| from typing import Dict, Any | |
| from deepsparse import Pipeline | |
| from time import perf_counter | |
| class EndpointHandler: | |
| def __init__(self, path=""): | |
| self.pipeline = Pipeline.create(task="text-classification", model_path=path) | |
| def __call__(self, data: Dict[str, Any]) -> Dict[str, str]: | |
| """ | |
| Args: | |
| data (:obj:): prediction input text | |
| """ | |
| inputs = data.pop("inputs", data) | |
| start = perf_counter() | |
| prediction = self.pipeline(inputs) | |
| end = perf_counter() | |
| delta = end - start | |
| return prediction.json(), "latency: " + str(delta) + " secs." |