app.py

import gradio as gr
import torch
import os
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

# --- Configuration ---
# Path to the cloned UniRig repository directory within the Space
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

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}")
else:
   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]}")


# @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]}")


# --- 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"],
)

# 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.")

# 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"
)

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()