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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("image-to-text",model=path)
def __call__(self, data: Dict[str, Any]) -> str:
"""
data args:
images (:obj:`string`)
Return:
A str containing a caption for the text
"""
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])
return prediction[0] |