Update app.py

app.py

@@ -33,14 +33,24 @@ transform = transforms.Compose([
     transforms.Normalize(mean=[0.5]*3, std=[0.5]*3)
 ])
 
+# ✅ Define classify_image first
+def classify_image(img: Image.Image):
+    img_tensor = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        _, predicted = torch.max(outputs, 1)
+        label = 'Fake' if predicted.item() == 0 else 'Real'
+    return label
+
+# ✅ Then wrap it in predict()
 def predict(img: Image.Image):
     return classify_image(img)
 
+# ✅ All set to go
 gr.Interface(
-    fn=predict,  # ✅ this matches the /run/predict endpoint your app calls
+    fn=predict,
     inputs=gr.Image(type="pil"),
     outputs="label",
     title="Luxury Item Authenticity Detector",
     description="Upload an image to check if it's a real or fake item."
 ).launch()
-