input: Write a function to create a simple linear regression model in Python using pandas, matplotlib, and scikit-learn. The function should read data from a CSV file, train the model, make predictions, and visualize the results by plotting the original data points and the regression line. The CSV file contains columns 'X' and 'Y' which represent the independent and dependent variables respectively. The function should not require any input parameters once the dependency libraries are imported.
  
  
  def linear_regression_model():
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      # Read data from CSV
      data = pd.read_csv('data.csv')
  
      # Set X and Y
      X = data[['X']]
      Y = data['Y']
  
      # 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 the function runs without error
  assert linear_regression_model() is None
  
output: def linear_regression_model():
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      # Read data from CSV
      data = pd.read_csv('data.csv')
  
      # Set X and Y
      X = data[['X']]
      Y = data['Y']
  
      # 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()