






# Function to print upper half of diamond (pyramid)






def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''


     for i in range(0 ,							__lowerCamelCase       ):
          for _ in range(0 ,							n - i - 1       ):  # printing spaces
               print(""" """ ,							end=""""""       )
          for _ in range(0 ,							i + 1       ):  # printing stars
               print("""* """ ,							end=""""""       )
          print()





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			str:

     '''simple docstring'''


     for i in range(__lowerCamelCase ,							0 ,							-1       ):
          for _ in range(__lowerCamelCase ,							0 ,							-1       ):  # printing stars
               print("""* """ ,							end=""""""       )
          print()
          for _ in range(n - i + 1 ,							0 ,							-1       ):  # printing spaces
               print(""" """ ,							end=""""""       )





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Any:

     '''simple docstring'''


     if n <= 0:
          print("""       ...       ....        nothing printing :("""       )
          return
     floyd(__lowerCamelCase       )  # upper half
     reverse_floyd(__lowerCamelCase       )  # lower half


if __name__ == "__main__":
      print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
      SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				1
      while K:
            SCREAMING_SNAKE_CASE__		:							Dict 				=				int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            SCREAMING_SNAKE_CASE__		:							Tuple 				=				int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")








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)
						_snake_case      :							Tuple     	=    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=1_28, 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')

						_snake_case      :							Optional[int]     	=    tf.keras.preprocessing.image.ImageDataGenerator(
						    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
						)

						_snake_case      :							Any     	=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

						_snake_case      :							Dict     	=    train_datagen.flow_from_directory(
						    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
						)

						_snake_case      :							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

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