1import pandas as pd
 2import logging
 3from datetime import datetime
 4import time
 5import os
 6
 7import pickle
 8import numpy as np
 9
10
11from flask import Flask
12from flask_restful import Resource, Api, request
13
14
15from PIL import Image, ImageOps
16
17#image size after resize
18size = 100, 100
19
20#model = pickle.load(open('model.pkl', 'rb'))
21#print('model loaded')
22
23
24
25app = Flask(__name__)
26api = Api(app)
27
28
29class Predict(Resource):
30    def post(self):
31        print("new predict")
32        start = time.time()
33        filer = request.files['file']#open the uploaded image, and transform to the numpy array
34
35        #process the img
36        filer.save("currentimage.png")
37        image = Image.open("currentimage.png")
38        thumb = ImageOps.fit(image, size, Image.ANTIALIAS)
39        image_data = np.array(thumb).flatten()[:100]
40
41        #load the selected model
42        model = pickle.load(open('model.pkl', 'rb'))
43        print('model.pkl loaded')
44
45        #predict with the selected model
46        r = model.predict([image_data])[0]
47
48        logging.info("  [result]: " + r)
49        roundtrip = time.time() - start
50        logging.info("  [roundtriptime]: " + str(roundtrip) + "s")
51        return({'result': r})
52
53api.add_resource(Predict, '/predict')
54
55
56if __name__ == '__main__':
57    log_directory = 'log'
58    if not os.path.exists(log_directory):
59        os.makedirs(log_directory)
60    logfilename = log_directory + "/logging"+ datetime.now().strftime("%Y-%M-%d-%H:%m:%s") + ".log"
61    logging.basicConfig(filename=logfilename,level=logging.DEBUG)
62
63    print("server running")
64    app.run(port='3000')