Update app.py

app.py

@@ -13,7 +13,7 @@ whisper_model = whisper.load_model("small")
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-emotion_classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion')
+emotion_classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores = True)
 
 def translate_and_classify(audio):
     
@@ -40,8 +40,8 @@ def translate_and_classify(audio):
     print("Translated: " + translation.text)
     
     emotion = emotion_classifier(translation.text)
-    detected_emotion = emotion[0]["label"]
-    return transcription.text, detected_emotion
+    #detected_emotion = emotion[0]["label"]
+    return transcription.text, emotion
 
 css = """
         .gradio-container {
@@ -141,7 +141,7 @@ with gr.Blocks(css = css) as demo:
 
         with gr.Row():
             transcript_output = gr.Textbox(label="Transcription in the language you spoke", lines = 3)
-            emotion_output = gr.Textbox(label = "Detected Emotion")
+            emotion_output = gr.Label(label = "Detected Emotion")
     
     transcribe_audio.click(translate_and_classify, inputs = audio_input, outputs = [transcript_output,emotion_output])