input: def predict_house_price(features: np.array) -> float:
      """Design and implement a neural network in Python to predict the price of a house given a set of features.
      This function uses Keras to create a neural network model with two hidden layers and a single output layer.
      The model is compiled using Mean Squared Error as the loss function and Adam as the optimizer.
      
      Example:
      >>> features = np.array([[0.8, 0.4, 0.7]])
      >>> predict_house_price(features)
      0.0 (Note: This output is placeholder; actual output depends on model training data)
      """
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(64, input_dim=3, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
  
      # Model fitting part removed as it involves training data which is not provided in the example
      # model.fit(features, batch_size=3, epochs=200)
  
      return 0.0  # Placeholder output, actual output will be generated after fitting the model
output:     from keras.models import load_model
  
      # Load a pre-trained model (assuming it exists)
      model = load_model('house_price_model.h5')
  
      # Predict the house price using the loaded model
      predicted_price = model.predict(features)
  
      return predicted_price[0][0]