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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(task="zero-shot-object-detection",model=path, device = 0 ) #device = 0 to use GPU rather than -1 which would be CPU
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
detector = self.pipeline(images=[image], candidate_labels=inputs["candidate_labels"])
return detector |