app.py

import os
import gradio as gr
import torch
import trimesh
import tempfile
import threading
from pathlib import Path
import gc

from inference import SkeletonInferencer

# Global state
INFERENCER = None
MODELS_READY = False
DOWNLOAD_LOCK = threading.Lock()
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

def check_models_exist():
    """Check if models are already downloaded"""
    required_files = [
        "skeleton_ckpt/checkpoint_trainonv2_spatial.pth",
        "skeleton_ckpt/checkpoint_trainonv2_hier.pth",
    ]
    return all(Path(f).exists() for f in required_files)

def download_models_once():
    """Thread-safe model download (only runs once)"""
    global MODELS_READY
    
    with DOWNLOAD_LOCK:
        if MODELS_READY or check_models_exist():
            MODELS_READY = True
            return True
        
        print("📥 Downloading models (first time only)...")
        try:
            from huggingface_hub import hf_hub_download
            
            # Download only the essential models (skip Michelangelo for now)
            models_to_download = [
                {
                    "repo_id": "Seed3D/MagicArticulate",
                    "filename": "skeleton_ckpt/checkpoint_trainonv2_spatial.pth",
                    "local_dir": "."
                },
                {
                    "repo_id": "Seed3D/MagicArticulate", 
                    "filename": "skeleton_ckpt/checkpoint_trainonv2_hier.pth",
                    "local_dir": "."
                }
            ]
            
            for i, model_info in enumerate(models_to_download, 1):
                print(f"[{i}/{len(models_to_download)}] Downloading {model_info['filename']}...")
                hf_hub_download(
                    repo_id=model_info["repo_id"],
                    filename=model_info["filename"],
                    local_dir=model_info["local_dir"]
                )
                print(f"✓ Downloaded: {model_info['filename']}")
            
            MODELS_READY = True
            print("✅ All models ready!")
            return True
            
        except Exception as e:
            print(f"❌ Download failed: {e}")
            return False

def initialize_inferencer():
    """Initialize inferencer after models are ready"""
    global INFERENCER
    if INFERENCER is None:
        print("🔧 Initializing inferencer...")
        INFERENCER = SkeletonInferencer(
            device=DEVICE,
            precision="fp16" if DEVICE == "cuda" else "fp32"
        )
        print("✅ Inferencer ready!")
    return INFERENCER

def process_mesh(
    input_file,
    num_points=8192,
    use_marching_cubes=False,
    sequence_type="spatial",
    progress=gr.Progress()
):
    """Process mesh with lazy model loading"""
    try:
        if input_file is None:
            return None, None, None, "❌ Please upload a mesh file"
        
        # Download models on first use
        progress(0.0, desc="🔍 Checking models...")
        if not MODELS_READY:
            progress(0.05, desc="📥 Downloading models (this may take 20-30s, only happens once)...")
            if not download_models_once():
                return None, None, None, "❌ Failed to download models. Please try again."
        
        progress(0.15, desc="⚙️ Loading model...")
        inferencer = initialize_inferencer()
        
        # Create temporary directory for outputs
        with tempfile.TemporaryDirectory() as temp_dir:
            temp_path = Path(temp_dir)
            
            progress(0.25, desc="📐 Processing mesh...")
            
            # Get file extension
            file_ext = Path(input_file.name).suffix.lower()
            if file_ext not in ['.obj', '.ply', '.stl']:
                return None, None, None, f"❌ Unsupported file format: {file_ext}"
            
            progress(0.35, desc="🦴 Generating skeleton...")
            
            # Run inference
            results = inferencer.infer(
                input_path=input_file.name,
                output_dir=str(temp_path),
                input_pc_num=num_points,
                apply_marching_cubes=use_marching_cubes,
                sequence_type=sequence_type
            )
            
            progress(0.8, desc="💾 Preparing outputs...")
            
            # Get output file paths
            file_name = Path(input_file.name).stem
            skeleton_file = temp_path / f"{file_name}_skel.obj"
            rig_file = temp_path / f"{file_name}_pred.txt"
            mesh_file = temp_path / f"{file_name}_mesh.obj"
            
            # Prepare outputs
            output_files = []
            info_text = "✅ **Processing Complete!**\n\n"
            
            if skeleton_file.exists():
                output_files.append(str(skeleton_file))
                info_text += f"- Skeleton: `{skeleton_file.name}`\n"
            
            if rig_file.exists():
                output_files.append(str(rig_file))
                with open(rig_file, 'r') as f:
                    rig_data = f.read()
                info_text += f"- Rig data: `{rig_file.name}` ({len(rig_data.splitlines())} lines)\n"
            
            if mesh_file.exists():
                output_files.append(str(mesh_file))
                info_text += f"- Normalized mesh: `{mesh_file.name}`\n"
            
            info_text += f"\n**Statistics:**\n"
            info_text += f"- Joints: {results.get('num_joints', 'N/A')}\n"
            info_text += f"- Bones: {results.get('num_bones', 'N/A')}\n"
            info_text += f"- Processing time: {results.get('time', 0):.2f}s\n"
            
            progress(1.0, desc="✅ Done!")
            
