app.py

import os
import sys
import json
from pathlib import Path
import tempfile
import shutil

# Safe imports with fallbacks
try:
    import gradio as gr
except ImportError:
    print("Installing gradio...")
    os.system(f"{sys.executable} -m pip install gradio")
    import gradio as gr

try:
    import subprocess
except ImportError:
    subprocess = None

try:
    import torch
    HAS_TORCH = True
except ImportError:
    HAS_TORCH = False
    print("PyTorch not available, using CPU mode")

# Ensure temp directories exist
TEMP_DIR = tempfile.gettempdir()
HF_CACHE_DIR = os.path.join(TEMP_DIR, "hf_cache")
os.makedirs(HF_CACHE_DIR, exist_ok=True)

# Set environment variables safely
os.environ["GRADIO_SERVER_NAME"] = "0.0.0.0"
os.environ["GRADIO_SERVER_PORT"] = "7860"
os.environ["HF_HUB_CACHE"] = HF_CACHE_DIR
os.environ["HUGGINGFACE_HUB_CACHE"] = HF_CACHE_DIR
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512"

def safe_import(module_name, package_name=None):
    """Safely import a module with fallback"""
    if package_name is None:
        package_name = module_name
    try:
        return __import__(module_name.replace("-", "_"))
    except ImportError:
        print(f"Module {module_name} not found, functionality limited")
        return None

def setup_environment():
    """Setup environment with error handling"""
    dependencies = [
        "sageattention==1.0.6",
        "insightface",
        "facexlib", 
        "diffusers>=0.30.0",
        "transformers>=4.44.0",
        "accelerate>=0.34.0",
        "xformers",
        "opencv-python",
        "imageio[ffmpeg]",
        "moviepy",
        "librosa",
        "soundfile"
    ]
    
    for dep in dependencies:
        try:
            module_name = dep.split("==")[0].split(">=")[0].split("[")[0]
            safe_import(module_name)
        except Exception as e:
            print(f"Could not process {dep}: {e}")
            if subprocess:
                try:
                    subprocess.run(
                        [sys.executable, "-m", "pip", "install", dep], 
                        check=False, 
                        capture_output=True,
                        timeout=30
                    )
                except Exception as install_error:
                    print(f"Failed to install {dep}: {install_error}")

def download_essential_models():
    """Pre-download models with full error handling"""
    try:
        from huggingface_hub import snapshot_download
        
        print("Attempting to download Hunyuan Video Avatar models...")
        
        try:
            snapshot_download(
                repo_id="tencent/HunyuanVideo-Avatar", 
                cache_dir=HF_CACHE_DIR,
                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin"],
                ignore_patterns=["*.mp4", "*.avi", "*.mov"],
                resume_download=True,
                max_workers=2
            )
        except Exception as e:
            print(f"Could not download HunyuanVideo-Avatar: {e}")
        
        try:
            snapshot_download(
                repo_id="tencent/HunyuanVideo",
                cache_dir=HF_CACHE_DIR, 
                allow_patterns=["*.safetensors", "*.json", "*.txt"],
                ignore_patterns=["*.mp4", "*.avi"],
                resume_download=True,
                max_workers=2
            )
        except Exception as e:
            print(f"Could not download HunyuanVideo: {e}")
        
        print("Model download attempt completed")
        
    except ImportError:
        print("huggingface_hub not available, skipping model download")
    except Exception as e:
        print(f"Model download error: {e}")

def create_hf_config():
    """Create config with error handling"""
    config = {
        "model_settings": {
            "profile": 3,
            "quantize_transformer": True,
            "attention_mode": "sage",
            "compile": False,
            "teacache": "2.0"
        },
        "avatar_settings": {
            "max_frames": 120,
            "resolution": "512x512",
            "emotion_control": True,
            "multi_character": True
        },
        "memory_optimization": {
            "enable_vae_tiling": True,
            "enable_cpu_offload": True,
            "max_batch_size": 1,
            "gradient_checkpointing": True
        },
        "audio_processing": {
            "sample_rate": 16000,
            "max_duration": 15,
            "supported_formats": ["wav", "mp3", "m4a"]
        }
    }
    
    config_path = os.path.join(TEMP_DIR, "hf_config.json")
    try:
        with open(config_path, "w", encoding='utf-8') as f:
            json.dump(config, f, indent=2)
    except Exception as e:
        print(f"Could not save config: {e}")
        config_path = None
    
    return config

def create_dummy_video(output_path, duration=5, fps=24, width=512, height=512):
    """Create a dummy video file for testing"""
    try:
        import numpy as np
        import cv2
        
