app.py

import os
#Define backend as tensorflow
os.environ['KERAS_BACKEND']='tensorflow'
#It is important to import keras after changing backend
import keras
from flask import Flask, render_template,request
from scipy.misc import imsave, imread, imresize
import numpy as np
#import keras.models
import re

import sys 
import os
sys.path.append(os.path.abspath("./model"))
from load import * 

app = Flask(__name__)

global model, graph

model, graph = init()

def convertImage(imgData1):
	imgstr = re.search(r'base64,(.*)',imgData1).group(1)
	#print(imgstr)
	with open('output.png','wb') as output:
		output.write(imgstr.decode('base64'))
	

@app.route('/')
def index():
	return render_template("index.html")

@app.route('/predict/',methods=['GET','POST'])
def predict():
	imgData = request.get_data()

	convertImage(imgData)
	print("debug")

	x = imread('output.png',mode='L')
	x = np.invert(x)
	x = imresize(x,(28,28))
	x = x.reshape(1,28,28,1)

	print("debug2")
	with graph.as_default():
		out = model.predict(x)
		print(out)
		print(np.argmax(out,axis=1))
		print("debug3")
		response = np.array_str(np.argmax(out,axis=1))
		return response	
	

if __name__ == "__main__":
	#decide what port to run the app in
	#port = int(os.environ.get('PORT', 5000))

	#run the app locally on the givn port
	app.run(host='127.0.0.1', port=1245)