            # Memory cleanup
            if DEVICE == "cuda":
                torch.cuda.empty_cache()
            gc.collect()
            
            return output_files, str(skeleton_file) if skeleton_file.exists() else None, \
                   str(rig_file) if rig_file.exists() else None, info_text
    
    except Exception as e:
        error_msg = f"❌ **Error:** {str(e)}"
        print(f"Error processing mesh: {e}")
        import traceback
        traceback.print_exc()
        return None, None, None, error_msg

# Create Gradio interface
def create_interface():
    with gr.Blocks(title="MagicArticulate - 3D Mesh Rigging") as demo:
        gr.Markdown("""
        # 🎨 MagicArticulate: Make Your 3D Models Articulation-Ready
        
        **CVPR 2025** | Upload a 3D mesh and automatically generate skeletal rigging for animation.
        
        ⚠️ **First inference will download models (~8GB, takes 20-30s)**. Subsequent inferences are fast!
        
        Supported formats: `.obj`, `.ply`, `.stl`
        """)
        
        # Check if models are pre-downloaded
        if check_models_exist():
            gr.Markdown("✅ **Models ready!** You can start immediately.")
        else:
            gr.Markdown("📥 **Models will download on first use** (only happens once)")
        
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### 📤 Input")
                input_file = gr.File(
                    label="Upload 3D Mesh",
                    file_types=[".obj", ".ply", ".stl"],
                    type="filepath"
                )
                
                with gr.Accordion("⚙️ Advanced Settings", open=False):
                    num_points = gr.Slider(
                        minimum=2048,
                        maximum=16384,
                        value=8192,
                        step=1024,
                        label="Point Cloud Size",
                        info="Higher values = more accurate but slower"
                    )
                    
                    use_marching_cubes = gr.Checkbox(
                        label="Use Marching Cubes",
                        value=False,
                        info="Better quality but slower"
                    )
                    
                    sequence_type = gr.Radio(
                        choices=["spatial", "hierarchical"],
                        value="spatial",
                        label="Sequence Ordering",
                        info="Spatial is faster, hierarchical preserves hierarchy"
                    )
                
                process_btn = gr.Button("🚀 Generate Rigging", variant="primary", size="lg")
            
            with gr.Column(scale=1):
                gr.Markdown("### 📥 Output")
                info_output = gr.Markdown()
                
                with gr.Tabs():
                    with gr.Tab("📦 Download Files"):
                        output_files = gr.File(
                            label="Generated Files",
                            file_count="multiple"
                        )
                    
                    with gr.Tab("🦴 Skeleton"):
                        skeleton_viewer = gr.Model3D(
                            label="Generated Skeleton",
                            height=400
                        )
                    
                    with gr.Tab("📄 Rig Data"):
                        rig_text = gr.File(
                            label="Rig Text File"
                        )
        
        gr.Markdown("""
        ### 📖 How to Use
        1. Upload your 3D mesh file (`.obj`, `.ply`, or `.stl`)
        2. (First time only) Wait for model download (~20-30s)
        3. Click "Generate Rigging" and wait for processing
        4. Download the generated skeleton and rigging files
        
        ### 💡 Tips
        - **Memory limited?** Use fewer points (e.g., 4096)
        - **Better quality?** Enable Marching Cubes (slower)
        
        ### 📚 Citation
        ```
        @inproceedings{song2025magicarticulate,
          title={MagicArticulate: Make Your 3D Models Articulation-Ready},
          author={Song, Chaoyue and Zhang, Jianfeng and others},
          booktitle={CVPR},
          year={2025}
        }
        ```
        """)
        
        # Connect components
        process_btn.click(
            fn=process_mesh,
            inputs=[input_file, num_points, use_marching_cubes, sequence_type],
            outputs=[output_files, skeleton_viewer, rig_text, info_output]
        )
        
    return demo

if __name__ == "__main__":
    print("🚀 Starting MagicArticulate Space...")
    
    # Check if models exist
    if check_models_exist():
        print("✅ Models found, ready to serve!")
    else:
        print("⏳ Models will be downloaded on first inference")
    
    # Create and launch interface immediately
    demo = create_interface()
    demo.queue(max_size=3)  # Limit concurrent requests
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )