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 function should return the model and the predicted stock price for the next day based on the input data.
  assert create_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])==(model, next_day_price)
  assert create_model([100, 101, 102, 103, 104, 105, 106])==(model, next_day_price)
  assert create_model([99, 99.5, 100, 100.5, 101, 101.5, 102])==(model, next_day_price)
  assert create_model([150, 149, 148, 147, 146, 145, 144])==(model, next_day_price)
  assert create_model([200, 210, 220, 230, 240, 250, 260])==(model, next_day_price)
output: 
  import numpy as np
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def create_model(stock_prices):
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      X = np.array(stock_prices[:-1]).reshape(-1, 1)
      y = np.array(stock_prices[1:]).reshape(-1, 1)
      
      model.fit(X, y, epochs=50, verbose=0)
      
      next_day_price = model.predict(np.array([stock_prices[-1]]).reshape(-1, 1))[0][0]
      
      return model, next_day_price
  