# Import necessary libraries from transformers import ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests import torch import matplotlib.pyplot as plt # Load pre-trained feature extractor and model feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') # Load and display the image url = "URL_of_the_image" image = Image.open(requests.get(url, stream=True).raw) plt.imshow(image) plt.show() # Extract features from the image inputs = feature_extractor(images=image, return_tensors="pt") # Make predictions outputs = model(**inputs) logits = outputs.logits predicted_class_idx = logits.argmax(-1).item() # Get and print the predicted class name predicted_class = model.config.id2label[predicted_class_idx] print(f'Predicted class: {predicted_class}')