input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day. The model should be built using TensorFlow and Keras.
  assert create_model_prediction([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) == 11.1
  assert create_model_prediction([9, 9.5, 10, 10.1, 10.3, 10.4, 10.5]) == 10.6
  assert create_model_prediction([8, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6]) == 8.7
  assert create_model_prediction([]) == None
  assert create_model_prediction([1]) == None
output: 
  def create_model_prediction(stock_prices):
      if len(stock_prices) < 2:
          return None
      
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      
      model = Sequential([
          Dense(10, input_shape=(len(stock_prices)-1,), activation='relu'),
          Dense(1)
      ])
      
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      X = tf.constant([stock_prices[:-1]], dtype=tf.float32)
      y = tf.constant([stock_prices[-1]], dtype=tf.float32)
      
      model.fit(X, y, epochs=100, verbose=0)
      
      next_day_price = model.predict(tf.constant([stock_prices[-1:]], dtype=tf.float32))
      
      return next_day_price[0][0]
  