Integrate Honest CNN Classifier with Advanced System

## Overview
Integrate the working honest CNN classifier with the advanced raga detection system to create a hybrid approach.

## Current Status
✅ **Honest CNN Classifier** - Working system with 50% accuracy on small dataset
✅ **Advanced Raga Detection System** - Comprehensive cultural knowledge framework
�� **Integration Needed** - Combine both approaches for optimal performance

## Integration Strategy
- [ ] Use honest CNN as baseline/fallback system
- [ ] Integrate advanced cultural knowledge with CNN architecture
- [ ] Implement ensemble approach combining both systems
- [ ] Add cultural context to CNN predictions
- [ ] Create hybrid confidence scoring

## Technical Tasks
- [ ] Modify honest CNN to accept cultural features
- [ ] Add shruti analysis to CNN input features
- [ ] Implement gamaka detection in CNN pipeline
- [ ] Create ensemble prediction logic
- [ ] Add cultural insights to CNN output

## Files to Modify
- `ml/training/honest_raga_classifier.py`
- `ml/training/advanced_raga_detector.py`
- `ml/training/hybrid_classifier.py` (new)

## Success Criteria
- [ ] Hybrid system achieves >60% accuracy on test dataset
- [ ] Cultural insights generated for all predictions
- [ ] Fallback mechanism works when advanced system fails
- [ ] Performance metrics show improvement over individual systems

## Priority: High
This builds on our working foundation to create a production-ready system.