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64 lines
1.5 KiB
64 lines
1.5 KiB
import pandas as pd
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import logging
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from datetime import datetime
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import time
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import os
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import pickle
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import numpy as np
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from flask import Flask
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from flask_restful import Resource, Api, request
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from PIL import Image, ImageOps
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#image size after resize
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size = 100, 100
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#model = pickle.load(open('model.pkl', 'rb'))
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#print('model loaded')
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app = Flask(__name__)
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api = Api(app)
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class Predict(Resource):
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def post(self):
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print("new predict")
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start = time.time()
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filer = request.files['file']#open the uploaded image, and transform to the numpy array
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#process the img
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filer.save("currentimage.png")
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image = Image.open("currentimage.png")
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thumb = ImageOps.fit(image, size, Image.ANTIALIAS)
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image_data = np.array(thumb).flatten()[:100]
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#load the selected model
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model = pickle.load(open('model.pkl', 'rb'))
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print('model.pkl loaded')
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#predict with the selected model
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r = model.predict([image_data])[0]
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logging.info(" [result]: " + r)
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roundtrip = time.time() - start
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logging.info(" [roundtriptime]: " + str(roundtrip) + "s")
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return({'result': r})
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api.add_resource(Predict, '/predict')
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if __name__ == '__main__':
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log_directory = 'log'
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if not os.path.exists(log_directory):
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os.makedirs(log_directory)
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logfilename = log_directory + "/logging"+ datetime.now().strftime("%Y-%M-%d-%H:%m:%s") + ".log"
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logging.basicConfig(filename=logfilename,level=logging.DEBUG)
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print("server running")
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app.run(port='3000')
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