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

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')

def recognition():
    image_number = random.randint(1, 9)
    try:
        img = cv2.imread(f"digits/digit{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!")

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

    print(loss)
    print(accuracy)
recognition()