





'''simple docstring'''






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def        UpperCamelCase__		(						__magic_name__  :   str = "laptop"      )				->			DataFrame:
 '''simple docstring'''






 snake_case__     :					Union[str, Any]    =  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__     :					int    =  BeautifulSoup(requests.get(__magic_name__		,       headers=__magic_name__      ).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__     :					Optional[int]    =  item.ha.text
   snake_case__     :					Any    =  """https://www.amazon.in/""" + item.ha.a["""href"""]
   snake_case__     :					List[str]    =  item.find("""span"""		,       attrs={"""class""": """a-offscreen"""}      ).text
   try:
    snake_case__     :					Dict    =  item.find("""span"""		,       attrs={"""class""": """a-icon-alt"""}      ).text
   except AttributeError:
    snake_case__     :					Optional[int]    =  """Not available"""
   try:
    snake_case__     :					Tuple    =  (
        """₹"""
        + item.find(
            """span"""		,       attrs={"""class""": """a-price a-text-price"""}      ).text.split("""₹"""      )[1]
    )
   except AttributeError:
    snake_case__     :					Optional[Any]    =  """"""
   try:
    snake_case__     :					str    =  float(
        (
            (
                float(product_mrp.strip("""₹"""      ).replace(""","""		,       """"""      )      )
                - float(product_price.strip("""₹"""      ).replace(""","""		,       """"""      )      )
            )
            / float(product_mrp.strip("""₹"""      ).replace(""","""		,       """"""      )      )
        )
        * 1_00      )
   except ValueError:
    snake_case__     :					List[Any]    =  float("""nan"""      )
  except AttributeError:
   pass
  snake_case__     :					str    =  [
      product_title,
      product_link,
      product_price,
      product_rating,
      product_mrp,
      discount,
  ]
 snake_case__     :					List[Any]    =  """ """
 snake_case__     :					Union[str, Any]    =  """ """
 data_frame.index += 1
 return data_frame


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





'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate


@dataclass(frozen=lowerCAmelCase )
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= field(default='''question-answering-extractive'''		,metadata={'''include_in_asdict_even_if_is_default''': True} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features(
          {
              '''answers''': Sequence(
                  {
                      '''text''': Value('''string''' ),
                      '''answer_start''': Value('''int32''' ),
                  } )
          } )
      UpperCAmelCase_   :   str   				= "question"
      UpperCAmelCase_   :   str   				= "context"
      UpperCAmelCase_   :   str   				= "answers"




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict[str, str]:



         '''simple docstring'''



         return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}




