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, viz_state, slider):
+def classify_toxicity(audio_file, selected_sounds, slider):
     # Transcribe the audio file using Whisper ASR
     # transcribed_text = pipe(audio_file)["text"]
 
@@ -93,11 +93,9 @@ def classify_toxicity(audio_file, selected_sounds, viz_state, slider):
             score = class_score_dict[selected_class_name]
             if score > threshold:
                 print(f"Threshold exceeded for class '{selected_class_name}': Score = {score:.4f}")
-                viz_state = True
-                calm_image : gr.update(visible=True)
+                affirm = positive_affirmations()
             else:
-                viz_state = False
-                calm_image : gr.update(visible=False)
+                affirm = ""
 
 
 
@@ -110,7 +108,7 @@ def classify_toxicity(audio_file, selected_sounds, viz_state, 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)}, viz_state
+    return {class_name: score for class_name, score in zip(class_names, scores)}, affirm
     
 def positive_affirmations():
     affirmations = [
@@ -123,7 +121,6 @@ def positive_affirmations():
     return selected_affirm
     
 with gr.Blocks() as iface:
-    show_state = gr.State(False)
     with gr.Column():
         miso_sounds = gr.CheckboxGroup(["chewing", "breathing", "mouthsounds", "popping", "sneezing", "yawning", "smacking", "sniffling", "panting"])
         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")
@@ -133,8 +130,8 @@ with gr.Blocks() as iface:
     with gr.Column():
         # out_val = gr.Textbox()
         out_class = gr.Label()
-        calm_image = gr.Image(value="./hrv-breathing.gif", visible=False)
-    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, miso_sounds, show_state, sense_slider], outputs=[out_class, calm_image])
+        out_text = gr.Textbox()
+    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, miso_sounds, sense_slider], outputs=[out_class, out_text])
 
 
 iface.launch()