bvLJf/Hm//U//n/8lWLbc+xuSj/l2Sr1HaTTDI62eb/dxCjOMQp4OCV4usEJljJ74GQ4kBgINrQJp+AFfpoU4kC90QQyUiidUgjtgayOpB30OvXK2t//wXv8X+C//VffFIc3MX/6p3/67W9/+2c/+1lYUhfoKWG1DfSClXKzDmRZsg1HCCPohSGKOMyuyAHJ9VnQNnoT8Lo+QzMcQaWmNC8XBGLgYyYUdtUF0w48q+jChagLkv2aKCRjiO3RQ4liLvCKITRv1dA51lJHFSfLiEsInOhke4KwsGaIgMKtUMujNmHhEEyTuTZDmmXI3fl+99++nP5N1XdECjZ1qkTIqQFfiZxfEAckQ+RdOrYfbXmhfzxDd0AbJSwPMBD8WDMQwmw3iT8d19QMWZYpkgAJURmWVLiZBir+UiD0E2dACnxdzJsVqhcNOI5a1jkgR5kd9ACSSH7uGR63//Zsesh6skhj0t5E+2tGGNe7nK3UPM3kUx8YZ3lIRTGl60VC8OiChqBO6Qotj4dVcMGazqq8Bepdfjxgd1Z+HSPw3vz8H7/46lvvvPP1n9mDWnY35x5bgN3dvMHZIDphwmuN0vYJJmJkVmrqFIquTgRj9fusKOT84bkzBKcHl0XBYhu1IKeEJKqgqds4HqPKBMOBJD5ec6xBS2cKckryzUmq+PJ37+7cuVPWVccBJdDPz891R4YYxsJ2MIcQGOqRuDOKkgY3xUAXE9m8sFL5NjRL4e7du20kN8oIOTXBLCzW8ixu1JYKlBWmvf3owowTc5BTytoIOPgp5a6Uw9geM55qKNMoxG7zjDvqbM1AIy8vDLGXwnBWPtunbKs3B3MEs59OxlZGHiZLqLCKeiPUwHHfYKDz6eXe+WT6fb56O1k2hJwuYl4Ie0vwRBINON5SfqOYZofDTcCQ1/JgabESiDMlusCMJKUZPGMZ1W/G6wADBqMtHXfU2afRQqHiFr4goz3e7EsMb0OgkOgRn7DSZlQqky7408Plf/lHZz96/p/PJ9zxvm5s7ayIaDh7vbWSlHypnbHFX6sBnxbXawJW6CgQaj45dQ7QEflxXXFmPzlwd0ie0Aic7j38zL3/7b/7P/f4yoEoOlhocmhQYreGAPsDfuyS4DcQHBrqdZXVT6Mbhpe7UKgHE8Agg687M8vgGwBwpCOPLXTWKSwjaeCgDZ0SUMTsh4w84ROXv8RB6omxKA26ois0isNsMTx6RyHQWfZfoaEOomBLXjCwEg8TRggKQwY2haSNksBs6PP3agwMqAIvhNxtbXS2lEEzFsNQy1EFMUedVXQV1gDKtbwKo9o3BbK9fBtJIIGTWgFnUYmgDnMQlLo1U7aCJDrLi7Ms2ZIjAIJUOSQFL1piDITAO4rodHjhrXXa1Y8m0ezxpgXAaQnTAP4lv8I4W3zyPt8zM9rxwcZhm48yQQEbbelHXHAB8CwAAtJvnzKWKeONEBrKExch6kSgCpxMUKTFgV+EYENjkQIG6gYTINRHcdg1/dwsK0cPAJggTDfrRIzh2rCipc2g5+D/7M357Lk/WUz9euOEQwLneGOFW07oMTwN+6jIqovpy4Scz3TmImm7jD7nRAwFhjqw6V2dcQSanyuZziepeJwsBLuyi8CACNxZXLl7/qkvvfy1v/h+9jVh6T5lj9OksM03HjXNKOKg4HxDLcK672LfNQ8v96JQOjleyOCpkYnPx4c8+isv1hBICXxUE5cFkibDKalYexrrAskQEKIz+44IOlSky4Pc8603esNGe2R1kiChi0miFp1Kgpkmde+IpNoG0jhegCHACmYzeDlSBsCouq6ycEtOe4WBX6s51S8l1LhMHUQqM5BG5xD3GUsBg8CLZiFBOF6rYoP1hor7pEfD9aQA2D5K48wBL/TyCmCJCb8inw5soPvl2Q0KwQDOutw9w52/oAd5udAA/t3p5HJ/8plL+47IsVIJQOqIb38osWBUGAgBbOEHOTFnrj/88ENOTkWgIUTtu1CltBhi+FrKo26w+T1dMFPQTBNiiH5iiHImfaCeiAyoWJkEOXQGeBGSREylDvzr1xe/9+k3vrr3AvKWmHtybp+TEZ3k8cZYZe1oxQ6RWbHKmXuGwTTXFtOyXrJRbgCaTsbbf6JrNX0UHb7CzfBYu+6jcGJn86ONwDcfvfpnb33v/31v/tAfM2o3FE5dtolyOXrjUOoHO44CTo+BOnXUUHMWCQzHETrhgJ+TGaj9QOJmRACFSuIH+q64Tb/yla8E3H7g0lGAE6yUuZFW4BALIh0CE52aJ4i0qzfdD2elOeDBpybZgoh5kjCAhTmISiV1zBFxhgmgstZj3dNUQQAixCBS5KLT3pRmYKoz7epNoxC1gbNOj8CrRgaCuoA8wNO7DZzoLMx7HdoCPwUv5CEgnSBXCX5vAoXS2VtDeSDrk8IcCbxqACOZA7fX8sAGzjPzyTOL6YjP3bGF4+AsHHSghaku6gAPYCWmquugVuqsE27JFx7WUpugATgK+qGpLdx5/HXfdNK5DZx1OgWeMAo8BAXfdTOFI3XDg3+wN/lv//jw3Zf/5YPpMUxffOpUIAp0MLVK8xGr1oqyvjyYTicMkZJdqwV8VSOYaTFqRUqz83bVLgL/ECLw8sHP7r39b/7nr9uXMDYX9j6bnZpdoP3eLN+mN3QizJXUsL8wjYxqaY4zp2CIVJskHkm0cX0UbpCCcMtm4EQnRad0y7GIaXj2gaH2w5olUVqXadEVpaBEDkRd6CVw/bKigp60CZI6nKlYSjNEMw2R8qGFnN6Gz0gVhrRvory9cIMk8IRcROpFrNohtlKFDTDad4FK71A1JIUdtmLNaPVvxI8AerpOfTNmTJMbNcsAOIokaQqtXCjjx3eYGz1qtpv2og3fKbKVdm2kQctAxORFyAs8XS1zzRjYQLxnsezzPTMNOoHNHOnxM2I0zYYfmiDXM5WuodbsjPsRF1YmanXipdOk+Cv44Az8ir+Q1LkvnVpOY6380AmANodz4I/gR/xRlUYenCynThfkv3hz/vCFf/pgek0RyJ+yuyk9cTdjPuW253KCh9/+9J3lwBHLdoSwrJ8qbzohXtZj3SoumdHrLzireVnV+TA3n629mMT10bvWLgK/rhH4wezpP379mf/1u+///L5dFg2FjaCzKD0ZOMcoDaOauxp0cqRgiCINSGKozcGFjMTQwLHMJ8nRAIcrAyXNeOp6U5xAUpIjSJU6kRH+AA8xZuKuu0FJTMQo0IOJoomBCH4zgZ4hc9msvNyrGMGvwy/klfgZMta9GMDKMKKrkhD+ylFgViGezJGWY6WSHkw0VxrtqirFDw1Oqc2xj/DuXFCpu2JrkCeYLH7qwB+wIWSLutPiRxsnRacMpgGhuqQTJJRKYdBSOCbohUCMGtpH1OJXsjXi4rfcfVr7PTOVyMUELWCER8gFnsgDT3+raMiZWzBNWJggzKF8rGMK2Kji05Yoj5BCw6TInQKMjU3hZDfpLQHNjUM2CkgnS4W3LqwrYWMU+HXNQCCjWaAXWnWzZm0icCIc7jcM+fRT8y/97ltfO3ydZWpe2T9bsRi31tJpuJ6Tr33JjAkRiCx9XxEGmbHel2TzSeZuvYz2ktI5z1/lPgbwG99VFA3oMrE2eNfYReDXMwLfWn7qP/nUX/wPf9327zGxF7TN2RGcG5ylEAOPU20uxU+HHlY4TzhbegeV4TyWpaAckPyaE4SYbY6pSrs6E1BCL9p0bELDF0ejBD746uqTuMe5LL2qZTiFkvb2pkdPMUEi/ES/jGp0/GUTXTmaxcIoXKAUmDQDf+WosvxHxYllCiHYEMLMOqYEDUFzRJxKXgcmxGX8pBpyAcDsZOEfCBs9rNIhx00ZAI6TaZG8xhISbGpNRA/8Cum4M/Wef0fkb9V/v7tOZOoCfsA0PEEXVOpRJkgRU+4OjK4nuMBH5OWI4g9CepmjgQs1XQDAYyuxoqi7Qk31BA1gEAJYnxKUTpRThoSXsXiN+4W3BGE3iBfPFv/5F29+6/gPLe22bDoSdG/Zdeh3YJq+i87Teh+GQV/9ytVdu2X0SfpurWLmnqbsKR3ojLBhSwtRWjTR3rs6/WgiY2N2ZReBX9sI/Hh2/YsvvfLG2z/6+/c3/BS64CKLPw4l9r7SMx0mBclCU6cQdfBRVT6CEIiUD0O99xoDOZ0omOP04wkLaGFy6YgZMNoTcRdrCFAp6KRETAraKj7jzhiEfKxVGoAK0a4tq1Jdo2cYwkBQuMNSQ21oQSUcgT8dhU5CzNSmzIG0plDfEjpQlYaDnKJbFg40QQ/NWpcpJ7raENuYrIJOAAs2kREBWjGz1dMiI0e+3wJoDkLLJ9lCq1UkOtTiQmEKtgd1SJYJKkphI4ijLdB7CUUogiDNQueICtH87HJ6fbn3vat2kGshoR/8ij/m+h3BLAAGjnsCsFBBCKQIiAjQQtBFSbtwQV5Qp/yUZixQWQCPB2pqOqWBQRF+iOgCFUX44SuqdTdNjOpEcKqguXILHO0v/5s/Orz/+n/84fR8XafFMwmqN40lqbTXpaI/F1hx17rEjuHyW81WNQPS4iBtoApdBSLv2b3uIvDrFAG+GvLN2//7f/+vp4vV6u6J309NOzbZHTpYOA0o8Kljy+ggammDgZzVEu53fVQaIoPnABROPTmqFIMZ+EOgDn8KNZWBb9+VFv6HIjiUOJSDX0egCPm63t785qQNoxSUF25EmXMPal3Atd6oKgcCtceNJRdUF9QqntQ93rcUVJWbiluZ35UjPcJPBJgIhR1O4B+yN1AyFtTUNSDFfKUuyBFJBn6INhtB8uF4am4IzeyTu5TTwVRnpQvCg5jwFxY8OuFTUj0DaU6r3rk7LuiMBpgOZYG5A8LL2acul1+dPSDRHogwhhNPDlmahbCEQA9Cqji+WV2aESlRnKmbJ7HSIqOIqmIy4jkAQtAqz04jEAtJJ2p4IReaN7Kgsq1QW5lnVzq4kYkqYGi5phHYm07+6y9O9l/78m2+/Z+ytpC5FDg3nG18o7KH8dEsPHFXP4eWqcIgr9ZIibxlbwnEd6sSM1sUGyaqqja96yWTgslAEAeBKsdSpWbH20XgiY4AXw25uPHxL7z0nb/8oeUDQ7AynGOH44iDhUNAO4hzoPksaraIzjhJUGv7bth3NcqcPkgDDU5+mUpQYXLAQqtIEgAUXFCzoUYshYpmhH300n5sGn0NKpq70NjjWmrWSW/ur72qmQ7BAZoKgei0t45GVXvhOiVlPhFgksp8OOGF6FQGJCrMAkTaJVqOl/mPmRMuCI/qACwXWEUQaR7wmEE2m5MLyCgpUXpBU17IhTZ7qcEKwzkIhu+m1IRCCmYFVuBjOoSfpvBTt3QBkERAo1JzA2klxOSadXqAigsUAkWhKReocRDwaKAGWGja5L2EAAAgAElEQVSwv2e2mLw1n3xjvO+IRCWG2LMAaBmxwAPBqChqUgszgdVhOOJGQCcRGzchBjCOoxMioGo9UBemAJmWhYFoIwKazZajmsXAw2QpAqH2n31sfvbGP/nuwSsE3u8GT91Xq8Z41uWvvppcKsvX6aJpvX6F6HZBSgm5EXSsGkrOERUh1SagTD3pMJKVYF11hZUTcc4IofFhGgcfMTRA1OnZ8XcReGIj8I3Lj/35W9//m/cWjxaWu7c/T3SustnDNe2RuFMQ0N0Bn9Ng4AYBmAxhkVMLerhadPI18MJJBo9OlIOfMgRtQJVme/AWMepHACuU9tOg2aJmuGoI1IJNNYRKP/3pKGZ93NRKylELoTpcUBfIIXIPhkZbOrdUx0Skc5Hib9gqDMF9BMbFxtJCbfsFttEFVMmjIMYCLLUAEDFELUpY+dQooeZMYdFKraagfUDqYJAM8YCc06ROoAcfhKglHVSmxcRR5Ih8QQD8gMcdTLdcLT/dszxr3O93xzswAE+xrXMW2BRcQABCYnihorkojMU7dDJkxHMGQxQCS9116t2DVZW6wDTRJAjoRHPBkX5NIqPZB+2IagEDQgJL+YPnFl/+wme/dfCWI9SkZOk7nORCy7v81flGeUZumbn/o60DWpsNOsvY1ZHNOce2P4b3VN0abrq6YkiOATEJx7Gfcaqy90wbAcxWmmMRF2a1sR13F4EnLwKPlgc/ufrWl1/96//l7+xXv+IuYwtTAu/qVFo/Wjk36hY8fJ2rjOXgoobTIB+2mokAJrUSbqmWIeEgA8FDQSHDdb0iwKl1fHyMmJjUzXgaelHO8Ipf0GwYU9nV8ooCfZSCHvc0e1gLrV5cHfHmK1js3YwZwpdUiVyTI+FCKvDk0EDFCy361IsAr63V1Yuu8i0DAhhlAAV5eRH4g5AXG11gaSm/LKgd2LQd1eWnT+FF4AeAIokL4QVoma/Rt0Mk2UO8BjnY8JpFRUEVHH7pHuVEA8x4oUkZYuUn/K7qcvLZ2eSrI5xYKyAgBDPgIWIuVt0+F+Cnt9Py1nyNfoIRUnQ2rNtwASK82LgjcBC1TCL6Y9RAAp1SS3i7rts6L1DIRfgfvnH5p5//7a8df34dofy1u8PyZl5WiW7WpZD49WIcpe1qZrSNcilL5Z0XOX3zh2ekLc/mbXAWfkBAqUa7GXCOaNspmp201tzZenPKCMa4RtHZ4N3LLgJPcAT+/vKFP37z7X/zg9u/uG8/3AMpa5hDhuMLmpXMdoawY6LXg3M06GBBLYeMNoi0DYlKG7UyJ4upL3XbEz5Hq05XIsDzMmqgBrMH4KHXoBCEYZyJEkz5prrlJBEaJFMNo9Bga6MHsVgK0Ol8QEcJVQizNLU6g/mRE+GFvAZ2ENAKL8SIOKV/RIWokhckFqlywaZWzMf1Ygh+kFCASp3qkRfUlOBLhomgFORDRoR6R98UqMU0W7jl0gUAxy7yEIEQJQwvJOjIINBSbahqJn6Kyvmk4XtmmodHL1MA/tQFaPwiKRxxOaEKtaP/TIM4gx+1Imw9ra8oTYcWTLi8kUjVsiQ2yrcUkFpiSzQq1QKeKLkTa/sCYUrBC34b9c8/Mf+TP/itf3/lDxeT1QpMwEhJjw/P2MCK9L2UxyPlGbXn31YlrfxZvbJz78igubRoDNFKGH7E4WmhaFphBmEId+l7FtHdy5MeAXbDT6avfezG33xwaZm6CutZD6G164OZ9/d5RWdcNNw7pApo0QHSR10+JtSy6dDJkQuHTmxBlE+nfNyGV4brK0xQi87T01MGcE7yQWvp3zA+7+6TuGOSQuhRgktYFU0T25SIY26l8ysToDconUc2DkAtaCUiL1QXBnX1ArXDXS5gaN+Mmw9fGAV4CGpollclMGZNAu2tbFsSzOFIpRejAyYyWr0juqbI6zcUU7WAr5uLVKyBZt2OngVijiCQV6XHkFYRkaGwsAMSLlDYlZy8EMGvJBTb0feF5e7T5W9dTr66ugsq7WdMrag4ncQFPP4Sz9QLvFZ+2aSuYx9WCJeSbOiOo1fi2hqpFyBnjeFCwYvVmO4UOplZgsCsjXj2ohZtWk6sCnyhBDp6iQz84NQRV/Yn/9UfLs/f+Kf//vDNmqw9hkr/hvTdVrD9M2E7L63pNFxSeO+Cb+z69D3tyqxa+s4gG2eLVU0jla07pf71mrAQjUJRrGAGwSCj+Xp407cruwg8uRHYm8zvX9pXQpUhxpbntOFwQAAO67ws2YlTVovOYLZUxf7SxRHyaNCRCF9d1BxcQwAzNv19Vn0xPEw+H98GcHXirmOCWiUcEIF2CtoJetc3CgVVlU2Mor+yqxMTPchnPjitJRJKsDIw+lKF2rrfTA1bPQjhZ2A4wnJRE+QwFaWxvOiBsDAkRVXoEmzV6YzEEAhK5ZJFnpVWuf/LVtpzmHrUtpcPSU2HNrDo6AovRsyoQrkSoLHioFnAC4LAx++EnBq+cql4Ah0AOhHgJAtEIdo6DWwW1ufdP3s5+Xr+u6oN06Hd3SYNBSdiJNnU0M0Y2veiStkwICvXdqoqZkQeRZdiWPCCJhPHyVPgx6h+BNpYEkPebAg8tTZIwMAL1gOrogfg508X/9nv7F974yt/v/8xz4qZoCw7Dv0lAgGbR78DnFrNas7LEnSTslzYc+ssfc9Tdu/zBWHpu3J6H57n5JbYJ12e66/6lL5ntaMWdNUlzHbUo65QFFKYQQDV/nPbZSU7zi4CT0IEzg7u/PhO3UrPAMapyGnGicEi59CmDMQfatGprE+aqQua2VNRoovhZUl6AxtDInmoEw5tGwlORQpiqOX3WcEMzTlJqYRBr30yNU1nkWOwpKkBWjcSMcbX9dLVuxCRiHsbJSCJUpAXPHmBDL0KUEFsYBPAw5ea4KWOaP5i2eHL8CUiT2VroNfl4SAMzRDCHxzkEVDpNL8MSZWU7fbmSDN1pQaMpiVk5ALNuumAT47CroshoxDCCaQ6wHVWmAgKq5Qij5CUF6xb0N65c4eu0QGjcNxUWOB/ts/PLqdvLidvH3h6lftSNx11YSnwCQhJ9pCctaAwmsSBXZwuCTnCpIiICYVgRigxtoGQv6MDxrrebABv42kJ/vACWo5QU8rTQRwQ7gr495+f/avfv/6jW//snb2bRMM2qyWtGxICj5sd+F7sWbr/yxn26jwX8QPAqDw1Vy/3qJ0MdvwYAynvXzGzPN67DZjLu5zJiBCf8RTHLOAJmfSJbJ2+Y2Nrt3AGZfeyi0DfCOzNH17OfYO10MBxQUGQm0g5jx+ErU7CBvWhhJNH1x9HE0yaMUoc6uBsJBCOVFsHIEPC1sbhdQKojb8FxDmpx/AY4uRUcGJg/x9SEIWNx3qYaU/UnUTwoxS0KeLUBE50QUBN4ltwvlKsB7PBaFlbeAFBr+pwAYIiR4iw6LKSJ4FTdkQcguxOVNzcTwLsFANQOSNYxuELvZoRCHkR05EO3EgzCj3UGyU7CQA1zf8KY7HImqGw1ClyRF7gKYuK/U9dGKWmxo6+QZQKY7dTKACTFiGUBrnzo+XimfnyzUer5+6VTvVgjp67x0RA8JMN7Q6AMREqPUDGEDS0T7JjVBsikuyYO2ZEvmhq0ukIpzZqDsCsUjQ3yx/sLf+jT86/9DtvfevqFx5O7YvVsuIfE1FWzX7NuQ2vftKunqmHpMYqMbcMW9ryfF1N67WeUvpezONdWTl9V77u/M2ZO4bCH+Ks1Z7WMO1gyo8pzYIkw6sdsYvAExGBy0eX1bdNE7q4gzgiRsng49TCqvYLmkW3P7jqEKMwAGMoAMOn1I1qw+dgpyCJTpL4/YP9ydHlbH55cHF/sje1L9ztYQCIPUZthMsBBEo0y3/JF86vfoBRtaXTrS4UWi6yGzUE3qm0cQRJhny0hbjhCxggohawsiP6afjogIkVmy12SG/9mpTUEe1hzUWbGWlpujnDbqmkUowgxB7BHQpi8gj8FASwDtFp8TCKues6qhJhgUl+hmadQYUuIccF8MuFEAC8OVPvBZ+Z2V/ad0R+rd3n3UPzRgKoAAZP1/UmRzQjYSUc0ftDbZBOUxOq6ggizDJG88ZUuE5DgY/v8gU+P7oFLaGgDqIg37UJTkxwG6GWUjn8tYv5f/H5k8vnPv/Vg9fn/Coq74SR8yNI8joaPcmOXLdSUzBdgafvsJKTVcPr0neNwrQdxfH0nbbScGNaQyozfCgXlXdZNwUxS/ezETmpLkvIs76VLEPK6XtmDnkwy3LOMoQf/ZXh+HfVb3oEHj2896j1E/dysOJk4CzicGNh60YoSxY4OruyreE7ovIeQQBJSnvNBUOFZsBDp/IKBPCi95Y8OFkcns32rz6c8gh3wW8MHE5nV6e/urmYTao/414AVG4Ca8gloZBFZNEPLfcguOHg9Pa2jFb6K/kDmaDVDJFLpe4APgomoHsYShX2GN51COZYcNQUYYYQcmoWpZhd1Y4iz+pn68ZO3qgzHJEvkteMFBxBJzLtNW80HQKYA4bMBbMrgQaWFgUixgowG7BHgh5KygQKyaXqMuyyfEsOcZBmarxIHUGDJkXxh26pU2Lv+m8otP9d1fbKgcp6I846i8oD5UisLgg5wnQzpM4R+HpXgNi4Sw5t6GT6tCTKgOs4IMcXagoy1OGIQAKYI47S4Fed8ga+QsHCJs4FzWdHy//0t5Yf//in3rn6udvTU0t18+yU5SKcodmSXU+pV6lr9FUTrsufn9tqW624Fd/YxldAXCprRnKfdXkf6Czj9pzeiRxvziGkArki3HCW+qMEdS6TIhY74+TzghjF3pNTFAoIxsdH3lfMVNmO3kXg8UZgb7J48Gi2sB0ytHAQUdDCSVXO4FnwOsEQ0DaRvO2LxoIAxyZFGsqaG0c3dQoAEmjm5JQohjZCIlM/urjcu5p9rG9+f+/yg4MHP5suV+9/+CmlPfXok7gTJsWxCbuDLgRUYWUUDlDwpKBh9Osh9IOkbC56NxKaWmok5YVovODrHfhY0hDlG62PKyBfuDLDhSBarvhx8QzRJl+oKcwFtbTFzoHZrB9Jduw2po90pH0eDHIWP0io5Us4AjZ+0xxHwhcEmD6Sy2bXevQqYR2ume1GiekQEnYKWWAhS+sBMh1C7s4hx++qjv7cnSAQZAorRO4o/podmMxLzEgKaSMdmgnFRuH2AoDRuwLQ1mnW6kKgky94StHFVqe5Pc5UEm1azOCBPjlc/vM351/85PPvnvyjr+49j6T2M72xlozO0tRMk7a4p+/Z9k9N1NBI6hG+p+6rQyIz6Mm4Z9cOwU4XFPkIe/GU21+BSJen7kFEHu/KrHv1kXfUuJwl+hnfEOTAnVkDGSkfxQMBghBFkaHJsKCDqNW169hFYJsROJ5e3ntov4+OEU4PDszh1lBCYW2jlkMMhSx7KafurV9KpEG3MBzZ6q1TA9ETByaadfCuNE8n+8fLKzceUfNAnd09u7f38JeHi0d7NJtLn8QdBLradTpQqxQsAZoCSupCV10Tx4YnDZXKQbgRRu6HvRaUmCdV7zQkNmTRFAwVmhgtcFo2U18K7rgrq/XUUmFLMZRjrjfsSivCT61TIJXR6hoY/21gFkiAsaQBqSYuqCgjhA5fwIAwiVcbXxBGJ9uwjXCYaENIM/DiuGkYJfw4SEGMZkRS7lCnw3VytdGcjtpI/5i/q8rfZkq+Z2bjkEoB8OMINUUCIlh1vHEaFzbaiAaaRz/uUIhmPXoPd2JeWDasmX6+oBmFaIYozGxlPFsyUcXvXt28Nv3yW/t/9ObFO8e//+/2nrcZsLzWLmal6bKoGdF3IcY0yRBrzfNjWqtt1YhBYrXpuyfjkdYbFs/Ps/Q9bZoirX0ReR5PS8ehdwseckbIuZRvDufI1S3wK67aCPlGIyBpySLDSBdw09m7HcTyobvXXQQeUwSuTB++d+cRRwX2ODd0d3P4xG3YEgcLm6KjTCuZOo4g+BROPLR11VwJAIQUYeYexDTNsTQvJnzuZXnt5uLg6sQy9f3lozvLBz8/WjxseWStIG9O3IGOuAXPCzEicHIJgkIvjolYKe5OMQGjBKhsGeDBlBc0MRdMEf18Ge54AUb7pvyili9qggeCWkWrsKCT6StwxmpiVNZ7KGRgWtAQ2iBwhN5+OUczGDQTEM6CZrFOvcwIaNksPGYOL+QRixwv7t27B7+TzlQYzByFo+y7VC20NBc2o+ZFTiFDU+CpKbhDXdBTbirOxASi3DuE45+ZmX56tvzGgWdajboATz++UAqCxBNsBV8QI9S8pyoID2zK0PA8OJ2amBccYeGxpMdd1ahFIdEAPGVgBBh+7XD56acWX3rt4NVXXv7h/if/7d5ztrhW+95XGlNqea7NGlMjQjR1NJ1DBYeluDrwjdVUJLkpfbfVbZJpvr5qeq/tCVgAtA5ol02IjCt4jtGEVg/j8W4NvDfRZcXFRWa15pqAIMYbeSey4BgLYwqkCA/d2vhdYxeBbUbgcDL70LN2jHBuUCA4/Ddm8KxbCqcutVYyNaeN6ALk0Iy8sprgFCS7NkNPaAYAzEoYdcr3Dpf7VxeH53NqO8aW0/m96f2fH/AZGIag2Qo7dL+z5lUC5+HKqgIOsFIALf42kic0M6ljXTO4gcLwyINjv5wLE0dU1y0Fep+QIi8AE46EXzDDEegnxxdQBey6MJbdkS9yCrpue2jP16ntzQ/rPTTgC+uWBaZarqGQghfMCw9rRfRQ3jyEbTj6+w3hBzBpn7Y5HHwBCUyVZlTNvehkEhWcZsmuve/ucyhOPn05+Ub+/e5okDtpLV+oKYCh3mgIrzmXhmfYZUNo5v0AmlknlLJAgSNHWGwQ0eWu2GKjBBNCy4MVonlMu4bQmAMzq71fQMB9eri8fnXx5q3lJ585fPX5p24fv/bu5MV/Oz3hyTpLDQGmJKuztWfu+kwFYTMrL+AHnXNMkdOZTAt/JenWZXs1xtqypnyYnix9NyM2UOYMiDV0wQTtncYs5vGWpSNvJ7k0mCGN1xjvzLoVlFVkbJgZx3dfADUfnlFoInamzEDvyi4C247A+d6D73h6mhqKg44DJPJsBFinKtAsUY6yrqeWn392AHI8RqbX5lBN4VXSqWZgB8LKrWSZ+rX54dli74gn6pPlbEqO/vD9g/m94kMi9IRmfAc2Rb7Dr0SSMvEx+yOajFFhGEQqFDTCo8QiFAYB9DqjIVMmstn2l0Jv6guwu66Dgra65rgBSd2BRjl2FZbUnTownfjo7xHwjSZYPHogh2TBHY3dkjsbgTULsKrZk81rGwEKu4sa18IdXGYguR11ZUgjIM0YuvZiC9Pgoe46FnlcYKzmKB0utfxBMZbf6I+ZMaQkmP3YD3YKNaVx5CdHe/uL6RvLyfcPlQTiSlawBZXKd6IZC2zmkXqInkqjBJlQU9IzqjA7NDNPfNJbhg6FTHG/DLsSajBZ6mAAM3XzrjnaW948Xn7sYvHs6fJjN/dPTk7OT44vD2/+fHLzV9NbfzU521v4IrRE1CaIf9nWyo2x1TR1Iio+OePdhCgfEfKa8RU/BGoIk1RSDpGhMdFMA5NgfKtcUk1jJc08fXdJx8TUsdsY5f/cQ2usCESrHHc2Fcot7Y8qi4dzJJMuD62TfCyjVu9sEtLYK5kdtYvA2BE43b/77u2KbJW1qoJBXaYQrMaxbgROJIqUc0BJefMZhUybEprBTw6gIcu92f7J7OB0sXewmO5PFo+ms7t7D39xMH/AF2G10WoyuA9CCpqJSTzbkiOVWuy2oFT2FZgolY0Cf5QmgQB3nSplGPQKQ4jhMEXuQQT/sRFcivGF+e2NanqoKeko92blDs2WU5MqaUljeqyIoSqdoNgtIMHEiLODKgw1rOaWvpfF0BnpNe7gAguSoglSrJDxvTIj0+qEAWFUNSzvMp6WHHSyAjem1wSNIl/QLHdisYkoW0Q5cdjGCgTwkGwS/HJHHqXu/HC6eGG2+IR/3r3s0RAOURJsAtJp9jcaFX5CTSHmqTtaM5jeqKROAKi82UAzhLTVSXblgwrNLGw2DjGhuTflafri2ZPFrePpx27Mjo+Ob14cn52cPDy8uNw/+8Xixk8mVx5NjmZ8t+PCzj3+8Vd6yddtNvlq4iICGDie1ZyUmLDgwLRE2MSNg4CPdNo0hhqFTdm264meZkIalIz75bsKP11tn75jQ6aTJ/H+pkApfOTyOSFf3WGrAG8ntPkTvISq8iBdOUQDEasjUobHXFNXSlcp2/F2ERgUgaPF/bv+4VNWWnpcs/x0FmkdygYydt36x7A5ptKu3iCwQmE41jmjIGgOPwOnB0u++IVMnc+37O1PF/f3Fw8P5r/c4/sjFnPbdEMKjoNQIIHNuY02RawQE+toWQirNLaU7ySmcySd4HQ4oKOk/DY0OjV/bYS7yqAcYHWjCktWYpK3NeWrqmF4ndrh/N5G8ZeiyQoYaKNobYjeRraHOSKmFCRMDyRY0mxpas0Ufx8HE/KFpIRETe4MtEJk4l3BQFXl4YQa5aBVlyaImibg8UsuyC/osoY6jmCjSgu1TqwfH8DA3viWQ/MidzCUuiOPytZ/yGOPyeQzs8nXO5xtZTXVHAADm77eMdEE4UhqAG1ohsnyJjKdpinVU0mjDZ1oTtdJpWQn5v50eXyw/OSt2bOnk9dvHjxcXnnp6WuLw+v390/uLs9+sbz+weTgF/ZV++5p9kwmdzt//mxr1K5pcndL31cAyMb9CbxlyjbvCFqGLp43s5QdjivzlJTxvuZXeozBeP7zl7RjA60JytP31b7J+f7q+8moPEc3Uk0bodGWfxvtaTs4/NWTcmusE+6LsDrgyspG1fqT7xEct6NMhQEGi4ZBDdJpZ9K5K7sIjBiB2aPLh5MjvlOX9cX5RmlYaHQpZ2Bx6jqGwwXUMKQ9VFlHnhMmTm+lK22U8Bz96Mbl4dl8yvk0nSwuJ5cfHt5/92Ax803oKtjFKOd3WWmFuTbKG2RCT2VM2l5u6W5vMNamC1U4KYWS15HNhAnrKLMVSFAbaU0wxyXkDh7JKfBDyIveHknVuDhDmxBGs0zgkZxKuxhF2bidtocc6ymeljR4KCQuFDmFHjjyhfOCj4VI87iZTQqPoLEO2x8W6dhKGvw+RZbryC9coLDesDLWGyc2Dh92Jz6VGIYwgYry9McFqUepZtzpun/5XVU+776N74gEGGBYJ6DdOJuaICQp+KtaE0Qz9VE0TNJ3YsL0IVYWGMJBJ3j6/aDjYG9yuLfkUfonbi6fPpk8e364d3R268bJ3cnF7enZh4uLHy5PyON/uiTuXFwNMHFbaSMyWQZr0guyd7vXfXsm6TtdKLT01xNYy9sVRcs4LYk3dZbrpvm6Qm1qo5ggkq4kmK0Iu4kNge5os6RS5hsnx+KkgTO7Ppo+8z3j8WoqM6bjctR5t5B6sBxzoTKtrloozHtMo25VDPD6t0ZiwIJlwgbKSadX1Gr4jtpFYGAEDha37z7o/D3uLE5dXqzKjyaD503+/vLoOp9Tv5zu26ZaziaPPji8+85hw1c0AjuuA2BzIzNQud/AMBqAPCbQaNZ+bZu4g6Z9NiDV1CoF6OCghJ/0bi9hKpju3cQRxlL71bI6I2kSStxhkqh76/9IBmp2Ch6BxKanRYJeh5mxaKauExjCb1Aud7RtcIoSGJQt6fdEh1jvPRYA5Ezpmm+pqnmO2JL4WMiAW2puKaZUkrql/Eax1CNofsqhgwVHiFLXHL3O3I/8I4ifnk2+2eJ7ZuqU1PEBya6nxB2DI6w3uRarDoIZj2adtgKfUHMeoq39eVvQUNckvJpNUFHKYpxrfGCF5+iH+5NP3ZqdXT144WJyce3k/OL04OrZB8vzD5fUZ99Z2vuWb2t/q+ZBFOoYT3N1OpYtwLH8UbIubAP8n/2gmZI9ei/pyXRbPmrnsEeVVzus7Hm6E6bWD2rvzegA4QPRa1LBbEeYcYG28dJhI3Oeo3JVLknTxJJeb3rb3TcXIEzGZP2fD1rL7lecLJE3g4wyPVaMdKfUVGQyWi8KFNFISzlE9EawjN6VXQSGRYB1uZhfDvnrS6zDOF05aYHD8UUZZX1KFTo5Zg+PDviN0r1rl/snj+zPGi0n/F3Syw8P7n6PL1fvE4U4WlEu5GAOZh+NyZhVTBJmLUkyVBkvjoAohcHIU9oEGvcQKwwfq9lVebgjImDgCzQ1EyCaJvcrDyNHv19lFABhKGD0INDDKGpCISKaIC941EN/eQizia2xVmpBP8q1GjEBQU1RrPBF7pBX9ZsUMEc2VrA7vAmkBuW4kJYwJ6fwmhLMMoHyLb37BUAz8jKY4MgjTVAwCx7RyzyOla+HFQjL3eeTt2aTb42Xu6dzBGwmVAcCTm2coxRbM000UL6lCSV3l/LZ7JLH5E8dL0+Plm/c5POaVz71LI/Wz8/OTy73zn85Ob+/vPbB8hofdzG0SZ7vJ0qNBxw2lk/W9GZsP0s5k2iacP6Plj16twPW1oy9iTB1UpmQYjAVCNrJFo/eTV+exxdoWbZT3JP8jRAln9QGVmDQIAfyXgfp/X5LGGUimVjStGF5pp48cc9kPZc3b1YP55WX55wsfTef3XHZt16AeW2cpCtjK1JEhi7WqRP2bMWFGWgxrKRh7souAj0icDSd373fdGG118ly1e3AmaA8WCetreZehU+87F9Z8CH1g5PZ3iFpOjfc3uzOwexd/kjzdKDyFFHcCIEczDB7I0+Vb37izq7moOfyptYOj/FCMBANXqE8dI5L1CnHkbTIaLjT0iNuVh7ijgs41SY8KaeZxlkEVOOdhKVEa4haTHo1p80K+/VihciMkrjjCzhVgzlcY8Ggn0IWEk71Q5uOQhXmUs6INMp17mjh4UvMkawg0HLhlVEJOQq7rpmyqjJHygEMUe6FI4+iDhnAUBhVNxBJujS/zGYMHIsgd+czM5/q9dw93IlVhy8CDMEK5DoR8ssNiRoAACAASURBVG0cX0QDK/0+2VIXvfMjfm108uL54ta15fXTq89dP7/cO71ycnZvev7B5PTR8sp3llctsTQn63S04OvgWWWSdUMsw6Qv8vZsBE+85mwEzjEy94rfWYVnI1Xb6NUT9+zROxzTZcrTnD7DYSyz6jCFNetp8SL5/Fm6KVLJ+f7qy8SoLH13e9Y0WID3rpz2hofCE3JDtoGQ+6hK03dl7sYUoCxE0Ybww6Hw+RmYDskhFmgtd6nb1bsIdInA4fTyvXv213787B9ymqyshipO3cjgW90a08nBNTL1+cHxfMqXBc+Wi0f7szt7d9+/wtc1rgxMLAtNlWNxlE0QyNluSjBQO1D5KnGPi0qbOfzBkqIz0FIoTAldiilnXBr9FLmGZrmmyaCmDHEKbQwfF3BoQznwopkS4ZHcUZd7s/Kobmyq58mhcYRlpoJ3AQwvWHsUcqOYqS09iZRRrABgrGktzBRuXr16FV/wS+6MOE2oJTK8k4nojUiQpEYSiVMUlGv5UeMFBY/kVFe7IOcgZvaZ6K5jN8rzu6ov+XN3PjNTV3BBJfySR6o1X5Vj5S+R2UbYUU7YmVMAQFcCaGBe2Z/wDYwvny2ePj145vrVi9OzydXT+eHFB5Oze8vjO5Ojb07yn0TEMZPeYg2q23RJ1SqTrBxjhi19T/95rsli4o0Lf9zcf2eVjNJkLEvPiFwbVmTCNJCMZ1k5DUtv8yXKPAadjVyl7z18zoZkiTgaTT1FfD1Lz+wbVzk61HrKnnS5iOfhFgp3U5m3DQpObgBmWhQA89jdVhchkwZ3fSXOOk/XNkIWHE/ZEYJGQES2vZ25Gr+jdhFoEYEr00cPHjzieSynOicY64oTrMchVmkqVHFcoxwZOGt3B5n6yeLodLZ3vNjbt79ROnuwP7tt39K4nqlXqE+VAx4JgbdtMrighMMcNewywGuvgbylcg1hFIUbc+173BXcVBFCMjYYdoUCQqOfg1T0dWGFSxoE5nR02am0tzddsNJbT+VAlDMLxAeCksrAj+WVzlQq88TS2mz4BQF4uSaPWGYUXGt2CgHGak5Hd5NdxFnQVTlelFcgXlBQRYEQVJI8fBwdNgoxAXgCu3aEDbOEU/JLavg8Ors1PAqnhhmx0cQkzvfh2goalLvzeXf+riql4JSENUfA6OoU8um7moLpgU2U85aAyLDAGqaV4+H6lenx4fSls8nz55MXzg+vn59OrpxdHvKJl5t3J8c/mxz9hC9hpBRuHz9XksxtIN7ScPQrryz1JAzDlGSnThsLXCxpXrQGORAtgTc/LAOW3qT2rN3PExsZB4sew0tf0Jl1S9+VZ3sgMm77F4NhIz1LzodJVSl9N7fwyaUjm89He5ef8mvpO/L4YT6aR5mGhONdioS7vIqGN9WT40peTYUVBPgBB3/TkVf+JUFzLM5bMRMFO3IXgaYI8NeXvuV/fYlTi8J6Yw/rhKepddU0vl2fbiJkUX58Y//g7NHkYD6Z8oxjObu3/+iD/fnPDzdm6nWmUuVKw4A91t2NKuKAaUWGGjosBqS0FyajdE/B3/A97qT1Y2ENNEEIbjTbELp0kWSspl8ENEWxCD0slG08CQv9WAx6CIEL+dWkQ9+UwaHWXI5lKAXZI/jp8I00+lk8TIFcCxfwiBXFR4zgBHOjtlQADWwk6pT52Gj80jaGEH4R4GlzJOkZ6ihvVssuA4A3BoVdUBar5MgpJismxabH9xS1hjBxCPTTX2k0ZW4j/Y3J+unhZH8+eXM5efsgezwTTqUY+tGoimkdUW2AQbk9X0mOYiaLNP3V8yWfe3nz5t71s9PplbPJldMPJ+e3J+c/XV57d7JnmV67wq3BDPvd0W5AJ6ksj+XIbh5mEMDhubm92Blv6467ipuedUdxDex7V0VluabV2V7MtySvdskpTVd2aiyTtP9Euy6r3HetcNcbHa0IDbE03cHGmDLfOJlLJpo2M9o1ONBI1CNrbyR89hjtw2TZ4GTz6lQAWxEeJfsWfWJFsUBYNOy9gr0YWteV0E6uNOyoXQQqI3C6d/fHt7OFiADLhluDworSRQNnYGLJp1+u3Lzcv0Yya+t2fm//3nuHk/khn2C3pv+FuEpsXZnc7BRGAZ5DmHo4+MCgyKhJZPSMBiZFTBwJOkZB1P/8ON+3lcNSFb1pgAKrPJzQUHROQ4SAebOeSUTX4ycIcaeVJ0dwynxLnAI5TrEyCqHoqr9TBLDYSb5OWL7gFAtaG1KScPCIhGMbX+QC+EIA6+D14zMRuEONlZiviJgmSzu5h/5tg48Msg6bnMKvgoBWYPOSRoaBlN7uF4wWmrzNjg/kFLqam1oPwpZKppP1zsQ+M/PxR5Nv2g9XRy4Y0qNxls2IwcEvyuH+3uHB9NZVvi796MWz+TNn+9dPTg5OSNNv/WJx/WfL0+9P3KUBe3r9QBo5OKZudYqvKw/MJkBDz5AlwyHluCzZ9uxd6XtBFem5CVDoMA16Q20vNPM0Pd130C4mK1brOPSsuqB9JdNI2ShL3avS94zvEB0kEN2gcQwzThpprzDU5Tz4q3cwSsoNORKCGoQ54EoTvnhodPey3oTpPVSKFzFREdvM2ED/DwmjDVhCSnBX7yJQEYHDycP7FZmdLSJdMZzVZDiM1L1ToWKdxVc07l9d8n3qB8cL+1rGPX77dXr/Z0eLh7Y2k7KY+y/oKAnGHPopiUB/MsCzCyKD58D3ndFfbXptyQQcbTSQ1ylvStzrEuv+GJORIAMfyBSC6AGosA6MCPqlJzSPSwC78nG+Ii7vsKimTLtn9tZzq8DGdVPa8ILCfLEkKFpY8oIajyjxbegMkQDMbYAJSKOEEb8EGMyBljOFDJjFiQujWAnNEBtz61S4Kw1aCr5QM1YTIR+lyvvt9JRAV/0M7JdbtzSk4FTuLGmwheglnS+6cIf5YniDIfvMzMR+V3XE75lJzWFdP/HosfLDKYirB5Nrh/bxdB6ov3p97+zalRP7+6M3Plxe3F2evL08n/N5kS3urdSnMWktubXPqxSuXctg9WzZ7XqOCmU5OHT+hZFca/aVM1aoLV22ytLcqHX4E0tbGNmjd98RxrJiWHLaGcYySybcsxgOG5wl4qHF+a7VIZiEiWRi+Yi86W1D5075iDx9T5jRKwJZ6fOQeQgtKFmWLyQu4qRpdzzWcjahICJeYNgbJWg0KhpO2wgjDL0NFm0admUXgfUIHM4f3ruMFbbe5y3Oagok6YQyeM5McSTNVzTuX10cXcyo4fD7LrN7fEj98J5/AkcyDTXaKAiQp2GCtUpzrBWLHr39YCOgnNq9qU2yU5wIU3QvCw9jpS3EhJwmYuCHQBKxFH9t4s4Y5FLR0NuJENCoBYIahzVPIwY0BYb+8D/lj0WjX17ItVCroFFvya8wtCWCeVfBQQi8kyF5xJS1eYiOGLtxe/FHP9i66mcIvqROhWt4l+ayNDFB2UaQUY5aAitiuInYXHIQhfzVJNwBvxwZy5Cgbsyth3gEVJDrjVPBr4iYnErnq71FcveX55NPziZ/m/9mZvuxbST13B2owGuQ1yLM6umE73u5eXXx0tn8+bMlD9Qvzs+XR+d392/8cnn+08XpO5NDy6HyTH0t62qw0bdrHP2W/QHa6/wGJywGypZ/Q7HuLLE0FXnTR2WP3m2hoyxL3l1XZkoGM7O+w2zZoM80mnUWT56OrtEZIBd1wRx01tHyRaPy9N0x+8ic7686AOB70g0MJ9ebeZewuEg8ki8TOOfTpsqi5hFEsenJm47EeaL8R5dZ218sVlZWn5+xBuMjdDmRMl3ZrtpFYBWBvcW9Dx+uHoStOkoUl7ju8cPj/eOT6d612d7RJQuQv0tKpv7gl4fze630lBRnDJ3DLGDyGS1vzGlV1w1pz0dPQb9nDWsZNto4riiyTpNRLTFImzQo4WSs0pLaxB255sdXBfeAlRb1KkDUFKURMQrhTvpjYEsC/RhtKdwgljoFHZLMBHTZrxB48gmmWEWrShHDI1YVk0UKUpiyJ8cjEDa8MYgp0xwBO1zTfGnpN7iDft7pbhRr0NDcxW5Hf4/1jyMxWQW/cA211PClHy+aYfTrlSHl1v00VI6SX5oyav1hJq3A8KtyYFfmD/Lcnefu2yhMK5NLATb60ymDhsNfAb9+Zf7K+eXzp4uXrh9dnF09PL55b//izvTiV5amX+F3rAxYzezpEGKek9PIxMcqnfV7zuf5YQIhf3C9OjGjs4KlvrUOnd2et2eZJ0LKwf3RO2m7/WTJosA4k1sbTsM1ZK86AUxD5PHIW1Zr/9nIJJq+h7LsWMg61hkSMyUYciZHmPGNGZYzm8bKRnnqbv0+1QjnfNce828d5mpmE/m2JYsJAzWc2ov4bHMVeCLgixZRpn30rvpNjwDLcXH54HLTUy+eqR+ezfg+9b2DxXR/sng0vbzLr5MeTB6dagVyf7HwRokmeuI0JufZtn5MRPKgvTPQF7RJIciFv/ru4kCUXDlqdDFYBUwQyCi+rtx/rrEp3Iyq01+22IODfkHqNLbONYUewKET/cwEpZP+TsJhq9OoSmH8Io1gviEQALwKS5lCgo6trU5HJaqBTFyQLzFraipumiy86xdGjcJEv+EbXWujH+tl1xiIaxRMNGCji8JwSW7E01UAtShnRfXbAuGXJlHW0QnmmDIdT/30b3RHuftbl5Nvjf15d1yTd+DnYzNy5+Jwdv3q4vXr8xfOli/cOD06OZ8cXXwwvfGr5cWPl8ffnxwslHt1uaRs+Svf2uhtL4Fa/QHS9p8XEdHM2e1eNwzLXLSX/B96Pd2e+98Ssmjzf2IM2lJx5O12Yk15sp5cVZawR8qu+8t0ZndZaLJLbNAnZ9BkWAybp9Y2X1lxvnVgVixwGGprxYi86RrMD4lYf/a2ImfipnrXCW9llTljoZA5901NI81u1qN+mh47O0eIjBvMaEUMXS5jeEt0rmP3+psYgYMpP5av+Hnj9GB5dH12eEqazv/TxcPJ5e2DB+/xsZo9X/cRq+x5hzIW7gVO0egbSLBWpY3lrSsGDreM1nBv5WjTsa9do2NfHHTqauutPB0o/FipTtxxCWfkGEIxkmEqA6GQR/Z43BgwNhLCXycWIVaU5WDuWfEnA5VK4nFaZe9HyAQYj0JjxchBJovZLCTooz8x3bbX+KIFKUPQJEZah0rvmMERMaCTMErziGpDFXs7dkHBNcngDt7JwRjVnkA/8WHS2w/pJIl+ltBGeJWuaVTzWCJPfJo3cifABWHL3SeTIbl7uAbBZKm2Y2Qxf/F0/tzp4vWb09dvHl65djI5vvlgevr+8tavliffnBzZJ9RZqsNXK/nWKhUr+DdO0/SjyRK3PLNb3QadTNQNsxw80slKjYpT/rgZcUdkozxzny/5TKddm/ETdaFdobYUPm95PkrT4mZ6NHGyCw0hfobEBLMcOeN0fpHjeTIuZ0xJzvdXWXau+VfZjLTdRlgSDnr3QatPzODghzsdFQPEksvBz6ZWbZNB0arkq9pAsWHpRDCClkYsaBErFTvqNywC+8vZr+5x7xzwHJ0vfjk848Bjh1qm/ujDg3s/OuJjMLaMGguriCsGETY2Fw0ENwIrsHFQh87QzwrnomFkcFpq4W4CG6O0R8BWyBaEll4kpRMB5FvqbxY7wLZwy7zMYAACw2OZSUGgOW1ugw5f8A6aElbwiDLQNWZCqyrUjkukgMua6dWsMXEhCaGl84R8xIUgA4m6jL+ZI++oKUiqtjnLE1kNJ/64v71ZIJj6RcNmtF17mTiKO7d6x49rI27pgLTtEPHeO31vgFPyLgBADHEN/OjXvKc6x6I75e46pvERPFqTrBDKdLI43l++eLZ44ya/Szp9/vrpydnFvYOn319efLC8+PqSL0Hgltp8UfVzii3iCVm/0cmowjbVeWkPppWp5XlmMqIduTp4a+TDxgZJAGZgPCG1pmPml1a5z7k/+Vw268+sePa5cohh4vi8MX06VNaJ7Jyx0fn8BmCPsI0KTndCY0HgGcsaOLOpeXS1JukZts2sG7VeCrjUZWzxIn33WOjElaTZ8X8mYotWOkUgYuoyFqSZ8WrVk7QNj4mzuy1ojgPpCJRHEhmDF0yjXNuu+k2LwJXp7PbFvfNPHC5mk0e/OrrzvUP7Hpi+Rccso1l+W8rg9RCZBSz9WCyk4MKu2y3WOWJtnj6zD5SlMJBLhBrO8OveMr+yljaA+k6E/VrqiPmW4khMIUQDDBNER6VyDnqD10D08yUqA5VsHI47+IUtJejQeKRROEW5d+9ecDZqe8wCLGsAA7LOriYLGQp06oh2TsopK6E3prvcOwoHGAVg7dUykCLXIMIXCGKiJnxmdnt7DfwsntR6e/wNkihUkXf6MDryOIXFcd3hXSi5OzojgA3AenSRu78yyX5XVcNT16CxqxrXOLV4VH5lf/n08YJM/c2n92+eHNw6v768en5v//rPFzc+WJz9VN/MmK568plVUtQD44Yhli/10F/IqizpqinexcGj9LFJqKavBdsTw0YDKMm8tJf8X8b1R+8LPXr3CTSwcsmiA60H5358aj6VaNqyom1qLB/NBmmxiS/wPjBLlsXpVZshT6fNF/8nNc7Pku81jotZr3nhyXUxZaeHRSqtCkuekK+4KWcl4/F0/xVZi6kot0dECI0z1bZAERkVeoHFgz2znwcwo5FYcdForV35zYnAxd6Dv/3u+d0f9vwgZV2gOIEp9HKpkWGzyuImrRvSiY9CXV7ca+hnVaOfmkIXNdaHJK4oGTGDt++rRmN4uNWnmLKiQITFloTFLy8MgQzYEBQckSrmFVpz3FJ5VzGmFotdR9XJ4wsKKSCnqOk+WSaEL7xJCO/qlDxpfPDjEajyScteU5zI4OC4qV6qfyDNNmMDN8MreEdTC0PTx6ypWYlEXTGkUmYgE/DpQ/Gu2sK7mMrUO+JDkxODenvrc3u5u7z70f701cXeJybLdw6zTY07LEsK4SJN55sZX7++eO50+vKNg6fOrx2fnN8/vHl7cvaL+Y23J1e+o8+9NH8Is1Ve2nVyEvkstbPMq6KkB1UIBFExoIJlWaMlcuWuClZZaDNHqmtsxHC5ohRXaaZ1Gddzyuncf2E1W62ersdQEdluyzcdr6uU3VJM8zHziGUQtAY7OowNdJnhenjuwFez43zXnd8thi5zM0YYS2m8O53RLiaZVWpuUANzmTBF2bSK8mZUZZ9dSqEjOCjc83dM6OE/xcq4K8p06c2Q8XflNyMCp/t33r/H1bCVB+SEkLuGwipTssTSojnKAkMnZ0esXzXRHNfBKBOIwuEZ/Npn3APxKPgqlRBrolzZJSYY0iKmZoWaQhCpGzQQ+uZkq2Fsmy7g6VJvI1yQwX3gqZabCMgpwsJ06tvQdfP0tlIw+tiaMXHCrx8UyEH5OK5HRGzjchrR94J3oRnXKP3OjoGJdWBoIFpGqeAdTXTKNWrl6JVW6OK9wcZdWTm2JVNRGvgzLtYkTmllQuCUHAT525PZc7Pla5eT7xzyzYzLW8eL12/wuZf9p86v8MGX/eOzD/aefn9xyifUf7z+zYwt8SOWJYh5Uth+YCdJnCJP8vzKx3kWSOo0uJgKrYhE72CtZQVmYwW/3G+9liRaT/KLo6uw6n6wn6sjw4S7OouAjfF/nkyaFkswbRXYi6uFQK3FMNenRRJN77XK+024b9FY9yNLxKUp5/urr1CjsvTdFmzSpGVACik7+F3GM3brTAiGu7/mexBoWWtaCyWabqGyoJgi65G4B43o2A85rJgOk6pJ3xloSJHblX/4EThYPrw/sxyJwrxz5G7pAbnSX/STBmAIc1x2neIreLoUGIgGFmlZCfpxAQG6kOlkokEYW3JBMKhlos1GWUvcH8PjdmKkrBqUFIUMQu4JscKnusHtyq5QVdk7CrNNsggMghlLCrtw8I7C3DNbeEepw8PAht66UY+TjzuApKakdoGNjzhITddW30ERSbZTeZulePrR7lZWXb16NUyk3vXTXB6Fzq1ON+DLibXmjho8mkFbmp71aO7KOBs4eihO3SAzpAtgnUzgUbo4abpz9p6/4N3B3vK5a8sXThcvXyw/e3p1/+zK3vnN+wcXv5rwuZfTtydHl/wBbdKNbtdBta8WZ2UuazumWrgVV9mS0il05k83x1KfY0j1GU0wtWZygXFfMYGFLEX1rBL9gUEPcM0i3lsuSVEWKpbxOWNJGUgqfRUwNhPNU08flFdmznT4IlkjPKBwzN+c1ijPQC1rToCpp1OdOWX4tDTkDg0r0o91aLdklTUMjrE0yl+9K2vntHuLvKtWA4kKAmWbii3dDJxhCTLfWTZhBiwrWh7GzBdKkd5kcNf/6xuB4+XDO/mXuDPvXEAUFkLv9Lo5FJzqFGTY7UqvaWKxchQw0qsBeAgXLoXyQPfAFG4vgxdgwQOhgMmvMh44q8SdMUhTKuV6M1FLiWBB6+0XCiO+IxrFUN2c9XahMLDw9oa5hEON6ZCUayAhc/Wgdo7qiDEJVP0I/KIwcelw4OEjBWYd1Dp+qucjp9PFmYLBNfAzfSzXrb794NTAhCKZAhiLxkGU6+0HHtGkpsDceGC1xyAvthcoALObKuciXZ/yDth4p3OAgakXN64snj3hF0mXT59MX7hxcvX0dH5484Pp9dvLs28uT0jQ+RN9l4vsSwDSgePQa3uoi0o9Io4R0uN1tUqcru4IFRCbJVLpoAlylsatnwkh0ItYmyZTLEajicxLe7EkEbv2T01b4cDk6gFv+ndWXSjbB1ovcsiGuWfW52os6GU6vPO8NIMQzF6EeSv0yWo1hvH9Nee7pKfiMQKfAZEJWhxEWzyyFL8iWTfYgb9MoCIXMBA23flEJKR3edTsQKHDaR1lNBU6xpouhTSXcZUmvyv/8CJwuLz7iwdavSvnWAXcEbQ5rjnGITiftVRWQsMotElhIb3GIhzpBobEfFV2tgdmCot5S16AqqWJVeJO9jnw3k1v0AgJUISGmgnb3mM5WQTDQC8CeYFgtsBPlPhtPP3UHo4WAStSfuFjYVS/JprHUtUegJYjNSUdpemD0wMSqnqMSq030yhvbwLJWKIpKiaR3ZJyUqP0Moo6ZY5LY3oUE6mDgVBLlE2BFdwM/rgEVrBeeE87rgmdmPz0AEc06arDL4iCRfK1508WL13M37w1eeb06NbF6fLazdvTi/cXNz9cXvvm5LD4zYz7k4P54nA2408oFVSN3LRcpl5lwQ8kVw+a60elPQwxEw02UunutDQT8D4mCu5h3T0soBD2TSakK3+67Jk2esx3zivee3P+sOvtG2f4ZQQvCo2RovK2VpOlmFpIEMjYYsujn/JdFb0m4oImPKBouFRJrZSV+caxXN0cdHK9mXVJBFcQ0ZuCbum7KScS6HYii6jHzIAlTGtS8s1IZkZvtkMtYh7HNHRlWhp29T+ACLD8FrMHs/rbUvcRniq9ZjEooR/Rd3RSWHdcFtRYJD2DM5YJVCm9RqEe2kLgxZZMxPuQMIFT2f3UPm9gDAV56jQQgKY0JEDII5AOeQJp8y1/OyUfwQwHqEw/3j148AD+nTt3tgp+q4GSO3ihN77hCEYpI+4iIoYVghYmRidQzrIuYA4HsZ5alINd39dhgs2JL6mqcWnwc8R0ek+rVYqDclZ4cBCclQ7Cx8S250IHJTCGxwe/KHpSAoFO1WjGl0pH9qeT44PlaxfzF88nL1/fe+nm8dWTi4eHN99fnv98fvO7y6vfYSNvmsbZ/t7+fHI4m18ebO25Oz5wolhSlMcpDRjM4Of9Va+bhCyxy86uquFj8OxgTN2o05n/vCCT3YQ8VYMJT/5SXpk2xYqh55kIrJp2K/G5GfJ2HryXkndijWi+uLKVZkyPngh8zPJPs2xRdX7AsPVun7indHEtxq8IDQeNhdX/qa/MN44n1hYetxujvMsQ02UzBDbXlg0x+D7WJUxJEBIVxyF4n6fvBsYUyigaim10ulpk8g++mxU0WIGgF8Jlqmkbvyu/5hHgXRufcG/jBGc4hYXB9Updd6q3UcVwrkKKhKWKxaarkF7uEWo4hVShjfJmmVAoLzCBU1rnzQPb96IQj/COH5ujGQKjWeKOY+l9j5PoVThEhxlGUnqAwwSjQs82CFzCwzaacYqCPKgoEIzCL5hooBCNmJJUIQKIpZxt0FgZrlZKqOVd6LT584KDvA43VKeBMBLbut6x+HinPYNCfJRH1FgfxUEpDM1jwS7oYWsQq/IG0azhIyV1ELqrgyxp3qql27yAYXiT9x5d34HIKdzEQWpKrMk6B/FC8xuAb1xd/JNXF//olZP9s2c+nN56f3Fxe3Ly18srJhCXSOstNd/fm9pz97Fz99hqORK8wF8DmXOMXpVq7qp/E2XKPdXaJDikPzWBh2Aup+mW6Frp55BCtMkR2fZ80sikiWE7DQi2fezdHr3nOJS0Iy0nlMJbDp5l6vZiqarJryZLHuZ884sip3Oz4vWtzRyI/J9pzkvCz5jOYccgoXR8NWqtyxuZGDvMxCOnN0K2/FUueLRVWTDNWUahxmpv5qBcV3SLq11MxDI1TlFFJBGroxO9O/LXLwI8Gvnw/v2JvhW3BXxWhRItzn89SeQSZLc2D2Xx2I2YZ+ooYUjd1VY2gXD5qm22uLFXXgCMe5wao5ig3jiwUgANFBykRkAO3rfAWiHhsfc6xEu1uNSyJ+mNQYxRzQQmervRrDl6CVl58nx+raI3DYS8I7gMae8jGtoLB7BtE8Q2LZiLUENULiDJh9iWEGJlLM0CHDMYauEzI1udFAKIXeowOjqBclJepiPclAk4KsPfhKAHndtewOymhtxd3lEDAzAQ1HKw/SRiQrn78cHiU08tvvKJg5u3Xvzh9NW/XjzHB3YUt4E1z935zMzBbD4b8txdh3YgCkLgLJu1TxUQhYFom4ajfLsm3ElfW7kfNUK0oQAAIABJREFUw9L0OmdMO7aaYpXF26SMtGxUpL0axAU3qT96t3VoH51xEUt5XTOjoL32sFmD/xgZhCnM/TR+TsOn2A5LvuxGzL61oXIHDJz/kybnW4ebs1cDaziNlY/Im942XHTltIsrROaBg5aCXJFphBNdK8LsOZosfbfBGS8f4MDQYKqt0I1IlopF0LIOB1WmpXJX/9pF4GAyfzjjorRPsXcqcf6Tp+l4T/MW1pKufu04Fgzy5WSv2WKYQFX7NwnNOgu9ACtk8DKqFV4QLjRTH5GnpBFIheHbN58gQQhc0jbZNgrvEOTPNpRLJ27rvU5dgs7jQIKIm0MwMN/bdqQZXixfrWAJa+6o2zs4MA7NIANVG7GyTIOPcjAFr0kvKxmRg1Hmnd0yos7UR00lHK0uzG1pjbHNr1y5MvB7FZuDwNTYseL7XT5SM0Q+0kuRgxDNqup6eWD6yumDL7569OmXLm5feeW7i5e+ycP1nsrqjEyUu7d97p5azxIZT6hq1ecdeYqUt7fw6rmRErFB2i2VCwVBWU5LsWpNICRHJNySWQnrFcqZCk9K9aKU08Us1bRn73p3zKrkcZal5C6iZHRdHX3+IB3nnDBXLaO1yY71HHQ21kZYoOy/oSXTkKfTjtF0iq9M3KIujsRo2BsNYxkM68xTdnV5nwXR+l2TNUQwigFZTo54oXifjBV6rIkKs2YUujPSAmV73j8/Q5fTVimA4lTQSJimXfk1i8D53sNf3LMDv3fh+qBwa8QHL3XpU4vorTkGShXNeJMQnJAZSLB+N2bwLHtK3I8MAQa+tzFtT9z13eFtpHvLYGXEnYirZAZK0IGEcgGT26Mk6HWeYpePudf1jsvHTQo6w0FiqIKnEAPNhdqBeoYMx0FgyM3Qg2vaSG18RJJJabncw0RXAiSAxFbXgcjLQdXoUdjlIHXqI0fVth0hd1dW3cOR5iF6ByXvtD3xBQc5v1Ifm5U09z51vPj8i4svffz08trL31++/JeL88k82/vNA/v11ubu6c5TupIfQX0M+dmouPUZ3nKMkqkOOEtObhybCTBwi5PCdrLMdCMYwmIeZAkqlKWk/sLBaQuSf3xuhgOWT8aQZSeQ3YLs2ADdXfaCNstxTZTRkZxrea9BclEXTPSa+X4FJZGMyy/0SLOycvCscdx1E/D0XJHwpr97iVReYuaZeSS8aDKPY6T3OsN0ZVm9Ed6qrkwFKldFQYxAQaggEfHMaDoK3JWaHfWkR+B07+537qzPfWvIbEZKrAc9TaYZz+a0NFrr2yzIDUXBBHZ5aoZ+muNaQZsyeEwoZQ39ENyP/SzaZ9xD0WZHe0kAF3w9hhJQMgBKTCcc5TrEopCgE3dmuoeVj3yI1g01BTDU+MukUJhUOBCjg0SnzI2uuaAQK8KPU5QwKj5dA3O7x+MI640FtnEZ41S4KWDUFAZStIEL8UmbTDebRZOe8kekgaFzEFS91eKmlGgSVaM5lqse7W/0tyUA/qTp7z43/8rHD89uvfzu3iv/z/yZ+cIf6lgKV0wRWupsKTbjGQE/QODz7nxykTH6V7iYCs2WqhMxArh1Twx8nZXCSpA/Ba8KzQR9kayzUpTr31amablsC1Q4p8fI5JjmKEMsCeZCms49EWfHwsr5IiRGrdMre7GmTmMnXKFRXthQQeccFLQDqQFNtazk6ftqxnK+vyrnRQ0w7V/mb96kY5Wdp7SLr6Xvvh6lRKOVuvt+s8q6MpZbW2/Saxl+MSDEB6Z02A8+vGFtpD2M6jNXEq5IeLvy5EfgeHr/g9J3QdbBZtJ1j0hAN0hhumnCRwBJrmAIrpWCTJ3+lny0cXNRAg+csS4vIfczxuDgCwVfoMM16+hYtv4JFvAAujmlJl7IMCv4A6FZgYmHhK+QoHd0cExxIA1Uh3eUgh7NH86G48RhxHUzEHOP4eEmjshZXKOwN6h7KGwegk6sbENzg10shpshBgYt2n5giM/jeeje8DH08EVE6mbEWW7iaUE4bbJt21tJBwbNR2I+dn3+H7wy+dzHnvrJ/qt/v3jx/vKo8LeQbEdakhCDhhGFtYn26WTGkwPmZb7Y8vfMjOtJTRzwyJemBWwVtBVVM6wj262Qi3UcthJvN9Iegdfl7krSXSNHA6+WWcrlXLklkPxIyO4bP5UtIvT5GtBrXueG/IxhR/idu8o1bRg8cfMc1E2bNgNpvS6RcXu/SIkplE+5ohUfjjJhiMxzExUCPM56PfOWEldmfJeJ9N0IHLYR+ueNrMq6Qkb2TZ8EclweEyJmejIZI2j6myXE/eBmCgTa+xyvizvXA54Hd6V4Rz2JEbi6fPjT+0fxKw0FiEylLk3xmd72KQGSFDRwHjOc24dmQf/AJniUdwFSbxL6WQFkZD5AAmchAdbVGVYQaL5My35l3ypT7hiLAzjtSRRCyyU9RA8TCICbkPX+0A5qw0qoHZ3ASpv4IoZpavmbwmA4k/QYoKZG62jhrOvdyG/jJs4iRr1R2xAB9BPq7VnBBRW+j0kLQDOoRTvubOIFu2Pbb9sqrchHLVpozQje4Wbh3GkzWQystLJxLJZvXV184aX5H71x9uj0lXcWL/0fi4tCvr6mJE8I1phtGpYgWLF0gxf+ZS/OTaoZf2TNn7sP+l3VRGENKU8yODUyPdi5nxpqUyuOzPVQ2GKIrZ8OVvpBIV9Wgu4EqDJPaWapdIo0Tx4RMnP2Yv/s0e+Cv7zln5vxkabF4LvW9dpiZymmB9FSXfvPk3NnuTkEYvvIvo9wYbWH1gJnIA2q/VNRFNO03jjr6bveReRpOoPNo0zMtQVt2n2sp91qpJxV1m4ByaOLwUJT2IpMQkSgrDjEvGEGDEHwc3KNL5W7+smLwOn+7dsPJ8p6Of+ZRi4UipDShEnpDRwNuozQGVZ0KffWWR6IQulsaYU1SyFZT93UEi4rD05YYWBXX7aSuOODzZV/Eh1AOKACYrAybXxMXHEJHwYS5Do9couuRvnFPp4jpqN8vlZVdMlfPIUIZksCdT1GtVQeYp1MrDx0KlWCngY36WVRDtmoYauBYC2xzEaxIk9ZuiLAD0GNCRYYVgoLoAFVvy684EF1v7HtR+EOixl5vFONjxDwG2azvX5JooqIEUzUthl7drT87NPzf/za4TPPvfKD5Sv/bvH0bJad+BuGW1ZQL1LeghLOh+Sv0lCtS7n74/jbTIaiGkO9h2mPp7K4ZDrW/VxJrXu84o9LuZUkoUu1D0SgfD1XaDvUV/ImrQaGQZ5y8moh4p9lrgv+ZC5a7NYiatnjYBPNAyk/fJfoSLC9k6bshgFxh6DdpM2FEisuqt0mxrBanirjNj/8n1QaZOu2jDzhZMDygVnS74J4bhqELmiLVfavIkdnhA2zGBQJs5vzNS+CYTZyYcvMiQ8FwoqHkzdR1qGWiUNZR05mDWeHzh3xBEVgObvkA2g8rGUj6fDnFuDqHH3KuFN0rZD7kWOgf8SbKwIaVpRbYyU4LEuYsTjp6p2CgpyCUVmBoCnvAkmBGJS4A9oOuvxD9xDSLvewzYNDSm9/Clg/wiaeUliI+MJCgQ5PcVb+Uo+CMJSPoq2rEvPTi2YTUn5pGTGnndzsJNwV6kB5XMNHOUuNNtCqxOYsmKCXIc07qjCkR5Mto5XWY2zlELkZE4oMjmCFldz7Z1yVhspM9gvvQ6ixWO4Vh4+wv35j8Wef2Hv+mad/efjyjxbPfXt+tU64ms/s2Q3vnSJya9bKNmv10Cpu6FrrzHJ37qH8ycpa92iN1I2NSj2DzdzXwCx3zKLRrMAzpGaRgb12gPiz6956KuczdzLX6seUdnHOqnk1ddk/JIz0FxJHlmj+6N2+cIZlY50WW/0zR2w1uaD15pn6ikDAM1BXuhJQk9o3QZYgB3MAoRkvpOnoy/n+6kZXHHMqH4GgmtaNX96V05G2m0/uly0XEZK2ufVhKWEBMK2mTlvP4mNRi51ovZgya2vpe/6GScH1Acghg6TNhwbktPjG3JUnJgKzhzNmSrmfQHHvcNFAb0xG+znBRcZAjCqN5nbGUD9VDaMEHl+4znwxZz86GDcZwAoFGOX3CQVsbRN3EKOLAgFuCorYOQoTsYOgFLVv/+s+sAiYEfewO7eq5BH6ZYKwiih4OmIT25q8EXVWqtKcytVUgHnER+Z0256mRgfSYMad8gqUWnyMdQstptxU3dJTYkJKve2H7kDSSdcjJqmnDKeJaxR0licUPmHZ9mIja68L2guniy+9vPjd1y4eXnv5u4tXvrc8afpITGU47E7PCu7EI888MYjXXKjt6yq3SEc8xufulrEwgSvrlscEL/h5BhuM1YAWlO2FdSstBrURSeEIos1OvvWaNXh+HCLZmFxh/hr9OSHlrd4k2JrxYJJcKsT24twlv7CqT73bn1nlP88es+6SbTwT2IzIMCSJpnWbKcUgwxo6c+wDX4VL7wdsOv0fL+t86zCO8nGTqknf1eWivubyUPGqRN2CFY0SYdZlGUVZoW3mKwrT6+GiKw4rd4H1kpVYNrRNyLVA52SW2Vco37EebwSYlDv37vANC6lZrh4KHLJHLgJktpFEoZYLDivcaFiBwIrspmC60mijaKVhAoV61AXH02HLh2W3q+ZmecBTsIJ+Bc2CmCTYxcRdgMgbgAUNVmpsMAZFQISASWk2rF5paCM5RAaoYOuhAXiaFfkoDfJOMSq4qa4ehjoN2UbQCp7KBL7b6vDQFTztBHij8DY8KhhlvlgG1OEp3uEUTSQh6Bru6VajlHqkZJc6ZZZpfKTIxwiyPG0DlYDw/AD5NsJl6y05KMcQR0occNcOlr/3wvxPPn50cPHqj6Yv/9Xi1mLe4iMxniPp2jbTolZtGP6kryWszWIVCQiDHkvunp2uhM7XL05aymKQE383e9BKQhqrnU0VDLRs67P06N39zJx15zwzHOBklkFbnDbhNY/NdBZZoXD77Af/5Lv+0Gq2MlFnI1R7tsmE+DWY7bzYgBCm1vpEMtDTd82gx9QHxuQ6a2glfyMZd6CmM+f7qwC7KbPu/luHkWlTkckABljJuKwHio4sl99EWL9ZU8bv9lW5iqRtISN0BiyjrEURg7qZTlXt6MccgcPJ/N4jsue1xD0wcAtQmFXlltBcPdE7FoFOqcVKj/cJwNOtKjyoAqeWXIoQTuF9giRTmeE0VixkedDwCJ2ye3Dt2jU6woZE6eONBcwy4pDcSHBVp5o3yvcW0AZvHq7JQBIxnArCJrld4oIGJJutPAm9hZUnSLjsjhZTtMcwQdhtM0H9Qsek6BRgOOuNpjzFr0gT+2muG4XaNAetExvIjyUaeoihPNXShY8MBU+pQ6wrwXsD3Nn4DqGr2oI8IDlDmZ23bj768mvL115+4cd7r3xt/sKj5YHnDgVxbxZ8YuNuzsU8Acl3d5XSrjxAeL6zPm7sz8wUXMVYZtSOK8tsVpx1IP1bJa9Wz2j6K9000rJqrdXcvE9p3vDha41NCiv7tUwwFDulUsyY2fQmITaOTQej0cDzd/uad7vKcx3gs9xYI62Op1thEcL5rsYy1synhJ9p82C0wJkbb/EqWw5RMLMxOd9f3a5RWb5uHidNWsr/iYHT3gedidtrCOQEGkyiJqH3Zex9SfpuYUK/RduCEE04Cqtx+EKIbDKQMjiKZzNtSnflsUfgfJ+/vqQprbXNxOlq5jrjsZGams3aMb06uHQorBYuOBSQh9Cs1MT2psS6QrL9hai0CrVoGPFJfwFnBA2QxA1bB/fv39/Sj/6xgVcFBNtuYlRzoGmQOdymMG3UvQHwy3yP7U8vtQEpByNtjSH42GnlxcAtEeBJ56KfFTRQmFkKGqRTnnIKQIgZdD8rbUYRWx0EbYT7ychTxh4fH2vRwtEBMfqGInQUojq65vCdb3V89mTxxZfu//7Hb9y/8tb3Fi/9RfkjMeWn6RvO/1BfJIhVnpAVu1q021rNn7vPe31HZOEUajRq7lhCtBH8ZolGFcSt/ES8ccTmTp/VlRgptVsxzkC0K6U1VFt3NBWWNLJqPMqQBs5eCPxivpjuQXOvs5DpMEmKU2ZEk+MnkHph0i9+RtlAI9f4aovlQzKJ4A8gTFWeZQdk55lSDmR3w1xxSbkfTYuELTp6vdtJDcqVepqNTDafEC7NmLr0PQ+ohQy9JqmF7RSjLWbWoWlwruLLiU+LLgu2F7V8RNZTScPclccZAf760t+1/utLXG0UZpX7lFrN0dGyXpSFc8dFYg3TMon87Th3n95LDLGuCxoNpGQytI1sBOSotaBBDYFbN5agbC8VwCjQVYgRhqjDEUUwmnUIu/JZXrzJ6Tqqhzx+FUbJU0Ja6MLHWC6FIU9OE5AsZbZlS0iVzmo2cbZhg2GCELU31BJPWQwYYy3v1FlNbnjKcQO9jc1f8AgTG39/tDCkZZNvifm952d/9NrVk6dfe2fhH4mZZcmP38u5GtZ7JDY5b9ArCtcMlJUVt1hZYiOnde5eOGDd243acwEDammO+TMC6Fxt5SuTMDB3X8/UAVwBWayBhirxF5iy3spQtlqypNHzROJtE0caubSfU7LlyVLtCJZP2QgziUM+O5ZXsom1g203O+UEApaPZtEwufUVb5fwaH+qKcJg5vJMW8mvugxw3ie+t+SvOe0CxrNcmmamJAaZazFEqbbJOTNJ8E1h1hspubE8XHms3YatcLcavd40BBCRsSfkKsqmwKxHxDM655uKXXksEbgyfXhvw6c7iziYu0Jivb0rz/bddMplBwgucd2wRUCD23oHgiE9VFWGpiU6WPdKQfEz7queYRSgNR/D1NhujIIqaGpFgZpCmEYxtBEntjbKDBeQs3oPCh1GNf3Uw008Zg3hQtmunC28IUGeIn/LQxo4DGFPPobEXZlu159TyVlq/MWLmNwGZzHESngMy3tcQwd7y089tfiDl/bfeuWZH++9+u3Fs/cujzwNwG12r9VZs2E6B3atDKyogSrLw8nd+RbSQ/6u6kF8rNNz18xNma4FUNtRskTAWCcsmlLPyAwZYnG20euuZoKa0vYT28lQGzB1MmbI8tRNHul09zwTVSbtC1UvBJ+f19p57L+yas/f3Z5n49nMaH6stv/ERCoIo7zLRlrofLCryXQZTtMr3aueYZRhkFZzUW5mJnL+iikAQBUGa/ooV5Bl9hmd8s1pcweeIpgxxPSetFtpei6cJvLGM4trPM+3dGD6JQyAhe0FAqq2R83ofOEW+cMiuBvdMgJnew9+eT8+VdZyUCamG58JjHx34FWOKqUW+crJPnOh+3REQ5V+sgK5VenakqGtJO6KVKU/DUxGpUWS+K/CvEIUhiNfZhZkxmpiayxV0hOrSl7LEdU90tZxsY2uDR+VsKqWfpylaGYhRjf6ESrU5MrZWKVylv3c0lmJxfDtucMUcFyCFqKHFU0u9Uvny889M33t2bPTp55/b/Hcv16eTxa65hOtI2+jZnVbXVSm/HJ/72C+4Ld5SOJpRmYGNW4hvChslYMOM5wZ8mwpNHkcqVZeQZmzA0ru0TAtLQAIZ6vQmZ+WdC4sCVSaC8zMcd+/ZMGLPbuO+eQMgrnWVWywBpMuf0Emv6RckXVRoM1Sknca1zhWeeeg2LqyqDJVhtYzYmEw+FYiWYfOJc3lLH03VtoUjQfmsoutaM/a1WGmJOAmM1O5BRtrKFTRaYqsGC9pOg9GfjsomLx/MnnClxdaFSGlN+ebsCvbVduLwNne3XdvxyOMPnaYJeW7XEbxyZb2txKjKLESGFiX/Q801N63LRnaSuLOY0JFv849Iqv4KsSIaVsRaEo064YHHyWSD86WCAzVrYA2FhlOCWch8Jfi7hbfkCD5eJxqg7yfjLu78pf1gB6cwuX2aWsP05hQbHuM7TQEL2KRY1GTq/lNJ3e4s2jg/Or6dL+TLxLmOQS/xaFfSW8eLn/T9Xz9yvx3np39wUsHV26++oPlyz9YXszWvxGsqNCu0yKvRbvPmBZqW4qs7v08Yc3wzP3vqvLcfct/V9VwWjq4nlK3RN9ZzJLXLKXDqLu6lfijtOdy6OiSZ9K16y5m15xU1u6yaipDJfwEn7tqQWD27GOy+Tp23faU2v8ZsIzjOSfSrmY9jRTTrBFnk7dhKp5jKvgJN+8d8JohxJb5m/ksE9lc5+mtS67E0qbJW4+l0xmdv+YpvAXQLXg8LAgaETl9luEncnoj4L4lKxwlZibTm3uenbGGI79KGePQk1B7r4fXxq115Ip2r6NGYG92/0Hbj8RuMKzUiEnjiRJXLU2uwvIYBJRsqAsx8jSthLJwJUeG6MKQPnHdVUOl2jKzYAgBDMEsSzZw5CxebyVxx3DELiylaICLgOqU35VG+WP4IAGo+AxGy+Sp4C8hxtNwFmKjj21kNippIwC2NmLNMgV/JczM6n1O+DI8hW2GoV6Mssm3uiTkL7V+oqf5LfvbBm0bGQJIkZU28kNkmKN4NxJ6wt/ggAd/EeYLNz737OyLLy1fffGFdw8+9rfzZx4t7At0NxdLA+xGri9NffWjyj2bLZXHREaTdKEng1SJLPu8++Xs8nBbJ2qAscVgCWIlkJDqQBSOJOlFv+dbYxqqw4TF/5+9N+uSJLny+zKzMquysvZ9y6qu3tCNbQBwGutInCEf+CTpVY/iZ5B0jo70YfSkNx3NcHQkEdQ55GhAzgyHQxIYAAOggUYt3dW1di1d+5aR+v3vNTO3cPfw8PBwjyosVpXu167d3bbrHh4ebkNvLk3Q5PIZwJ7G5UrLqjU+wx/CLAvNMIiw9IC7MfxzbeYFYVPn2KqPEss5DSUhWm+Tct8iDSf39S9UovGVfDU2zHUOMdBJ+XQMfRhOefoeSQJZXdXiEq/vLI2flKnLvfhngbXQGJT8dEBxkIdEaiwgiUyNKrRCY2HTORVv4gimBBd9QMPvS98RePls7Hfl5xdPD3q+zjZEVk2PU+VIFeEAvvN6sjGnOoRQkOk7e9rT5xRbZU+K3Cl8BONjtUoMBjJM8qZkVW/bjEfT3cYI/5gDgNJsVq2trxuShKZ6M9IDisv4iMEOeGQd87p5UbJnViPxl4KbLqe9v8QERoZByYDeq3iUzJtfOKLM48Jl5FNwxHNc/KLMr6hZAroWc9Mdv9bX13E5twcH3d+E5BGBzX1b3zu7/Y1zez/feIe3xFzZ3r09a98yiJQXITUMpyS/b8Czp2Ytmr9ZaSbOCDNQz7tvv0zPzGQt/YMhPewaOk8TWbDcsgZvpajXOTUpFm7DQLrKvWtXPlqwJ1njeLEp3YQyDFWdCqx9hri8vcLOClEhDMjoIU1nLUt4J6UGEFjjEC4tWXI/4INltrF4Ml3ID21znVyaZ9lmbpAW8UqyHS+MajLbwFQVgbUyRgy2ihxy8uKc8vO8wQQaaWqWIHWORl3oHh8SqSojtOFaqwgFUPVCJKH3AsYDS7UBDpy/P80dAeL88MljdUnfhX5MiYen774lec/2qw2Z+aUCwtE1xC6PIiRT8M4vFRzjToHBZWBa0V66Fwly5sQdHorv7gAeNVfmezzIkpp+I5ukYcMQAU3yHXAf0eXBzVvRTh+77zl+Hhh1/QrsYAw2eMFrAB89HCmMM46zyiRQXPnAOyvjwuhzf91lV43NkwYzTTi1gBFoge/5jY34iIP0b97FYHC29hqVJhgOro++eXr0X761vnzwncujzb8aHZr5h07zZd12aI/zsMc4hqOW0gDGs7CORYKmM8yTqBU7RkV83r1JytxtZCwMDO/HZmHmbeGypTrKD1sWTYfJLrcU0pLMdFny2pJhApkbnMyu+ur+k1hPD4SkxD/UBaEBo2R8xJKoaaR/Kdd1MvWP9xJHmL1qJwQp+5Rp1uagYSM+OQfvMJ0g7THLdtNdp/wIbTKbYjXFwbPkjNH9Eom5qKsMzK37E0HWYMFxNsNas4c6+muOS6O771WZg3j1inWfQAspcYSCmgXdaqEqhthUC4P8fZkzAvz60oNnejaBPbFbnpAMoAcpfpsZwAV6Pu00IFlrOYIcaAtGrEvGDK4WGD9zOpW8KwFIxgv3iKdVvZXnOyYlHk4wMXFHUCqQAqPAjw0+4KR7WzJuiCq6mn2bVWnyl2U4uQyAvw0uz6qlmd4j3EzTY2ty2QGXjLMA9GNf4UUg8ns0u0EUs4uBwXESjXvqCWtOg78zzUyPEtIcyEX1DtMR7R/WqmpPLvvATgbjb20Xsybmq+SeNb0l5rvnVs6eOXNl6Y2/Hx17/nLiupFpn9rj7NfakzOWIUDbvG0Wx0E4r0YkThLhz8wsKHdXOjOWerphclhFNew0UyfZ65RTjs5c0jWFp2vz9Ey6Ijn6qwY3NT9WyAsEujxiBaoWElH4o11gOAWsnh3jB5vsqfdoPyY4nUwyyJcKjUGth6ANQJ7NAE3KOD49y0w1abMlWUlxdFHInooMdMGys7BaFdPo2qkapUgdU65a/k1TFBbqctScleuxgrPyWAiP4mTAbCql9TIg2Pb79F0d8urLvh3PP3+8RcbJLsMOgkHsL+2zQbgoDHoKk6E5/YDAdy4UUaiyYWkKDVDwgoJV5BUcPU/oRU9yGWkYjxZ3Cjy6KGAmBVDxVaiy4jYhyAucAC0NRWVtKtCSfWFkyV0MRilV95Ej/pYGgUewfRDm8SJZMo+QWt6Sy9D4iMcvyky9XCu/GYmKZoK+WnEEvxjxCMxdBgbjZri/UM6pFC2MjTzHnVNgAzs2txwbDGko/egCk79tTMUpLhIkYXn7/IGt72xuf/XckXvrb1wZnbo82phsoe3nk5vrW7SVxx24nmJWrG5kkg/Y0U2yY8invG1WmTX0DYIWmruTRvrMMpd9kLvbFoUayzujCOFicvepFtpSEnoAZ6O/U/lqCCxsLSRIW9CYnQAtFdWJNVRd4ZPPc0oXbqPR564fQfDOdsUyrUkJ0jgN/kia45Pd1tWBLSH7AFxlzLjllpeIt7OHeTr2AAAgAElEQVRpLzDmfE3VEnHioaZSpq4g4pTFTOuZA45oSt/dGAXeYiLAZ7hV1eojk5ARMQoAxdnyKrCIrWkS7Fy/P84agY2Vx1ceK+ZsrBTCq9xzxw6iXbvvQMAm5fuUesuSEBhn0uuSEcWm76p9659JSBtizHNdGOzPgaNoVmuxk+IuJ2uRXDJA4bM4EL1JuvBX1yteoAYoSWlfxaZ52NsrghLn3bepXClSABAnI91ZOmOqzcRuYVcj+FU7yqe6WSLwwcExx1ddZvBNdT+X8DrDqaN9rHtH4x1et+nlDq4h2deLDryzsjACa2+6M7fxlIJAP1Z7eVZdh3Y++0fnl795ZnXH4Xcub5/996MDoy3Jt5KAiJjzLMtZc2YXy0pVsCYo5j318hLZnEaLvcHoXnL3K1eunD59Op/C+UWJO4A/3ukypt7lHjxNIpRxmt/Dq5JOLU3F8NDG5L1r2nszwQW1C6BswIrqydPRJRYbpY7bvjmTu9ZZGaKIZ77ZisEgAUpBTYd1Z8Kb3nAAaY11snO6meEgUFbY8HZLFHUVV+o2C2N/Rhly9BCYkLIHIZ6cqylk7MLbnwsNlYQ0QN1exRhD4vIaRkBYlKLuUfUAcvTiOTtN4gCF9iocWgqhv4faRGDn0vNH2XdTiW7KdNm5qHpK7ZmJh7+vrRnhnqGxG7qugXZ8zMZmCoBn1e4Xx0khYoDhMiW53D7pgpLiEshqcl36TJwySetMeJKYhSW4RK1qdh4jLKeKq17w3wM3k0dOjK6Wr5TpILzKgsFVZAPGhwXO5jSMLeQQogZpDU25qNcQ9o7mSME8jvhCsTmlJ3yaHe/RIzQSf5/JPYqtFYWD6ZrECZLLcn7GYVNVsWvH9tdP8q3T5eMnz15aefNno8PPt9q9JaYqq2dMKWW1oe4Dvhj1BTRNufbraTSt2hsEzZm73713ly8K61OPIjHBYTe7xnhQUEaCVsZ3IxpaRb72+bzGaXO4xutuLtRyaTQp1sq7awkypNsYs8+sJtBmoqWr/tB7Ic00yBfI8Ml6Vtps5gZAvhoHyBxGcG6YzfWB0nepwkZZITfMXI5u1lj67kiZEcmMSTl6JiC0GpXg7C94AH1CWoLvWXvK9FMeHwFCRjgUxgSEqCacLKYQNF8YHRDMH0dv4+gVD25oCKGONdH+vkyNwP6VZ3cej90ohIW4UogkRzYv9kp2Z8pUad0ILNENj7Wgy6vdRE3lynW5jykjpUrSyBEh+E56MI/LSKjqavOs6lQXROBWtiLtgyiFBsCHBUcKMeo9aVtYZjY1MA1eu/tTJbwqgnkSXOJPwXeK2+8dzWgGqHrEAKi9rqtSzo/BBtajIa7r3GuOyUicxS/eblTrdSLrALx7eOt7m6Ovnj14c/0Ll7dOX1jatc09BW2KHYR1YGHb1k4cOat9GrKZPuxBS1V+1Nx4TvY1UhWNM+Xunsa5bQzgz2599u677xayWkDEiIGR5kgLjrlIlOPO3R+lniDCpSC7O5CV8F1MnyrCM+W2ykTn9ktwqFk2SW20vbW8xUPv+soqa5f3q07qpXT0rNLUhq7TMNBUcJGiTNNCFIRBLaHYpGlrbmRqeXY1MX13P8Ua8Xa21VdQzLuDn1k1a43CRO2wxU9jVj77nyoKrIJgMa0HZIovGokIfhMSDmFFMSRB9DXTAWAvEmIBpppgxFol9kKoqf33pSECe1ceXH2o3cp3LuJKIZZskXnayhbmN8VzZIPYDk0o9RwaS4a+AZ904dfGxgZVUh3Ps4E7GN/AknThV2+J+0Cpkne/1j4rHguquEeAKA1+9tXUewc0GIaX3gpASHOvwWNJv14jfzHeYTYfyEztL+yhcyluGEe8dpen8qao4pEzJsxwQC/Ry71OprrXvgAlJOoIDutCwswDHFkffe/s6INzu5YPfuHC9rkfjPaPvSVGwbd9bx4d03l9y9TYTmN/OtNcFBP9CnNvLuFjzCF3r/ttJiVoocjvmJHJhMuXLp8/fz62znDOZ80MbJ1IMbjD4Eg+u86WAYdMOSxBWkAxZdIzXZt7E+OgcAijJNTA0fZID7Pz4Dsdw0qLA5FWBMpfTQ1IrVhaS+Sj8JJl08GQBplkjRpvoCqMKbT26eY6i47JxwJVC7nMMDFNl5MVeHfYJMpwM94VxCq1mKa7sWpWVAo8COrGHhuCLFGG1jIQF3lxelhFiQn5IQwbQ4YQoxcSCzfBVXytytFhtRWVUo2G35f6CGy/eDla2bm2pjCyQ4VgVmg9qSXG/ukx1fY7e0XYFASS/Z4autBItbSfTuGf1mypiiY3hAh//Ji3YeoRGvBkO+iaFIRpgqe0I7a3xB3r57QSCRRP2gCSNACCQkne0A3DBSVpcYBuyFWXWuev4ilCkteo8wFd9Xp+XSUJKcIl/BBVdLmnSThVSnI8GUO0e+lchCeZSekQANb6LG0jHKsgw2t33Fmwk9LGa2h4Y9SciTuPxPzhqa1vnt2xefLUpeV3fjg6Ntoq7uuPeWEb3himh4p2SpUgXAGhKDC2B3t1uKPp09jwvhhOkUtW7s4PyeodkUWQlcKRX9SV27dv79u3zxeBuvYpOA37ReW41mNNYYw9jc3qbOjrfZ7ik5oJl0ubKGFiQwvpJRIT1XowyjX9IcRNtJNAW4KWeV+k3Y6AwLwgYox0amLyVcowVgEZIsoIFUZSlVwWo7U6dI1SNCU/JlZlXCR3eCJpQScjqRX0ckEq5Y7zW806yjChMRKYM8w8CTFhQaDJEWx/Fkt5FBE6J2QAFCOTE4LlS4e4jEBGmnBZIJN10eRIj7iFVDEF8AKZh5iqeKwS4FKTmn9fyhFYfvH46XOS9zK+tk5g/Y47WyeFTb8h16+VMBPSdbHn+rra+WqBQWFbd1jJPVcpWYJwCpT4RZO7VqKZv9pP4k66ia0trfHp4YmLw87o8wc/Kc2i4Eozqply/lai39dnOphdKu4FR/faq32pa+M79rQhm5PGvfZp4xrdU/d6and30I5M1LUfkx1UJBZ0+RRNmAQkxx0Aj+Ne2qTpSU4OwDjTdEu89DSPxPzRuaV3Tx24t/HuJ6NTF7fXtZk1FG2v2gwbSCY3RdG2idqu6XKitHguJHRUVAhIUFV2anKAHqEjfDSWmuavmufuvlx6uarcfe3lCKBZOLfmb9/+7AtfeK+ZrLk1ZIdkKsMXhdGzItMVHdew0ZgvDMjAAjkb5CJydbPxz0iN9a3HPkMpDHAliBQdostqYpEgIPxWE1JDWzEtlPcrswwywuam8Elulk+K16Rm+OCVCC0dLvSGloknJInJzRTUXEQuWgtLJA3M3tNmg1EESxRAV6BhotVPVROiyWdtpQwdGsOLnkBIm2kESQC9IhGxKcjVONRCFbkMkHtOKZHULMg+MkWvInsC6FXVRaymAIeKV71B8n5fxiLw9Pnjlll7zsZ2RpXNmj2UyFMdIh9wjUh24eiiAJMhTO1PbPOCEBPQ6oF1xPZ1tZCHK8H9JO44Vptu0hN5ca0eKQ/B1KglQxOAQI9+wgwK0MFPnjzpoIKYYCpHeAHcU46dHe9gwythqXXcu6xztjqrI6ij4xaTuGMb3Zo62t13g1N39zhicYrn9mZy7eCu0Z+c3/ra5p7VA2d/vf3m327v4wnctiHVNmYb4DQGnM1o4Yoq/Bxr08S0au9FmM9Kjq1UTiayfCs0p7SscN9alLvz20x1z8zkgi9euPjmm2/lmG6wZ4Ehy+smogWXJ5oQegRQZ6H0w7xRHdOfCZNTyqlKAR4j76vi/lhGOCbSkro4vsfc9YTRZgxWqslzSS0R28tkCXronV0ByZlPJpy6cZhzYccQlW0dBlicRaN/UAczsgFsGJuGRjFmdKUS2YN2D2qIbYU4EOnk8o0w8ARXLG/GLGjqMYHAZbgLUMY46cyf4RU3wcjKgBTNKN/MSdpUS0WcsjFYkvCGkYmUuC8rxKmAB07LArDojD6HY/it4Xf+sLI04m47GXi3SHhSRJi5J+UptSf03aRN5cqvFiBmMy1t0GziXnxU0NrZHncNLf4wBQJRp4E0X+khccdDdxuA0Htxq9xtjhDMb6vLpF87B7FDrJJ3DbxVxyF2lzG1L8cbDOjWhGF0Vjde54I9+Q6GKjIp+F51HEqa5lHXnhdFc7o2VRfzEBVeIOYdIDxOV+v4VFGzEjDz29x0379Tb4n59tnVw0dPfbzj3Z+ODr8YdZrvtduf9jOlGrYzahQRCjky14CySNhOaVAPwlzOpCM2zzpUSmm6BaCVz1O/q3r37h0+yfUPcycZ3B6vjK+P3sg1pkzdHfb8MSnpXV2huiTa1GfDpCAcAkKbL1zBX04TO1yG8kcRSahFjMLH11ZHJJQshjFu1lG6zWxOQqulSwrVEKpi5J9PMYPDbJMS1jrRSqkXeEEXFBFfexaxcbsAu99tttRSC5kRWmosw0ORFx4csyEQyx1A/YnX0nPMFhhgtRpfoAxyFAN5DakaIvE4MrWOA1Jgigt2N1x2mLRQRXcoHnfva1Ch082uKhw7w/yIEn5nzxvLL+4/UY/OUwiyp3bMDl8Gq1nEPPJLvGzWFJBsptwLA0hdD55N1o0pcXWuIpDCGPPMwVV0ltZlI0d3nq75rU0f9B6I5H9ns5oZh5afay/pct/dfZrcawAcp5SIczmvJ4z97Q3Day+wuO/uMsOxjePQwA5Le43zULqFbQybqiXv9ESM1+5+UtHvPE+KqgCqm590/+rxl9/d3H7n9LEbu965PDr54fbOpfmizgAfHyps9TZyZhg+VT8KzJgY7bSWixTtA0KawpPVKUnKirk9bmzWOhX03H3t5daLyjMzjKXLly9//evfmCqkPcGYoe3ZIqUSHGa6JTouaiw9jGTpLOLOF8yz28oA7K4uGV0HpFwsGCW3DfSI1LFkOB8wlkWCFUuGUTSZTFoz/BPZ3G/zSBPLDEAntBqeVN0EQVnCmNnllJkVoS/A5BrGCIqKuANdcllGu+EFXQ45oeX5wWBvjXg7myPg5RMKoky1yS9OUUBsTZSRQ8IDPUwWChMVkS7XpBmp0YrMDAgdoCraQr5urUU12O2BhlABV5F1djRrJc6rvhKGJqskMhf1O3hcX3566xF7zJRHAVtGhsWQQmjJcckZ2O8oLXnbk7l8jhR6EBWeUpu2/tW5YSgqXZygDmfbm+2UUxJ3XCJw5lqYB7Chm4I+ju7zwlIW1M3qYWd6dHHZx9HfKOSe4jJRrn0uqLOinBH5efVVwTjrg9i9Tr7T6Z37Gl7/FGwxTqEOFzpY6xcnHJOdbTodRYv8LKhW3em9o+9sjr55bv3R3i9e3j73V9sbMzwSk7xN27W2NsPS/QEqiDpD7SZwO6rORlQY3VcSmNBidbzu0XGXTO6+xkTQd1XHNrkPP/zFnI+2V3zKXZnUWOCj50VPh84vEAXxJEgjJS/jtbylF9jWJUkqqZ1VuC+6wVjr9XoJygyTrum+eUhrU3ieeufuG59B8tyMPfhuia2GH2J1tFwiHUHpn4XXd10ZmCFVk232LxlvfmmPTpiJgA39nFA8Sp7NnIlsLhkVJcoCLwGSEuUVlMYUqlnrWCqvZkJhQoymyL8D0iicKiMtsDLMlHjWbjXDjeNDz8omCQ4dQEy9CGVIC3OEPeiOj7AR/s4ddi8/u/a0Z6+JvG/f7MV9vdWRucA2nQwlSciTZodJG/pSlxRVAXQldeSZOIsxHKuUtZjgg8/t5hy9lh8n0VfbNASyWyrWbEnyPc/VYCGIqOO12UO8pbvWpJIBtTQ9InEQ3ynopQC4cPCUNLD60ojMpKIvmQ1ysD+forWUVd8hc8dnzfjbqKu1oRuSSeef7sG+sbb9ndMvPzi7duT4uYtLb/3N6MgSn8a3LdWVIvLGc5RkO12stDmXBbThMRoYSSR6T5pL+s3z6L5GZ5b1dDa9pKOu+qKSu9+5c4ePUHgNcB35vDh3ZVLneRAaktUa9S2Cs7DJ7osW+11cvWrsraJCcqZhJsb2vB3USYVF2U6KXcAss8Zq1K0oS9S73ikc1Vo5ykzllGq0nF600ZhiXR1P5U0iorTwuuyAmXSqhtH5ZL7+JpVIZQlyRik8nGa582aUEphVjVCoPE1Hlrw2shyvO6QJaRm5KDMAOXJGvDLKm0LSH8Nl3vrBLFFsZbzGgxQEiw2mGjAe9VARpfeKWS6TYotz/64c9+94ervy60t9Oc/eSg5GqH1DL2XbzVrgopDIORm9w87e3EfIp8CFOo5ebdYyT6vLd3XIwVkwUwWuerLvdC15cqHoa45CTjw/TH45a0aVK8VaChjvSGA3niOl2qPspiTuuYRB4WTPQFrkfEzTXQVxmOT7EDagawixbWS67+4+R2fBHsZ8td/bCKzSIIrxybHaNARme/TynaMr//SNl2+fOnR957sXR6d+MWr+odNq8IlDCEULC6G0ba2OtL2UOu4aHLtn77m7pzXucczWouHasqvxqTGsFxS5u94zw0+T2IdCn3zy8Ve/+ge9SJ4ohLEuX9WJ+J7IDCqqWUsi6QIwxSwl6sLbgcfSJ7M9cyWXkxYetce+NzCnaoDH5Hq2lpaRBjZvCtNGJx9jnkKqMdx6X7an3lP2Hsa+zwAdfadSx2nLcs1aTQsogCZT3o5ZZ+7HhNVtmnA01vIowHkNHrd9AmMcWjqbukRn3Ba/iFcFjzgaxqpID4yh1ZprYONSQByQaUaFKEPawJM0H4GQlQD4lF7HADlBjKnNDhOlCEJGgT7BXlXdpXgbR5OZaoHM8L8Lh43lRzeecBdYsRqoEFJ/0oF8muLJam2c6RzdibR7kRB02+KTOnIkf+CefHq4zT2pw+w26vSuw86pMOFrc3HQV0f65GkvzXsOLi/exxwpLXM1JAzXVVVHsLOveEanNYJRRBWvAXCHknqcIZLgqj29YzCjd5m1AlHkvc+llzuuXrfSsutrxTYj6TtfUJrJ5m89uWf0x+dHXzq959metz5ZPvtX23snP8Ju6ShR1/bUT/D7kdIiCkpSbB9tQVsmiRtIPJvzlveUKVM9z4kScjiA98zwsDsL6C8++og3yfg87VGdea5eRyZ/PvVUn3sYfPTRr86ff5Oh3mwtephw86tr1pJaTZ3GufyLzrvv+D+j09PHuPyyFbWlaLOomrcbhlvvRGrkt96RWzykZ0pQRbOciN9VVdV70iCPcaCxDqeVajn2hgoh4TSx1M06H0ZS657UM3vcUGxUBWWBl4CAB5lTmoYsm7e60TpVkBnIFA9LzolDiYhwObIRkAOlGGVVl6AIUsBT3OOxKhUTkppcJkdrkXxv+q0/rm0/fTHioWI98jHcDuth9IyFBdOXIM+nqVI87JCR5PT1PDPyKUgmg/eMF+HD9azys/iVWc/ga+M5ZfFtHnA44zqayfpqJXBEsFaa9xn2UBIBwbUgdI/ycN2TjMwBjO+m0d3niDSPAHIouF/b67nSRcKYNIS65H7qffedqctMW9iTTihNBgzh5vqO7f/i3NYHZ3fuP3zm4so7Pxod2mKPTyEF0DZimotZoGRVpZgWVp12mECuTHpmWdN0NbQrWWmXu2toFUYn/wtUg5bU5BNw0E5MugD4iupnV69uP3u2f//+HN8BDt2eOEO3l92nPuco5dcB13etX792ffPsZtI2CVAky5ZNop0P71pMhgaC+V12vpWGWZhcTRh4rRhDMDhZhuonB+Otd245aA1PI7iwGg2FmxBoyCMhA2jXP3W+zHGCsCO4HGNRIjxlFts8D2Kc046JT5oLYzIKgU4V5mNGKTxsKWyRkrNR6WC8Xk20qio7tzandA8ivkxgxAnpgEQb/RhgBsUBY9oVITfUq6IAytJ3wuoFPEDqKmCndHyCUxXgt7Usv3j+9MXWc5L3+JOoJB6DJocunJyQR0a9d/ytbgNFmM5NFwzk01TJRQd1UJcL8YKBDMdzueRd98SdkA1qdzIxAXQPGjniBsXx3mfEkSaKT57EMg+Aih6ltbEEX6aSNbj/WuXotY4QT6JKN9W2tkQy8Lz3U7is51+LSxS6gEsFn+Et3WlD9qWjW//0ze2zp47eWHv316MTz7bXlriAZbBogPrOETef6SOoRmF7pvaZdI2aTqiQgma8nlQIYXusE6TBkBF2AZGjkKa9u4uMtjyMk19duvRHH/wh700ofVe1LCLvIaUHKn72NSNv99ZJRzmYZRuTyCbh79y+ffrMmRvXbzx7+nTX+voksgKPZQQ0Ds8CPwckacau/nfIjg7SEghSvaxrYkOZsE3dLZjFQ5kdOk8n98bTSzaw5ZHSRO4e2gxPFlAV3xLNVkyAvtlqmtUaAcVbQuNuou4WYxKl+EBv7Rm2aI+QxMQIR5yfxRZugY83FDWXrEdsRGnmWmPE2zniqcges8sYXHzAmAxl46JS8zgMgjZNXEUhiMqQ1moRgSKRFoB5GSWYyuyQTxafO2YA3CqmUUeKB9yRORyavMEof/sOz58/fvpSXYOX/jw627Tfnyb77NHftPsjk32fJdTvrJMS0EQBM2ikUZo76Db06GBJFL54OoGDXDCkST1X4j70vUysxFwKAA4wDvAKwIM1aPegaIgMrNQrU6vJfY+AHxfj/lTbOhAwh5la2N+SNx8AzkKnU3woA7SUszAyXPNR2pdGBsB/+5Xt99957xejdz7mh07DDyfFbThsyNqPWmpsS9cgbgZtDVJmaLJXX0eX5Wp0Ip5nkNWCFPkMLcZeC9q5SP7hpz99/4vvj3bu2mHPzEz6XVUN82ykJ7s622fLSH1C1uwP85fMkjF54uSJy5cuvfnWW830odU6zBKjVuRVIvEaVkMPadHzeC5ziMCDNomizDF3HZWzeChH0Ond6jk7GKty653Rx3/2PUJNpm7E3mjeb9snq1GAaQ6puS2JYeRqDENjESil8u6tRVUJb7PzkEUxZUI4LZGOjpTbqbvwPCl3ogJPXSqMUgmzJc0WGdEorFIjMGsdg60teSNzTaArNSaPsMu2rD9gc0yiMUAapTuExxcEP9r0cYO0UHiBVChZKbO9lsNjDUb5W3NYW3r55OlzDdNYcN/3aLZ7f0PLPA+uIEQTwcqk5Ac8hYBDzBG43wuG6Fk4JwcxzB9o0drYOrEpSWuuogKnnMadotoxcSc0caA2K23bikBKsi+xEQvvcpr4nuhAoUnqcoD+4HPhHDMojPs+CABcERGm0FUcB1W9SOHJu5JS8BQi4ECKQBoAJfqWVaK34AswOgsvOgxUd5xxDpC827e29bU3j/z1yL65WKBT+xRgdo4pAmnGPHzMjZzOMwuF3eYzBka9pR4kMCBDDjKLqM608nEIjVl/8CaZvfv2Hjp8GCNfrNnz7vF3VZUuZKZnwyHDzgcis0Mn3rp168SJE48ePXrw4MGBgwfvf/75/gMHWhoyk8Z8wYMxRSAPyxS9Ih12oJYNcCvVea3MDHkeUjTOw5/JHG2vbDECuFHJDfUV3qTAl86R6XOOnJ5/YhCL78M+XAOBJY0+PUUjc4I96nGbUcly2dBmLgdlia8AXLRya4rE1RY1W65sJAVZ5LazdXqVUp4bM/Kz1hxGepalm8sKjs0j5zUZHmQFJLZOBII4sbkct9Sc84kTQm+YAKOPtoiRUe6SxT+0xM6IhEb9G37Yu/Lkxv3nS0s1r0Zg/6WwIc70gkWClO+DSGiZ9xPV0gUD1UFDzUbvez1pBgXYDZizS4lYigAyS+6D6Zi4Y2Jn++gVSkrRPKwcKQ1Rhr5k/ZyhmcqORkyaStaBoCECC/YRSzrY35mFeLpGHwA+Blyauj8+79RZfpWROZ9em1htHQLDGPYJ3Czcx0CanBB7BEpT4IsnXn62ek57eFOhE7XBLKwvMZ49av5M2mZXaYrhRfQjnnHdMpXU0BSL17aN/n308OHeffuuXLly4MCBixcu/OEHH6jfrPOe8+4CRo7l7ovpSHVinI+tgra99PTp05s3bvzZn/3p40eP/vv/4X+8evXqvv37kdOKXQPUcqkSuUYug7+QoRFQoika20AFc/QRrgLZRkR3GkzHmdYOWGbNtb4ydYsEo4Hr/lVLzvk1ds7LoxVuJerZGSdQKmrzzzowVEJ0rU2iFNDQM+plUcknqZN1Y9GwYUDjGLIagQa3AqdZKCvrC1Qp3bZBH8iEB/TIGU7yJEyOGJiq1FyGcm5ac8oKXEndzXf54QBSzSlElQBkSzo2qU3mlYeuzx0fYBZtRVWA/Zdx8FicU6uJ9JaxJqP9DT6cXv3sL24o+JMKiaY/XsLOSEyUy1eenyEZYDd0CUQMAo/bJJnNeNMQXukIJVVsaGaZs9XzYbzrfAOeCHjBcUopEyiZ1yVxz4djSVypCiUFaxzAGgjMqnk7pqRoiKpbO6dkdzxFwN3nyDCqDk2I51T3GrKXIsDU9SAQgeah+Rr60sYkhk21H30A+NGFQNYmAt88u/rp6ESt3nysaGOpJXr9kJ46yNyQQLQ3fKFehkynvwDS3ax/n165curU6V/8/GcHDx5MQ8VD8HJtVe+Ziffd+9M8URIDFZNarjr3Pr/3yw8//MEP/pKsHcYf/OAH3/3u927dvHn8RP34rNUak7PUlelKvpa8B6Qmo/ad1n7Or1MB1TCvj6xCPtL7YcLT6vQBubll6qjWUIgzQhIYI9sjMhA+c+Jd7yTvJtuJaBdxkcSbr6ZakHbamCAWSPFo4IlRsBdFyO5aF9pjU342snq3nExao4U5Y4RdZUy3zQFring7uxa3xDJn60Az1zJuhbYmfVdgy/hIn52JNgL4M0MVJ4tyBPAOMWFeJEiA1Ko/LLZmNdqsCgaAIlkGqyG5YaJTq2zMm1IliPzNOx0a3bz0+djPydX6gKN+X5IcgLtpth7q0+kUvd7zgaSRSwLXWE26ak3tjJxVI+57BNAIL+b5BcBUA7ok7sS9Kp3ouwUAaE2dgTX0EGWqKQ0ExH1OCeBNUJUAACAASURBVA3Ca5vc/tqmSUhYKKkbgPEdYvOePVtlEq/jYWkm6L11qkmzakwRAKDAbn5rDDgAZuivRpRsRjWdssjxg6f+yaBHwIOAAbMuTPt2bp88dvzC9vrUYaHde3xHKQWh92rIF/LNv6LDkpeEDYO7yBimepVYDWijcZyjh5rP2BktHdPrGdy1q59yQ+nggYNnzmzev3//wcMH77//fhoeiYH3zPg7Iqd8VzUxzA14r0wdPNxr/7M//dOf/uTHUKKT41/8m3/93vvvM7HZbNKtsnpzLIghkvSioslwDUA9Sw12jk6ITrrKGtn9ozTMCZPPj+0VMnV2wBgDfi91ewfJeq6WNnmok8ic00ByVDZR5ZkrJI3Kc/S8b6A3EVJlgB+gsG7S+PL+8pE2BsMhHm8JzN633kGZvDJoZBNjKS/0Z+Jzswox1hyuE3KyWrzJ85iYwSbeMm/pyFvDyDLVlpynFFw8MPuoMyDYSIP1kmm2MZlaBci4YFQExFGwGNanuELpEeaYihEjJQQafIJDU+wBbwL5m1V2Lb94+Pnde888TFNsx1fWCrZj6IDJJNkWyeaH9p01ioJeNKKXjXjofMA1uo84izowHh3MoNBEFcfBd3B/bO1wuVOPSbHr5ohiCsY5wHGqkJkI0Fi9VJhJwqzEfN7RrBGvUxxcuPuegjCrRuh7j1sHG2ZiySOQBiIRaAgCTXBxnEnRPMRMDHpzuKuFFASPAKbiHcsTX8mYs0O/dmLr1vTnZOaJzVy8pUxac15bX5LJylRUEnYeoKRxHlEteXFAnWiLbEuWtPa58x6E48d1W5pt4/r1az/96U//5J/8k/sPHjx5/Jg3tJTEhtx9gffdMcDW8IlefvLxx3/+5//iwq9/nZv65MmTf/X97/93//yff/rpp+fOncubSHfGgmCBKA0FIjoe11L7mLx+KhWV/YitkcKjLyFTJw5kjKTpK9urnmXWkEeUiIE9dp6qGsJyTsLF/spey77Dw+8akaJXGMO08LzRK6FLNR31zxaisD4BO9LU+ugeC771S7AlmlZzHu++GgIJtdS3GA1jVK7UU28jccfhUYn4DImDatAhyg7yrRpa8yY1R65Ao5CU/iy+yWsBzhZ4s3S/uDmCERle1popUhKKzSnCGxxwwPsgIVWNlVJTFPO6nw8uP/zlDR5w3znJ0Hx/xFfPaN1pUna89mTa8ZOE9IJnX6agEZP8Bnxzjje/Utz0DxnIQPxeHhiUdkvWc3umJO7uJH4mnnTzG90gvQNS63DAwhS5C0SZ72Ald/LLRJBuDBGgDxZsWDJp8QDDgDj4+uJBwHdmAkdKS3ugRw7HlvTzk7W3rY2uUhBgQb7Pw1xRilIbmZNoGp6TqbKgkXxJ+8dCiudm1u3a6PjnG+PQylG0SDdxpyGw2aDXRu6hr3YAo51vc7I/7du37z/8h79dOfweXqxZvTZcryh3ZyRHH6JZDOl/+4MffP9f/j/ccY+44vyzn/3Dj374w7PnzrFUbmzs8QZFQeOhtoyhRVlVWcvXF9K6CKd6mZ6ZUXpIXU+/WP6pkTDawXPqCoSX1m76iBKbBlTkBrQG3Xqn8FNNuvWePTdjtMZBYq/5YUm8vNQ8De7CqcFssoVOQVBDUZNSIcTGMRohdLm0cctlmMAyu9VdfilNpyXi7RzZqcgqsy7QqBYwIpXjnJI843C80M4debJz1qBYZ0m5ApUwrhpiM0s1dUmKqga+cIqmRTUADqOeRjNG7KEWmOuajPg1P5xZvfm/3yo/J5Pvkqx+DXkqQSg9P7OAZBp7KBjpz6MPlMWVguBLKKs+SRR9igHz9OxY4o4mL3GAaWxRcscYZsPdvKz1BJOwobZpCKRHgPjSqej1Gcjgy4MwhN401YcQPqtMjPE4ACTeXoLgIU0yX3OAIOTXKljbMggsRlz7zTNTNta2N0/oOZnXJETa6ovCsNDAsE10cVcLrn9iWliY1zOERtuOPQQOSkUxN3KwopxRtG6vPH/44MG+ffu/+Y1z/9/f/OTN82dPHJ7YucrdX+h3VRf2zIzcIetQmlI48Dd//df/6vv/ks+OClQObW//2Z/+H//T//y/fHbr1hvnQ+I+FpWcuBZ2lTTlWmsp+0NqTSv5ObNwz9TT3QfP1IvbW2V58s4mUDs3wwjTiT9KGH6eFJK5c5XAT69Z8m7eKFVUgu5HTxrdS0W2yCE9WQyLuu2qAfY0MlXcfu26XAg0jm3fmae6JWMQ6t649LGjjzk8KpEVeHGLXRhRFZSKbcAagcFG6/ICpaEtIQ/c4CMfQYIhirVzQEiEohDTdx855gtNavOS9rXQDZY5QGBBDn0g2P57GJzdCUSBoFAp1aKO1/K8++WtK/eVibJRUvAAV8hKZ82XoEcIS6XfmZ6VvUNs2MopMJLsoVe5/HzJtNuPKCJAQXjVC79Kmd/NHe+9914yHQBlWC+HYoljiUaF1WlUWVQhrN6p/Sr04DLUkE+xUaenrzziYPgsmBh4NPIg9GtGkoYBwAtQlDQC+EzDa4+DB4EjGCwh7BrLsRCNnLczTGwR2Zm9G6N71MBLv3sQ0mCgCn30PpzbB2FON7+7+fLA5pfvbB9ssLnUlHbvEr5DVVkG2wubiR2RPP4/F5ltXzl6SNh3xyE1BNm+xarCjks8KAqFAqONtnWB7/79zz/88MMPPvgmS8zb508/enD73suDB3br61m1Yrhpu2OLV3Zv8xFVLcEQSHyzHCXI3jx79h//4z85fOQID8Ps33/gxcsXPg2SapaI58+fffFLX+F+0u7du2eMionxiOZak/ThgJKfUxWRgHFPfeWlHQG4r6176npU3f9PHw4+cHxSTdUngmKACQq1BNp49EFZtGaQ02u8CumnHEBgEFm0ijSj8aphdEjVWiBqqG0UEn7RNIlRWx1Z4IE9aolUOgcmnUI1grFJ6NBk51inorqLEJwakoRIEVpFY2RG7MzOVUsgvYEeiSY0CvR6iWCcerwWGV6n8/rS8933fvl313emhMFTps5pDIysMBy5bcr+C0AZ2mO3mWijdGqeUDUGFvZ6CgBCUsLQkCrgFGQQuJswNhBXNaJllSsAvwioNlcxGDfPTcSqwDYYrGxD1kyDEAoBojilDQkdqldFUIJvFth7K0pn6rxuBigKWRyYG1TRSxni6qibkb1z4R0zhKmSJKcgADjSx0N1MCSWWQGmFfOlc1S/ubl6ZXRyVqXd6C1JD6xsLEA6emCmTT7aF5ZGJ+8wbgil2ZwPNxoL722czJB2JVsNYCRcv3GD9JdBSAbPKnTu7LmXW9ufXr+1/8iZQ7tfjJOHmt7v/uLl6vbWpN9mquWaE6kO1epn+QiP9Ozceer06W9/57uIZR598snHv/7oozt3bn/0q1/dvn0bzH/8u7/70pe/srGxwTsu0+o6sw225KbJODN7Bwb55x1edHISY79f6s+pO87vqY99QJ2IZwGalJbkFMapN/QHQQCBbI/i7ju3ONnT7C4frWFmxHDSkwpq6NAcsFlrg1qtxXy33i91hG2GYzeYS6bKMDPXBVZbE0Y+6I52jH1qCID3RboTnsgCXtx2bWnkUVjAZFWaXYbihm15k9SP/Vlso4+xMSKNklibRMLq4mLgsIYmQztAs8XTUaqFKiElzhQwDqvBqo5xSo5qLiqlmtpfn3J67c6Pr5ESyqkeC2HxPJNlk+KpcI/ya0WxlKGU2KORY7NS5ZGWpiMKazunDZ57I4r8BFEoxYxa80rIGVaiMNpKAgaudlAKS17cQE0ReyC7TSI150MOnUOChZ15axl9bHGklZhYDMbiwHDpPOZqNb6eSHz3uQHgFqbB0HvMUwSYgR7ehGkP7NqxffbUsY+2drVnccqWGW0pU9e2PUcxpfOJmF17L0pjHHxfz12oD0g9toXxPAUO7/Hjx+/cuXvs2HF+CxZlbBBvbB6/e+/uJ483Th5YWlutubP+Qu+IfDnsMzO26nAI3mmSaK1gyXDPeA0hGwyDmUn0xhvn+e/4G9evX77ML6he+Psf/vC/+q//G14NeeJk90tNqc2Utgjq/CRyEE+lGY958Uv4mVKwLJf+nPr8WkoSLKqtPQ39opP1U34K5uuCSus7yTs/2RQLjc5rbDR7lwotjyNvgKBVhwtLEWlRc1wIVBomATt+qnPLDRmjkxplxGbiWItX3IyYbpsD1hDxdpaNKiYsZNSxKmdcft5qMH5wjrKNTKTEB1GyP/COI8daRSYRIVyRMdGYapNlKBmZgg4T0RV/gFRT1TEcQ9Wlx7bxmlG/BoejS7f+/DN9Lj1Q8WyN24ukZAxy8toYj4EUKvieTFeVsvpRUkf0aAyuUXDJrlP0pp2pwmdI3BHaJuvtN6JTlXooiaYHFO2E3gv+A3Swhy0q/2ZqBwkdWJL9HXhhgT0PhTvuEWhIzbvFp5uFiQurMJVjwvQLIJyiARH3IY8DR+ZDv7qapXX29Funt64svdEsfHKr9qdUYm4qREp2ARLBby4wqw9pLUiM3eKAnCShPnrjzcyyDz/8xR/8wddW11aPHjvKINy1s7gkO3Rw7/7tHVc/ubRx/AtH1p9UBfLBTfptpmprO0xukCZ9XmftQMgYxtYTzyCYRCdPnbrKq2PeKA9I0nT+f+vb3/Zthhe9e37fzqQ6KlliAzbO3DqifnCk6Zap05l217ruFY39aKqV4g6qJ0qBr6H2casckBJTQdW8H5UILpHR2AtnyCaVv5eKhVXM4gkZo0joXN8CfKkMMGSSkLeImGLbqbQ2TByjyd0KdptqFxOOUmI5royqKWqnWEptJIHM8XiqVuH0Z8IsRoaxKg1BftbqfIHSsnNxqydEbuG1ipChwQHpMEwEpFksnAUKPQbU1KAQXQw7IU5FIix2eV+I1MVkTYHO8K/wQDR2Pb997WFxrTiQMSwvFCLh98LJZ4ZLHpILrhSNfD3J+wi9IL2PElm/gCfYZC/oRXJDxjJD4p5GW7+2TpWWIoUBpYQMXlpxj5LIpgqcSvCqPJ1qWCJQZhov/pK1BIGBBc1MoUjsSfjQAOahtC8txMEf+HGBCKf4eABIWsBAmaqLAZiHXAR2eLrsu+eWL22d7mCkp+n6+NwizP5a2mJ7i/u4cbYhcRdrIPHjyrIa/jYr9RHgZvVnXNyk20nktjSPiZO1Y7j6JRuW7sqO5a2z587ef3z/0tUnZ04cXNtRvqZV7s53Vae/I7LOIE0Du8EY49Z29gW6bXaRXevr/O7Sxp70DVTJenD/Pl/hZ5CTrx8+fPj06TPXrl7lJTNRT7ez9Scm97dEuB2WqfMDpWFNWFamvsKvkhZWSmlRWwTkg6Gdp2HM6eQuyNZQMwzm2wsj7V2R4ZUzBNOniI6WLvoRVvUuQ8M72ZZKB5Gpa4AYCrWokkcGChPXECK4TEeJBCky2Y6hSYItI1ZLfRF9hUyIAh94M0phIlOQb9UQq3KTSNJfQUN8xhsCVaQIrUUYYxChE43a6QPVzFw54jhVvcBClBVoKw5k/SLsWHWs4kyv4Li2/eLKvRe8KYsvr3fY42a1mLCwzuA62z0rkifWswppQ+8ZhVOi1N8Gk9IMz63byOlMQ65Cgb161z/JbJu4E6kFWJzMAvBI+ZWH4wkiZeiLntyGBcN4V6sxDRoIGLiKQl1uWsv7GiIxnhHp1xizmlcKBewMcR8SiG2QBlk+lhooe2xqNmmSovUdo1MnTn440kNvzUUbqBfOtrv6BusLezNv762o1naOHQssSWkMBLoFJjP6sWZcipTGjGeqr2w2pLPf/d73EmVmasIJ2L+xtnf3rqtXr+06uHlsT/mpd55338nz7sV7ZsZtyiXZsKOZ3fTZ82e8x2Yyac42CSbP2D5x4sSlixfOv/lWTrRz167PP7/Hszxra6sPHz5gh1vbufb48WOed8/JusAxx+zCG3mqmbpepj6aHAzLZEOGFYUMfnalqPHMrlGfjxzLGaGnJl90iiCjkp2BTcLfF0n27i0cnTBq821E64QPFlMuUBNHXPqXlpFS1W20KQA4OZ6ojAmrz0pjdHqOZpTLcilm7Tg6NgctMX0vWF0aHovSdoB6jGQH+SKwpBp6g/0cYSMzcgurxcR5q8hEIUBiYhwtvi7JO4f2YtEI66RwCrJ1QQAcRpS7mATKPZktg0PjWMWaF3t4a+f1H159+vDhaNeuXfv37yd9pwxtAr57IupJLVUW2PmVkld4eoBACio45mL9OgEyFjvwVYKcuC/YldLP2MYRpZ7QI79t4o7FHq++bMrlYBPyKTnSA8dmUIpgTjMEjDEL1ph7kULhZvgRewYaKK/E05Y5NL5TmJ8cU4jgHSgUSUW/ANYy62aaO//s7dHF5XerZthCUqwmWvNZ+L3Ec+IKe0Oq/9YBSiYUAZVxZyuxmMn3dtyMSe3oLYh//vOf/eEHHyQT6LLxHSG1COAO8OaZEw+fPr/0ya3Tp0/v3KFXpGl7t/KcTYPcfWlUfkekTePCljhh+BasBt720r79+11CxyN+bm8fOnzk9u3Pjhw5ihCy8zu3b+/Zs8d/WIpZSeJ++PAR1vBLFy+ef/PNjooyNjnRMsSRS5n6ikfMULywJ3+ZeiRrOrtSKNp0bZOgWdpMF30Y+20Kr8Y9JDr5auBZoyFDAqknZ3gfkSTqyRldTlNs4Nms0RZnCAkIgBYTpaaaTO69D6sUCfEYTbLPBQbqhB0HfLQnIW63kfiAdRcCj1CywagCLj8FFqSN01TxtZiYm5vXUhVCYtZFWHjE8ydyYiGERqIj/WzZeESm1hB9C6iEZ4xGY0aljvZVy6uhD2QSlCoa/DqpeIRjbbxaajP6xRxOrtz8T9dWsYokjcJj6KTvpNHAydThLElJLXrRgt5ZlbJYpeyCFayNBMgoqGNPh91tGM5Hl4xffnGSrlXQ2ypxdxPnt8/HGEcsyKVhGeFw43I8+9SsnZGzd4MZB1VLuolq5iIOFGLrgF/wwUIofrNy02Y3W7Z6KDwazkLXU3oMBYFl4DHoW5rUCxlKGcYzJe4fnNv1n7eOo52Vm3/sFF60a6bKdOMi23TK3iiwEIvNzt5k5oJ8GwNjgQgOdlc3X4SUPVj6k1tYgm/cuHH40GHfWkKTlCY/SuShund9Zc/myauf3V1bWz92YM1Sh9DEd1VZnlg9X7JuOI4Tk6dOEh8pHz5y9NbNG3v37ZtAUsdWi1teZlfmpvuhQ4eZpNxT54hf3GP79a8/+sIX3tvYvXH3zh3uuB86fOj2Z58dOar8ft5iScxky4k+/4unX5aUqa9pAM5TLJZTe3YeDbW8clOZYCvjQz5XnMSlmjI9CQIkwQAiIlwPmkbLQzVWcE4zyLi9aspBhJTdCXxiGRytshVJxHnJqXN8DkfPTGfeUCxoRZPEuw8FLueR7XU0VXykstDasoEc5eISrD8DQ9XcUoIdxcfMO7KILvy5/khQztGD+ZJVNBXEKAhLB7pkYqyGvjFMgN0cO3rcFe1I4IyqldqMYNADZjy7f/vxSzdfqvh8j0Ias3fvXnY9f5v2oDYgHDNQCsAmSwymZtKsWp6sw0jpnF34hu5r4DxyZoqPe4ebBLlV4g6ZR2cmNSig4BvHxGjhahWvnCuxLwDAWcZc74o8DoqIFeKACo4McYqrW8wFQ8k1zClhFlBFKfMnHxseis4TqY3Nuk59Fa+QRy+epl6umkorxQfGkd2jZxtv8NuI9Ip2zq6dA9+gOXTVC8d0t3hcou1Ovq+xSYYoNATDScdlZLUGzoxqVpAuY9xOmkEktbxo5ctf+cqsYuk6JuWZY/sfvVy/dOkXp859YX0lfga9vMR9d56ZWa7ed6+oYbQzrng8/cWL5zuzr8NWCFshcPPM5ubVq1c3NzevX7+G43xy+3xrdbS6/+HDhwcPHqSX+PoWD7vzphme6Z8UllbKIpEJsbGgucCiqW+U2iPYHqTlme+pR8nNZ+lF24LXRlfXWq/HBTvj9OAcLTaUFlX9VhPDSeuLEvjAo2Bamu7jlyPxwOkEaPUwAsXJsKk/C5oihjl1gR2DpCLMZDeiNCe9GuyDU3XrhDEpoRKIAw3IwDeOz5CWQBtVyvmVg2sUcYjpuMAAhybP8iOOs+iFVHuELZ93pCXrCFYodUBgBmQ0wdJE6QIhoME7QtalLvGgO8ZUR9/MlNDqXZRIJXKocnDl8cXbLEq6250XdnB+JZrN3Z+XI5Vi+8sJBoLze9JstShNYbDdVQcwlB5zDBSRGBN1T2aQ3LDL9+W4uzA9ccesqdZA44XoYB+whUgHeKey17r0SnIsN76bwbkXxIEg5NGgFbH5eMrpgYlVCbOAKkrdyEF1oYLiI8Q9dbjHKdTGfpxFbxvKfmnodO4HpEvfajR8mnCkfOvNl3dXT/FiuvmLbc62/80va2AJ2vdlqY7eQ3bMwGkGjDFPI+6xnRFlG3ONSN53/vY773QYchJoScWeHU/On3/j2u37K8tbJ47sdSSanuu+O8/MTPldVV46yYhiB+VnDJfmTtzRS/ZPnJ8+ebK9dvDh/XujpZ1nT+ylStbOw+78YNSevXshO3ny1NWrn545s1kTlJlQCi1PvzAZcISRYV8n3V5daXhOfSb5zcQ+Eif1bjPvPK3Nem1wmHifHYAJ0A6isQNKMylU7aF3e/BdO/PY86gmxw82kMURAOXY2sYdwzGHQ9VogEMpUUR0drZ5Tt1clIlZm4OOkdZQN4TqBS62RfaYVieaINbyZuMTr5AZZVGVbE/fY8RSEH0iGikifKYbIB68CLCdQSEnIhPgSr1DRCJ7bCIHIwucQRxoEI0lUaLPildFE5bJ8V4Z7wCvQTxE2Vy9+Zc3J40lvY+ci3l2On6UjSMPzyzmpiR6KWjksXu8JgIkXRxBkmwMEQfX4sJJ3/0hEdQNGnyUTk/cSaBLPhMXxo2XFAsPEJR9WYz8+RPoZF57AL3tiaGEPgXEq0SAgvE9RmMmk14tMdHwkswgFBQPiyNL1UQ5NIAN9BfHoRUl+ahjPjOJEsaCMRaN1ATwjc31n44O55j5YNsm5hPRgVs362ynnMTrZiXjIvEsU69Cqw1P++UknUPhpTF5EpXwkAw/NWo/JhpRk84xm6FdthOLwgWBp4+uP3m5dvnjj4+fPL2xE00qbXJ3JPHCFybj855uejF0dxx85x9++cN3vvCljdXnT548xj5+ockWOlnu49x2zWW+GTv7bX77gVLlQe4me4A9p2551avqXV80POyLOxLZMJotFBwqgyMZExqzk0D+KHZCki2/bFXpp5okT9NFy6ErE7WvjjrRGG50aL3UDJUBOSyKSCNVXlycmxtx5XOcp8HicrNpimNAje66HHKnygyx3TLijEYuxjbnzTASpUbRBMZIG6s6R1jUCqal76pYVLw90hUEBaViHNkyQNp9M6IdWJEl7AJks0l369QYOkbRt5KAwOJ4q9Q3OVt/x1PL1/7h1tgDz1XZLBf6/YrlZRYEXqrIratBv73KQsdW6wFhRdJiFX9fMsWkamSPGF00xNdWIhYYG3qUn4sq8okcm2CikIeDKiGgYBAFIFH2C7iifmW2kYZe/GqghMBHA4CTeTTopHmikaQ1qB6iCZvnVJ0PDyxEIIUYNgcEGhibQz2Ev1hF9zGrhxCOTIKJX5QUVTxFKQsWmKl6T+8drR0480LfqOun+Gbbj6xZpFT12krBIUwbnzzZxtQovS2dpGs7bU3fqHW2xlwvt5du3bz15a98eZKItHDK0mqwCjZFjNru1RdvnD158+Hy3ZufnD6z6Q6G3H3y76q+ePli69HW40ePeWVbIbITxDMw1+48JrjHDi2vbR5fenZ3fffu/fv2Xbt+jeyc+1lIPXjwUJJ98uTJT69cqb76PRFEwDP1tOSy/vKzR7hc14Xeu3AutoOZucxi66k6q6In/Z2VD3sApNcU18Yj12jzy7N0eMUfZGQnpLEw64dWWZ+QiGRj09n+3FELs9isauFWYlrk6DlsS77Jkp2h2PCesrPE+WIW1LvnEp1AklW3CR7UlE/mg9NwDHxBSMbrGNwThZkaGiVcqKyaw6FJPeJkOsse6COdw6HBVaQEfxzIcnh1Vly1rMfdQpQEfN4TEFCwReZEAIJUzWGQparY5i4r21u37tx9tjUlcXc9GKnvrmbfXu3xUWSGckrWSSfsDkIIDto9wYCAb+N469yuTxeAv6Xndqbu+9OFVijGUgT6OGWlqKdKXNDanIRVZPaAwAyU9iBoRhGl7xGmYeEzhIDQ/V5mFDyFHMmuYgrdq25OAUmGEI3SbElNDQBc+U3oBsp+m3rXWwoInYiK2o8Fx76hOMGrb53Z+nTp3ITGjmjtt7bldeSfnc0WTm3uYdMwCbYX+YY0TWI7qslSpHhy6zAtKCz2iyV7SOZtJrUri9urao6LLW2MCe4g7cS+0bOdxy5f+/zogfW9e/RZcMjdi3dEjgnk26K8VZ3n0Rml+w8cGGtrXXn0dHT77r21/WdPH9F7heFbP3KEn0s9t38/j67wkhmU7969kZx1wVDyaQMfl/NNtXFVpUyd3yrl0ZexbWicfrymwDG0Ypoz3jhcLXg3oN40enAwztd4bu9vGCs6SaDyygBGjL0vkiVBL5the/cUE2Lt9tqCLL5isqr7bXiLvAVAMj1p9GZV7F+qGoGllXLB6R03dkQaxeaCQfWUQV3ihN59S5gMCMQ6KaVOLRFv54inoiycYhhBMeVWi6ohuc+bIkk6xxQ8SrNs22KvcMZLL1SYNM/FsWwS4N6pP2SZauoYqhT1hKwS4LAokj+RwGlKTXkVeJ5ybMe9X94iPWuVuCdF3HGnsO9zHwGbuRmfLE80bQDcRwhHiNukH2SSFNZAtmA0dkhX2lhVpUl6ySpp7Vev3sGbVOIVgaAkDK6iL1UXBhDlBetlHKCUz3TIuohJmhv9hntSAF3dpNbh8A0zxwNCTADcAIgpiwnIQpzkZwAAIABJREFUcC53lkwcKH5lm4QwWVoGhNARzHxyJSEOEOWvbe752ai4bVki6FZt2Di7CaxyaW8xrAMGS602maklDK6pdG0J6CNCzbEtQ090KJTepeVbn93i1Svr67t9PwU/ty0pwEssUOdP77r1ePe9K788fXqT2TnpmRl+L+nAwYO3b9/m8+nr16/zshdMau8rvtx7vufB7Y83Dp48d4pntx7l+/TRY8du3LzBs+xsEC+eK5OoSobm4sULe/ZuLPGtMN79EstsmXrkys42tF5FF6sjLf/KjOkM2uQIHUv0agJYiJ5FbxCZnTIQkarxWfmy7rxbDjj2FmZim5J4n0mwqHt9bFsiKgne4SATLNHjVdlvGX3t8FCrFUUUQH/BUsePHz0+EKg4vSoBYdjiQLsaZPYYTRCS4WsxnrOrq4Oq8fRdzZHEAiIhrDocjEUXA5ayq1ENZkSBjJSJpQT4GDOmsXVMXWDF4oxOdUOCHfBQJ2RzVSI6lfNr1/+367Nl7UkP26V/e5VLeuzkxbINe2LiYutMOy9cLffcxA6AFi4bCIjfMSSrbqM3l9ANRosr8kwbGNXdROVcq+SplByVYCLVi44ksCWQD8eWLDORIZ/iKSlAGuUeXz7TmUlaL8SY4dp7kdZBiEfDj85OWDCpwwzpoH2RLDiFm1OjrSFigwTiZN48ASGSTF3WjiStBJzcO9p14NTz9ncfS/yTq9qKbbObTDJDi3aUULRXsWv57gcuATlBhO1coRhr7alCxykbWIiuYLISFMoyv21Krvz+++/35Eq9mGMbT57vOP7x3R2Hdz3cv3fDcvcX3LVO73fnNTK8xP2LX/oS75W8cfXS5V/96NHDRy0T95dbo9v3X5DuH9g/OneatzrW3LjhDe7czmdIr66W92/e+qLM0L5evbF//dGTB3s29q6Mym+fqHesNZYu9vymNUdPhN31hiFik4ShaaOz/RD10dxuWPsMtWwTPZqktRjWdzJBXveudc5zTG2G0cOwMdpkUsbICNdCYob4nlnAWl6CLyZh3C9RZxTVnpB5YcLK2ipBxHiTe2N0sqo2fQ9CzK0SDU0p73ZeEWeUQYu5nBFoURGrUQdYjBZd4hZhW3zkT/zzLsgQDN0oJQEux3iDfkXENCroqec9voYBVLvDAJSESTBA0U8iKVcNN9th59NbVx6UJ/5MIkgsSd/ZW/nqPOaRdFWzUFq9IHmenTc3jPi4ItJ3CmZQcoLhYNdLUu0ZfIfMikB5QACaPqNER0OeMZyHGDc1r2qv3b3l6AVGjffKZwvtBf4WUBJhouFHhi+wB6TDYJonGq53HgkdeJmo1QTax4YHBBixNkbCpUsHLVUWBLrkapNjvn1mdGXp7KTWefBpU+0mRJuD9j7CogsA31ZMVAbWifb+bfa6ju/1xlk4MNHPIRwxxBcvXHz77bcHcMDjXwjmV0vP7xp99vjgJ1evnD553N8R6e+Z4SbWzRvXDx46/Jd/8RfLGyd279JsZ2oXzBOgJy+W79y+s7Tn9NGNe7sOTfnNppOn9OqYc+fe4K0vo+UXuq1uQ0SZzTbPqWtnefH45Z4j+/X7P0MUW7Veweiiyy1hGpsKtQ4q2/KpwykOkVrKlkiNNoV5hoCKnD+K5Y+cc4z2QcTpj4dnZKQIRWo8aQazepkD2ihEYRYUS1qW00tBRiN5KuMUjisdpUL6ZV+pabxKq1lnWJEqRc5xObmIKzRCFPggLFKF7s2qouUP6xQF0zUGW5uhY/rudJHezhZ+C6P8DNQFIBkWASeJm0VCJXazPYbeO8iNC30Twi9zReqdYRY2VSFoWfYsP7t6+9GL0VyJu+sizeNpOuzkq6v+7VWy27QL08pm3Wbhaml5TuZi0fVKnp/BZfIu7HEfc8OqMEZSvKehJ0Rir9I5BlKIJrUOip/ngsF8DE93uKsMXAq+THXnFbrspg4U1RQTtKAiBSSl6a/k8ozuGNTrScHEfZomxWTqIJkktg2egDNda1ciNs0/2Nz9s9GRNnI60Ohe2ZTtMEh1SmJkG4SQBngt4SomTGjxLvaBV+EZEKF9NeQBc2tRzqEg6MgpehrPhfzP793jyRAuC/tQXRVfKErQ0Y3nz3Yc+PTeyoEd9w4c2M87Ij+/ffPhc721/dmzpwy2F49vPH7C+w1G+/btZxdkaU28OfD58933bn68e//xk8f279jBr1js4nU0d+/d5W0Q/O5STglsb1Ifrewa7dy948HTezt38I7IHcu8Wd6Wl0SMOiWD48jU2gsQhPcR7tnsMadYScre2doSR0gMSKuebK9f4mpUNwjQgse41UkJZQAjhuFt4laU6dFfovUBL6goKZ2HDAYacoCqI4U1pkRTiIgo8RbYDIrxC2ZmLSUwsbtv7pXMtr964phGJ5ogJCTPgVfIDGPVEDY89KqHzvJsUZvVMWU3MSYgpdrGLpe0Iim0MES51i2qIDy0yoUCo6asxLYMlbqB8HpRo0GcY3eE7glVWk1CqWq4KYejK/c+vEVmWL+STGGua8YGklG/ocajgOyPfHs1GljH0B+O7T49P4MZqB40AUiG4126Ac+VA0pxP3c55SewVFtBTkzcWeJrM4ykezggd6BBC4FO7sFClSMFPx1o4K1teoUu19rTDZliAvv8MelmQxsuuglTObYhnpPGY8IROcSEjkYvpds46WwMk9MXpqqE4xujjUOnX7zsbTUsqZiUtfs+4KmpbRZsGb6nlASMV510HDepNkn1JPq+8OQPthfOKM9iAQ8uhk0yOtDsNMOJR8nfs4dkZlTdLHiK/Yyoc7tGt2+//OT6/bXtRzzG/sapU3cfP+JW1e07d/iVM553PHjiJMHg4RaeO8/FMf5v3X36ZGnvwfXH5zaP82s93HF/+uwpnjNZeDv77o0N6JWpr/CBsu/1pBr2PvXR6sljmxcu/JrfYwJj8VLqkeRzGXPgYMdvxCYhrQBb+ZnXrYj7I7JeVkyk2LXPZ8MMDnRw2XvPE0cLgmXyWK5hbvumPivRpkooeYAm7Ki+h4TpID9F68HOAQjCPBE2wpY/Fk0ee8sZgwjHlI6yNGSxQM1R8Vb3zUiVHqehWhJsHSXRJRrwISWPvCXKoEU9HoQbS6harysbz8TQVKTyahNt+rPQK4xlpFGGHiHWiCxh5JHHXW0yB4KxYHoPyRiJVxGPavIiVMcqoS1RGvnEw6kdN/7PaS+CnMgcG1he2IXdClZOdkayTb+ByI0P/+1VPjlsaVKU2vGMFr+NjUnc7MCehaW+OE4hDujN+wSTwDe4X5+45yI6BqMrGz1K4KrcmOQ9DYA/fvQQN7hXldOAIXav5N4zJrk7DbZNasonADSEgkJYHJjE9TrgMZKAD2GJDxUik4Sji5JmAk0UMIlgYQBKa1V/Z3Pr49G5Qc0I67fp8I0I0IGYmk7Wnxgmk0xsse2DTplIMFiD7Vzav5pKzNSZPb6DJfIENLHHtksXL55/881YM6WSlxAJ4He5GXg1DYmiA3D48KH7Fy8+WdnYuX8nN9gPb68c3bV+5I//+O7dOyv/9t+defZsdXPzV0+fsSx4R7wc7bh26+7y7pNHN57tfHr7we379z9b4n7r4cNH9KOnO5Y8U99eeiEn+Fo1j75YIlKy7ejRo/fv3z961K4H4srsNHwI3uJ1kCV5HatySslRz1GtWGNzyA5qQqsrjLlRhX42RP14mSAD3TIkdugEqgpaOoKe7GRYu/XOz8brfZF623vOywphv1db7L20ygRLSbV+aBrFBDKHoStVXW4airmaHPagmpdjpuQ0EQ7d4FVVZI9N6EiRnWkvpenGIIqIFzMloxQmCg7Cs6q1Cp03xf4RJ4Gytkhj55SXB0aQRDBWUmvgVROSJEgAxgpCc6wp+qFLpFF9E/wQbQDBG6tVxypiKZqkoKasLI1Wn9y8/aTYW2uIJqDY+PJkneQ4WZVzkENToOSLNOBbfns1l9ANxhjP11Htz89gRjdRM3GhjgKLR4NeIEmYeuVQnzmRUU3lnMm49sSoJl5KrKwkRpzxkjC9A4SP3K53sX0JpEc9Jj7BEEtPUyZNgL70vuZyasNCPzYMYAbSQNcMU2PF2GZRKF0f8pzM1zd3/3jA52S0uPM/rfJT7Qy7wXS6VhT0UbHftOLojcj2ori5uVTlERGy3Uo0KvHstVmO9t7Dfdz8LjMp4mWx/mPgZco56gz1ixd+ff7Nt7hZde/u3V/fefb2T/7uyv49P921c2XHytPjx3+xsvzdAwe++423f/mrXz1+sXb9xtWtHYdWn95bfnLnppL1w2fObm7vSA/Bs6GurGyxNZQtr9rIEzi//PBD0n2WJlq1NFlPY9Kip1hUXTVyLkzMeCTEu7IaFUuS0rI8j7oZZqh3TwfV0hH0ZCdAeYi73PkR5PfetWjY9CWnhUvJIXuOtwPI6SwvV5OyeIUoh2PVA0jNShIUETVnCZS1lGrcS/RO4MRGLdts1SsRBlExTS9oggQ30900Vk+aNbTdjJhdI1wsbiHRsGaFwPRSTYwWJ/BjNFY3JotbcNXWSoVd1EFkjELEWFhkj1SZehFa0OkSSootgMPealTq0aI6VhlvcursuH/lyUdXn7wcFa8izBprQJYF0irXwLbbPldhB2dRhd2/vcrDMw0beo3iOVCopmAzOzVi0Ivlc8irYc3Dgq5yPmBP3sNGtpD6riSlPnGHaBJDiX/OqvcoblAcZrnXQBv4V2przXYDapsWj8SYPCwYQExmGvqLt3kBGieFZTEXx704WB1mRzdGuw+d2XrZ5TZGblLMSFmU2Th891G7r+k5ZRmeTlHmmL0uk2bnmptD258SDhNkYfG8QvXe7GFzOnb8eFmgqe5Nx4RIPHr08PZnt9959ws+rg4eOvTG3r3PVr71Vx/+53ffeYvP3nhk5dLlSz/60Y/ufXb/5a8uXti7/8ie0f4jDw/uPcG3SF0qS8sKm3GI0gRNE9BnzpzhVfGnT58J7UpRtrnTjyUTOAZDa8jPPcw0XGxkeM8V06jJbAXfk6kJVMMNA1TL5JgvT9A/jsZDywxxVKCfZGJwnY3GhhMHHNP3jk2J4Yw8Vi1YSvYSgAhNNw+bwSEXlHQLbSmiIA0zMULGhH/BUulvKi7HidWPGtShVmILlBWaiLeztNtwkCQ3ImBiDh4JCAKkldRcOGMUc4VGWboTqBPUjR7rCLhEY7PmqMPp5FjGlQxVSFW8V5CRwznSxMsC76T6JjWHcnzl9r+7OTFpdCLPWzhSRWD7ZD0qKc6MQ//2Kj8KQeF3DEs5bkHaN4Tlrou7ISSllszPdVc3pXP0BX41ZCy0JtUQ16qu6QO3su84SB5GUHDAAVeBlal3aaK6sEur3EdMSqM2xy8GRjt7v0cGjW6Jh+IVWrUY3xu0+DhJl+wemV7CQlR9sDVoH6iJ4c0Uywf59zZHlzs9J2MrrtZuLf1Mr7DBYLjvPWUPRGeUE9rL9D3WbX7JxMGLkoVU0gbmqvtVX0jjF4iSyhzwVYVjjuwR5rF1fsek9ETKsRPHf373zlvvvbdr585Du1fuXLu8Z+/eCxc+Gi2Pvr706N6+jfU964eOr6483LnUx6sheL/N9v1t9tTiA4flZd71duwYVzKLL4TaRsBMMbeMybIgTaOOveV8ElUjwGfpQOFwk1nTZrNdNgW7Aoh9CcM3HrjtqO/x8+yMzuaWK7Ej25QWGv4lzQAePwOsEZFqjrDVnKsIhgQpu62JWyAqujQYXDDXQy5KcgVZzbRUqb3dU2aNnVjAG/tYso6kRGlCkR1y6aAmVnOxkYmzZ/sx/TavxRgj4DChCEjjVGsAnNR0GNZ7SzEpaNQFsFvYXVDMbYiwOsbbdFJJg6cEV6tgjizduvqQJ0nKiSybqRfkU8g18w0OxnkKAnlgBgm8eYbvypPUstqAnEdme17PsElC/PkZ/Gqvmth6Xoc68hbC0p4XlpJq2BHiltck7nRAL0HHaO9LNKXBgWIK1tc6gJO9qHbfZjqWcqmZeGcl9rAQkxQWB2YaE7MqfQ3pGQOEgvHgthGEFBnHQEBrPl778oJQ0+NJdV9i28jBndJXVD84u+s/tXhOJi5UtguaJluf7eBb0yT1TuIsBfck6qHw2kS0xfQtP8vUJVs5QioBRLU2tqwhUbQAOrK5ZE3tOXRPMo+pwdsYN3ZvbJ4tv0L0xZUrFy5evv/w3pnTJ/9o9Ld7juz+ydGv8lr3t996562du7Z22Jp/f5LgWfHKRPglpk+vXEnXD0yu9V3rEjSA4y3ssxEwRXUcND4mwrGF7DqSsfGhpEqjfAxZx9U7jpGmJWKm9F2WiimcCJpXhJAH1EfbXPGRtfPlHDCZW4BaTOSuQDH6YAc0AJywBZzSdzVoPmbSZHiklqia4ta5l2OW1NA6yuWbabJDSFUCwmn8qDZLjnOCYJ7jxWcGJsoxjMdeKBMVuyIRK0hy2XUXWXfmVBAR7AzEQopTMTbL3aiISC1qDe2pzXE6EmoPL0cviHHAibwvwDixI0vVle2XW494axX7tX4AlTTaN1CXAzx08sbr3ikk0Pz2KroW9vIZosGuTSE/wXGqaPfgeKDyI5H0NMaRcM0ZFtMcvsCKdqqEupy454lUbs1UOKVcaTbimPflJA+rMtPwqjYNjeEDkYE+hSlFJo1yB5Jf9EeCf3cAJqFPflz2AePjcugIoCsN1KF1VeXjcppovE9m7dD50Yv652S0joY121fuVLOGSQdf3Gtb2QRene81W0qtkc1IzweMRo6OZeoTOdkO2ZXGU4VJxA3hm8TSiLdds8fxxvj59NMrhw4d2rt3nyt+Pnpy9/6NTy89uv/o6ZefPFk6/8bJU+d2ri79XzffXXry9M7zm//oGx9s7N6z9sX3l37+i0ZbZ2xUj+qu0saePVwb7N+v18jcunUzvL6mb8dnMK7U3+p7z2u8c9sNmnb6SqPaktlXM8tsOihFaz/YLFXzZFNdmXkc3IKAndwE1qXuNv/44qrdMUczAqBNAFXBbo8mbmaaEZUtNeqmOW3SJFaaWxUnk2UUKvI21ByXjqK0/NraC5ogQWMKCv3VYkIYjcZEeSqe0Jloi3RK34lRptphy9jlLPoUUCk2AwICG4hVlBMABHknWpMbCaP6IAXau0dxRoR1lcNedWKOFGdJrYdXHl78jF+M2O14z0fZyCCYMzd1dS2P5GkUUtiZfnu1pfBmMhZeCu6jnaOn1M5CHFgGQRINaBoy+2YVk1oRm27A6+mdEh0GEZQSslTFuJSMIs5tdXM5Ukr0LasupyVx72Q47h/HzCnZPxnBlyTH+nrihwyQ5cSJ67cPSCM7uUZkGC0Mx85jJon6zQKY1elC8XtneU7mrLY0lTBs0uhJwBQH29JNETN0szaesLO0VmWBSWuKJlbXpGtCkCagWxvYhlBzfGbP6wXzhvWLly6eP3+ed79cvXbl43s7T+999nLf6pE9x7/4pY31taXTd24/P3Dgtn1Z6Pm+g2Rbq9eura2srK+t8YxovdCuWPrFw8frZT6+fJnvquLp82e8SD78VGqPjs9io80nhTwkQZ7NxOk1i6ROtIqJbeGvZG1Hqa8n1YkS55GffTLJ1uhl6E+dwl9oJZJ89KlPSGHSs67E1q5Q4tHcNbc9n5RIhV86HArzVou9DAs6S1URsxbyryF0nmLIyyjF2JoPrg8W43GrTFeFjTYfNrYoi8NLFS8pRML+AlPBm1TJTguXEeeiQxjB5zQFTBCkwVYOhBgQ1FkHWHOKcgkIWrMwJYLgz3ifqSeseOSr1VM7bv6LqzurKSk5j9+AW2T6ji7/7VVePoPBJG8L005kPIcmaeS7s57gDZGse3eUjjV33PE/9VaiBulXEt7qxyGsJAoLC33yLgFVx1PTJIBQpAsYp0FIh8h4SCdpWQB+CAOQSYdyTPbXRoYAdoh8kjkPwAR4JUOOmOC1Bwffv7a5/qOto2kfa+tREde2HInOujtkMgm5MGCq4bZ9aJdyk3wETeVqZT9SJLgfYa005kTotR04x80C82CZ3rTwkx//eP3g8f/3X//fu3ftOXXizKl9h3aurZ47eCqJunH06Fe+8qXHH13go+QnTx7z1MrJc298dunSezc/+xk3ZXp1nwfZeHGNq+btNDdv3iSD37E6/nME8zqePGsGPFk1GjRaJwdfW4R9iDGhifaKBpz7z/ISbfDQpb6vc9fTNmWNatWpgmF7Q2L2U022lii99KQvVkMe4VWEASCwyMZz2HQJIaKsaF+QG2PIrN0mk7HlyGmwS3PHJFppdKjlrIFMJ1meaMbx42JEKUykKRjBWIwkx520C0pP4Ys0nQiYKrPJ6JSpB95xpGQrW/NWaQx9nWEsQKJJxFE3RqgowtY3DgBTEgxBXuU5qSOja5/c416b0WUHv/HMprb49J2kgvQdU0mgKax3nlJn1vUPsnfjLHKJFRrR7hji0L+yCRLH7rhzF9CzGTfLWTAOJAGaIKE3dBpJvUlsLcjHaDM5NISFkhMTGS420lhvljCp9RU6jklzGu9O4YJ/1JB8XNiwSRpnBRjSDPhZuWalJzIeHI6Jl2HD12vAHF9/tvPg20tbRVOiKQMtSMosr2fdtrcsGNpfGIbaQcxgO/XrbSYNUFtZhllklNA7g3Zl6vrxo5gLLI9WXzzdOn3y7Nrq2oF3v7pr5y42DMbS40dPr129yr61k/c/skrt2PH3P/7p106fenDhwi/ZTQ8ePHvu7DeeP7v6+DmJ19KEH0/tFobV1WIG7d23787dO1c++fjsuTfK0mZzvMw9oa5xYwOHEx0ak/UqteUo+QSskgyEwSasVMYa8rmB9JTFKhG2gGh5n8lz8RmvhRa5VgMjkD8EjvSqd0ktHp3RtAJBYzyK3KvO5EbIHBps/glWWOJcFHVWhU6BM7mC64qESSWnKKWOrIKTuY50VpNToQoylTIDZjSuy/VqQkftZYxlzM4YZQQ5brKxuvgiZSc6FhjTp3ljukGiJTCYMBGNIa0ibYks2OmYRGy61Y90AyV1CYDD+JMACLy6Z+nplRt3n21V3nhLsxX2Ux7WIEEifYd9AQl01Cxr+Y4+VX/5DGb0/u1V/PL712gh5aPk2tFIoPxmHE0LyJZXyV28b7ADy1BJyc1K9g0KpAE0qJZJwnE8dxljvORpOoPDI5PG9CRpv934GBtdxgC7s0SGsEx9yOq1isxA/ehhYUQlZwlO7aUvC9y3NkefLpW/XJgYZ9uMCrbpkPYDrfvTKfun0Ia0tE06GkePmdGjKdNE0f7KnG8Kp3J0+x8cYGPl0YRReObEOfUDSYePVKUwwNitnjx5+uTxY8Yed8G/f/Xq0sMHhx48OPn+e7dv3/54564l8voXL7hV8zIbnFVR82B4KeQvfv5zpkCNkDnGnAfEB44kGxSGz7TedksgZr7HEVdj3XAoM3AR2ov4aGZ7XMJ5Nt8RZHkkJw+2p4QW94BhL7RgcqCou2kQn47W04Jo8lU2ByQydYVl5mklgLcmUZeIckY/1l20mzK3YqxpckWOyFgrqoSU2hH5MVCaQ4nDGESFk3aSpEhZiBIm5t6iLapGX26ysNuViFyu+bOeSP4agRtAoJ3DgDEy7z305U1hLnhPpO6BxroshMU7TzYvL59ZvfXXnxabGsjawmaXp++kVUlILX2/SH3E+OQJ737o5durrGNecIHSfCmSCEjfKZ4r9utdLk1Pp3i+hTJg9OXNC4NLqfPC9LoieppeSdcw1k06LGbYMSteVdjbxJmxi4V+dHoi4+MSoI2EBhok+FLRQDNcE9rxa57ge2SqwWE4TQ0OBH94ds8Pt7I8zFf+4Rwek6ylfAwxUEW7McVup3G2XIJdxje8PnTO6gU7mW2hfeieWYZlNXgPI2n6kmXqgIoLU2F7h36jdPbC+skrKfVWysOHc+579+5+eu3a+vpuR/LNskFzdzaRr3z1q7kBY3DmO/ip3aZh40mKUdvAMaapnGNaQ8XXmR6HXZ2SibghtNu0kkaPR0NoZtbuoj3ls9GpLFIKbKCalyxfJAsMWfDsEEtk75riNtuhLNb1XDlM3gNaHVM2rkqxIIw1mSqp1Zw1CwKmcpLAaHClcRLCIxcCqYrSZfOyzBEbywRyVXwFvkqZYYzW6E1PnspLr6SZKImb+GduWgCDz0wTi02emodoFGQxPhYouZd3hsdWqt0bJBnkZBxPbl/95Z3pibvk2qsPPX0np0LOYvIoV82R2+0UVPPtVTZ3Uvn2WzxxYDdnOZX/nd5ribPYgBD/5GEg3/VrR+4wmlxl8n+RwCK1o4tCD1HwkQjQx/SuOipGY5G+Y8Yr0VvrI8ak+DgBtvWVplc1cqfwFV6zufb2sxr7PTgc8+DU3lCvOlvCHNn17OXet7e0472C4qPOp0DP6uPeHcRawhWzi4izHcLm36zKNWfnLAP63miZ3VDfsjtfynG4ob6ke+pdMvVGPUXj8tqB5R1jESN35y4FFEPcdx/TVFiRQb7kKkOpp9VKGFqMtNMQyfSNgT7aUTHIsB9TVVMJ2if7XsMzjgpbtTJFxaU+guMsqdZFu/R5d0hV6BlljRTHWHfp+hN7+KsaBc5p8oxQ7NBaNh5g24cRGsbFeJP0USTIPZ/gurWHFNY4Wh48kO6XiZZtcr1SRKlG4zB1hjC6cXyV0jAWPROeVcfS9yimyM5RabGX2uyvIAApaSJKSAPMLrR5RUCBCUyGyA6hw6QyFDA7Ri8f3r3x+bPiubiMYyLIxkr6DvsrSd+5d0Yhu+AZQmwgwZuU39Kach5sdrMnetWuwYUgmXsZHFENph1rK6qwZ+AeyUcrjgGIcMzn5ACy9fyPp1loQT6K1DPj75Lvlnj1aO1w7jcbSUwIjl8aOqXHZ6DLxKoxdAQju4pfDGaqduKTxo+bpKEz+y8p1Lrznc3RzeXNuu2hlrx/pM+IeeVamo4Q3xMkbVIwOt4uAAAgAElEQVRSNq5J0zFtJeNN47VC8Dh+rhq+M9T7icBkQ5Spr7CuaqemkKlzT503Yqti26yAIcvqrg1uyJCl50q4GdXtvnt/PUEmodzd4hKCYxbSIf0pyX3OYHX9HNlzJqkLKK9bD7yQv9pUgTGGJp5n128xn3jVVC9Pk9T7SKcMhFyWaMvAI+XidufdssswvTTKtR7YIqHAqy5KAeCjADBS7v6GiiFsloz5axiTKoKaYpIl3c2roahHuRbZR3FuVQLCsOEQKLF5nKCKr8WkPB3hpsdiqhBKryw3sWNNEV85e584Q2ocQxJrFxvVaPy7EjXJiDSyqIwPTu+q82vX//0neQRmgBGY0nfYSKZ9EMwgYg5SUju+0E+Sw1vnyeB5Dbw/YIJI39ndQciGyHnwFH/RRZJDIXOYdPEwq4tF4p78mVXE/PQ9XjakznCrCFwKVsNwyQfu/O7MKmGR2hnBhChp9JgwmF7hZdus4RqUvhofxs9Aa83XN3f/ZPuQFmlf3gd1bIJwBkAaDBNIKmjbhMEGq33zrVDNgVhQOOS4ZQFzmFpmrWbquqFuW3KZNKQ2wzq7Z/VpWa/V/b47G/pWfCeMkw1rjekIw2d7acTHDcoYFqCzHAOlkh1GfllMxzoDT/nShIsHa1IrfzG1NZBDH6WcLLeRGdYondw8T/3MSHUfSJJ3XmxN7h6fmzG5NDL4odYyo93GPAqxtzTdlx81IdwTSKsEWMLVVlQNo8MYCkRWJAxd0cqsZSroo1ECKKooG5a0SomNZYIqvoRRVfZzsBUo6LHQ1qXveCpvmv7MUwtvIhQXkoUca00VA+RYkb4bsRlns4MIe8ecW/n4f7061/015LzC9J1NnPdF4g7fXuX5GSyhDJSsK3yV4vl6ukk6/0WCOgNx3j0VdQtCYECHCxG6AUZKPoE7dAZXQh20Lyg086khOHmajjC/jMm7G4L5lPwGczNyKFyLExCPSTU+A7l3Zt9obf+Zl9urWjgH0tFCrO/izYTaN634ToO5vRls+xZd0GzA69xqmbrfz7ae5NGXLRbVVh7J8bRVLtBJN86fd8cCHlYbVHm80LPsxxI3qbNRxKR7Vb2PXqWT7Xqq9/hYFygrcvctROA0hOLQiefedZtAuR+1t9Lgi4Ane26ojffQqSZCC4U/9+6verdU04a4vJI22i3qrhymmBmGMGQWaT1OgwU+myhjMRFKEsaQuS/WHvLXHN8Cdpnusw0R+WK+lpnNJ5BlgipeMkUVKIMKRSZINoKYRWfEkal0VuYfs38XbL1jcQvZurQlZAIc64Z4s0zw8SC1FmoABZ3wri1t3bl14+HzuRJ3iTOBZMwAPDyDZPIuNlxrGfZAjkcuhA7uwfHMDI8YkME7PKzicek4i/s4jj1zuq/OQIrfzx/Xsriaz8AGfThJq5L0LE2Hq5c0i2+m8gFKg/bfiCZCRPEQJYNbxgfGxLJ4YGHaU3ySRoYQaweXLr6aLNL3b53ZurJ8Nq7Zry7+bAZa3Mdctw0vYGgqtY6RdqwU+gYQPotNnkHMYARbmd7SaHu4djs9pL7Fo5+FR7Oohw9GkzMbWyvqqTZ57o4RpfvuraQ3EoVk3TwrkuOKQcxETw4ahQ3ViGFuZ2HhUKrKcgmMTbyUkHpoKgEq8/VZV/BN3gxa1aGBaRxEkMSAtFvv9rp3fZ7CP+YITcals/d5WFVipq4UwJZlA8Qly9Q7ohdMUZtTeT0ijSASZU0OmmQxVlqmIpzFLHd+Wy0rbIFMJwg4Zhxy2PEFMsdEJpNhC6/iFeVEVs6RyZqMJCbi423hQkVeuxwLvocohWIMEJnJMsdsPspioxHqvZUL/+aCtfV38IST9N1vm/aevjNoyIX8piQe+f3cZH769iovn0E1N+N7NyDpqgLY4+7jOwXbKFWyZowSdwRRmumGayW4pagRdC9DpOlVRzCgQ+CqchaJyeMDjGrvRCI566cHMLqERdqf68JmDOh9BKYQMYpcHSo8PiVdTJ7cngXA/JjFH5zZ8w+jQ+gy3zkuQG29Cm0KjAHWeS+21daTzoWN8qtC0hZRbRoe42OPY70qBmZ48YvfGWKkznBPvV5mFTtMBFjTJ3hVWBBz96V5cnfbPCwHlj6FMgynqerD+H9lS5DbqfRweqiKoHWAQojE6fHxsyL1yrZeKVdpM06MMFJz9qzREHkm72RaYPXgO9kTE8ueh/J1Tv2sRqLtwyUu/Jp/tiwbgEXFiDD6YnqWqlKIQA7inFBEwNtvOnZxCpKky05O+iuVQKaTfEmtEW9nwxeYKKqEUVV6gi7TGeGiSRk59iBDSbYiYFFw+ywYwo8RRKEJWQBS5/SIUl+4BKDDzy/9x2uDbJHz56+yNSuerPsgw4Xm+3Fop5D+8duryCB9X3Ae6KkaBnR4/4ze485nB5nviwax2+96pjSdiHsBDzC0QQtQMb8LBMfj48uTx4c01IF55COhYcmbR3JLXrzAtfnnjEVIh6QXyZSpVzLQ+FRPjEMDJ/eMdh08/aJ4nYgW36GVFvJLU0q7aNdNrRBahVp7pI1J20ZVxGIwpjl2AZtX8YpG9LOb2YtfbJscxh5tuMNIltypYe2cu1vuJdttAek+hHz2vcJVSDNgmGXQ01MNI3VwfVek7q9vHmhkZGLNgBmXAA2sMLiyEyBFfggipnrfjFZXPinP3Fe8LdO2qIsjhT8H1OCrAtgEu+C8CsaRTUNIPRGTUWeY7eidI7cEhTS6KiKQaUmjUX9ewBuvtVtSE8UUlCVMVrXFR4SB2JoUf2KcYEu7A02k4yyC4LhWWScYQ6bWBBB3mU3YT6x89ncX7r0czfY+Gfe55dE3aG6fkb+SBsyaCdDp5JC+7/t2b6OlpfIl1PHbq7Dz8AxySIbJ5tsy90FnHvPlED0/gzyqeDFVsB61mcnPqRKnEii3ik+8eNAxlLKYNH2qeQsmqI1/ChGtbo+HiJ7qvbN6FzhrAHHNPzCaiZHIpCg5I44gilk3q0dMFQxY5HT99ubok+x3l3wMpL6eKQ5tiG29zgjDmBrHsAtU8RnJNHAuZm0UsnI+IdNMnNBuP1C6xP1BdrP/n703DbYkue777vbe69f7LBhgBjPAACANgNgIYAAQAElIFsJhOmSHTMkKM0zaomXaEaZC4bA/+4vDHy35g0XLCoYsh8IRXhgOO8zgAlAmSJFYCGKAAWYGA2D2tXt6e92vX/db7ubfOacqb926VXWzqrLq1gDKmb43K/PkyZP/PJn5r3xZdTGgBaa+Yog0vG4HrCiNEnz0wt23dLkq2HdXDurMFIsjqh6i02QIyJZhCF15QBSmqwEBqhcyJV1JSECk1wUf1mwmruYmgYLaxVZterkukFYKE6R01ODVL5oUNYqW2da7lFpqKBqEMSqbBbSIjxgSSIp+gVJKufjqJSmRhDbGEJXEZFANET1NpnvHTa3aQRm9MruXNUiGUWQisYAzyQyw9FXJRYpUIwxbpiSJOpVpyh5nSfqCvkvczFBDMUP1rCaiW4xRkC2ieqRs79HZ87/3arPPwEj1+nOkfHrSdxwiSdaNPaqaih9o4LdXUcsDbzB4O0tTUVelYjg3DMTaZXQIWlKgqXG+Ar5YYwE7sA+MzCbi5BK3ywIrm8sCo7U7ss3VbpoBBzOgoa4iICKAD8ElNhfBgOaUr9VMG30MAB/nSwYLbhPkZg+o4S1r7QwloOdkdp+aLf1Qjk6SYWqQKdhp0phP9+oU74p5Rhb1eBYoEKtkQIG+gqz5jEPqvHw6Wq3YE+Tx/C2hWz5IFSiuldXgrYsPtvz+HO+IxHWm8UQkTD0uCbELSNMzcUI/tRFCelVmTTmJ1KsGlLt/sCKqUrxHja/YApsGy7HnnLZUS65I3+N+i7qPLwEg+pKYHDJmkpeXCDGNSzNVQCd+nf4RIUXWAbmkiEb0eyHJJUHm/sR6oZeoE7E4qOMmZOL0+FsXVa9FJy6R+rbKrLkyMCQWXSUlIzFxC7HbZa2mZ6cY9VflIhBdyrVeCkUX0BJZUToCLo92mn1qhqP1sYCWQDji9MlIb77dO7557fXX9hfMhLoaDUbGWOjZfWddTnEz0glmAFkQAOn+cAFt7LgTeO7x/PnzVMH5mXDq12vCgCQCXObtJy5+gGm9Vg8JYLWRSZU2CkG/AF/ki88hedRZS4Qeop9qqShfWLwv9j/j6014YXm7ulUidTODR+FIuFMTVqK8CbWZOh84PTt9z0OT6dI2hsyfVYNMu7pw6CQsC0mF4Feqkmo/a2Sh1QXET7yU1IKpR2vebNRfnFOKVUn9dZbzWE/l7wYR8LLJ9t1loOk7IoUNNdjhGSZZbRtlrlgFDy3i7omZQsTU5pAwobPYgAzggiapAWV63hAR+i44yJdSZ4tKkoEkB2fkvWb2tndJVgkiGjO+IJ8kxAPRjUiboWWS0FypxOJyKf8tLkVCNMi0KP/lBFVNXq5ATjmXbAXFWolF9NjlukgkhrXasIL0VclYcVRWLw0f1BglX80SKi952KQYCA72L1FmIZAQXiQaj3/P7OUvP2fLizO7jQgLPQGOBD2zFd96l8R26KJ7ehX6jgFsxpv/tdF4rUMBkPMztqUIP0wZUP2ZA5RCOgluxKCaRq7W0VprK1SE/SlEKigpKAI4BFyQT8T4pLqk/1lWQ3y0wLBOZQELHeFQMtuSKDVtLfhTOzU2XRH6P/fI7KX5u9MVMbcyuYqPrA9IWlCXikr5FS1Sjmealy4L1Ve8rK/gKlRVDLI+W3yyp661saeexdRXLJHm+3fDSvEACTo/ZPVCXd0AkYmukJ44wHJs3x0D3L57nNnmtxC/AsrVtClUnRoLC4yWDMtENIB1qwYEUFpGBQ2LRo5/KaXPiEdlzYkTCIlCGAJDUzm3nHoXUeQMbPcpOjQxFdHyEbe0OApUCVdG1FMTqJpkhojIStCJNHPSWxHNTbAWSuMkFtHoVWltaobAanqsZqEqIZOsR+aqlLBKalrctJSMKNV/gI41EZpx3N0ziaHbvZOtvaefura1szNk37dNikKXsRwbNyNOYGm2rehVZJtLodUELOHV79TS/tOrDAkMoGrou4HgKIoXcVfo5MNouhthNWk62tr0hrwOpjl5WWXTDagUAS1GiSJdAKFsS+vIG0r0PoE4+IMYIOCUm4KCqhkbblTUaV1xWVarn33k1BPpczKFhdQ9zUdtnk4tT4WFS2TSF7JcWx0lygUUlbXI9/YlUa1w9MUTpdqA2XAw206IvGWi2gtVQFjbwniai7+lq+lygSsZ5FnVk3FvtDgzk8xtIa4GCYdIW9ZC3XEV9IJSTL2WLnEhEXVpDUSoU/tpYzcw1s7iXlh4kiIgReJ/RKWsSSQ6UxIEWX59Cz5BK1ePYUjTkVBNxjVEmaRIkiOZxKlCbvFc9yBEne5SJLSkOHq+N7nKTL7Sp1gm5sQASEz+JUMko+3SzEggMz2W0imZNqoiA8UgIEUutaKkcJwk3/pP+yCKx2nyrZ0So4l6ofJRAVX6wPSNb7zMD41yiHTC4miHiptboOk2+ABMAFPoEdbiJFMnyw7PkK69nAS22Tg12tOr/PAqRvLqcCPTzda6rN1qBBx7/wwIZBB3jLNgvAoNyFkIu5uOHe1DsAxIrSuHEkCZIkOJnvb3b5TUMuKtUJg2AhHBNRZ8wMr92csu/UEL3miMcbYFV55UyDmZnYsPTyery5UuLIm53qLiHeogDXtJpF6WQasvaXSbcVmLZFUprjPB1FWQ3pN3v4Rg6hFrWGNAsXl1c6X59H9AG4ThSBAvX6/2ZFvPu/OKg3hmq9ui8uUVgra9UWGKnI/ZqrVpIRMe7afN27DqiOZK4k2rdkueEEDLjMrKVxTVbKEUwtzlfTPikmxnyJiP2qqoIyEeECeKPtLlS7sn2TPJdFGvBHTZzaVfi2Z40Rzg1VqGh1opluqYk6tkiGQMIW2R5Wamk6icXSdF0SiBhpjmSEdcUXRpuCGnoAqwi3+xskXaMn1XYdE/mM8eOHz6t1+N+KHb9w1O3yGBjhhAnBwl0JYuPuAGZCHJLQQdHJaILqrJj2HAwcEBzsejq+0/vUq9tN3uarCRiHQMSQQbEHyCCwFDm0bHaszHqvEc80L/agwoPq1IEih/JSlJQNggYU0ZE+oSx6JdFtC51p1AgCKM21AGdFbPZx+ZvTx7ZMk8ndnj6X2xutgkvSQZ8iJbPV1Gb7E4hqwqhC4WeU6/6PIv6qDp8stHq+fUQ9TVDRDqdERM0wUp+7fgMXiaT+8u3hEZT3choC2nA6OFb3nZW06zk44HX+TxqbrEE5i+GrbBGZMZwQabHHx6LVNDzUTqFQMSdHi9JVrAppHI+CgFRVFp4awsDEnyHmeJwVKrTUZypZfypfSEK3ENZb3WRWTJpaaLuAwAqdHlauJyAUlKhswySQHfuDVQq6eI0GYxaDlEIFjfJgSisuaOmp6dYmRcFEdcnIpUXECL41q3SUb0XfIQA7vlvJi+q7AIPTR7/RvP7h3IdvsihKLvMChjCKguxTMR3ix9x8nscVVePsPxdwBhA94ccQFTuBjuC1DARRXWdtNNurzHnSQCeYRwla7RRN1Uukao4Wz+7sBTCHmVGE3HTgIyhg82wy/bBCrPvO6kg4+5lzNJ/KmMRyHMTZQrvpEIg5CxYHNTQwYA1Effefq70/vRbyNNJ1mpTSIEnYUt2sBnVEkDmoOq1PV6zmSV+IFSeSPFfMjrX4LWlK9MbdjwDYxMO7LK5lvpcpSmROJ8pfinE4sinkqVu08os9F9dyFoNgOnm1H+WgadchYgMlj106K56oQkbpa7q2kBcchtaiLDJigS1J8iwDw9J1ITQ81lVFC+FjqISXdAStg2jN/2rtsGQkSoV6ZD6XzX9CiiHNucghSx0bozkW4muIJ2KZ+axHdup68VWOgqipl+NU5r08auFohaiTkJgaisfJEe64jURJKxzKKgigsnN+BUpRSP1FiL9dJuiRbpMa2nqHH66dvvfu+fvJi9IrNEAjfrNYG9Xdbu1VatplDEkQQQrrMpTI3QdxSyXlMR9qBwtcZGU+DrBAgkv70KIYTNB7TBgKKBtBTlYLXaFnkd5EZ2Oq3XVw1qMwWAnNslHQsb6AYCuRZp06ru12WOZTeCzr3e6vcz9LVNBAHxBxz8nE8Cah88M52feyTaE8msRqfpiFNkCpROlHrLBapnHgxqhI8Bsqc+YIbSxYPt9MZ/9midUYKcGLNOrsn83Pp1oVrkxky90NjCzOxWnGyN5B2Rm+Xusi0J+6hgvjRKyFxUVpVUcuyNc3cawhzCYmQziTSsgaBeJXrBOhNuEiM0M7OzTYpKmHLh7eiQ4pEKSZBT7zSN9IGcm5EpKII8bjRNlyJ6SUnJjVNEoc5YplDjKkKGZWlBvYqSJL0ASdVcJLDQtSYWmYSUtdhav1woklkRiNP1W00yNTETV0A0yfCUaVtaqvBqRaoyQeW11VG2oC3oxNXGxUh/aPr6157duzNe2m5H2AUgth0uVkwWuALqDFUgWEEIaCYHdWpLRbBh4/QdAwggAH3HHl4+4/hkqbYgjDcaVSBuZF38OD9k31TlywfLiYZgMH3lFFE7vBOkHFcDprybm3Kqf+ykwYqxB1y0zHoN72QEFjvWjx0MXg0CJYcVBYAIoNxg/vijk6vDd/a8dii8qssRshk/J7MzycbUmZxkyZAPfj6XpSI2nmmrEscK1z5MilbEcDpLapJVOMYhOY8bMjFUKaU5ySkp38vOnJlxnrHecl3x1KnEnxweLrJew6qEUUUmwNWs1lJk+tXKAhrhbmzQ6aMWGbEh9kq/tkd9oeW0FklYTsQOdsrYeJdFhh34heKoxmjxkRxYfky8KRT1ia5HcRz74nRVpGLEEmpVXpQualqOZZRZFvC+sioENonpfKcgJhVEMnwJlxZZC3G6fuscsEgRqVirlIs0i4BE48tkfJFllH2Vvs9777z7+D9+3osZGn1n4xm1jr4DaZKDQm3JbSjQR6YfIke9zoaGqstUyxK/v7/P0s/Tq3yy++5/f4I8WKGWhpTiVF7dk2lunUQgtmFTR4l/WaoDIAIRK0Xtxj4b9Sp/C7sjmYlVO7c0LXvFKua4BE7iePaqQCrFsIKpO78yp8rz7U++69S3ZvellKQuYw9NJa+9jBx7rZyXgA5P1yivIuuFZFMtefolzdRTGmQVKscOUgoCXG7WhpipC5/BkhXPCNrlRWh1g7sLRclrsvIc/VCYVCxPtqilxXk2IgrMKC4eJNdaVceGGCvRxL/EjY2vgZEN5UZn1DvUEcWkDelEJe90oZyd6elyLVvCEU1nWqUHdFRYJKaeRmd1fMjUG09cGl+0zxVcaqYpXUpKXMRlSLJGJ/JKR9Fg7VVdEatOaZFaEjzb5UZlbQ6w9q5IikxcPhGVeTROjipVSUmLsxb0ff7w9OU//sH+4SR3u93Z5CKQKNZNaCtgQhWKF0FXKmwEyk6/2w1DcrMsbC0F2mi1Pb0KDgQO0uRxS+x0u3uUyhMrqIuszRB38PW/KSluwGquEfRMmi5DOQ7cojVnQ1xJ17/xIXMju2nGXCDayMCjUixJdlD72OEPeEXBQMKpLDhTMdvzLv/hc9Pe+UfhYGvbhZOukLTMQutVZRZrKVGWWE6/2MvUGXccUu8Ppkw43mZ7CzbYInrCuz8CmCF1MQgBacHUhVoJZwmgvrKKznB3g0YZkGGlY0WxaQkg6R+lkJXBrF9QbCgxkGLCqEW8sPLAUnHwnKlcixdWSyy6SiWCrt6q4vLC3flLr023NhRRFV/GdFOHxqJPiKmMVCp/TxCWvzBgtetUXpQ6oVREJ4CFkancMpdWBaokCIDyz66Sn1GOjHuSF+LRhQARR/mWqygl0i9fi5Ro6lim73G2loiy+vPt+fi+vb/4vWe9WDuIsRrCQcUW/c1RPm3nm5T2A7WzFlMv9NIYJqtzy2ZgAwdmqJQ3z/D0KlyCpyhJJMWwMt+tvxO6GeJu1gfB1LwHUIiYQmDyoZ7WtUFseAspcbzT4AIrCwVstZ3WYQa2tT/Skq3DBudFpCddi7gB5c/Uk5qJf+qds9d670olZl5GfpyZx3zcSlAcmG7KVAdCi5epYyWQ9eXFL7rfU8lqVhffm5hK+jdeSHhFFARpIev2HafG3+p+lhkntf29ce6uUIlLsBrLjmw84bcNhM4MJcdGeBvxk9R8lapD4RIzkRSn8gwlRFW1unCZrlC71CKJ6Y2qo/CaIHky2WI2r5Ji551T79YCybCZ2D4Rz4yIGpnLJd/ifMt/GkR14tIS14GptgYagJEd0hgqjyh0ZEb8ZTKxgGEmeZnpsRqdL1dk4iJRRQnhuG7rPhB7z/SZ/+tJXtex9KvesUnRt+0WG+6shinmwCVZ0HdQZiNMuzGloPFL25BV9j6CIhMar3KlgkMN/PKr/fYqsGBGQEA2QNzdUFtp7PoEyhpH59Ok8Qy8B1DKcj78jz9nrK+ySYk6UPjYZXAZYlYXKBEAjeA0ODBdSvsRrGK0t19vskaDy/744OCq4FpJnS7+8UdOf3d2j7ssijC1yqplItFXkXxDeQsb8iqQoy9y3jQ6ts+eur6isTpTX6mIJWXj3D2sDcoatH/R68jESsNXEswh+VzJaS+hfe6uk5R+OJ6uEDB9bRYKG6PKvTbWI4ZAcpjGcImXqFnlbYvA9nYqdWFbTMr45qIasT/6R6VicJSnaxR7ASRaiNq0MI1kW8cswqeUVmM0Ra4sEZ12k5x9aSqtpA7KbNyc6oUJtWJWS8TOxdjon1MamUEzl3Pjgvod58lFQjIlI7RdJVQ8viMgSZIlrXexd3Pvlacev5zB2mEI8CXDjpW6mICCEjyVIkbfbRdcKmg3OPrOgs4iTmitfgcXWLm3vwNLwJfPbIC4cyfkD6K7vTPQaTxYUNx6pU5PAK6NwzpKulbWeUwSruJh1pEm0BfRnNqiQQ4uqxobzK+CzzUPn5/2z71nMs+YE7OayzzNzGozb1Z+O2ksg2nSzIvbeJk6S6ksjljYn40Gcvhn06Y2DUj1DW+jC5F94mbGbioBRvGNDJMkusLdm/xd1ZhgCVmjXsUpAyyBYtNjxGzbrBnKuJSrKl55cC16MAPLRWblmDm18L9y+qMC0ooFrYxUWNPweDaaaJyem7HRQ1zHgc5CVBkPCxJRxKWoi9eTZGIynXh8yXfSbHVB06Iy6Q+re7lMWqbMtdWtplPMWLQ1fqEFGce1BSsNUUH5WpSKr6KUZRlXCQBSyqlU4UcPn/gfnuJV19GWKAgY+0JwLVlXe5Y+KGL0Hd4MYsGX1KXK8i8cfWfzm7g/88xXmZsDs4VRkL0KF7+9CpicfQdV9uLNqlxFHhkbIO6YTsMybaPZFlzuKgQuq2Ykz4aaassWt3mlbCkn73zFUpqDy9X4lo7gXSDmJmXgYiSnPIERHryNn+YHLfqPxBNupnqbYaMssTBNmjNLNZyIFYOJbnoxz+uSMBv1Z9x+LFnbrBHdgMKnRwBFl0P5sCClwkLVAcKqv6sa7P3uClaEmN7W+CKGnFKosPjGPef93b4Zzr1oedR446ruMtP4FnCySYvaS9RlrZECxFJb7y6RjmYWYjTJEanMkzM6XUrN6hRxRL9Vt8ym+h/1JPi91CAWrOwaiVBcgPxUiMuQXKKpKSXLl+iJOlY0KpIrAiQ4rr0kvkhf1hHrMSNppKiM2su3XarKt09ff+rZl17dl511glUNv6xJMVlbO0LfaQjtYnEPsu1r+PCJTxqjwGGKNZPr+fSqU14Q2QBxT44boIRLOfuMSNX0FaetIALWKbpWINxcFm03NHyqwEUIIManya/1FR+1yPjb4Kmwmhhm0LSAxqANhJOIMXi49Q9YhU9L6a2PPXLmyYxzMlE/Ziphei3KzixTPxH4+5x+kftq20rkBQ+Dmbzta5NBHH4zeCy1erk/3EIJsWUAACAASURBVJXu0ImgroDRjt9SwXAXBkQ4fRU1yfvda+y7C2KRd4MZbXJYlrPHpgs3H5YrHE5azJDOr9gKH0OEbEmQWvKqIb3IDAe5aWrmM6KGcMI8K7PrjYyTr0TUZOME2PJcD71D3/mPZSvuf4lGBS2RghrR75iUi9fFcclfxGMxvpNmLxcgMxWU25duakpJ4tLVTYsFQPmSf8lgMjQ3mm1igThdv/W+glIqtUiJZays6CCAAtBNL+498b88v7O93WeJhGprVrAPR9/hzcQ3tftO0wj16TtuZ7xC4Ct5lB/5vKdXS8HdEnG3uZXWJs8x0wZ6sQWavooIxH1T3pM0Bljy7h/IsoCfWRHgIgAXn0klQeLU1YTaUrZhQB0zDC58jOD02DRRqmkUQUNev5RqkQm/89xsdP6hsWxUW7AZM77K+fYSyinrnywcPXqiNCqkp18S04KuTv4Km5LE5dtBJKcBWrmOkZibuEGIs7Vpm6yzy6Qjx+Rmk3XfvcRvM8kYBETpxyRedTtVRrrS2WZbu0678kZhj+sES+QvHGzh+2v0Z5ixpkQJe/xF6eBo2ihRuzVXeDtOIuU0ak13efQ1zyYzaw1YK5a33s0N4nXMrQDJiKiWoSPKLS4xvdZL+XCXXEiwtSOdanliqUjk5cZSZb8xThTHdlJDSoPlaOWRrAnEBSXfSmlM2XkyRW4KohsDEXjvyfe/9L3LNw4S074oCBxYUnnFCmtrd+i70QPPdtLLkDHsR54bgJq3N+7pVX68CS7K85bmaZ7GNNVVRp7sE1OwyQJI1WywZ8OKxSDu+FCxTAu5uAKwWEXEgcshRiJYkdsOXElLWmh4ZhW0FzMyszITk4iZ/cBV38Hsphw9mZVWSPxM9D6ZEk2TWqLVr2SpdfYlmDqOx+SNCxbuqTdjxjozV/LbNSMFutCFeO0kttlXmqhrdIO7b23tFP6uaoSajupoaKeQXennCgkduZMJYkbsZULafKFallMztOxyegVg6xSRiVyGjUxjZYJOSspb12y9o3gacXdci1nMHAwWTXW6rmrNi4GCM8YiSCzItjipZaidegl+y2a7wpltkVytNTO3UqJVH7mDTH5occ4RaUTG6k3mLgoa8jpxRYkJPYuy56Y3b770ra++1hQVTDWfhdXoO2ffiW9k3xaTZO99OoVu2V1EwX6okXXxsHWHYVIt9bkECgLb2WfPnkU/T696Eo8wvWV005FOLCBYrxBxDaDxbv/YJW4kgqlJwzZiA1DY6SirPROxdgyj6sTM1U6dGbXgMHgwwykjT5PoNQtmrSGGvKev56lNpZsZqcTKl+wKfeTh00/N7q2ggZFjM26Fsq4ITN3IepQC7bR3vziJtRFbAdaKNS0gy05pIrDWqDyEF/ODLOKLhVxWvA6MF1tINj5sWXMcd4enxWhHHtOaedGEIDwmrz9j05r8jswo4x4KmthsdlexfmWaMPQbGCslsdPOEDNc87wUmBcVbb3LCBS10t1G3q0S47ZUFzmixBCwoZyMYIdLFJv0wnpPL1EgM81SbywVEKmlIDVKv/O1VGpJqPSFU7VK0E1XJCDEXqPAoiFRcJEusYSkyczed/Bn/+13rVR7nyyy7EhCgTZL3zED3gypsBfgOPpO1xtfBxEIhktvCCB23AnU6P/0ahXiTquMP9EMc27aT1jbPCwroGUNgbKqFpuxdjW90RTHOA0x6lLMfH++p1HbmJKcVY1WVKx81QxAw2fMNus1QMPFbQ4t1lYn1ywJUss7zsx2Lj40nrpzMiXs0oYzE7tZ2KusHFKPfvZI5KHp8j/vU68RVrumhrJNFs2D0tbdhWXKCBaXyzH6pQuAbNwMYTfz3tFotK3n92bxob5ltNq7otO63y/KrIxeCc1TctsIRGgvT5rDW2Lst3y/RPOefJmfqWk6JRKLxjFn3mVXQv8Exsv9NV0pbMTXbYiIvA1ZjWGLpfApcekC0ZeMc6nXafa+IiSCi6Cag9N39GOfGm1Ru5AEFwwQqxojk+nWWkmxhmteJPn2w+e//L0rNw6rLE+ujsoR2CCBJT74M6OlTIJUcBcB2Th16hSOgkmk8AmhLaWnpjA18vQqZvDjTcW/vUpFXss5yKIuHjPiu9YqGwH+5qKknYMfxSZhBh1TLFM/NxM0EEuChiXJy/qVVtOADa5zq2kIVQozuAV3xtj4EcgSU1Gougr04N50DZ8FMp5ZP/fw7JX5uzyFM8SYdaNFKiPTkmRPfbCYYqINdd2jyS1TMsP1SMlyocUjMrIOkUS1BaKRUyFhu2tGMRJlux+lX2hFm72THIlSr+I73traPT7ml1cYNpsFrX1AMtubMkOYZzSM7W8CBV6Z0OcnlSiQjlYlzWk99a8VEFhwCU3maZRQ/9aS8uGQjGPyt30RQblwFLYp5EqHhTBVrTMaJUJh4+GyGDd6XyDFVbtcRReaICr0P7mykCwQpyW/tdaFncmsGnG1TxotgQuZ4KMrS7NkPmm/pMRr5qJgIl0Sz832hpe+8ZVXd7e3ey1sh4lNWYF1lmD0Ha5MPEuqqTT6moMPxsQAAcph5yCSE11TdWfpxQCeXsUq7iLst1czf24og7jTBkB0vksD0LV2Nz3Lho6m8WeR4PdSwGVM3bUZ/3urgLaYw5z1rUQcaM7Z8D3+dLWpMeMajcMXH9pxksURzsl87OHd787uKxYryrVZd1kiwdR1eeDoy5TfrsoSXS5Y6ypeBGspqV+YxSffkmIIlvwKn7N1DJOKi2XZTNlNjZqUOWKJLOLl25BSlH8p9EAwF4kFaMvyh2ya1XjPzLKyWlctAOJln3qISApkrncMRC8FoYQEkIjAhlJZRY+MNqG8RqWzNWCnC250kmLJenqG8qLDxFy6DG3+jg5zl3+xDnDXsaHc30a/gmHMVkYwktI94t9C96OCmpG4FLsXlyaULBwVS3yp5pUyCYGqUTNR7UaFHHyJ0VloRMYalsxNFNRSo/n0nXt/+t99cyw33PpbpyjY4L6qkPcW6XuS5cLToOwOP0BwgGzqfgbvsqdX2ce8cOECZnD83VlIRG417IbDUilAaIJxwmvhQ8m6NxWnvfZGnsoG0K/gRiDicMPtgt8PVLawgwXBiuBww80wEtCSkwVu2gXLsQ1T61vyds7J3PPO6aTONiRmsKHEE5GMHSBTitYOU0+1PwAeKY0BLtcaZW5mNQXpU2c02kL5idNZLRLzk7Vg+KpXfhUJo1z0egyH4+3FeXffmpqRE0ACDeFSBiZwU18DNEnaPGsWQzZvhRBNfCltyRJqmXizUogPJr5k7bBEl06P88IZmDvQy8Is2Ecjw0aqFIj8QteeaI53nkJERaJh5NJJJEiumB/lWqKkak9Hl6kvzSVtuUxKqMqlKVRzVb3EDJJIWyQgX0rT49xF+vuOn/jdb1+5fihbt9jJKszSvNkjK1gi5H06haFhCWyKeNSgEF/0lNtdLd6MNkCQZ/+OmrFkU9yVfiFgBrvvoAGbN0uEuEPnW2CcQIYFIfCvq4Mm0zH+Wug/gjFOK0Xx4o73V94FSZpDA4Nb4kADOpRTCwHnK3A2UEU47HCt1i6zxAZJNQ2U+twjs5dn5c/JsLpA1qNz6ixr9A2gMMOG76MyTevE4q9LUS4tM5oUNUqX2SYc24GGcly60SpcXWsizGc1vEMXfp0SDbSqupLPqq4xuOFs6Rq7yw1bUQrkyOFIFTdI0zqqVjy74CQywYu1PrdgYSFLa8MSANFUMUohSsusXIvhJEbFFleSaOmajV4Bm84Q7i7vi+TfojIdrCJAkWU2rwmxCu000aolpV8taG8vLqPElFAkGxcRixuaIswsqUDbo+AYQFHlkcBKbv++yaUXf/jtb7y+dOCCxY4/d8PTmiDNkUV+XzAEgtF32GnNVRhGQaPoNroTVf4sFHkThjdjDCbVtMSv9RlSgECgFWfOnMEqDs9s0poMA1tJsuFYUJVxdHqagBjyBPqMniso9a+yDDc+HW6ABgvH5zzBQTjIGRXP6grEsISG1BmouM7HH9593OOcDAtN/DJ15l3+55xmf8DjpLrAqJEk2ixcYPKPZ1Z2s91aqnjFSzOLcQZ3+vHEZblVNJw5ysbdck7RVUQ7hcSAWzbSReWz8oS7d+TMTHRQomS71oor/bWmq7tZgaJi1XonC916aTo4KvhJvVrj0ku4CVzCeDNgy0iKVUTfUo5/BPmK5BcJqljf9u6ou+sBbT1zrNB5SURHPG4WwCwTdCuyqAezY2avNujHovAibRGTXK4CDbGFXolZ86UhWoU2fkkiEohye73t6eHFK3/yP353MDPIloSX9rxZuOusgMuKS18JeVf6DmlmOSb4q6Brobms4BQx5kYX+BdPSRqBMfpe1pKUqjqXVH379m0axdOrS7dcdZQWl63JgYqVl8pdtYQ+JpFAxFTR0wR6uk5nl7LqLSoMaHYvK1OgBnDDyyvjRkHXC5vFhIYwUJk4KpvxjrOz0cWHZ9nnZDj6wg+UMpVwAAavAz7uEpJMfblaBCPfXE5v+YoBIqtWg6b4qDa6Ke+TkB01taZJk4oxNs/vgtOKJev2mJe4k/iUUrniFpbP5czMduH73currFjCmrcYPT7ulVWVTmgUFk34WgU1UkzR35yrRg1r2RLjS9LqFdwMCp1RSiNKTxCMeGt/RBridOkxIcucnJEu0+ojAMQS2QSPKC6Ztl4lIqpaLdeiJikzn6vFRpJLENWmZSHkqtOIGNbg6hYZZg2TqqJ/Wrd8RAL9+eynDv7sn33r9uGk6E0yypknRlXrLOiu+soRW4I9d98dIaE6aK7/5rqPeUbfzRKsKnUj4aPfUwZmwnn3log79K4OB/Jsko8Yp6hBHI90Y47xBhaYFw08Hy0hZDCg5RoLrHZoFMiQRT8SiJjx2A+YfBKKC/4E5n72Yc7JPBI3fD4bTFlFZPIGPP7nTJD8lqqbcGPBjn/L0kVfhzHbU8uSd8nyr//p8IlX0k2iZmPBcwQ1aii4KF1Z4Gr8QisVqAS4VgKnMrc3uO+eaqWiUMpVdD4zP4f+0bemMaW3JJSKPs4caytZPKC4OUoz9+BuKRCwBLs1diNQGZNoJpKvpUkpTrdfWQVvduXMEuJam5BoZbm6CEvPaLqYTISvqJdk7tFcaYVciDNIXEOW4StCsbB8a+aSimRuiLi0Cj1io9wq2tVC8fuPH//jJ1565loRa3fSUFVaC1Xlc+P0HYZmNxJEIGzOSCJYCGUnArxNEzn0EzZO31si7jZckli3FqdquKb1K5XyVwb+3GB0szUbMivaICaZ9qwmGnSgZ/MTn+C22QG8amRzKcwOuE1qjvCsbrA9/+Ajw+8Pzs76/ECvzqKzUb8yU9fFzVYeTwOaE5OVbLF0la7Hp6gQLVlzooAfxtHl75zkZaE2rmws59rZhgmuDl4OGv0CsXKVjWF00sK+u1/jxH1wqHV+K6xM3E6UqseZdr86HPzrIl1xFWGjQnTCOG1ivOYN1hVgFsBSBEsU9kXiinx2gs0T+Vvv2vM6Imip8Hf5M512sjRdAQAHveHlkpwYkgU2Oh05nKIi6iYqnmW4NMeVkPqWguSKj5Vu7JKW3AtTq1VINTqVylXv/snrz37/W196Xh649AyYavQd0mxxz4JNiGEJah19h5kYhtCSsJvra41P0ndq53JtkbACbRB3wDVPDWt6pjbqIrgeRYaq4V6uX+3Bi8yyLSdCCluDxadpDjp3k4N5OCUPrPgU//GTYZrAW3ya3x/0BtuznXvHW+cmMhsP+tOj3pOXhx8///yTs5+Zyi+D/PgE/IRRpUufV6M8V6doLKhuqcKjGEKUUnu8LGlWyMPghgxIsCZgA48BKV4INmRQrJZ9d867z0c97oDjtErf9bFVOJRNLRkQz8DidupI9Wta0p95QUWrlmRKNp1ollBLNQ6p/FNHaoa3lUNSwa9+IxHNR/K1NDVFCShWJsDMHL8vUpuuu+7SgJjEKxQyqcQR/ZYr8RRppcUlwUwmJhcZ85D6VlLIRN1nVhmXWT8ihmIY/7QxvdOz/Yuv/d5vPTWyXtNc3w9MhUFBDFgQN8JTnaGO2nF0wqxSmF1+qxGj75iEMZDMNul7G8SdOyS7VQoOKqOCgD8RTDm9SABBPoNXF1YhljMGwuospc1BB3p2Pw1omNRQZ3nahgGMhM0is9bU/nA+PDXbvjDdOjvuD3ts6EyP+ie3tu5e2tGpUhT8v7wW9+5T//qHrj0++tzRvPZrLqstrWtbUk1A1sOikoWZUlAHZ4JwsiC6Bq4tnKiZUh0hqbSA4bNoRcLIJqIyv0VAZdzltGxMQQPlHZEn47Endy/T9QWV5mWBVGJdkMoS/dVw3cs2qSUV6fKyprpXNuxKuK5bV2XMFtS+bo7IKirDWQYReUWqs4rGewling2NSENkh/IBTli490Xa1rt2hKtWCuogVi3RaF5gIxPWYoiLK4FBbKlcLjmUWqlCuTipaimlsk18mOb59uzop2/84X/95/274+p1wU038toZugdKYBwPYuA2YUnvwjEeTCK0TN/bIO74ozl1fcek8+gt+zS1aCa4viyuwgZosUxrue0bA24WqJpmMg6Bzm5y/DFsGh/GAHcRfDZdkY9+YLFugqmPzky3z0+HpyaD7f583JscDca3R4eXz3COMi/84fNbL9288h9/9ivP7nzmyuxinphvuiwU1add31p85HS5SxrjY5Yue5F29UC35vlUmS8Trcz5Am3liKuwsDezDC/QM+zWeoIY04wpJfE07j4Z9ZZ+V9XHY0pWlCsu2FkwSBbkK7dI8xk6htRb2oQip106yyUHdEJOsYt8CVNLWGuolyhArerdmTvYCZNyoov6xNzIZGQT6cqu2WmhHn3hu7ZHJJWxI6pILHbcxXi9m9BvuWJNiIxUzTLiE2NRVwyR5V8cpMSiSJwafatBSypSAvUvB73Zo7f//H/68739Y6+j7cU1CkuNX7XOHh8soli+ci5UmEBx8KPG1f1s0jEA9LtG382wyg33Kdj/4he/2OgOq8FarQrKQjT5tGBdiKMQdCz4NHBJhl+RxQOqGbOkKMQFf17xvN+oXBt+7wAEMUIeep5nQipbUqpgF4xhH51zLzsXZiP20EeT2Xg+vTs6uT2c3OEn50q1pveOM7Nf/+To4G0/9+rsQVtCypVfSMv8v7jaXAwj8CVGZbEJC66JXGJvqrhUhVwfYyqorVYklDHiKsIRxIrKNwOhjKkGRdJbeVZ1OhoucfeKSr2KGR8SvqTwpcpsGJZla7pojJun1gxx1xJfOVfAIwIwuXx3bfHIoJU5yqWTA7+IyYVNZs5b0pdUF2ty055oihM1uoSBE1uyNDs1FinOjaXKfcPaf+bwq//iL578/edKHG33rIMtNggGhApe4VmkWAwEjIUjhk7YmjiBR6CgnRrowmkLMKEVWN6QMYKSBya1RGjA6q1SnkYabMEJGNEM5RZ0Le+ud8p/zCJ0pzF12kUcvzHXD4Xejxlc6eb0e9sXJ9sXxsPtOcNidjKf3N06ujYcTE8fH9U65X/5zuC3vjH92x/60w+/9+NPzt6frrfEtewKJdeKEkVriy6tSrGD4WYpxYt51ph6Oj8lHubSvH3VmDDaS2qpY0Z0q6PQCXIr8Ja0JYQG/yoL+5pnVcOcd8+3xy3xGKLI5RrUNYdh1NRxm3xIyuREf5fwOfGVC2yZ+tbK0ocAUxGZaJNDJkwqWhjs0omwQsLcxVfio7YiKBVatepQeomK2BBnkRDKOFGjUpX8pwExvtO9mp1qJURbVpk4t9r3e48e/7PHn/yDMg+k+lcEZaeF0CqKEDf7/Ys7SVgfRJFLNEDWKzAWCrINGsQYZ1XlCPZjDI1q7l6iceJugyAPArclbALWbf63WXlqf0LSwTbF1O3vCZXHz08IblEzlanv3HMy2Japez6dj29vHV7emY0HyT31nZ0AqOyf9P/nJ7b+1tF3fuHDN78xfWw8D/AnywBmrVOxWO5yJG3tcpk4JMFd/sRGGICeOMhQNcDkjkxIRLzwhwEP3fK3/LBKU6Z5d3j2mZmUtpKXxq2MIZVyPQEb9/U2vqRd5cQxxt9nyqkulFb0kIhO7MRgYIwCE1+v6GgLOOmk6nRWZid6WIxdMji60HR+hIjWUgtVqf+IW2jryZbS1jMaky4CF72dsCw+td/kin8y1tafnInK5IPrPXtIlYXhwZMXXnjm2//PD3khTKFcjUza7xgz8VLHGSDrEBhKwXTr8H5nfh1jnJJQEaPvOFD9G5tVk5ol7jYUXK1c0lUu0Tqsob8muEp/nCLcw+HoSQBh6v5/0HirQIFjNGEq59S3zk1P3Xs8ONXvMWHPeyf7ozuv7c54Xie/QhAGcz5rmkSFv/PM1tNXX/71z995cvTpm7OzVRTq2lKloHeZ3PUk1iC9g5AhFm6NidVX/GZQYJgbGhW1BCpWbIyu7loTJMB16Frcq9pGJc0igyd4G2/cvdZ7ZgQ+gtRKtYqfd/UpDJWCGRdL5bR/KZ5gY6pqa3xtVgCpJEJQiqWrNK+UgZ7OcZVIaXfRaMTGSGUfFkMFWJ21YpslIU6HjcMcZetdf9BN2qwN1+lEq9WydklUMqXpziJdrhZzD+lUI/9pSOVa4pq7Eau1JsIPjl/qv/Tl//W7vLwuqrS5Lwy2Pea1r51xm+sYA3Vpgr2YMXSSvXmm1L1EcIicMaHouwHY4Bl3gMNWPjHd4OAWxEJwdHwU0uDTp08fHBz4CLcgs/aMO9BhM4GI2QN6MEiHZ0Aju3Cs3DWHuzvaWJcrMw9vyROlOxdPhruyoc48ON4fHl3fnp2UWHLMjZmVnHk1I49emP5Hn9h5/Z4vXJ1XelxVloUS9q+1dq2uJX9zvqh6yVpOWFtbgwIsqW61bLAab9XOHmVKWixe8711BBNsvKfWulGiKZyZmZQ6775AMKzvi02NI5NouE+0IXviRbgcgPFkU6Z3fRpZSaYOMlED5GupLcl0plX/U+8JTYspMMHXpaLleVqvlioXFFJCkpQIxbkJwXQU1t574Q//wdcHR5OVKtOyga/Z52IFh5G7FZxWQGNIpyY6kSw+A9eao46q7bzKZum7WWfGEK/w5wXQMwCNBAYj7thEQDWfZiV9w2V3zpTzZCptDsjAclzFN3mVuCtLlw8HoN3ntODlnSLuNsgrjDRe0ajvfpkQkR/Nm/Lil8Hxze3Zca3JKzg4F3fmv/lzvcn9n/jh7L2lJ7B4LfX1syy5YjiEJsVmueGcpUbSEO0OXWakrDU4ryEB0xU8xXB5JQ9YRQVVjYNT7FXLFus7IocF73fXlR0gs+nOsrLaV+btZeyvXWWRgmA9FflgiqwWVJ0BQTBjCqr1z8KadWS3QJk0L/6XFEukMX8sgoirA9pR+JjaWJrLTEZScd14XwLVlUVyEbJT4/zi3Fhq8f3Q5JX5C7//D762AdbujIC7EyAwGE+nQeIdj3cyrUVgFNB3DGhig79sKwDEdt8xBnwKiiMpbF1PEyl+0Z/9Sa9O3IVgarCK6RuCEU1LMfu6Q5TPnj179+7dYqQKQAyeBR2k54AQoByGmLcR/w7OTevA5e85w93Z1tkJB2CG27xoqDebDMf78pbG6VG9H3xZtp5brAq3yMs60lfDfu/f/9DkZz7w/u/MPlL6yHsl7r60fCybw8hdJJTkmpR1DrxQsrmYrOsbql1okgLpAOkeOA3fZZWBPr3vHoGnxIweLKMqSId3qrMqGhNhKHjEq0pZbDJwxxhVmJFVVnsQ+YrgaN1RG+RrqTmJdMki2NIsMRVOfaiQSfJpwSQlvjyJLtJVzvRZkcVnSmiREZVZsjaVm7x85+Tlu8995be+cdL+Xjtm0Apwg2sSd90UfPVMttc/jmF2L9ER+o4xwLVK30kEQKwFwExCiIDvGXfrD3NlQ8o4OizTRvUqfNS9EQ66aoml0ITNsnbrDz4tgA99BkSbtcrAyevEPDAbTS8wRn/2iB8onfWHM36vFILOOfW7b+yEZeqp1tFH1lmp9DqXnNz5358ePXbjh7/86YNv9j876YV/XLV4pl+ArO5YuS2URhWflTUELljprqaaDSmatLxai8qugYOFzXZWmdsmzrtvH5/MR6Op7mcq2RHMqvVF/VKd6iwbUJ6+LGKGnLhgzZDRhbExXRnmdeyRWxCcTFoZRTVBp0Owi5ool6zLVGR77QKr5MrtELOdykUR00eSji35EpnoHt5ZGqWrMLpFgSZpQpQqyVZgkRrH1L7c3FiqB2ufvvDlf/xN+YHAwSzYWxqd/ryIkXUznuUyydRJNIaaTMzT02g6HWoH8dmJI75Z+k6HAoiBAz6ARgo0gxQiXBabl0vckz0BmuiyphLxBBcN3dlux2bzKk/jg4g5DK1qACRYD1mHEQ9SURAl5jFBVNVX4jprsDM/dd/x6Ox80J/y8NDkcMAPlB6/sl38RGl9A5Ia6DX721YysX4c7v4Xb4yOv/76rz72B0+f+sKN2TlfncKSGYYy9adCRpJKLHYzubQlIyDVzqs1ZVwrl+bGzn/C1imzX9RYgc+HJok9XTpN1Lg92Y656IfkAnKys82+OxtMcPeFxOZijTpPhWYJBcy6KzY3jPwKL6wFnm9hGVPWefXqq4BDZpE6nSUkJuGI0jQHgxEcoU8iJD/VJE+tilsjEmUKElK/qlFDJCZqRO/iBicVJ9dkRExF3SVXErTWdKJlxbkLFS7dIg+NX5y++Ef//dd6hxN4hfyOIQEOZi1JCQe5hGga1zSimVkRicZQMcbiQaqurITV3Og7pwzW8uPKtXgWBBBsgLhzL0GvYxhY+ZSNjspQhg6AaLoyjmK6lLKR1TPcZTUElKdpu7u7d+7cCahzVRW12M2lebD1Cp+ElLC5e/FNVapIo5cMKoxZtbPRSjOV80TpqQeOd8+PpvNjJszpYe/4xs74zmDeB3moaQAAIABJREFU+kM2SfMaPUp076n5f/H50Zv3fO616QPJSovieJSuEsi4FScln+zN3KUgVabqJXU1XUUp00LZE++cGUnPQ3q9aZ3i7kxG1Vuyvq0Z2p0rCj9aqXvneDzeKjrv7lNnQJlQzhPKpMge8UUBTz5XMFxXV+kCBQo7ik+BxckstxYriIJL/M9JRWDF6cxsFjjWKthreiJTVLjL+EK/JdWClVqNk+LKRqKZQkt5WmihW/MeHL/Ye/HL//Br/cPEQkmttrvsSQfTtWRdoxOSY1wRzlBq85FSMI2N02XXLGiY7Xa3z8SomoAlSaaNMSSuRZUu6P/SL/2SMTbQRIVrUs2I2VSqU2vWWFy8iSdTQQ9HpKVEbGVK9kGxPfQQwoRisdZyN2UPx10G27Ode8fb5yYygQ76s6P54fVTvbvyG7fdwcemm+bsubAz/5UPT88/+qlnZ+9hj2d9v2cRZXPCqGwmRVqvt6JEp4gpbahjj8BoPSA3Rx594YFZHXs81JcWadwe3SpecKJ1KO4cn4y3RgXPqpZuYb0CXeGm9FPUEH04YR2MKuslVA+epV3lmqqCFF/TX0rW4xueDHziUb6UJRdRgkYTp94lJ0rTaCzm9KxEkmLE116azJr5x81OvK99/uKX/uHXhkfTyJSovH4ZXV5LB5NFVuPwHEgC6UCNqjpLoamqac+qhZVT2rSHugiYCr0hZNqMAFCTC0SZAvR79YdTMzW6xE5tt2PVmTNnDg8P63ibgKVD1+410YkHo5BAxDXcM9KdHW4zGEehFXme5NkoHzFepi7n1C9Ot86M+0P+EtmfHvVPbm6f7HOxUADIhDzHXci1FcMYhlPAfYtVw0eD3i+//+TjH3jvt/qPTbJ+oSk1JYvXsT4Yy1To8M9Vta2lYM9mDUi1tJQ9CmakIGNPOKW60mUpeyrVUK5QE9zdjWBxxJLOuPY9M+WaV1t6g/1l60kKQxvuiWaVxDdRMkhU8EHRRuecpYbIGF5muoajn5EJtBfARrE4TxiAhsRLZriWCuJKFpEEMCZjxubFV3MtJdWkODH+HvTmP330l9defOIffbNf/DQqS7wtYTCWuPSab2ylCAE5oZn5DzSuUZSVXcGeLDXB0rCHVd6aGUypKjIYUc6Vv35gxyTkV1kQCnPPuIc1fePaQM3fX81a0KEUgYilCEkP9EyD07lxZMyA5lYpmPro9HT7wnS0O+lv9efj3uRoMOaJ0ktnevkTCPaAfEfAwQz6nXutRu2ZzHr/5zPbL9966W99+vCpIb/QdNqqi5xPL2QDzpGjxHJRliQ12pCOKC8eYhGO8VJfLBykRVQhtQXRFUIJ9ydBRr36o3ygcKl1JVvLs6qnjscnW72O7LtLfy3Oo4VAvEBHPKwFwDSOmkSy/hFDBZZgLtDaaJYNmSAuFMZO7TD20qKBBl7L/lhci3hr5LELx10kMO3qJolulPT155roBVYoE2HPghyrOYroXcMqSEnAiGOUyZh1ydzYXpVKCsUZGDz70NFXv/KXT/3us+vXJtvhtlWMHSir26lKRpSrR+cIKEVI5oaKm2bsgZ4SZ4UNpbmaHmsmxtjr/mDM1fS4UnQZ2vgE6kz+7SQzI0bx6YvMs/iNEHcq23g3ZGJRnGgcHeNNDLhpBfA11JaCkVNsZxO5GBOKKLOPvnVusn1+MtyZwtRnJ/Pp4YgNdd79wpM+ngF7Mqcqz+JNiJlJTfcaj6te/srV3/j0//fCuV+8Mb9AQ6RG1ghZIGQtkngcWO6XruP0jXzbDNWpXkviI0ukrpMKoSJJSgLMFkCLurGFmvyqqNZlMcmMfFEhzMHRWI2fMUgd7WzpeffucHcxvam5SHEU4PiXGthSbUawwdWUPRkVrk+q5kLr9ZaREECQFxglSFEb6WWUSCH9p6okqho10S7EmWUulupmsjzJJKLV8aEmRKWU4asakRU98VlaE3eJqlf8y2QyLy0xLUTq7uzOT9364688+ZoPa3eq7blMDkSkqCSWGV9HkqxiZh/ZFOKLiqja53YiRG3rddjtBITbIAKK9WWWJeBRIGldXp89ooGATug7RNT9zb+RozJ0A+03f1xu1GauwJEz7qtPpjqIzSwMBhpCC5Z37SgRfoazOrco10/93s7FydaF8XB7zp9w5uP5+M7Wya3h9Gg4L+32i5obfR50UY13DHxwDEaRd4kqgjZ73r87/7ffPz/3yMefnT1SQM2xh46rUs1PRhmhRrqYdgclzOlah/l4kaIYOU1p+0sW6NqZGZotfK1kK/JGmLI0p62iUp8uyzOgofS2TTIcrTFZfVPHnqhX5Gupg+J09828wp/l5VPZuchLnl5rUbt0SZHChL0mSrpkJdLzUiKp89Mb77n5L/7Rn+49txftM1oB/08WGlY0I4VUDFzCEBte3QrMwwZ4I2ZUJCEFqqtmgQ8U0RMWW7ipCgJJERpStdrcchhjJAR23Qhx7xorhQICxvHxMeDSeDc+DGKymkA5F37N6BpENmy4HS82O8rt97YvTnbuORlsM9kw4nvj21vHe6PZycB/T31tRV2DCIObMMlGo/kkfuiG/dag93c/PnnkvR/89uTDvAgzEy7knTNnCrSfuFmT3GYwDXc3PF3jypuFKNMlwM3BZQKZSGaWLUqMGE6RSGbeqZPxyahD75nByMrcPbmCBxyt6kXYVRXiTNzrJTbu2A7KFYabbbjJewqvqFAKbqkLkKNYIs8YBT1rnRt/UtCiLqJl4vIJo5xYVFciS8vKh2XJJ9v575y80H/lT/75d2Yv38peFxbSOTH4H1xItGllUCP6Lke21WRbDVkE7eBKq3XnVGb0HXuwalWkBb6eqhSI5HxRKrX+JXo74gQ4pTF1tttxTVgXd0Id+VNAarDWh71RDf3BfOv87NR9R4Odfk//IHGyv3Xntd1GX6aOF+FLmaOl0cY2rZyut7nAKqKB7DGsDpnxrPdPvj36/JVn/q3HDh/vPTbN+oUmVFGwW76UWGOaRtL0C8W0RUe4Z2b13aLu0msrRLkdrPJqMdwURwAUuHKQzFOg6ZnYF5bIyzyS8+4nJ116zwyIwG2WSVWm+YJCcjg3NDxNbaeGvzh2E9ORoimweqC/1CUm79dtSwX1QoZBNHMsJhBiBElnmKh+JnAaHj+uqiX05IyBIcIRKlommq3NQVwfqlQ0fFIQKqGO5/id3gmPon7nye/93z/Yujsux9qpDMJnVcKCYETUahVDjWwZspQNfmKGe8k6Rm7wLwAOBMwgLlw5PosPhkYsScdC3+1Op7FexCAKT9xpkjW1nnmlS4MmAapHcIUNVnbcj46OXOK/iqwiYBNJlM4PTmzNt85Mt+85GZ7CN2UqGd8eHrxyWph6WwEHpSv5bKtCr3rwsSWsPApRxMY5EcRpEW7p8wdB5uw/f3U0mb30Nz92/Xun/srtefS4qkedGxOB8DXNSnXttAVUmLpQzEKvFJOa4BM1MBaTusHdIxz15sf8E6bh1TI/KS9VWUJHO9vK3bd4EDArfwNpuFoWCVyYpzODIBoj2biRVNQp39aGCww1EZBGxXeQdXXldJtP30hf0hbpURsoUV9zITFpp+bxl1IdzvpTTZISs3YYN1KRBZphLUNCy8eMXHQtxU0JnxYk99759ffs/+k///rVb13ajtPXfGMHayiMEzlUZO4QkQ6JRwya1BGuzBKJSZjNHQUmdYEDAB1gsgUMUJjE5UZYruvv8EdlmjhO4MxNRswpwZGIpdPBFpJixM+dO3f79u1U4mYvu3WAu98b7sz4zaP+mTvDU0xCnH4ZjG8Pjve2ZycRtu3DRbcydH0Ibmu24WxQ8LUmmWciSYRpkYBb1tk8ePDs7O99dnTtws+9OH1wtbEdoYDOsGhVc9e1I7pm2sLJQlnFIbsGEZBsyqQIR+mUjOMxRfBWAb5u33eNuwuDSzAsYUNxE90yFCe0971kUnvVFtVUzSQphdZ4QS+qoHxeNZOsHrVqEXWVR2MiNhgfsAArUfqOIAn6KR9LkaWLSJGmJeNaCvL/8PjZ4etf/e1vTd+4vdiXJDMz2DrFJ01m6fGnmCy4tsBRKlNz+4n2V4IN0ncDk14ETBZxkDGToAGktA8INYqPffGLX1xLRPyNQ2FzZAsECWK0DgVQIwAlnwUWUqSF30wtMCAza+PEfbg72zo72To3hbKzXTAbD+cHu4e3ZtPjxbSRaXmbia3dBPo3KrPjcEjczJg6qhjbFoo9079SJLeHvf/0E9Odd33mpdkjqSPv1GIjopTCRoWDmCTo4Yy1d++spUFMCgtamyZxn2DGF1Hz5O1EZ6YBfUfkBs+7rwAhfgmYkt6dcdemL3mOAl+TdPkWQGP666m/gpivSVmqtb8tY8kloovYeC5pSPTIqsQoov/sQz4lxYmvRJK5Inp6dvC+O1//yx+8/Hs/GtzJ/4u31hmtQaw+RjGlfMmAHogphTZITFMmYxKsks82TWI1twUdMLltkEGfCBsxydVP7YGJO12+2khXX9kIwBlTt4JgV4EPQf5opjvRVdaGJuSt1wPeL/kYCVPfPj/eOjfrD+WnOafHw/Gt4eQu736J7uDfKizZp7HNyTiUnHPSm3imTZSp4R3WDLg7v9D04Q+8/zvzn01y94hEhK1sE9oictnYGk7v0FmbaFlunc2ZFDN1jwYvQ9KcSbkorMtohbsvo5AySQe2I12SaQu5B7opTY1edrDvMk1SOBd8tlFMMpTTd1U7Tnwg9oOk5jjNfTPTJIKU0iq1fFx5MsmUORkt0Ju/a/Ls1uW/+N+eOP7B9ey3x6ACUgtNAlLj64Zt0rYKcRTC5VjU/HfrK9RSqggtxSRa1yhxErauj+36gGng89nmHQWgSb2lsFsrjMbKfkNZ3IVAxCoy7PhcW2+BAJ3dtQPuroEFZtfP4tDLzr3HW2chK9C8/vRwcHyTd7/knn5px6pS7arsS6Vq8RTGMxnSNkvik4SA96g+NpxMe//H97cv33n2r3/s+hOjXziY8/yBBEYLLK14J9Uk2/yk79Z6VEwurRUMex340ehvwNjmNFc1FohC3XfpjpB+KJxF/lCIg5jk0XdVW1ylHO93h7uPt3q80LhK+ewyhSgIM4+PWVN81ZVlkcJ/q1PAbKPqpWJU1/pu4U5Ypq0DuFU467W7ZGmFCSNKFhNxGa1RMyTqNEQX8e0cbSXIE6t6ZkbqMgmZFMmRgvE4E3WLFLkQeC7M9t5396tfe+by7/5ocDRJs3all4vfSArOZVnd2Ouklu4cfAc19xJ6W3wd+PUjtqyjh3sVfxaOSQjTW5BMyvoXDGBwfRVOQ9nRiDyeAR8yDaDQBBmiCjrDGdmFCE2mpcEtGWzPdx845vQLR1/6Q5h67+j6zuGVwXyymF8KKgX/gtyNZGFSQ1j5NMf8E/9B2DknZN1tuvsoCStDD/3Jy6Pnb9z8zZ//4+dPf/bq7J5Iv60KYSurpw30Vo0Spm5eZitXYuWrV5tXabgs/Vh2mvJSvSGhCE+hIHbnljPSc5LzrJa+6xhQxt3ntX5X1QMFmh2DYn5SWEZujjoGlFjdIZNiPMWkjfP1uGflW4aMTOsV6Hs0gcnstpjLRJlo1BktVsvDYvpTTRGB10N/hgSzEJVH40xKqSobc4PZ5KPTb+29/vR/883B3tESZYcgGl8qxS/Vriof1EKgUngtpDnuzCqqQpWBOAV87QxNs2HOsk6oZiSwYBJ6jL4Tr6anVKmQR2Wwm8bn9S4NI0CD+DQTkcQtmqCwSQg6+GQqQNVveH/QG2zzisbx6OxEEJU9dWHq4/2loZ6Eoji+QT6aZxjzhTlJnkDYdDyTadFc1NwYf171z8xj7mEtWavt7Wdm/+HHescP/vyrs3ewHqyV34gAGAKpISk26sK1EUs6XqkB5WnkgqzbOp9ZLIRHlLIq04rgiSXfEemHQuSgYqxbm0pZ3kGgNmySQirox2u94blhqzI7NSbZmZnFiZF7aTtTknFaLJI49B57mfmaAqTSfIz6k3dNnjtz49u/89T8O2/IL0GiliWJddAm0sz1KFV1E5fUDj1gKQy+tV/HWmBhscak1TW6QC1toSCo4o3B8US50fdGd9+pJSRxTzE/lIMOgYjhCL4QVh3UBcCGzKL206dPHxwchFRaW1fxHU6e+v5wPjw145ePts5M+sPefNafHvVPbm6fwNTtZj+vpF96F/hoylI8hzHW3GThXBQ/oWo8E/8kpMxIXeLnjQ7LVHV5lzvD+a9+5OTR933kO7MP8ycW+FzR6Yg8LQ2kqzPqzjrjvntkvYvsQX3PzZOrfRJBqhm5vRzNsqulK6YIUNxvhVZb0Zq4WOF7ZvxtjW5/0FoAe1zn+u8OOpV2X5o6r29JZQnq07LFeHYQKLUa2/2dZ4FRVEa+loovLmLyAyzLQYrE5fpbvcm7Zy/s7n33S98/+Opro1lvwJsHwcooE0tSjO6i6vZjrJJGXdYukW3ahkkYtpa+I+O25Ar2l4NYTkfbfU5DFaE/2Bl3c0q7lbHGm9s1ZLonvnQVBngKtyYGVj519UfzrdPTrfPT0e6kv9Wfj3uTo8F4f3T30k4v/EEbH4valgk+y4M8A5hg7lrNRfGoRm8nPFE+nvb/6RM7/+b+9//qx+9+b/6zBzN+xtazaHgx5emm1pw7Inzz6K/J4WusrJGu785NjmuFWrXowARTFzy1Y1d6dyXBaQsSEZO699ec+P3uI37lQZvpj0JEfqQAoAa9JVGsunUKC5PkKIh0oT9EpR1HpugIT69qOghU5EWVgIr4vgxXGyoR1Dp+ddhGQ0j+8ChYsQCBkx18F9x6W/PJu+cv7t743peeufMXl7bm/dPDLX7wUN6tTj4LjRVUIzf8wV2EvV6dPb61RLk1W7EEVAGKsLqhBgMkYAzGt8ZF6TKAgmZwU0G8iXqrE3dxQQ1EwIUo0BA6cndofsN9z+HhYWs+5F8R3bkqzD761jl9ReOpyQCmfjIfH47YUL/7xg7H1n8yg3lXnbbjmQxdp8dclM/KOinLgKxcPGzBL70wenX/5V/9zP6TO79wZ7bjmhm2llVtulTJh9WYx4JI7yJLNia82qrNptikEPEggXbJnOWrpazGLpjaY+LRWB3lFAsKRzs7uu8+8Phtpmii1TWq2cGBdiprto5yWOn9ybo/5pRVibw0k6+qmBpE3cSqwn1OgqMLNjpqo7EaZelcaXDRajn4LrNm/0z/6J3Tl3dvPfPHzx5989L2eHZ6yl98J0tvB4dNsdawfq1S0godF6QINNSsQltHrAJVo++OKIMY6z4WslhvykiqtuP4ZhVmBMHflJQg7riaEvX00RfjQKlzMgFNrKOK/jPz6ihprizn1LcvTLbOj4c7c/74IUz97tbxjdH0aGe+5rxGc0Z1SzNjsqxBOKqNWyIyUdZ4qW3ZqtuXB52nrw1/+6t7f/dTf/DG+c+/PnugORuibXXZwosW7ebqalpzR24n1Ln5EDzNXRe3QNHq3zQSa/RjGYNoYdUa8eDZ2Sis7Lsv1WuTRlViuaSq1EXUg1pxqYKNCgexKoIUQ1EXooFBrAqPm7q7Dsdyum2VEmeV0Sz/XPkoK74DHsyn75i9+cDk1ZvXL/3Za9PvXB7ePeH9GblPNBolhV9BlyHNTu0GIziDUdJOWSXYz+cs/RBlFv2OvEsQSwwr/kxBPBR9LzrjztByNy4GChUTbAyn/KabxL1zT6b2exxSP3NPf7Z9JDPXZH5ysHWyN5qdDDa+p97BM+74GH619o7ZOSoRiuCi9mefTEdN+W21S/4qp0Oh+rZ9tXoLSp3emv8njw13H/roj2bvs6WiQNgzK2LqKl2HutER1jWe9bYjtinurr0jS/tibXcNNpacleNENhJptwczgMlstXtWNTnSN+5p7WKVCUxGolhFcinXcpsmpUplVF6U1E24Kmy9WyMj31Wsk83emZ+8rX/lvsnlw703v3vp6InLgyt3eDNHiamaRQc+BgtMenuyio3EMcnOqLAgbsQAxjtMnU9g4cbGzDCrunOrY8iw8Q1W2FmTvtPYJeKeOlRABbTcpz+ACcsQ3kjP5VVK686ePXv79u08gRbSOa22dWF66t7jwU6/p7c8J/tbvdtnD++cyK15l0I3b73yKDIuR6CLgdActU33o14mC+bQLnWgnJz8az81+vmPvveJ2YftT7YVzANM2S1SYhktQhW0LBfZFEVetiJ91aZVOtblI+P+ZwVlusAcO23xRq9bsWoFi4ImC62c74wnJ6PhfFDxVVoF6mtlySAq05ZalZUpvNYwGf8SxPq2mtCKa2mrSn2sxSpfm/U9+G3PTx7uv3bu+NKbV649cWn27I359bul6Hq6jrAbt2ntVa9ZDWGPbd5UGAlmnsR58rb27FaHXB8SW7XppcvBWzCszk0FrR7ZzYpVTvOqnaPHlK6RGFoEOjXvbEr3CY+dbM23zk53Lp4M+HmcKVR9ML49vP3y6eTL1Hcg8TY7lq7gJ64APol32cxOhOEKBFySvkGX6+ZKw/bNn748GEyf/uR7Lr9w4QtH821Pd6E5IimkkjlBF52grAO1HUSsaavk7xXgaqgavtYfQbE1lS182hoZuUdT9SleRcojV0VELekfb2+fOj4+2fI5716kN3CeDCIxtWG4ylutRCfDKp0BBP2MvPK1lCxBnR2cH8BCNzBoTLkRO+xNz/cOHpi+Prx7+ZUrt37/jf6Le9PDic6xJZFZFbenHjv1eChGQrToRPb+WJcbJV0+fN2BBpslQHEJbd5UOAMyI1B2AmSGTiSChZlixYlLO+7Fonm5dJjhkiewqfQzZ87wZCqe1KAB/d5wZzY6M90+Pxnu8qea2Xw2HN8eFPxAKcZ0c2+7U1bhVARGKQHvYi5gZsfFF+t2g53qpRq4zCov6VaEmAU42Le7u/vQmfF/9VfPv7D7uUuze1drVjoZJQurbCW0ub1dqkEBGUO0MmfytZIwB7SqFBprhPVehIG5Rqxu9rL+xJjPq7rwHZF1raleProfXm5OdXXBShqiZpaR9WCq6yhSs/K6uI7iumV1RK9VsjM/Ptffv//k1Tt715578+DJK71Lt2fTMHQ9o3KWRbDq2hoEH2XDlJWaxSjD6EpJNBMagFpKV74xYL2meDfhKkvfASQAcaer0FPtvqFSP/oWOn/+/P7+vq+0t9xwd7Z1Vt/9siOPiM/GwtTH+1vTY98JulMU2bV740xUWbp84E7M4QxRC908fN8pt2c+grKDG6ydHy5gV+bc9vw3PzO8/cDnXp+9nS6W5UOWH4kh5jq9zYhQhDbr86sLT6sJiO2rZzQtI8nPJpWqb1iJyrxF/TiMt7o8wcTNpWfvCHff3pptyLfz2iHpLUFWZMJSnvJjUsQ9uwdXN90+rxP50/mF3v69syvDw2tvXrn+4vXD710Z3DpqjK0vdaS8yg/6Xpb2Leto5AqSzfrIqsQKXrkCBj5KCGgI0kYUAhcO1qkdQFpHG0vd7dCQAMR944QvzzNCEXeY+vb58da5WX80Y7KbHg15RePkzsifqSctZLDRT/h0MrELcZsC6oy0sq2wkemYOiOK8blqQDfvc2wW2OBxHUMbvo4vMez5wQ5MIhHvAlLAvLA7+Bv/2uQd7/7g0/P3d4Ez/9hsuru7oPTfK+ox9dXh01ES0xgXpb2AoGeL9HsVkcIU96xqodQGMjfej2IA7V6h6Rs3LLszbIuhg7f58T0Yu0oPDa5eHF+6c+vac5cPvn9leuXO/O6GlnRjyd05CmJ9ausj8bJLJAVpkS1h8AFCtpNUTUVzl+92fP5YIRBVbf5SOZtwl5I6cFHZKn6gdOfek9FZtuJmvLFxejg4vrlVfPrFv7mAXtkw/1oqSLYwidN2o5VEsNA21OGd3QSkGMMW4CowgNo5BgZ0bLHz6iQkAZZgwDLxkXvjYPrPvj3/5TtP/PyHb31z9smTeZjBXmBVcRaErJvc3YakQZfZBNtTtz9Z0IolDhSarCcNEMS6+pSjHd9PWlsh7ga+kfWCLvBRXvyOSB8NDcmsdbAG6o2gNV/NA3YThnm0VUfYZifYTCvP9u7eN79y+ujKrZtvPv7q5IfX5vvH/NE9U7a9RKN6tpfcnQ1Bug/KDkv2PMmNK9IEc0ha1FxDYB38RZq10tOw1jqSVhNYuzGMSPEdS90ddzqGQDWtNc+zIpzA6IuP/GBnvvu24xF76ozCYX96t3d0Y2dyd5B8otRHj48MVuHTxb3ioye4DK6MzuCGCZ2MCaU1nJFDxN9+uz9e3Yn319CQ5EYMA4c7d+7QIh7hYIQz+qx1dBzBgE0axiL4M/dNf/3z9z81+szN2ZmGoPBUS683SXQ9rcgQWzUsIuvKT9M2p68zFIZKokPzWFeoKqrpqWyYDn3De23TSgNNgR3OzGy531Wt1rhGSlVGzN8aqUJOw5U7DteCYf5NSEp2wTBseHBw9b7p1fHB5R+9vv/stenzNwe94TastGtLEmsBMz+Ut2uGQQCgPZmGmc10OjgjwGfSAZqOYxW2ZRrWdNXF+osNY0WoS9w7e06GF0HevcuLlzKOWLGJPtie7dw33j7HoyNzfoN4etg7urbD61+K0QySS5dgVaZhQfRXVmL0uv49GEMRVUlCWVOnKWR0VW5aQwVbNgxezsEYRhy+TYuY4wA205EY18zgyb9RvvvC7O98cuuNe75wdXZPQ2h4qu3mprvgKYTHWA9fuukn13EozSHjgrW/6ei3PHdfgFuOU1a70evos6o6ZhvoSgFXyTquVtFNO+tjeYfLa4+qIgWcXB/Mxw/13zxzfPnOzSvfeuX4mavzm8sn15mHAa2DqxKG0bbk5F/U1BbzWJJwfmPnMAQCl6xfG8cQw1jKQYwObRGP9VWZYeCTWuXBLQBx76CLAEnygHt/NOfdL/zyEQ+Vwtrns/70qH98c3u8P7TFej2E4SToDBhYqifCqa/NgO7pAAAgAElEQVSuCd8lVCDZeBGDkLJEqJ6mBW8gk1E33Yw9b/7uVh30dSUNWzehcOm21YuLrhp2fnv+9z/bm7ztEz+cvkcfUy1W0FQuHEPORXQmRNhGFi3z9c6YiZE2uDoDW2RIkWHVyXqqlVW6obPPqhYhlmp30aW4LbgoX6+CT4Zu09ilsbkwUmwL1MyF0nSMU7EX5/v39K70bl168/qNJy7Nf3R9fpJ/xJolzxamrq3mzBUYxlLOYpFu5OaubS3jWSx8l9V843w9iQS2QcxIsfuKZNbG46v0HWtrEXccF8bWqQ4wlGHqF+87c7K1N9qd9Ld683F/csSLX0Yn+6P2mXqq4xlRHXQOjMQZ7O9ZKYNXL+l3CxRhEBKCM/VUpas0NCWwqcvgvQmkYGu7EaDKX434pBZSSrXRNKRuw4b93t/+mcmHPvj+780+vMEj7zgMzSzVnLDCbms9ZYQYxq9YdS+0wlsqNnupN6PNX1EVtIurdMout9T8+Hl8lqxi85ooVrk7wVrtCYrtUguXenMpZ9MXlUFbZ/h27+TC7MbF6aWjm1eeeu3Oc9dnr+2XoAlMzoDWQRZkxy02vpGMr7IYEUCJtYzAAgcZ7dp9BW6Cqa4346G2znvaygcxzAM07hKJ1CLuwVlLZRD6w/nWOV6mPh3uwNT785Pe8Oje27cOePfLPOOwTOV6AhQEtG5uHuMMecSdLAYe440IEOA6Flpz7s6CBixgkuLHZb3EmDqfFARSsOVIDE+XksL+BFWUVWjymXc7cPdPPTT5G4899Pjgs+Pehh9XrdauCqXEUcV1dYMyVX6ZEyJpTp6S2vhllw0zcBrGbbmf/Ppj9/hEuXuVsn41VJfy7FARC7uzvtbkxijy2prXCniCtlbPoDc73z+4OL0yuH3p2s29H14+/tH1/t7R2nLZAkzRtnQydWdLbCiVIQnhA7T27yvcokbtxtdTGNh9BYZ1DTQsBzSsah+0FESpS3oT0PgEz1orNyrolZT2di458bJ9cczPHg2257waZ3Yyn9zZOro+mh7tzKfyC0ewoPFRrdY11BBAa0hzWLVMRgR0Wi/jK/jxpro7bNMCagMW+HEF4s4IZIIwZ2COMHgxjF1C7utAnoPsNV0FtVSRmhZ5quMbr4+Oxpf+g8f+8Ae7X7g+kzfStB9wpJqtW2uz+Koj69GgSwy9RDSpSo7xCL1PpnUibsOwadD8m6oTv94IKbbNrwNVeuVwZ1u5exefVV3ToQYoQgpvq/6oy7rV6+8P7UiKVdG4rlJhfz69r3/9/tmb+1ffePby4b+8MX7lVn8iZLsWwMzeTLObosgFQDAH2mrCItUORWbFMXKZXNQyLbQdd0Ajt1NbmVjezdfO0Jt4GojBb6vvuDOEUNEa4gzY7Xsm2xdPBts9Oac+np/c3jq5OZqdDFb31HnhBnuWNDLTYzabmLkPulmTrHbIYpKD2sAT9tOBgKfZ5NgBW9ImeHaom9EobyMQhB28RNhiZy5ji51hma6j0nXxCL24M/8vf3545Z7PvTaVX2hqOVRhYetMtCf0ZHdSGHvWSpyVtqqVvsguviraesoGbXO+SqMz8DH0/RCuAVuVCjr7rCo4LDpUZ9poXGR6bw3UKhW1mb8K4JWqK1FoAZpfoZ350dvnl86ML1+7cuV7lybPXp/dPJIXyAUPrKHQVkhRcrAEr6WaQtZQhm1DhM0ajmEsaqxipZqPVSx5rO8V9r+qQeFfit6kaXaP4V8quKSRB9QaKyNSnbiDdaNbsP3BfPvCdOfe48FOv6cM5+TW1vH1rdlEt8QKseHN1gcHB6W8p1BfyExPnheyyhxdDBjb97V5EJ/APxp91DLHkPXJGMb46dqfrszugoFgw97E8mY00nm9I13A3SZtXI9FGYliZ7uwM/+VD08vPvrYD2fva2AVW2MoTK/+U6oLsi6bZjkkIye5wD4bEQUCG8tqmU0JvlQJgutvZloBrXxfald19llVYe52o9gJsp7261b6NF1pmOv5jN80fWD2+vTW5Vev7n33cu/5Gz3+3th06DINZRmFvgekoY6v16fdpqqdPwuU9QFAA7r2bcOXqBprGYbJ2yHSqxP3Yk5QFhqWhsFIzqnvsKd+qjeXEdYf3+KHhLcrvEw9+UqZ0pY0XCAwbmWspbsZHrggEVkuVrwBL2nojryMmdmyG8Qt26A4NXlTYXE+LbN4OmMisIPsUHZ6JNYX8pvuRnPBTgZH3n/5Ayef/MB7Hu99atwLfNtQ3BLjg8Uymbn4rZyB0c9c1HIzMlWmE62GdGo3rml+Q97i2hdv/mbtrDuhrEiTttXr0V6vS9x9cT8knpzcd89CdcNpHR4Mq/42mh8/2Lu8e3Ll5rU3n3vz7tNX+2/eqes5FfA3GtrNrXfbTqpsG5MPrSMAvu1GVcAnr4hR5Mq25akNks4OHW1vwTZqsR3VvM0+EajWJErSbdXKRqX6PV7RODrDE6Xj4W5vzl9XZoPx7cHBa7uzk1ojDdtocC3bmixcF7cytgGFUUlXCCqJ57nLZKRNw5L1vqXjIEzgpsLIOo4HwsV/GQBnO8jORMAdZqPNxxhsKyDu3CD/zjPbL918+d/79NH3R59q8xeaGOTwl9xt8mVcxDmVrBvgkpl5q1Nr5lhUqVU1zo8X9ZWJgQBo8Fmm0DrZiEkiJopVdxX9oW2rYkNeU6PfVd3e4qUMeTJNpuufLhRg+QvGst8L6J3lx034WyCgDbdhjx+WO3jb7I3enTcvX7n+B6/23jiY3R3Ty9EGSqDaSqhh4mUtYIYnUjD9ltAYThR7sKqsbUDt/j5P8Yb+Ms/SSUXYBnrFy2g4PHw1QZycbWAYnC85hGn7Wv0Vd9y5MTLX9G20yg13Z7xJnW314Q40fT4bD8f7A46qz45DzqT0OiyKM+6lbGtHmL5pdFcb/bTdAi3CAyx4OhnQ5dH6dvApqKU7O+5sNjiEAZaBwIzmM5HRF7zekSIcZMcNChobMIs+ZRag6mKd77kw/fVP7b524eevbPoXmsxO3RWIPvDqIuMLM4sKFufZvkRDyour9sjFi9bAslZJzCR9jsGsVZYUqGdbs4i3ve+u21u4kk9n1cMt2QMNxLt3X7E9P7qnf/Pc8RvT25efuzr+1qvjywdyrqtTgcWC1aGFbdoKrTbb4McFqwMrna13OKfPOlLBjMwiZhs1srxmCmwwEUxYvgEtyK0FM4PbX/ck1RSpSNw9Gd7w9Gz73Hjr3Kw/Et4wPYKpD3lF4zQoU0914enTp2FRHexv7LRhEKS/XattXElf6tYI/mTBCfhHPLvVX2FAyQ0Sd4B1IDN/xQAvqDADj/QCl6PHuZOk9+u83rEamBjvea94/+781z42Gz38c89O31WtrgqlkmTFMcnl7cgsrc2yu6jG7nGVJSiS0C1lFF4ok5RD6/qvULRGZhnbWunLRFuaflZV2m7VFd9wJkxKRHUQVCmY0NFYtEy3NmIEBlzo7983u9q788blK3s/fPPkub3+/rEsrLZ4MTk3UnENpczA2MbqAA2toaapoqwOWJjasGO9I5AOnp5ssgn7WFgxo/jWool6fXRiGOZV7lawpTif1FX2johSVYg7pfKowPDUbOfek9FZdoNm/J1qejg4ubk1uTusefrFB0cn0+UnU20w1BzAxv6l82ozdQeaRbpM3HG5NmcQeso6C2RYLaxqIinE3GXefQV8nXmHIQplp7+cfJuRPNtWbRgNer/+s5NH3/fBb08+PG3+D82KZkzX16KzCfA2zlRW+8ileN5XJH22TQcsNG8TfemAa+K8u0M5BMRd9roNHOmZz8/1D8/19i5Or97ee/P5ywdPXJpdu9tP/aYp80eX+TFLAAs3a0HBIpLw0FajGMbyarTEVj3WO0IXTKVbsQ04ugkd3UrANuDy7DPkARzhsnzd6QeTKmfc6VpHPQc78923HY/OzXjxS3/Yn9ztHd/YObwyqPBEqTOrZoRWdcHh8lpRwTbpp7izUdvcTTAV5Zm98XRajbs3t6GSAplxWHOm4F0xKIE0cye5WfQYrfiPG7MFxvBK43/6ndHnrjzz73zq4Lv9T9yZnyoQrpYV83QprTvrfVKKHG+jLimGyUGHam1ttpSYlndgRlCO7N7UmFbzkk8idAjE4+3tUycnx3XPu8tcHrUqKMp4XW7PNutTHtrbsW0+v7d/+77e1e2TG5ODGz968+jJvckPr82PpxANjMzwJRDjL+1MdP77FB6tDSbC9Eu3cmtBxJ/kBau+UJHxSPgxEd6XIG7dmYAx/DUAw7p5V0ZvEoAOx4MwFPATR+GsSE2AyxF3XqA+2J6deftJ//SRzCyD/vRu7+j6qTuv82b1mpaEKW5TXhhdDWjxNA8xPDW540tn4xYNWPSWUcmQsDvvUBaHBVmGQ/wHkOZe71it7Xb/gAv5FGdd/PPXRjeOXvvVT978wZm/sj8/7VOqWEbnBj5YbjOeRhXcVpfijKW5uJKmcuW+IosoNFVfGb3mcnxaIbfgktAFm8Us6drO9GWMLT3KbzNVPO+eQLm5lqV6Nja8E9/ib3LLErJb0Tiaj+/v34Ssn51euXXr1jNXZo/fGLx8k3eulzj9wizHdMefN2F7BSxqIziyRrhbi42feqcTIRgEoAA0e0aLxG7yY7oSC7G2mz1r3AzoADDVs9gMZTeQyQrleGuOyvRHc979wi8fbZ2Z2LLL8fTpzbOHt6YdYeopIOzWh7vGVHpHLjGPaWV1QqG/YeoWMJURjhihZbO7uVfhQGBg1HT95GwFyHQEIBNxVVSO0Hfo2d/fp4rmXu9Y2TygY3ZedbwChW8/M/v7nxtdu/CZF6cPFYjlZemOb5S5lkQuyHFIMpBnWul0epZuLV2slQKx96p9XbFxYUeXocPKHX6n2Gff3aFMn7boCV1GT7h7PTR4G8zu/O47hlfPT671jq5dv3Xwwt7gR9dHL93qn9Q+UsJaxtzuuVvRykhdVMJkYhNy+0s8VdvWL66VtyIY1yzeP140pvUYJIpWYF48KFu3IL9C61kMo2ftpog1N7gTUkuauMPUR6en/PLRcHcyGPZnE54oHZzsb/FQqTF1s6Z9h8vHaimHJ1PhdsGRWqqjxgXD1RwO5GOiLoQPlQFJZGUDO07cK5hnOOO0RIDFRlRY70UnmwF43YULF6wrK+PfXEGaj+/Ztop/LVuD3m98Yrr77k+/NHv31GPXWci67rAuiJtPZSodegvPp2JfGbrV/Me3QKNyMcry54vYqztgXm6fdwu9la6RvVneEZFBx9WZkZeW5bZuRV/ghI6jV3brfbs3Pte7/Y7Bm6eOrx8e3Li0d/cHN7Ze2R9dvSvPQYZduGGozPx0bzen5TYJKGQDNJgljGbwudZNWS+QAb21ku0L0BDQo1ttq7t9A/JqxN8MZwDHtobMo/mj/nC+fWa8dZ5XNE4HW/3ZSY9nSU9uDiev78yzOhfLGrImD4tS6ZgXlpaVqr1YmL7EPGTAHZ+zIdQpMLs5wRWjupprOAOywxmQm2gaPcjfdvC33d1d3shegRmvGt9QSrXmj2e93/728G/e/fonPnD92/OPZz6u6tgN1KbEAY0MIiSUv6Hm11RrjsRnTT11ilsPGsLxXzAiezZkni8aGzLPF2zOuy/tuy82kmOYfTU1Itdx9FjM8MzCoTHfnR/f2997oH+5d3j91q39l64ff/3G9pt3tw7G8Mhd5k9erNcEdrajzHYP838HWQFWsVSxamBnQ+bBNwj0DktV2UUQyo55oNeceZU7HZfrlHkOZ/rR3SiSCHqkhL0dNdD6/+5/9tnDWz021KeHw0ymngIXP+vmTZjZ2anfTE0xSLyNvuzmC+YNvY53LublzT725z9b5MCZ0cInIeW9oS4ZilB2qoOy06emFhuosaEpuL7l2IlDAmAFVV941+Sv/+x9j49+8Wi+rZjKRxVeU0j2QK+QAVQwPGSR9s1T/5X7GZ9bhrbMK+zCfLzbMi/fgoIcflrhWJ5VnQ9oXcUGFqivn9Vp9KR5NtNG0PXns3O9O/cPrr9tfvnw9o0rN+88f23y9LXR/nh7PJf3aTBJEmhUfWR8NLBwUFe1qc9Hf00Z23oPSKvcahgEZ7Sxdjg+WrOxwYubeXSuz58RwtbOekrtzM4FOJt5YW9+qDF9VKa4YfQfRZq4gSiu1zMX286ePXv79m1P+eBiGABEBNNse+pMGQRLqXDYI7iRBQo7TtwB1rlf6qaIkQPaDueCNtbMslMxjMbV1ztiG1NwwPm3pqmrxau5H7DT3ofOzv7OY6deO/upqxV+ocmPC9F9YLhqdldShEI3a4tj6u4YTKn6GgMwTLMbM68USLFwPCfbmXVaCHc/grt31gMxuLO29Xqj+eShwZVzvf3z08t7ezffuHn8w6vz718bnsxHvYE8nEfvwxzaZ1fW30xiTM7M3pgRe0CHvlnObFuKhayaWcycRiJtiaysJ7N2dGIeOjvL/TAPy9tZfEHD7rVwZgDx8Sjk6eJQNz8YUI64m2/5GJrZ/U0n4rhYyI9TNl1RUj8zAgEkSQQZnJuQFEjGqzGnpIZG4x3vX7yfYPD6j5kgiNGz+BVVw9dtjshU2/3+9Vw7GUp4tbVRHFq3x3hc9dc+1h8/+LmXZ36Pq5bne+BsQykT3o0nNmJedGZdGle37YFvLcr337oeagTAdZUm8uODXYp1Il2iwt1PTo44O9tZftwl7s57YE7Nj2VbnZc2Hl+5dPXGU1eGVw63f3B1PJ31udeHqYAqU0dH2B6Di/mZrVlMSnV9Ry5tZSnFPmkUczVQM7I85/bKjbVFIRT7rGxGXkGgAMDm/A390A8+gRov4jPPksx0V7w+gKgqTdxLeVVmA5pL5NwCvtvoX8QEsvh3NK0hpRwFx+oygPglAJb1yOY6lPkI+mgLAFZB1om3DCCV8npH2oh3MXMVN5b+rTCki3UGzMV16eJMAMmyBcCqww0yl7ftYe/XPnL83vd96Fuzj7p3WKctrMH36GUsSSvs1HUgcmyjzM6sB2xfCACbxT+EhSUBU6ylVetcC5kddmU5WbFOsqQFAcX13mND5g170/O9O/f2rt4zvzo7uPrKtds/uNZ//sbw0h3h6EzOTC9MI0wdTDLMnAGbHUpVBXIcqmofPWBoJKEYPcTAmakS126ar6fMxjwqbZRlpWosdQkscAbMKwbQX6dbGa3VOpf4l05Los0AzFyF09I51ygpQdzxFUMkR9vmk/mlm+DnZAQjZeoWodsI1dgtGvJo0+axUwswjzk3lMdXaBQQmZsRMahT9rS5pc3g5yA7Ps+rirDHpzkII4l7+AhvRAYAmTLAltoNaj7tEqgJPlb9G+8Z/7VPPPq9+cfvzHcW8l4ILcRzY4GYca7+ehnVNz3jnfXo2YBQcK00h970dNfloo0ZtFyNvoeEpIarS+6H+Q1eM5N5Z+dkLOfdy5RKNbH5y5YGCdvq272Te3r79/aunB5fO7i19+L1uz+4Onhlf7h3JD2IpzHpEYizcNjUBzUh4jmZNI9VugasZaXr7LEZzM0jx8zVxtcNapu3081r/hozALDLXYx5GOlWugqQ4NhAbYsjLQ1LigxAdFa7/xHb/JtEM6pV419FTclKy1VGncBqAYWMDQJzUNiey6i1A0m0tH0rgJqZlE+qBmQCbrYRS1zbeYAYG3B4nnV2iT4R/ARmbKuXj3zLMubPnPYBZKrmE4MrDOovv7j1+u1XfvXT+0+d+sU7vcC/rgq/rfLYa1tQQudKcHcl6zRHGiXTU8NsVUGQenx5XRv2pHtGJ9VGWLFOX9J0mlW1AvrpeEfeM9Nt7i4Lk3R0A2HQm53u3X1gcOPi9M3Jnet7tw6evz7+o+uDyweDO2NqlMPETNfb29GOL3MIJDhpCJfGnCrMLUk9DcVtNe/y3QWMkzXRDvYwS7MScUmPd2FxpFMww7oYw+qQ44b6F7U4HqODLjbE/CuiFFAb2qzjDTmwAUgtdDHeWIEwlNhxBwU6yR+CliVBvPKTqdZbTEZEGB4EG9thm2AMtSFXCGIqnoQe2h5EW56SFNo4sYU8+WQ6TgiAdFAyMVQcM9hi55PhREXV1FI2tYxV0xOq1CrazLZBXm306IXZb3xm6/L5z706eyCUtaanBDMOW7G3tjUWxny9FaKeZbSNj2xel52apaXBNIYwnhmkApsNVFkYhViFos5zd3mkKhSGw/n0fP/Wg4Nrp0+u3Lm99+be3Weuzp/bG9w4HEzjuTY5kzBJwjaK52FWE6aaTk2GKX/j7oJGdZPVGNpssgAyGLIoF6Odalo7lxjJQlmNerZjIaTL848D7u4Ix8a92zGPWqxeKvXnXcDuS9yRpIKOk044k+eTqTSHacWYOtgxJGwaanRsUB2BilrzibIVNUfcHdogz6iwaagC2jghpfxd3BMBOgXKjjCnYgwEz4KrYkwTmNfmyF+1ATczwMkyuLDHoc1US3uDWHh6NP/PP93bfedHn5r+dO6R91X71qWsocXrireQD5h4sqvIYUtKMt0JtB9JWahctH0rimpcsbBIeCkvAXdYvr5Ui75nptv77rW4+/b85N7+jQcHV0aH127c2nvlxsnTVwav7g94vXpyY4TJhFkXrwZ1ZjZCCqWCS8oyH3ZzU9bMxkLmQywMMh8WQOGZBc6gjVUObWZyw7AjFq42BIMxssu9bBZCX1cxNLSBnTWxlG+v4lAnhS6m0zMtXFWLtb7EHedubqdz1bIKKVAuLCTklcW3CDQZARsV9GJiCcgrFyyd2tG1QedY2xJchxDk1gKcbUhYpTLfh9gzwDxgLOjltW1MCbCZQcBUnj0130gJlL3EQrS1v4tj9VoTcGzQXp2krC3IMJxpddmmZcqPBr1fet/ksY988Ikej6sGCwzMIN0RzKAVRfHUwXzSUVP1/mdxd7HSgs0nlOplETaTE7dMjbYBrtr98+6lMOTR0rcNrr6t9+bk9t6lvVvPX5/Jq2DuDk5W2DiTmC1YTCOsCLG3l8bbZpvNsqJio81CJswgC19xXXm52AB14xOcMSM1dZuFJAZc+PIsqZzOmkLZ9hc+f4OTFuLbeDhlO+WZWEhfr70FQsb3jLu5lD9G7UvSDantdiN5fGIM4wG/36xXYQmzQ/vItFYjg4GAqxjgDIngEw2dyARXv0X4A1vsWMhfacoeZC+uHQsNgWKx+rnUYh5u1dm07rO++sj4mzeZ9X732dHR5Pu/9JEr3xx94agXoHf8a29fMoGe2+3tIDkWk7APa9vxxmodgW1rLFS4aQz3hO03BOyOdraj32Zq626hLJLFGA57k3t7t+/vv3lh+uadm9dfvH70J1cHz94Y3j7huQs2kmQvyQVUGX0khbkxyL09HYgeGAmTVfDlwFleJ2IW0vD2TzmCCbzFehBw1NkzmmIWsrZycgYw88QySraYBLmiOVhIQ7rJc7AQtDlQbXgGce+wAGMhzsBgwcJisupF3M2xwprYhDbsxLnxHjOYxjP7dMrLO2VMZhcAXWZ6ZqIBDuaWy3AtmH0yNbSfCMHlBo+OYIudv9I0ZIB5YHDlScBpAm0pHt55BjAumML4zBMom/5HL249v3fjNz//ped3P/vG7L6yxVflDUA+V7PaT4mHrZiTNIn0dp439W5yGq70tbei1gSzOzqBuDVhgw3htEzH992TGHKzdqp3dO98777+1Z2TKzdu7D13bfIvrw1fujU4noJi+tEdyhp9pMdBvaEJnGmKZaJ9ZuzvxjTceCemMq/6F6wgCRRMvxT0321BmOUVeVgdkYBTdwX784pgHk9PcQtEAxtypLyqi9PNw3F1oDs4ODD8sZZQXLD9XMYgdxQYzGDBPGDMtMHrqAw9YU6TqWKDiTTPAl0CCaNLaCqmbtCkgqoZctVoVoHOsFmAaUMuT63N8oY5MuL4rQNeefZnDNjrHaHsNCGvjfXTmZQZfqH80DrFDDa1QTSzNRLkEdUkXGe25n/vM4O7b//ca/O31z/yTmPxt6T+9uL6dKmeC19jwyaNXMBRhFI3LFzYmhkTI3VbndyNdXqmZZqIbV1+VnXUm57pHdzfv35ustc7vPzGtdtfe+n4jZvTm7Nzs5zno5OzCvNJO0SQSuESLIJ0dz7YG85hmWZRCw6ILZ0srzSvpn7WF/R0GUYa24UbDPwNrDDGAE95Ha5IVqduMFKubzcYq8MTs72Ie2WqlLKj/iUG24xDxAYAXUIgESJiP5RTv5aGNHSfuIOqTaxJBBzg5OL6oI0npcZAUr7pODCWHWwwVKY5moaTmOc0bWTNIcOIJTgnB3BgD2tzBRh9DDi7Pf+VD00efO/Hnpx/4C3G3YWsS5CZRf75hUUZP/lgUv4mdvjAzPI80s7YrNYDXTvvvtM7udi7+Y7BleHh9YPbe6/eOPzR9f5rBzs3j4fH+vI3qCd/WmTGY6S7JruJBeARCD6ruIryInQx9lA1c1qezMbTAzJj2muY29IZ6n7AYFyldBuHLmlAQBiTatfGjbHwCT6E5Tnm/2fvXnstudL7sO/TV5IzJDWckeQoMBLQieVLDFuau2QDeTF+Y8TIiyAIAgQIkG+QfKZ8iby2ZY/mJsW6jcaW7diSZobsIdnNazebffLb5+lerK69d+1VVatWrTrTBbK6dtW6PM///9xq7dp1nusdMPKCUwvbz7Ve6UPA2HUZYp8v3LU5LOZqqsDucbCX9dkP2wHdI8Nyu8qslFcspN3MYm4hqbrDQji4ZiuBuavQZv31Q3xXsO4xCTPlYSRSF8kh381e3dEWOh7LNeSTnUeIH444RcQeK2TmpDcudv/8v338u3/vN35041vtP/J+tfY36wlqfKEvE5zZzSZOVFfIM1ruhdHk2f9PW1/9lvZMz1Uveyr87qMV/67q5RcuP/7yjXd+/eJnjz/4xS/e++Av7j38k7dvvfXhxUePP7cKYUT0Ts/vCoCM0894xHPgiTFo9y0AACAASURBVCoVAstZlkRjNtBytQQuiYaE0xIfzCOHUlNZMm2QszBGSdf40jsY63CNMoBAHtowN+lZAKNBcN2tjDM71mwGRnKGQdLxfOGeXyeVUiMRYEDoCzSYGKZBYHrw4EEpARYaZ6E6qYi0CXOmzzhyMC8y74RBZCZ2O3yTRv7IWJ6K0X7CLDO7yExgHDZaWkRwN1emnc+Uqtd9iadl0hR/58uf/R+/++U/vfPN9558MZ2ccFC+nMt+DCZfWvRhM7/9+JZFBt/fn4yfukSPbhI9DdTyMBbQpeZvVS8un7y2++DXvAfm8qfv3/cemI9/cu+zP3n75oOHNz49/Q2cYB7xJ0Kl7Pnee+/J45UXL4axjtCn6Bxutu7VsTcYKY2y5OXq9S4mwg4h5bvhhNjtUv8YLMxv8l3QsMApjY6t13vDMkj+spCQvbmmfaQpGO0Jeb5wr1BuJuhDH1ZoY/r56r366qvvv/9+fvtVWnKwpuIUM7UBHxqMHub2rQl5lKkBISEcv+3wJUyodnSEpU+amo8d0h3nQ7Bqwf2UstgXUkWBUw1mnv+vX//sf//t2z99479/6/JLk4cqVbhfhZN9SFnCKkoJeYBSyTq7flm8n/HqyfV8zOsLeYD5mRM0WvR595uXj798sX8G5tVPf3rvnff+07uPfvz2xZ/eu/npk4sn51IinKMgdsC748/JhT4WMtR2EmU+F2eAmH05bjCEa6TPHmypAaKeE8kHhIzClAQza8fJOuQIOXnwUh2j6DzMidPGT6aOFylsgJ388Y1JSO1LCZk/dX5LQqp/1inczb2vGZ8thaaqMV/6XstNrLgPlJs9dRb6KL7A3N74DD2iTG+u1YXsyXP04+HNJHVkKfceHM9C8tFelU+GkEw9YHdAgLUi+yndD5E81XLa+S/evvw/f+fy8a/+9o+f/K3JyRm5gd5YGZ4V69N6j5ttspDHpilZr3fHLypkd+DOsTmefZqGew0hn0k47V/PzLz06NEnd26zy2kjdHvd3D15Zffxly9+8aXLt299/NbP37n/H9558qdv3/z3791MSHbbHx6LMEq3QDstGfrIu5UgYk50ceD9DU7aDgdZ5cyhkKuIMTxpCAlY+aXbUjIFuzOAXW75ozvjwPEpIQe61L9ESAUGGCd/P2AEmDN4UWKh7zQMroqYI+TSwALhTOEOIwD17HWCWCZK5UsEdmPOHzYk4T8ikRWFCYJV6wIB1lD5Tq4He1CZ4vhR3TdXuLMivz21t8QeYfSoXjVPBuye0omVAIATL8y+phg5c6F7ofCXZvfI+//8dx//g7/3m390+d89vMx6/2zq+/RA/ZJXID2r1PXbdyhRVvVlOfk5iqw8OU8MMqvziTH7p9kh++yfnfn5Ge6lQF9EyJk6Pt995m9V7+w+fW33/hu7e69+9vajD37x/739wZ/fu/iP7938+Ue5b7tCosRnI5cIw4sPI4w2MmO3dtdYtBSXvvCFL8jIz+u02idCipCTi7k6cguVJgJdgr1BmdUYSK9cZozFX5qGISEPLfbUUMnadVk6YYUMJCSnubB8Sqq1zkPjTOHOWKP4GCuikVOlrq/IEls+VfkzbuKXqQCpU7gn2M34DPUzvxDoQi2Gph82dc83dRxCskxJiL5Lv94xR3dixAZ2Rs7bfYR/g27fVYe0fHxpxtXu3/iNx//j1/6LP7j57UeT/kITSInalbx7nKLKQJtu+4WOh4U8PelJvU53mXFFkX1VX88Y4lnXZ7gvA3v27dozcSr/e/Vb1Ufe8p617n55+crFJ29cvGdZ/c7Dex46//f3PvnxvRt//cHN+w9HGICoop4AOOzFlpzwIlr26g9xyRvY5KNGvpxEHKWo1mzFCXAS2tRzkk7L9xhEjeWYHNuo7DJpOkIyP3Y4jKRmcZuUb+1piiIHhAyzfBbqiow6d5C9NQ6PoUW+xCCmpC7RCyvTiv5hkQ6vmrdlG00C5yOZuuQcQDshb4ooFu1z+m60jULTd77CqL+CRv21tAjY2TwBAvZuGGKTsZK0lng58y5kk72pPZv73b+69dGnP/vfvvb//OTlf3Lv8vVeg7Mf9yw/X8jFIu/+7x89/0eRzg61XIOruJe/zL+S3e6RnP6iTgZD7qdULOt7+9Sz7AzzTAEQD+/eGXje/cblk1cvvF793defvHX50b2fvvPBn917/JNf3Lj30Y2P9++B+fwtjWcFEesit4ozgsyo8C5aquR0l45jIscednfeGx2E0IhgZ2VYtAGlhFMBc9RC7KIiGRwykL/y6/1CLwAdA5NlNltykA2zKk54NnsjREiyBeOAPQQz7pECZ+osTfSp8YNxYJIkuc+pxjXPD624g5WZgvWUQMw62oRliyaxnWq/0PlN/DIVVrYBMEeBA3ajgd0Gc3bPsGyjBjnaOBazj15a/SQFLRTZ+543FhXqi5QyqKkDeftTYrQMZpL5rJunlvMPfuWly//rd2++/8bX/u1nf3PsaM/Me183Rr0+doQ67cnJK0/PNXDpdKfSV84J+dx8z5CPe5K68u/vMerO+Jzq5z8QTu3+6PYtf0xE61uXj3/l4r3fuHnv5Yc/e/f+e3/97ic/ubf7d+/eeH/wPTBHpxHho2p0VeIQZxIRR9sPn1R8aNArPowpoprFl5bD3etc5TgCOyEHgmoFSRLyxJBbD4UBJlGbLYsDIlqQ82hZXAHD/CkCTKQzb8nIpi/YbfmDLN2yKTDZ3lDhflh2aB+FS8T9U2a9NIi98Tfxy1S4AW2yLUI+bDpidyAfxz00Zn48JH3mgEW609cSO+dJr3cU36vFTdxFNKHLqAwaSWgJmoqgmgZZ9L2QaZY4uH1z97/+/c/+y7/923/25L/pXTry8emrG/dXuEDzVdxezhNCNld9MkuQ7iU+vu3N1uX94vpQs+Odi509gWax8UsMdPfJo//q0V++/sr9i4/ffuud9/7jO5/98Vs3/vL9Gw8fe5pm9JaiDQKimhk9xIkOgpjBD9cvnfH4h/UvAfZE16qn5SCJstQiV77ogY/20o3Zh+M2rIR30A03y599oZabuMdglnIQ2OX0+rznIx++ufqNpYB8/lEZBsqgI3azUbCSO1/VpVuSbXI1vLRs3fEBOFZOyDOULvIt23RX2YLHPFlwBEXvRWYBS8GJukMZHPImdTJsfhryehmnKX/pqpmORUzK2qczyx18+tnu//6jm//Tp9//nb/z1vcvvvnp7uAt+1fFTvwZU0TsF9c/3/Zl5Oefmjwi8fPVZqMC76F9XlBwRhWyl/gK+v2/64J89azmXtSWthu7J/4W0q/d+PlXdj//9ME7f/3Oe99/+8blf7j9+zd3H+8EjdHlLwWjwKKlNHFYWxfRPhLQ4V26QlkNGg8ftrD0Tn1oVFuaiWpsLPKipbudte4x8u1B9hHbkS6T1onw+bIRLCocgoX5RTnX7L2QhG5jmXQE7Ipy9lfcRRBogs/mmJS8PRw+n4+aLQUaCJKz5qQT5soJQwAHO/wdsAnWDPnKxpEj5wT1x3ahdfwGiGMfzSVlvxkAuC2CSADOooqEubJyjoUxsz3dZcpq32CEVF/9G4//l2/++h/f+saDy1ecSXZOmAGxD0rNgbarXXom5JAiqwnXmRjme7QT9OsurncE6x0+lbN3tuJHGHkPzJd297+8e+uVx2998N79//zuR35a+ue/uPn+o6cs377cfe3RxR/eufw4j3bIR+FCDwqWCjhnUTFpOHuH9qedRABR14OI0tDZcZZuQAYBeaF7GOAb30aLKHImqxN3XJWD5wRpo9xsQc4wexTIsMDv2qGT5HS+8dWucCLlGfkncDGzC5Qu/uk//aemJ4fNcECMepE5rntLkamb39bEc8+Z7ddqdrQghn8gbw/5BP5aQpr3qJw15WGNkgcjtEjACE9NPb8gDvDFbgcBfi+InJp61Pn5co6abnLjynLCHPJ/643d//B3X/7oK7/14c4jtvt65/Hu1qPLW59e9tcstQ/VMJWOJyu7ZMeNyAnHK8T3eLbxgMQwKfV599PSVy4++srFe35aevOTt95+5/1/d+/xn//ixl+9f/PhiYdv71zV7n8wWLuzXvVoZNvDwmUYhFJXyRD+fmUFz43qjHxKPO9qe+7CGh+IEfkIUEXmN6CwY09NicZWatiychaR6nCQ4ngeTjFwJoF/1uxxxEfUn6V4H5BqziVCErW+nPz34p/9s3/mphZAPYwqJ/LJ8G3iAXfaJTyZb2zAFz7ALnwcBtDJgMzsKACtdcNm3lhfkTNANKzINDmfYV/vOw2+HUliWJ3Vr5KTDIuuHzB4Yc7m4Cre7C3/yy89+fpvPP7SsyLhC3du/cort19+KfH/tA7W5arO3D80/PDJnZduPtrdeGV366XLW7c7P17cN75xccMtwNMHbK7K/Y+evPLgySsfPnET+HS0QDvmuBr5KfyPL289vLz9yeWt3ltXum2eNj3yz3OD768/W3g/0natU88CTVcj57of1xLt/LzL43nz8rNfuXjwlYtffOHxvU8/fOs/3/vo397b/cW7N97+6MZnV09wnRXyqnbf/ejO7pPnzSFZfkSDUiXjWXlONcC4lGT9tZf3o71Q7O/ZtfCXMcgp1ItLcxDj6eKboSLbHlX5FFD55+FJyEVDaL4wAy3hyQhRPNCm4CWwAx8FYB+1NEZOYqxVjeQjEF+51JQTpP1HZUJcKAs01ajNx+iw5SZeKQPM8GoOY+PeC8WOQ3zGnplWEI+dpdfeErsUwgGssrPJ3tWjH/MLzTBme+Mk8B0cHbb4SerQq4UvKM+qdvjw69kuZxtQP0qWaMnybTPBj3vguzcvX7m9u6vGPrd96e7l3/jik195uVONf97pOWO7e+vGV75w69VXLm4+vW2Mq5RIzdIdgRcBvry7fefCO+qf3SYQxFdoV3cN+/ZM7PHljcuLOw8+e+WDy7tPnn/02b3j1a8+r0a+2j16cufjy7sf7W8wrrark3F0Nf/nn582yPsH2nrSuKNFv+e+zec69q+283kJOV/aPXrj4p2vXNy788m9Bw/e+Yu3H/7k3o3//P6N+59M+WkprDwz841Hux/c2T268fniehHLL04EVzpVE4Pa3zS095128XnHDhhfvY4tSNLN0tiScax4qb2sZNKFHu9Js8w/ICRR4blcHSKeTKvXu9rJ2qg/ZaLdluseU1bhBMyxJjpNbNMdL9xXqd4m6MAyiNrg30wNq93je7WsLmoTtX1/RgE/Ie1y/txlOXKD6ZSMeOxeyjnW5VRBnEK2cehSTaOjYkehefRSUycLej1rR0FoJ+yiANellBXNjX+K+lKzDI9z82LnzuHWzc5yvw59HS/u3H3p9pOPf+PVJ196+cmzm4GnA1+V4c/V4i/fvvi1L9x47ZWdryQ+/25g/+rLaPb5SQXhKy+9cuPunZu33GGkQa7CzdVdxLsPX3rtzuPbF48f7e68/9kXHzy5+1n8CPhp2/0EtquuV6fcY+xuf/jk5Q/2LdOAe1H3zZ7tn4q+6j8MKUSaLsXl5asXH//qxVu/dvHzhw/e+dl77/+7tz/7yS9uvvXRxdXr1acPHD1Z/ku3bvkrwX92Z/eh0FPoeYy5Yp3oz+u5Jyc9et15T85Yeo/S+WibOicjpOTk0BR8rrCfu0wwVjvRKRJTnRw6Vrxue3LyprK1Jt+EPxCMHJG/O+O04yBUwC+YRKZJMtyr2m0GkI8X7lspNRR8nLOs5Q1zM3B1uFgMZx7o3silOoU71uQD5jfnL2/3rDTFa0iuErJPMYh6Jtp40CE8OoiakxqPagp/W6gpZKPgaLMiJ1HffhynacSE4vcYSmm3ATcu9kvpT7erfBk3S/uU+emnbr5duntj57bhjVeePPfVRKdfdHdTdfPWy3/ziw/dYNx+7k1jMUPU7truD6yCvfryK7de8hde9hkkRrgyn6uPT8/sz1/ubnzw2Rffv3z50eVztziuXdy4uq/bt9r//+Ry3/L+k5c+3X8vsT/zdNvfXMQkn5/c3x99Pu+zloP/Xlw++dLuwa/feOvLlz+9/+67/+mdj3/89u5P7/mjYBeZz8AMDr+/KHLK3A6iDuYJ33i4X3cf83dRz06ySIMoygfSqKUxSq2+9B41sQB1NJYm/JcOPjkcCFAQG4A0Z5AKbUQMuJ2CdJQAgf8++BR6r0N3diFAbtoEpIHDoulpH2e/853v9MzLSXMXTzZdGkodCyURU0oNOGoccYTdR7BmrxGvT43QqzJPNVv9vNqLIraFJGFXHoyBm1UcljZnFpCqDqNYMY5jIWPOgAv1JSFN25Stp/IoK6UU1WwxSM18yeki3/Tkb/AjSAXYhRwK+By2CP7YJCrfLIjhjd3l3/jCpduGL3j0e3DzDYaWv/qFy5efa7mPD/G9wFXvq48XN37l5bu7l1+7e9udYQog+1b7nzfsOzi5P/9kd/OTyy96QOruo5//4t37//GdT//s7Zs//sXNUpW6Kdgh/O2jXundrMYzMz88eN59L2RjW2gxkPSp5oV9LSy9W63jUAE1ow3Jwbmcl03jimC2sg41TZKzvSKTTshQKQUsVK/3JBfuojRdKJz2ppvzEaQwGXCoOYOD/UjhvpUFQppX/mUquMRo1uMAKzbhI9OGoLoQi3Ms4LCvWBN6HV6ac8aYfu0ELo4n8k4eKvC3N0KExYjgkwes05EbT17JriNhzBK2PRDBwwUgHy4A/IHGi0oOUg7FrhadpcjgDL5g/mb8gT/ZBB/4lwIB+7ZNhCnsU3zY929eXL7x0qXfHLz10Y2CVsLyAyj4Q169OIB/vGfmR3cue79VLWJXZQehFLsaDlMRw31NCoSys48aLTII2G3MIDMFj5qiSGMoRZhqVsKkJurD90GaTp46SC6gMR8cdsNTg0w+r5TSt/0wJVCrdoBTPEXC/7mvRQNKZ3PIm4x7wY4V5IxKESYhNg8cjtQFtVtlqOKQMlxVi2GFMAs2Y5WCfKLAIDYDoiDGqRwyxgq/ufbwjJKoKzkKxHQsRGTgAi1UzGQQwYfrjK4WKx4zVxE8Ge0EScIF7PWF/0LFCvajJG3frfDOUEWDgZLos8uLtzPfqZ5BSVAAnwhBmYbnJe8/uHPp/e7t1+5Ip1oUmg6OQuJPamj2/vvvK52jfjrabKGTYZyiUNing8arNzDKfYByMMf9F8KzO2zcBhMVtgOFJgqU+NTRLNMFurMUOUY6Z4w8FZZQZNjigwhNIIIYUbE/EKnyp45cvGeh1ydyc+9kmx9JXwSLnnYBDcsIKEzBOGZOtCFUoUHaHibTPjJWhms0UR5Z+YPoon1QoBf8DXWYS5zXrGXXTSqzn62ICucwgESBM+Q/SkFSsP5B2AMjmembFSRnorFGfmjDA7OHF2BBGx3DCwbaF7kkKfJWkI4StcjUYwcRW4hqv6iosRJJNphMoyBq968/vPjB3cvGn3enIwNQZwygyiB90W3pXfnuUdWIFWO5G9XejDYTBf6Jbr5fwQBGiXq0MUjJP3xHdLRj5ZOAxTuDPxT1ioF9IGIh2lQW7HC6EOOoqIeN1z3DaG0Wbkg7J4cmCozGovqPysTaPlzW1TZn9kgwdMhpPNwGKFEmig50jy0FiOG+OVeNrxnEcxqv24aoQBi4584RzzIDGzWUFZrMyK4Zy8aC8SEPKywMU6ALcy1iADlKzWmzCVEJiTJbhJgcCuZgMr8vgbey6J4valCgPXxQYBv2gvkw9kYwtdCqMuudb/AjUYerzGkyi0JcoCwFnnf/5sOL7zdfu0MsUBVXReABAF311Dv8bQPNJl9CAV/QPb5fOuoFIapcw00mT1SnI1EFq1jYrjPj5FlC1Ig8KPBxH4aqB6Ic+cmmBmCKMyuWnLlmtglRmTFzzR8qevUo8LG/4i5mjRo3X4LiLTn25BdB0pxFRrEOyiD+aGgoIrbphoNgkVmKDAKEqJ4njKYvRriQet379c+O0E2Q8BEaRtme6QB7dpYWGsxBdWn5U4I0Ee7iRmgTt0NQDWDbdy5yEhLURxMMM5Z+wpg14wXaL837qfFNbVFNNdbC0topIeN8ErXITwhkBARFrEZTWaP69GL3+3cvv/Xw4nvN1+5QhScDYIcDIIjelt61fPDggWhfJBQbJMIRGVBw1gJDVAWxjkc9a9h+al4NW+XmzKzx6BpUEhXFEjrJawI1ai6yAROk1hocDJjrqGGXaByigpRnsVXVzvAsyRGOpoN+4T48VlNXQTCKJ+0jLtMCiLCz1dHI1NXmqqNRbxZWGDdRnmLnRb2r3Y+u2iI0oO+oUXbbX5tjJkdr+xY0iqAQkrDMXoJ0RoNNWKxgLWQXqduW5oWpEzUtXDEGjgBnJmFryhE4JpE2YQMB3eTbDCyELxgHNT1HKGsSUbv720zf38I7IkEBVZgMxwEmrZnHZlTPjqchhoV048oRbKPGEQR0J4CDUR3rN6aaYgBQ4GVy9QUYmDGxwP0hSTxnNvEtASu1EZV2jdsAbFEv5vCaALnHCPNgzE4e5uVuy+cKdx3G+kx3rMrHZ+2e2UmNgHCgsY129pXl3NZ08AFXvswgVbIzxFPPO3ZZMCwH++VkgeKscTgL5sM+tiUWOAKaoiNhBmoUV4WVtUQdq5pQSDX7sR3rtxep5ewkKoSbveVIoiZp68OVOSNzZd6j6ra4ZdKLdliolvU8765q/9rD3Q/v7tp/z4wQAdWzVQEY09K7LMAZM4mLiBTOOzMp6I7TU8VQpjx1mjG5AJbhMd06kw7Mgj4Uhy/0WFAMpCpzIF8MDF7zkpDFliJztQDsgO6ot8WdRmAOf9k5fCEH6ucKd92MMjBfO5d46WFGoTwVbA6Iyuy0QefqYoc8q4tRUADYqjkYH59//fXXeyOjAEH2zmvJi1pgoSdk5Y+wqlwNhzsgwkH4Qk5ECFi0R9+hi1UGLWe6KDGbrYCpgIJgwTFgWULjeSVgZy0stmVgk3lEIBouMXlBEKFX5AVcpBGqHfh96g/u7r76aPejLbzfnXNBdRjYgC6W3j31rv7wqOQAnsECOrDQqxQHep29xKcMGLV744GL4XEuqMIqPyafRWBUA/hHpUiYYRYiXm2iIMY7POmlJh5WahRWCzXmXJIsZ0EB64Vzfvn9XOG+kHxLDIsbehqZ5rGl6sT5VSLyEmrWH/MsdHwjnorhyd0ALRwHEWTWhiHmW+FkNU1k3k2UQZN1HNUxqhNEhDsEC2c5PZwCd/LKVm63qNmaGaAgPAK2rFRQsg+clTibsFhmwwy4+Vq1xaFZDpwhqmx9aAbO2NARQakFXdTu/irT1x5t4++qAla2zTEDMcfD7hD21Lt3vYM98RVxKc5MDkpptFMHLNZ9JjMIjzvVrJHzgGWWooEwm4LD0rIhItXr+ZUiYNEqI2CQ2BMSytJ6dcenFzWZAUtz3L3UzjHqEUFOwIZ3jJLt88LdKJtIJ9RjPeIIc7dn8bbGK4zGDb1nMcyodyY+8lhhkcGp11EQ1uZjtGc8Nm2O9l3opBlJYr/Q+GWHZQawKm4MQQQcSGvwYGHmLCFqWfWXG43VtVANHyXiUGvBSlJpPGSF2GwpEkyz+a8Lb2QEpmuL6sTVIu7QnaXI8X7d/c7uGw/9YnXn+ZnGN+yDNKd2p4hmzPvDDz9kOcr3VCYiAkHGWVpZsygxbZWT0QS9lC7qtgolptCUiMDmNGT0wil+HWBzgr7VujAzwEahSFo4V5t6eKJEBJEIltwBKWG0mT7yeeFOSaoOz7rW1dDW3kZVRuNgE9/hroVY2XlBzchYiAUV5sV7Y/wIAcn4yk6aM5qpWUJOyxbawBB6PLOIMAIxRsIjEJHp8PlTG9MU9vldVmwpJkBjlXRiXkCF7jlECNlY40TtpJMB4hhtWh8ZaNbCpYhLikUyx9aCVKdkULur2r/1cPfdLdTuPEuwdXusSDgb8BHxxhtv6CIohbWfAmGh89jnley2eFQsLjAwQSqAkHaJAizytVmEJrDMlB+bigFjgrdUIpsp0kB3FmhzX6TN6tJGvh4gAjvyAmy1OcvU54X7gP6rXGIZtijLmAsCunmue7yKeKMmDS1GdWmhMQOKt0HJhfEg+4DZrSIweTaE7Xxpw/kD6pwacQ4pPE6e3krhLtLVXHSPCBu2B6ix9YEMTdqtLD2Q1pdszb7ZPTkF/0KELe405hh/nb7W2ve1+6Pdd+9sYN1dzk12C+pDiLhDFEmuihvaq+AtvTsvgxy2X/RMFAxRDR+VdtHZxw4ecIkJrLdIbZOcwshnq8Cx0hoQswWlHSvAqPYgDWmJzSpG9Z3fOGUKRJy9MYsIRlp2q/2AtE8LdyXyQKP50ueMQEPWRmiNKcB8IX60ozbtu+JRybdyEhc2+KvXoc3iz9rcVlRbV06ojhJAe/jzi+jFSWsSEam3SCIZpfW0xsABlHg3rfvZXriIdCj42DiF/dlepxrorso5FeJO9VrrPKtbaEVwmkbhFOFNhxkRqqTdxH3Rft39jr/NtPve3Z3jxjcG7/6tV7HhwjIhyV2FfNcpEOQ9M0z9/v378dhMTQVJwgbO1kA1RRqYS5gNaR1AbKDlwKUIUBocOsVArwmXQtpGFrPPyh/SslL49Ez0bN9pDSJZcA1GODZTkFa8JSrT1dfHQxme/uVUBIwd/XCssWfoRrFYVqeeTeo9KmVvZPo4U7OC6Qkw9mP7GTpxweExwhicwchYTeu3bx/bLia8Mey8e7J3HFxoGec5xXLFaG/q3keSgHdDjlZ8GbvLRRCHjh5Kkz8OxOXJYy7XMb7DXcsU6dXjgiQDySKqya2Y7kuXT3+r2v47IsPAOBpHALKPWMDF2WThhTPa++oGj8tZ6dGR475CUjt6tbWTIr8UPKq+1D7yRf1kgdNIwQPO2A7CkdEWRSm44A7DASoHE9Im0+36l/M333zzTYhjvU5ERrO5YnMcudDUoCRGV7gBxTj/JlZTQoU9ITP9BgAAIABJREFUyg18oXGIJ/zDyNBBSEsp/o4GFsCLjsP2bZ6hBcEyLWd1FcArzB3CC/9nbvH0SydOkfxiRbEFDmKsKMDYqdkDkMf26rXnFxS3hXVJ+SizlTWzU8bQE6aRj6QFCATKgnBWOxRwDVMjxdRXbrHPF8NixFW9tDw7xeoNHl/s3r65++rD/d5xs1uEKaiSEB244BqZ7ibu6eXJGR3DraqpSUKSHw281WTIn4i0Nnf19sNGDklK8Q7NIkbpkj9RkZamZgbhnpvwNUIyPwKT3FYEBIPEmF0uigwegQ7LDDiRu3fDmDKdKqVGGscc4agOaGIiosycDkZFQElCLnpA2pn6FhSPMCluksrmFigeZLeI4renBeeqMxQVmkI4X+uea/ALwbc1wyaSGGefr9eKLaUQZmw/QYaea1TgAtdI3xC8FrCLf6dxiikpECN8hIOHa5xqeeq8XkbYCrwfX+x+dHf/t5m+394zM1gIOkDNudL3GLAdBW+0V7tLOp6cMewp7oqfZwzcjfVayWZRxccvOyBRQaRcc3AYe2VwG/QoNWphvqyQ3dGIQaStfIXIhkHHGo/C29Xr7DEWrtjY/1mhhVKGkXkcB+zCu39Uxtxlp6RJxFwjgwZG5nZwFoXMBv5OmzfFZjZevVnchAFhLUnCsHBBAEQEHY6Zr1V25y2xa5PEYx8pNKeTzR6QnwcKHM1K2BWMtJIHFpxcwjW6c5U6rlarFRGYJQM5s3bXMlUkXdcoIknmIODlbgXDY+a805pFKbxQfOjSIUzZ5sMS66yGmqZv5V6emfGOyEbeM4Nr3oEULHCoiFo9QMJ9xoZfo1ktkneE7t6Ai36kC3sw+1bsQTSDcFTniQ5EUGG+aywB9ambjSXmmj9mRLNp9hDUYGEfp2qFF/7Chgm8X3EPz5yDghGoAQUHxqHGcoY1X9o5mk7oS+CjIW/CUPldeDtGoj06ejdm7uad0cYvh4Ky7shtRoSuhN1j0h6q0G3QwnGXDq5B4LGpbkUtWC/Xrm/D01Rm7UphIA90j5gbDbS0rWjzimC3ylxyQOB2LjEDCMsfAkgpqYKOiJO4KDgyCTkaePG7CQP2jLv3zHz74e5fr/eOyKhmQAe0XuI4ZBxfgtvYtR5drL5ZNmL2Nb/mpRF3U1zSsayZHSJT5AxfM440HUn8LB1FJp0zCHfjzuwhbjbmDFWhr5jAHkQzMmdiK0xF9YyRhdYvBhQnJAEY8NP76YGmpy4xfe5nFA34wz77DSbLU+OMPU/oDRU9oR18xqo5tj0i0IGU6IiLQ6sihnUOxgrDmuFyrC7Xo32vOuzSIa5tSMcofbryNy686HZYWXbj1Sox9xRovJJLspZI0qeatXNebGHA8JxTCke8ojW9KL5omme6G/paw7tlrLir3e1rvmcGF7xmAh3sgSVMQNiKu46+PGdOtmoWztgm3GxUEy8m4iBRIIoPftebjiuLMWE67mxDKHKF4gkjVO4S1TCBWbLt1Oxsho+gQ3v7U82WPm/qvQEzCMVczmQsidzitQOdbasooOj0G8ocgRtpA66FJOnWIuhgc6f8hCNZ3tDmlVdeiWQ5LFJQPNymnav0akEYoAUjIQzMN1TsDgO4nA0PzzvtKuSFtQjH6W52rXiVo4JArIjhoTmNW2jDsAlsuXSs63EQ1IQKA/GquI4ElpjtxwpcXJKcAdXr3g75zUf7N0UuWrvza+kgMoIya/LXPvqy4QkIs4dYepfTPfXuYw4+89uwPTITmNhNmQRGImT14hU50TTh7mg+VtNG4GsEbhDho+pAm/HH7VzPJFLIYjOUOtq9/smhFfcoRMhto5gt/LO+lN0Zt1VDhOSg66ow+TgY4Q8Orgg5+ehhmkL5wiK1r/xjoCRAhQNQMFHGWWGu3hQ9RsiQ49sqSyTa90Zr9iMrilK4WQmTYBiBrY8MnthAtk9Xmz1gNlH3NCthTzACW0PJsfZgJEjhqr282Bt2oY8xL4Qn16YLCXZqWL9V/f6d3deX+a0qRtINLQfJIfGUnOl8hL4o1MaG4lh6t66suy2NuegBIZkiGxYfxgpcXLDECEM9dUOLKXIS2IE2xWUoPmASmNibCMJQtUE4WAgfIXwRBykL7/5FQt0Rr6r0/Y4luURo6G8C9K4W1+k40l6XEXT0WDvUVwPWFoHp2j8Vw0rhY3+IwxJnpjHSlYRPqYPtuydbPiaqKNayhOpCW4pajD9S8llPaUQp1ktUKmzFKiI1AFmQOYohl0yZT+PVkx8ZiKouXF2So3Adnoz3zPitaqm/zRSMhI8ws+JpPZIOk1D9jDVjsll6d1tVc+k9Ccw2yHxIwdJncBE+QhICnGUkBJY7BvxuaZlHjR8C05Hf8T4fR3VfpTFjgLCNwM3Gilts5Srl7XMemAjN5VYx4hySCEnCnJbttAlgR8kjiiHFXi/62nKK9ZhCY9+52/taTSgcNW805l1k3oSPJZW52QRNR3URfZCiC2RGMXI4ixGC3MNLzZ4JmSnejoQwREr4VxQiXaPlMhF82xF4WBJ5YlsPzEgT8nHvZiPcBCmRR5oyGMKQeStFD2v56GL3w7u7b/mt6ku747dHwyZ1dTXcxD7UX5QRDqjWiVXzsbU7YeUs7HhfJM91nKFcgSb8jtFWtopwE3BF4BqlhsiGTfiQfFE2R0k10JjTURPCERMGWq54KWUT0sZTi4yQzPmlV03h98+4475N4Q6BCBwPz1+DM2E3FJHzMDLBnxmcnysYR/qPEvMawJKjgvAX1VtO41FtIrxGF/AKlN3ScNRQvcYoJnOp0XqDL/FRiOB9q69AAA0pjNxBpIFTqSsQ3hbIDAzI9kswuMSY7EEBwYzlkRi/rJsUl5nNMB7Ssufigy8xoNrdivvvfLL7vbu7T8f8WkoK4ClE4giV8zurYMZcjzGMxQQ7viK29O5Hq96m4uPYESa0Jye4WDLJF43JKaGYcU4sxWmA3HIp3CUCqgSmfmtL76xUNIhs0nMTH51nyQ2CvH9F6ATv6lJS8xjEmT+lrSnV5LlSbDXCnPDKJcIrhDymNlme6Lho5JopW4XuXVK4RsFivSu8WGCiDbkeq5hvWl0ERh2nhKeXeGrL6Y47EWNDdTCroKliRTTIUXDdNkS1sQo/ebdKupW4wXhYBcm34n2emfGGmd99uPu9l3Zn7f6Kk329zpYWil05VheuB+dMV+2NqYZW4XnqnTrWoXpXl/gILk4XZWVx76OFaE9sJidTF5E/SmEIk7nUmEUEGxiE+nBupBQOUsBIqlOMB8hatna/0fRzq4cWIEmcgviwcSNnuvmM/Bw4fJh4M93YyOn1jtOeimkEovlidEEeOxpSlEpIcaCvyFIhDppFLGAAY6Vdsb0cLIRVkxkp0lKQMs1TWIXutjnmURlwFQ/DaPY3lMBESqyDIiXk5DtObugGiSUDmVVww8r8Tpsu3u9u3f3o+92Rsk8qz/70WCPGE+ECztPCKaWsQzGqakvv7AF0BGYVRaIcRgRMjC+XU4DMGTf02AyQ2cOKpTC4zM668nOKljaGofgE+DQXLttre4V7Wf2XHo192FKqi1QxLZB1RRUI4vWOViMiNHSvzj8m8/xBKo9AZvBmTqoxB45UpxeH3FDZkalj8WasToYQwoqPnAYMUuydCVLyOU2DdA/QaoFnvsd1x1z0mL4QFjEayRCUDWeJbIcU4tl3QWAY+7JxO7+sJTyTYMwUmWlgXRwWPd6/Z8bz7p13ROIFKWAPm2nQyJkK2SbX7vDkvHzB0js1fbGzKMIxOBjNaN7JGWHvCVc3UcvV610c2HDcbzhoJ2h0JTw8ZhjAqbn0Hs4iswRKE7yeYejOmIFskEOlap65+eabb64uRKbCoOdRk90pc5YizfitkEpae2RjOiyVsU6wmK5I1LfKbhxRTOKJEqfbYP4xnG1bsYrQN2QexlYbvCRSdERK8LKKssHdsMzz2Sw7ApkJXFZmvHSdhW2XJcXgxWUui2pvNNYYKa13vvJHXAcvvMbUKYgdZR9rsVRZWcg50zEzIZTkcwap2dcz7vdu7v7R4xvvvHTr5tWf/yQ8XuyPklJTtlNzhevNsQ3xQXfjeBwr8umpuUqdj3TAB0flBf4SyYUY+7xycH9bSryj47CBEGCUzEeHqnaSzJGRF5U5prA33UxSGKFBGKFtUZmHKdhXMhsq3HkvsAA3rNUqV6P4CEYjz4WVYLqUzJbY3VibyAuqxRQHC2lqZCFgRbucoBecwX4oc5cXShn5KqLub/cPG0+Yd04XMgv0bdrzKb2AVqqmDGeBQBhb1B/QAMup2aedLyjzNAEm9IKDcMdWJ/Sd2SXxwneIQQb7HGdZUebJKm9I5qg/Lm/funfz4rc/evyXTx4/elLeWSYjOdCRRwfO9gPNhi9RX+SxaMUgBY3hxvOvkjnihgPbwIBRLvMa/hLJJcdZBgacfMm8RIXSlchDMk+eonjHJLORh3EeOzVekMJUDFv25jbJjPFVuN5XNWPhWLE9DuI1PSvK0J16D9+VuzpgHKIS++gZ33xqDRhL7J6KsXUFeHGcEIBS1OVxBi8+At/Ho7ykjise9GReUZL8qXvmnd8xWkYwrcxLyBxOOlbgtdqTmd0y4zq1O3DCZeg72V/ILACqGzbxpWgwS2bSukdq8DmTkBAvalYEJV78tvGHt3fffrj7Vxm/VV3LgHvzwhnCYRuOe1czPwLBe2bYmKfefeEMmcyO05qFzHH/DPzeICmUKd0O836vcbWPhAmcCU+qavPOmShkVt2x8yKhw1DYMazgOdnYhjUKmVkg8yDzQrMMyLCs6Q9MPOFSBK8JHQt2Se5qTGyxDBQOjE/mgavDlwyuZDdCzb8FHXrNEXtYqeWuooYXxfgpyS03XZGRmRCo67v9HOGZpYBlnzkIBbXHjvacZZUkJ7YqGpotzo4iCSg36vk4Hx1k+CRSJLloY6L5mZ7fyb62w0JnWJIVr0aCb6125zJgBAskDyuDeEfkP/5k9y+3Vrt7NomZzTEPFgscmdH3z+r4pS1H0IhCMLxDNPMxgrYzbYZuBsN4QE34NiU8ZA2YwtEc86AywzCyoWyHUxQ/I2bahI7Ia8XHHxhwY4X7gCbLXWJPDIKvmmJC5THBc/ibqCRAFHm941hkJgg8dopS7fmqLaix31ZlBgRuT377UoBUGEfSFV7PysxlbGFL1SLpKfVDDI7Mf0+1afC8IABq+7KyBTXGhMZhUThzLgOS2cgbCiNMOu5h6uT7UwiLYKgREDQgyXA0e/qOSO93307tTi/GHMsrc2p3QFnMEoLu37/vAGinIC1yHhemsMZPZlbtY/u2TVQ+CGrSzoG6CICZgxCYeVhh4QLCSGYvxhC3UroPu0zmgGObmZTAATUZxnaf1n5Zi58m09FeNZMuUzAdMhwQht1P9tUY4ahGhyeFA88CiUeM4PXXXz9s8OLMITXJw4HmavshtUsi0yL22SK426WFY+HpKNRR/cQlqhWvOOfoLrwuUQTPEelsX8YMaoFofupFjeLD3pjsbVFqQM2qF53iLHRjG4jw0j+I6jtjlxpi2DKFj/fM/I6/q3pn92j6N7uZsxVrxjxATet8TY/OjSxZ0i9WWZryXdg52mzOSWOahQPyGonGsf1WUgw5ITO2Dp4DV5G+EGYbYrWDgToYNXgJahjSuqSIz7YIIMQugsPwIJsp3PnMTD8fBoKtxJbKjlIE5JgU1tPrHeu89GoYjSWC4PCMA1cJgxouGtRw5qPUnKomB0Z+cWkaAjwxPXkS1NgbKqjJMfhp887sRTxy2s8cp2Z3pi6HiQ8TJuUvV6ltv4JL62rpjQGEhRz10wmK1OlCZvcbhJ+G9lghmaKkFgbpbmGaWX54sfvBnd03Hu7/uuqGandQq3LEkPkWomTH1/vvv++5MmOOZeFoe44T1DAG1JA2mjnmjD7WsZCjso09CWFBgNjul5qNzD2l+ELccjh/aCEppmGBUr2+K35kGNw5VuKWtpDNvFUm7sDKWl6EzrhP4qvMhWdC3FZqIkZmtAFjYpfx29PlXu84MPupS8LWsNinOpY6HzVHUIMmw6Im2BnIcNAeuFpKtrLjbFFm7Ei64T6OmcpZasqCNm00cm70+w2OkO+PeNHexrTEsagzKvuFeYlhqzzvNMNIvYDMsAlvSyfLHiAFNQIsx7kKafuMM2c674h85+buq492P7u5+6z8onNZ7T8fjWFAABT5hv155+ePmBm/VsPZDGjY56/nftIRL0YIuw3H6VGDstQgd9y121EhDJsgPXXWFm1o/jCM5I9hLcCnQqSbBnUJqPk4Q1ou9O0NdQi5lq7BYr6HU8g4NpqDGLLG5J/LKWqiU4NbYjc1u/QLm4Fmp7pfy/M9duCT75zY5NWbg4WOxD5cV2hQEcEofAfU4TWxb1DUUyLxdyoUiSSnpih+PhLA2SK4y47EFr2KC5M/INuI7wryXTh/8OVaKv6WWJ4MdogdGacsJh9c7P7wzu5bnpnZ1PPuYaXQLvJUVTyDbundgLLqKAtJ7BCJAQz3Fau1j1uF4ZbtXGVvcVcj+m0i1wR0nEUci5fpoca2iYxDThVd3GMsJPBmCvfJPgBBxirtOWC+UXOUjZujZDP1i9c7dhHDji1uXcIzV2SnK1idY8qG7nWmGzsLxxGAwneEUUE/sSN1jR1t9fYMTFLfVuEONNEf2of1DV6CHW167KwONQHUCkfFbkG2ARlKiY2dqAi5DMPr+s7A7NMuqd1/eHf3u1fvmdnQT92lY2iXulOSR1577TUrYsr3nOWwUfV6lxdskryU2N2RFz0WRsRzLrmoKZZSgagI4kQsxJiOS41cYRwuD2QGCW2ws5ayk24DCxSmciFH/4iY9rYImrLaqBFyZhnbhgxKdr2sDWB0bPfr1J76yQ9/CYv1xqnkNakcFHFORXkOxTGLh6RFwSEzgdkeq1t0orKDRxBDSqzfhPtEcFsiK5QSntjEk7oi9ZYadulxiM3mJ68Eh/tEzorgtrTAMb7n3T3p/k8+2f2Ll3YbMm5ox52SveP5WFmg5eMDS+8p+8jIky3TFPpaBWjZAQ/BJDbbjsWLNmNgyj5EhW0yCWSJJHHyUK82z5DZFt//gL2gkBff+c53oFNwxCWGork4eLjg1J1Lg7g/cxK78LLvNljlmOQIsxFevPBLmlXEGDtp8VzLGxEEAQeEwc4SUaO42GNxm9Y+7mGWACRfHrxgJwgSK3OyUURYtp0/SyMtJ9dkK8oPbTf8Yhp2uE/7QTthJQYSe13zTsLkH/AFjplf2Ilv6bupHPfJl2RUy1cud994tPu9u7vWk/qBVrxSMCmYteMVbWnpPQgyrSkKus9GzZutAiTfvA/oKn8iPM64A+6jDcmJnQr68nIsMGLYnhgodM8fXi7Yxop7UNVTmPTgQGScj2qjNTpZmFt/vu0rvJ781/5jIsgBXiJcLkqQKZhEwdBfhyPOzELqVzZBUNzu4oUAo+K4LkawLcrpEhTQlNb1AZ+gSyQzILNqN/8i4fD6xYQplu6iGlOTMfJt2QmBwQ7w4SIPQbbwApqurqO/zeQ9M9/6ZPf7L+22dUvNsK288MoixQ2rtvSODt9yS778nQctQZAxDS6AO1jalQqOz6rlyuI3SxMkJEbkILyfTUDa2Da39M72qCaYiBWQnx8ltlG4ozayrPhIcx/DPsBRBIUJ1jbchW253Senv6D0S1WyI4jWKZNVJgjspjbpMDutXeXGcKsmFYIESnvzwmpOMuOVAN9EBdyFl8AyVrNiMwYEAfaQoOCuWcm7IHeP1WQA39wtB5zlWlwcAo4d5+lYOcR1UT117Hn3P7j77B2Rpxo1eT6KG/Z/CPhYecNTDKWkVruzveWeUCUte1BNzomlYxWc357pxs2SvDkf8LHyRJRD0wQPYie8b3OAK1ZpLaRQeSbgG3gdJGrjqTW+4VgyY2ex0X+suSzdHjfx21Nhgth4alDIsyCAGsJnm0UDOmofGQ5BTjJKmxEq6842iJEveaaCFZqFYZN/obkiXgRHjkHEUOcTtF3A4cxoK9vnMLnhRDhiDAQ7SpDzCpEtWvhGJSc2RhDH1DmOciGcyMeIchoM07rKVe90f/fqHZE/vbVrUb7ToMCTI8B5mpHznRTlEkHhWfJyYvP0/BOvsAeDx0qw44mjrNENzgFaNUtGhyCWoty0efWyGYdXOlgDuYlzApzMJGcn00xF90YL93Dd8EAE+xjVMIamqToR4zHd3LwSkrRK9rAnYl9RsyU3Do1z4ibtEkF6RZRklOt6UY7kY1it1JadLCG5MRNHqEl3UwWdaKNGHkEfIJUIPjGNEIyg2DRJTjRAEB6FF/sTQzZ6mkYRydeNDxPQIbAlmCT80RuqCcMu3UXt7v3uX3+4++sN1u78YlQ8jEqIHzlAED/qmZnzUVXL0UbG5hL4mzdk6M2+xFwFxyQt91z6rgPmwAG+ucKJBqJcpnbCYAy7LcApTnJoyJ4TIvneOzIBqtCs605o4APBK/XQXEGAaVMQ0l9dJrDg3nsqBr7bsqeEAKUIH/inkw7YmS0uMTi8HLbptn9xnIkAO4FqZuPhZgjiSsERJ1r621s2wEO39XxnABiSrxJbItkkjkbJwONsWJ4Q8YctZ+mr1FQc0H0TURE7DJu00Pbco5slX9A7XhqlguPHMzPxjsht3ecJXCyctcB8AJDgKPnRWbsyIB4/+OADEVK+Hhh58iWRkNmYZVshEXRWHonNwkeFoxygAIJNUxi5uAcxFZGQ5PaOc+RppA0LEV7YZKgwSqo1C3f+hk5bcAn3o7aOkmHvHaVwwcYEVrLTwrtiEHA4cgSUw/PbOoMgYS5kPsXRtjS6ftKyNMHRnmriIz8qHh9PgWaimPdUg2bPM2agFc9SA/ryo3ClmRzhN37uOTBXm5dE8sYfdhfJw5V6dUZIroJvE9hTUqndf3D1fvd/tal3RFKHe4otR61FwOFHUTnwX0ydUv/wvL5+eMaD7t+/7+Bo4j7sNeoMkch29q5j1Jh1GoMlJC+SQVLlABDbciqwE+7JJGBeRPLlRO2NzHRJLuAQfpTktQv3cLnwFnC71bD1lOl9RP/ZNr0uS3+EtY1gvSX2peetMz6OEOR+KaYTQClbZ+r5szAqwo8K5fMnLTICnFmUfeZoaNLepj2tRUb7zL5lm5FZ3GnNSXN0FCuXrt1ZI3DssQMoy1o5gp1tQ/ItVgb0alNyfoQm4qHpaAZ1HndH68izZK3bwPvdf3R39+1Pdt/d1N9VBVqUNTCXgODfjXhXpeD0WlB2s3nhG9492lqcIJ5OeJKzpW0lo5BcbIGw4wnIoClufXWvWTlIQGZEaxxMkHytLqAGGsnJn5lGFy/cCSRpxcb3bIQjaD5GuuQ3XrQlScRuwoM4p2SnezvCDyMTNIliIbN9xMrhXg1eZV202FasDBgj6NgPoBo0qTCCJk5eMzKeEowYUlRmxDk1yCrn2QlfHhWOcuTEDiO0aSwCQKa4QcaA4mrxkXMUnNOGwIx86fulHAm7NJHqrCuhkqls8X7Juvsf3nn6npkRqTcHxIXbwBwvHmtxwGxsZ2nKl8iKO0IfPHigdo/btvy+Z1sSWLXAWsi8rdgYkguMwkt+bEzepLte9mchKt7ApMxjwgJ2cUnGDhiSSxlxy3Q2qi9SuKMQ5YRwQCBCMNxpRBrBNhaF4u35HiekiAJloWfjist8dkDA9mhK/sb0z3ZvswGOomBqU7wBqTjIKVOnkQ1Z2jDFoyuCAyNXuAR2wk/z8QriDUwBTCnKfqBN5iUEdRfXl6ZJihKOxKVM8dppJh3IT+BiNvWlYqhBk6nHFoLa697CXcdY3N6/sfs3Ubvf2X26fkbNEh9N4p6mXMkx8G1ZPbMbRZHnF6u8Sfl+KgJnj9dvGHVkqQjTH33JzzAHPj89G8c0ww5Z+DV9lxQqa2xlDB5hTp7iBpMlwdRGpBUSo/oC+8AwxQr3VAKCLMqLVAUOTH/2EpuA/tlmyzWghdRIKV5Nx+UmqjYySG1BEys5RVM0qCZVqYkGyt9SUyw3DjpSNRMOFSbnPC+wX27qmSOzIoGyhag9VpHAfI61R3lhhPo0BezDIX4sIHXaM5XKdx1dh+JNoJumqb4yq23yCNPmnd/r/Yvdv7m9X3f3t5nWzKnnNEn1ujomhRSwizC8zMG5AcZdN6ZfqRnWkzNW5TA7rv+51uxEwlUBJ13O9WjletSRpypguMEq4t7Z4r6ySsoAaBOPLW0LdpIDU7BiMGzy1I3HrMLd6HGnFeQFzWUZIv1aS0rmhSDu099MLqtazdGuavXzxXoSifWk4xcH1RDgQaJkgG/Pb7dSHJBWEKgGVNmJws3t84eNpCUAUhxr+gZr+SMUaWnqyJ2rzD5TBaBVqN1xlMqLUg7FK/mpoAr/mSBU7r5fd7963v33Xmru/e7wjHLilEMxGLBjc4moaGqPv8bSu4xflhca8VDWToWW118OtSZ2VMDYIXw0gJWPLnGoltVhJ9wf7A625apQTbAT/jC8jy7cEWbDXxTrRwc9pH/yGSZSGXEY8V6TuvnOeZB9QLVDuAcaF7+EpoiDRqbOWKa2W4cVR3LpAYMpgDMYgYb5LT3jEuMzMPYmlC8x+KJjipLAD/yHJ0pM6dJI0hLfV1zdGIZr+GpguFAd1mVKwVE8FBsT7IalxbCarV217u63qv/4k92/bKN2T3dWHOosUxowGOW7gyWA9b26XOmFMw5MVHAKdmIpcHgZteB0ZYcS29GkIgprx9RWlrEDdlQKCAvZTFmou6OBXVZi7Wyyl1jPF+560tkWIxqipv7FA24Xl94x1YZf79hrP/wRbsMNlrgaTMXU1JnsXWBfRf4imBBelGk8oZJQmQtkW5cpkb0ICPUHoYW1jV58qS/GtBmFSPHxqL8gCFMRALtMTZuoeC/WDvON3jKRHOywBWwRZAwV9VYFpliLUka59oGEAAAgAElEQVQ1VjNJFUHJe2Z++OwdkWVwHy9WqtfHMsVVsSxOHvXW8YL0exj89ddfj6V3j9Bw/36LGZ/JzODpTosZw1TtGgGQzJiy57OlvLWaGtAm+Xa/8QiDV3inUHO8cEeVprZoN9a1qvFRaiKIiL/09TPzso5aSsJT45CWRZLcAbJsbNT+VPvM80YwbGbj1ppFfGmwcE9uBbFTTBEb8g0Kn8PydoUPwMOJQlMsKAHjjFzVcqIlnpzE7Oc7fg7LZduIvYSH/2Thw63cuhjBOCJ5WQkHRos11JozDggz6pLa/Q/v7r75cPe9u1Wfdw+3IiqyuoXIKOGjcGQ2yyEfS++eejeLUnuUeMONac1Wl7vxGJ591FV1RbhVNwCKigChxaihVm8ckYHwNNqc8AzeBnZeE5no4jvf+U4cIYlT2VyzaRfJbEXEQUzW5Z4coKY/qEFTdPLPsppCEqRL5HuJKgaPqgJNVKBLQfmNDJPN2XcgsBzyExCOqiLurIIpZA2Ms2nkCc9hF1oJGwCtyCXCxxfZyDIgsqSrsm5VRM6jg4Twy9UxRyctdZLDijZjzYbKEgSycISpYbcqJerhOMSQOzb3h5lCkdcud3//0e77y9fuKEaWPbeSE4u4ldEi2hQZ7ZDZOBMVQvGl9zrCn1Jq+HyQxbD5FM86bBzCKw+weXi18TOEF20Y4XaFx8stOtCE6VPD5yUKzclEyqMLBUSaGpnWYq5768kSDnRk92UDihRli2ELhr+jKpSV/OgUy50EDqtebvyckYOs8CzGJsZlQqoZinOmaLBNpo6tSR5kgd2BRDW2gmxBHcjvo/lmf2NA+MimZ8HkU6kE1Gv1hAV5BrOJBdRDbB9c7P7k9u7rD5eq3fkUWiNn8ayypZLRIK92z4+uhwicPeNpKGZm6R3FtrPtMxsk4Ve85+yJiqbkWcM3V0l4yByt7HsjN/UxCU+qzS1NhvB8av9jsmall0cJWpZ1FmldSvRXr9uXHbw7WkSr7pkJx6mk0DeyVLXaiPwTBP5l7hIlReA2k6wixrMKFxTfSvlITv4FJUEmqgqRUB2wuVQURItsliE2KjyxVUXoYD9H7dYlfPGLRNbRZqucDJGiglxFgDmTPrix+5Or97v//t3dcejHj46m5er1rjjpronxFy8V0kRsz2sqlA3K94KvmAvhmY3EQf40Xf2DcC7ycMNMSUJ4Lsltt7jSIdTTmvCLWs4SVIbY++cClxi9wTE5HraYWkHfG1AzcsxAg1OXwovi6sz679QUL86XQgDLLErkLVhSiJ5sYLsVWMvlI5pkymAfwgJCNwDGsTbdk6VMpcI41NloHgWO9M9ylF9d8CMYYkQk7JFVAc/8KYgnCGR+aZA/bJ2W1t3/6Pbu2w93v3d3N7MagAAciF2zHopvPLjzqbu+IjAyTqFD7W7vuMiYBomaZBW35VZxfwW3acU3zDksNGix3I1TKah749Dahk0BJ/N2pTdC5Y+MBNRk9rTI8R+nVhZo0emw4il5FmaJfebrHUfJOSr994r1aV40Srzr3Rjpo/Afi0aXLz5f1u15Jv9ksWOlaqS9CC55NxXHE18MY7j+43q+HF/oCb2lCYI5BVsDP1/rqMCssLgZtunIC7YSDAUBmZWl8d98lRtp6f3uf3hn97uf7P7VpHdEqv8oThfOVTYYZuLDSFRgYv6iYZNnxdL7gwcPCr7Hgsw8V8ynBf/NVHlOs7i/MheyZs5Icg67ofK3hxuLZbot33uwarWreMg8vKiU/M60W7izrZlOKIDGD1upHWGlx9nSHwdcAvR79K++/CUGUYfriaVFvWbjiyaCbNkMasAuX1upJ+ozKxlwXvZcf+rujPyLGPZOsoR8vjTebu0LdklIKu1CsYnjiIeQ940o+beoAhtTuwv74s8mMO8K+cHF7v+9u/vWJ/u/q5p55yEeRmKVqVep17vys3z+bltaEv6F5bJL7wwmjIcWyxkPslAGNBCVRYnYRgZLfpjtcrfusYBvIzzky8IyUy+Qql0Noo71pvLuaDfffPPN5QylO9PYY+tejGCg9h0YkBl98MEH+voxOD4kg4HGC10COmvoDi4z8RyRxSUi+Sje2TRrkIJD+bu6tH8M6vmoogkOQVlNvszLem3t43xUQqDNB//oyMMnw8Us/4SLTfOvCOL6Ds/V7FVmw2J7wadZaZk6aW0clsxCN1EZz0aNnwoKu62A37OKRxe7+zd2/+jR7me3hv6uagqJOFLoULYRsphNROyl8Rdn1BVmUVExXR97SE77KObEaGWDJ/EiJC7KF5ltjN8stmkIrNgLmwEULVYUw9TEAKOSnTGr15XBPZE0aHfFndA9cc+iyVys04T1+xsKZ9tXaABiishJDohHI5Fui2ZdAauCU0AY4NMG1BFfNt2Nw5/r34WbUQSPImaaFuv2AhoAq9W+4WL2tDY13Ga6GD8lv6HWhXHa7MRWWkXAmTZChV7IipsrULOTbqhHn7zlzEwSK2hxdAo5KJYetyi/36r+2e3d167eM9Ozfh6BMiozsGYXVtkSOQP/o+wUPGkWBbElQrBYZywyMuMX+Q07P/inRMaV6lQdDJ7xE55310+a8/Fn2LCCv3215NUVG24MiQHzL49jdS/1jtst3EclHkDHUzF8qZQL9ZAa9THFOKZczW1GSXi9G4Od/YzSEWW2sDoOPL/4GzV7r/EE+XsjrPtR1OOJS8c+fEUlAS6UFUwVhiK/MdeFcfLs4r7at8GnTYIyXgZb5iE2HtWR/PBvUP6j0vZOssZNy2/R/U+fvSOSA+zD4tXfeMHXulGxh/Opj0wLBewfCw5ONStyniV72B0syixf7wNq/rCCjwJujvzEEBgjKgJhvkijRoAGAfjvJqylpxrQVpHf3Q7Gze7nVTl5s93CPdPlWLn4nu5UejTU/MhYbVH5xR1bfZ8pqG8m/gVnrD8UsxHgUGbqqPx+GbSugzMkwxfKTmdMlCEuKFsoNxCePXBn+7LyVxtN9Ld0VPBmZo7kKIvYCM8cyuBP8iLrjnPEntyX/NRUu2w0BVh3//FLN37ns5s/ePmGR2FwQaPJaNTvKP9CPvCvIDlD5WueahCX4qHkmSon+VnRqZvbwynERmLYZ3rZ4QilzhCA2GARhbYYQpP8DnLK6Dm4MRisIdqvJvJttdFn3GkCi2HI1Ovx8geaM5HI5XMQnNBXNjK1tGQDOoFtwbozWzTZBMKmqxZakP9UyGNdNgSJcV3Kku4tHJCNSZ9SoQUJh2WIwF3KBUARjobWOpSRX+I317CazV4ti/80NXlZsEYYxR8wHWQOJZziWuP8ZJY5cp1mITYESrlAHbHD0Yj98GJ378njf/ThZ//pxpMt1ewdmCDPhZlcBRMSrpm6yT05A70i1Qh/MaaRz3qNXBaTjvWyDlrlD+FPMNu2XCABQexwhyXkN7LvauLe0tMioxINk2h0xR3ZTDAh2D3ghPFtgm8WaN69VOcY4sSDnekwenRNxdUKwWJRfUPBRadYdHDBrsuCIgBrMSPTOmVdi4o0anCmJWqP8udR4y/dmP3PNCHdsWYjKjaPOtqiWqCAzWyXglh0rPzACdaihmAAoJvjaFahBPk6hdcShsR+JAtozAFhCcEOx8RaqhHBHsnr06v3zHz7k4vvvnSZe791OPR6Z2jB+JnQqHXrOfLi2o/r4gUgnpyZM1T05cLGRA0VDkdLpQhHqx8eD+U5PMPyCYkC4oVRHbZp+QxgIxHbl0oE2PQ4t7A2aom9h1K7hfthvoGdkp3CXKLIF1I9LE59FNQYnwLCgTYEYI5btMJTCl7L8wgS8uyDNcbTZmg7BX6S/FSD9s9zkwmFb8pGFFw3IZFfyikVr+vzxYSY/QQKJogarNmbFG6C5IRBDrvwWYtSh7ngsGWbZ0AhT8sdiGhQwuRrWEv1eldO74j84zuXX3+4+0G5v6vaHb/CMeNhQvCv5sjqE7x75baDtFo0WVNDsR8qJIJkNMPibhOlCCHDi+FfjYLJaB925BrkB3iXgsNmZ89gTb2uJsFjvI79bJeBBo0W7pSEV5KbqtyP+fKEqMPSpYUOzMIxolgnyVgP6cm/kJAvhj1EIFizdwl97vS6hnTYvuUzkk1E55aFHJCN/JmFL39Bls1ofE18tx8YudolktQpfBfSSNZHASIW8gKUwQd9C7FG7Kh9pYCFIFp6WJJL+WbBwtJzZY6Pr6j8wHvW1/bPu9/effXh7oebrd0VXm6f1ExsKROimc3MFUvvprbICPA5A7Ic/sWKkMXjgrVqusyRPPoSWP0Gk1NfHcyfYukRII8F8sfBqOn4WrwxBQjxDpVR3Y82brFwZ+WYJm7wHYG7zh893dcOnR+YmjokOYrdwMmkwkCbF5dKIQDtIM6AAhwHi6Am0k2jr5RgM8ehCJ/fbskSdAz4QrrLQhNladoaX8J05r3HTK6X6w5V+UYBUXAKZsnjgt+lWWMY5jIjLgqqUHMo4LMioWld8+aJYOR0xABmfuXnPTN/flW7W3dv4n56PHmsVOFoy9d6/CT9HtYZ4eyJiFhq7V/O/owyxGEtHJlHZHdtqCHkKcIRcMEXGpIsTxT4c2RE5C+9UxZl+PL0VFmVWyzcQYPj+AtKNI+blTxsp7SKmk9Q0xnEpp4fXgeKlSkivuhzDIFEHL4Qt3QBcUyEZc9x9TDLZadZcnTedFg1RvVANQqGxy0pwtyxpd7K+X6uxM/3BzIH4Sygfv7K6E9wkHoRB5Oa7mauKHzLJr/R+s/oIOXL9/bzk8sEKYI4HUfV692Jonb3zMz3N1u7i0VRdWGhq9qix2a05mid1aRjnxfgaBEqmX0qS4RTPujjomIvNDhF4BC1LFNcaJZFhyU2LfgyCk5FVAT5msWesnMeZB9QpLnCPej0hENoPiD6nEup5jNIlA5l4+l2XSuhWhaQNOzMA8CKZVHOniWOClq2qchMHLbSPSggLSJUD0FcVA9b4YW01g42minDTqLwPZVmhm3pkLjh9gtdlQIlS981LzT+0sOydiq4/ZDalp4rjS9UynQ+hsel89MO1O7+NtM3H+6+e3f3+WOs08ZaqRccEIGFyoakmOF9ajhTK1vPao813Gkm7PQiD192NW4Cz47TYINwBNptVwWFezz5gwh0dEGW4yQLOrpVmxZvu6MNHDf0OkhYxBPJX/rSl+gcOX5A9LGX1HxgZTE2yPJhm4nQMHaos+0xuihtZwWY36AdFaJ0CO4c5xOHcUTb5qOx1gj0tW1XBVbE3cRoAMpA4XGbU4fAzG/THj1WBVanwqA1BpPHrUucQB1rdWs54/x566jA4yJamo7TsdtSxHlH5Hs3dl97tPvL5lb8cskBBVhEJFad26dEO8nIpOo8dQ4z5l+HozqpTdQnA8RRwabldiMSCkKFODiEov0zJLfFN4F0Ua/7RoXYnopxi+7Mciqwk/X9j4bqdSbImuP1jlFvzVebensNsxdo58+YRjBvOt7uAS0Wtb8BZEzNDHCnDRkE2d7Cw0DfdIlfGcQ+ndncQfjFtgJ0OB3koU1y3i269VYmtkUELVRC25K5Jy0VeNNZdwins+d0KFvL/XvCx8d9nnzyhBaVS66jwkw7SX7Cy3RLuEPcJBMM10qHaRKe7fXBjd0f3d59Y8vPzGABPiotNXRlCzep2f2ZVSFR5R1op4CZ73ShghGivj/LWoMNqBD3MLSekNxb0Cj48itkDHq0e/67YvKVWrNwj6ROedYcJRq5QeBMvgK9lrrLOkKYA5cYB5uYM2Bv/F+ej6uAhrjEnQw3M/0wMDly05RhISy5fS0Qd+pGixueLRkbV1CltVC9VU3xKFaOPqrR5a7lgEm2WOJiUdVwKzuRoMQX3AeWKlYSdzCZGTAzNX3/6pmZeM/MRmkQV9OCQmVbwr5ST83qyRkHsSIwYWWKClH4GrCULWUaQMFm4qqs4Q6ktWWCszpKBxGLol7HI0/k3Wc7FmmwTuGOJCGGwR0+yO7kBOVBZouinx+2nHuK0FZhkCgZ7ZeeC+O4szcR6styt5WqdwBk9hyGPdBmrUsEE3aDO3KeCr7SkmauriXn/HlpF2rOH2rFEThXt2RESoRNfneKuxWlPTq1xCHNK1mOXt3ESSzI+pCfkOmSgrqjz8dVuPOOyJ88e0fkRmt30LEitiQ6zSEiMZJ/IJLEW/LU7u4fyJDft9eS2zIDI9S5Z+vNXuRj4E8FflGZiAnyS3kWmkVRyHd/eAr/cOo6aa524c5MI2yd+qOnEEFkDqAily1qGmlVL/ucji/a5CAQhXtOywltegUfd2UVE8b5ZegCHKG5KXy6rpfDHcfE+NbJEpG3vuguSEqQYmy6T6ZUhZvzgtSTlsybrlSgESo4GFupIE404E18al3u/Fb1GtTuUW/BM7PqmGnJ4XpsOMoVZhxL7x6PnrwuEENZ/aXLtnw5gUns9r894HdKdjIT9ejr2PkjEhHhYOlatFLhTg2q2tM8lE+c9Q5YtqeFeifTx1QxOBMVw0YtNWnU7AFgoT02rwyrw/RZtihpcOGmqWJ0WPIVr6IAbisKEFNjLejzkSPjbpTrhRZ1suNCWNEaCLZRii8kzNhhiZ2KBgdHE8/YMddqjwjmRItNBxBlVnzVnmNOUa/bU3ys6y1HU9Tu//jj3b94eavvmQGOMostLWpOMmnE8MOsxwYsB/hFowbDpdEAjzyCOW1l0fqUIogAFC0c5DjFqXGKnw+OuN7Z17EjIu5AyECL4pKkARcvCEJbEefwqZgkRPdAyx5nznCqaNNU2OqKff2OeyxMVrBLX4StUiPniGSujVZaOdpVaBP0BYaHWSdfAH2FP/v8Lg22VDOJRYtG5LJaI05ZEPQRXl4xPhVUCZvmgvASPOMMjcqCVm00OV6tZn8qJCIOWYm+U82qCXw4kdr9D+8+fUfk4dWtnOEaPKL492nJ+9QtCutTaDBjDyCIKn60+sUvftHHUy0HzrMNhsQpTLpd1wYUj47afXXXhqRvQtxLABY1A+D3LqFSL/nOwUJaLFi4s1Qbf8j/o6eQCj3Zro4+QmTY6HuQvfhYEIHAf8KAOqIvAhBCme9aKYfxcKHtBrIAH4bAXCgEHPKLPqDZHJiUFx+2mXCmshYTJDzbhQowsa1lz2cljAasJQo+Ah96nzJFUuEajWsxrCyzpIX9prWIYsu+qywDQx8SqXZIX7dlC8eed/+T27tvb/n97mCUI+QsJWORcGco8XMUfW4bkO5xAx2tck5jlvAGKX4HMk2Yab2Axh1oAQeRatogM3uhLyiwxC5OThhNLxsiqLOEFuXf405Q38MyILi7X0FAptriFLfR3ggiF0eyUV7uyRyhkWaEpwvJG5FnshjMN18LWmuPPvtQvwX62NIoLSZjtWhHWggB+VxMEwZrQV+kHOEmGJw22mEvjlxBi8N5y56pw8UEmTkg4iDM4B0P03c9uKCFlMFKJ8DVTpfEBdY4IAbxSCkMLu3ypUB4dLHzqpl/+Gj301sbfmYGETO9WwgNBzTOBPoYAHvWN56cMdoEgmihl3G2YjxHdaQFNIAZ6hxtU/ykGa0FxLMhKJi/KICC0AKntlICG7PkirtYEysHlJc8cqQUoVhnGCglHfj1qjWGnL7NtgFrQZKaVZNgNEUZEkNlDBZZriirMtnKDlh/NOa0kBbBYHirWbjwcoFyOS1qMhL4MPvlgBqlDgY5oE2vfAckPDr00mXUdE01pgX5FbuKpKYEGyVM+IVnJGgR26jujTT2zMyf39597eHuB3d3G1tp6yDIopQfY7/J4YNCaHjT/G9IVKtMWuEoyExbeucUFFG7E8ZBR78tHYYW0JCVlg5TuPPQGsAVMKOeijkLKMltuEAE7z7bPrNBVnl9diz2QeGwM/vh9jCysXUBizJYcRBdYLfpX02FFqHaMAibvoriWNWjBaNkjonBTevVsvBlEcYgHwxXxeD8ZJMJHWdnOfaZ7dtsBi7pZN171FQrMAwMThCGFgoUfdsEOVMqwUdSZMybUwSD+0R49UAFLUL+7ZZZ+PJHVffvmXm0++GdDdfuHIo3ZVa9Ua/rIqYVLMvYRtzIKSLV7mbJdIfUjEjxFBa72m68DS7cxnAT8SppV/BAJBcG+Z0l4+ViCIuiAqNiJEV8fLRB9CCjLbOALDtjbb2r6eM+PnWKddKjJF1NBwMjpDbtH6iHlrOAtdRHjfARpV7cbhWxv7XU2dy84WUzc0MwiMRgcOZoEzCkhSi53UQSKkfs4hFHg9gEWPK74C5ILMIgA5C3FsqI+UrNbCkpbusOBIOyIa05QrrjkjKkUZc27R1q9397a/Pr7lEvcg0OcjTNhRuKAFhLDM4048PuYQ/x4LGnjg8bnD1DNoNs3cexAHA+LlIdpeMsDocNcGeZGDgg6r6O/bBlqTNMhfC4sJ+feScW7iw7Htt3O0j/o7pd1er7lXVXCU3W+nnuqGBLn6RyKfNaWtSz46ccgzsZZb7BnZ2xeIPrYXWR1yfgzxqFp3bcEB2E2TopUojlk95vCoubbhpQ3goSBRZuWCq8MKp0G5Dm2uIBOuT1anRMgyjF0m693h2KdzMqroGX7vltHce6+zce7n7/7oafd8eCkg4duEi3UimWRu1VIYiZUclOgMlL74xKJUaR5W4wKtgnwEt9gQCNuAuy6Fz5+Y4wKnEAHULWHPsZXbizYyV72JN00uMs3Yk6ryWjyRdO31IJqSdV5Y/AqTxj2emiSjAmCzuVY8rOuOhojCoUWXSW1gZPJHJAJLbjWQKWVYdNZxFcQ9W2dMiSY9TrpkPfzEB/yj4RYeVFDjvVYBPn4cPIYTXhznZpBZEo/phFQjxr9hq4A6EO61pasOXGV7v/2e3dtx7u/vX0Pwm6nHQjRkaHYMWuMBIkMjDHI4Yo0dTUXs2n7lK8erRh7JAMj8zsijqbtivyg2LyTYiOQIBGnSX2UzSJVGRgV/aOTzUbPj+icJc5xHdG3Hu9o+wFzahW2ceoYr0r3GQ+uoO8OJ6GAPqQiErdkZiqBLY1bcB2evENTtKOPJMlwcVwpRgkKhREZyQKc5PnWq4j2bZ+ZxvggHeJVV7gRKkXJFZYRebsbZa8o4yQm8sgoGskZKV6fawnYtytlP2mayy/Vf3Tfe1+8b27l7Xr3FF2M9hYvI2giovVb26tE7Pt+/fve8yBww4K3r9IfhbFQYwwuVjsD7rGZ8JzKIFX1HKQIwISOZTyRsSGXk6XpdugI92EpFpr1KTnC3dz+EIBRqqf119/PUaHRZFivSsrW1zdN7ryXPvjKBEkGJpimUs0uF5VhIXrUSkiiA8eluMYtEVNLy63TyJFRA/7IuSuOIioGLDPlyHqAzzyRCRWqNeTzKaLm/bMRJg6tnYQt1K0gOFasrGHKKqIMS0lkzxqrJo2sARcavcf377c4ntmkjMmEjmI2IvQJYDKH1N8UIOlp97H5jUOwjht7eeIAUyQknkTQlMlu2igsPR9xcCYq1ySAcVedmU/NhsOFe6Gi0ra/Q3DjeJAYAIE7CavrK+C0YtJAwGujkFUOogS4bAQfIFVmwh0yxH0IdFG1M05oyBl7WdsqGqQFIl85qJ7OCMekSjerpVQKYKRrVeKLEQ0W0URPEqRvBKPk+v1ZOE8PRjZenDe/1bVe2Ye7n64kXdE8kQ8RmbsOqNg5RLTaoERK+5ihec9BB9lXzKbnANKUaSFm5AcaQfaIAJTGDl0N24YyFB23adiBuSPSyxtQJGB7scLd1gYDsH+Eq+7lijylk4tZhkQ9MWlOQig0pZ4ZNDdKnDOyC/61kRAfSDmBo9idwtZZJr6zC+0mNa9nV5Y4Fn2o0SKOs9eUFUTdEuEUeOUaowOYmx9KQ4aoUi1uqRbr5cNqmEYjVSKc8zs3Ru7v2i+dk88cuTDQjDUd4l1qQhFXQdzMJnfV8zxxLJ1VX9p1SM0o2qnphSZA4WQxU2idndgKLBYYsemNYhGnorJUTAUEbVQ4zinS79wZwqMEhZf/vKXWYOBysajUzLB2lynrr44PwEB9LklBay+1XicIGeFLkx6u2Vi4jGpsN16PXEtqohT10ARcVa0zIxd8kpal60TVxPgZw8ooiixX70oOSvqcAMqMK0Jd1PDw3av8kQ3OUKrcmE5HhlVTGSK7uybO35HCrq9+/rD3ffae89M8MjmmU0UfwPwaiBkxd1UC26iSCXShKX3UISb0Dozdg3AsuIliqjRw99R6aMKdosaBSNUoEtOSLn55ptv6iNAiHRsUey2+Uh5m0t1WDEpcatNt6hScjPoFp1iYHCzoz9KdmKEZ04Ddl1FBnQce4k9R5Yd23HF9mTmwyiIEoR3BJXXhpRro8hZ66JpUMmcEo8rmtapqUUJcq4Yu04JNvY8FfLvpvIHD5eMUq8Ojxjh/puLXYeQfnyx++jG7h8+2v1lf6nwsG2NM1BlISm0Mpj8QjysCzX5XZZTiU1SxLcEClbO62P+XBQJY55WHuRPtGhLXzhAABfUAcKmdQmjwiPEBqwLy/uywG8d8EdtrWO/KNBHB5fbrsETlqHaAOJHdZ9/UmQHYDit8u4aLGTOx6Q7QkSo7plmj/kjKonHihQHPVvycVRoblZNgiGFpsy1ZSFzZEOTdYeeIvvY2vk7O5sIbtKG7XqQEiujRWAPKsVYxnDqOYocO5nWhnWlumTaCI308szMn9/effvh7rvrrbujMkpVwWeybQjC+sZS41r1Uo9TS+/ksfTOVGy9qwMf2TN/12VzNYNE6Vl/Xql8jR+eIpci/KURUgZgH7gkAuOClVLwsABIHW/++q//OubonE6tcrBF0zkKFCQhXsF0olhHMPpIom6wmbdX6h0VMvOkkSsokinMnGZIaVmXoNJ9NstBX4SeU8hrrI1tDiAt9BWhqMxoWxBmvgx4oRFL45JBJQZRSUHn549fZ4Qg5ZTt1ZGh1Cx8BBeTdQkqI4PuY+t6DxVQgUVdOXFF+9cAACAASURBVP22vf6Ti92HV+vuf32r6t9mivgfXlnKJdmDAY3ciHeTRBFFKqvOjNbHTD8iP9PSd7KnZE5UqhlRvcxerlSyu4Pq4k99ukcoLjXdKuMkUo56PXJv/uZv/mZX81WkNKn7xc3d8x3FitEs58xGVoDaWK1jnhbpZCEGTbQVZz7KRfdka7rsfe/q1/0RZOEc9fpZKnmyjmebdXVv9vgaRNiEbfwoCjWJSkylqxs6YFoqkmvg+OEpkB9FBMfkkramqETHhkqrAWtXu398Y/cPPt399GaN2j1irD0bsLpc1qqNZuSmgpg0x3mtQNPX8QAR3Usa00XHsc7SHWTpYzhbYo/H+il46rsvinBhumw9RSIl7kMiEHXhpWAutd1uxY9RUtajikuYP2BxN44Kz7AO2KKNyebLM6cl05nTvam+0GtBHjxGrCcMmz8VfQZEZQCikip/oM1WLtFChVTNnovDgspgM7ySv1wDXugCqOJxrDj4OQMyLUtCNDobyiLMYtOwHLPBVSQiqd3tz+qSg8yKbX7hmaNbu0Xf745NZSs2p8XYfHAiggnI7QQxunsZIDt58OCB57/DpM9qxKh0oYju6sWz7Ws2kCN4sRl9mZAjG9K5PGfBjuOaohafi10JxaFLROaYoonCvSm7nwk9lIskb/5m40U8ivEZc5V4HQLMxKSF7ox+RV1QKZEQgBhCz0wLYQkr6lKQzVCk4IB1hoqaAAU9x5RdZjJbR/6zs0jhVrakybMt229AF2lv4IHmYJMi2NS4WY04iyw+rEuzwvcEU7tf3N597dHuB3d2ZR8jw6YNVjyxjjOaxYx4acp4yKOssjIt9fDlHv6nPjIwurRTj5Fc1S5jUqRbtp6SP51nAFw+vjdr554qiTfqgOJMCyl6JV2evlVm1EDFG0t4BIJ18ZHrD8hPJt/kKQXQsw88V6GHvdqMthYydBnlLfXRzp/RDZXGNZHEpsBhK86mkW01dcnHeWxLimxlcReV4Z5x98U3e96BEVz3To4FpJH2dKHv5FDWiBYhxlFdBLdg09WrQNtnsykVkiKkJfY14OWji93Di90/eLT7qxKLh2JIYlMtUTlpcnm8qJUZUjtmI7TChGCqXnEpMuBZ8ZIuK9oYUX1XoGpHpQfZ3RrR4qzkhw1a0OVQqmlngo4g1AglnGaaIJ1eLOx6ZDs60aWj2flD7aUQm6asc7KNnp9pfAvyEG+az4yfbdke7B7IFYKRoKPiCWWwaSuuGC2uR/KGjFTnvr2phNfli/1jM3LeWTbxovESjHdFqnNMl0j21yAy04XvU4eZoZLvwNDJppZIM2klNpukQlp7y+zYYLN7N3aXt3bferj71yPeg/KcHsk9Wem6a38EYE5qTQu9TWVMpiIo+TtN7D9z6T100VEoq+z+RPUgu0nV60z9ObInfUi6GK3ZLJOpGRVsEb5aWXHfYgw9Cjf3AO7RS+mkcCORsFGNHbAtHnJFypmOaYQ6B1GvNBWG5iieQ8208RFqcB6FTcfYFCMQuhx0jGfrYShBzcwqp4c09akDJEbCQyicg9AcNimCmrMR4NS8TZ2nS2uLiJPxwZ01vAhoKjyctmZy+aqRnCKROPJ7tdnSD1X99w8/3f3VfuVqxEZ9xpncsxGPIwapGhEmoSmakcrHUUvvtBDK9K3gKWZxU8FD+al7jOL3YHRhKtcjMod1rb/iDtDWDD1ZfNmDiLb2rJMzqAZySoGyMowajXg8alSXZhsvATXTtQWhSoGB52iLwxLJu0JILS754YAcQZXcwq07Kt2A2QN2Wm0XvBzquMUzXAYIsh2Ctig/mVGZig8FATPbri5dCmgRZes1SJ37591v7b6e97x7IrRyvO2CP3DMZcQx1Cg9WwvOHEFws5JNfqvaOZmdFnrxGgcDWs+5JIGGMH5Rs+iPargM44mnslujZiyA5F+/cN90YjiLOFuJ2k5LcAs3S1SQZ8WY1oCo5J/Wt8FeOaHqrNgG2VfrV482IVQ4WIVQhkSGrQegQBuARag5y92pBpCU0shQhFBJbtFUd0qLJc4zs3iQaRUjn6yRqJVuwNCRhI96t4VbxMmqpY5RIFLtGgQBz8zsbg69Z4ZvBqH07RKa0GjngIRWcNTufKe12yowWtXmBf5UkxVupddZ3DQWHuNWJPnR2V45DYxpY71jf3iaM/jRNuYKaiLOH22zlZPrF+7pLnArkJ2Vk3uwdZuWTIT3sv6zvV40WBoBcQc106JPtxRA6Oq5n13lhN2lIS01PgcRB6S6UgPmjGNGTsokEFqwGkCNMW3TLC1H8pptmHoj34ec1RrmnIKrooAt2fe6IBrj9S2tJ0apj6hxW2V/DSzt3s2d73a/+nD3w7vPvWcm+IIYQjeURsNrGGTlmJZjWnzEplwmpL9kdLYLr+FK8W3VoU+d7d5rwD3dM6BVyHX/UN90aS0CuGcoGPN7Olb4uH7hHnG2gqpLT8EWbVyCLZYtBZaW/NT4FBF6Tl3d3HlBZ2w5lWo7fY+WApsDoU2B+Uudn6gyAJyKOZFTF4rdKgxxwOBtoj1KKkGA8YtsOBrVsVpjVEJ7oF7vSoIUlkYjW/f8Ro9VIUoQ+/oFUHHE3rbSdbFfd/++57GfcSrqLuSkxeXvDUhsZmlr837D0juPvn//vidVCNkTvveRdYWloWNyHBB449exHolZ934mUnnU7huNA+sX7j0T2dZH2YI5yhzEZtBc1PH1SNjbIiJHWi6ac5eY6gDRCqfNpo1MdXKQaaHNoup0ORW1l06ldDGj7RqUU2wDXDLuok+gTrNAq4A8GsgkhHnmIL4ub1OdTPm7zVJFVfMHNl0Byh6/fWN3++Xbv3V58Se3d49WegqxoEbMUnkQd1YFhy01lLvx119/3VPvivKcpXfNlPh6ja1wuFss2PudSSMhUbjgMqSK6FEK0mrjrFy4M4L8gFsNlOGJ5GNi2zQL1ru22D0eHufF1coIsLSBpQWECrJHOa0sZ+Z0ClBxRzDNbN94M0muuDqqOoxHAT2qtpuPldxWXJ35Uk0eIdbbGjG2qB7oAuRp6aMpdSaTEh0jBzVbHWZqF66q8c/399Wf/dYnu9+f+o7IzBnrNBOlEdQyO1bcY+k9yuthWERRiTLWqs+WOjg1uPbu+S3tD4+8ylUBJO6sHJxVZxUJT026cuEONaZwSrh2zsv9rJAJRhHA0I/mMG22Rf9ZhOl7ts1WGqCmp46PLBBrVFABHOW0We2umaUVVAehQStO14rIoU6Ei2ZNKF8wSNJIABT68nsVbAlJs6OVGPO/M6GOzQ3AJrLPWRiRAh83iqz9bOOmGhA7bq3RkcJvPDPzjYe7793dTfm7O01pePVVPKNt+dcIPKu79I6UAQgZG7LiViSi3GFjnLoNcLXU69gPpyh1RjChEXWEgrWC2wRd1i/cW/6OLyoAdswEGevZsBgtJ9DwoksdBCIkiVM25OKUr16P5F0HwEVnEUNVZvbTZsGp7vqiFaenksq0wSf0Ei6u06I7deo/YcJhg1ZsctWCyYKFyNYRBCaQ21oXXqNasm0imgWtmAUjgTlsD0/PzFzczn1HZK9vgx8pmL7kWT0uncInlt79YJRf2E41c54KPFGbqHpTS7S6PxH0cLrKD0+TJKMOQh1iM8izNd6okZdrvPIfYJIJCsbiIjBFsS4CRlhhmmGdh8HlcDpdNGvWMw8FPntGJbSh29ABdcQUnHJOB5olWjdNFuERlGOZA8i0c4k6OBplb9iEgF72uget7QByndhhJxMImmxdQauIalLZlFUUp9WYUXxMFrKpjvABWtDUlGBdYRAaFRJplXcowG+3QTr+8GL3+GL39z7d/fTmdVh3p1fYG8VPqZx0X+tA8cMj8KIqS7nylDDUEXjj1hetfu2qatfXKrv9qV7NnqcO2WhdPM4UV3mf9YoPOmrARiw40r+9DW0onGZ5ujei0SgWrnFjAUUyS7SqANR27XtmJiMUEWim2WrmFJWbcZ+cRVCEolVL4iE0fcleWdqz07E3BDUr3ln5ew0ExoB9OQ9KDotW6C0dTk2hUrk2BIU6WIsqpEffih85rCIv4nA+rT/n37d2X8v720wrapc5NWNWEEd12xpBXRUsp3JwPyRFGWm7l3rHqLRO/+qrrzrwIHvLSvUkP/qR/JFSBZ/GdVmzcBejl47LR+lJJyNJ+EgMVNnb0tUJBwLThF4vupRFoFvV8cNunnAppy4sK8+io8202EVlmzA4N1QaouxoX/RF+qe1lu3fsQQ7xL42NKFGpVv8a9JUr8uXXYc9agYFT4oPZmRU7dtSptZuQhRb9GrE5LizjTAQJlWmFqlZ1O7+rur37lyTdfdYzKZgy6WhLKkcJ+qDBw+8cCaWSBIpDsQ0JTtm2dt77723iYXqrvynjhkqjQQEEelUGjrVt+b5lQt32bemtuZigqyN2TmunCQqa/rLNh1OhY/Q+lRVF0n6OiEjF16nW5FQp0cQBUVSJyP9N1KR9IQ89TEKqeKV7qnplj4PfM5VqtINZnkuxwTU0sIfHZ86Kl0CTCgrjw64+klIxtcIK3pKYla9PtP497X7zd03H+2++zS6rw7wXAFUhDwIRI3fLvILhuRpdYk1Lb2raD0VAwKPxPgrToFFmBy9rocT4YWaVKbXik40YGdrFu6sAfcDwpW6JDEo1vmJARkWVpYjY7mRS6FxncbBLAezUQryZ9fqtNHlOiEQVVTLawNj0aYRb7VHa9xj89mzzI6dpWZ78gs+1yOlwQ010jk/mhzrUlXXCLMqy/i11WSNaprT2bloEYXUzIr57ESHDSLV8lzMkuGwwbQzP796zF3tfm3W3RUhanclUOOhG6GvvfYaKpXvX/nKV9Sy3P/wh6dMjrEJC26ANZjGclO9KGJrVqM1C/dFkxmDM74IEgmGMVW4uzVXU8Z3XYXBbNyJiRe8a1SGuGYcQeA6aRTe6ktY+ayRqm6+E9FFwqtfRc2X/NQINJLSxmqU3LZBZteqdE8hPPO8sBAcjYqNcyaN22wjyLNLpNq3bvqzqtfnPTMBlCzWfu3Obd944w38emzGUzFucU/ZCWPbhEan5D88TyP3V2JdNT86lOHomTULd8HlqExzTjIvW6R/JZ3gNWe0F33bQQCniVyJ352Y/QTxlrC6CWKU7RIGX3bMmqMlck0abiv3T+O3ptj5c1GK9drnd2m5JSeiiySds7rWrdcnu+3SaERIKfUI0NLS5owfGi1dFwa5/Jc9LF3cWHdXu3/10e6Hd3ZTQn8OanXbcAdhodm7esZDNs6eXseObuW7R2WIfRQqGmmj0lV6XY9UKxOFRk2lpNUKd1gcJX7CSUMxoxhQ+IDv9bCYCVBcvy5oVR9IDFRD7vxwwDa2Xub2WI7ob9873/5HRCiVgo5u4nfs/HW668ZOfOvaPimZEgqz1t4GrC7I5b9RQbZ/GxYcXaf7K34E/yXuRpArLBtcOK2ZcH92VS5ep9odRzBUH7vtaaRuQS55wmx6T8UIYqp5j7aj3veiR2MFTxe6NWuq0j0qauZJGmEnAvhAxMscrUiz1Qp3bo/XyTqkwGEEsLL+OaNNFuNFx4UQYB5xT49crmJfaiLBUdBhMKUGXH0c4IiSjQSUHDTgH1n/FLnBUc5QW2lDIyZ3nYpCyMdTrb111m5k3lzmpouShVk2UkLNN28UqMAKGp6hOC/BBJxVcq7a3SqOd0R+/7q8Z4a9RV1ov67hIVc5zoW929F21Pxc9Z4Z1Ft6jxfLHDajRWgk6G0oMR0q0j1DI5bfyJcJqxXuSo0Jv0xN9RzLWCtwdLnsHa/rdT1hNvdRRJBj7EnO4ecvrh9FwMisyP7o1S2e3IrVqdeDX4lK3B8Wm3ej6doEfXZFZUXhNTM8JKaiMApEZ2hq26IrkTkKjrGP77esLC5UGyScY3vCspRtzyVBtK6+8SeZvCPy2tTuvCYMT9YTHuvDy0IiOllNzxGAs4vPSjhix0tmDmV2SRtmc22+O2X8wInaPQelQ0xKnVmtcJfFMzWP9bmo51qIGqegDwlPXX1x/hQCUc+5KniFY5xqWeQ8qzNjkaHaGSSUynSoymLHEh3vUDfk34yxBInEvrK0i04HgWt2N4JQq270wu9Vub7Vej3xHlHoOlUbVFNCTf4mIe7HxJZ8501gLncQz8x84xq9Z4bhxVdYgt6cW6xRmHPbWDVnIffv3x/bV8lOWh0diGyH3V0V/xtZpT4Ub8IZjhA0OVhxeeII1hOUmdCFmQ70UlrhOwqsAGi4/cBQ1S7xgfaFHIvGQhrBCr828uBX1Bgr2Jz2Cyk1R6SZfaMibGdhowi/DOOa+ZRAf23uRuTpSNVyM5qu0xI1jZQaolO1+mmm++d0F2MpZZ8Z/aif+F2xQBlQLWr367TuTlkEpW82BnSffwm5fqPCc62ap9exTxjWOK+//roRSB6P2fQG4UQiOaVYkYPe1Y1+pCzFw6FWUWG1wp3F9BR2Zl/KPSvm8N1mvOiJnT6S/9rYZVLKAb0yY32319Hj4DcGxC/rP9rsxcmxCESNO7ZX8faYFc7ifns+v9xfuL9mRqIoBJF9cfDrDMiFyW8uJpeKdTQ5j/E6MlSYhdVdpze7Q0wMp5RSI7F2FEYunJ5na9/10vPuP7guz7sjxfoLh8LCEvUPftmA8XnrBx98UCqzW3EnbTz1TvKeaSXbM+l2Q19PKYpQx0IMvuoXfusU7qiN1UFmFIvrDrALiPaDRY+/9DFUSB+vxwFSZuqle9TrAGHf3LtUsLgeCBfUYiZTkyXhwoq5mJ0Ll8o3YXuTpWqzo3C/xUX3VK/jV+juubAzlHKpTcynSRVfiA+XudNGXqsX1jB1apmQC2NZGyZavxCZjInn3S0Bxntmhr7EnzzBGh3RhA7ltYNS84vS7kVRrMRSYZcaNo2jqIuldzIfXXo3rzZllUqzr3LAWcQHGjkolfUyFbn55ptv1vdS92eifDdSxO0L/TPlbrAZl4DkplU4RDVC+QSlhAleimIHUa9jvL6lHWrkDJHsJyh1dLRGTqqbo3SuJg/bQLHNjMhdwoVxFG5VTakKE1GKXzTiC8P6Al9aIq1mMtNASUcplnDNavfrpxSNUInTxFSiOEXpaDNsGE1dff/qJ0u/+XiniL82W8SHLlOTVeOY3gPDi1WZtkT95AEHOhJYevXkjGTEtHotCyrVG3nFj/CkLMWrhXTT1Vtx30/27Gt0BwW/plmRs+7UFNxcyOvKf/SYRvQ6eunoScyGu0YaaBMQ9YdYVnAx4ygUlU8KH0Wi/Fmx8YtlzSpQTKktrk8PY0gp5ld5hWZYpN7VuOvm+ETlJjlerCWruGYxkFKs3eagB9F2P9IFv2IFFfDFizMpblnlv+68333Di3/PQ4wpricAWqvO8cHne+8/WfmOUNN7Hfthy4JnxIHXXnvN2yRN6oahVz9QisnNUaqgqKWGCqV8lyWqOy417MA4ixfuqZJjeQqmSFc0nGaIA5q0cOn6KUWjnuMd4qyB6BDNUNz+w045Sh2q2fiZpW0vObLAVJNiUV6dYd84/qPEEwYbrN25sHrOfloxp/4T2C3pjYKi8caUUmQAZGn/qoZDODKfYoS0qzbv0hNF7R7vd196rmrjMzzBNp5uyrdA5HqogSOrnie8dLuIdh7LIbxncrgP+btjUkSUcFK1UKfM7c6+0HEoJaq7z6/gVosU7uxGdBio5PJNcCGUXww7EwHkBsvGwaYcsC1OwzhngtBadxTQqyARRuPIiKbpWrdkUeNWCIU12ZSu2incsUwYLLMcOE+2Hx0ZSTt6lSI0Kqet35DgFzW4xlHoonK6Zl8mqN0tt6vdf3iNfqvKrcIC+aZSeNiq3XhHxZz5Ovbh0WZeZXKeemdsR5feKRVhR4SfOVE73enCp5b+PkFSLla4I4nE9kCUlgYqObZ1nahKRiMmpuNrcyBqBKehUY9lvndtNL0GivA7Hj3fuXr1+vwB52DLAsljczBnnNb6SsMi4Yo3JCBlLYI2YFF8tibIAVBRKGmtazA5co5qE/isS9YogbuNg2Vxmxa9u7IoB50vQn130hWP47eqv/1o96PrVbvHEvUpP8Wyej2C/9jXsS9NloDAd9TubhRt3emoQ2Ynr1MhIQtzKCo7EA+7+s48Tr5s5Fk/To2B3DZB33EIGnIPiMjCWKF4MdBmi5eAQPctSj4gM1qldg2CZQkgk+WBMVu4RKnrRxafEi8mBwvkHrLcgp8yQrJdp/KCCwAW2vWNEJjmZSdcgKnInWQoyLLRpOrJRthCcDiUAT4RBgsCdThL2TPyEaKDZXQctTQnlU1HL5UVpuZofqtqCe1vP97Fi95rTr3oXGhCqCm6RohlT8VwZ3dlHoxp0+/EHGZGNhJSwccElKhOnetnhMjieqVqQoih2GgwtEFs9Iq7IQgkjzrQn3xj75Z0vGY5OFnhdTpAE0PBtQ1fEnw3XmxdUxpdv1oQKRM4wi+i9YWJoNCgb5Iq4v7Wra4nv1wrjdVZdE/uzELgyZ17wpT6GEkhckypMVsYB01uSHjHBBerKX/cfpsxMzXHuns8PFNTzkXn8syM2vC3Hu3+4Bqtu0OMESrgUMx/YyUbyy08FZPDZghMWkHPcerCp8IIYxEhnd/6AXUw9f+3d289thtVHsB3SBAPTJ+Ty1MyhEi8cQnw/T/FQIB5H0DRZEgUDkFCHKLw616jkrUv3na5XK6q036w3N51WWv91+Vftb3dNIVadlWVt2OpY7TpIEuJ+xN/e9sorBl5Pzt/xSC9o3Ipf7ZBLoc68E4CmjqAdvAb5wNF2mPqWBBPI2GPWeqPSSMI0u7u1LEq00xjmaVx7yXeeAstYEmGjl2NL365RISz7H/XMbY3MItyxa921Wu7nGtHwG59XWzXcG3HCu15kYgWI2y+KpyjvYXx2g24CkptmeLPT++ZGY+7q8Xe2QJuvzqt+a6YLVikvvzz4eGBp/nRqhfO+DM+4oSCK61JUvveL2R4ytJLMlxLokCsIwucUfawyRxx1zPIvqYm1n97Ijbm9kEahJNeDUq1UCTCR9K/BLprvWbUF1F8e6ZBpx/JDpEmbslP60iXXfD1pIXkQ3KMJN0Z42I/vUS0g5VM4ahsLkjxw8G4IBvSqDWOa1W2EWjJUN5oTa/tHjsYd4ey1zuiT/i6uhyLtO1Wqj8CT5McvBCc69IoCSBp0NFHI+V5eEkaitdyvYCrsY7scIstXyHubMegrKmPeBbVybLbLyhQv4psF/vuCGx9y8R3+x7VQOSDw+wB9JC4HGXbo+YF5eVayx1AR6IX0T0CfVWvo4xccF7ZFS4Fs4fUHUEN6AOff4iqQbWy5aOg5fOGoo5DiWTevBFK9eIzqruzFfj2BZJBIkv0mBxmTBrc3W9VvWem0wMu8ZAJaKb/8VSk97vWopStd8TUT2ltvQfhBJCwkjToNcNZe8QRdvS6+9iMNrK3YndX/Ufizog6sJ2LsN32RHDLuMQ6sJzckmr7faZj7u3j7D0COQHtMJEKdNc/RiVM1A9v39vg9ccPyJwFNYbh7FriAHd9YQrOiFu0QJgKahRDSbYY2MaUK6IZh0uzUiMJlkYKVSPCFERNKCEWj/z9iICKBA5rs9/N3qu0xplU5/FCDHdXmD0z81+9Pe8uqD2aJYELost3xYh0ib3rEJP3uNzZ1ns4dnD3Q0JsVdQsb0wXOEbqkEPOOkYC12ZhIXh8F49coIOx+MHZcMX/5IUjgTG1TwXrTadbdR0cDtCEFPALnWPVFN01bhmvLcYUX9J9pHXFeJhwo4sq5bzFOA32DT+M2Fwr3pSvNxjUaorajF+u1avx9oesSQJrlhECOy2HYk0SNaJxCFaJF79V/XU/3F3UyHVStx9UyOS3lNUgXNG503Im79l653i+TPB6HE5OWbrwcEag4GAJH1LWxrEsCcjWUvZwhncMxGq3PKP4/bz6VFyM4gM2qBeR+ISDshEAzsUV73dAYdMgatn2lA4sz2gE5VHXZoNBlrBGbVUpqTjdmb8AtJ0qWAO67Lbr/LxrPyVeuOV4mYfZVd/lkK01XWqf9lzYsALWNKKXQBsMsnhm5lf/PP2m4X13EW3PRcigZAt/eAqpgIxv9AtZfNXjhTO8PT31TiP3x1v5W4pASpQ5Rw7PSCOb3uOeksvCC1JCAh4L23fUDD8Ggyg6XOYo6qleSgHK5xbZDGWEw/XaQwC2Ypk9Rq42JnQi6/E9GSGSgjsuqslQbSJgyeNDeiMc77pihDZwGVxcB9bVjJ83EaWUqPG8UbiBw3EXtTy76QVohylYz7HfRGcSii+QjRdl3u9uE+sn/zr97+NeVluH8N/yOnbuwVWo1AIDybMscii364u+Y1Pc3nX4vPvVnD9P+LW9wE3fII0Z++b6Xvlx6lohlrfnWzxsefuOWjLlsTHDFZ4311c5jFwQyW5VrxYaP8bt0/46YQTU5Xpd0d2VUhxlhGNDbFetgXjrcVVwK13OPFaW78sIpA1WkVGfdjX49sFpFNtmZVmFyI28xG5HGS02cXd6IGe75bNHaHDfXdTHynbj69jth0aW6Jpi8XylzZsu+Z4frQJaLRNfVBMLZQMt24u2dKRgLMUjtHE2DiDc1mb1qjvuHHQM618iB4D6WxRR0Tm6I4p6ePlaJ7hUZ3rH4PVVmwqw33VfqiW4OZvs7IDLVazdlCBGRY36A2TwS6+mF+Ac8VHAHYle2gy4L3u1fyfq7pDeSCl1FzTbUUhwGwoJuxXa2ydaOALUglAubN9Ls7Tvfuz/VQW3LXaPhXAelF2Ap8DPtiSfkUMcXafHCATs1sNCCrQLBqFa2rzIts+xHclPHXA7EtYuqCbQXDgWSshEVXfcuRQpFwrXVzOmrCYwM/IA00G6SMBXk/x5ogwLpB04fVX0JeGtTU2HzFAqu0ukObkve4RmO8amu3OQ9YjurmtwMvWoO7gUDNS2bE5LGwOF5QAAEBRJREFU5o7W4OZ4sg1KQcGE4xgXse8ev1WtrxGs4zFufL04HcIHjI8jwq6+agVnpMXLly+ZSGRZ4ShnkRi7U43k8rkzaK4mc4FPR3rhdctRq0rcSU/Kgui+OUMxHVeO1Sf4x0umbw6UCzWdLs8W8vWzkUcNNzlEIFxNgmcW6OjPCHAx7kJ0D6YdyHAU2o234oIUpdRmhXmVv6UFOcts4f2rJl3VmGpky1Bt1SyHNMbd7bTh7tV+qyqurYIcvGXhD0/zLGN8tcNEA5AElJ0HMpfvtVBbIRaq5RXEPHtm94I4mXVfIq02VFv+2Ew94k6NZ9a+1glg6QiaIiDX1oa1071p7VujgGIkEAeEqrkk4G9BFunb+VaDfu9TCgVknH5VSJJDPC3I6WWHScUdjLWHsnJXqadKkvUauQAc1BYuJgNxYY6RbAnwOrpTDXEfkrv/yU9Uvzt9+vr02fcfX/S+3wHreB07Jn35OvY95uVaStsY3J31Xrx44bWYXvcePF56pJrYaTZPxpocBKsCHGqhmlTpet4x6hH3IbdbknELrkmmtRx+UCw4eBL4+YIFxMnCcruruc4QL7JTQjXLfXV3V8kPGZxqLHbI1AUnRYb4ngFhNF2QC/lgdQXnamQoZWwMMnFpTzF7d7dMBYSsvuCeIn45WlN3iAq1IX3yT+jPt6dP/3n6bJ93RMrAvEK+mn8d+x5wmzTCbRV33EOSImPGfyZJW+++oeKTMuddgltk9uWDCBNhvpayp/F1pJrSYBzApfuXF/WIO2nmRbkU7o26AyoHRgK84OuNqD/wskF2E2ZH2XnK18/Y23aRBkaNcQRIpxsBib3dQtx9Nam1grTdIY0g3LilLDekdrg7ona56rY8A7pgp/Xlp0UMu/cgxOaTsIPg3nNVHv9x3/30uO/+28e3KZY5YB2vY0d4dn0qZl5ceBEAcM4DACdvxNY7k3rhDJ+MvY9G1sDEE+bsvD3GaWS0+Y2AeqyFKGrSvKt1+qlAzZY8bbyBHGBt8i0KtilYttmjI5tvwS5vdjMq8GFSiDvyxrnbi3YtfJ9wV86MBjJJX89dRFoHOsnnM7tA63dZchdKHAJwQxL3AE4+TxEtzCMA3ek9fwZwtgN7V+TSRYO7x/9muvx01R1h7nEOXWyxe6fhqr57NAaWbMMPeaBysMcUlce0IqII7m5BYmoGdzGfUfeWEF93lF2WG42agHNxlTbXI+71GdLegKXxg4SlP+9epD0YLaGSEv3djs8NilugmlsCXVE3nYBUBSvUPzEvoZiruNFaGJAZ21+WqCtAZ667fH1qUgmhr2XJVPi717RTkIZ0SyijEWkvZhi2BFP5Kp5PcL4LcXcNgrv/csO+O9xjRXrgFvtVswd3Jx7nVBGutunrprRv650fxta7QsDyuHuFknpmqCfG/viD+z2CIoAzxdWVSSXivpbanhmo/T/vOg0LqOIOumgsp9/t0ojW5BwYvr1RgLjYC9Dr8PXkNjIa4NKfg11Il80uSwJ0xgdBXk4P7ZwHQ406sZ5sf9G11vLgjkWaZYnd1r0Ty1rxtrenEXqEJOW59HYBdh3h/5+ZeXrefflE3DieimGZOj88XS7btCXxuCVpEY/p/X6v+SFG6xergo5nVl6ZxOJ8J8o+BcUUsTJBHlykjyoRd5VsjNVeMtz0YoYeCRX0IhrAQPxMOz5fH24BMQ+dslXWgBzeQTtufyzoxbU7HLIQoMFliWAP6ka2jYs0uUJlcm7E2mXFEBHojscJyg57yGgR7JI80GM7xhmNGJLdypOh3bE5bSegcXf7HPFb1btTiHQRqhk39jKou+0PbyAjkdkxDHcXeg8PD1zx1atXnlCSUtzZNWcaX6TL82apZkaJRTKRUiLDhCNV+s+pXPxsxXC4HxcUAEWT0RwxZqArQoKyo25gdk4NCk5dYShuapZOhb9rH2A5imhnnKfE+PiTcAMKbKALtrsy7NfgSbky2u0nZPbItOOcx1qY8MHXQU8eoAv2UiJxpFJDZRt5v469a0d+u5ihRQR72CqSSe/a3cKddvx8VO1efe/k0ekff3v64ulHq5dGoDuC6D8oufC9CtbYUYQSVbJCSxSmS9U6vUMdYWjtJAax2520A7dZDC69O4oQhlUGB1kUmvC3SsR9mP2Vq7YOX4mQUL8lNbgGwKxcH+OrQmbf5LL69q7FjPobi1DibS7A3QJfT8pCbadElqY48IK1JVOBdogMCXcBQoYpdSsiz7HaFVFhZpDQrkcCESVcnqcd0K9WcdqJOw2GTJu0kzM3ps0Z3zj2o1vcXbzb1rUFCXSU3flYOfNmh52O0maPoXdLZSGJsos1T72DCUah5q32q+5HvB9I2ZO0lKIj7B7Lerr7fJFnAYjGZhuzCoZO43led74736DfT6EWNXitCsHbWMYIirfz2hEqtBfhA2PHgKGgcwVjxhTsyWGgXwF3ycRcQ6YUxrRb2dcjJVI9tsrZlsS7b5iDQNR0zmpRwCcRCNYYif8l6/3xabv9F69Pv3t6RyRN7TzStP7r2JNIBS8owieF3sbH+QqKVGQoMMmWtt4llnfffZeLbizKUn2wZEM1EsU0irRZg7iPRx1oJIkr3hxOGMjjrFnE+Z4HqW+BVf6pVgXRh3sXiU/GoWAjeac4uCCQW+tEn4mEPEtuLwkL7cDHBibuLEnB2MpaaJBDmsUS3dRr83yU2CEfdmcNoRfbnBvp0SGY3p30ibu/9ek7P/ifHz4SXE9Rj5RCQUYpC0vOOZJeKp1XvFPtiy++eP/997NzS4Q8yxiqQfuQqgZxZwUp726otN8AaXMEDaKR+h0yu9m+8NkSclwIZndvv+OSyJQCHHRBNfqqxMGNkq+2D8cqCWG3at21anCNDQ73CHDpsj5HAZwFg6nXSt5Fe9phD1x0SQxW1gj0Vk1SH/HyVoaUGhs+ZrEVPRj542bi3VPsr/CWv/39R3/5x+/L/W+myj48Mx3n9AWC6Dskrc0Itv0jCdPhJT/OweOXj5koe17IL59oY8saxD2+lNko6FHdpW9h7CCAsi0RN1hjjjLOMPNC+RLWgB5v81F3fD1BQ/KBd22pKSRhVHZrYAr9dImerFrtIuCrNl39ifAG9bWpMpmWalxr41INfFE+XNS3bYUZsfa+nneatwldHED3xIXzX0+nd946pWdm5vv2+GnAJ8WN558ou/j96quvXr58CcrL+n6GlzgV+Fo2lYvOhEx/1iDuzNHCfxFLOi+5gKK1lyNIWxdYLtErrw0ek9exi15idUrcXfPYBH1f++tXDX43Z13t1ctNJUetLUXcE2kzbCPQB7UdddM9aqp8ezh1EPLpKbiCCR9wAz/sLrewVTx00UvGuJQT9L464ITA8lTMtMEfUaR/nX7++jTkvjtNwWflrOqVSqFT6x17LZwdfrHKP4OUX5Uncn4vlD1UqEHcrxqrwZt8V+g6yAZFfjw241kIwfBGwBgC9ODrAb3F+kL7tN+MRiqTc/uiZks4XXplDDLl6wVJW4Ykl10Cvo0KXg7bzp2gtkcRd4ZNgU+SPdIdj7K2bGQdWBx3FgsEe1QQ9Cg7m3hu5BZzxd0//nZk7g6+4LgjVb3k5556klscqvxZEYy076PWcn4S/tbFM3F/fA0z/KIuZkCo4y3jDnN/YB0FM/SlLaErcZ8F9hgI0qu1pxHKGjYKj/PaYQN9vQS+7nuQtrUiXW2freDV0Rq8SUHUtnL5jLLNGvjKrpSFXxlfDGa4aINgXYokbXanIL4OEWLHUzGXSk3v+K3qx9+NzN3ZQTiM6qKxMQdQF1K9CxUf8VMZK+ecqVNtud6duCdLbZFyj74pa0fS2VKzt/TdQ7WyYw6p3RR91dTKROYqa7d2RhsSwal5KSgLT+/MX/fC15MWchQFY3Mh3RzpIhbMdIyLXVVjRgTFFBnbNNmCmYt2zVbDbL1Sx1AQH2o8kULB69idbbH7/WKS/+7F8PvuWKyyWH/9fNfypRpEFhX7LhyNO+q81rsT96a+IpSyZRZnRul3sTWP6POnMxZIfP2sZg9PbWdsMsZHsWMkqGfUUa2FvwayttVaX6DbGVJyOt0fmgElfUQ1m6DoVLpT9kLaj/A/Cn0uOvDD7sCiIBdtdnEi9j01AX2/uJP/M7zrcd/9dPrZ69MfRnzPDIPIn+wzJHdP3I+Xhg/INkEFMzzh8C67E3e58thfpsJGwSYGWyvVYCtbsI1fdsDDfWIwAQAUBRtMM6u1sXHk9u1vhm1xPJVYvblK3IOvw1eb7vh6skkkmbG9FDp7eGn6gkUUOJJJ619YnDS1k1XcAhCkoGHzmHFxeQwoZFC08KtVW+xXhRmeu8ey1gqTr45BbDgA9J2nyd+rZr7++mv348u3q1i3fHN34n6U8qq1fO1MAElkv50qU4zh30chtdO84QB8QCbiAPMFG4JjUyJGkKR2MnUjwwIR6DQNeVxbsAWs05TdiLQZYtBi7E13cZogy7DPWZfkAFxiv/x/Nun8n1xUIhpyRzMpztSNfLEg+dtilwH8arbg7mFw95++Pv33oAmVlwIxvNR1Qra7CxlA1YugO1NEGL733nu8wjtnvDiSk/Sl3WjEXaw6wLCErhWBylzt7C4U0ajrQaJaO3MA+6/zfD1pCsHoku6Md9FdbloLAbjRPmcZAJoytevE49eO1mD7qD1wPCtCDYqaLVJs2W55Pwn7cINwAOHfmq0i1YSjZlup8Y6H0z6k0+KBqZf88DTDmPFb1eG5u20CEdRjChX+Qdlntmwi1fg25ssvv+QwjgxPOKrL25988sl+qe2JRb+9kDxlm0CmBpLDdAZRraNg76fXVFQpeHji3r6OJAwfSA4AlFUOoPvYOAoTxyqbTP288Wuq8QHJl4JwlAHWOkDjCoZ4iigdadeFtNlC5q2i5f/0jLWi02w4A1G26ZEPLQeU8VHnyo7KbWyxO8xuM3WGtC1X5FbLV987eYvkj749/d+4/zadGeNJko6qRiQB5WBJBgi9Xrx4Yanp1zWiUsdbiLdzX+p4+4MPPghs9hDLakbw7JShAiHVWrjysCjVO801YxwCVE5PM8Ls9BEdWXinwbcMSzDB5iwCoeDIdgDj6L5FmPb7NotjtunkWemLXrIB+HhpEPfsAdvvSFM6OtoXNU9CIQzT5QmHD1jMiF/TqdbcoH3j0HH4BdhaHPO8JXrJAB544Da+q5l5I/uWKS774u7/8d3pP0fn7kFnswvrpd12uhOE0OCrOKdcIWN40RAX+uabb/zpYicJSw3rpxpvffjhhx999JE1B7VLjZvGeXh4CFukO9svmBVTl6yDrG8fcOMI8q/l2sZBGu8uIQqGdoSUSvgAeYKrFRHsTcBxJB3lgUhZuNq0qIyk4y3Htpe55WGSW8O2cz/WY3fzKh9wqLW77q3uZBYZjPBN5dXimopQx646incHMmCL/ZAF28d2e946eXhm4COtitvUkY8JpY2EEMlRRzxbJe0grlJQg8oiPFj7559//m/GSv2XwGIMmQAAAABJRU5ErkJggg=="}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_2ETENqluHzW2mWvBnC2B", "content": "Code executed successfully: Aligned all viewport spaces to active camera!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "0", "tool_calls": [{"id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7", "content": "Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": " eyeint", "tool_calls": [{"id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7", "content": "Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Check backdrop coverage\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": "_", "tool_calls": [{"id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nimport math\\n\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\n# This means we should rotate the ground plane to block the background correctly.\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\n    print(\\\"Rotated and shifted Ground Studio!\\\")\\n\",\"user_prompt\":\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\"}"}}]}, {"role": "tool", "tool_call_id": "tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF", "content": "Code executed successfully: Rotated and shifted Ground Studio!\n"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "tool_get_viewport_screenshot_VPYrHxD2TzAZM1r0zZ18", "type": "function", "function": {"name": "get_viewport_screenshot", "arguments": "{\"max_size\":1000,\"user_prompt\":\"Re-check backdrop alignment after rotation\"}"}}]}, {"role": "tool", "tool_call_id": "tool_get_viewport_screenshot_VPYrHxD2TzAZM1r0zZ18", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], "tools": [{"type": "function", "function": {"name": "get_scene_info", "description": "Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ", "parameters": {"properties": {"user_prompt": {"title": "User Prompt", "type": "string"}}, "required": ["user_prompt"], "title": "get_scene_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_info", "description": "\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name"], "title": "get_object_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_viewport_screenshot", "description": "\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ", "parameters": {"properties": {"max_size": {"default": 1000, "title": "Max Size", "type": "integer"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_viewport_screenshotArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code", "description": "\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_categories", "description": "\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ", "parameters": {"properties": {"asset_type": {"default": "hdris", "title": "Asset Type", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_categoriesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_polyhaven_assets", "description": "\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ", "parameters": {"properties": {"asset_type": {"default": "all", "title": "Asset Type", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "search_polyhaven_assetsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_polyhaven_asset", "description": "\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"asset_id": {"title": "Asset Id", "type": "string"}, "asset_type": {"title": "Asset Type", "type": "string"}, "resolution": {"default": "1k", "title": "Resolution", "type": "string"}, "file_format": {"default": null, "title": "File Format", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["asset_id", "asset_type"], "title": "download_polyhaven_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "set_texture", "description": "\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"object_name": {"title": "Object Name", "type": "string"}, "texture_id": {"title": "Texture Id", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["object_name", "texture_id"], "title": "set_textureArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_polyhaven_status", "description": "\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_polyhaven_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hyper3d_status", "description": "\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hyper3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_status", "description": "\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_sketchfab_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_sketchfab_models", "description": "\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "categories": {"default": null, "title": "Categories", "type": "string"}, "count": {"default": 20, "title": "Count", "type": "integer"}, "downloadable": {"default": true, "title": "Downloadable", "type": "boolean"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["query"], "title": "search_sketchfab_modelsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_sketchfab_model_preview", "description": "\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid"], "title": "get_sketchfab_model_previewArguments", "type": "object"}}}, {"type": "function", "function": {"name": "download_sketchfab_model", "description": "\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ", "parameters": {"properties": {"uid": {"title": "Uid", "type": "string"}, "target_size": {"title": "Target Size", "type": "number"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["uid", "target_size"], "title": "download_sketchfab_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_text", "description": "\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"text_prompt": {"title": "Text Prompt", "type": "string"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "required": ["text_prompt"], "title": "generate_hyper3d_model_via_textArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hyper3d_model_via_images", "description": "\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ", "parameters": {"properties": {"input_image_paths": {"default": null, "items": {"type": "string"}, "title": "Input Image Paths", "type": "array"}, "input_image_urls": {"default": null, "items": {"type": "string"}, "title": "Input Image Urls", "type": "array"}, "bbox_condition": {"default": null, "items": {"type": "number"}, "title": "Bbox Condition", "type": "array"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hyper3d_model_via_imagesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_rodin_job_status", "description": "\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ", "parameters": {"properties": {"subscription_key": {"default": null, "title": "Subscription Key", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "title": "poll_rodin_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset", "description": "\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "task_uuid": {"default": null, "title": "Task Uuid", "type": "string"}, "request_id": {"default": null, "title": "Request Id", "type": "string"}}, "required": ["name"], "title": "import_generated_assetArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_hunyuan3d_status", "description": "\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ", "parameters": {"properties": {"user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "get_hunyuan3d_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "generate_hunyuan3d_model", "description": "\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\n\n    Returns: \n    - When successful, returns a JSON with job_id (format: \"job_xxx\") indicating the task is in progress\n    - When the job completes, the status will change to \"DONE\" indicating the model has been imported\n    - Returns error message if the operation fails\n    ", "parameters": {"properties": {"text_prompt": {"default": null, "title": "Text Prompt", "type": "string"}, "input_image_url": {"default": null, "title": "Input Image Url", "type": "string"}, "user_prompt": {"default": "", "title": "User Prompt", "type": "string"}}, "title": "generate_hunyuan3d_modelArguments", "type": "object"}}}, {"type": "function", "function": {"name": "poll_hunyuan_job_status", "description": "\n    Check if the Hunyuan3D generation task is completed.\n\n    For Hunyuan3D:\n        Parameters:\n        - job_id: The job_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ", "parameters": {"properties": {"job_id": {"default": null, "title": "Job Id", "type": "string"}}, "title": "poll_hunyuan_job_statusArguments", "type": "object"}}}, {"type": "function", "function": {"name": "import_generated_asset_hunyuan", "description": "\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "zip_file_url": {"title": "Zip File Url", "type": "string"}}, "required": ["name", "zip_file_url"], "title": "import_generated_asset_hunyuanArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-15T04:47:50.699628+00:00", "finished_at": "2026-07-15T04:47:59.890582+00:00", "duration_s": 9.203999999997905, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "917348", "content-type": "application/json"}, "body": "{\"model\":\"google/gemini-3.5-flash\",\"messages\":[{\"role\":\"system\",\"content\":[{\"type\":\"text\",\"text\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.\",\"cache_control\":{\"type\":\"ephemeral\"}}]},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\\nglass enclosure around a warm glowing core, standing on simple ground.\\n\\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\\nwarm light passing through the glass, a soft pool of illumination on the ground,\\nbelievable metal and glass materials, and a composition that presents the lantern\\nlike a product shot against a calm, dark backdrop.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Get initial scene info\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_scene_info_El7NNW4G2qFUh4s67C60\",\"content\":\"{\\n  \\\"name\\\": \\\"Scene\\\",\\n  \\\"object_count\\\": 0,\\n  \\\"objects\\\": [],\\n  \\\"materials_count\\\": 2\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check if Sketchfab is active\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj\",\"content\":\"Sketchfab integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Sketchfab' checkbox\\n                            3. Enter your Sketchfab API Key\\n                            4. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Check Polyhaven status\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU\",\"content\":\"PolyHaven integration is currently disabled. To enable it:\\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\\n                            2. Check the 'Use assets from Poly Haven' checkbox\\n                            3. Restart the connection to Claude\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef get_or_create_material(name, shader_type):\\\\n    mat = bpy.data.materials.new(name=name)\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    \\\\n    if shader_type == 'glass':\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n        principled.inputs['Roughness'].default_value = 0.01\\\\n        principled.inputs['IOR'].default_value = 1.5\\\\n        set_transmission(principled, 1.0)\\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'emission':\\\\n        # Glowing warm flame/wick core\\\\n        emission = nodes.new(type='ShaderNodeEmission')\\\\n        # Deep warm orange (approx 1800K)\\\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\\\n        emission.inputs['Strength'].default_value = 35.0\\\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'dark_metal':\\\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 0.6\\\\n        \\\\n        # Texture nodes for realistic metal variation\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 10.0\\\\n        noise.inputs['Detail'].default_value = 4.0\\\\n        \\\\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\\\n        ramp.color_ramp.elements[0].position = 0.3\\\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\\\n        ramp.color_ramp.elements[1].position = 0.7\\\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'shiny_metal':\\\\n        # Brassy weathered/brushed metal for cage and burners\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        # Aged brass/bronze color: Hex #C29D60\\\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\\\n        principled.inputs['Metallic'].default_value = 1.0\\\\n        principled.inputs['Roughness'].default_value = 0.22\\\\n        \\\\n        # subtle bump\\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 30.0\\\\n        noise.inputs['Detail'].default_value = 5.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.03\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    elif shader_type == 'ground':\\\\n        # Soft-reflecting textured studio ground plane\\\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\\\n        principled.inputs['Roughness'].default_value = 0.5\\\\n        \\\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\\\n        noise.inputs['Scale'].default_value = 15.0\\\\n        noise.inputs['Detail'].default_value = 6.0\\\\n        \\\\n        bump = nodes.new(type='ShaderNodeBump')\\\\n        bump.inputs['Strength'].default_value = 0.06\\\\n        \\\\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\\\n        \\\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n        \\\\n    return mat\\\\n\\\\n# Clear and initialize scene\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\n# Create standard materials\\\\nget_or_create_material('Glass', 'glass')\\\\nget_or_create_material('EmissionCore', 'emission')\\\\nget_or_create_material('DarkMetal', 'dark_metal')\\\\nget_or_create_material('BrassMetal', 'shiny_metal')\\\\nget_or_create_material('Ground', 'ground')\\\\n\\\\nprint(\\\\\\\"Materials created successfully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Initialize materials in empty scene\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Robustly create standard materials with clean names\\\",\\\"code\\\":\\\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\\\n\\\\nimport bpy\\\\n\\\\ndef set_transmission(principled_node, value):\\\\n    if 'Transmission Weight' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission Weight'].default_value = value\\\\n    elif 'Transmission' in principled_node.inputs:\\\\n        principled_node.inputs['Transmission'].default_value = value\\\\n\\\\ndef create_glass_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Glass\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\\\n    principled.inputs['Roughness'].default_value = 0.02\\\\n    principled.inputs['IOR'].default_value = 1.5\\\\n    set_transmission(principled, 1.0)\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_emission_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"EmissionCore\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    emission = nodes.new(type='ShaderNodeEmission')\\\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\\\n    emission.inputs['Strength'].default_value = 45.0\\\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_dark_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"DarkMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\\\n    principled.inputs['Metallic'].default_value = 0.8\\\\n    principled.inputs['Roughness'].default_value = 0.35\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_shiny_metal_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"BrassMetal\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\\\n    principled.inputs['Metallic'].default_value = 1.0\\\\n    principled.inputs['Roughness'].default_value = 0.22\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\ndef create_ground_material():\\\\n    mat = bpy.data.materials.new(name=\\\\\\\"Ground\\\\\\\")\\\\n    mat.use_nodes = True\\\\n    nodes = mat.node_tree.nodes\\\\n    links = mat.node_tree.links\\\\n    nodes.clear()\\\\n    \\\\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\\\n    principled.inputs['Roughness'].default_value = 0.45\\\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\\\n    return mat\\\\n\\\\n# Clear and rebuild\\\\nfor obj in list(bpy.data.objects):\\\\n    bpy.data.objects.remove(obj, do_unlink=True)\\\\nfor mat in list(bpy.data.materials):\\\\n    bpy.data.materials.remove(mat, do_unlink=True)\\\\n\\\\ncreate_glass_material()\\\\ncreate_emission_material()\\\\ncreate_dark_metal_material()\\\\ncreate_shiny_metal_material()\\\\ncreate_ground_material()\\\\n\\\\nprint(\\\\\\\"Recreated robust basic materials successfully!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU\",\"content\":\"Code executed successfully: Recreated robust basic materials successfully!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=vertices, \\\\n        radius=radius, \\\\n        depth=depth, \\\\n        location=location,\\\\n        end_fill_type='NGON'\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    \\\\n    # Smooth shading\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n        \\\\n    if bevel_width > 0:\\\\n        # Add bevel modifier for nice rounded borders and highlights\\\\n        bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n        bevel.width = bevel_width\\\\n        bevel.segments = 3\\\\n        bevel.limit_method = 'ANGLE'\\\\n        \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n        \\\\n    return obj\\\\n\\\\n# Test basic cylinder function\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\ncone = add_smooth_cylinder(\\\\\\\"Test_Base\\\\\\\", 0.08, 0.05, material=mat_dark)\\\\nprint(\\\\\\\"Base test model created:\\\\\\\", cone.name)\\\\n\\\",\\\"user_prompt\\\":\\\"Test cylinder creation helper\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h\",\"content\":\"Code executed successfully: Base test model created: Test_Base\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Clean up test object\\\\ntest_obj = bpy.data.objects.get(\\\\\\\"Test_Base\\\\\\\")\\\\nif test_obj:\\\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\\\n\\\\n# Define materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\nmat_ground = bpy.data.materials.get(\\\\\\\"Ground\\\\\\\")\\\\n\\\\n# We will build a highly detailed classic lantern:\\\\n# 1. Base oil reservoir: A wide circular tapered shape.\\\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\\\n# 3. Glass globe: Rounded hourglass glass tube.\\\\n# 4. Wick / Flame: Located inside the glass globe.\\\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\\\n\\\\n# Let's create the base first.\\\\n# It can consist of:\\\\n# - A low wide cylinder at the bottom.\\\\n# - A truncated cone tapering upwards.\\\\n# - A small ornamental lip.\\\\n\\\\n# Helper function to create tapered cylinder (using cone or bmesh)\\\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\\\n    bpy.ops.mesh.primitive_cone_add(\\\\n        vertices=32, \\\\n        radius1=radius_bottom, \\\\n        radius2=radius_top, \\\\n        depth=depth, \\\\n        location=location\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    \\\\n    # Bevel for highlights\\\\n    bevel = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bevel.width = 0.002\\\\n    bevel.segments = 3\\\\n    bevel.limit_method = 'ANGLE'\\\\n    \\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\\\n# Let's place the base slightly above the ground (zcoord 0).\\\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\\\nrim_h = 0.02\\\\nbase_rim = create_tapered_cylinder(\\\\\\\"Lantern_Base_Rim\\\\\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\\\n\\\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\\\ntank_h = 0.045\\\\nbase_tank = create_tapered_cylinder(\\\\\\\"Lantern_Base_Tank\\\\\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\\\n\\\\n# Collar rim top: radius 0.055, height 0.01\\\\ncollar_h = 0.015\\\\nbase_collar_top = create_tapered_cylinder(\\\\\\\"Lantern_Base_Collar\\\\\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\\\n\\\\n# Let's add a decorative oil filler cap on the base tank\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=16, \\\\n    radius=0.012, \\\\n    depth=0.008, \\\\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\\\n)\\\\nfiller_cap = bpy.context.active_object\\\\nfiller_cap.name = \\\\\\\"Filler_Cap\\\\\\\"\\\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\\\nfor poly in filler_cap.data.polygons:\\\\n    poly.use_smooth = True\\\\nif mat_brass:\\\\n    filler_cap.data.materials.append(mat_brass)\\\\n\\\\n# Parent the cap to the base Tank\\\\nfiller_cap.parent = base_tank\\\\n\\\\nprint(\\\\\\\"Lantern Base parts created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build lantern lower base and details\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W\",\"content\":\"Code executed successfully: Lantern Base parts created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\nmat_emission = bpy.data.materials.get(\\\\\\\"EmissionCore\\\\\\\")\\\\n\\\\n# Let's locate the heights so far\\\\n# rim_h = 0.02\\\\n# tank_h = 0.045\\\\n# collar_h = 0.015\\\\n# Total base height = 0.08m\\\\nbase_height = 0.02 + 0.045 + 0.015\\\\n\\\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\\\nburner_h = 0.022\\\\nburner_center_z = base_height + burner_h/2\\\\nbpy.ops.mesh.primitive_cylinder_add(\\\\n    vertices=24, \\\\n    radius=0.025, \\\\n    depth=burner_h, \\\\n    location=(0, 0, burner_center_z)\\\\n)\\\\nburner_base = bpy.context.active_object\\\\nburner_base.name = \\\\\\\"Burner_Base\\\\\\\"\\\\nfor poly in burner_base.data.polygons:\\\\n    poly.use_smooth = True\\\\nburner_base.data.materials.append(mat_brass)\\\\n\\\\n# Flame/Wick emitter\\\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\\\nbpy.ops.mesh.primitive_cone_add(\\\\n    vertices=16,\\\\n    radius1=0.007,\\\\n    radius2=0.0,\\\\n    depth=0.030,\\\\n    location=(0, 0, base_height + burner_h + 0.01)\\\\n)\\\\nflame = bpy.context.active_object\\\\nflame.name = \\\\\\\"Flame_Core\\\\\\\"\\\\nfor poly in flame.data.polygons:\\\\n    poly.use_smooth = True\\\\nflame.data.materials.append(mat_emission)\\\\n\\\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\\\n# and illumination through the glass! This is critical for getting the \\\\\\\"soft pool of illumination on the ground\\\\\\\".\\\\nlight_data = bpy.data.lights.new(name=\\\\\\\"Flame_PointLight\\\\\\\", type='POINT')\\\\n# Golden-orange light (approx 1700K color)\\\\nlight_data.color = (1.0, 0.48, 0.08)\\\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\\\n\\\\nlight_obj = bpy.data.objects.new(name=\\\\\\\"Flame_PointLight\\\\\\\", object_data=light_data)\\\\nbpy.context.collection.objects.link(light_obj)\\\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\\\n\\\\nprint(\\\\\\\"Burner, Flame Core, and Light source created!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build internal burner dome, glowing flame core, and Cycles point light source\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty\",\"content\":\"Code executed successfully: Burner, Flame Core, and Light source created!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\n# ----------------- 3. GLASS GLOBE -----------------\\\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\\\n\\\\nimport bmesh\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\\\n# We'll sweep this profile in a circle\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.026, 0.0),\\\\n    (0.033, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.11),\\\\n    (0.026, 0.13),\\\\n    (0.024, 0.145),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\n# Create profile vertices in the XZ plane\\\\nverts = []\\\\nfor r, z in profile:\\\\n    v = bm.verts.new((r, 0.0, z))\\\\n    verts.append(v)\\\\n\\\\n# Extrude by spinning around the Z axis\\\\n# Spin 360 degrees, 32 steps\\\\nbmesh.ops.spin(\\\\n    bm,\\\\n    geom=bm.verts[:] + bm.edges[:],\\\\n    cent=(0.0, 0.0, 0.0),\\\\n    axis=(0.0, 0.0, 1.0),\\\\n    dangle=2 * math.pi,\\\\n    steps=32,\\\\n    use_duplicate=False\\\\n)\\\\n\\\\n# Merge doubles to join the spin endpoints\\\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\\\n\\\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\\\n\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate glass to correct height in scene setup\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # extend inward\\\\n\\\\n# Subsurf Modifier for perfect smoothness\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\nprint(\\\\\\\"Glass Globe modeled with realistic physical thickness!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Model glass globe with realistic physical thickness and hourglass shape\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: spin: keyword \\\"dangle\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\nimport math\\\\n\\\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\\\n# Custom vertex rotation is super clean and easy.\\\\nmat_glass = bpy.data.materials.get(\\\\\\\"Glass\\\\\\\")\\\\n\\\\nglass_mesh = bpy.data.meshes.new(name=\\\\\\\"GlassGlobeMesh\\\\\\\")\\\\nglass_obj = bpy.data.objects.new(\\\\\\\"Glass_Globe\\\\\\\", glass_mesh)\\\\nbpy.context.collection.objects.link(glass_obj)\\\\n\\\\nbm = bmesh.new()\\\\n\\\\nprofile = [\\\\n    # (r, z)\\\\n    (0.027, 0.0),\\\\n    (0.034, 0.01),\\\\n    (0.040, 0.03),\\\\n    (0.043, 0.05),\\\\n    (0.040, 0.08),\\\\n    (0.032, 0.115),\\\\n    (0.026, 0.138),\\\\n    (0.024, 0.147),\\\\n    (0.023, 0.150)\\\\n]\\\\n\\\\nnum_radial_steps = 32\\\\n# Create grid of vertices\\\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\\\n\\\\nfor i in range(num_radial_steps):\\\\n    angle = (i / num_radial_steps) * 2 * math.pi\\\\n    cos_a = math.cos(angle)\\\\n    sin_a = math.sin(angle)\\\\n    \\\\n    step_verts = []\\\\n    for r, z in profile:\\\\n        x = r * cos_a\\\\n        y = r * sin_a\\\\n        v = bm.verts.new((x, y, z))\\\\n        step_verts.append(v)\\\\n    grid_verts.append(step_verts)\\\\n\\\\n# Create quad faces\\\\nfor i in range(num_radial_steps):\\\\n    next_i = (i + 1) % num_radial_steps\\\\n    for j in range(len(profile) - 1):\\\\n        v1 = grid_verts[i][j]\\\\n        v2 = grid_verts[next_i][j]\\\\n        v3 = grid_verts[next_i][j+1]\\\\n        v4 = grid_verts[i][j+1]\\\\n        \\\\n        bm.faces.new((v1, v2, v3, v4))\\\\n\\\\n# Write back to mesh\\\\nbm.to_mesh(glass_mesh)\\\\nbm.free()\\\\n\\\\n# Translate to Z = 0.08\\\\nglass_obj.location = (0.0, 0.0, 0.08)\\\\n\\\\n# Shading: smooth and add materials\\\\nfor poly in glass_obj.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nglass_obj.data.materials.append(mat_glass)\\\\n\\\\n# Solidify Modifier\\\\nsolidify = glass_obj.modifiers.new(name=\\\\\\\"Solidify\\\\\\\", type='SOLIDIFY')\\\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\\\nsolidify.offset = -1.0 # inward\\\\n\\\\n# Subsurf Modifier\\\\nsubsurf = glass_obj.