| import requests | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import tensorflow as tf | |
| from transformers import CLIPProcessor, TFCLIPModel | |
| model = TFCLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32") | |
| processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32") | |
| url = "https://huggingface.co/flaviagiammarino/pubmed-clip-vit-base-patch32/resolve/main/scripts/input.jpeg" | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| text = ["Chest X-Ray", "Brain MRI", "Abdominal CT Scan"] | |
| inputs = processor(text=text, images=image, return_tensors="tf", padding=True) | |
| probs = tf.squeeze(tf.nn.softmax(model(**inputs).logits_per_image, axis=-1)) | |
| plt.subplots() | |
| plt.imshow(image) | |
| plt.title("".join([x[0] + ": " + x[1] + "\n" for x in zip(text, [format(prob, ".4%") for prob in probs])])) | |
| plt.axis("off") | |
| plt.tight_layout() | |
| plt.show() | |