Update app.py

app.py

@@ -6,6 +6,7 @@ from evaluate.utils import launch_gradio_widget
 import gradio as gr
 import torch
 import pandas as pd
+import random
 import classify
 import replace_explitives
 from whisper.model import Whisper
@@ -115,6 +116,7 @@ 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!! Launch intervention")
+ intervention_output(intervention)
  
  return toxicity_score, classification_output, transcribed_text
  # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
@@ -153,8 +155,29 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
  if toxicity_score > threshold:
  print("threshold exceeded!! Launch intervention")
  return classify_anxiety
- 
+def intervention_output(intervene):
+ if intervene== "Audio File":
+ gr.Audio.play("calm.wav")
+ elif intervene == "Therapy App":
+ out_img : gr.update(visible=True, value="hrv-breathing.gif")
+ elif intervene == "Text Message":
+ phrase = positive_affirmations()
+ out_text : gr.update(visible=True, value=phrase)
+ else:
+ pass
+
+def positive_affirmations():
+ affirmations = [
+ "I have survived my anxiety before and I will survive again now",
+ "I am not in danger; I am just uncomfortable; this too will pass",
+ "I forgive and release the past and look forward to the future",
+ "I can't control what other people say but I can control my breathing and my response"
+ ]
+ selected_affirm = random.choice(affirmations)
+ return selected_affirm
+ 
 with gr.Blocks() as iface:
+ intervene_State = gr.State([])
  with gr.Column():
  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.")
@@ -168,7 +191,8 @@ with gr.Blocks() as iface:
  with gr.Column():
  out_val = gr.Textbox()
  out_class = gr.Textbox()
- out_text = gr.Textbox()
+ out_text = gr.Textbox(visible=False))
+ out_img = gr.Textbox(visible=False))
  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])
 
 iface.launch()