input: Write a python function to perform linear regression on given X and Y data, read from a CSV file, and plot the results.
  assert linear_regression_and_plot('data1.csv') is None
  assert linear_regression_and_plot('data2.csv') is None
output: def linear_regression_and_plot(csv_file):
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      from sklearn.linear_model import LinearRegression
  
      # Read data from CSV
      data = pd.read_csv(csv_file)
      X = data.iloc[:, 0].values.reshape(-1, 1)  # Assumes X is the first column
      Y = data.iloc[:, 1].values  # Assumes Y is the second column
  
      # Perform linear regression
      model = LinearRegression()
      model.fit(X, Y)
      Y_pred = model.predict(X)
  
      # Plot results
      plt.scatter(X, Y, color='blue', label='Original Data')
      plt.plot(X, Y_pred, color='red', label='Fitted Line')
      plt.xlabel('X')
      plt.ylabel('Y')
      plt.title('Linear Regression')
      plt.legend()
      plt.show()