Update app.py

app.py

@@ -49,7 +49,7 @@ def classify_emotion(audio):
     return  emo_dict[text_lab[0]]
     
 # Create a Gradio interface with audio file and text inputs
-def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, explitive_selection):
+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"]
@@ -57,6 +57,8 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
         transcribed_text = text_input
     if classify_anxiety != "misophonia":
         print("emo_class ", emo_class, "explitive select", explitive_selection)
+
+        ## SLIDER ##
         
         #------- explitive call ---------------
         
@@ -134,6 +136,7 @@ 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.")
+        sense_slider = gr.Slider(minimum=1, maximum=5, 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")
         text = gr.Textbox(label="Enter Text", placeholder="Enter text here...")
@@ -142,6 +145,6 @@ 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], outputs=[out_val, plot, 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])
 
 iface.launch()