input: Write a function to create a simple linear regression model in Python using the `pandas`, `matplotlib`, and `sklearn` libraries. The function should read a dataset from a CSV file, train a linear regression model on the dataset, and visualize the results by plotting the original data points and the regression line. The function should not return anything but should display the plot.
  
  
  def simple_linear_regression(csv_file):
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      # Read data from CSV
      data = pd.read_csv(csv_file)
  
      # Set X and Y
      X = data['X'].values.reshape(-1, 1)
      Y = data['Y'].values.reshape(-1, 1)
  
      # Create linear regression model
      model = linear_model.LinearRegression()
  
      # Train the model
      model.fit(X, Y)
  
      # Make predictions
      Y_pred = model.predict(X)
  
      # Visualize the results
      plt.scatter(X, Y)
      plt.plot(X, Y_pred, color='red')
      plt.show()
  
  assert simple_linear_regression('data.csv')==None
  
output: def simple_linear_regression(csv_file):
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      data = pd.read_csv(csv_file)
      X = data['X'].values.reshape(-1, 1)
      Y = data['Y'].values.reshape(-1, 1)
      model = linear_model.LinearRegression()
      model.fit(X, Y)
      Y_pred = model.predict(X)
      plt.scatter(X, Y)
      plt.plot(X, Y_pred, color='red')
      plt.show()