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 2\\\\nsubsurf.render_levels = 2\\\\n\\\\n# Recalculate normals to face outward\\\\nbpy.context.view_layer.objects.active = glass_obj\\\\nbpy.ops.object.mode_set(mode='EDIT')\\\\nbpy.ops.mesh.select_all(action='SELECT')\\\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\\\nbpy.ops.object.mode_set(mode='OBJ')\\\\n\\\\nprint(\\\\\\\"Glass Globe profile-modeled manually and solidified!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \\\"OBJ\\\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\\\nglass_obj = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\nif glass_obj:\\\\n    bpy.context.view_layer.objects.active = glass_obj\\\\n    bpy.ops.object.mode_set(mode='EDIT')\\\\n    bpy.ops.mesh.select_all(action='SELECT')\\\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\\\n    bpy.ops.object.mode_set(mode='OBJECT')\\\\n    print(\\\\\\\"Normals consistent and mode returned to OBJECT!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix mode_set parameter and compute correct normals\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_1NsobuZ1llbupAIinQUi\",\"content\":\"Code executed successfully: Normals consistent and mode returned to OBJECT!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\\\n\\\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    obj.data.materials.append(material)\\\\n    \\\\n    # Bevel\\\\n    bev = obj.modifiers.new(name=\\\\\\\"Bevel\\\\\\\", type='BEVEL')\\\\n    bev.width = 0.0015\\\\n    bev.segments = 3\\\\n    bev.limit_method = 'ANGLE'\\\\n    return obj\\\\n\\\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\\\ntop_holder = create_tapered_cylinder_simple(\\\\\\\"Chimney_Holder\\\\\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\\\n\\\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\\\ntop_hood = create_tapered_cylinder_simple(\\\\\\\"Chimney_Hood\\\\\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\\\n\\\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\\\ntop_riser = create_tapered_cylinder_simple(\\\\\\\"Chimney_Riser\\\\\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\\\n\\\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\\\nvent_rim1 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent1\\\\\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\\\nvent_rim2 = create_tapered_cylinder_simple(\\\\\\\"Chimney_Vent2\\\\\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\\\n\\\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\\\n# Bottom radius 0.032, top 0.01, height 0.02\\\\ntop_crown = create_tapered_cylinder_simple(\\\\\\\"Chimney_Crown\\\\\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\\\n\\\\n# Eyelet loop on top of crown for the hanging carry handle:\\\\n# Create a small torus for the hinge loop\\\\nbpy.ops.mesh.primitive_torus_add(\\\\n    align='WORLD', \\\\n    location=(0, 0, 0.327), \\\\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\\\n    major_radius=0.008, \\\\n    minor_radius=0.002\\\\n)\\\\nhinge_loop = bpy.context.active_object\\\\nhinge_loop.name = \\\\\\\"Hinge_Loop\\\\\\\"\\\\nfor poly in hinge_loop.data.polygons:\\\\n    poly.use_smooth = True\\\\nhinge_loop.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Upper Chimney structures and venting details finished!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u\",\"content\":\"Code executed successfully: Upper Chimney structures and venting details finished!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Build thick side pillars using Bezier curves with proper thickness and material\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\\\n# Authentic vintage lanterns (Dietz style) have:\\\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\\\n# - A thin wire safety cage wrapping around the glass globe physically.\\\\n\\\\n# A - Side tubular pillars\\\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\\\n# Let's write a curve generation script!\\\\n\\\\ndef create_side_tubes():\\\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        \\\\n        # Create a curve\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 16\\\\n        \\\\n        polyline = curve_data.splines.new('BEZIER')\\\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\\\n        # P0: on base tank (±0.065, 0, 0.06)\\\\n        # P1: out and straight up (±0.073, 0, 0.14)\\\\n        # P2: straight up and in (±0.070, 0, 0.22)\\\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\\\n        \\\\n        polyline.points.add(3) # adds 3 points to the existing 1\\\\n        \\\\n        pts = polyline.points\\\\n        # Point 0\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\\\n        \\\\n        # Point 1\\\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\\\n        \\\\n        # Point 2\\\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\\\n        \\\\n        # Point 3\\\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\\\n        \\\\n        # Set all point handles to professional automatically smoothed angles\\\\n        # For Bezier curve, use select and automatic handles:\\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Give the curve thickness (extrusion) to make real physical tubes!\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        # Apply dark vintage metal material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n        \\\\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\\\n        \\\\ncreate_side_tubes()\\\\nprint(\\\\\\\"Thick side pillars created professionally via Bezier curves!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\\\n\\\\nmat_dark = bpy.data.materials.get(\\\\\\\"DarkMetal\\\\\\\")\\\\n\\\\ndef create_side_tubes_fixed():\\\\n    for side in [1, -1]:\\\\n        name = f\\\\\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        curve_data.resolution_u = 12\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\\\n        \\\\n        pts = spline.bezier_points\\\\n        # Point 0: Bottom base junction\\\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\\\n        \\\\n        # Point 1: Mid-body curve out\\\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\\\n        \\\\n        # Point 2: Top shoulder curve inwards\\\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\\\n        \\\\n        # Point 3: Top hood junction\\\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        # Extrude/bevel\\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\\\n        curve_obj.data.bevel_resolution = 5\\\\n        \\\\n        # Add material\\\\n        curve_obj.data.materials.append(mat_dark)\\\\n\\\\ncreate_side_tubes_fixed()\\\\nprint(\\\\\\\"Side support pillars drafted perfectly with bezier_points!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Fix Bezier curve creation code using bezier_points instead of points\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU\",\"content\":\"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\\\n# Let's create:\\\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\\\n\\\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\\\n\\\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\\\n    # We can model a thin torus for beautiful shading\\\\n    bpy.ops.mesh.primitive_torus_add(\\\\n        align='WORLD', \\\\n        location=(0, 0, z), \\\\n        major_radius=radius, \\\\n        minor_radius=thickness,\\\\n        major_segments=32,\\\\n        minor_segments=8\\\\n    )\\\\n    obj = bpy.context.active_object\\\\n    obj.name = name\\\\n    for poly in obj.data.polygons:\\\\n        poly.use_smooth = True\\\\n    if material:\\\\n        obj.data.materials.append(material)\\\\n    return obj\\\\n\\\\nring_lower = create_guard_ring(\\\\\\\"Cage_Ring_Lower\\\\\\\", 0.043, 0.13, 0.0016, mat_brass)\\\\nring_upper = create_guard_ring(\\\\\\\"Cage_Ring_Upper\\\\\\\", 0.032, 0.19, 0.0016, mat_brass)\\\\n\\\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\\\n# Curves make visual presentation far more organic and high-quality!\\\\n\\\\ndef create_vertical_cage_wires():\\\\n    angles = [45, 135, 225, 315]\\\\n    for idx, angle_deg in enumerate(angles):\\\\n        angle_rad = math.radians(angle_deg)\\\\n        cos_a = math.cos(angle_rad)\\\\n        sin_a = math.sin(angle_rad)\\\\n        \\\\n        name = f\\\\\\\"Cage_Wire_Vert_{idx+1}\\\\\\\"\\\\n        curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n        curve_data.dimensions = '3D'\\\\n        \\\\n        spline = curve_data.splines.new('BEZIER')\\\\n        spline.bezier_points.add(2) # Total 3 control points\\\\n        pts = spline.bezier_points\\\\n        \\\\n        # Point 0: bottom anchor\\\\n        r0 = 0.048\\\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\\\n        \\\\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\\\n        r1 = 0.051\\\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\\\n        \\\\n        # Point 2: top anchor near top hood\\\\n        r2 = 0.033\\\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\\\n        \\\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\\\n        bpy.context.collection.objects.link(curve_obj)\\\\n        \\\\n        curve_obj.data.use_fill_caps = True\\\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\\\n        curve_obj.data.bevel_resolution = 4\\\\n        \\\\n        curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_vertical_cage_wires()\\\\nprint(\\\\\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build horizontal and vertical protective brass cage around glass globe\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H\",\"content\":\"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Materials\\\\nmat_brass = bpy.data.materials.get(\\\\\\\"BrassMetal\\\\\\\")\\\\n\\\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\\\n# A classic bail handle arches high above the lantern.\\\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\\\n# Max height at top around Z=0.48.\\\\n# Points for Bezier:\\\\n# Left side attachment: (-0.076, 0.0, 0.18)\\\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\\\n# Arching over top: (0.0, 0.0, 0.485)\\\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\\\n# Right side attachment: (0.076, 0.0, 0.18)\\\\n\\\\ndef create_bail_handle():\\\\n    name = \\\\\\\"Bail_Handle\\\\\\\"\\\\n    curve_data = bpy.data.curves.new(name=f\\\\\\\"{name}_curve\\\\\\\", type='CURVE')\\\\n    curve_data.dimensions = '3D'\\\\n    curve_data.resolution_u = 20\\\\n    \\\\n    spline = curve_data.splines.new('BEZIER')\\\\n    spline.bezier_points.add(4) # 5 points total\\\\n    pts = spline.bezier_points\\\\n    \\\\n    # Left attachment point\\\\n    pts[0].co = (-0.074, 0.0, 0.18)\\\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\\\n    \\\\n    # Left curve shoulder\\\\n    pts[1].co = (-0.092, 0.0, 0.38)\\\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\\\n    \\\\n    # Top center arc\\\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\\\n    \\\\n    # Right curve shoulder\\\\n    pts[3].co = (0.092, 0.0, 0.38)\\\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\\\n    \\\\n    # Right attachment point\\\\n    pts[4].co = (0.074, 0.0, 0.18)\\\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\\\n    \\\\n    curve_obj = bpy.data.objects.new(name, curve_data)\\\\n    bpy.context.collection.objects.link(curve_obj)\\\\n    \\\\n    curve_obj.data.use_fill_caps = True\\\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\\\n    curve_obj.data.bevel_resolution = 4\\\\n    \\\\n    curve_obj.data.materials.append(mat_brass)\\\\n\\\\ncreate_bail_handle()\\\\n\\\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\\\nfor side in [1, -1]:\\\\n    bpy.ops.mesh.primitive_cylinder_add(\\\\n        vertices=12, \\\\n        radius=0.0045, \\\\n        depth=0.008, \\\\n        location=(side * 0.074, 0.0, 0.18),\\\\n        rotation=(0, 1.5708, 0)\\\\n    )\\\\n    hinge_plug = bpy.context.active_object\\\\n    hinge_plug.name = f\\\\\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\\\\\"\\\\n    for poly in hinge_plug.data.polygons:\\\\n        poly.use_smooth = True\\\\n    hinge_plug.data.materials.append(mat_brass)\\\\n\\\\nprint(\\\\\\\"Iconic carrying wire handle and attachment hinges complete!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Build high carrying handle arching over top of the lantern with attachment pins\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1\",\"content\":\"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Inspect list of all objects in scene so far\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\\\nfor obj in bpy.data.objects:\\\\n    print(\\\\\\\"-\\\\\\\", obj.name, obj.type)\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz\",\"content\":\"Code executed successfully: - Bail_Handle CURVE\\n- Burner_Base MESH\\n- Cage_Ring_Lower MESH\\n- Cage_Ring_Upper MESH\\n- Cage_Wire_Vert_1 CURVE\\n- Cage_Wire_Vert_2 CURVE\\n- Cage_Wire_Vert_3 CURVE\\n- Cage_Wire_Vert_4 CURVE\\n- Chimney_Crown MESH\\n- Chimney_Holder MESH\\n- Chimney_Hood MESH\\n- Chimney_Riser MESH\\n- Chimney_Vent1 MESH\\n- Chimney_Vent2 MESH\\n- Filler_Cap MESH\\n- Flame_Core MESH\\n- Flame_PointLight LIGHT\\n- Glass_Globe MESH\\n- Glass_Globe.001 MESH\\n- Handle_Hinge_L MESH\\n- Handle_Hinge_R MESH\\n- Hinge_Loop MESH\\n- Lantern_Base_Collar MESH\\n- Lantern_Base_Rim MESH\\n- Lantern_Base_Tank MESH\\n- Support_Tube_L CURVE\\n- Support_Tube_R CURVE\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Ensure there are no unwanted duplicated objects in the scene\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Clean up any duplicated objects if they exist\\\\ndup = bpy.data.objects.get(\\\\\\\"Glass_Globe.001\\\\\\\")\\\\nif dup:\\\\n    bpy.data.objects.remove(dup, do_unlink=True)\\\\n    print(\\\\\\\"Removed duplicate glass globe\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR\",\"content\":\"Code executed successfully: Removed duplicate glass globe\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\\\n\\\\n# Let's create a large plane for the floor. Z = 0.00\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Add studio back-curve (cyclorama shape)\\\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Let's find the edges that are furthest back (Y coordinate positive)\\\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\\\nback_edges = []\\\\nfor e in bm.edges:\\\\n    if all(v in back_edge_verts for v in e.verts):\\\\n        back_edges.append(e)\\\\n\\\\n# Extrude back edges upwards along Z\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts up by 6 meters\\\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\\\nfor v in extruded_verts:\\\\n    v.co.z += 6.0\\\\n\\\\n# Add a smooth curved bevel to the back corner\\\\n# Find edges connecting to the extruded verts that represent the fold\\\\nfold_edges = []\\\\nfor e in bm.edges:\\\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\\\n    if len(e.verts) == 2:\\\\n        v0, v1 = e.verts[0], e.verts[1]\\\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\\\n            fold_edges.append(e)\\\\n\\\\nbmesh.ops.bevel(\\\\n    bm,\\\\n    geom=fold_edges,\\\\n    dist=1.5,\\\\n    segments=16,\\\\n    affect='EDGES'\\\\n)\\\\n\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Set smooth and ground material\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless cyclorama ground stage built beautifully!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create a large seamless background cyclorama stage for realistic studio lighting\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC\",\"content\":\"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \\\"dist\\\" is invalid for this operator\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Model backdrop using modifier-based beveling to ensure robustness\\\",\\\"code\\\":\\\"import bpy\\\\nimport bmesh\\\\n\\\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\\\n\\\\nground = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground:\\\\n    bpy.data.objects.remove(ground, do_unlink=True)\\\\n\\\\n# 1. Create a large plane\\\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\\\nground = bpy.context.active_object\\\\nground.name = \\\\\\\"Ground_Studio\\\\\\\"\\\\n\\\\n# Edit mesh: extrude back edge\\\\nimport bmesh\\\\nbm = bmesh.new()\\\\nbm.from_mesh(ground.data)\\\\n\\\\n# Find edges on the +Y side (Y > 5.9)\\\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\\\n\\\\n# Extrude up\\\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\\\n# Move extruded verts straight up\\\\nfor geom in extruded['geom']:\\\\n    if isinstance(geom, bmesh.types.BMVert):\\\\n        geom.co.z += 6.0\\\\n\\\\n# Write back\\\\nbm.to_mesh(ground.data)\\\\nbm.free()\\\\n\\\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\\\nbevel = ground.modifiers.new(name=\\\\\\\"CornerRound\\\\\\\", type='BEVEL')\\\\nbevel.width = 1.5\\\\nbevel.segments = 16\\\\nbevel.limit_method = 'ANGLE'\\\\n\\\\nsubsurf = ground.modifiers.new(name=\\\\\\\"Subsurf\\\\\\\", type='SUBSURF')\\\\nsubsurf.levels = 1\\\\n\\\\nfor poly in ground.data.polygons:\\\\n    poly.use_smooth = True\\\\n\\\\nground.data.materials.append(bpy.data.materials.get(\\\\\\\"Ground\\\\\\\"))\\\\n\\\\nprint(\\\\\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E\",\"content\":\"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Set up dark world lighting to emphasize the warm lantern flame illumination\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's configure the background environment lighting.\\\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\\\n\\\\nworld = bpy.context.scene.world\\\\nif not world:\\\\n    world = bpy.data.worlds.new(name=\\\\\\\"World\\\\\\\")\\\\n    bpy.context.scene.world = world\\\\n\\\\nworld.use_nodes = True\\\\nnodes = world.node_tree.nodes\\\\nnodes.clear()\\\\n\\\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\\\nbg = nodes.new(type='ShaderNodeBackground')\\\\n\\\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\\\nbg.inputs['Strength'].default_value = 0.65\\\\n\\\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\\\n\\\\nprint(\\\\\\\"Dark studio world environment set up!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2\",\"content\":\"Code executed successfully: Dark studio world environment set up!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\\\n\\\\n# Let's check existing lights\\\\nfor l in bpy.data.lights:\\\\n    print(\\\\\\\"Light found:\\\\\\\", l.name)\\\\n\\\\n# Create Cool Soft Key Light (Front-Left-High)\\\\nif \\\\\\\"Studio_Key\\\\\\\" not in bpy.data.objects:\\\\n    key_data = bpy.data.lights.new(name=\\\\\\\"Studio_Key\\\\\\\", type='AREA')\\\\n    # Soft industrial cool blue key (approx 5500K color)\\\\n    key_data.color = (0.75, 0.85, 1.0)\\\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\\\n    key_data.size = 1.0 # large soft shadow lines\\\\n    \\\\n    key_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Key\\\\\\\", object_data=key_data)\\\\n    bpy.context.collection.objects.link(key_obj)\\\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\\\n    key_obj.location = (-0.6, -0.8, 0.6)\\\\n    \\\\n    # Track to constrain light focus to lantern center\\\\n    track = key_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\n# Create Rim Light (Back-Right-High)\\\\nif \\\\\\\"Studio_Rim\\\\\\\" not in bpy.data.objects:\\\\n    rim_data = bpy.data.lights.new(name=\\\\\\\"Studio_Rim\\\\\\\", type='AREA')\\\\n    rim_data.color = (0.9, 0.95, 1.0)\\\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\\\n    rim_data.size = 0.8\\\\n    \\\\n    rim_obj = bpy.data.objects.new(name=\\\\\\\"Studio_Rim\\\\\\\", object_data=rim_data)\\\\n    bpy.context.collection.objects.link(rim_obj)\\\\n    # Position back right\\\\n    rim_obj.location = (0.5, 0.7, 0.5)\\\\n    \\\\n    track = rim_obj.constraints.new(type='TRACK_TO')\\\\n    track.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\\\n    track.up_axis = 'UP_Y'\\\\n\\\\nprint(\\\\\\\"Studio professional key and rim lighting rigs configured!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj\",\"content\":\"Code executed successfully: Light found: Flame_PointLight\\nLight found: Light\\nStudio professional key and rim lighting rigs configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# Let's clean up any default lights that might have been present in the scene\\\\ndefault_light = bpy.data.objects.get(\\\\\\\"Light\\\\\\\")\\\\nif default_light:\\\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\\\n    print(\\\\\\\"Removed default light\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Clean up default initial light to prevent overexposure\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m\",\"content\":\"Code executed successfully: \"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\\\",\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\\\n# and the side profile (support tubes, arching handle).\\\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\\\n\\\\n# Let's create a camera\\\\nif \\\\\\\"Camera\\\\\\\" in bpy.data.objects:\\\\n    cam_obj = bpy.data.objects[\\\\\\\"Camera\\\\\\\"]\\\\nelse:\\\\n    cam_data = bpy.data.cameras.new(name=\\\\\\\"Camera\\\\\\\")\\\\n    cam_obj = bpy.data.objects.new(name=\\\\\\\"Camera\\\\\\\", object_data=cam_data)\\\\n    bpy.context.collection.objects.link(cam_obj)\\\\n\\\\nbpy.context.scene.camera = cam_obj\\\\n\\\\n# Camera Settings:\\\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\\\ncam_obj.data.lens = 85\\\\n\\\\n# Position camera\\\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\\\ncam_obj.location = (0.50, -0.68, 0.26)\\\\n\\\\n# Track camera to center of glass\\\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\\\ntrack.target = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\\\ntrack.up_axis = 'UP_Y'\\\\n\\\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\\\ncam_obj.data.dof.use_dof = True\\\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\\\\\"Glass_Globe\\\\\\\")\\\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\\\n\\\\nprint(\\\\\\\"Hero camera built and configured!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr\",\"content\":\"Code executed successfully: Hero camera built and configured!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect initial camera setup and model visual quality\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Align viewport to the camera view for next screenshot inspection\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Set all 3D viewports to camera view\\\\nfor area in bpy.context.screen.areas:\\\\n    if area.type == 'VIEW_3D':\\\\n        for space in area.spaces:\\\\n            if space.type == 'VIEW_3D':\\\\n                space.region_3d.view_perspective = 'CAMERA'\\\\nprint(\\\\\\\"Aligned all viewport spaces to active camera!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B\",\"content\":\"Code executed successfully: Aligned all viewport spaces to active camera!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Inspect camera composition framing\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"0\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n\\\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\\\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\\\n# Currently, the height of the lantern goes up to Z = 0.50.\\\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\\\n\\\\ncam_obj = bpy.data.objects.get(\\\\\\\"Camera\\\\\\\")\\\\nif cam_obj:\\\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\\\n    cam_obj.location = (0.75, -1.15, 0.40)\\\\n    \\\\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\\\n    cam_obj.data.lens = 60\\\\n    \\\\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\\\n    # Alternatively, let's create a Focus_Target empty!\\\\n    \\\\n    focus_empty = bpy.data.objects.get(\\\\\\\"Focus_Target\\\\\\\")\\\\n    if not focus_empty:\\\\n        focus_empty = bpy.data.objects.new(\\\\\\\"Focus_Target\\\\\\\", None)\\\\n        bpy.context.collection.objects.link(focus_empty)\\\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\\\n    \\\\n    # Update camera track constraint target\\\\n    for c in cam_obj.constraints:\\\\n        if c.type == 'TRACK_TO':\\\\n            c.target = focus_empty\\\\n            \\\\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\\\n    cam_obj.data.dof.focus_object = focus_empty\\\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\\\n\\\\nprint(\\\\\\\"Camera composition refined for perfect product-to-hero prop framing!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Reposition camera to cleanly frame the entire lantern and background floor pool\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7\",\"content\":\"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Verify updated camera composition\\\",\\\"max_size\\\":1000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\" eyeint\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"user_prompt\\\":\\\"Scale up studio backdrop to cover entire camera FOV\\\",\\\"code\\\":\\\"import bpy\\\\n\\\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\\\n# Scale it up by 4x to be safe!\\\\n\\\\nground_obj = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground_obj:\\\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\\\n    print(\\\\\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\\\\\")\\\\n\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7\",\"content\":\"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Check backdrop coverage\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":\"_\",\"tool_calls\":[{\"id\":\"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nimport math\\\\n\\\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\\\n# This means we should rotate the ground plane to block the background correctly.\\\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\\\n\\\\nground_obj = bpy.data.objects.get(\\\\\\\"Ground_Studio\\\\\\\")\\\\nif ground_obj:\\\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\\\n    print(\\\\\\\"Rotated and shifted Ground Studio!\\\\\\\")\\\\n\\\",\\\"user_prompt\\\":\\\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF\",\"content\":\"Code executed successfully: Rotated and shifted Ground Studio!\\n\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"tool_get_viewport_screenshot_VPYrHxD2TzAZM1r0zZ18\",\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"arguments\":\"{\\\"max_size\\\":1000,\\\"user_prompt\\\":\\\"Re-check backdrop alignment after rotation\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"tool_get_viewport_screenshot_VPYrHxD2TzAZM1r0zZ18\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"},\"cache_control\":{\"type\":\"ephemeral\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"get_scene_info\",\"description\":\"Get detailed information about the current Blender scene\\n\\n    Parameters:\\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"user_prompt\"],\"title\":\"get_scene_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_info\",\"description\":\"\\n    Get detailed information about a specific object in the Blender scene.\\n\\n    Parameters:\\n    - object_name: The name of the object to get information about\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\"],\"title\":\"get_object_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_viewport_screenshot\",\"description\":\"\\n    Capture a screenshot of the current Blender 3D viewport.\\n\\n    Parameters:\\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns the screenshot as an Image.\\n    \",\"parameters\":{\"properties\":{\"max_size\":{\"default\":1000,\"title\":\"Max Size\",\"type\":\"integer\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_viewport_screenshotArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\\n\\n    Parameters:\\n    - code: The Python code to execute\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_categories\",\"description\":\"\\n    Get a list of categories for a specific asset type on Polyhaven.\\n\\n    Parameters:\\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"hdris\",\"title\":\"Asset Type\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_categoriesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_polyhaven_assets\",\"description\":\"\\n    Search for assets on Polyhaven with optional filtering.\\n\\n    Parameters:\\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\\n    - categories: Optional comma-separated list of categories to filter by\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a list of matching assets with basic information.\\n    \",\"parameters\":{\"properties\":{\"asset_type\":{\"default\":\"all\",\"title\":\"Asset Type\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"search_polyhaven_assetsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_polyhaven_asset\",\"description\":\"\\n    Download and import a Polyhaven asset into Blender.\\n\\n    Parameters:\\n    - asset_id: The ID of the asset to download\\n    - asset_type: The type of asset (hdris, textures, models)\\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"asset_id\":{\"title\":\"Asset Id\",\"type\":\"string\"},\"asset_type\":{\"title\":\"Asset Type\",\"type\":\"string\"},\"resolution\":{\"default\":\"1k\",\"title\":\"Resolution\",\"type\":\"string\"},\"file_format\":{\"default\":null,\"title\":\"File Format\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"asset_id\",\"asset_type\"],\"title\":\"download_polyhaven_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"set_texture\",\"description\":\"\\n    Apply a previously downloaded Polyhaven texture to an object.\\n    \\n    Parameters:\\n    - object_name: Name of the object to apply the texture to\\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\\n    \\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"object_name\":{\"title\":\"Object Name\",\"type\":\"string\"},\"texture_id\":{\"title\":\"Texture Id\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"object_name\",\"texture_id\"],\"title\":\"set_textureArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_polyhaven_status\",\"description\":\"\\n    Check if PolyHaven integration is enabled in Blender.\\n    Returns a message indicating whether PolyHaven features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_polyhaven_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hyper3d_status\",\"description\":\"\\n    Check if Hyper3D Rodin integration is enabled in Blender.\\n    Returns a message indicating whether Hyper3D Rodin features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hyper3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_status\",\"description\":\"\\n    Check if Sketchfab integration is enabled in Blender.\\n    Returns a message indicating whether Sketchfab features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_sketchfab_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_sketchfab_models\",\"description\":\"\\n    Search for models on Sketchfab with optional filtering.\\n\\n    Parameters:\\n    - query: Text to search for\\n    - categories: Optional comma-separated list of categories\\n    - count: Maximum number of results to return (default 20)\\n    - downloadable: Whether to include only downloadable models (default True)\\n\\n    Returns a formatted list of matching models.\\n    \",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"categories\":{\"default\":null,\"title\":\"Categories\",\"type\":\"string\"},\"count\":{\"default\":20,\"title\":\"Count\",\"type\":\"integer\"},\"downloadable\":{\"default\":true,\"title\":\"Downloadable\",\"type\":\"boolean\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"query\"],\"title\":\"search_sketchfab_modelsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_sketchfab_model_preview\",\"description\":\"\\n    Get a preview thumbnail of a Sketchfab model by its UID.\\n    Use this to visually confirm a model before downloading.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\\n    \\n    Returns the model's thumbnail as an Image for visual confirmation.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\"],\"title\":\"get_sketchfab_model_previewArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"download_sketchfab_model\",\"description\":\"\\n    Download and import a Sketchfab model by its UID.\\n    The model will be scaled so its largest dimension equals target_size.\\n    \\n    Parameters:\\n    - uid: The unique identifier of the Sketchfab model\\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\\n                  You must specify the desired size for the model.\\n                  Examples:\\n                  - Chair: target_size=1.0 (1 meter tall)\\n                  - Table: target_size=0.75 (75cm tall)\\n                  - Car: target_size=4.5 (4.5 meters long)\\n                  - Person: target_size=1.7 (1.7 meters tall)\\n                  - Small object (cup, phone): target_size=0.1 to 0.3\\n    \\n    Returns a message with import details including object names, dimensions, and bounding box.\\n    The model must be downloadable and you must have proper access rights.\\n    \",\"parameters\":{\"properties\":{\"uid\":{\"title\":\"Uid\",\"type\":\"string\"},\"target_size\":{\"title\":\"Target Size\",\"type\":\"number\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"uid\",\"target_size\"],\"title\":\"download_sketchfab_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_text\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n\\n    Parameters:\\n    - text_prompt: A short description of the desired model in **English**.\\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"title\":\"Text Prompt\",\"type\":\"string\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"required\":[\"text_prompt\"],\"title\":\"generate_hyper3d_model_via_textArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hyper3d_model_via_images\",\"description\":\"\\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\\n    The 3D asset has built-in materials.\\n    The generated model has a normalized size, so re-scaling after generation can be useful.\\n    \\n    Parameters:\\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\\n\\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\\n    Returns a message indicating success or failure.\\n    \",\"parameters\":{\"properties\":{\"input_image_paths\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Paths\",\"type\":\"array\"},\"input_image_urls\":{\"default\":null,\"items\":{\"type\":\"string\"},\"title\":\"Input Image Urls\",\"type\":\"array\"},\"bbox_condition\":{\"default\":null,\"items\":{\"type\":\"number\"},\"title\":\"Bbox Condition\",\"type\":\"array\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hyper3d_model_via_imagesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_rodin_job_status\",\"description\":\"\\n    Check if the Hyper3D Rodin generation task is completed.\\n\\n    For Hyper3D Rodin mode MAIN_SITE:\\n        Parameters:\\n        - subscription_key: The subscription_key given in the generate model step.\\n\\n        Returns a list of status. The task is done if all status are \\\"Done\\\".\\n        If \\\"Failed\\\" showed up, the generating process failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"Done\\\" or \\\"Canceled\\\").\\n\\n    For Hyper3D Rodin mode FAL_AI:\\n        Parameters:\\n        - request_id: The request_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"COMPLETED\\\".\\n        The task is in progress if status is \\\"IN_PROGRESS\\\".\\n        If status other than \\\"COMPLETED\\\", \\\"IN_PROGRESS\\\", \\\"IN_QUEUE\\\" showed up, the generating process might be failed.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"COMPLETED\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"subscription_key\":{\"default\":null,\"title\":\"Subscription Key\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"title\":\"poll_rodin_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset\",\"description\":\"\\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\\n\\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"task_uuid\":{\"default\":null,\"title\":\"Task Uuid\",\"type\":\"string\"},\"request_id\":{\"default\":null,\"title\":\"Request Id\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"import_generated_assetArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_hunyuan3d_status\",\"description\":\"\\n    Check if Hunyuan3D integration is enabled in Blender.\\n    Returns a message indicating whether Hunyuan3D features are available.\\n    \",\"parameters\":{\"properties\":{\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"get_hunyuan3d_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"generate_hunyuan3d_model\",\"description\":\"\\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \\n    or both for the desired asset, and import the asset into Blender.\\n    The 3D asset has built-in materials.\\n    \\n    Parameters:\\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\\n    - input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.\\n\\n    Returns: \\n    - When successful, returns a JSON with job_id (format: \\\"job_xxx\\\") indicating the task is in progress\\n    - When the job completes, the status will change to \\\"DONE\\\" indicating the model has been imported\\n    - Returns error message if the operation fails\\n    \",\"parameters\":{\"properties\":{\"text_prompt\":{\"default\":null,\"title\":\"Text Prompt\",\"type\":\"string\"},\"input_image_url\":{\"default\":null,\"title\":\"Input Image Url\",\"type\":\"string\"},\"user_prompt\":{\"default\":\"\",\"title\":\"User Prompt\",\"type\":\"string\"}},\"title\":\"generate_hunyuan3d_modelArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"poll_hunyuan_job_status\",\"description\":\"\\n    Check if the Hunyuan3D generation task is completed.