"""
Quick CNN Training Script
========================

Simple script to quickly train the CNN model for the Image Deblurring application.
"""

import os
import sys

# Add current directory to path for imports
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_dir)

def main():
    print("🎯 AI Image Deblurring - CNN Model Training")
    print("=" * 50)
    print()
    
    # Import after path setup
    from modules.cnn_deblurring import CNNDeblurModel
    
    # Check if model already exists
    model_path = "models/cnn_deblur_model.h5"
    if os.path.exists(model_path):
        print("⚠️ A trained model already exists!")
        print(f"   Location: {model_path}")
        
        choice = input("\nDo you want to:\n  (1) Keep existing model\n  (2) Train new model (overwrites existing)\n\nChoice (1/2): ").strip()
        
        if choice == "1":
            print("✅ Keeping existing model. You can start using the application!")
            return
        elif choice != "2":
            print("❌ Invalid choice. Exiting.")
            return
    
    print("🚀 Starting CNN Model Training...")
    print()
    
    # Choose training mode
    print("Training Options:")
    print("  1. Quick Training (Recommended for testing)")
    print("     • 500 samples, 10 epochs")
    print("     • Training time: ~10-15 minutes")
    print("     • Good for initial testing")
    print()
    print("  2. Standard Training")
    print("     • 1000 samples, 20 epochs") 
    print("     • Training time: ~20-30 minutes")
    print("     • Balanced quality and time")
    print()
    print("  3. Full Training")
    print("     • 2000 samples, 30 epochs")
    print("     • Training time: ~45-60 minutes")
    print("     • Best quality results")
    
    while True:
        choice = input("\nSelect training mode (1/2/3): ").strip()
        
        if choice == "1":
            samples, epochs = 500, 10
            break
        elif choice == "2":
            samples, epochs = 1000, 20
            break
        elif choice == "3":
            samples, epochs = 2000, 30
            break
        else:
            print("❌ Invalid choice. Please enter 1, 2, or 3.")
    
    print(f"\n🎯 Training Configuration:")
    print(f"   Samples: {samples}")
    print(f"   Epochs: {epochs}")
    print(f"   Model will be saved to: {model_path}")
    print()
    
    # Confirm training
    confirm = input("Start training? (y/N): ").strip().lower()
    if confirm != 'y':
        print("❌ Training cancelled.")
        return
    
    try:
        # Create model and train
        print("\n🏗️ Initializing CNN model...")
        model = CNNDeblurModel()
        
        print("📊 Starting training process...")
        print("   This will:")
        print("   1. Create synthetic blur dataset")
        print("   2. Build U-Net CNN architecture") 
        print("   3. Train the model with early stopping")
        print("   4. Save the trained model")
        print()
        
        success = model.train_model(
            epochs=epochs,
            batch_size=16,
            validation_split=0.2,
            use_existing_dataset=True,
            num_training_samples=samples
        )
        
        if success:
            print("\n🎉 Training Completed Successfully!")
            print("=" * 40)
            print(f"✅ Model saved to: {model_path}")
            print("✅ Training dataset created and saved")
            
            # Test the model
            print("\n🧪 Testing trained model...")
            metrics = model.evaluate_model()
            if metrics:
                print("📊 Model Performance:")
                print(f"   Loss: {metrics['loss']:.4f}")
                print(f"   Mean Absolute Error: {metrics['mae']:.4f}")
                print(f"   Mean Squared Error: {metrics['mse']:.4f}")
                
                if metrics['loss'] < 0.05:
                    print("🌟 Excellent! Your model is ready for high-quality deblurring!")
                elif metrics['loss'] < 0.1:
                    print("👍 Good! Your model will provide decent deblurring results.")
                else:
                    print("⚠️ Model trained but may need more training for optimal results.")
            
            print("\n🚀 Next Steps:")
            print("   1. Run the main application: streamlit run streamlit_app.py")
            print("   2. Upload a blurry image")
            print("   3. Select 'CNN Enhancement' method")
            print("   4. Enjoy high-quality AI deblurring!")
            
        else:
            print("\n❌ Training Failed!")
            print("   Check the error messages above for details.")
            print("   You can still use other enhancement methods in the application.")
    
    except KeyboardInterrupt:
        print("\n⚠️ Training interrupted by user.")
        print("   Partial progress may be saved.")
    
    except Exception as e:
        print(f"\n❌ Training error: {e}")
        print("   You can still use traditional enhancement methods.")

if __name__ == "__main__":
    main()