Update app.py

app.py

@@ -1,23 +1,22 @@
 import gradio as gr
+from transformers import pipeline
 
-def predict_voice(audio_path):
-    # Load the Hugging Face model directly through gr.Interface.load
-    model = gr.Interface.load("models/Kabatubare/ast_celeb_spoof")
-    
-    # Since we're loading the model directly inside the prediction function,
-    # we use it immediately to make a prediction on the provided audio file.
-    predictions = model(audio_path)
-    
-    # Format predictions for display
+# Initialize the pipeline for audio classification
+# Ensure you have the transformers library installed
+model_pipeline = pipeline("audio-classification", model="Kabatubare/ast_celeb_spoof")
+
+def predict_voice(audio_file):
+    predictions = model_pipeline(audio_file.name)
+    # Format the predictions for display
     formatted_predictions = [f"Label: {prediction['label']}, Confidence: {prediction['score']:.4f}" for prediction in predictions]
-    return formatted_predictions
+    return "\n".join(formatted_predictions)
 
 # Define the Gradio interface
 iface = gr.Interface(
     fn=predict_voice,
-    inputs=gr.Audio(label="Upload Audio File", type="filepath"),
-    outputs=gr.Text(label="Model Predictions"),
-    title="Voice Authenticity Detection with AST Celeb Spoof",
+    inputs=gr.Audio(source="upload", type="file", label="Upload Audio File"),
+    outputs=gr.Text(label="Predictions"),
+    title="Voice Authenticity Detection",
     description="This model detects whether a voice is real or AI-generated. Upload an audio file to get started.",
     allow_flagging="never",
     theme="huggingface"