pipeline.py

import base64
from io import BytesIO
from typing import Any, Dict, List

from fashion_clip.fashion_clip import FashionCLIP
from PIL import Image


class PreTrainedPipeline:
    def __init__(self, path=""):
        self.model = FashionCLIP("fashion-clip")

    def __call__(self, inputs: str) -> List[float]:
        """
        Args:
            inputs (:obj:`str`):
                a string to get the features from.
        Return:
            A :obj:`list` of floats: The features computed by the model.
        """
        image = Image.open(BytesIO(base64.b64decode(inputs)))
        return self.model.encode_images([image], batch_size=1)[0].tolist()