input: Write a function to create a simple linear regression model using the scikit-learn library in Python. The function should read data from a CSV file, train the model, make predictions, and visualize the results by plotting the scatter plot of the data points and the regression line. If the input file path is invalid, the function should raise an appropriate error message. 
  assert linear_regression_model('data.csv')
  assert linear_regression_model('invalid_path.csv') # should raise an error
output: def linear_regression_model(file_path):
      import pandas as pd
      from sklearn.linear_model import LinearRegression
      import matplotlib.pyplot as plt
  
      try:
          data = pd.read_csv(file_path)
      except FileNotFoundError:
          raise FileNotFoundError("Invalid file path")
  
      X = data.iloc[:, 0].values.reshape(-1, 1)
      y = data.iloc[:, 1].values
  
      model = LinearRegression()
      model.fit(X, y)
  
      y_pred = model.predict(X)
  
      plt.scatter(X, y, color='blue')
      plt.plot(X, y_pred, color='red')
      plt.show()