Update app.py

app.py

@@ -40,15 +40,17 @@ class_options = {
 
 pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large")
 
+def classify_emotion():
+        #### Emotion classification ####
+        emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
+        out_prob, score, index, text_lab = emotion_classifier.classify_file(audio_file)
+        return  emo_dict[text_lab[0]]
+    
 # Create a Gradio interface with audio file and text inputs
 def classify_toxicity(audio_file, text_input, classify_anxiety):
     # Transcribe the audio file using Whisper ASR
     if audio_file != None:
         transcribed_text = pipe(audio_file)["text"]
-        
-        #### Emotion classification ####
-        emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
-        out_prob, score, index, text_lab = emotion_classifier.classify_file(audio_file)
     
     else:
         transcribed_text = text_input
@@ -75,13 +77,9 @@ def classify_toxicity(audio_file, text_input, classify_anxiety):
         # classification_output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
         classification_output = text_classifier(sequence_to_classify, candidate_labels, multi_label=True)
         print(classification_output)
-    
-        #### Emotion classification ####
-        
-        emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
-        out_prob, score, index, text_lab = emotion_classifier.classify_file(audio_file)
+
      
-        return toxicity_score, classification_output, emo_dict[text_lab[0]], transcribed_text
+        return toxicity_score, classification_output, transcribed_text
         # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
     else: 
         model = whisper.load_model("large")