from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

# Load the CIFAR-10 dataset
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0

url = 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog10.png'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('nateraw/vit-base-patch16-224-cifar10')
model = ViTForImageClassification.from_pretrained('nateraw/vit-base-patch16-224-cifar10')
inputs = feature_extractor(images=test_images[10], return_tensors="pt")
outputs = model(**inputs)
preds = outputs.logits.argmax(dim=1)

classes = [
    'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'
]
print(classes[test_labels[10][0]])
print(classes[preds[0]])