#!/usr/bin/python3
# wut-ml

import os
import numpy as np
import tensorflow.python.keras
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from tensorflow.python.keras import optimizers
from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.preprocessing.image import load_img
from tensorflow.python.keras.preprocessing.image import img_to_array

datagen = ImageDataGenerator()
train_it = datagen.flow_from_directory('data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('data/test/', class_mode='binary')
batchX, batchy = train_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))
img_width=256
img_height=256
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

prediction = model.predict(x=test_it, batch_size=None, verbose=0, steps=None, use_multiprocessing=True)
print(prediction)

if prediction[0][0] == 1:
  rating = 'bad'
else:
  rating = 'good'
print('Observation: %s' % (rating))