from keras.datasets import mnist
from keras.utils import to_categorical
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
import imageio.v2 as imageio
import numpy as np
from keras.models import load_model
from icecream import ic

# ---- Explore MNIST Dataset

(x_train, y_train), (x_test, y_test) = mnist.load_data()

image_index = 35
ic(y_train[image_index])
plt.imshow(x_train[image_index], cmap='Greys')
plt.show()

ic(x_train.shape)
ic(x_test.shape)

ic(y_train[:image_index + 1])

# ---- Cleaning Data

# save input image dimensions
img_rows, img_cols = 28, 28

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)

x_train = x_train / 255
x_test = x_test / 255

num_classes = 10

ic(x_train[image_index])
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# ---- Design a Model

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=(img_rows, img_cols, 1)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# ---- Compile and Train Model

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


batch_size = 128
epochs = 10

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test, verbose=0)
ic('Test loss:', score[0])
ic('Test accuracy:', score[1])
model.save("test_model.h5")

# ---- Test with Handwritten Digits

im = imageio.imread("https://i.imgur.com/a3Rql9C.png")

gray = np.dot(im[..., :3], [0.299, 0.587, 0.114])
plt.imshow(gray, cmap=plt.get_cmap('gray'))
plt.show()

# reshape the image
gray = gray.reshape(1, img_rows, img_cols, 1)

# normalize image
gray /= 255

# load the model
model = load_model("test_model.h5")

# predict digit
prediction = model.predict(gray)
ic(prediction.argmax())