from PIL import Image
from transformers import CLIPProcessor, CLIPModel
import torch


clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")


async def get_clip_embedding(image: Image.Image):
    inputs = clip_processor(images=image, return_tensors="pt")
    with torch.no_grad():
        embedding = clip_model.get_image_features(**inputs)
        embedding = embedding / embedding.norm(p=2, dim=-1)
    return embedding.squeeze().tolist()