| from typing import Dict, List, Any | |
| from PIL import Image | |
| from io import BytesIO | |
| from transformers import pipeline | |
| import base64 | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.pipeline=pipeline("zero-shot-image-classification",model=path) | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| """ | |
| data args: | |
| images (:obj:`string`) | |
| candiates (:obj:`list`) | |
| Return: | |
| A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82} | |
| """ | |
| inputs = data.pop("inputs", data) | |
| # decode base64 image to PIL | |
| image = Image.open(BytesIO(base64.b64decode(inputs['image']))) | |
| # run prediction one image wit provided candiates | |
| prediction = self.pipeline(images=[image], candidate_labels=inputs["candiates"]) | |
| return prediction[0] |