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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]}
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