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        for i in range(duration * fps):
            # Create gradient frame
            frame = np.ones((height, width, 3), dtype=np.uint8) * 50
            text = f"Frame {i+1}"
            cv2.putText(frame, text, (width//4, height//2), 
                       cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
            out.write(frame)
        
        out.release()
        return True
    except Exception as e:
        print(f"Could not create video with OpenCV: {e}")
        # Create empty file as fallback
        try:
            with open(output_path, 'wb') as f:
                f.write(b'dummy video content')
            return True
        except:
            return False

class WanGPInterface:
    """WanGP Interface with full error handling"""
    
    def __init__(self, config):
        self.config = config or {}
        self.device = "cpu"
        if HAS_TORCH:
            try:
                self.device = "cuda" if torch.cuda.is_available() else "cpu"
            except:
                self.device = "cpu"
        self.models_loaded = False
        
    def load_models(self):
        """Load models with error handling"""
        if self.models_loaded:
            return True
            
        try:
            print("Loading Hunyuan Video Avatar models (placeholder)...")
            # Placeholder for actual model loading
            import time
            time.sleep(0.5)  # Simulate loading
            self.models_loaded = True
            print("✅ Models loaded successfully (simulated)!")
            return True
        except Exception as e:
            print(f"Error in model loading: {e}")
            self.models_loaded = False
            return False
    
    def generate_avatar_video(self, audio_file, avatar_image, prompt="", emotion="neutral"):
        """Generate avatar video with comprehensive error handling"""
        try:
            # Validate inputs
            if audio_file is None:
                return None, "❌ Error: No audio file provided"
            
            if avatar_image is None:
                return None, "❌ Error: No avatar image provided"
            
            # Ensure model is loaded
            if not self.load_models():
                print("Models not loaded, using dummy generation")
            
            # Create output path
            output_filename = f"avatar_{os.getpid()}_{id(self)}.mp4"
            output_path = os.path.join(TEMP_DIR, output_filename)
            
            # Create dummy video
            if create_dummy_video(output_path, duration=5):
                if os.path.exists(output_path):
                    return output_path, "✅ Video generated successfully (demo mode)!"
                else:
                    return None, "❌ Error: Failed to create output file"
            else:
                return None, "❌ Error: Video generation failed"
            
        except Exception as e:
            error_msg = f"❌ Error in avatar generation: {str(e)}"
            print(error_msg)
            return None, error_msg
    
    def generate_video(self, prompt, duration=5, resolution="512x512"):
        """Generate video from text with error handling"""
        try:
            if not prompt:
                return "❌ Error: No prompt provided"
            
            # Parse resolution
            try:
                width, height = map(int, resolution.split('x'))
            except:
                width, height = 512, 512
            
            # Ensure model is loaded
            if not self.load_models():
                print("Models not loaded, using dummy generation")
            
            # Create output path
            output_filename = f"video_{os.getpid()}_{id(self)}.mp4"
            output_path = os.path.join(TEMP_DIR, output_filename)
            
            # Create dummy video
            if create_dummy_video(output_path, duration=int(duration), width=width, height=height):
                if os.path.exists(output_path):
                    return output_path, f"✅ Generated video for prompt: {prompt[:50]}..."
                else:
                    return None, "❌ Error: Failed to create output file"
            else:
                return None, "❌ Error: Video generation failed"
                
        except Exception as e:
            error_msg = f"❌ Error in video generation: {str(e)}"
            print(error_msg)
            return None, error_msg

def create_gradio_interface(wangp_interface):
    """Create Gradio interface with error handling"""
    
    try:
        with gr.Blocks(title="WanGP v6.3 - Hunyuan Video Avatar", theme=gr.themes.Soft()) as demo:
            gr.HTML("""
            <div style="text-align: center; margin-bottom: 20px;">
                <h1>🎭 WanGP v6.3 - Hunyuan Video Avatar</h1>
                <p>Advanced AI Video Generation with Audio-Driven Human Animation</p>
                <p style="color: orange;">⚠️ Running in Demo Mode - Using placeholder outputs</p>
            </div>
            """)
            
            with gr.Tabs():
                # Avatar Generation Tab
                with gr.TabItem("🎭 Avatar Generation"):
                    with gr.Row():
                        with gr.Column():
                            audio_input = gr.Audio(
                                label="Audio Input",
                                type="filepath"
                            )
                            avatar_image = gr.Image(
                                label="Avatar Image",
                                type="filepath"
                            )
                            emotion_control = gr.Dropdown(
                                choices=["neutral", "happy", "sad", "angry", "surprised"],
                                value="neutral",
                                label="Emotion Control"
                            )
                            avatar_prompt = gr.Textbox(
                                label="Additional Prompt (Optional)",
                                placeholder="Describe additional details...",
                                value=""
                            )
                            generate_avatar_btn = gr.Button("Generate Avatar Video", variant="primary")
                        
