Update app.py

app.py

@@ -5,7 +5,6 @@ import sys
 import tempfile
 import shutil
 import subprocess
-import spaces
 # from huggingface_hub import HfApi, snapshot_download # For future model management if needed
 # import spaces # For @spaces.GPU decorator if you add it
 
@@ -14,172 +13,178 @@ import spaces
 UNIRIG_REPO_DIR = os.path.join(os.path.dirname(__file__), "UniRig")
 
 if not os.path.isdir(UNIRIG_REPO_DIR):
-   print(f"ERROR: UniRig repository not found at {UNIRIG_REPO_DIR}. Please clone it there.")
-   # Consider raising an error or displaying it in the UI if UniRig is critical for startup
+    print(f"ERROR: UniRig repository not found at {UNIRIG_REPO_DIR}. Please clone it there.")
+    # Consider raising an error or displaying it in the UI if UniRig is critical for startup
 
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using device: {DEVICE}")
 if DEVICE.type == 'cuda':
-   print(f"CUDA Device Name: {torch.cuda.get_device_name(0)}")
-   print(f"CUDA Version: {torch.version.cuda}")
+    print(f"CUDA Device Name: {torch.cuda.get_device_name(0)}")
+    print(f"CUDA Version: {torch.version.cuda}")
 else:
-   print("Warning: CUDA not available or not detected by PyTorch. UniRig performance will be severely impacted.")
+    print("Warning: CUDA not available or not detected by PyTorch. UniRig performance will be severely impacted.")
 
-@spaces.GPU
 def run_unirig_command(command_args, step_name):
-   """Helper function to run UniRig commands using subprocess."""
-   python_exe = sys.executable
-   # Ensure the command starts with the python executable and '-m' for module execution
-   cmd = [python_exe, "-m"] + command_args
-   
-   print(f"Running {step_name}: {' '.join(cmd)}")
-   
-   process_env = os.environ.copy()
-   # It's generally better to ensure UniRig's internal scripts handle PYTHONPATH if needed,
-   # or that it's installed in a way that `python -m` works correctly from its root.
-   # Setting cwd=UNIRIG_REPO_DIR is often the key for Hydra.
-
-   try:
-       # Execute the command from the UniRig directory for Hydra to find configs
-       result = subprocess.run(cmd, cwd=UNIRIG_REPO_DIR, capture_output=True, text=True, check=True, env=process_env)
-       print(f"{step_name} STDOUT:\n{result.stdout}")
-       if result.stderr:
-           print(f"{step_name} STDERR (non-fatal or warnings):\n{result.stderr}")
-   except subprocess.CalledProcessError as e:
-       print(f"ERROR during {step_name}:")
-       print(f"Command: {' '.join(e.cmd)}")
-       print(f"Return code: {e.returncode}")
-       print(f"Stdout: {e.stdout}")
-       print(f"Stderr: {e.stderr}")
-       # Provide a more user-friendly error, potentially masking long tracebacks
-       error_summary = e.stderr.splitlines()[-5:] # Last 5 lines of stderr
-       raise gr.Error(f"Error in UniRig {step_name}. Details: {' '.join(error_summary)}")
-   except FileNotFoundError:
-       print(f"ERROR: Could not find executable or script for {step_name}. Is UniRig cloned correctly in {UNIRIG_REPO_DIR} and Python environment setup?")
-       raise gr.Error(f"Setup error for UniRig {step_name}. Check server logs and UniRig directory structure.")
-   except Exception as e_general:
-       print(f"An unexpected Python exception occurred in run_unirig_command for {step_name}: {e_general}")
-       raise gr.Error(f"Unexpected Python error during {step_name}: {str(e_general)[:500]}")
-
-
+    """Helper function to run UniRig commands using subprocess."""
+    python_exe = sys.executable
+    # Ensure the command starts with the python executable and '-m' for module execution
+    cmd = [python_exe, "-m"] + command_args
+    
+    print(f"Running {step_name}: {' '.join(cmd)}")
+    
+    process_env = os.environ.copy()
+    
+    # Explicitly add UNIRIG_REPO_DIR to PYTHONPATH for the subprocess.
+    # This ensures that Python can find the 'unirig' package located within UNIRIG_REPO_DIR.
+    # UNIRIG_REPO_DIR itself is the directory containing the 'unirig' package folder.
+    existing_pythonpath = process_env.get('PYTHONPATH', '')
+    process_env["PYTHONPATH"] = f"{UNIRIG_REPO_DIR}{os.pathsep}{existing_pythonpath}"
+    print(f"Set PYTHONPATH for subprocess: {process_env['PYTHONPATH']}")
+
+
+    try:
+        # Execute the command from the UniRig directory for Hydra to find configs
+        result = subprocess.run(cmd, cwd=UNIRIG_REPO_DIR, capture_output=True, text=True, check=True, env=process_env)
+        print(f"{step_name} STDOUT:\n{result.stdout}")
+        if result.stderr:
+            print(f"{step_name} STDERR (non-fatal or warnings):\n{result.stderr}")
+    except subprocess.CalledProcessError as e:
+        print(f"ERROR during {step_name}:")
+        print(f"Command: {' '.join(e.cmd)}")
+        print(f"Return code: {e.returncode}")
+        print(f"Stdout: {e.stdout}")
+        print(f"Stderr: {e.stderr}")
+        # Provide a more user-friendly error, potentially masking long tracebacks
+        error_summary = e.stderr.splitlines()[-5:] # Last 5 lines of stderr
+        raise gr.Error(f"Error in UniRig {step_name}. Details: {' '.join(error_summary)}")
+    except FileNotFoundError:
+        print(f"ERROR: Could not find executable or script for {step_name}. Is UniRig cloned correctly in {UNIRIG_REPO_DIR} and Python environment setup?")
+        raise gr.Error(f"Setup error for UniRig {step_name}. Check server logs and UniRig directory structure.")
+    except Exception as e_general:
+        print(f"An unexpected Python exception occurred in run_unirig_command for {step_name}: {e_general}")
+        raise gr.Error(f"Unexpected Python error during {step_name}: {str(e_general)[:500]}")
+
+
+# If you are using @spaces.GPU, you would import it:
+# import spaces
 # @spaces.GPU # You can specify type like @spaces.GPU(type="t4") or count
-@spaces.GPU
 def rig_glb_mesh_multistep(input_glb_file_obj):
-   """
-   Takes an input GLB file object (from gr.File with type="filepath"),
-   rigs it using the new UniRig multi-step process,
-   and returns the path to the final rigged GLB file.
-   """
-   if not os.path.isdir(UNIRIG_REPO_DIR):
-        raise gr.Error(f"UniRig repository not found at {UNIRIG_REPO_DIR}. Cannot proceed. Please check Space setup.")
-
-   if input_glb_file_obj is None:
-       # This case should ideally be handled by Gradio's input validation if `allow_none=False` (default)
-       raise gr.Error("No input file provided. Please upload a .glb mesh.")
-
-   # When type="filepath", input_glb_file_obj is the path string directly
-   input_glb_path = input_glb_file_obj
-   print(f"Input GLB path received: {input_glb_path}")
-
-   # Create a dedicated temporary directory for all intermediate and final files
-   processing_temp_dir = tempfile.mkdtemp(prefix="unirig_processing_")
-   print(f"Using temporary processing directory: {processing_temp_dir}")
-
-   try:
-       base_name = os.path.splitext(os.path.basename(input_glb_path))[0]
-       
-       # Step 1: Skeleton Prediction
-       temp_skeleton_path = os.path.join(processing_temp_dir, f"{base_name}_skeleton.fbx")
-       print("Step 1: Predicting Skeleton...")
-       run_unirig_command([
-           "unirig.predict_skeleton",
-           f"input.path={os.path.abspath(input_glb_path)}", # Use absolute path for robustness
-           f"output.path={os.path.abspath(temp_skeleton_path)}",
-           # f"device={str(DEVICE)}" # If UniRig's script accepts this override and handles it
-       ], "Skeleton Prediction")
-       if not os.path.exists(temp_skeleton_path):
-           raise gr.Error("Skeleton prediction failed to produce an output file. Check logs for UniRig errors.")
