Update app.py

app.py

@@ -14,21 +14,38 @@ eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", fi
 xcp_model = load_model(xcp_path)
 eff_model = load_model(eff_path)
 
-def predict(image):
-    # Resize & preprocess
+def predict(image_path):
+    # Read the image from file path
+    image = cv2.imread(image_path)
+
+    # Check if loading failed
+    if image is None:
+        raise ValueError("Failed to load image. Make sure the input is an image file.")
+
+    # Convert BGR to RGB (OpenCV loads images in BGR)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Resize for each model
     xcp_img = cv2.resize(image, (299, 299))
     eff_img = cv2.resize(image, (224, 224))
+
+    # Preprocess
     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 dict instead of string
-    return {"label": label, "probability": round(float(avg_pred), 4)}
+
+    return {
+        "label": label,
+        "average": round(avg_pred, 3),
+        "xception": round(xcp_pred, 3),
+        "efficientnet": round(eff_pred, 3)
+    }
 
 iface = gr.Interface(
     fn=predict,