input: Write a python function to predict stock prices using Linear Regression model with given dates and prices.
  assert predict_stock_price("2020-10-01", 'xyz.csv') == [/* expected value here */]
  assert predict_stock_price("2021-01-01", 'xyz.csv') == [/* expected value here */]
  assert predict_stock_price("2019-05-01", 'xyz.csv') == [/* expected value here */]
output: from sklearn.linear_model import LinearRegression
  import pandas as pd
  import numpy as np
  import datetime
  
  def predict_stock_price(future_date, csv_file):
      # Read the data from the CSV file
      data = pd.read_csv(csv_file)
      
      # Convert dates to datetime objects
      data['Date'] = pd.to_datetime(data['Date'])
      
      # Convert dates to ordinal (number of days since 01/01/0001)
      data['Date_Ordinal'] = data['Date'].apply(lambda x: x.toordinal())
      
      # Prepare the feature (date) and target (price) for the model
      X = data['Date_Ordinal'].values.reshape(-1, 1)
      y = data['Price'].values
      
      # Create and train the linear regression model
      model = LinearRegression()
      model.fit(X, y)
      
      # Convert the future date to ordinal
      future_date_ordinal = datetime.datetime.strptime(future_date, '%Y-%m-%d').toordinal()
      
      # Predict the price for the future date
      predicted_price = model.predict([[future_date_ordinal]])
      
      return predicted_price.tolist()