| 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() | |
| latency = end - start | |
| return { | |
| "labels": prediction.labels, | |
| "scores": prediction.scores, | |
| "latency (secs.)": latency | |
| } |