Update app.py

app.py

@@ -1,5 +1,43 @@
 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.",
+)
 
-module = evaluate.load("toxicity")
-launch_gradio_widget(module)
+iface.launch()