pipeline.py

import json
from typing import Any, Dict, List

import sklearn
import os
import joblib
import numpy as np



class PreTrainedPipeline():
    def __init__(self, path: str):
        # load the model
        self.model = joblib.load((os.path.join(path, "pipeline.pkl"))

    def __call__(self, inputs: str) -> 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.
        """
        predictions = self.model.predict_proba([inputs])
        labels = []
        for cls in predictions[0]:
          labels.append({
                  "label": f"LABEL_{cls}",
                  "score": predictions[0][cls],
              })
        return labels