input: Write a python function to train a linear regression model and predict bitcoin prices.
  assert isinstance(predict_bitcoin_prices("bitcoin.csv"), pd.Series)
output: import pandas as pd
  from sklearn.linear_model import LinearRegression
  
  def predict_bitcoin_prices(csv_path):
      # Load the data
      data = pd.read_csv(csv_path)
      
      # Assuming the csv has columns 'Date' and 'Price'
      data['Date'] = pd.to_datetime(data['Date'])
      data['Date_ordinal'] = data['Date'].apply(lambda x: x.toordinal())
      
      # Prepare the features and target variable
      X = data[['Date_ordinal']]
      y = data['Price']
      
      # Initialize and fit the model
      model = LinearRegression()
      model.fit(X, y)
      
      # Predict the prices
      predictions = model.predict(X)
      
      # Return the predictions as a pandas Series
      return pd.Series(predictions, index=data['Date'])