pipeline.py

from typing import  Dict, List
import numpy as np

class PreTrainedPipeline():
    def __init__(self, path=""):
        # IMPLEMENT_THIS
        # Preload all the elements you are going to need at inference.
        # For instance your model, processors, tokenizer that might be needed.
        # This function is only called once, so do all the heavy processing I/O here"""
        # raise NotImplementedError(
        #     "Please implement PreTrainedPipeline __init__ function"
        # )
        pass

    def __call__(self, inputs: str, *args, **kargs) -> List[List[Dict[str, float]]]:
        """
        Args:
            inputs (:obj:`str`):
                a string containing some text
        Return:
            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
                - "label": A string representing what the label/class is. There can be multiple labels.
                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
        """
        # IMPLEMENT_THIS
        # raise NotImplementedError(
        #     "Please implement PreTrainedPipeline __call__ function"
        # )
        return [[{"label": inputs, "score":0.2}, {"args": args}, kargs]]