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()