feat: Enable MCP

Hello! This is an automated PR adding MCP compatibility to your AI App 🤖.

This PR introduces two improvements:
1. Adds docstrings to the functions in the app file that are directly connected to the Gradio UI, for the downstream LLM to use.
2. Enables the Model-Compute-Platform by adding `mcp_server=True` to the `.launch()` call.

No other logic has been changed. Please review and merge if it looks good!Learn more about MCP compatibility in Spaces here: https://huggingface.co/changelog/add-compatible-spaces-to-your-mcp-tools

app.py

@@ -214,7 +214,30 @@ def run_triposg(image_path: str,
                 progress=gr.Progress(track_tqdm=True),):
 
     """
-    Generate 3D part meshes from an input image.
+    Generate 3D part meshes from an input image using the PartCrafter pipeline.
+    
+    This function takes an input image and generates multiple 3D mesh parts that compose
+    the object in the image. It uses a diffusion-based model to create structured 3D
+    representations with separate parts that can be individually manipulated.
+    
+    Args:
+        image_path: Path to the input image file
+        num_parts: Number of parts to generate for the 3D object (1-16)
+        seed: Random seed for reproducible generation
+        num_tokens: Number of tokens to use in the generation process (256-2048)
+        num_inference_steps: Number of denoising steps for the diffusion process
+        guidance_scale: Guidance scale for classifier-free guidance
+        use_flash_decoder: Whether to use flash attention decoder for faster inference
+        rmbg: Whether to remove background from input image using RMBG model
+        session_id: Unique session identifier for tracking generation process
+        progress: Gradio progress tracker for UI updates
+        
+    Returns:
+        tuple: Contains:
+            - merged_path: Path to the merged 3D object GLB file
+            - split_preview_path: Path to the exploded view GLB file
+            - export_dir: Directory containing all exported files
+            - zip_path: Path to ZIP file containing all individual parts
     """
 
     max_num_expanded_coords = 1e9
@@ -282,6 +305,18 @@ def run_triposg(image_path: str,
     return merged_path, split_preview_path, export_dir, zip_path
 
 def cleanup(request: gr.Request):
+    """
+    Clean up temporary files and directories for a user session.
+    
+    This function is called when the Gradio interface is unloaded to remove
+    any temporary files created during the session to prevent disk space issues.
+    
+    Args:
+        request: Gradio request object containing session information
+        
+    Returns:
+        None
+    """
 
     sid = request.session_hash
     if sid:
@@ -289,6 +324,18 @@ def cleanup(request: gr.Request):
         shutil.rmtree(d1, ignore_errors=True)
         
 def start_session(request: gr.Request):
+    """
+    Initialize a new user session and return the session hash.
+    
+    This function is called when the Gradio interface loads to create a unique
+    session identifier for tracking the user's generation process and temporary files.
+    
+    Args:
+        request: Gradio request object containing session information
+        
+    Returns:
+        str: Session hash identifier for the current user session
+    """
 
     return request.session_hash
     
@@ -322,7 +369,7 @@ def build_demo():
                 with gr.Column(scale=1):
                     gr.Markdown(
                     """ 
-                    • HF Space by : [@alexandernasa](https://twitter.com/alexandernasa/)  """
+                    • HF Space by : [@alexandernasa](https://twitter.com/alexandernasa/)  """
                     )
                     input_image = gr.Image(type="filepath", label="Input Image", height=256)
                     num_parts = gr.Slider(1, MAX_NUM_PARTS, value=4, step=1, label="Number of Parts")
@@ -384,4 +431,4 @@ if __name__ == "__main__":
     demo = build_demo()
     demo.unload(cleanup)
     demo.queue()
-    demo.launch()
+    demo.launch(mcp_server=True)