input: Write a function to create a Python neural network model that predicts the stock price for the next day based on a given trajectory of stock prices. The function should take an input of a list of stock prices and return the predicted stock price for the next day. The function should use TensorFlow for creating the neural network model.
  
  
  def predict_next_day_stock_price(stock_prices):
      import numpy as np
      import tensorflow as tf
      
      def create_model():
          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'])
          
          return model
      
      model = create_model()
      input_data = np.array([stock_prices])
      predictions = model.predict(input_data)
      
      return predictions[0][0]
  
  assert predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) > 11.0
  assert predict_next_day_stock_price([20, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8]) > 20.8
output: def predict_next_day_stock_price(stock_prices):
      import numpy as np
      import tensorflow as tf
      
      def create_model():
          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'])
          
          return model
      
      model = create_model()
      input_data = np.array([stock_prices])
      predictions = model.predict(input_data)
      
      return predictions[0][0]