Update app.py

app.py

@@ -2,19 +2,9 @@ import evaluate
 from evaluate.utils import launch_gradio_widget
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, pipeline, RobertaForSequenceClassification, RobertaTokenizer, AutoTokenizer
-import tempfile
 
-tmp = tempfile.NamedTemporaryFile()
-
- 
-# Define the list of available models
-available_models = {
-    "mskov/roberta-base-toxicity": "Roberta Finetuned Model"
-}
- 
- 
 # Create a Gradio interface with audio file and text inputs
-def classify_toxicity(audio_file, text_input, selected_model):
+def classify_toxicity(audio_file, text_input):
     # Transcribe the audio file using Whisper ASR
     if audio_file != None:
         whisper_module = evaluate.load("whisper")
@@ -26,7 +16,7 @@ def classify_toxicity(audio_file, text_input, selected_model):
         transcribed_text = text_input
  
     # Load the selected toxicity classification model
-    toxicity_module = evaluate.load("toxicity", selected_model)
+    toxicity_module = evaluate.load("toxicity",  "mskov/roberta-base-toxicity")
     #toxicity_module = evaluate.load("toxicity", 'DaNLP/da-electra-hatespeech-detection', module_type="measurement")
 
     toxicity_results = toxicity_module.compute(predictions=[transcribed_text])
@@ -39,11 +29,10 @@ def classify_toxicity(audio_file, text_input, selected_model):
 with gr.Blocks() as iface:
     with gr.Column():
         aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
-    with gr.Row():
         text = gr.Textbox(label="Enter Text", placeholder="Enter text here...")
         submit_btn = gr.Button(label="Submit")
-        radio = gr.Radio(available_models, type="value", label="Select Model")
+    with g.Column():
         out_text = gr.Textbox()
-    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, radio], outputs=out_text)
+    submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text], outputs=out_text)
 
 iface.launch()