import numpy
from keras.datasets import mnist
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import np_utils
from keras_preprocessing.image import load_img, img_to_array


img_rows, img_cols = 28, 28
input_shape = (img_rows, img_cols, 1)

producent = []
def imageClass(model):
    model.add(Conv2D(75, kernel_size=(5, 5),
                     activation='relu',
                     input_shape=input_shape))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Conv2D(100, (5, 5), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(500, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))

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

nmodel = Sequential()
imageClass(nmodel)
nmodel.load_weights('model_weights.h5')

def imgSkan():
    img_width, img_height = 28, 28
    new_image = load_img('cyfra.png', target_size=(img_width, img_height), color_mode = "grayscale")
    new_image = img_to_array(new_image)
    new_image = new_image.reshape((1,) + new_image.shape)

    prediction = nmodel.predict(new_image)
    prediction = numpy.argmax(prediction)
    print("Producent:", prediction)
    producent.append(prediction)