
"""simple docstring"""




from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
      A__   :					Any							=   input('Enter image url: ').strip()
      print(f"""Downloading image from {url} ...""")
      A__   :					Any							=   BeautifulSoup(requests.get(url).content, 'html.parser')
      # The image URL is in the content field of the first meta tag with property og:image
      A__   :					Optional[Any]							=   soup.find('meta', {'property': 'og:image'})['content']
      A__   :					Tuple							=   requests.get(image_url).content
      A__   :					Dict							=   f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
      with open(file_name, 'wb') as fp:
            fp.write(image_data)
      print(f"""Done. Image saved to disk as {file_name}.""")


"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A__   :					Tuple							=   pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
A__   :					Optional[int]							=   dataset.iloc[:, 1:2].values
A__   :					Union[str, Any]							=   dataset.iloc[:, 2].values


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


A__   :					Union[str, Any]							=   PolynomialFeatures(degree=4)
A__   :					List[Any]							=   poly_reg.fit_transform(X)
A__   :					Dict							=   LinearRegression()
pol_reg.fit(X_poly, y)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  plt.scatter(_UpperCamelCase ,				_UpperCamelCase ,				color='''red'''						)
  plt.plot(_UpperCamelCase ,				pol_reg.predict(poly_reg.fit_transform(_UpperCamelCase						)						) ,				color='''blue'''						)
  plt.title('''Truth or Bluff (Linear Regression)'''						)
  plt.xlabel('''Position level'''						)
  plt.ylabel('''Salary'''						)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003
