Initial commit: Infinigen Agents Space with HuggingFace Inference API

README.md

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+---
+title: Infinigen Agents
+emoji: 🌍
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 6.0.2
+python_version: "3.10"
+app_file: app.py
+pinned: false
+license: mit
+tags:
+  - mcp-server-track
+  - agents
+  - 3d
+  - procedural-generation
+  - infinigen
+short_description: "AI Agents for Procedural 3D World Generation"
+---
+
+# 🌍 Infinigen Agents
+
+AI-powered agents for procedural 3D world generation using [Infinigen](https://github.com/princeton-vl/infinigen).
+
+## Features
+
+- **Scene Composer**: Compose nature and indoor scenes
+- **Terrain Engineer**: Generate procedural terrain
+- **Asset Generator**: Create creatures, trees, materials
+- **Render Controller**: Manage rendering pipeline
+- **Export Specialist**: Export to various formats
+
+## AI Backend
+
+Uses HuggingFace Inference API by default:
+- Model: `openai/gpt-oss-20b`
+- Configurable providers: Cerebras, Together, Nebius, Groq
+
+## MCP Server
+
+```json
+{
+  "mcpServers": {
+    "infinigen-agents": {
+      "url": "https://dev-bjoern-infinigen-agents.hf.space/gradio_api/mcp/sse"
+    }
+  }
+}
+```

app.py

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+"""
+Infinigen Agents - AI-powered procedural 3D generation
+"""
+import os
+import gradio as gr
+from typing import Dict, Any
+
+# HuggingFace token from Space secrets
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# AI Model Configuration
+AI_MODEL = os.environ.get("AI_MODEL", "huggingface")
+HF_MODEL_ID = os.environ.get("HF_MODEL_ID", "openai/gpt-oss-20b")
+HF_PROVIDER = os.environ.get("HF_PROVIDER", None)
+
+
+def get_model():
+    """Get configured AI model for agents."""
+    if AI_MODEL == "huggingface":
+        from pydantic_ai.models.huggingface import HuggingFaceModel
+        from pydantic_ai.providers.huggingface import HuggingFaceProvider
+        
+        provider_kwargs = {"api_key": HF_TOKEN}
+        if HF_PROVIDER:
+            provider_kwargs["provider_name"] = HF_PROVIDER
+            
+        return HuggingFaceModel(HF_MODEL_ID, provider=HuggingFaceProvider(**provider_kwargs))
+    else:
+        return f"openai:gpt-4o-mini"
+
+
+def compose_scene(scene_type: str, seed: int, complexity: str) -> Dict[str, Any]:
+    """Compose a scene using AI agent."""
+    try:
+        from pydantic_ai import Agent
+        
+        agent = Agent(
+            get_model(),
+            system_prompt=f"""You are a scene composer for Infinigen.
+            Create a {complexity} complexity {scene_type} scene with seed {seed}.
+            Respond with JSON containing: scene_type, seed, assets, lighting, camera."""
+        )
+        
+        result = agent.run_sync(f"Create a {scene_type} scene")
+        return {
+            "success": True,
+            "scene_type": scene_type,
+            "seed": seed,
+            "complexity": complexity,
+            "result": str(result.data)
+        }
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+def generate_terrain(terrain_type: str, seed: int, resolution: int) -> Dict[str, Any]:
+    """Generate terrain using AI agent."""
+    try:
+        from pydantic_ai import Agent
+        
+        agent = Agent(
+            get_model(),
+            system_prompt=f"""You are a terrain engineer for Infinigen.
+            Generate {terrain_type} terrain with resolution {resolution}.
+            Respond with terrain parameters: heightmap settings, erosion, materials."""
+        )
+        
+        result = agent.run_sync(f"Generate {terrain_type} terrain")
+        return {
+            "success": True,
+            "terrain_type": terrain_type,
+            "seed": seed,
+            "resolution": resolution,
+            "result": str(result.data)
+        }
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+def get_recommendations(scene_type: str) -> str:
+    """Get AI recommendations for scene generation."""
+    try:
+        from pydantic_ai import Agent
+        
+        agent = Agent(
+            get_model(),
+            system_prompt="""You are an expert on Infinigen procedural generation.
+            Provide recommendations for assets, terrain, lighting, and camera setup."""
+        )
+        
+        result = agent.run_sync(f"Recommend settings for a {scene_type} scene in Infinigen")
+        return str(result.data)
+    except Exception as e:
+        return f"Error: {e}"
+
+
+# Gradio Interface
+with gr.Blocks(title="Infinigen Agents") as demo:
+    gr.Markdown("""
+    # 🌍 Infinigen Agents
+    **AI-powered procedural 3D world generation**
+    
+    Using HuggingFace Inference API with pydantic-ai
+    """)
+    
+    with gr.Tab("Scene Composer"):
+        with gr.Row():
+            scene_type = gr.Dropdown(
+                ["forest", "desert", "mountain", "canyon", "coast", "kitchen", "living_room"],
+                label="Scene Type",
+                value="forest"
+            )
+            scene_seed = gr.Number(label="Seed", value=42)
+            complexity = gr.Dropdown(["low", "medium", "high"], label="Complexity", value="medium")
+        
+        compose_btn = gr.Button("🎬 Compose Scene", variant="primary")
+        scene_output = gr.JSON(label="Scene Result")
+        
+        compose_btn.click(compose_scene, [scene_type, scene_seed, complexity], scene_output)
+    
+    with gr.Tab("Terrain Engineer"):
+        with gr.Row():
+            terrain_type = gr.Dropdown(
+                ["mountain", "canyon", "cliff", "mesa", "river", "volcano", "coast", "plain"],
+                label="Terrain Type",
+                value="mountain"
+            )
+            terrain_seed = gr.Number(label="Seed", value=42)
+            resolution = gr.Slider(128, 2048, value=512, step=128, label="Resolution")
+        
+        terrain_btn = gr.Button("🏔️ Generate Terrain", variant="primary")
+        terrain_output = gr.JSON(label="Terrain Result")
+        
+        terrain_btn.click(generate_terrain, [terrain_type, terrain_seed, resolution], terrain_output)
+    
+    with gr.Tab("AI Recommendations"):
+        rec_scene_type = gr.Dropdown(
+            ["forest", "desert", "mountain", "canyon", "coast"],
+            label="Scene Type",
+            value="forest"
+        )
+        rec_btn = gr.Button("💡 Get Recommendations", variant="primary")
+        rec_output = gr.Textbox(label="AI Recommendations", lines=10)
+        
+        rec_btn.click(get_recommendations, rec_scene_type, rec_output)
+    
+    gr.Markdown(f"""
+    ---
+    ### Configuration
+    - **AI Model**: {AI_MODEL}
+    - **HF Model**: {HF_MODEL_ID}
+    - **Provider**: {HF_PROVIDER or 'auto'}
+    
+    ### MCP Server
+    ```json
+    {{
+      "mcpServers": {{
+        "infinigen-agents": {{
+          "url": "https://dev-bjoern-infinigen-agents.hf.space/gradio_api/mcp/sse"
+        }}
+      }}
+    }}
+    ```
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

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+gradio>=6.0.2
+huggingface_hub>=0.26.0
+pydantic>=2.0.0
+pydantic-ai[huggingface]>=1.0.8
+numpy>=1.26.0