import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.utils.np_utils import to_categorical
import random 

np.random.seed(0)
(X_train,y_train),(X_test,y_test)=mnist.load_data()

assert(X_train.shape[0]==y_train.shape[0]), 'The number of images is not equal to the number of labels.'
assert(X_test.shape[0]==y_test.shape[0]), 'The number of images is not equal to the number of labels.'
assert(X_train.shape[1:]==(28,28)), 'The number of images are not 28x28.'
assert(X_test.shape[1:]==(28,28)), 'The number of images are not 28x28.'

num_of_samples=[]
cols = 5
num_classes = 10

fig, axs = plt.subplots(nrows=num_classes, ncols = cols, figsize=(5, 8))
fig.tight_layout()

for i in range(cols):
    for j in range(num_classes):
        x_selected = X_train[y_train == j]
        axs[j][i].imshow(x_selected[random.randint(0, len(x_selected - 1)), :, :], cmap=plt.get_cmap("gray"))
        axs[j][i].axis("off")
        if i == 2:
            axs[j][i].set_title(str(j))
            num_of_samples.append(len(x_selected))

print(num_of_samples)
plt.figure(figsize=(12, 4))
plt.bar(range(0, num_classes), num_of_samples)
plt.title("Distribution of the training dataset")
plt.xlabel("Class number")
plt.ylabel("Number of images")

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

X_train = X_train/255 
X_test = X_test/255

num_pixels = 784
X_train = X_train.reshape(X_train.shape[0], num_pixels)
X_test = X_test.reshape(X_test.shape[0], num_pixels)

def create_model():
    model = Sequential()
    model.add(Dense(10, input_dim=num_pixels, activation='relu'))
    model.add(Dense(30, activation='relu'))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    model.compile(Adam(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])
    return model

model=create_model()
print(model.summary())

h=model.fit(X_train,y_train,validation_split=0.1,epochs=10,batch_size=200,verbose=1, shuffle=1)

plt.plot(h.history['loss'])
plt.plot(h.history['val_loss'])
plt.plot(h.history['accuracy'])

score=model.evaluate(X_test,y_test, verbose=0)
print(score)

import requests
from PIL import Image

url = 'https://www.researchgate.net/profile/Jose_Sempere/publication/221258631/figure/fig1/AS:305526891139075@1449854695342/Handwritten-digit-2.png'
response=requests.get(url,stream=True)
print(response)
img=Image.open(response.raw)
plt.imshow(img)

import cv2
img_array=np.asarray(img)
resized=cv2.resize(img_array,(28,28))
grey_scale=cv2.cvtColor(resized,cv2.COLOR_BGR2GRAY)
image=cv2.bitwise_not(grey_scale)
image=image/255
image=image.reshape(1,784)

prediction=model.predict_classes(image)
print('predicited digits',str(prediction))