input: Write a python function to predict stock prices using a given dataset.
  assert isinstance(predict_stock_prices(pd.DataFrame({'Date': ['2023-01-01', '2023-01-02'], 'Price': [100, 101]})), pd.Series)
  assert len(predict_stock_prices(pd.DataFrame({'Date': ['2023-01-01', '2023-01-02'], 'Price': [100, 101]}))) == 2
output: def predict_stock_prices(df):
      from sklearn.linear_model import LinearRegression
      import pandas as pd
      from datetime import datetime
      
      # Convert date to ordinal
      df['Date'] = pd.to_datetime(df['Date']).map(datetime.toordinal)
      
      # Train the model
      X = df[['Date']]
      y = df['Price']
      model = LinearRegression().fit(X, y)
      
      # Predict prices
      predictions = model.predict(X)
      
      return pd.Series(predictions)