Update app.py

app.py

@@ -6,31 +6,28 @@ from model_utils import load_model, prepare_input_for_model, interpret_predictio
 MODEL_PATH = "best_audio_model.keras"
 
 # Load model once when the app starts
-
 model = load_model(MODEL_PATH)
 
 def predict_audio(file_path):
-try:
-features = extract_features_with_time_series(file_path)
-if features is None:
-return {"Error": "Feature extraction failed. Please check the audio."}
+    try:
+        features = extract_features_with_time_series(file_path)
+        if features is None:
+            return {"Error": "Feature extraction failed. Please check the audio."}
 
-```
-    input_data = prepare_input_for_model(features, model)
-    raw_pred = model.predict(input_data)
-    result = interpret_prediction(raw_pred)
-    return result
-except Exception as e:
-    return {"Error": str(e)}
-```
+        input_data = prepare_input_for_model(features, model)
+        raw_pred = model.predict(input_data)
+        result = interpret_prediction(raw_pred)
+        return result
+    except Exception as e:
+        return {"Error": str(e)}
 
 iface = gr.Interface(
-fn=predict_audio,
-inputs=gr.Audio(type="filepath", label="Upload an audio file"),
-outputs=gr.Label(num_top_classes=2, label="Prediction"),
-title="Audio Deepfake Detection",
-description="Upload an audio clip to detect whether it is Real or Fake using a Keras model."
+    fn=predict_audio,
+    inputs=gr.Audio(type="filepath", label="Upload an audio file"),
+    outputs=gr.Label(num_top_classes=2, label="Prediction"),
+    title="Audio Deepfake Detection",
+    description="Upload an audio clip to detect whether it is Real or Fake using a Keras model."
 )
 
-if **name** == "**main**":
-iface.launch(server_name="0.0.0.0", server_port=7860)
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860