Update app.py

app.py

@@ -151,27 +151,33 @@ def detect_language(audio_file):
     try:
         # Convert audio to WAV format
         wav_path = convert_to_wav(audio_file)
+        logger.info(f"Audio file converted to WAV: {wav_path}")
         
         # Define device and compute type for faster-whisper
         device = "cuda" if torch.cuda.is_available() else "cpu"
         compute_type = "float32" if device == "cuda" else "int8"
+        logger.info(f"Using device: {device}, compute_type: {compute_type}")
         
         # Load the faster-whisper model for language detection
         model = WhisperModel(MODELS["Faster Whisper Large v3"], device=device, compute_type=compute_type)
+        logger.info("Faster-Whisper model loaded successfully.")
         
         # Detect the language using faster-whisper
         segments, info = model.transcribe(wav_path, task="translate", language=None)
         detected_language_code = info.language
+        logger.info(f"Detected language code: {detected_language_code}")
         
         # Get the full language name from the code
         detected_language = CODE_TO_LANGUAGE_NAME.get(detected_language_code, "Unknown Language")
+        logger.info(f"Detected language: {detected_language}")
         
         # Clean up temporary WAV file
         os.remove(wav_path)
+        logger.info("Temporary WAV file removed.")
         
         return f"Detected Language: {detected_language}"
     except Exception as e:
-        logger.error(f"Error in detect_language: {str(e)}")
+        logger.error(f"Error in detect_language: {str(e)}", exc_info=True)
         return f"Error: {str(e)}"
 
 def remove_silence(audio_file, silence_threshold=-40, min_silence_len=500):