Update app.py

app.py

@@ -15,25 +15,25 @@ xcp_model = load_model(xcp_path)
 eff_model = load_model(eff_path)
 
 def predict(image):
-    # Use the full image directly (no face extraction)
+    # Resize & preprocess
     xcp_img = cv2.resize(image, (299, 299))
     eff_img = cv2.resize(image, (224, 224))
-
     xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
     eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]
-
+    
+    # Predict
     xcp_pred = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
     eff_pred = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
-
     avg_pred = (xcp_pred + eff_pred) / 2
     label = "Real" if avg_pred > 0.5 else "Fake"
-
-    return  label
+    
+    # ✅ Return dict instead of string
+    return {"label": label, "probability": round(float(avg_pred), 4)}
 
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="filepath"),
-    outputs=gr.JSON(),  # <-- very important
+    outputs=gr.JSON(),  # ✅ Now it actually returns a dict
     live=False
 )