<reponame>Ali-Parandeh/Data_Science_Playground
from sklearn.externals import joblib
from sklearn.linear_model import Ridge
from azureml.core.model import Model
import numpy
import json

def init():
    global model
    model_path = Model.get_model_path('sklearn_mnist_model.pkl')
    # deserialize the model file back into a sklearn model
    model = joblib.load(model_path)

# note you can pass in multiple rows for scoring
def run(raw_data):
    try:
        data = json.loads(raw_data)['data']
        data = numpy.array(data)
        result = model.predict(data)
        # you can return any datatype if it is JSON-serializable
        return result.tolist()
    except Exception as e:
        error = str(e)
        return error
