Update app.py

app.py

@@ -84,13 +84,14 @@ def classify_toxicity(audio_file, text_input, classify_anxiety):
     else: 
         # model = model_cache[model_name]
         # class_names = classify_anxiety.split(",")
-        class_names_temp = class_options.get(classify_anxiety, [])
+        class_names_list = class_options.get(classify_anxiety, [])
         class_str = ""
-        for elm in class_names_temp:
+        for elm in class_names_list:
             class_str += elm + ","
         #class_names = class_names_temp.split(",")
         class_names = class_str.split(",")
         print("class names ", class_names, "classify_anxiety ", classify_anxiety)
+        
         # tokenizer = get_tokenizer(multilingual=".en" not in model_name)
         tokenizer= WhisperTokenizer.from_pretrained("openai/whisper-large")
         model = "whisper-large"
@@ -116,13 +117,13 @@ def classify_toxicity(audio_file, text_input, classify_anxiety):
      
 with gr.Blocks() as iface:
     with gr.Column():
-        classify = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
+        anxiety_class = gr.Radio(["racism", "LGBTQ+ hate", "sexually explicit", "misophonia"])
     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, classify], outputs=out_text)
+    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class], outputs=out_text)
 
 iface.launch()