#!/usr/bin/env python3
"""
Deployment script for Hugging Face Spaces
Prepares files and provides deployment instructions
"""

import os
import shutil
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def prepare_hf_deployment():
    """Prepare files for Hugging Face Spaces deployment"""
    
    logger.info("🚀 Preparing Video Transcription Service for Hugging Face Spaces")
    logger.info("=" * 60)
    
    # Create deployment directory
    deploy_dir = "hf_spaces_deploy"
    if os.path.exists(deploy_dir):
        shutil.rmtree(deploy_dir)
    os.makedirs(deploy_dir)
    
    # Files to copy/create for HF Spaces
    files_to_copy = [
        "app.py",           # Main Gradio app
        "config.py",        # Configuration
        "models.py",        # Data models
        "storage.py",       # Storage management
        "transcription_service.py",  # Core transcription logic
        "logging_config.py", # Logging configuration
        "restart_handler.py" # Restart prevention
    ]
    
    # Copy core files
    for file in files_to_copy:
        if os.path.exists(file):
            shutil.copy2(file, deploy_dir)
            logger.info(f"✅ Copied {file}")
        else:
            logger.warning(f"⚠️ File not found: {file}")
    
    # Copy and rename HF-specific files
    if os.path.exists("requirements_hf.txt"):
        shutil.copy2("requirements_hf.txt", os.path.join(deploy_dir, "requirements.txt"))
        logger.info("✅ Copied requirements_hf.txt -> requirements.txt")
    
    if os.path.exists("README_HF.md"):
        shutil.copy2("README_HF.md", os.path.join(deploy_dir, "README.md"))
        logger.info("✅ Copied README_HF.md -> README.md")
    
    if os.path.exists("config_hf.py"):
        # Replace config.py with HF-optimized version
        shutil.copy2("config_hf.py", os.path.join(deploy_dir, "config.py"))
        logger.info("✅ Using HF-optimized config.py")
    
    # Create .gitignore for HF Spaces
    gitignore_content = """
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
*.log
.env
.venv
env/
venv/
.DS_Store
*.tmp
*.temp
flagged/
"""
    
    with open(os.path.join(deploy_dir, ".gitignore"), "w") as f:
        f.write(gitignore_content.strip())
    logger.info("✅ Created .gitignore")
    
    logger.info("\n🎉 Deployment files prepared successfully!")
    logger.info(f"📁 Files are ready in: {deploy_dir}/")
    
    return deploy_dir

def print_deployment_instructions(deploy_dir):
    """Print step-by-step deployment instructions"""
    
    instructions = f"""
🚀 HUGGING FACE SPACES DEPLOYMENT INSTRUCTIONS
{'=' * 50}

1. 📁 PREPARE YOUR HUGGING FACE ACCOUNT
   - Go to https://huggingface.co
   - Sign up/login to your account
   - Go to "Spaces" tab

2. 🆕 CREATE NEW SPACE
   - Click "Create new Space"
   - Choose a name: e.g., "video-transcription"
   - Select "Gradio" as SDK
   - Choose "Public" or "Private"
   - Click "Create Space"

3. 📤 UPLOAD FILES
   Option A - Web Interface:
   - Upload all files from {deploy_dir}/ to your Space
   - Make sure app.py is in the root directory
   
   Option B - Git (Recommended):
   ```bash
   cd {deploy_dir}
   git init
   git add .
   git commit -m "Initial commit"
   git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
   git push -u origin main
   ```

4. ⚙️ CONFIGURE SPACE SETTINGS
   - Go to your Space settings
   - Set "Hardware" to "CPU basic" (free) or "CPU upgrade" (better performance)
   - Enable "Public" if you want API access from external applications

5. 🚀 DEPLOY
   - Your Space will automatically build and deploy
   - Wait for the build to complete (5-10 minutes)
   - Check logs for any errors

6. ✅ TEST YOUR DEPLOYMENT
   Web Interface:
   - Visit: https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space
   - Upload a test video file
   - Verify transcription works
   
   API Access:
   ```bash
   # Test health endpoint
   curl "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/api/health"
   
   # Test transcription
   curl -X POST "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/api/transcribe" \\
     -F "file=@test_video.mp4" \\
     -F "language=en"
   ```

7. 📊 MONITOR PERFORMANCE
   - Check Space logs for any issues
   - Monitor memory usage
   - Test with different video formats

🎯 IMPORTANT NOTES:
- First model load takes 2-3 minutes (downloads Whisper model)
- Subsequent requests are much faster
- API endpoints work exactly like your local FastAPI
- Both web interface and API are available simultaneously

🔧 TROUBLESHOOTING:
- If build fails: Check requirements.txt and logs
- If model loading fails: Try WHISPER_MODEL=tiny in Space settings
- If memory issues: Upgrade to CPU upgrade hardware

📞 NEED HELP?
- Check Space logs in the "Logs" tab
- Visit Hugging Face Spaces documentation
- Test locally first: python app.py

🎉 Your Video Transcription Service will be live at:
   https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space
"""
    
    print(instructions)

def main():
    """Main deployment preparation function"""
    try:
        deploy_dir = prepare_hf_deployment()
        print_deployment_instructions(deploy_dir)
        
        logger.info("\n✅ Ready for Hugging Face Spaces deployment!")
        logger.info(f"📁 Next step: Upload files from {deploy_dir}/ to your HF Space")
        
    except Exception as e:
        logger.error(f"❌ Deployment preparation failed: {e}")
        return False
    
    return True

if __name__ == "__main__":
    main()