| # 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 = "https://cdn.akamai.steamstatic.com/steam/apps/821880/header.jpg?t=1652241767" | |
| 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}') | |