input: Write a function to generate a simple linear regression model in Python. The function should read data from a CSV file, train the model on the data, and visualize the results by plotting the original data points and the regression line. If the input data is not provided, the function should return a message indicating that the data is missing.
  assert linear_regression_model('data.csv')==None
  assert linear_regression_model(None)=='Data is missing'
  assert linear_regression_model('nonexistent.csv')=='Data is missing'
output: def linear_regression_model(filename):
      if filename is None:
          return 'Data is missing'
      try:
          import pandas as pd
          import matplotlib.pyplot as plt
          from sklearn.linear_model import LinearRegression
          data = pd.read_csv(filename)
          X = data.iloc[:, 0].values.reshape(-1, 1)
          y = data.iloc[:, 1].values
          model = LinearRegression()
          model.fit(X, y)
          plt.scatter(X, y, color='blue')
          plt.plot(X, model.predict(X), color='red')
          plt.show()
      except FileNotFoundError:
          return 'Data is missing'