Zalmati / handler.py

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from typing import Dict, List, Any
from transformers import pipeline
import holidays


class EndpointHandler:
    def __init__(self, path=""):
        self.pipeline = pipeline("text-classification", model=path)
        self.holidays = holidays.US()

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
         data args:
              inputs (:obj: `str`)
              date (:obj: `str`)
        Return:
              A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        inputs = data.pop("inputs", data)
        # get additional date field
        date = data.pop("date", None)

        # check if date exists and if it is a holiday
        if date is not None and date in self.holidays:
            return [{"label": "happy", "score": 1}]

        # run normal prediction
        prediction = self.pipeline(inputs)
        return prediction

	from typing import Dict, List, Any
	from transformers import pipeline
	import holidays


	class EndpointHandler:
	def __init__(self, path=""):
	self.pipeline = pipeline("text-classification", model=path)
	self.holidays = holidays.US()

	def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
	"""
	data args:
	inputs (:obj: `str`)
	date (:obj: `str`)
	Return:
	A :obj:`list` \| `dict`: will be serialized and returned
	"""
	# get inputs
	inputs = data.pop("inputs", data)
	# get additional date field
	date = data.pop("date", None)

	# check if date exists and if it is a holiday
	if date is not None and date in self.holidays:
	return [{"label": "happy", "score": 1}]

	# run normal prediction
	prediction = self.pipeline(inputs)
	return prediction