# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# SPDX-License-Identifier: MIT-0
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# software and associated documentation files (the "Software"), to deal in the Software
# without restriction, including without limitation the rights to use, copy, modify,
# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

import joblib
import numpy as np
import logging


logger = logging.getLogger(__name__)


class ScoringService(object):
    model = None

    @classmethod
    def load_model(cls):
        if not cls.model:
            cls.model = joblib.load("/opt/ml/model/model.joblib")

    @classmethod
    def predict_fn(cls, inp):
        out = cls.model.predict(inp)
        return out

    @classmethod
    def input_fn(cls, request):
        features = np.array(request.get_json()["features"])
        if features.ndim == 1:
            features = features.reshape(1, -1)
        return features