Update app.py

app.py

@@ -8,32 +8,36 @@ from tensorflow.keras.applications.xception import preprocess_input as xcp_pre
 from tensorflow.keras.applications.efficientnet import preprocess_input as eff_pre
 from huggingface_hub import hf_hub_download
 
-# Download and load models
+# Load models
 xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="xception_model.h5")
 eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="efficientnet_model.h5")
 xcp_model = load_model(xcp_path)
 eff_model = load_model(eff_path)
 
 def predict(image):
-    # Use the full image directly (no face extraction)
+    # 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  label
+    # ✅ Simplest format: just the label (for gradio_client compatibility)
+    return label
 
+# ✅ Output changed from gr.Label → gr.Textbox to avoid JSON schema issues
 interface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy", label="Upload Image"),
-    outputs=gr.Label(label="Prediction"),
+    outputs=gr.Textbox(label="Prediction"),
     title="Deepfake Image Detector",
     description="Upload a full image. The model classifies it as real or fake."
 )