Update app.py

app.py

@@ -116,11 +116,11 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
         # plot.update(x=classification_df["labels"], y=classification_df["scores"])
         if toxicity_score > threshold:
             print("threshold exceeded!! Launch intervention")
-            intervene = intervention_output(intervention)
+            output_col = intervention_output(intervention)
         else:
             intervene = " "
         
-        return toxicity_score, classification_output, transcribed_text, intervene
+        return toxicity_score, classification_output, transcribed_text, output_col
         # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
     else: 
         threshold = slider_logic(slider)
@@ -157,16 +157,17 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
         if toxicity_score > threshold:
             print("threshold exceeded!! Launch intervention")
         return classify_anxiety
+        
 def intervention_output(intervene):
             if intervene == "Audio File":
                 print("audio updated")
-                return { out_aud : gr.update(value="calm.wav", visible=True, autoplay=True) }
+                return { output_col : gr.update(visible=True), out_aud : gr.update(value="calm.wav", visible=True, autoplay=True)} 
             elif intervene == "Therapy App":
                 print("therapy app updated")
-                return { out_img : gr.update(value="hrv-breathing.gif", visible=True)}
+                return { output_col : gr.update(visible=True), out_img : gr.update(value="hrv-breathing.gif", visible=True)}
             elif intervene == "Text Message":
                 phrase = positive_affirmations()
-                return { out_text : gr.update(visible=True, value=phrase) }
+                return { output_col : gr.update(visible=True), out_text : gr.update(value=phrase, visible=True, value=phrase)}
             else:
                 return " "
 
@@ -181,7 +182,7 @@ def positive_affirmations():
     return selected_affirm
     
 with gr.Blocks() as iface:
-    intervene_State = gr.State([])
+       show_state = gr.State([])
     with gr.Column():
         anxiety_class = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
         explit_preference = gr.Radio(choices=["N-Word", "B-Word", "All Explitives"], label="Words to omit from general anxiety classes", info="certain words may be acceptible within certain contects for given groups of people, and some people may be unbothered by explitives broadly speaking.")
@@ -195,10 +196,10 @@ with gr.Blocks() as iface:
     with gr.Column():
         out_val = gr.Textbox()
         out_class = gr.Textbox()
-    # with gr.Column(visible=False) as output_col:
+    with gr.Column(visible=False) as output_col:
         out_text = gr.Textbox(visible=False)
         out_img = gr.Image(value="hrv-breathing.gif", visible=False)
         out_aud = gr.Audio(value="calm.wav", visible=False)
-    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class, explit_preference, sense_slider, intervention_type], outputs=[out_val, out_class, out_text, out_text])
+    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class, explit_preference, sense_slider, intervention_type], outputs=[out_val, out_class, out_text, output_col])
 
 iface.launch()