Update app.py

app.py

@@ -66,7 +66,7 @@ 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, intervention):
+def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, explitive_selection, slider):
     # Transcribe the audio file using Whisper ASR
     if audio_file != None:
         transcribed_text = pipe(audio_file)["text"]
@@ -119,8 +119,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
             affirm = positive_affirmations()
         else:
             affirm = ""
-            
-        print("output column: ", holder)
+ 
         return toxicity_score, classification_output, transcribed_text, affirm
         # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
     else: 
@@ -175,7 +174,6 @@ with gr.Blocks() as iface:
         anxiety_class = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
         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.")
-        intervention_type = gr.Dropdown(choices=["Therapy App", "Audio File", "Text Message"])
         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")
     with gr.Column():
         aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
@@ -186,6 +184,6 @@ with gr.Blocks() as iface:
         out_class = gr.Textbox()
         out_text = gr.Textbox()
         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])
+    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, out_affirm])
 
 iface.launch()