Use torch no grad for predictions

app.py

@@ -13,7 +13,8 @@ labels = dataset['train'].features['labels'].names
 
 def classify(im):
   features = extractor(im, return_tensors='pt')
-  logits = model(features["pixel_values"])[-1]
+  with torch.no_grad():
+    logits = model(features["pixel_values"])[-1]
   probability = torch.nn.functional.softmax(logits, dim=-1)
   probs = probability[0].detach().numpy()
   confidences = {label: float(probs[i]) for i, label in enumerate(labels)}