| 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) |
|
|
| |
| image = Image.open(BytesIO(base64.b64decode(inputs['image']))) |
|
|
| |
| prediction = self.pipeline(images=[image], candidate_labels=inputs["candiates"]) |
| return prediction[0] |
