# -*- coding: utf-8 -*-
"""787antitheft.195

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1RuQfAM5faBjQTkTWdhahfka6eF7S0MGu
"""

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, epochs=3)

model.save('handwritten.model')

model = tf.keras.models.load_model('handwritten.model')

loss, accimaguracy = model.evaluate(x_test, y_test)

image_number = 1
while os.path.isfile(f"digits/digit{image_number}.png"):
  try:
    img = cv2.imread(f"digit/digits{image_number}.png")[:,:,0]
    img = np.invert(np.array([img]))
    prediction = model.predict(img)
    print(f"This digit is probably a {np.argmax(prediction)}")
    plt.imshow(img[0], cmap=plt.cm.binary)
    plt.show()
  except:
    print("Error!")
  finally:
    image_number += 1