docker_images/common/app/pipelines/text_classification.py

from typing import Dict, List

from app.pipelines import Pipeline


class TextClassificationPipeline(Pipeline):
    def __init__(
        self,
        model_id: str,
    ):
        # 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 TextClassificationPipeline __init__ function"
        )

    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.
        """
        # IMPLEMENT_THIS
        raise NotImplementedError(
            "Please implement TextClassificationPipeline __call__ function"
        )