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://d168r5mdg5gtkq.cloudfront.net/medpix/img/full/synpic9078.jpg"
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()