






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)
		__UpperCAmelCase								=						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')

		__UpperCAmelCase								=						tf.keras.preprocessing.image.ImageDataGenerator(
		    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
		)

		__UpperCAmelCase								=						tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

		__UpperCAmelCase								=						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

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












from math import sqrt

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int							)					-> bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5			,			int(sqrt(__UpperCAmelCase							) + 1							)			,			6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int = 1_00_01							)					-> int:
      SCREAMING_SNAKE_CASE_							=			0
      SCREAMING_SNAKE_CASE_							=			1
      while count != nth and number < 3:
            number += 1
            if is_prime(__UpperCAmelCase							):
                  count += 1
      while count != nth:
            number += 2
            if is_prime(__UpperCAmelCase							):
                  count += 1
      return number


if __name__ == "__main__":
   print(f'''{solution() = }''')