






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







def 			A__							(      _a  :					str = "laptop"	):





 '''simple docstring'''







 snake_case__  :    int							       =f"https://www.amazon.in/laptop/s?k={product}"
 snake_case__  :    List[str]							       ={
     """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
     """Accept-Language""": """en-US, en;q=0.5""",
 }
 snake_case__  :    Optional[int]							       =BeautifulSoup(requests.get(_a		,  headers=_a	).text	)
 # Initialize a Pandas dataframe with the column titles
 snake_case__  :    Optional[Any]							       =DataFrame(
     columns=[
         """Product Title""",
         """Product Link""",
         """Current Price of the product""",
         """Product Rating""",
         """MRP of the product""",
         """Discount""",
     ]	)
 # Loop through each entry and store them in the dataframe
 for item, _ in zip_longest(
     soup.find_all(
         """div"""		,  attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""}		,  )		,  soup.find_all("""div"""		,  attrs={"""class""": """a-row a-size-base a-color-base"""}	)		,  ):
  try:
   snake_case__  :    Tuple							       =item.ha.text
   snake_case__  :    Optional[Any]							       ="""https://www.amazon.in/""" + item.ha.a["""href"""]
   snake_case__  :    List[str]							       =item.find("""span"""		,  attrs={"""class""": """a-offscreen"""}	).text
   try:
    snake_case__  :    List[str]							       =item.find("""span"""		,  attrs={"""class""": """a-icon-alt"""}	).text
   except AttributeError:
    snake_case__  :    Optional[Any]							       ="""Not available"""
   try:
    snake_case__  :    Optional[Any]							       =(
        """₹"""
        + item.find(
            """span"""		,  attrs={"""class""": """a-price a-text-price"""}	).text.split("""₹"""	)[1]
    )
   except AttributeError:
    snake_case__  :    Optional[int]							       =""""""
   try:
    snake_case__  :    Optional[Any]							       =float(
        (
            (
                float(product_mrp.strip("""₹"""	).replace(""","""		,  """"""	)	)
                - float(product_price.strip("""₹"""	).replace(""","""		,  """"""	)	)
            )
            / float(product_mrp.strip("""₹"""	).replace(""","""		,  """"""	)	)
        )
        * 100	)
   except ValueError:
    snake_case__  :    Union[str, Any]							       =float("""nan"""	)
  except AttributeError:
   pass
  snake_case__  :    Any							       =[
      product_title,
      product_link,
      product_price,
      product_rating,
      product_mrp,
      discount,
  ]
 snake_case__  :    Tuple							       =""" """
 snake_case__  :    Any							       =""" """
 data_frame.index += 1
 return data_frame


if __name__ == "__main__":
       __lowerCamelCase	:		List[Any]										=   """headphones"""
       get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")













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	:		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=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')

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

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





