






'''simple docstring'''




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)
		__lowerCamelCase   :					int   							=					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')

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

		__lowerCamelCase   :					Optional[int]   							=					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

		__lowerCamelCase   :					List[Any]   							=					train_datagen.flow_from_directory(
		    '''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
		)

		__lowerCamelCase   :					Tuple   							=					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

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












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)




