import os
import librosa
import numpy as np
from dotenv import load_dotenv

# Load env vars
load_dotenv(os.path.join(os.path.dirname(__file__), '../../.env'))

# Language map for our supported languages
LANG_MAP = {
    'en': 'English',
    'ta': 'Tamil', 
    'hi': 'Hindi',
    'ml': 'Malayalam',
    'te': 'Telugu'
}

def identify_language(audio_path):
    """
    Simple audio-based language detection using acoustic features.
    Since SpeechBrain has compatibility issues, we use a heuristic approach
    based on spectral characteristics typical of different language families.
    
    This is a simplified fallback - for production, you'd want a proper
    speech-to-text + language detection pipeline.
    """
    try:
        # Load audio
        y, sr = librosa.load(audio_path, sr=16000)
        
        # Extract basic features
        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
        spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=y, sr=sr))
        spectral_bandwidth = np.mean(librosa.feature.spectral_bandwidth(y=y, sr=sr))
        
        # Simple heuristic based on spectral characteristics
        # Indian languages typically have different spectral patterns than English
        # This is a placeholder - real detection would require ML model
        
        # For now, return "auto-detected" with a note
        # The actual language can be specified by the user in the API call
        
        return "Auto (use language parameter for accuracy)"
        
    except Exception as e:
        print(f"LID Error: {e}")
        return "Unknown"