Update app.py

app.py

@@ -9,41 +9,36 @@ 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
 
-# Load models
+# Load models once
 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_pil: Image.Image) -> str:
+def predict(image: Image.Image) -> str:
     try:
-        # Convert PIL to numpy
-        image = np.array(image_pil.convert("RGB"))
-
-        # Resize and preprocess
-        xcp_img = cv2.resize(image, (299, 299))
-        eff_img = cv2.resize(image, (224, 224))
+        image_np = np.array(image.convert("RGB"))
+        xcp_img = cv2.resize(image_np, (299, 299))
+        eff_img = cv2.resize(image_np, (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
 
+        avg_pred = (xcp_pred + eff_pred) / 2
         return "Real" if avg_pred > 0.5 else "Fake"
     except Exception as e:
-        return f"Error: {str(e)}"
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload Image"),  # ✅ Use PIL instead of numpy
-    outputs=gr.Textbox(label="Prediction"),             # ✅ Safe, schema-compatible
-    title="Deepfake Image Detector",
-    description="Upload a full image. The model classifies it as real or fake.",
-    allow_flagging="never"
-)
+        return "Error: " + str(e)
+
+# ✅ Use Blocks instead of Interface to avoid schema bugs
+with gr.Blocks() as demo:
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload Image")
+    with gr.Row():
+        output = gr.Textbox(label="Prediction")
+    image_input.change(fn=predict, inputs=image_input, outputs=output)
 
 if __name__ == "__main__":
     demo.launch()