cnn_training_demo.py

"""
Demo: CNN Training Interface Usage
=================================

This script demonstrates how to use the new CNN training interface in the Streamlit app.
"""

print("🎯 AI Image Deblurring - CNN Training Interface Demo")
print("=" * 60)
print()

print("The Streamlit application now includes a comprehensive CNN training interface!")
print()

print("📋 **Features Available in the UI:**")
print()

print("1. 🔧 **CNN Model Management** (in Sidebar)")
print("   • View model status (trained/not trained)")
print("   • Check model size and creation date")
print("   • Quick model testing and evaluation")
print()

print("2. 🚀 **Training Options:**")
print("   • ⚡ Quick Train: 500 samples, 10 epochs (~10-15 min)")
print("   • 🎯 Full Train: 2000 samples, 30 epochs (~45-60 min)")
print("   • ⚙️ Custom Training: Configure samples and epochs")
print()

print("3. 🧪 **Model Testing:**")
print("   • Test existing trained models")
print("   • View performance metrics (Loss, MAE, MSE)")
print("   • Performance interpretation and recommendations")
print()

print("4. 📊 **Dataset Management:**")
print("   • View current dataset status and size")
print("   • Add more training samples (500 at a time)")
print("   • Clear existing training dataset")
print("   • Automatic dataset creation during training")
print()

print("5. 🗑️ **Model Management:**")
print("   • Delete trained models when needed")
print("   • Confirmation dialogs for safety")
print("   • Automatic UI updates")
print()

print("📖 **How to Use:**")
print()

print("1. **Start the Application:**")
print("   streamlit run streamlit_app.py")
print()

print("2. **Access CNN Management:**")
print("   • Look for '🤖 CNN Model Management' in the sidebar")
print("   • Click to expand the training interface")
print()

print("3. **Train Your First Model:**")
print("   • Click '⚡ Quick Train' for a fast test")
print("   • Wait 10-15 minutes for training to complete")
print("   • See celebration animation when done! 🎉")
print()

print("4. **Test Your Model:**")
print("   • Click '🧪 Test Model' after training")
print("   • View performance metrics")
print("   • Get quality recommendations")
print()

print("5. **Use Trained Model:**")
print("   • Select 'CNN Enhancement' method")
print("   • Upload an image and see AI-powered results!")
print("   • Trained model automatically detected and used")
print()

print("🔄 **Training Workflow:**")
print()

print("   First Time Setup:")
print("   ├── No model exists → Train new model")
print("   ├── Choose Quick/Full/Custom training")
print("   ├── Wait for training completion")
print("   └── ✅ Model ready for use!")
print()

print("   Improving Existing Model:")
print("   ├── Add more dataset samples")
print("   ├── Retrain with more epochs")
print("   ├── Test performance improvements")
print("   └── 🚀 Enhanced model ready!")
print()

print("💡 **Tips for Best Results:**")
print()

print("• **Start with Quick Training** - Test the system first")
print("• **Use Full Training** - For production-quality results")
print("• **Add More Data** - If results aren't satisfactory")
print("• **Monitor Performance** - Use the test function regularly")
print("• **Keep Model** - Training is done once, use many times!")
print()

print("⚠️ **Important Notes:**")
print()

print("• Training is done IN the web interface - no command line needed!")
print("• You can use other enhancement methods while training")
print("• Model is automatically saved and reloaded on app restart")
print("• Dataset is reused to save time on subsequent training")
print("• Training progress is shown with progress bars and status updates")
print()

print("🎉 **Ready to Start?**")
print()

print("Run: streamlit run streamlit_app.py")
print("Then look for '🤖 CNN Model Management' in the sidebar!")
print()

print("Happy deblurring! 🚀✨")