                        with gr.Column():
                            avatar_output = gr.Video(label="Generated Avatar Video")
                            avatar_status = gr.Textbox(label="Status", interactive=False, value="Ready")
                
                # Text-to-Video Tab
                with gr.TabItem("📹 Text to Video"):
                    with gr.Row():
                        with gr.Column():
                            video_prompt = gr.Textbox(
                                label="Video Prompt",
                                placeholder="Describe the video you want to generate...",
                                lines=3,
                                value=""
                            )
                            duration_slider = gr.Slider(
                                minimum=2,
                                maximum=10,
                                value=5,
                                step=1,
                                label="Duration (seconds)"
                            )
                            resolution_dropdown = gr.Dropdown(
                                choices=["512x512", "768x768", "1024x1024"],
                                value="512x512",
                                label="Resolution"
                            )
                            generate_video_btn = gr.Button("Generate Video", variant="primary")
                        
                        with gr.Column():
                            video_output = gr.Video(label="Generated Video")
                            video_status = gr.Textbox(label="Status", interactive=False, value="Ready")
            
            # Event handlers with error handling
            def safe_avatar_generation(*args):
                try:
                    return wangp_interface.generate_avatar_video(*args)
                except Exception as e:
                    return None, f"❌ Unexpected error: {str(e)}"
            
            def safe_video_generation(*args):
                try:
                    result = wangp_interface.generate_video(*args)
                    if isinstance(result, tuple):
                        return result
                    else:
                        return None, result
                except Exception as e:
                    return None, f"❌ Unexpected error: {str(e)}"
            
            generate_avatar_btn.click(
                fn=safe_avatar_generation,
                inputs=[audio_input, avatar_image, avatar_prompt, emotion_control],
                outputs=[avatar_output, avatar_status]
            )
            
            generate_video_btn.click(
                fn=safe_video_generation,
                inputs=[video_prompt, duration_slider, resolution_dropdown],
                outputs=[video_output, video_status]
            )
            
            gr.HTML("""
            <div style="text-align: center; margin-top: 20px; color: #666;">
                <p>Powered by Hunyuan Video Avatar & WanGP v6.3</p>
                <p style="font-size: 12px;">Note: This is a demonstration interface with placeholder outputs</p>
            </div>
            """)
        
        return demo
        
    except Exception as e:
        print(f"Error creating Gradio interface: {e}")
        # Return minimal interface
        demo = gr.Interface(
            fn=lambda x: f"Error: {str(e)}",
            inputs="text",
            outputs="text",
            title="WanGP v6.3 - Error State"
        )
        return demo

def main():
    """Main function with comprehensive error handling"""
    print("🚀 Starting WanGP v6.3 with Hunyuan Video Avatar...")
    
    try:
        # Setup environment
        setup_environment()
    except Exception as e:
        print(f"Environment setup warning: {e}")
    
    try:
        # Create configuration
        config = create_hf_config()
    except Exception as e:
        print(f"Config creation warning: {e}")
        config = {}
    
    try:
        # Download models in background
        download_essential_models()
    except Exception as e:
        print(f"Model download skipped: {e}")
    
    try:
        # Initialize WanGP interface
        wangp_interface = WanGPInterface(config)
    except Exception as e:
        print(f"Interface initialization error: {e}")
        # Create minimal interface
        class MinimalInterface:
            def __init__(self):
                self.config = {}
            def generate_avatar_video(self, *args):
                return None, "Service temporarily unavailable"
            def generate_video(self, *args):
                return None, "Service temporarily unavailable"
        wangp_interface = MinimalInterface()
    
    try:
        # Create and launch Gradio interface
        demo = create_gradio_interface(wangp_interface)
        
        print("✅ Setup complete! Launching application...")
        
        # Launch with error handling
        demo.launch(
            server_name="0.0.0.0",
            server_port=int(os.environ.get("GRADIO_SERVER_PORT", 7860)),
            share=False,
            debug=False,
            show_error=True,
            prevent_thread_lock=False
        )
    except Exception as e:
        print(f"❌ Failed to launch Gradio: {e}")
        print("Attempting fallback launch...")
        try:
            # Minimal fallback
            import gradio as gr
            gr.Interface(
                fn=lambda x: "System Error - Please restart",
                inputs="text", 
                outputs="text"
            ).launch()
        except:
            print("❌ Complete failure. Exiting.")
            sys.exit(1)

if __name__ == "__main__":
    main()