demo.py

#!/usr/bin/env python3
"""
Demo script for the EEG Motor Imagery Music Composer
"""

import sys
import os
from pathlib import Path

# Add the current directory to Python path
current_dir = Path(__file__).parent
sys.path.insert(0, str(current_dir))

from data_processor import EEGDataProcessor
from classifier import MotorImageryClassifier
from sound_library import SoundManager
from utils import setup_logging, create_classification_summary
import numpy as np

def run_demo():
    """Run a simple demo of the system components."""
    print("🧠 EEG Motor Imagery Music Composer - Demo")
    print("=" * 50)
    
    # Initialize components
    print("Initializing components...")
    data_processor = EEGDataProcessor()
    classifier = MotorImageryClassifier()
    sound_manager = SoundManager()
    
    # Create mock data for demo
    print("Creating mock EEG data...")
    mock_data = np.random.randn(10, 32, 384).astype(np.float32)  # 10 samples, 32 channels, 384 time points
    mock_labels = np.random.randint(0, 6, 10)
    
    # Initialize classifier
    print("Loading classifier...")
    classifier.load_model(n_chans=32, n_times=384)
    
    print(f"Available sounds: {sound_manager.get_available_sounds()}")
    print()
    
    # Run classification demo
    print("Running classification demo...")
    print("-" * 30)
    
    for i in range(5):
        # Get random sample
        sample_idx = np.random.randint(0, len(mock_data))
        eeg_sample = mock_data[sample_idx]
        true_label = mock_labels[sample_idx]
        
        # Classify
        predicted_class, confidence, probabilities = classifier.predict(eeg_sample)
        predicted_name = classifier.class_names[predicted_class]
        true_name = classifier.class_names[true_label]
        
        # Create summary
        summary = create_classification_summary(predicted_name, confidence, probabilities)
        
        print(f"Sample {i+1}:")
        print(f"  True class: {true_name}")
        print(f"  Predicted: {summary['emoji']} {predicted_name} ({summary['confidence_percent']})")
        
        # Add to composition if confidence is high
        if confidence > 0.7 and predicted_name != 'neutral':
            sound_manager.add_layer(predicted_name, confidence)
            print(f"  ♪ Added to composition: {predicted_name}")
        else:
            print(f"  - Not added (low confidence or neutral)")
        
        print()
    
    # Show composition summary
    composition_info = sound_manager.get_composition_info()
    print("Final Composition:")
    print("-" * 20)
    if composition_info:
        for i, layer in enumerate(composition_info, 1):
            print(f"{i}. {layer['class']} (confidence: {layer['confidence']:.2f})")
    else:
        print("No composition layers created")
    
    print()
    print("Demo completed! 🎵")
    print("To run the full Gradio interface, execute: python app.py")

if __name__ == "__main__":
    try:
        run_demo()
    except KeyboardInterrupt:
        print("\nDemo interrupted by user")
    except Exception as e:
        print(f"Error running demo: {e}")
        import traceback
        traceback.print_exc()