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