Update app.py

app.py

@@ -1,43 +1,5 @@
 import evaluate
 from evaluate.utils import launch_gradio_widget
-import gradio as gr
-from transformers import AutoModelForSequenceClassification, pipeline, RobertaForSequenceClassification, RobertaTokenizer, AutoTokenizer
 
-
-# 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):
-    # Transcribe the audio file using Whisper ASR
-    whisper_module = evaluate.load("whisper")
-    transcription_results = whisper_module.compute(uploaded=audio_file)
-
-    # Extract the transcribed text
-    transcribed_text = transcription_results["transcription"]
-
-    # Load the selected toxicity classification model
-    toxicity_module = evaluate.load("toxicity", selected_model)
-    toxicity_results = toxicity_module.compute(predictions=[transcribed_text])
-
-    toxicity_score = toxicity_results["toxicity"][0]
-
-    return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}"
-
-iface = gr.Interface(
-    fn=classify_toxicity,
-    inputs=[
-        gr.Audio(source="upload", type="file", label="Upload Audio File (Optional)"),
-        "text",
-        gr.Radio(available_models, type="value", label="Select Model")
-    ],
-    outputs="text",
-    live=True,
-    title="Toxicity Classifier with ASR",
-    description="Upload an audio file or enter text to classify its toxicity using the selected model.",
-)
-
-launch_gradio_widget(iface)
+module = evaluate.load("toxicity")
+launch_gradio_widget(module)