added vggcrop split=val

app.py

@@ -52,7 +52,8 @@ def predict(image):
         output = model(image)  # Perform inference on CPU
     _, predicted_class = output.max(1)
     # Fetch 9 random samples from the predicted class
-    class_samples = ds.filter(lambda example: example['label'] == predicted_class.item())['train']
+    class_samples = ds.filter(lambda example: example['label'] == predicted_class.item())
+
     sample_images = random.sample(list(class_samples), min(len(class_samples), 9))
     
     sample_images_urls = [sample['image'] for sample in sample_images]