Update app.py

app.py

@@ -16,6 +16,7 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 emotion_classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion')
 
 def translate(audio):
+    
     print("""
     —
     Sending audio to Whisper ...
@@ -42,12 +43,12 @@ def translate(audio):
 
 with gr.Blocks() as demo:
     gr.Markdown("""
-                # Emotion Detection From Speech Using Whisper
-    
+                ## Emotion Detection From Speech with Whisper
             """)
     
     with gr.Row():
         with gr.Column():
+            gr.Markdown(""" ### Record audio """)
             audio_input = gr.Audio(label = 'Record Audio Input',source="microphone",type="filepath")
             with gr.Row():
                 transcribe_audio = gr.Button('Transcribe')