input: 
  def predict_house_price(input_features: np.array) -> float:
      """ Predict the price of a house using a neural network model given a set of input features.
      The model is trained to take input features and output a predicted house price.
  
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      140000.0
      """
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
      
      features = np.array([input_features])
  
      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.fit(features, batch_size=3, epochs=200)
      
      prediction = model.predict(features).item()
      return float(prediction)
  
output:     model.load_weights('house_price_model_weights.h5')