| from typing import Dict, List, Union | |
| import os | |
| import joblib | |
| import numpy as np | |
| features = ["fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "free_sulfur_dioxide", "total_sulfur_dioxide", "density", "pH", "sulphates", "alcohol"] | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.model = joblib.load("sklearn_model.joblib") | |
| def __call__( | |
| self, inputs: Dict[str, Dict[str, List[Union[str, float]]]] | |
| ) -> List[Union[str, float]]: | |
| """ | |
| Args: | |
| inputs (:obj:`dict`): | |
| a dictionary containing a key 'data' mapping to a dict in which | |
| the values represent each column. | |
| Return: | |
| A :obj:`list` of floats or strings: The classification output for each row. | |
| """ | |
| data = inputs["data"] | |
| X = np.array([data[i] for i in features]) | |
| X = np.transpose(X, (1, 0)) | |
| return self.model.predict(X) | |