input: Write a function to create and visualize a simple linear regression model in Python using provided data from a CSV file. The function should read data from a CSV file, fit a linear regression model to the data, make predictions, and then visualize the results with a scatter plot of the original data points and a line plot of the predicted values. If the CSV file is not found or the data is invalid, the function should handle the error appropriately.
  
  def create_linear_regression_model(csv_file):
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      try:
          # Read data from CSV
          data = pd.read_csv(csv_file)
  
          # Set X and Y
          X = data['X'].values.reshape(-1, 1)
          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 True
      except Exception as e:
          print(f"An error occurred: {e}")
          return False
  
  assert create_linear_regression_model('data.csv')==True
  assert create_linear_regression_model('nonexistentfile.csv')==False
output: def create_linear_regression_model(csv_file):
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn import linear_model
  
      try:
          data = pd.read_csv(csv_file)
          X = data['X'].values.reshape(-1, 1)
          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 True
      except Exception as e:
          print(f"An error occurred: {e}")
          return False