import os
import numpy as np
import cv2
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')
image_number = 1
while os.path.isfile(f"digits/digit{image_number}.png"):
    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 a {np.argmax(prediction)}")
        plt.imshow(img[0],cmap=plt.cm.binary)
        plt.show()
    except:
        print("error")
    finally:
        image_number+=1





