Update app.py

app.py

@@ -28,20 +28,6 @@ pipe = pipeline("automatic-speech-recognition")
 def classify_toxicity(audio_file, text_input, classify_anxiety):
     # Transcribe the audio file using Whisper ASR
     if audio_file != None:
-        '''whisper_model = WhisperModel.from_pretrained("openai/whisper-base")
-        feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
-        transcription_results = whisper_model.compute(uploaded=audio_file)
-        
-        audio = whisper.load_audio(audio_file)
-        mel = whisper.log_mel_spectrogram(audio).to(model.device)
-        _, probs = model.detect_language(mel)    
-        options = whisper.DecodingOptions(fp16 = False)
-        result = whisper.decode(model, mel, options)
-        # Extract the transcribed text
-        # transcribed_text = transcription_results["transcription"]
-        '''
-        # model = whisper.load_model("base")
-        # transcribed_text = model.transcribe(audio_file)
         transcribed_text = pipe(audio_file)["text"]
         
         #### Emotion classification ####