from __future__ import absolute_import, division, print_function, unicode_literals

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

from PIL import Image
import PIL.ImageOps


#print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
                       'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# train_images.shape

# len(train_labels)

# train_labels

# test_images.shape

# len(test_labels)

# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.show()

train_images = train_images / 255.0
test_images = test_images / 255.0

#Display the first 25 images from the training set and display the class name below each image. 

# plt.figure(figsize=(10,10))
# for i in range(25):
#     plt.subplot(5,5,i+1)
#     plt.xticks([])
#     plt.yticks([])
#     plt.grid(False)
#     plt.imshow(train_images[i], cmap=plt.cm.binary)
#     plt.xlabel(class_names[train_labels[i]])
   # plt.show()

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

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

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels)

# print('Test accuracy:', test_acc)
# predictions = model.predict(test_images)

# predictions[0]

# np.argmax(predictions[0])
# test_labels[0]

def plot_image(i, predictions_array, true_label, img):
  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])
  
  plt.imshow(img, cmap=plt.cm.binary)
  
  predicted_label = np.argmax(predictions_array)
  if predicted_label == true_label:
    color = 'blue'
  else:
    color = 'red'
  
  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)

def plot_value_array(i, predictions_array, true_label):
  predictions_array, true_label = predictions_array[i], true_label[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])
  thisplot = plt.bar(range(10), predictions_array, color="#777777")
  plt.ylim([0, 1])
  predicted_label = np.argmax(predictions_array)
  
  thisplot[predicted_label].set_color('red')
  thisplot[true_label].set_color('blue')

# Plot the first X test images, their predicted label, and the true label
# Color correct predictions in blue, incorrect predictions in red
# num_rows = 5
# num_cols = 3
# num_images = num_rows*num_cols
# plt.figure(figsize=(2*2*num_cols, 2*num_rows))
# for i in range(num_images):
#   plt.subplot(num_rows, 2*num_cols, 2*i+1)
#   plot_image(i + 9000, predictions, test_labels, test_images)
#   plt.subplot(num_rows, 2*num_cols, 2*i+2)
#   plot_value_array(i, predictions, test_labels)
# plt.show()



img = Image.open('test1.jpeg')
img = img.resize((28,28), Image.ANTIALIAS)
img = PIL.ImageOps.invert(img)
img = img.convert('L')
img = np.array(img)
img = (np.expand_dims(img,0))

pred = model.predict(img)
print(pred)
print(class_names[np.argmax(pred[0])])

img = Image.open('test2.jpeg')
img = img.resize((28,28), Image.ANTIALIAS)
img = PIL.ImageOps.invert(img)
img = img.convert('L')
img = np.array(img)
img = (np.expand_dims(img,0))

pred = model.predict(img)
print(pred)
print(class_names[np.argmax(pred[0])])

img = Image.open('test3.jpeg')
img = img.resize((28,28), Image.ANTIALIAS)
img = PIL.ImageOps.invert(img)
img = img.convert('L')
img = np.array(img)
img = (np.expand_dims(img,0))

pred = model.predict(img)
print(pred)
print(class_names[np.argmax(pred[0])])


img = Image.open('edited_7.png')
img = np.array(img)
img = (np.expand_dims(img,0))
pred = model.predict(img)

print(pred)
print(class_names[np.argmax(pred[0])])