input: Write a function that creates a neural network model to predict the stock price for the next day, using a given trajectory of stock prices as input. The function should return the predicted stock price as output.
  
  def predict_next_day_stock_price(input_data):
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(loss='mean_squared_error', 
                    optimizer='adam',
                    metrics=['accuracy'])
  
      predictions = model.predict(np.array([input_data]))
  
      return predictions[0][0]
  
  assert predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) > 0
  assert predict_next_day_stock_price([9, 9.4, 9.5, 9.6, 9.7, 9.9, 10]) > 0
output: def predict_next_day_stock_price(input_data):
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(loss='mean_squared_error', 
                    optimizer='adam',
                    metrics=['accuracy'])
  
      predictions = model.predict(np.array([input_data]))
  
      return predictions[0][0]