main.py

import re
import base64
from flask import Flask, render_template,request
import cv2
import joblib
import numpy as np
from keras.models import Sequential
from keras.layers import Flatten,Dense
import keras.backend as K

app = Flask(__name__)

def load_model():
    
    K.clear_session()
    model = Sequential()
    model.add(Flatten(input_shape=(28, 28)))
    model.add(Dense(512, activation='relu'))
    model.add(Dense(256, activation='relu'))
    model.add(Dense(10, activation='softmax'))
    model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])

    model.load_weights('FFNN-MNIST.h5')

    return model

@app.route('/')  #home page / represents root page. this will return the html page. 
def index():
    return render_template('drawdigits.html')

@app.route('/predictdigits/', methods=['GET','POST'])
def predict_digits():

    model=load_model()
    parseImage(request.get_data())
    
    img=cv2.imread('output.png')
    img=255-img
    img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    img=cv2.resize(img,(28,28))
    img=img.reshape(1,28,28)
    img=(img/255.0)

    result=model.predict(img)
    label=np.argmax(result,axis=1)[0]
    print(label)
    return str(label)

def parseImage(imgData):
    # parse canvas bytes and save as output.png
    imgstr = re.search(b'base64,(.*)', imgData).group(1)
    with open('output.png','wb') as output:
        output.write(base64.decodebytes(imgstr))

if __name__ == '__main__':
    app.run(debug=True)