input: Write a function to create a natural language response generation system using a specified architecture and predict the response to a given utterance. The function should define the model's architecture, compile it, train it, and then use it to generate a response to the input utterance. The function should return the predicted response.
  assert create_response_system("What time is it?") == prediction
  assert create_response_system("What is the weather like?") == prediction
  assert create_response_system("Tell me a joke.") == prediction
  assert create_response_system("Who won the game?") == prediction
  assert create_response_system("How are you?") == prediction
output: def create_response_system(utterance):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Embedding, LSTM
      from tensorflow.keras.preprocessing.text import Tokenizer
      from tensorflow.keras.preprocessing.sequence import pad_sequences
      import numpy as np
  
      # Sample data
      utterances = ["What time is it?", "What is the weather like?", "Tell me a joke.", "Who won the game?", "How are you?"]
      responses = ["It's 3 PM.", "The weather is sunny.", "Why don't scientists trust atoms? Because they make up everything!", "Team A won.", "I'm just a program, but I'm doing well!"]
  
      # Tokenize the data
      tokenizer = Tokenizer()
      tokenizer.fit_on_texts(utterances)
      sequences = tokenizer.texts_to_sequences(utterances)
      max_len = max(len(seq) for seq in sequences)
      padded_sequences = pad_sequences(sequences, maxlen=max_len)
  
      # Define the model
      model = Sequential()
      model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=64, input_length=max_len))
      model.add(LSTM(128))
      model.add(Dense(len(responses), activation='softmax'))
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(padded_sequences, np.array([0, 1, 2, 3, 4]), epochs=100, verbose=0)
  
      # Predict the response
      input_sequence = tokenizer.texts_to_sequences([utterance])
      padded_input = pad_sequences(input_sequence, maxlen=max_len)
      prediction = model.predict(padded_input, verbose=0)
      response_index = np.argmax(prediction)
  
      return responses[response_index]