






"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def _a			(		_snake_case = "laptop"							):







      """simple docstring"""






      UpperCAmelCase          =	F'''https://www.amazon.in/laptop/s?k={product}'''
      UpperCAmelCase          =	{
          """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          =	BeautifulSoup(requests.get(_snake_case				,	headers=_snake_case							).text							)
      # Initialize a Pandas dataframe with the column titles
      UpperCAmelCase          =	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          =	item.ha.text
                  UpperCAmelCase          =	"""https://www.amazon.in/""" + item.ha.a["""href"""]
                  UpperCAmelCase          =	item.find("""span"""				,	attrs={"""class""": """a-offscreen"""}							).text
                  try:
                        UpperCAmelCase          =	item.find("""span"""				,	attrs={"""class""": """a-icon-alt"""}							).text
                  except AttributeError:
                        UpperCAmelCase          =	"""Not available"""
                  try:
                        UpperCAmelCase          =	(
                            """₹"""
                            + item.find(
                                """span"""				,	attrs={"""class""": """a-price a-text-price"""}							).text.split("""₹"""							)[1]
                        )
                  except AttributeError:
                        UpperCAmelCase          =	""""""
                  try:
                        UpperCAmelCase          =	float(
                            (
                                (
                                    float(product_mrp.strip("""₹"""							).replace(""","""				,	""""""							)							)
                                    - float(product_price.strip("""₹"""							).replace(""","""				,	""""""							)							)
                                )
                                / float(product_mrp.strip("""₹"""							).replace(""","""				,	""""""							)							)
                            )
                            * 100							)
                  except ValueError:
                        UpperCAmelCase          =	float("""nan"""							)
            except AttributeError:
                  pass
            UpperCAmelCase          =	[
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      UpperCAmelCase          =	""" """
      UpperCAmelCase          =	""" """
      data_frame.index += 1
      return data_frame


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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase					      =  {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""GLPNFeatureExtractor"""]
  _UpperCamelCase					      =  ["""GLPNImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GLPNForDepthEstimation""",
      """GLPNLayer""",
      """GLPNModel""",
      """GLPNPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



