import torch from PIL import Image import open_clip model, _, preprocess = open_clip.create_model_and_transforms("hf-hub:yyupenn/whyxrayclip") model.eval() tokenizer = open_clip.get_tokenizer("ViT-L-14") image = preprocess(Image.open("test_xray.jpg")).unsqueeze(0) text = tokenizer(["enlarged heart", "pleural effusion"]) with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True) text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) print("Label probs:", text_probs)