models/embeddings.py

from sentence_transformers import SentenceTransformer

_embedder = None
def get_embedder():
    global _embedder
    if _embedder is None:
        _embedder = SentenceTransformer('clip-ViT-B-32')
    return _embedder

def get_image_embedding(image_bytes):
    from PIL import Image
    import io
    img = Image.open(io.BytesIO(image_bytes)).convert('RGB')
    embedder = get_embedder()
    embedding = embedder.encode(img, convert_to_numpy=True)
    return embedding.tolist()