Update app.py

app.py

@@ -121,7 +121,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
  else:
  affirm = ""
 
- return toxicity_score, label_score_dict, transcribed_text, affirm
+ return transcribed_text, toxicity_score, label_score_dict, affirm
  # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
  
 def positive_affirmations():
@@ -143,13 +143,12 @@ with gr.Blocks() as iface:
  sense_slider = gr.Slider(minimum=1, maximum=5, step=1.0, label="How readily do you want the tool to intervene? 1 = in extreme cases and 5 = at every opportunity")
  with gr.Column():
  aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
- text = gr.Textbox(label="Enter Text", placeholder="Enter text here...")
  submit_btn = gr.Button(label="Run")
  with gr.Column():
- out_val = gr.Textbox()
- out_class = gr.Label()
- out_text = gr.Textbox()
- out_affirm = gr.Textbox()
- submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class, explit_preference, sense_slider], outputs=[out_val, out_class, out_text, out_affirm])
+ out_text = gr.Textbox(label="Transcribed Audio")
+ out_val = gr.Textbox(label="Overall Toxicity")
+ out_class = gr.Label(label="Toxicity Class Breakdown")
+ out_affirm = gr.Textbox(label="Automated Text Message")
+ submit_btn.click(fn=classify_toxicity, inputs=[aud_input, anxiety_class, emo_class, explit_preference, sense_slider], outputs=[out_text, out_val, out_class, out_affirm])
 
 iface.launch()