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 use TensorFlow and return a compiled model. Additionally, write a main section to show how the model can be used to predict the next day's stock price based on the input data.
  
  
  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
      
  if __name__ == "__main__":
      model = create_model()
  
      input_data = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
      predictions = model.predict(input_data)
  
      print(f"Predicted stock price for the next day is: {predictions[0][0]}")
  
  
  Expected output:
  
  Predicted stock price for the next day is: (some floating point value)
  
output: 
  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
  