Update app.py

app.py

@@ -5,24 +5,26 @@ from tensorflow.keras.preprocessing.image import img_to_array
 from PIL import Image
 from huggingface_hub import hf_hub_download
 
-# Download the model from the Hugging Face model hub
+# Download and load the model
 model_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/cv_GP", filename="xception_model.h5")
 model = load_model(model_path)
 
-# Preprocessing and prediction
+# Inference function
 def predict(image):
-    # Resize image to expected shape (299x299x3 for Xception)
     image = image.resize((299, 299))
     image = img_to_array(image)
     image = np.expand_dims(image, axis=0)
-    image = image / 255.0  # Normalize
+    image = image / 255.0  # normalize to match training
 
     prob = model.predict(image)[0][0]
-    label = "Fake" if prob > 0.5 else "Real"
+
+    # Based on your notebook: label 0 = Fake, label 1 = Real
+    label = "Real" if prob > 0.5 else "Fake"
     confidence = round(float(prob if prob > 0.5 else 1 - prob), 3)
+
     return f"{label} ({confidence})"
 
-# Gradio UI
+# Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),