input: def predict_house_price(features: np.ndarray) -> float:
      """
      This function uses a neural network implemented in Keras to predict the price of a house given a set of features.
      
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      Random predicted price value
      """
output:     from keras.models import load_model
      import numpy as np
  
      # Load the pre-trained model
      model = load_model('house_price_model.h5')
  
      # Predict the house price
      predicted_price = model.predict(features)
  
      # Return the predicted price as a float
      return float(predicted_price[0][0])