\\n\\n    For Hunyuan3D:\\n        Parameters:\\n        - job_id: The job_id given in the generate model step.\\n\\n        Returns the generation task status. The task is done if status is \\\"DONE\\\".\\n        The task is in progress if status is \\\"RUN\\\".\\n        If status is \\\"DONE\\\", returns ResultFile3Ds, which is the generated ZIP model path\\n        When the status is \\\"DONE\\\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\\n        This is a polling API, so only proceed if the status are finally determined (\\\"DONE\\\" or some failed state).\\n    \",\"parameters\":{\"properties\":{\"job_id\":{\"default\":null,\"title\":\"Job Id\",\"type\":\"string\"}},\"title\":\"poll_hunyuan_job_statusArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"import_generated_asset_hunyuan\",\"description\":\"\\n    Import the asset generated by Hunyuan3D after the generation task is completed.\\n\\n    Parameters:\\n    - name: The name of the object in scene\\n    - zip_file_url: The zip_file_url given in the generate model step.\\n\\n    Return if the asset has been imported successfully.\\n    \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"zip_file_url\":{\"title\":\"Zip File Url\",\"type\":\"string\"}},\"required\":[\"name\",\"zip_file_url\"],\"title\":\"import_generated_asset_hunyuanArguments\",\"type\":\"object\"}}}],\"reasoning\":{\"effort\":\"medium\"}}", "body_base64": "{"model":"google/gemini-3.5-flash","messages":[{"role":"system","content":[{"type":"text","text":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote, based only on the published visual outputs they can inspect: the interactive 3D scene and rendered presentation.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe final published capture is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. The saved scene is also exported to GLB with geometry, materials, the contestant camera, and supported lights for interactive inspection. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work. Before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — position and aim the camera, and add or adjust lights so the subject reads clearly. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. The saved scene is also exported to GLB with geometry, materials, your camera, and supported lights for interactive inspection. When you are finished, reply with the single word DONE and no tool calls.","cache_control":{"type":"ephemeral"}}]},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classic camping lantern as a single hero prop: a metal frame with a carry handle, a\nglass enclosure around a warm glowing core, standing on simple ground.\n\nThe floor is a recognizable lantern that glows. The ceiling is what the light does:\nwarm light passing through the glass, a soft pool of illumination on the ground,\nbelievable metal and glass materials, and a composition that presents the lantern\nlike a product shot against a calm, dark backdrop.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","type":"function","function":{"name":"get_scene_info","arguments":"{\"user_prompt\":\"Get initial scene info\"}"}}]},{"role":"tool","tool_call_id":"tool_get_scene_info_El7NNW4G2qFUh4s67C60","content":"{\n  \"name\": \"Scene\",\n  \"object_count\": 0,\n  \"objects\": [],\n  \"materials_count\": 2\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","type":"function","function":{"name":"get_sketchfab_status","arguments":"{\"user_prompt\":\"Check if Sketchfab is active\"}"}}]},{"role":"tool","tool_call_id":"tool_get_sketchfab_status_qlLYu1PJlRJJufmtICDj","content":"Sketchfab integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Sketchfab' checkbox\n                            3. Enter your Sketchfab API Key\n                            4. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","type":"function","function":{"name":"get_polyhaven_status","arguments":"{\"user_prompt\":\"Check Polyhaven status\"}"}}]},{"role":"tool","tool_call_id":"tool_get_polyhaven_status_yXW3lIrUXkllLe8AL0tU","content":"PolyHaven integration is currently disabled. To enable it:\n                            1. In the 3D Viewport, find the BlenderMCP panel in the sidebar (press N if hidden)\n                            2. Check the 'Use assets from Poly Haven' checkbox\n                            3. Restart the connection to Claude"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef get_or_create_material(name, shader_type):\\n    mat = bpy.data.materials.new(name=name)\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    \\n    if shader_type == 'glass':\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n        principled.inputs['Roughness'].default_value = 0.01\\n        principled.inputs['IOR'].default_value = 1.5\\n        set_transmission(principled, 1.0)\\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'emission':\\n        # Glowing warm flame/wick core\\n        emission = nodes.new(type='ShaderNodeEmission')\\n        # Deep warm orange (approx 1800K)\\n        emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0)\\n        emission.inputs['Strength'].default_value = 35.0\\n        links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'dark_metal':\\n        # Beautiful dark forest-green painted vintage metal with subtle roughness map\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.05, 0.025, 1.0)\\n        principled.inputs['Metallic'].default_value = 0.6\\n        \\n        # Texture nodes for realistic metal variation\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 10.0\\n        noise.inputs['Detail'].default_value = 4.0\\n        \\n        ramp = nodes.new(type='ShaderNodeValToRGB')\\n        ramp.color_ramp.elements[0].position = 0.3\\n        ramp.color_ramp.elements[0].color = (0.15, 0.15, 0.15, 1.0) # shiny parts\\n        ramp.color_ramp.elements[1].position = 0.7\\n        ramp.color_ramp.elements[1].color = (0.45, 0.45, 0.45, 1.0) # rougher parts\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Fac'], ramp.inputs['Fac'])\\n        links.new(ramp.outputs['Color'], principled.inputs['Roughness'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'shiny_metal':\\n        # Brassy weathered/brushed metal for cage and burners\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        # Aged brass/bronze color: Hex #C29D60\\n        principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0)\\n        principled.inputs['Metallic'].default_value = 1.0\\n        principled.inputs['Roughness'].default_value = 0.22\\n        \\n        # subtle bump\\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 30.0\\n        noise.inputs['Detail'].default_value = 5.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.03\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    elif shader_type == 'ground':\\n        # Soft-reflecting textured studio ground plane\\n        principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n        principled.inputs['Base Color'].default_value = (0.015, 0.015, 0.015, 1.0) # very dark\\n        principled.inputs['Roughness'].default_value = 0.5\\n        \\n        coord = nodes.new(type='ShaderNodeTextureCoordinate')\\n        noise = nodes.new(type='ShaderNodeTexNoise')\\n        noise.inputs['Scale'].default_value = 15.0\\n        noise.inputs['Detail'].default_value = 6.0\\n        \\n        bump = nodes.new(type='ShaderNodeBump')\\n        bump.inputs['Strength'].default_value = 0.06\\n        \\n        links.new(coord.outputs['Generated'], noise.inputs['Vector'])\\n        links.new(noise.outputs['Color'], bump.inputs['Height'])\\n        links.new(bump.outputs['Normal'], principled.inputs['Normal'])\\n        \\n        links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n        \\n    return mat\\n\\n# Clear and initialize scene\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\n# Create standard materials\\nget_or_create_material('Glass', 'glass')\\nget_or_create_material('EmissionCore', 'emission')\\nget_or_create_material('DarkMetal', 'dark_metal')\\nget_or_create_material('BrassMetal', 'shiny_metal')\\nget_or_create_material('Ground', 'ground')\\n\\nprint(\\\"Materials created successfully!\\\")\\n\",\"user_prompt\":\"Initialize materials in empty scene\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NoRGZNJg6iMmwvHXahp8","content":"Error executing code: Communication error with Blender: Code execution error: Error: Node type ShaderNodeTextureCoordinate undefined\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Robustly create standard materials with clean names\",\"code\":\"# Let's fix the shader node types. In modern Blender, they require capitalized strings or specific type names.\\n# Let's inspect the names list or write a simplified node network that doesn't fail.\\n# Usually node types are 'ShaderNodeTexCoord', 'ShaderNodeTexNoise', 'ShaderNodeValToRGB', 'ShaderNodeBump'\\n\\nimport bpy\\n\\ndef set_transmission(principled_node, value):\\n    if 'Transmission Weight' in principled_node.inputs:\\n        principled_node.inputs['Transmission Weight'].default_value = value\\n    elif 'Transmission' in principled_node.inputs:\\n        principled_node.inputs['Transmission'].default_value = value\\n\\ndef create_glass_material():\\n    mat = bpy.data.materials.new(name=\\\"Glass\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (1.0, 1.0, 1.0, 1.0)\\n    principled.inputs['Roughness'].default_value = 0.02\\n    principled.inputs['IOR'].default_value = 1.5\\n    set_transmission(principled, 1.0)\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_emission_material():\\n    mat = bpy.data.materials.new(name=\\\"EmissionCore\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    emission = nodes.new(type='ShaderNodeEmission')\\n    emission.inputs['Color'].default_value = (1.0, 0.45, 0.08, 1.0) # Deep warm orange-yellow flame\\n    emission.inputs['Strength'].default_value = 45.0\\n    links.new(emission.outputs['Emission'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_dark_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"DarkMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.012, 0.045, 0.022, 1.0) # Forest dark green painted vintage metal\\n    principled.inputs['Metallic'].default_value = 0.8\\n    principled.inputs['Roughness'].default_value = 0.35\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_shiny_metal_material():\\n    mat = bpy.data.materials.new(name=\\\"BrassMetal\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.76, 0.61, 0.37, 1.0) # Brass/bronze\\n    principled.inputs['Metallic'].default_value = 1.0\\n    principled.inputs['Roughness'].default_value = 0.22\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\ndef create_ground_material():\\n    mat = bpy.data.materials.new(name=\\\"Ground\\\")\\n    mat.use_nodes = True\\n    nodes = mat.node_tree.nodes\\n    links = mat.node_tree.links\\n    nodes.clear()\\n    \\n    output = nodes.new(type='ShaderNodeOutputMaterial')\\n    principled = nodes.new(type='ShaderNodeBsdfPrincipled')\\n    principled.inputs['Base Color'].default_value = (0.02, 0.02, 0.02, 1.0) # Neutral super dark studio floor\\n    principled.inputs['Roughness'].default_value = 0.45\\n    links.new(principled.outputs['BSDF'], output.inputs['Surface'])\\n    return mat\\n\\n# Clear and rebuild\\nfor obj in list(bpy.data.objects):\\n    bpy.data.objects.remove(obj, do_unlink=True)\\nfor mat in list(bpy.data.materials):\\n    bpy.data.materials.remove(mat, do_unlink=True)\\n\\ncreate_glass_material()\\ncreate_emission_material()\\ncreate_dark_metal_material()\\ncreate_shiny_metal_material()\\ncreate_ground_material()\\n\\nprint(\\\"Recreated robust basic materials successfully!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_mln5Y3KJpGmHRNlZ4dKU","content":"Code executed successfully: Recreated robust basic materials successfully!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\ndef add_smooth_cylinder(name, radius, depth, vertices=32, location=(0,0,0), material=None, bevel_width=0.0):\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=vertices, \\n        radius=radius, \\n        depth=depth, \\n        location=location,\\n        end_fill_type='NGON'\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    \\n    # Smooth shading\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n        \\n    if bevel_width > 0:\\n        # Add bevel modifier for nice rounded borders and highlights\\n        bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n        bevel.width = bevel_width\\n        bevel.segments = 3\\n        bevel.limit_method = 'ANGLE'\\n        \\n    if material:\\n        obj.data.materials.append(material)\\n        \\n    return obj\\n\\n# Test basic cylinder function\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\ncone = add_smooth_cylinder(\\\"Test_Base\\\", 0.08, 0.05, material=mat_dark)\\nprint(\\\"Base test model created:\\\", cone.name)\\n\",\"user_prompt\":\"Test cylinder creation helper\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_m7FyYHK5Bjz6aYeAj23h","content":"Code executed successfully: Base test model created: Test_Base\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Clean up test object\\ntest_obj = bpy.data.objects.get(\\\"Test_Base\\\")\\nif test_obj:\\n    bpy.data.objects.remove(test_obj, do_unlink=True)\\n\\n# Define materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\nmat_ground = bpy.data.materials.get(\\\"Ground\\\")\\n\\n# We will build a highly detailed classic lantern:\\n# 1. Base oil reservoir: A wide circular tapered shape.\\n# 2. Burner collar: Small brass cylinders resting on top of the reservoir.\\n# 3. Glass globe: Rounded hourglass glass tube.\\n# 4. Wick / Flame: Located inside the glass globe.\\n# 5. Protective cage: Symmetrical brass rods surrounding the glass globe.\\n# 6. Upper cap (chimney): A multi-tiered metal cap on top of the glass.\\n# 7. Bail / Handle: A wire arc handle hinged to the support tubes or chimney.\\n\\n# Let's create the base first.\\n# It can consist of:\\n# - A low wide cylinder at the bottom.\\n# - A truncated cone tapering upwards.\\n# - A small ornamental lip.\\n\\n# Helper function to create tapered cylinder (using cone or bmesh)\\ndef create_tapered_cylinder(name, radius_bottom, radius_top, depth, location, material=None):\\n    bpy.ops.mesh.primitive_cone_add(\\n        vertices=32, \\n        radius1=radius_bottom, \\n        radius2=radius_top, \\n        depth=depth, \\n        location=location\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    \\n    # Bevel for highlights\\n    bevel = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bevel.width = 0.002\\n    bevel.segments = 3\\n    bevel.limit_method = 'ANGLE'\\n    \\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\n# ----------------- 1. BASE OIL RESERVOIR -----------------\\n# Let's place the base slightly above the ground (zcoord 0).\\n# Center of base will be calculated so the bottom flat face sits exactly at z=0.\\n# Bottom rim: radius 0.6m, height 0.04m, center Z = 0.02\\nrim_h = 0.02\\nbase_rim = create_tapered_cylinder(\\\"Lantern_Base_Rim\\\", 0.08, 0.08, rim_h, (0, 0, rim_h/2), mat_dark)\\n\\n# Sloped tank: radius bottom 0.08m, radius top 0.055m, height 0.06m, center Z = 0.02 + 0.03 = 0.05\\ntank_h = 0.045\\nbase_tank = create_tapered_cylinder(\\\"Lantern_Base_Tank\\\", 0.08, 0.055, tank_h, (0, 0, rim_h + tank_h/2), mat_dark)\\n\\n# Collar rim top: radius 0.055, height 0.01\\ncollar_h = 0.015\\nbase_collar_top = create_tapered_cylinder(\\\"Lantern_Base_Collar\\\", 0.055, 0.050, collar_h, (0, 0, rim_h + tank_h + collar_h/2), mat_dark)\\n\\n# Let's add a decorative oil filler cap on the base tank\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=16, \\n    radius=0.012, \\n    depth=0.008, \\n    location=(0.045, 0.025, rim_h + tank_h * 0.7)\\n)\\nfiller_cap = bpy.context.active_object\\nfiller_cap.name = \\\"Filler_Cap\\\"\\nfiller_cap.rotation_euler = (0, 0.4, 0.5) # angled nicely on the slope\\nfor poly in filler_cap.data.polygons:\\n    poly.use_smooth = True\\nif mat_brass:\\n    filler_cap.data.materials.append(mat_brass)\\n\\n# Parent the cap to the base Tank\\nfiller_cap.parent = base_tank\\n\\nprint(\\\"Lantern Base parts created!\\\")\\n\",\"user_prompt\":\"Build lantern lower base and details\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_PM5T9gDNQwp91nHMOF7W","content":"Code executed successfully: Lantern Base parts created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\nmat_emission = bpy.data.materials.get(\\\"EmissionCore\\\")\\n\\n# Let's locate the heights so far\\n# rim_h = 0.02\\n# tank_h = 0.045\\n# collar_h = 0.015\\n# Total base height = 0.08m\\nbase_height = 0.02 + 0.045 + 0.015\\n\\n# ----------------- 2. BURNER & WICK ASSEMBLY (inside) -----------------\\n# Inside the bottom of the glass sits a small burner dome and holding brackets in brass.\\nburner_h = 0.022\\nburner_center_z = base_height + burner_h/2\\nbpy.ops.mesh.primitive_cylinder_add(\\n    vertices=24, \\n    radius=0.025, \\n    depth=burner_h, \\n    location=(0, 0, burner_center_z)\\n)\\nburner_base = bpy.context.active_object\\nburner_base.name = \\\"Burner_Base\\\"\\nfor poly in burner_base.data.polygons:\\n    poly.use_smooth = True\\nburner_base.data.materials.append(mat_brass)\\n\\n# Flame/Wick emitter\\n# A small flame standing in the center. We can use a cone-sphere hybrid for a realistic flame shape.\\nbpy.ops.mesh.primitive_cone_add(\\n    vertices=16,\\n    radius1=0.007,\\n    radius2=0.0,\\n    depth=0.030,\\n    location=(0, 0, base_height + burner_h + 0.01)\\n)\\nflame = bpy.context.active_object\\nflame.name = \\\"Flame_Core\\\"\\nfor poly in flame.data.polygons:\\n    poly.use_smooth = True\\nflame.data.materials.append(mat_emission)\\n\\n# Also let's place a point light right at the flame core to cast soft, beautiful Cycles raytraced shadows \\n# and illumination through the glass! This is critical for getting the \\\"soft pool of illumination on the ground\\\".\\nlight_data = bpy.data.lights.new(name=\\\"Flame_PointLight\\\", type='POINT')\\n# Golden-orange light (approx 1700K color)\\nlight_data.color = (1.0, 0.48, 0.08)\\nlight_data.energy = 22.0 # Good brightness for a cozy product shot\\nlight_data.shadow_soft_size = 0.04 # soft shadows\\n\\nlight_obj = bpy.data.objects.new(name=\\\"Flame_PointLight\\\", object_data=light_data)\\nbpy.context.collection.objects.link(light_obj)\\nlight_obj.location = (0, 0, base_height + burner_h + 0.015)\\n\\nprint(\\\"Burner, Flame Core, and Light source created!\\\")\\n\",\"user_prompt\":\"Build internal burner dome, glowing flame core, and Cycles point light source\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FjQNroOGtmKAoQByS3Ty","content":"Code executed successfully: Burner, Flame Core, and Light source created!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\n# ----------------- 3. GLASS GLOBE -----------------\\n# Let's model a beautiful classic tapered/hourglass lantern globe.\\n# Range in Z: from Z = base_height (0.08m) up to Z = base_height + 0.16m (0.24m)\\n# We can form this by starting with a cylinder, then shaping its vertices or applying standard loop cuts,\\n# or create a loft-like curve and convert it, or programmatically create vertices of a profile and spin/revolve.\\n# Programmatically constructing path/spin is incredibly precise. Let's do a BMESH spin or construct vertices directly!\\n\\nimport bmesh\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\n# Define profile of the glass: Z goes from 0 to 0.155 (representing local offset relative to globe base at Z = 0.08)\\n# We'll sweep this profile in a circle\\nprofile = [\\n    # (r, z)\\n    (0.026, 0.0),\\n    (0.033, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.11),\\n    (0.026, 0.13),\\n    (0.024, 0.145),\\n    (0.023, 0.150)\\n]\\n\\n# Create profile vertices in the XZ plane\\nverts = []\\nfor r, z in profile:\\n    v = bm.verts.new((r, 0.0, z))\\n    verts.append(v)\\n\\n# Extrude by spinning around the Z axis\\n# Spin 360 degrees, 32 steps\\nbmesh.ops.spin(\\n    bm,\\n    geom=bm.verts[:] + bm.edges[:],\\n    cent=(0.0, 0.0, 0.0),\\n    axis=(0.0, 0.0, 1.0),\\n    dangle=2 * math.pi,\\n    steps=32,\\n    use_duplicate=False\\n)\\n\\n# Merge doubles to join the spin endpoints\\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\\n\\n# Solidify the glass so it has a real interior and exterior wall. Glass with thickness is 100x more realistic in Cycles.\\n# We'll use a Solidify modifier to guarantee uniform thickness and perfect glass refraction.\\n\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate glass to correct height in scene setup\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # extend inward\\n\\n# Subsurf Modifier for perfect smoothness\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\nprint(\\\"Glass Globe modeled with realistic physical thickness!\\\")\\n\",\"user_prompt\":\"Model glass globe with realistic physical thickness and hourglass shape\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_KQCbHoHGmOjWFGqdbuWu","content":"Error executing code: Communication error with Blender: Code execution error: spin: keyword \"dangle\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport bmesh\\nimport math\\n\\n# Let's inspect BMesh spin or write a custom math rotation which is completely bulletproof to API version mismatches!\\n# Custom vertex rotation is super clean and easy.\\nmat_glass = bpy.data.materials.get(\\\"Glass\\\")\\n\\nglass_mesh = bpy.data.meshes.new(name=\\\"GlassGlobeMesh\\\")\\nglass_obj = bpy.data.objects.new(\\\"Glass_Globe\\\", glass_mesh)\\nbpy.context.collection.objects.link(glass_obj)\\n\\nbm = bmesh.new()\\n\\nprofile = [\\n    # (r, z)\\n    (0.027, 0.0),\\n    (0.034, 0.01),\\n    (0.040, 0.03),\\n    (0.043, 0.05),\\n    (0.040, 0.08),\\n    (0.032, 0.115),\\n    (0.026, 0.138),\\n    (0.024, 0.147),\\n    (0.023, 0.150)\\n]\\n\\nnum_radial_steps = 32\\n# Create grid of vertices\\ngrid_verts = [] # 2D array of verts: grid_verts[step][profile_idx]\\n\\nfor i in range(num_radial_steps):\\n    angle = (i / num_radial_steps) * 2 * math.pi\\n    cos_a = math.cos(angle)\\n    sin_a = math.sin(angle)\\n    \\n    step_verts = []\\n    for r, z in profile:\\n        x = r * cos_a\\n        y = r * sin_a\\n        v = bm.verts.new((x, y, z))\\n        step_verts.append(v)\\n    grid_verts.append(step_verts)\\n\\n# Create quad faces\\nfor i in range(num_radial_steps):\\n    next_i = (i + 1) % num_radial_steps\\n    for j in range(len(profile) - 1):\\n        v1 = grid_verts[i][j]\\n        v2 = grid_verts[next_i][j]\\n        v3 = grid_verts[next_i][j+1]\\n        v4 = grid_verts[i][j+1]\\n        \\n        bm.faces.new((v1, v2, v3, v4))\\n\\n# Write back to mesh\\nbm.to_mesh(glass_mesh)\\nbm.free()\\n\\n# Translate to Z = 0.08\\nglass_obj.location = (0.0, 0.0, 0.08)\\n\\n# Shading: smooth and add materials\\nfor poly in glass_obj.data.polygons:\\n    poly.use_smooth = True\\n\\nglass_obj.data.materials.append(mat_glass)\\n\\n# Solidify Modifier\\nsolidify = glass_obj.modifiers.new(name=\\\"Solidify\\\", type='SOLIDIFY')\\nsolidify.thickness = 0.0025 # 2.5mm thick glass\\nsolidify.offset = -1.0 # inward\\n\\n# Subsurf Modifier\\nsubsurf = glass_obj.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 2\\nsubsurf.render_levels = 2\\n\\n# Recalculate normals to face outward\\nbpy.context.view_layer.objects.active = glass_obj\\nbpy.ops.object.mode_set(mode='EDIT')\\nbpy.ops.mesh.select_all(action='SELECT')\\nbpy.ops.mesh.normals_make_consistent(inside=False)\\nbpy.ops.object.mode_set(mode='OBJ')\\n\\nprint(\\\"Glass Globe profile-modeled manually and solidified!\\\")\\n\",\"user_prompt\":\"Manually construct the spin grid in a robust manner to avoid api version issues with spin operator\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_7NXSyfOyNEiRQzWVrEvH","content":"Error executing code: Communication error with Blender: Code execution error: Converting py args to operator properties: enum \"OBJ\" not found in ('OBJECT', 'EDIT', 'SCULPT', 'VERTEX_PAINT', 'WEIGHT_PAINT', 'TEXTURE_PAINT')"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Fix 'OBJ' to 'OBJECT' in mode_set\\nglass_obj = bpy.data.objects.get(\\\"Glass_Globe\\\")\\nif glass_obj:\\n    bpy.context.view_layer.objects.active = glass_obj\\n    bpy.ops.object.mode_set(mode='EDIT')\\n    bpy.ops.mesh.select_all(action='SELECT')\\n    bpy.ops.mesh.normals_make_consistent(inside=False)\\n    bpy.ops.object.mode_set(mode='OBJECT')\\n    print(\\\"Normals consistent and mode returned to OBJECT!\\\")\\n\",\"user_prompt\":\"Fix mode_set parameter and compute correct normals\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_1NsobuZ1llbupAIinQUi","content":"Code executed successfully: Normals consistent and mode returned to OBJECT!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 4. UPPER CAP / CHIMNEY & COVERS -----------------\\n# The top part of the lantern spans from Z=0.23 (top of glass) upwards:\\n# Z: 0.23 to 0.245: brass collar connecting glass to dark metal cap.\\n# Z: 0.245 to 0.265: lower flared shade/cap (dark metal).\\n# Z: 0.265 to 0.285: slotted chimney exhaust cylinder (dark metal or brass inside).\\n# Z: 0.285 to 0.295: top finial cap (dark metal) where the handle hinges.\\n\\ndef create_tapered_cylinder_simple(name, r_bottom, r_top, h, z_center, material):\\n    bpy.ops.mesh.primitive_cone_add(vertices=32, radius1=r_bottom, radius2=r_top, depth=h, location=(0,0,z_center))\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    obj.data.materials.append(material)\\n    \\n    # Bevel\\n    bev = obj.modifiers.new(name=\\\"Bevel\\\", type='BEVEL')\\n    bev.width = 0.0015\\n    bev.segments = 3\\n    bev.limit_method = 'ANGLE'\\n    return obj\\n\\n# 1. Brass glass holder rim at top: Z = 0.23 + 0.006 = 0.236\\ntop_holder = create_tapered_cylinder_simple(\\\"Chimney_Holder\\\", 0.024, 0.027, 0.012, 0.236, mat_brass)\\n\\n# 2. Main lower hood/shield (classic flared hat structure): Z = 0.242 + 0.015 = 0.257\\n# Flared downwards: bottom radius 0.065, top radius 0.035, height 0.03\\ntop_hood = create_tapered_cylinder_simple(\\\"Chimney_Hood\\\", 0.065, 0.035, 0.03, 0.257, mat_dark)\\n\\n# 3. Slotted exhaust riser cylinder: radius 0.031, height 0.032, Z = 0.272 + 0.016 = 0.288\\ntop_riser = create_tapered_cylinder_simple(\\\"Chimney_Riser\\\", 0.031, 0.031, 0.032, 0.288, mat_dark)\\n\\n# Let's add venting holes on the riser to make it highly authentic! We can do high-detail inset slats.\\n# Actually, modeling small ventilation slits using Boolean or simple geometry slices gives gorgeous results.\\n# Or simpler: add a small horizontal accent rim around the riser to give venting silhouette details.\\nvent_rim1 = create_tapered_cylinder_simple(\\\"Chimney_Vent1\\\", 0.033, 0.033, 0.004, 0.280, mat_brass)\\nvent_rim2 = create_tapered_cylinder_simple(\\\"Chimney_Vent2\\\", 0.033, 0.033, 0.004, 0.292, mat_brass)\\n\\n# 4. Top crown dome capping it off: Z = 0.304 + 0.01 = 0.314\\n# Bottom radius 0.032, top 0.01, height 0.02\\ntop_crown = create_tapered_cylinder_simple(\\\"Chimney_Crown\\\", 0.032, 0.015, 0.02, 0.314, mat_dark)\\n\\n# Eyelet loop on top of crown for the hanging carry handle:\\n# Create a small torus for the hinge loop\\nbpy.ops.mesh.primitive_torus_add(\\n    align='WORLD', \\n    location=(0, 0, 0.327), \\n    rotation=(0, 1.5708, 0), # rotated to face side-to-side\\n    major_radius=0.008, \\n    minor_radius=0.002\\n)\\nhinge_loop = bpy.context.active_object\\nhinge_loop.name = \\\"Hinge_Loop\\\"\\nfor poly in hinge_loop.data.polygons:\\n    poly.use_smooth = True\\nhinge_loop.data.materials.append(mat_brass)\\n\\nprint(\\\"Upper Chimney structures and venting details finished!\\\")\\n\",\"user_prompt\":\"Build upper cap, flared hood, and chimney venting system with brass rings and top eyelet\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_FY5NJmURZh0FUyjupB9u","content":"Code executed successfully: Upper Chimney structures and venting details finished!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Build thick side pillars using Bezier curves with proper thickness and material\",\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 5. PROTECTIVE METAL CAGES AND TUBES -----------------\\n# Authentic vintage lanterns (Dietz style) have:\\n# - Two tubular side support pillars extending from the base tank to the chimney hood.\\n# - A thin wire safety cage wrapping around the glass globe physically.\\n\\n# A - Side tubular pillars\\n# Let's create two thick curved tubes on Left (+X) and Right (-X) sides.\\n# Each pillar goes from base at (±0.065, 0, 0.05) up, curves inwards, and hooks into the upper hood at (±0.038, 0, 0.257).\\n# We can represent each side pillar using a Bezier curve converted to a mesh, or segmented cylinders.\\n# Let's write a curve generation script!\\n\\ndef create_side_tubes():\\n    for side in [1, -1]: # +1 is Left, -1 is Right\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        \\n        # Create a curve\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 16\\n        \\n        polyline = curve_data.splines.new('BEZIER')\\n        # We need 4 control points for a beautiful organic smooth hollow tube curve:\\n        # P0: on base tank (±0.065, 0, 0.06)\\n        # P1: out and straight up (±0.073, 0, 0.14)\\n        # P2: straight up and in (±0.070, 0, 0.22)\\n        # P3: inside upper hood (±0.035, 0, 0.25)\\n        \\n        polyline.points.add(3) # adds 3 points to the existing 1\\n        \\n        pts = polyline.points\\n        # Point 0\\n        pts[0].co = (side * 0.065, 0.0, 0.06, 1.0)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05, 1.0)\\n        pts[0].handle_right = (side * 0.072, 0.0, 0.08, 1.0)\\n        \\n        # Point 1\\n        pts[1].co = (side * 0.074, 0.0, 0.14, 1.0)\\n        pts[1].handle_left = (side * 0.074, 0.0, 0.10, 1.0)\\n        pts[1].handle_right = (side * 0.074, 0.0, 0.18, 1.0)\\n        \\n        # Point 2\\n        pts[2].co = (side * 0.068, 0.0, 0.22, 1.0)\\n        pts[2].handle_left = (side * 0.073, 0.0, 0.20, 1.0)\\n        pts[2].handle_right = (side * 0.060, 0.0, 0.24, 1.0)\\n        \\n        # Point 3\\n        pts[3].co = (side * 0.035, 0.0, 0.255, 1.0)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.255, 1.0)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.255, 1.0)\\n        \\n        # Set all point handles to professional automatically smoothed angles\\n        # For Bezier curve, use select and automatic handles:\\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Give the curve thickness (extrusion) to make real physical tubes!\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm tube thickness\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        # Apply dark vintage metal material\\n        curve_obj.data.materials.append(mat_dark)\\n        \\n        # Convert curve to mesh so we can smooth and add modifiers if needed\\n        # Or just keep as Curve because Blender renders curves perfectly in Cycles!\\n        \\ncreate_side_tubes()\\nprint(\\\"Thick side pillars created professionally via Bezier curves!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_pbFTKLAQgHW5MXFMCVcc","content":"Error executing code: Communication error with Blender: Code execution error: Error: Bézier spline cannot have points added\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's fix the Bezier curve creation. In Blender, a 'BEZIER' spline's points are accessed via 'bezier_points', while 'POLY' or 'NURBS' uses 'points'.\\n# Let's write a robust poly curve creation, or construct it using bezier_points correctly.\\n\\nmat_dark = bpy.data.materials.get(\\\"DarkMetal\\\")\\n\\ndef create_side_tubes_fixed():\\n    for side in [1, -1]:\\n        name = f\\\"Support_Tube_{'R' if side == 1 else 'L'}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        curve_data.resolution_u = 12\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(3) # Add 3 bezier points (total 4)\\n        \\n        pts = spline.bezier_points\\n        # Point 0: Bottom base junction\\n        pts[0].co = (side * 0.065, 0.0, 0.06)\\n        pts[0].handle_left = (side * 0.065, 0.0, 0.05)\\n        pts[0].handle_right = (side * 0.071, 0.0, 0.08)\\n        \\n        # Point 1: Mid-body curve out\\n        pts[1].co = (side * 0.073, 0.0, 0.14)\\n        pts[1].handle_left = (side * 0.073, 0.0, 0.10)\\n        pts[1].handle_right = (side * 0.073, 0.0, 0.18)\\n        \\n        # Point 2: Top shoulder curve inwards\\n        pts[2].co = (side * 0.064, 0.0, 0.22)\\n        pts[2].handle_left = (side * 0.069, 0.0, 0.20)\\n        pts[2].handle_right = (side * 0.056, 0.0, 0.24)\\n        \\n        # Point 3: Top hood junction\\n        pts[3].co = (side * 0.035, 0.0, 0.252)\\n        pts[3].handle_left = (side * 0.045, 0.0, 0.252)\\n        pts[3].handle_right = (side * 0.030, 0.0, 0.252)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        # Extrude/bevel\\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.007 # 7mm radius\\n        curve_obj.data.bevel_resolution = 5\\n        \\n        # Add material\\n        curve_obj.data.materials.append(mat_dark)\\n\\ncreate_side_tubes_fixed()\\nprint(\\\"Side support pillars drafted perfectly with bezier_points!\\\")\\n\",\"user_prompt\":\"Fix Bezier curve creation code using bezier_points instead of points\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_a0BDgzG7kL50JD8u1caU","content":"Code executed successfully: Side support pillars drafted perfectly with bezier_points!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 6. PROTECTIVE WIRE CAGE Around the Glass -----------------\\n# A classic lantern has vertical and horizontal wire rings forming a cage around the glass globe.\\n# Let's create:\\n# - Two horizontal guard rings (brass wire, radius ~1.5mm) hugging the shape of the hourglass globe.\\n# - Four vertical safety rods going from base collar to chimney hood, positioned at oblique angles so they don't clip side support pillars.\\n\\n# Horizontal ring 1 (lower wide part): Z = 0.13, radius = 0.046\\n# Horizontal ring 2 (upper narrow part): Z = 0.19, radius = 0.034\\n\\ndef create_guard_ring(name, radius, z, thickness, material=None):\\n    # We can model a thin torus for beautiful shading\\n    bpy.ops.mesh.primitive_torus_add(\\n        align='WORLD', \\n        location=(0, 0, z), \\n        major_radius=radius, \\n        minor_radius=thickness,\\n        major_segments=32,\\n        minor_segments=8\\n    )\\n    obj = bpy.context.active_object\\n    obj.name = name\\n    for poly in obj.data.polygons:\\n        poly.use_smooth = True\\n    if material:\\n        obj.data.materials.append(material)\\n    return obj\\n\\nring_lower = create_guard_ring(\\\"Cage_Ring_Lower\\\", 0.043, 0.13, 0.0016, mat_brass)\\nring_upper = create_guard_ring(\\\"Cage_Ring_Upper\\\", 0.032, 0.19, 0.0016, mat_brass)\\n\\n# Vertical safety wires: 4 wires equally spaced (e.g., at 45°, 135°, 225°, 315° angles).\\n# Each wire begins at bottom base plate Z=0.08 (radius 0.048) and curves slightly outward then in to join top hood Z=0.24 (radius 0.035).\\n# Curves make visual presentation far more organic and high-quality!\\n\\ndef create_vertical_cage_wires():\\n    angles = [45, 135, 225, 315]\\n    for idx, angle_deg in enumerate(angles):\\n        angle_rad = math.radians(angle_deg)\\n        cos_a = math.cos(angle_rad)\\n        sin_a = math.sin(angle_rad)\\n        \\n        name = f\\\"Cage_Wire_Vert_{idx+1}\\\"\\n        curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n        curve_data.dimensions = '3D'\\n        \\n        spline = curve_data.splines.new('BEZIER')\\n        spline.bezier_points.add(2) # Total 3 control points\\n        pts = spline.bezier_points\\n        \\n        # Point 0: bottom anchor\\n        r0 = 0.048\\n        pts[0].co = (r0 * cos_a, r0 * sin_a, 0.08)\\n        pts[0].handle_left = (r0 * cos_a, r0 * sin_a, 0.07)\\n        pts[0].handle_right = (r0 * cos_a, r0 * sin_a, 0.10)\\n        \\n        # Point 1: slightly outer middle point (flared outwards to bypass glass bulge)\\n        r1 = 0.051\\n        pts[1].co = (r1 * cos_a, r1 * sin_a, 0.15)\\n        pts[1].handle_left = (r1 * cos_a, r1 * sin_a, 0.11)\\n        pts[1].handle_right = (r1 * cos_a, r1 * sin_a, 0.19)\\n        \\n        # Point 2: top anchor near top hood\\n        r2 = 0.033\\n        pts[2].co = (r2 * cos_a, r2 * sin_a, 0.24)\\n        pts[2].handle_left = (r2 * cos_a, r2 * sin_a, 0.22)\\n        pts[2].handle_right = (r2 * cos_a, r2 * sin_a, 0.25)\\n        \\n        curve_obj = bpy.data.objects.new(name, curve_data)\\n        bpy.context.collection.objects.link(curve_obj)\\n        \\n        curve_obj.data.use_fill_caps = True\\n        curve_obj.data.bevel_depth = 0.0016 # 1.6mm thickness brass wire\\n        curve_obj.data.bevel_resolution = 4\\n        \\n        curve_obj.data.materials.append(mat_brass)\\n\\ncreate_vertical_cage_wires()\\nprint(\\\"Protective brass cage horizontal rings and vertical safety wires completed!\\\")\\n\",\"user_prompt\":\"Build horizontal and vertical protective brass cage around glass globe\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_NlyYgDL9m1l3g6nSI50H","content":"Code executed successfully: Protective brass cage horizontal rings and vertical safety wires completed!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Materials\\nmat_brass = bpy.data.materials.get(\\\"BrassMetal\\\")\\n\\n# ----------------- 7. WIDE CARRYING BAIL / HANDLE -----------------\\n# Let's create an iconic curved bucket-style wire handle (hinged on the side support tubes or top eyelet).\\n# A classic bail handle arches high above the lantern.\\n# It originates from near the side supports (Z=0.18) on either side, curves out, and goes way above the crown:\\n# Max height at top around Z=0.48.