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()