Update app.py

app.py

@@ -6,6 +6,7 @@ from evaluate.utils import launch_gradio_widget
 import gradio as gr
 import torch
 import classify
+import replace_explitives
 from whisper.model import Whisper
 from whisper.tokenizer import get_tokenizer
 from speechbrain.pretrained.interfaces import foreign_class
@@ -48,7 +49,7 @@ def classify_emotion(audio):
  return emo_dict[text_lab[0]], emostr
  
 # Create a Gradio interface with audio file and text inputs
-def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class):
+def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, explitive_selection):
  # Transcribe the audio file using Whisper ASR
  if audio_file != None:
  transcribed_text = pipe(audio_file)["text"]
@@ -56,6 +57,10 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class):
  else:
  transcribed_text = text_input
  if classify_anxiety != "misophonia":
+ # explitive call
+ if replace_explitives != None && emo_class == None:
+ transcribed_text = replace_explitives.sub_explitives(transcribed_text, explitive_selection)
+ 
  #### Toxicity Classifier ####
  
  toxicity_module = evaluate.load("toxicity", "facebook/roberta-hate-speech-dynabench-r4-target")
@@ -68,6 +73,7 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class):
  # emo call
  if emo_class != None:
  classify_emotion(audio_file)
+
  #### Text classification #####
  
  device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
@@ -121,13 +127,14 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class):
 with gr.Blocks() as iface:
  with gr.Column():
  anxiety_class = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
- emo_class = gr.Radio(choices=["negaitve emotionality"], label="label", info="info")
+ 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.")
  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...")
  submit_btn = gr.Button(label="Run")
  with gr.Column():
  out_text = gr.Textbox()
- submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class], outputs=out_text)
+ submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class, explit_preference], outputs=out_text)
 
 iface.launch()