Update app.py

app.py

@@ -116,9 +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")
-            intervention_output(intervention)
+            intervene = intervention_output(intervention)
+        else:
+            intervene = " "
         
-        return toxicity_score, classification_output, transcribed_text
+        return toxicity_score, classification_output, transcribed_text, intervene
         # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
     else: 
         threshold = slider_logic(slider)
@@ -156,16 +158,17 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
             print("threshold exceeded!! Launch intervention")
         return classify_anxiety
 def intervention_output(intervene):
-            if intervene== "Audio File":
-                out_aud : gr.update(value="calm.wav", visible=True, autoplay=True)
+            if intervene == "Audio File":
                 print("audio updated")
+                return { out_aud : gr.update(value="calm.wav", visible=True, autoplay=True) }
             elif intervene == "Therapy App":
-                out_img : gr.update(visible=True, value="hrv-breathing.gif")
+                print("therapy app updated")
+                return { out_img : gr.update(value="hrv-breathing.gif", visible=True)}
             elif intervene == "Text Message":
                 phrase = positive_affirmations()
-                out_text : gr.update(visible=True, value=phrase)
+                return { out_text : gr.update(visible=True, value=phrase) }
             else:
-                pass
+                return " "
 
 def positive_affirmations():
     affirmations = [
@@ -192,9 +195,10 @@ with gr.Blocks() as iface:
     with gr.Column():
         out_val = gr.Textbox()
         out_class = gr.Textbox()
+    # with gr.Column(visible=False) as output_col:
         out_text = gr.Textbox(visible=False)
-        out_img = 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])
+    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])
 
 iface.launch()