\\n# Points for Bezier:\\n# Left side attachment: (-0.076, 0.0, 0.18)\\n# Arching up to left shoulder: (-0.090, 0.0, 0.38)\\n# Arching over top: (0.0, 0.0, 0.485)\\n# Arching down to right shoulder: (0.090, 0.0, 0.38)\\n# Right side attachment: (0.076, 0.0, 0.18)\\n\\ndef create_bail_handle():\\n    name = \\\"Bail_Handle\\\"\\n    curve_data = bpy.data.curves.new(name=f\\\"{name}_curve\\\", type='CURVE')\\n    curve_data.dimensions = '3D'\\n    curve_data.resolution_u = 20\\n    \\n    spline = curve_data.splines.new('BEZIER')\\n    spline.bezier_points.add(4) # 5 points total\\n    pts = spline.bezier_points\\n    \\n    # Left attachment point\\n    pts[0].co = (-0.074, 0.0, 0.18)\\n    pts[0].handle_left = (-0.074, 0.0, 0.16)\\n    pts[0].handle_right = (-0.082, 0.0, 0.22)\\n    \\n    # Left curve shoulder\\n    pts[1].co = (-0.092, 0.0, 0.38)\\n    pts[1].handle_left = (-0.092, 0.0, 0.32)\\n    pts[1].handle_right = (-0.082, 0.0, 0.44)\\n    \\n    # Top center arc\\n    pts[2].co = (0.0, -0.01, 0.485) # slightly offset forward for depth\\n    pts[2].handle_left = (-0.040, -0.01, 0.485)\\n    pts[2].handle_right = (0.040, -0.01, 0.485)\\n    \\n    # Right curve shoulder\\n    pts[3].co = (0.092, 0.0, 0.38)\\n    pts[3].handle_left = (0.082, 0.0, 0.44)\\n    pts[3].handle_right = (0.092, 0.0, 0.32)\\n    \\n    # Right attachment point\\n    pts[4].co = (0.074, 0.0, 0.18)\\n    pts[4].handle_left = (0.082, 0.0, 0.22)\\n    pts[4].handle_right = (0.074, 0.0, 0.16)\\n    \\n    curve_obj = bpy.data.objects.new(name, curve_data)\\n    bpy.context.collection.objects.link(curve_obj)\\n    \\n    curve_obj.data.use_fill_caps = True\\n    curve_obj.data.bevel_depth = 0.0022 # 2.2mm wire thickness\\n    curve_obj.data.bevel_resolution = 4\\n    \\n    curve_obj.data.materials.append(mat_brass)\\n\\ncreate_bail_handle()\\n\\n# Add small mechanical attachment hinges/tabs on the side support tubes where the handle connects:\\nfor side in [1, -1]:\\n    bpy.ops.mesh.primitive_cylinder_add(\\n        vertices=12, \\n        radius=0.0045, \\n        depth=0.008, \\n        location=(side * 0.074, 0.0, 0.18),\\n        rotation=(0, 1.5708, 0)\\n    )\\n    hinge_plug = bpy.context.active_object\\n    hinge_plug.name = f\\\"Handle_Hinge_{'R' if side == 1 else 'L'}\\\"\\n    for poly in hinge_plug.data.polygons:\\n        poly.use_smooth = True\\n    hinge_plug.data.materials.append(mat_brass)\\n\\nprint(\\\"Iconic carrying wire handle and attachment hinges complete!\\\")\\n\",\"user_prompt\":\"Build high carrying handle arching over top of the lantern with attachment pins\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_BNA4tT1zfgP7ob6C9by1","content":"Code executed successfully: Iconic carrying wire handle and attachment hinges complete!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Inspect list of all objects in scene so far\",\"code\":\"import bpy\\n\\n# Let's inspect all objects in scene to ensure everything is perfectly named and grouped\\nfor obj in bpy.data.objects:\\n    print(\\\"-\\\", obj.name, obj.type)\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_I0H9Ar8o39BjNomVUMyz","content":"Code executed successfully: - Bail_Handle CURVE\n- Burner_Base MESH\n- Cage_Ring_Lower MESH\n- Cage_Ring_Upper MESH\n- Cage_Wire_Vert_1 CURVE\n- Cage_Wire_Vert_2 CURVE\n- Cage_Wire_Vert_3 CURVE\n- Cage_Wire_Vert_4 CURVE\n- Chimney_Crown MESH\n- Chimney_Holder MESH\n- Chimney_Hood MESH\n- Chimney_Riser MESH\n- Chimney_Vent1 MESH\n- Chimney_Vent2 MESH\n- Filler_Cap MESH\n- Flame_Core MESH\n- Flame_PointLight LIGHT\n- Glass_Globe MESH\n- Glass_Globe.001 MESH\n- Handle_Hinge_L MESH\n- Handle_Hinge_R MESH\n- Hinge_Loop MESH\n- Lantern_Base_Collar MESH\n- Lantern_Base_Rim MESH\n- Lantern_Base_Tank MESH\n- Support_Tube_L CURVE\n- Support_Tube_R CURVE\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Ensure there are no unwanted duplicated objects in the scene\",\"code\":\"import bpy\\n\\n# Clean up any duplicated objects if they exist\\ndup = bpy.data.objects.get(\\\"Glass_Globe.001\\\")\\nif dup:\\n    bpy.data.objects.remove(dup, do_unlink=True)\\n    print(\\\"Removed duplicate glass globe\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_UXa3ifS7RKwyEFn4ZETR","content":"Code executed successfully: Removed duplicate glass globe\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 8. GROUND FLOOR & BACKGROUND STAGE -----------------\\n# Let's build a beautiful floor and an elegant seamless back wall that acts as a studio cycle.\\n# This prevents hard edge lines behind our object and highlights the soft warm falloff of the lantern.\\n\\n# Let's create a large plane for the floor. Z = 0.00\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Add studio back-curve (cyclorama shape)\\n# Instead of a simple plane, we'll extrude the back edge up and add a bevel to make a seamless studio background.\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Let's find the edges that are furthest back (Y coordinate positive)\\n# The default plane goes from X:-6 to 6, Y:-6 to 6\\nback_edge_verts = [v for v in bm.verts if v.co.y > 5.9]\\nback_edges = []\\nfor e in bm.edges:\\n    if all(v in back_edge_verts for v in e.verts):\\n        back_edges.append(e)\\n\\n# Extrude back edges upwards along Z\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts up by 6 meters\\nextruded_verts = [v for v in extruded['geom'] if isinstance(v, bmesh.types.BMVert)]\\nfor v in extruded_verts:\\n    v.co.z += 6.0\\n\\n# Add a smooth curved bevel to the back corner\\n# Find edges connecting to the extruded verts that represent the fold\\nfold_edges = []\\nfor e in bm.edges:\\n    # A fold edge connects a ground back vert and an extruded vert (or is the original back edge)\\n    # Easiest way in python: bevel the edges where the Y was +5.9 and Z was 0\\n    if len(e.verts) == 2:\\n        v0, v1 = e.verts[0], e.verts[1]\\n        if abs(v0.co.y - 6.0) < 0.1 and abs(v0.co.z) < 0.01 and abs(v1.co.y - 6.0) < 0.1 and abs(v1.co.z) < 0.01:\\n            fold_edges.append(e)\\n\\nbmesh.ops.bevel(\\n    bm,\\n    geom=fold_edges,\\n    dist=1.5,\\n    segments=16,\\n    affect='EDGES'\\n)\\n\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Set smooth and ground material\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless cyclorama ground stage built beautifully!\\\")\\n\",\"user_prompt\":\"Create a large seamless background cyclorama stage for realistic studio lighting\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_GzVY244Mha4VP7H0yhOC","content":"Error executing code: Communication error with Blender: Code execution error: bevel: keyword \"dist\" is invalid for this operator"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Model backdrop using modifier-based beveling to ensure robustness\",\"code\":\"import bpy\\nimport bmesh\\n\\n# Let's write a robust bevel on the Cyclorama mesh by doing it step-by-step or using Modifier, which is 100% robust.\\n# Let's recreate a clean ground plane, extrude simply, then apply a standard Bevel modifier to round the back edge.\\n\\nground = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground:\\n    bpy.data.objects.remove(ground, do_unlink=True)\\n\\n# 1. Create a large plane\\nbpy.ops.mesh.primitive_plane_add(size=12.0, location=(0.0, 0.0, -0.0001))\\nground = bpy.context.active_object\\nground.name = \\\"Ground_Studio\\\"\\n\\n# Edit mesh: extrude back edge\\nimport bmesh\\nbm = bmesh.new()\\nbm.from_mesh(ground.data)\\n\\n# Find edges on the +Y side (Y > 5.9)\\nback_edges = [e for e in bm.edges if all(v.co.y > 5.9 for v in e.verts)]\\n\\n# Extrude up\\nextruded = bmesh.ops.extrude_edge_only(bm, edges=back_edges)\\n# Move extruded verts straight up\\nfor geom in extruded['geom']:\\n    if isinstance(geom, bmesh.types.BMVert):\\n        geom.co.z += 6.0\\n\\n# Write back\\nbm.to_mesh(ground.data)\\nbm.free()\\n\\n# Apply a Bevel Modifier with high subdivision or a subdivision surface modifier to make it perfectly smooth.\\n# We can mark the fold edge as Bevel Weight, or simply use Bevel Modifier by Angle!\\n# Fold angle is exactly 90 degrees, so limit by angle works perfectly.\\nbevel = ground.modifiers.new(name=\\\"CornerRound\\\", type='BEVEL')\\nbevel.width = 1.5\\nbevel.segments = 16\\nbevel.limit_method = 'ANGLE'\\n\\nsubsurf = ground.modifiers.new(name=\\\"Subsurf\\\", type='SUBSURF')\\nsubsurf.levels = 1\\n\\nfor poly in ground.data.polygons:\\n    poly.use_smooth = True\\n\\nground.data.materials.append(bpy.data.materials.get(\\\"Ground\\\"))\\n\\nprint(\\\"Seamless Cyclorama built with safe modifier-based bevel!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_3zZ4g7dKDeTWGUEVIW7E","content":"Code executed successfully: Seamless Cyclorama built with safe modifier-based bevel!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Set up dark world lighting to emphasize the warm lantern flame illumination\",\"code\":\"import bpy\\n\\n# Let's configure the background environment lighting.\\n# For a product shot against a calm, dark backdrop with soft pools of warm light,\\n# we need a very low background environmental light level so the lantern's own glow is the hero.\\n\\nworld = bpy.context.scene.world\\nif not world:\\n    world = bpy.data.worlds.new(name=\\\"World\\\")\\n    bpy.context.scene.world = world\\n\\nworld.use_nodes = True\\nnodes = world.node_tree.nodes\\nnodes.clear()\\n\\noutput = nodes.new(type='ShaderNodeOutputWorld')\\nbg = nodes.new(type='ShaderNodeBackground')\\n\\n# Dark slate gray environment mood: Hex #030406 (super dark)\\nbg.inputs['Color'].default_value = (0.003, 0.004, 0.006, 1.0)\\nbg.inputs['Strength'].default_value = 0.65\\n\\nworld.node_tree.links.new(bg.outputs['Background'], output.inputs['Surface'])\\n\\nprint(\\\"Dark studio world environment set up!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_RmnAnLg4kzVoFWj0Xqy2","content":"Code executed successfully: Dark studio world environment set up!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# ----------------- 9. FILL & KEY LIGHTING (STUDIO) -----------------\\n# To complement the beautiful warm glowing core, we should add some cold/neutral subtle studio lights:\\n# - A soft cool key light from front-left to highlight the lovely dark forest green metal paint.\\n# - A rim light from back-right to illuminate the silhouette of the side tubes, handle, and chimney structure.\\n\\n# Let's check existing lights\\nfor l in bpy.data.lights:\\n    print(\\\"Light found:\\\", l.name)\\n\\n# Create Cool Soft Key Light (Front-Left-High)\\nif \\\"Studio_Key\\\" not in bpy.data.objects:\\n    key_data = bpy.data.lights.new(name=\\\"Studio_Key\\\", type='AREA')\\n    # Soft industrial cool blue key (approx 5500K color)\\n    key_data.color = (0.75, 0.85, 1.0)\\n    key_data.energy = 16.0 # Subtle fill contribution so it doesn't wash out the core\\n    key_data.size = 1.0 # large soft shadow lines\\n    \\n    key_obj = bpy.data.objects.new(name=\\\"Studio_Key\\\", object_data=key_data)\\n    bpy.context.collection.objects.link(key_obj)\\n    # Position front left high, looking down at lantern center (0, 0, 0.22)\\n    key_obj.location = (-0.6, -0.8, 0.6)\\n    \\n    # Track to constrain light focus to lantern center\\n    track = key_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\n# Create Rim Light (Back-Right-High)\\nif \\\"Studio_Rim\\\" not in bpy.data.objects:\\n    rim_data = bpy.data.lights.new(name=\\\"Studio_Rim\\\", type='AREA')\\n    rim_data.color = (0.9, 0.95, 1.0)\\n    rim_data.energy = 28.0 # strong back-reflection highlights\\n    rim_data.size = 0.8\\n    \\n    rim_obj = bpy.data.objects.new(name=\\\"Studio_Rim\\\", object_data=rim_data)\\n    bpy.context.collection.objects.link(rim_obj)\\n    # Position back right\\n    rim_obj.location = (0.5, 0.7, 0.5)\\n    \\n    track = rim_obj.constraints.new(type='TRACK_TO')\\n    track.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\n    track.track_axis = 'TRACK_NEGATIVE_Z'\\n    track.up_axis = 'UP_Y'\\n\\nprint(\\\"Studio professional key and rim lighting rigs configured!\\\")\\n\",\"user_prompt\":\"Create soft cool key fill and rim lighting with constraints tracking the lantern center\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_hnUgye9yLT4O9mxy8tlj","content":"Code executed successfully: Light found: Flame_PointLight\nLight found: Light\nStudio professional key and rim lighting rigs configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# Let's clean up any default lights that might have been present in the scene\\ndefault_light = bpy.data.objects.get(\\\"Light\\\")\\nif default_light:\\n    bpy.data.objects.remove(default_light, do_unlink=True)\\n    print(\\\"Removed default light\\\")\\n\",\"user_prompt\":\"Clean up default initial light to prevent overexposure\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_cdClUbHwspRU8x0v1m7m","content":"Code executed successfully: "},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Configure high-end camera with 85mm portrait zoom, tracking and Depth Of Field (DOF) target\",\"code\":\"import bpy\\nimport math\\n\\n# ----------------- 10. CONTRAST CAMERA SETUP -----------------\\n# We want an elegant product shot framing the classic lantern center as a hero prop.\\n# Let's place the camera at an angle that shows off both the front details (oil cap, glass, flame, protective cage)\\n# and the side profile (support tubes, arching handle).\\n# Distance: close enough to capture details of glass reflection and the soft ground illumination pool.\\n\\n# Let's create a camera\\nif \\\"Camera\\\" in bpy.data.objects:\\n    cam_obj = bpy.data.objects[\\\"Camera\\\"]\\nelse:\\n    cam_data = bpy.data.cameras.new(name=\\\"Camera\\\")\\n    cam_obj = bpy.data.objects.new(name=\\\"Camera\\\", object_data=cam_data)\\n    bpy.context.collection.objects.link(cam_obj)\\n\\nbpy.context.scene.camera = cam_obj\\n\\n# Camera Settings:\\n# Focal length 85mm for clean portrait/product proportions (removes perspective distortion)\\ncam_obj.data.lens = 85\\n\\n# Position camera\\n# Z position: level with center-middle of glass globe (approx Z = 0.22)\\n# Distance: let's frame perfectly (the lantern is ~ 0.5 meters high)\\ncam_obj.location = (0.50, -0.68, 0.26)\\n\\n# Track camera to center of glass\\ntrack = cam_obj.constraints.new(type='TRACK_TO')\\ntrack.target = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ntrack.track_axis = 'TRACK_NEGATIVE_Z'\\ntrack.up_axis = 'UP_Y'\\n\\n# Enable Depth Of Field for a cinematic, state-of-the-art product feel!\\ncam_obj.data.dof.use_dof = True\\ncam_obj.data.dof.focus_object = bpy.data.objects.get(\\\"Glass_Globe\\\")\\ncam_obj.data.dof.aperture_fstop = 2.5 # soft defocus on the far handle/backdrop, pin-sharp glass core\\n\\nprint(\\\"Hero camera built and configured!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_CS8LQbp7EfFDnRtRZbIr","content":"Code executed successfully: Hero camera built and configured!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect initial camera setup and model visual quality\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_NQsTvikeEgQootZa9x1b","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Align viewport to the camera view for next screenshot inspection\",\"code\":\"import bpy\\n\\n# Set all 3D viewports to camera view\\nfor area in bpy.context.screen.areas:\\n    if area.type == 'VIEW_3D':\\n        for space in area.spaces:\\n            if space.type == 'VIEW_3D':\\n                space.region_3d.view_perspective = 'CAMERA'\\nprint(\\\"Aligned all viewport spaces to active camera!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_2ETENqluHzW2mWvBnC2B","content":"Code executed successfully: Aligned all viewport spaces to active camera!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Inspect camera composition framing\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_yN9jzvTEFnNx8D3kEW9x","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"0","tool_calls":[{"id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n\\n# The camera is extremely close and only framing the base/bottom middle of the lantern!\\n# Let's pull the camera back, increase camera height, or lower the lens value/adjust the focus distance so the entire lantern, \\n# including the handle arching over the top, is perfectly positioned in the portrait frame.\\n# Currently, the height of the lantern goes up to Z = 0.50.\\n# Let's adjust camera position and focal length so it has a beautiful, elegant, professional product spacing.\\n\\ncam_obj = bpy.data.objects.get(\\\"Camera\\\")\\nif cam_obj:\\n    # Pull back more in both X and Y, and raise Z to get a slightly higher angle look so the base ground pool is visible!\\n    cam_obj.location = (0.75, -1.15, 0.40)\\n    \\n    # 55mm is a cleaner, slightly wider lens than 85mm, perfect for solid table-top details without clipping the top handle\\n    cam_obj.data.lens = 60\\n    \\n    # Let's track to a slightly higher point in the lantern (e.g. Z = 0.24) so it's perfectly centered in the frame\\n    # We can create a lightweight transparent empty to track to, or tracking target remains the Glass_Globe, but with a camera shift, or simply tracking to the entire group center.\\n    # Alternatively, let's create a Focus_Target empty!\\n    \\n    focus_empty = bpy.data.objects.get(\\\"Focus_Target\\\")\\n    if not focus_empty:\\n        focus_empty = bpy.data.objects.new(\\\"Focus_Target\\\", None)\\n        bpy.context.collection.objects.link(focus_empty)\\n    focus_empty.location = (0.0, 0.0, 0.22) # Center of the lantern's main visual body\\n    \\n    # Update camera track constraint target\\n    for c in cam_obj.constraints:\\n        if c.type == 'TRACK_TO':\\n            c.target = focus_empty\\n            \\n    # Also update DOF focus object to the Empty so the camera focus stays pin-sharp on the core glass/wick area\\n    cam_obj.data.dof.focus_object = focus_empty\\n    cam_obj.data.dof.aperture_fstop = 2.8 # slightly higher fstop so more of the frame is readable while keeping lovely bokeh\\n\\nprint(\\\"Camera composition refined for perfect product-to-hero prop framing!\\\")\\n\",\"user_prompt\":\"Reposition camera to cleanly frame the entire lantern and background floor pool\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_JH7bayPdDq3R1Z4EGpP7","content":"Code executed successfully: Camera composition refined for perfect product-to-hero prop framing!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"user_prompt\":\"Verify updated camera composition\",\"max_size\":1000}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_kSavli6GRXfIpH95PPtk","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":" eyeint","tool_calls":[{"id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","type":"function","function":{"name":"execute_blender_code","arguments":"{\"user_prompt\":\"Scale up studio backdrop to cover entire camera FOV\",\"code\":\"import bpy\\n\\n# Oh! Magnificent framing! The lantern is perfectly centered.\\n# However, the Cyclorama (Ground_Studio) corner curve is slightly visible on the left side of the frame.\\n# We want the background to be completely seamless and cover the entire screen behind the model.\\n# Let's scale up Ground_Studio or rotate it slightly around the Z axis, or move it so the curve perfectly envelopes the frame.\\n# Scale it up by 4x to be safe!\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.scale = (5.0, 5.0, 5.0)\\n    print(\\\"Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\\\")\\n\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_F6ybPFt8EXzsaWsqMpA7","content":"Code executed successfully: Scaled Ground Studio Cyclorama up to 500% to ensure absolute backdrop coverage!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Check backdrop coverage\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_Gm7btywRopW1AZnIAfb0","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":"_","tool_calls":[{"id":"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nimport math\\n\\n# Let's rotate Ground_Studio so its back wall faces the camera directly!\\n# Camera is looking from X=0.75, Y=-1.15. That is in the front-right quadrant.\\n# This means we should rotate the ground plane to block the background correctly.\\n# The angle of the camera vector is atan2(1.15, -0.75) roughly.\\n# Rotating the backdrop around its center (Z axis) by -40 degrees (or approx -0.7 radians) will align it beautifully.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(-35))\\n    # Let's also shift it back along Y/X slightly to keep the curved fold far from the camera\\n    ground_obj.location = (-0.3, 0.3, -0.0001)\\n    print(\\\"Rotated and shifted Ground Studio!\\\")\\n\",\"user_prompt\":\"Align backdrop orientation directly with the camera perspective to eliminate corner gap\"}"}}]},{"role":"tool","tool_call_id":"tool_execute_blender_code_QeFBnTUNpsGeVkGrDcSF","content":"Code executed successfully: Rotated and shifted Ground Studio!\n"},{"role":"assistant","content":null,"tool_calls":[{"id":"tool_get_viewport_screenshot_VPYrHxD2TzAZM1r0zZ18","type":"function","function":{"name":"get_viewport_screenshot","arguments":"{\"max_size\":1000,\"user_prompt\":\"Re-check backdrop alignment after rotation\"}"}}]},{"role":"tool","tool_call_id":"tool_get_viewport_screenshot_VPYrHxD2TzAZM1r0zZ18","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"},"cache_control":{"type":"ephemeral"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"get_scene_info","description":"Get detailed information about the current Blender scene\n\n    Parameters:\n    - user_prompt: The original user prompt that led to this tool call (required for telemetry)\n    ","parameters":{"properties":{"user_prompt":{"title":"User Prompt","type":"string"}},"required":["user_prompt"],"title":"get_scene_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_info","description":"\n    Get detailed information about a specific object in the Blender scene.\n\n    Parameters:\n    - object_name: The name of the object to get information about\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name"],"title":"get_object_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_viewport_screenshot","description":"\n    Capture a screenshot of the current Blender 3D viewport.\n\n    Parameters:\n    - max_size: Maximum size in pixels for the largest dimension (default: 800)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns the screenshot as an Image.\n    ","parameters":{"properties":{"max_size":{"default":1000,"title":"Max Size","type":"integer"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_viewport_screenshotArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code","description":"\n    Execute arbitrary Python code in Blender. Make sure to do it step-by-step by breaking it into smaller chunks.\n\n    Parameters:\n    - code: The Python code to execute\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"code":{"title":"Code","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_categories","description":"\n    Get a list of categories for a specific asset type on Polyhaven.\n\n    Parameters:\n    - asset_type: The type of asset to get categories for (hdris, textures, models, all)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n    ","parameters":{"properties":{"asset_type":{"default":"hdris","title":"Asset Type","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_categoriesArguments","type":"object"}}},{"type":"function","function":{"name":"search_polyhaven_assets","description":"\n    Search for assets on Polyhaven with optional filtering.\n\n    Parameters:\n    - asset_type: Type of assets to search for (hdris, textures, models, all)\n    - categories: Optional comma-separated list of categories to filter by\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a list of matching assets with basic information.\n    ","parameters":{"properties":{"asset_type":{"default":"all","title":"Asset Type","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"search_polyhaven_assetsArguments","type":"object"}}},{"type":"function","function":{"name":"download_polyhaven_asset","description":"\n    Download and import a Polyhaven asset into Blender.\n\n    Parameters:\n    - asset_id: The ID of the asset to download\n    - asset_type: The type of asset (hdris, textures, models)\n    - resolution: The resolution to download (e.g., 1k, 2k, 4k)\n    - file_format: Optional file format (e.g., hdr, exr for HDRIs; jpg, png for textures; gltf, fbx for models)\n    - user_prompt: The original user prompt that led to this tool call (for telemetry)\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"asset_id":{"title":"Asset Id","type":"string"},"asset_type":{"title":"Asset Type","type":"string"},"resolution":{"default":"1k","title":"Resolution","type":"string"},"file_format":{"default":null,"title":"File Format","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["asset_id","asset_type"],"title":"download_polyhaven_assetArguments","type":"object"}}},{"type":"function","function":{"name":"set_texture","description":"\n    Apply a previously downloaded Polyhaven texture to an object.\n    \n    Parameters:\n    - object_name: Name of the object to apply the texture to\n    - texture_id: ID of the Polyhaven texture to apply (must be downloaded first)\n    \n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"object_name":{"title":"Object Name","type":"string"},"texture_id":{"title":"Texture Id","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["object_name","texture_id"],"title":"set_textureArguments","type":"object"}}},{"type":"function","function":{"name":"get_polyhaven_status","description":"\n    Check if PolyHaven integration is enabled in Blender.\n    Returns a message indicating whether PolyHaven features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_polyhaven_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_hyper3d_status","description":"\n    Check if Hyper3D Rodin integration is enabled in Blender.\n    Returns a message indicating whether Hyper3D Rodin features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hyper3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_status","description":"\n    Check if Sketchfab integration is enabled in Blender.\n    Returns a message indicating whether Sketchfab features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_sketchfab_statusArguments","type":"object"}}},{"type":"function","function":{"name":"search_sketchfab_models","description":"\n    Search for models on Sketchfab with optional filtering.\n\n    Parameters:\n    - query: Text to search for\n    - categories: Optional comma-separated list of categories\n    - count: Maximum number of results to return (default 20)\n    - downloadable: Whether to include only downloadable models (default True)\n\n    Returns a formatted list of matching models.\n    ","parameters":{"properties":{"query":{"title":"Query","type":"string"},"categories":{"default":null,"title":"Categories","type":"string"},"count":{"default":20,"title":"Count","type":"integer"},"downloadable":{"default":true,"title":"Downloadable","type":"boolean"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["query"],"title":"search_sketchfab_modelsArguments","type":"object"}}},{"type":"function","function":{"name":"get_sketchfab_model_preview","description":"\n    Get a preview thumbnail of a Sketchfab model by its UID.\n    Use this to visually confirm a model before downloading.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model (obtained from search_sketchfab_models)\n    \n    Returns the model's thumbnail as an Image for visual confirmation.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid"],"title":"get_sketchfab_model_previewArguments","type":"object"}}},{"type":"function","function":{"name":"download_sketchfab_model","description":"\n    Download and import a Sketchfab model by its UID.\n    The model will be scaled so its largest dimension equals target_size.\n    \n    Parameters:\n    - uid: The unique identifier of the Sketchfab model\n    - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.\n                  You must specify the desired size for the model.\n                  Examples:\n                  - Chair: target_size=1.0 (1 meter tall)\n                  - Table: target_size=0.75 (75cm tall)\n                  - Car: target_size=4.5 (4.5 meters long)\n                  - Person: target_size=1.7 (1.7 meters tall)\n                  - Small object (cup, phone): target_size=0.1 to 0.3\n    \n    Returns a message with import details including object names, dimensions, and bounding box.\n    The model must be downloadable and you must have proper access rights.\n    ","parameters":{"properties":{"uid":{"title":"Uid","type":"string"},"target_size":{"title":"Target Size","type":"number"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["uid","target_size"],"title":"download_sketchfab_modelArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_text","description":"\n    Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n\n    Parameters:\n    - text_prompt: A short description of the desired model in **English**.\n    - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"text_prompt":{"title":"Text Prompt","type":"string"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"required":["text_prompt"],"title":"generate_hyper3d_model_via_textArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hyper3d_model_via_images","description":"\n    Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender.\n    The 3D asset has built-in materials.\n    The generated model has a normalized size, so re-scaling after generation can be useful.\n    \n    Parameters:\n    - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.\n    - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.\n    - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.\n\n    Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.\n    Returns a message indicating success or failure.\n    ","parameters":{"properties":{"input_image_paths":{"default":null,"items":{"type":"string"},"title":"Input Image Paths","type":"array"},"input_image_urls":{"default":null,"items":{"type":"string"},"title":"Input Image Urls","type":"array"},"bbox_condition":{"default":null,"items":{"type":"number"},"title":"Bbox Condition","type":"array"},"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"generate_hyper3d_model_via_imagesArguments","type":"object"}}},{"type":"function","function":{"name":"poll_rodin_job_status","description":"\n    Check if the Hyper3D Rodin generation task is completed.\n\n    For Hyper3D Rodin mode MAIN_SITE:\n        Parameters:\n        - subscription_key: The subscription_key given in the generate model step.\n\n        Returns a list of status. The task is done if all status are \"Done\".\n        If \"Failed\" showed up, the generating process failed.\n        This is a polling API, so only proceed if the status are finally determined (\"Done\" or \"Canceled\").\n\n    For Hyper3D Rodin mode FAL_AI:\n        Parameters:\n        - request_id: The request_id given in the generate model step.\n\n        Returns the generation task status. The task is done if status is \"COMPLETED\".\n        The task is in progress if status is \"IN_PROGRESS\".\n        If status other than \"COMPLETED\", \"IN_PROGRESS\", \"IN_QUEUE\" showed up, the generating process might be failed.\n        This is a polling API, so only proceed if the status are finally determined (\"COMPLETED\" or some failed state).\n    ","parameters":{"properties":{"subscription_key":{"default":null,"title":"Subscription Key","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"title":"poll_rodin_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset","description":"\n    Import the asset generated by Hyper3D Rodin after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.\n    - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.\n\n    Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"task_uuid":{"default":null,"title":"Task Uuid","type":"string"},"request_id":{"default":null,"title":"Request Id","type":"string"}},"required":["name"],"title":"import_generated_assetArguments","type":"object"}}},{"type":"function","function":{"name":"get_hunyuan3d_status","description":"\n    Check if Hunyuan3D integration is enabled in Blender.\n    Returns a message indicating whether Hunyuan3D features are available.\n    ","parameters":{"properties":{"user_prompt":{"default":"","title":"User Prompt","type":"string"}},"title":"get_hunyuan3d_statusArguments","type":"object"}}},{"type":"function","function":{"name":"generate_hunyuan3d_model","description":"\n    Generate 3D asset using Hunyuan3D by providing either text description, image reference, \n    or both for the desired asset, and import the asset into Blender.\n    The 3D asset has built-in materials.\n    \n    Parameters:\n    - text_prompt: (Optional) A short description of the desired model in English/Chinese.\n    - input_image_url: (Optional) The local or remote url of the input image. 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The task is done if status is \"DONE\".\n        The task is in progress if status is \"RUN\".\n        If status is \"DONE\", returns ResultFile3Ds, which is the generated ZIP model path\n        When the status is \"DONE\", the response includes a field named ResultFile3Ds that contains the generated ZIP file path of the 3D model in OBJ format.\n        This is a polling API, so only proceed if the status are finally determined (\"DONE\" or some failed state).\n    ","parameters":{"properties":{"job_id":{"default":null,"title":"Job Id","type":"string"}},"title":"poll_hunyuan_job_statusArguments","type":"object"}}},{"type":"function","function":{"name":"import_generated_asset_hunyuan","description":"\n    Import the asset generated by Hunyuan3D after the generation task is completed.\n\n    Parameters:\n    - name: The name of the object in scene\n    - zip_file_url: The zip_file_url given in the generate model step.\n\n    Return if the asset has been imported successfully.\n    ","parameters":{"properties":{"name":{"title":"Name","type":"string"},"zip_file_url":{"title":"Zip File Url","type":"string"}},"required":["name","zip_file_url"],"title":"import_generated_asset_hunyuanArguments","type":"object"}}}],"reasoning":{"effort":"medium"}}"}, "response": {"status_code": 200, "headers": {"date": "Wed, 15 Jul 2026 04:47:56 GMT", "content-type": "application/json", "transfer-encoding": "chunked", "connection": "keep-alive", "access-control-allow-origin": "*", "x-generation-id": "gen-1784090869-dNQt7jjBI0HW2OQpQ4jy", "access-control-expose-headers": "X-Generation-Id,X-Provider-Name,cf-ray", "set-cookie": "[REDACTED]", "permissions-policy": "payment=(self \"https://checkout.stripe.com\" \"https://connect-js.stripe.com\" \"https://js.stripe.com\" \"https://*.js.stripe.com\" \"https://hooks.stripe.com\")", "referrer-policy": "no-referrer, strict-origin-when-cross-origin", "x-content-type-options": "nosniff", "content-encoding": "gzip", "server": "cloudflare", "cf-ray": "a1b6219d8cf2f443-MAD"}, "body": "\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n\n         \n{\"id\":\"gen-1784090869-dNQt7jjBI0HW2OQpQ4jy\",\"object\":\"chat.completion\",\"created\":1784090869,\"model\":\"google/gemini-3.5-flash\",\"provider\":\"Google\",\"system_fingerprint\":null,\"service_tier\":\"provisioned\",\"choices\":[{\"index\":0,\"logprobs\":null,\"finish_reason\":\"error\",\"native_finish_reason\":\"MALFORMED_FUNCTION_CALL\",\"message\":{\"role\":\"assistant\",\"content\":\"/The rotation was in the wrong direction, opening the gap wider. Let's rotate in the opposite direction (e.g. +35 degrees or around +50 degrees) to close the gap completely!\\n# Let's adjust rotation_euler to 45 degrees.\\n\\nground_obj = bpy.data.objects.get(\\\"Ground_Studio\\\")\\nif ground_obj:\\n    ground_obj.rotation_euler = (0, 0, math.radians(45))\\n    ground_obj.location = (0.0, 0.0, -0.0001)\\n    print(\\\"Rotated Ground Studio opposite direction (+45 deg)\\\")\\n,user_prompt:Correct backdrop rotation direction to eliminate right-side visual gap}\",\"refusal\":null,\"reasoning\":null}}],\"usage\":{\"prompt_tokens\":0,\"completion_tokens\":0,\"total_tokens\":0,\"cost\":0,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":0,\"cache_write_tokens\":0,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0,\"upstream_inference_prompt_cost\":0,\"upstream_inference_completions_cost\":0},\"completion_tokens_details\":{\"reasoning_tokens\":0,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": "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"}, "error": null}], "response": {"text": "/The rotation was in the wrong direction, opening the gap wider. Let's rotate in the opposite direction (e.g. +35 degrees or around +50 degrees) to close the gap completely!\n# Let's adjust rotation_euler to 45 degrees.\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.rotation_euler = (0, 0, math.radians(45))\n    ground_obj.location = (0.0, 0.0, -0.0001)\n    print(\"Rotated Ground Studio opposite direction (+45 deg)\")\n,user_prompt:Correct backdrop rotation direction to eliminate right-side visual gap}", "tool_calls": [], "tokens_in": 0, "tokens_out": 0, "cost_usd": 0, "cached_tokens": 0}, "error": null}
{"ts": "2026-07-15T04:47:59.909607+00:00", "type": "assistant", "text": "/The rotation was in the wrong direction, opening the gap wider. Let's rotate in the opposite direction (e.g. +35 degrees or around +50 degrees) to close the gap completely!\n# Let's adjust rotation_euler to 45 degrees.\n\nground_obj = bpy.data.objects.get(\"Ground_Studio\")\nif ground_obj:\n    ground_obj.rotation_euler = (0, 0, math.radians(45))\n    ground_obj.location = (0.0, 0.0, -0.0001)\n    print(\"Rotated Ground Studio opposite direction (+45 deg)\")\n,user_prompt:Correct backdrop rotation direction to eliminate right-side visual gap}", "tool_calls": []}
{"ts": "2026-07-15T04:47:59.909607+00:00", "type": "status", "status": "ok", "done_declared": false}