Update app.py

app.py

@@ -33,21 +33,17 @@ 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)
-        print("🔍 Prediction index:", predicted.item())  # Optional debug
-        label = 'Real' if predicted.item() == 0 else 'Fake'
+        label = 'Real' if predicted.item() == 0 else 'Fake'  # ✅ Flip if needed
     return label
 
-# ✅ Then wrap it in predict()
 def predict(img: Image.Image):
     return classify_image(img)
 
-# ✅ All set to go
 gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),