input: Write a function to create and train a simple linear regression model in Python. The function should read data from a CSV file, train the model, make predictions, and visualize the results by plotting the original data points along with the predicted regression line. Return the trained model and the predicted values.
  
  def linear_regression_model(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']]
      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()
      
      return model, Y_pred
  
  # Test cases
  assert isinstance(linear_regression_model('data.csv')[0], linear_model.LinearRegression)
  assert len(linear_regression_model('data.csv')[1]) == len(pd.read_csv('data.csv')['Y'])
output: def linear_regression_model(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']]
      Y = data['Y']
      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()
      return model, Y_pred