-
-       # Step 2: Skinning Weight Prediction
-       temp_skin_path = os.path.join(processing_temp_dir, f"{base_name}_skin.fbx")
-       print("Step 2: Predicting Skinning Weights...")
-       run_unirig_command([
-           "unirig.predict_skin",
-           f"input.skeleton_path={os.path.abspath(temp_skeleton_path)}",
-           f"input.source_mesh_path={os.path.abspath(input_glb_path)}",
-           f"output.path={os.path.abspath(temp_skin_path)}",
-       ], "Skinning Prediction")
-       if not os.path.exists(temp_skin_path):
-           raise gr.Error("Skinning prediction failed to produce an output file. Check logs for UniRig errors.")
-
-       # Step 3: Merge Skeleton/Skin with Original Mesh
-       final_rigged_glb_path = os.path.join(processing_temp_dir, f"{base_name}_rigged_final.glb")
-       print("Step 3: Merging Results...")
-       run_unirig_command([
-           "unirig.merge_skeleton_skin",
-           f"input.source_rig_path={os.path.abspath(temp_skin_path)}",
-           f"input.target_mesh_path={os.path.abspath(input_glb_path)}",
-           f"output.path={os.path.abspath(final_rigged_glb_path)}",
-       ], "Merging")
-       if not os.path.exists(final_rigged_glb_path):
-           raise gr.Error("Merging process failed to produce the final rigged GLB file. Check logs for UniRig errors.")
-
-       # final_rigged_glb_path is in processing_temp_dir.
-       # Gradio's gr.Model3D output component will handle serving this file.
-       return final_rigged_glb_path
-
-   except gr.Error: # Re-raise Gradio errors directly
-       if os.path.exists(processing_temp_dir): # Clean up on known Gradio error
-           shutil.rmtree(processing_temp_dir)
-           print(f"Cleaned up temporary directory: {processing_temp_dir}")
-       raise
-   except Exception as e:
-       print(f"An unexpected error occurred in rig_glb_mesh_multistep: {e}")
-       if os.path.exists(processing_temp_dir): # Clean up on unexpected error
-           shutil.rmtree(processing_temp_dir)
-           print(f"Cleaned up temporary directory: {processing_temp_dir}")
-       raise gr.Error(f"An unexpected error occurred during processing: {str(e)[:500]}")
+    """
+    Takes an input GLB file object (from gr.File with type="filepath"),
+    rigs it using the new UniRig multi-step process,
+    and returns the path to the final rigged GLB file.
+    """
+    if not os.path.isdir(UNIRIG_REPO_DIR):
+         raise gr.Error(f"UniRig repository not found at {UNIRIG_REPO_DIR}. Cannot proceed. Please check Space setup.")
+
+    if input_glb_file_obj is None:
+        # This case should ideally be handled by Gradio's input validation if `allow_none=False` (default)
+        raise gr.Error("No input file provided. Please upload a .glb mesh.")
+
+    # When type="filepath", input_glb_file_obj is the path string directly
+    input_glb_path = input_glb_file_obj
+    print(f"Input GLB path received: {input_glb_path}")
+
+    # Create a dedicated temporary directory for all intermediate and final files
+    processing_temp_dir = tempfile.mkdtemp(prefix="unirig_processing_")
+    print(f"Using temporary processing directory: {processing_temp_dir}")
+
+    try:
+        base_name = os.path.splitext(os.path.basename(input_glb_path))[0]
+        
+        # Step 1: Skeleton Prediction
+        temp_skeleton_path = os.path.join(processing_temp_dir, f"{base_name}_skeleton.fbx")
+        print("Step 1: Predicting Skeleton...")
+        run_unirig_command([
+            "unirig.predict_skeleton",
+            f"input.path={os.path.abspath(input_glb_path)}", # Use absolute path for robustness
+            f"output.path={os.path.abspath(temp_skeleton_path)}",
+            # f"device={str(DEVICE)}" # If UniRig's script accepts this override and handles it
+        ], "Skeleton Prediction")
+        if not os.path.exists(temp_skeleton_path):
+            raise gr.Error("Skeleton prediction failed to produce an output file. Check logs for UniRig errors.")
+
+        # Step 2: Skinning Weight Prediction
+        temp_skin_path = os.path.join(processing_temp_dir, f"{base_name}_skin.fbx")
+        print("Step 2: Predicting Skinning Weights...")
+        run_unirig_command([
+            "unirig.predict_skin",
+            f"input.skeleton_path={os.path.abspath(temp_skeleton_path)}",
+            f"input.source_mesh_path={os.path.abspath(input_glb_path)}",
+            f"output.path={os.path.abspath(temp_skin_path)}",
+        ], "Skinning Prediction")
+        if not os.path.exists(temp_skin_path):
+            raise gr.Error("Skinning prediction failed to produce an output file. Check logs for UniRig errors.")
+
+        # Step 3: Merge Skeleton/Skin with Original Mesh
+        final_rigged_glb_path = os.path.join(processing_temp_dir, f"{base_name}_rigged_final.glb")
+        print("Step 3: Merging Results...")
+        run_unirig_command([
+            "unirig.merge_skeleton_skin",
+            f"input.source_rig_path={os.path.abspath(temp_skin_path)}",
+            f"input.target_mesh_path={os.path.abspath(input_glb_path)}",
+            f"output.path={os.path.abspath(final_rigged_glb_path)}",
+        ], "Merging")
+        if not os.path.exists(final_rigged_glb_path):
+            raise gr.Error("Merging process failed to produce the final rigged GLB file. Check logs for UniRig errors.")
+
+        # final_rigged_glb_path is in processing_temp_dir.
+        # Gradio's gr.Model3D output component will handle serving this file.
+        return final_rigged_glb_path
+
+    except gr.Error: # Re-raise Gradio errors directly
+        if os.path.exists(processing_temp_dir): # Clean up on known Gradio error
+            shutil.rmtree(processing_temp_dir)
+            print(f"Cleaned up temporary directory: {processing_temp_dir}")
+        raise
+    except Exception as e:
+        print(f"An unexpected error occurred in rig_glb_mesh_multistep: {e}")
+        if os.path.exists(processing_temp_dir): # Clean up on unexpected error
+            shutil.rmtree(processing_temp_dir)
+            print(f"Cleaned up temporary directory: {processing_temp_dir}")
+        raise gr.Error(f"An unexpected error occurred during processing: {str(e)[:500]}")
 
 
 # --- Gradio Interface ---
 theme = gr.themes.Soft(
-   primary_hue=gr.themes.colors.sky,
-   secondary_hue=gr.themes.colors.blue,
-   neutral_hue=gr.themes.colors.slate,
-   font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
+    primary_hue=gr.themes.colors.sky,
+    secondary_hue=gr.themes.colors.blue,
+    neutral_hue=gr.themes.colors.slate,
+    font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
 )
 
 # Ensure UNIRIG_REPO_DIR check happens before interface is built if it's critical
 if not os.path.isdir(UNIRIG_REPO_DIR) and __name__ == "__main__": # Check only if running as main script
-   print(f"CRITICAL STARTUP ERROR: UniRig repository not found at {UNIRIG_REPO_DIR}. The application will not work.")
+    print(f"CRITICAL STARTUP ERROR: UniRig repository not found at {UNIRIG_REPO_DIR}. The application will not work.")
 
 # Define the interface
 # Note: The @spaces.GPU decorator would go above the function `rig_glb_mesh_multistep`
 iface = gr.Interface(
-   fn=rig_glb_mesh_multistep, 
-   inputs=gr.File(
-       label="Upload .glb Mesh File",
-       type="filepath"  # Corrected type for Gradio 5.x.x
-   ),
-   outputs=gr.Model3D(
-       label="Rigged 3D Model (.glb)",
-       clear_color=[0.8, 0.8, 0.8, 1.0],
-   ),
-   title="UniRig Auto-Rigger (Python 3.11 / PyTorch 2.3+)",
-   description=(
-       "Upload a 3D mesh in `.glb` format. This application uses the latest UniRig to automatically rig the mesh.\n"
-       "The process involves: 1. Skeleton Prediction, 2. Skinning Weight Prediction, 3. Merging.\n"
-       "This may take several minutes. Ensure your GLB has clean geometry.\n"
-       f"Running on: {str(DEVICE).upper()}. UniRig repo expected at: '{os.path.basename(UNIRIG_REPO_DIR)}'.\n"
-       f"UniRig Source: https://github.com/VAST-AI-Research/UniRig"
-   ),
-   cache_examples=False,
-   theme=theme,
-   allow_flagging="never"
+    fn=rig_glb_mesh_multistep, 
+    inputs=gr.File(
+        label="Upload .glb Mesh File",
+        type="filepath"  # Corrected type for Gradio 4.x / 5.x
+    ),
+    outputs=gr.Model3D(
+        label="Rigged 3D Model (.glb)",
+        clear_color=[0.8, 0.8, 0.8, 1.0],
+    ),
+    title="UniRig Auto-Rigger (Python 3.11 / PyTorch 2.3+)",
+    description=(
+        "Upload a 3D mesh in `.glb` format. This application uses the latest UniRig to automatically rig the mesh.\n"
+        "The process involves: 1. Skeleton Prediction, 2. Skinning Weight Prediction, 3. Merging.\n"
+        "This may take several minutes. Ensure your GLB has clean geometry.\n"
+        f"Running on: {str(DEVICE).upper()}. UniRig repo expected at: '{os.path.basename(UNIRIG_REPO_DIR)}'.\n"
+        f"UniRig Source: https://github.com/VAST-AI-Research/UniRig"
+    ),
+    cache_examples=False,
+    theme=theme
+    # allow_flagging="never" # Removed as it's deprecated in Gradio 4.x and default behavior is usually no flagging.
+                           # If specific flagging control is needed, use `flagging_options` or similar.
 )
 
 if __name__ == "__main__":
-   if not os.path.isdir(UNIRIG_REPO_DIR):
-       print(f"CRITICAL: UniRig repository not found at {UNIRIG_REPO_DIR}. Ensure it's cloned in the Space's root.")
-   
-   iface.launch()
+    if not os.path.isdir(UNIRIG_REPO_DIR):
+        print(f"CRITICAL: UniRig repository not found at {UNIRIG_REPO_DIR}. Ensure it's cloned in the Space's root.")
+    
+    iface.launch()