



"""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)
      A:     Any										=	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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation="relu")
      )

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

      # Adding a second convolutional layer
      classifier.add(layers.ConvaD(3_2, (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_2_8, 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:     List[str]										=	tf.keras.preprocessing.image.ImageDataGenerator(
          rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      A:     Optional[Any]										=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

      A:     Optional[int]										=	train_datagen.flow_from_directory(
          "dataset/training_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
      )

      A:     Tuple										=	test_datagen.flow_from_directory(
          "dataset/test_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
      )

      classifier.fit_generator(
          training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
      )

      classifier.save("cnn.h5")

      # Part 3 - Making new predictions

      A:     Tuple										=	tf.keras.preprocessing.image.load_img(
          "dataset/single_prediction/image.png", target_size=(6_4, 6_4)
      )
      A:     List[Any]										=	tf.keras.preprocessing.image.img_to_array(test_image)
      A:     Union[str, Any]										=	np.expand_dims(test_image, axis=0)
      A:     int										=	classifier.predict(test_image)
      # training_set.class_indices
      if result[0][0] == 0:
            A:     Optional[Any]										=	"Normal"
      if result[0][0] == 1:
            A:     Dict										=	"Abnormality detected"





"""simple docstring"""
import requests
from bsa import BeautifulSoup

def    _snake_case		(		UpperCamelCase		:	str = "AAPL"		):
 UpperCAmelCase			:      Any			=			F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
 UpperCAmelCase			:      Optional[int]			=			BeautifulSoup(requests.get(UpperCamelCase		).text    ,	"""html.parser"""		)
 UpperCAmelCase			:      int			=			"""My(6px) Pos(r) smartphone_Mt(6px)"""
 return soup.find("""div"""    ,	class_=class_		).find("""span"""		).text


if __name__ == "__main__":
      for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
            print(f"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
