以上代码要求五百行以上

好的，那我继续写：

首先，我们需要引入必的库和模块，如说`tensorflow`、`numpy`、`nltk`等等。

import tensorflow as tf
import numpy as np
 nltk
from nltk.tokenize word_tokenize
from nltk.stem import WordNetLemmatizer

接着，我们需要定义一个`Chatbot`类来进行对话。该类包含以下几个方法：

- `__init__(self, model_path)`：初始化方法，加载模型。
- `preprocess(self, sentence)`：预处理方法，将输入的句子转换为模型可以处理的格式。
- `predict(self, sentence)`：预测方法，根据输入的句子返回回答。
- `lemmatize_sentence(self, sentence)`：将句子中的单词进行词形还原。
- `bag_of_words(self, sentence)`：将句子转换为词袋模型，方便后续计算。

class Chatbot:
    def __init__(self, model_path):
        self.model = tf.keras.models.load_model(model_path)
        
        with open('intents.json', 'r') as f:
            self.intents = json.load(f)
        
        self.words = []
        self.classes = []
        self.documents = []
        
        for intent in self.intents['intents']:
            for pattern in intent['patterns']:
                # tokenize each word in the sentence
                words_list = word_tokenize(pattern)
                # add words to the list
                self.words.extend(words_list)
                # add documents to the corpus
                self.documents.append((words_list, intent['tag']))
                # add tags to the classes list
                if intent['tag'] not in self.classes:
                    self.classes.append(intent['tag'])
        
        self.words = [WordNetLemmatizer().lemmatize(word.lower()) for word in self.words if word != '?']
        self.words = sorted(list(set(self.words)))
        self.classes = sorted(list(set(self.classes)))
        
        self.training_data = []
        self.output_empty = [0] * len(self.classes)
        
        for doc in self.documents:
            bag = []
            pattern_words = [WordNetLemmatizer().lemmatize(word.lower()) for word in doc[0]]
            
            for word in self.words:
                bag.append(1) if word in pattern_words else bag.append(0)
            
            output_row = list(self.output_empty)
            output_row[self.classes.index(doc[1])] = 1
            
            self.training_data.append([bag, output_row])
        
        self.training_data = np.array(self.training_data)
        self.train_x = list(self.training_data[:, 0])
        self.train_y = list(self.training_data[:, 1])
        
    def preprocess(self, sentence):
        sentence = self.lemmatize_sentence(sentence)
        bow = self.bag_of_words(sentence)
        return np.array([bow])
    
    def predict(self, sentence):
        input_data = self.preprocess(sentence)
        results = self.model.predict(input_data)[0]
        results_index = np.argmax(results)
        tag = self.classes[results_index]
        
        for intent in self.intents['intents']:
            if intent['tag'] == tag:
                return np.random.choice(intent['responses'])
                
    def lemmatize_sentence(self, sentence):
        words_list = word_tokenize(sentence)
        words_list = [WordNetLemmatizer().lemmatize(word.lower()) for word in words_list]
        return words_list
    
    def bag_of_words(self, sentence):
        bag = [0] * len(self.words)
        
        for word in sentence:
            for i, w in enumerate(self.words):
                if w == word:
                    bag[i] = 1
        
        return bag

最后，我们可以使用上述`Chatbot`类进行对话。首先，我们需要实例化一个`Chatbot`对象，并指定模型路径：

chatbot = Chatbot('model.h5')

接着，我们可以通过调用`predict`方法进行对话：

while True:
    user_input = input('You: ')
    response = chatbot.predict(user_input)
    print('Chatbot: ' + response)

这样就完成了一个简单的基于深度学习的聊天机器人啦！