import torch
import torchvision.models as models
from PIL import Image
import torchvision.transforms as transforms

model = models.resnet50(pretrained=True)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

image_path = '1706678996799-2.jpeg'
image = Image.open(image_path).convert('RGB')

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

input_data = transform(image).unsqueeze(0).to(device)

model.eval()

with torch.no_grad():
    output = model(input_data)

print(output)