


import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
	# Initialising the CNN
	# (Sequential- Building the model layer by layer)
	A				:       str      =  models.Sequential()

	# Step 1 - Convolution
	# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
	# (3,3) is the kernel size (filter matrix)
	classifier.add(
	    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
	)

	# Step 2 - Pooling
	classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

	# Adding a second convolutional layer
	classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
	classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

	# Step 3 - Flattening
	classifier.add(layers.Flatten())

	# Step 4 - Full connection
	classifier.add(layers.Dense(units=128, activation='relu'))
	classifier.add(layers.Dense(units=1, activation='sigmoid'))

	# Compiling the CNN
	classifier.compile(
	    optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
	)

	# Part 2 - Fitting the CNN to the images

	# Load Trained model weights

	# from keras.models import load_model
	# regressor=load_model('cnn.h5')

	A				:       Union[str, Any]      =  tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	A				:       int      =  tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

	A				:       str      =  train_datagen.flow_from_directory(
	    'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
	)

	A				:       Dict      =  test_datagen.flow_from_directory(
	    'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
	)

	classifier.fit_generator(
	    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
	)

	classifier.save('cnn.h5')

	# Part 3 - Making new predictions

	A				:       int      =  tf.keras.preprocessing.image.load_img(
	    'dataset/single_prediction/image.png', target_size=(64, 64)
	)
	A				:       Any      =  tf.keras.preprocessing.image.img_to_array(test_image)
	A				:       Optional[int]      =  np.expand_dims(test_image, axis=0)
	A				:       str      =  classifier.predict(test_image)
	# training_set.class_indices
	if result[0][0] == 0:
		A				:       List[Any]      =  'Normal'
	if result[0][0] == 1:
		A				:       Dict      =  'Abnormality detected'









A				:       Any      =  '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A				:       Optional[int]      =  [{'type': 'code', 'content': INSTALL_CONTENT}]
A				:       Optional[int]      =  {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}





