update README

test.py

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+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)