handler.py

from typing import Any, List, Dict
import torch

# from chronos import ChronosPipeline


class EndpointHandler:
    def __init__(self, path: str = "") -> None:
        # self.pipeline = ChronosPipeline.from_pretrained("amazon/chronos-t5-tiny")
        pass

    def __call__(self, data: Any) -> List[Dict[str, float]]:
        inputs = data.pop("inputs")
        # # parameters = data.pop("parameters", {"prediction_length"})

        # forecast = self.pipeline.predict(
        #     torch.tensor(inputs["context"]), prediction_length=5
        # )
        return {"response": [1, 2, 3]}