

"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def 					a__						(						lowerCAmelCase = "laptop"    )	-> DataFrame:
      UpperCAmelCase__			:							List[Any]							=    F"""https://www.amazon.in/laptop/s?k={product}"""
      UpperCAmelCase__			:							int							=    {
          """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""",
      }
      UpperCAmelCase__			:							List[Any]							=    BeautifulSoup(requests.get(lowerCAmelCase			,	headers=lowerCAmelCase    ).text    )
      # Initialize a Pandas dataframe with the column titles
      UpperCAmelCase__			:							Union[str, 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:
                  UpperCAmelCase__			:							Optional[int]							=    item.ha.text
                  UpperCAmelCase__			:							str							=    """https://www.amazon.in/""" + item.ha.a["""href"""]
                  UpperCAmelCase__			:							List[str]							=    item.find("""span"""			,	attrs={"""class""": """a-offscreen"""}    ).text
                  try:
                        UpperCAmelCase__			:							Tuple							=    item.find("""span"""			,	attrs={"""class""": """a-icon-alt"""}    ).text
                  except AttributeError:
                        UpperCAmelCase__			:							List[str]							=    """Not available"""
                  try:
                        UpperCAmelCase__			:							Tuple							=    (
                            """₹"""
                            + item.find(
                                """span"""			,	attrs={"""class""": """a-price a-text-price"""}    ).text.split("""₹"""    )[1]
                        )
                  except AttributeError:
                        UpperCAmelCase__			:							Dict							=    """"""
                  try:
                        UpperCAmelCase__			:							Dict							=    float(
                            (
                                (
                                    float(product_mrp.strip("""₹"""    ).replace(""","""			,	""""""    )    )
                                    - float(product_price.strip("""₹"""    ).replace(""","""			,	""""""    )    )
                                )
                                / float(product_mrp.strip("""₹"""    ).replace(""","""			,	""""""    )    )
                            )
                            * 1_00    )
                  except ValueError:
                        UpperCAmelCase__			:							Tuple							=    float("""nan"""    )
            except AttributeError:
                  pass
            UpperCAmelCase__			:							Dict							=    [
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      UpperCAmelCase__			:							Optional[int]							=    """ """
      UpperCAmelCase__			:							Optional[int]							=    """ """
      data_frame.index += 1
      return data_frame


if __name__ == "__main__":
    _A							=  """headphones"""
    get_amazon_product_data(product).to_csv(f'''Amazon Product Data for {product}.csv''')




"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels

