input: import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn import linear_model
  
  def simple_linear_regression(data_path: str):
      """ Create and visualize a simple linear regression model using data from a given CSV file.
      The data should contain two columns, 'X' and 'Y', where 'X' is the independent variable 
      and 'Y' is the dependent variable.
      The function will read the data, train a linear regression model, make predictions,
      and plot the results.
      
      >>> simple_linear_regression('data.csv')
      """
      # Read data from CSV
      data = pd.read_csv(data_path)
      
      # 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()
output:     # Print the coefficients
      print('Coefficients: \n', model.coef_)
      print('Intercept: \n', model.intercept_)
      
      # Print the R^2 score
      print('R^2 score: \n', model.score(X, Y))