Update app.py

app.py

@@ -50,7 +50,7 @@ def slider_logic(slider):
     return threshold
 
 # Create a Gradio interface with audio file and text inputs
-def classify_toxicity(audio_file, selected_sounds, slider):
+def classify_toxicity(audio_file, selected_sounds, viz_state, slider):
     # Transcribe the audio file using Whisper ASR
     # transcribed_text = pipe(audio_file)["text"]
 
@@ -93,7 +93,9 @@ def classify_toxicity(audio_file, selected_sounds, slider):
             score = class_score_dict[selected_class_name]
             if score > threshold:
                 print(f"Threshold exceeded for class '{selected_class_name}': Score = {score:.4f}")
-                show_state : True
+                viz_state : True
+            else:
+                viz_state : False
 
 
     '''
@@ -105,7 +107,7 @@ def classify_toxicity(audio_file, selected_sounds, slider):
     # miso_label_dict = {label: score for label, score in classify_anxiety[0].items()}
     holder2 = ""
     holder3= " "
-    return {class_name: score for class_name, score in zip(class_names, scores)}
+    return {class_name: score for class_name, score in zip(class_names, scores)}, viz_state
     
 def positive_affirmations():
     affirmations = [
@@ -129,7 +131,7 @@ with gr.Blocks() as iface:
         # out_val = gr.Textbox()
         out_class = gr.Label()
         calm_image = gr.Image(value="./hrv-breathing.gif", visible=show_state)
-    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, miso_sounds, sense_slider], outputs=out_class)
+    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, miso_sounds, show_state, sense_slider], outputs=[out_class, calm_image])
 
 
 iface.launch()