Update app.py

app.py

@@ -36,7 +36,7 @@ def predict(img) -> Tuple[Dict, float]:
   vit.eval()
   with torch.inference_mode():
     # Pass transformed image through the model and turn the prediction logits into probaiblities
-    pred_probs = torch.softmax(effnetb2(img), dim=1)
+    pred_probs = torch.softmax(vit(img), dim=1)
 
   # Create a prediction label and prediction probability dictionary
   pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}