import numpy as np
import logging
from .base_processor import AudioProcessor

logger = logging.getLogger(__name__)

class MelSpectrogramProcessor(AudioProcessor):
    """
    Mel Spectrogram processor using mel-scale frequency analysis.
    
    Future implementation will:
    - Apply mel filterbank to frequency domain representation
    - Use perceptually-motivated frequency scaling
    - Feed mel spectrogram features to deep learning model
    
    Currently returns placeholder '00' for testing UI functionality.
    """
    
    def __init__(self):
        super().__init__("Mel Spectrogram")
        logger.info("Mel Spectrogram processor initialized (PLACEHOLDER MODE)")
    
    def process_audio(self, audio_data: bytes) -> str:
        """
        Process audio using mel-scale spectrogram analysis.
        
        PLACEHOLDER IMPLEMENTATION:
        Currently returns '00' for UI testing purposes.
        
        Future implementation will:
        1. Convert audio bytes to numpy array
        2. Compute STFT of the audio signal
        3. Apply mel filterbank to convert to mel scale
        4. Take logarithm for perceptual scaling
        5. Feed to trained neural network (CNN/RNN)
        6. Return predicted digit
        
        Args:
            audio_data: Raw audio bytes
            
        Returns:
            Predicted digit as string (currently '00')
        """
        logger.debug("Processing audio with Mel Spectrogram (placeholder)")
        
        # Simulate processing time
        import time
        time.sleep(0.15)
        
        # TODO: Implement actual mel spectrogram processing:
        # 1. audio_array = np.frombuffer(audio_data, dtype=np.float32)
        # 2. mel_spec = librosa.feature.melspectrogram(
        #        y=audio_array, 
        #        sr=sample_rate,
        #        n_mels=128, 
        #        fmax=8000
        #    )
        # 3. mel_db = librosa.power_to_db(mel_spec, ref=np.max)
        # 4. prediction = self.neural_model.predict(mel_db)
        # 5. return str(np.argmax(prediction))
        
        return '00'
    
    def get_model_info(self) -> dict:
        """Get information about the mel spectrogram model."""
        return {
            'method': 'Mel Spectrogram',
            'status': 'PLACEHOLDER',
            'features': 'Mel-scale frequency representation',
            'classifier': 'CNN/RNN (not implemented)',
            'n_mels': 128,
            'fmax': 8000,
            'expected_inference_time': '<500ms'
        }