Update app.py

app.py

@@ -116,10 +116,12 @@ 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")
-            holder = intervention_output(intervention)
+            affirm = positive_affirmations()
+        else:
+            affirm = ""
             
         print("output column: ", holder)
-        return toxicity_score, classification_output, transcribed_text, holder
+        return toxicity_score, classification_output, transcribed_text, affirm
         # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
     else: 
         threshold = slider_logic(slider)
@@ -157,19 +159,6 @@ 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":
-                print("audio updated")
-                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 { output_col : gr.update(visible=True), out_img : gr.update(value="./hrv-breathing.gif", visible=True)}
-            elif intervene == "Text Message":
-                phrase = positive_affirmations()
-                return { output_col : gr.update(visible=True), out_text : gr.update(value=phrase, visible=True)}
-            else:
-                return " "
-
 def positive_affirmations():
     affirmations = [
         "I have survived my anxiety before and I will survive again now",
@@ -195,10 +184,8 @@ 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()
-        out_img = gr.Image(value="./hrv-breathing.gif")
-        out_aud = gr.Audio(value="./calm.wav")
-    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])
+        out_affirm = gr.Textbox()
+    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_affirm])
 
 iface.launch()