Update app.py

app.py

@@ -65,7 +65,7 @@ def slider_logic(slider):
     return threshold
 
 # Create a Gradio interface with audio file and text inputs
-def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, explitive_selection, slider):
+def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, explitive_selection, slider, intervention):
     # Transcribe the audio file using Whisper ASR
     if audio_file != None:
         transcribed_text = pipe(audio_file)["text"]
@@ -114,7 +114,7 @@ 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!!")
+            print("threshold exceeded!! Launch intervention")
         
         return toxicity_score, classification_output, transcribed_text
         # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
@@ -150,7 +150,8 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
         average_logprobs -= internal_lm_average_logprobs
         scores = average_logprobs.softmax(-1).tolist()
         return {class_name: score for class_name, score in zip(class_names, scores)}
-
+        if toxicity_score > threshold:
+            print("threshold exceeded!! Launch intervention")
         return classify_anxiety
      
 with gr.Blocks() as iface:
@@ -159,6 +160,7 @@ with gr.Blocks() as iface:
         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.")
         emo_class = gr.Radio(choices=["negaitve emotionality"], label="label", info="Select if you would like explitives to be considered anxiety-indiucing in the case of anger/ negative emotionality.")
         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")
+        intervention_type = gr.Dropdown(choices=["Audio", "Therapy App", "Text Message"])
     with gr.Column():
         aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
         text = gr.Textbox(label="Enter Text", placeholder="Enter text here...")
@@ -167,6 +169,6 @@ with gr.Blocks() as iface:
         out_val = gr.Textbox()
         out_class = gr.Textbox()
         out_text = gr.Textbox()
-    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class, explit_preference, sense_slider], 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], outputs=[out_val, out_class, out_text, intervention_type])
 
 iface.launch()