input: Write a python function to read stock data from a CSV file, perform linear regression using the open prices to predict the close prices, and plot the results.
  assert type(model) == LinearRegression
  assert len(predicted_prices) == len(open_prices)
  assert predicted_prices[0] > 0
output: import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn.linear_model import LinearRegression
  
  def predict_stock_prices(csv_file):
      # Read CSV file
      df = pd.read_csv(csv_file)
      
      # Extract open and close prices
      open_prices = df['Open'].values.reshape(-1, 1)
      close_prices = df['Close'].values
      
      # Perform linear regression
      model = LinearRegression()
      model.fit(open_prices, close_prices)
      
      # Predict close prices
      predicted_prices = model.predict(open_prices)
      
      # Plot results
      plt.figure(figsize=(10, 5))
      plt.scatter(open_prices, close_prices, color='blue', label='Actual Prices')
      plt.plot(open_prices, predicted_prices, color='red', label='Predicted Prices')
      plt.xlabel('Open Prices')
      plt.ylabel('Close Prices')
      plt.title('Linear Regression: Open Prices vs Close Prices')
      plt.legend()
      plt.show()
      
      return model, predicted_prices