Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from audio_features import extract_features_with_time_series
+from model_utils import load_model, prepare_input_for_model, interpret_prediction
+
+MODEL_PATH = "your_model.keras"
+model = load_model(MODEL_PATH)
+
+def predict(audio_filepath):
+try:
+if audio_filepath is None:
+return {"error": "No audio file provided."}
+
+```
+    feats = extract_features_with_time_series(audio_filepath)
+    if feats is None:
+        return {"error": "Feature extraction failed."}
+
+    X = prepare_input_for_model(feats, model)
+    raw_pred = model.predict(X)
+    return interpret_prediction(raw_pred)
+
+except Exception as e:
+    return {"error": f"Prediction error: {e}"}
+```
+
+iface = gr.Interface(
+fn=predict,
+inputs=gr.Audio(source="upload", type="filepath", label="Upload audio file"),
+outputs=gr.Label(num_top_classes=2, label="Prediction (probabilities)"),
+title="Deepfake Audio Detector",
+description="Upload an audio file (wav/mp3). The app extracts MFCC/LFCC/etc. features, then runs your .keras model."
+)
+
+if **name** == "**main**":
+iface.launch()