Update app.py

app.py

@@ -42,10 +42,10 @@ class_options = {
 pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large")
 
 def classify_emotion(audio):
-        #### 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)
-        return  emo_dict[text_lab[0]]
+    #### 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)
+    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, emo_class, explitive_selection):
@@ -55,7 +55,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
     else:
         transcribed_text = text_input
     if classify_anxiety != "misophonia":
-            print("emo_class ", emo_class, "explitive select", explitive_selection)
+        print("emo_class ", emo_class, "explitive select", explitive_selection)
         # explitive call
         if replace_explitives != None and emo_class == None:
             transcribed_text = replace_explitives.sub_explitives(transcribed_text, explitive_selection)