input: Write a function to create a neural network model using TensorFlow that takes in a given trajectory of stock prices and predicts the stock price for the next day. If the model is successfully created and trained, it should output a prediction based on the given input trajectory.
  
  ### Input:
  [10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]
  
  ### Output:
  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
  
  def predict_next_day_price(input_data):
      model = create_model()
      predictions = model.predict(input_data)
      return predictions[0][0]
  
  assert predict_next_day_price(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])) > 0
  assert predict_next_day_price(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])) < 12
  assert predict_next_day_price(np.array([[9, 9.4, 9.5, 9.6, 9.7, 9.9, 10]])) > 0
  assert predict_next_day_price(np.array([[9, 9.4, 9.5, 9.6, 9.7, 9.9, 10]])) < 11
output: 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
  
  def predict_next_day_price(input_data):
      model = create_model()
      predictions = model.predict(input_data)
      return predictions[0][0]