import numpy as np
import logging
from .base_processor import AudioProcessor

logger = logging.getLogger(__name__)

class MFCCProcessor(AudioProcessor):
    """
    MFCC (Mel-Frequency Cepstral Coefficients) processor.
    
    Future implementation will:
    - Extract MFCC features (typically 12-13 coefficients)
    - Apply DCT (Discrete Cosine Transform) to mel spectrogram
    - Use traditional ML classifier (SVM, Random Forest, etc.)
    
    Currently returns placeholder '00' for testing UI functionality.
    """
    
    def __init__(self):
        super().__init__("MFCC")
        logger.info("MFCC processor initialized (PLACEHOLDER MODE)")
    
    def process_audio(self, audio_data: bytes) -> str:
        """
        Process audio using MFCC feature extraction.
        
        PLACEHOLDER IMPLEMENTATION:
        Currently returns '00' for UI testing purposes.
        
        Future implementation will:
        1. Convert audio bytes to numpy array
        2. Compute mel spectrogram of the audio
        3. Apply DCT to get cepstral coefficients
        4. Extract first 12-13 MFCC coefficients
        5. Optionally add delta and delta-delta features
        6. Feed to trained classifier (SVM/Random Forest)
        7. Return predicted digit
        
        Args:
            audio_data: Raw audio bytes
            
        Returns:
            Predicted digit as string (currently '00')
        """
        logger.debug("Processing audio with MFCC (placeholder)")
        
        # Simulate processing time (MFCC should be fastest)
        import time
        time.sleep(0.05)
        
        # TODO: Implement actual MFCC processing:
        # 1. audio_array = np.frombuffer(audio_data, dtype=np.float32)
        # 2. mfccs = librosa.feature.mfcc(
        #        y=audio_array, 
        #        sr=sample_rate,
        #        n_mfcc=13,
        #        n_fft=2048,
        #        hop_length=512
        #    )
        # 3. # Optionally add delta features
        # 4. delta_mfccs = librosa.feature.delta(mfccs)
        # 5. features = np.concatenate([mfccs, delta_mfccs], axis=0)
        # 6. prediction = self.svm_model.predict(features.T.flatten().reshape(1, -1))
        # 7. return str(prediction[0])
        
        return '00'
    
    def get_model_info(self) -> dict:
        """Get information about the MFCC model."""
        return {
            'method': 'MFCC (Mel-Frequency Cepstral Coefficients)',
            'status': 'PLACEHOLDER',
            'features': 'Cepstral coefficients with delta features',
            'classifier': 'SVM/Random Forest (not implemented)',
            'n_mfcc': 13,
            'n_fft': 2048,
            'hop_length': 512,
            'expected_inference_time': '<100ms'
        }