





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def 	A_       (  _lowerCAmelCase = "laptop"  )		->				DataFrame:
 UpperCamelCase			:      Union[str, Any] 			=     F"""https://www.amazon.in/laptop/s?k={product}"""
 UpperCamelCase			:      List[str] 			=     {
     "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (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			:      List[str] 			=     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			:      Union[str, Any] 			=     item.ha.text
   UpperCamelCase			:      str 			=     "https://www.amazon.in/" + item.ha.a["href"]
   UpperCamelCase			:      Any 			=     item.find("span"							,     attrs={"class": "a-offscreen"}  ).text
   try:
    UpperCamelCase			:      Tuple 			=     item.find("span"							,     attrs={"class": "a-icon-alt"}  ).text
   except AttributeError:
    UpperCamelCase			:      Union[str, Any] 			=     "Not available"
   try:
    UpperCamelCase			:      Optional[int] 			=     (
        "₹"
        + item.find(
            "span"							,     attrs={"class": "a-price a-text-price"}  ).text.split("₹"  )[1]
    )
   except AttributeError:
    UpperCamelCase			:      int 			=     ""
   try:
    UpperCamelCase			:      Any 			=     float(
        (
            (
                float(product_mrp.strip("₹"  ).replace(","							,     ""  )  )
                - float(product_price.strip("₹"  ).replace(","							,     ""  )  )
            )
            / float(product_mrp.strip("₹"  ).replace(","							,     ""  )  )
        )
        * 100  )
   except ValueError:
    UpperCamelCase			:      int 			=     float("nan"  )
  except AttributeError:
   pass
  UpperCamelCase			:      List[Any] 			=     [
      product_title,
      product_link,
      product_price,
      product_rating,
      product_mrp,
      discount,
  ]
 UpperCamelCase			:      int 			=     " "
 UpperCamelCase			:      Union[str, Any] 			=     " "
 data_frame.index += 1
 return data_frame


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







from PIL import Image



def 	A_       (  _lowerCAmelCase  )		->				Image:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     image.size
 UpperCamelCase			:      Union[str, Any] 			=     0
 UpperCamelCase			:      List[str] 			=     image.load()
 for i in range(_lowerCAmelCase  ):
  for j in range(_lowerCAmelCase  ):
   UpperCamelCase			:      List[Any] 			=     pixels[j, i]
   mean += pixel
 mean //= width * height

 for j in range(_lowerCAmelCase  ):
  for i in range(_lowerCAmelCase  ):
   UpperCamelCase			:      Union[str, Any] 			=     255 if pixels[i, j] > mean else 0
 return image


if __name__ == "__main__":
    __lowerCamelCase :  Union[str, Any]										=  mean_threshold(Image.open("""path_to_image""").convert("""L"""))
    image.save("""output_image_path""")
