score.py

# initialize the scoring logic by loading the model
def init():
 import numpy
 import scipy
 from sklearn.linear_model import LogisticRegression
 
 global model
 import pickle
 # load model file
 f = open('model.pkl', 'rb')
 # deserialize it into a scikit-learn model
 model = pickle.load(f)
 f.close()

def run(inputString):
 import json
 import numpy
 try:
 input_list = json.loads(inputString)
 except ValueError:
 return "bad input: expecting a JSON encoded list of lists."
 input_array = numpy.array(input_list)
 if (input_array.shape != (1, 4)):
 return 'bad input: expecting a JSON encoded list of lists of shape (1,4).'
 
 # make prediction
 score = model.predict(input_array)[0]
 return str(score)

if __name__ == '__main__':
 import json
 init()
 print (run(json.dumps([[1,2,3,4]])))