Update app.py

app.py

@@ -8,17 +8,17 @@ 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):
+def predict(img):
     try:
         # Resize input
-        xcp_img = cv2.resize(image, (299, 299))
-        eff_img = cv2.resize(image, (224, 224))
+        xcp_img = cv2.resize(img, (299, 299))
+        eff_img = cv2.resize(img, (224, 224))
 
         # Preprocess for each model
         xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
@@ -29,20 +29,18 @@ def predict(image):
         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 plain string (must be str type)
-        return str(label)
+        return "Real" if avg_pred > 0.5 else "Fake"
 
     except Exception as e:
         return "Error: " + str(e)
 
-interface = gr.Interface(
+# ✅ Use literal type-safe components
+demo = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="numpy", label="Upload Image"),
-    outputs=gr.Textbox(label="Prediction"),  # ✅ Explicit textbox to return a clean str
-    title="Deepfake Image Detector",
-    description="Upload a full image. The model classifies it as real or fake."
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Textbox(),
+    allow_flagging="never"
 )
 
-interface.launch()
+if __name__ == "__main__":
+    demo.launch()