从我给你的代码后面接条形图代码

好的，以下是在你给出的代码后面添加条形图代码的完整代码：

import openai
import re
import jieba
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer

# 配置OpenAI API
openai.api_key = "YOUR_API_KEY"

# 聊天模型
model_engine = "text-davinci-002"

# 对话循环
while True:
    user_input = input("You: ")
    if user_input.lower() == 'bye':
        print("Chatbot: Goodbye!")
        break
    else:
        # 发送请求
        response = openai.Completion.create(
          engine=model_engine,
          prompt=user_input,
          max_tokens=1024,
          n=1,
          stop=None,
          temperature=0.5,
        )

        # 提取回复
        raw_text = response.choices[0].text
        reply_text = raw_text.strip()

        # 去除标点符号等无关内容
        reply_text = re.sub('[^\w\s]', '', reply_text)

        # 分词
        words = jieba.cut(reply_text)

        # 统计词频
        word_counts = {}
        for word in words:
            if len(word) > 1:
                word_counts[word] = word_counts.get(word, 0) + 1

        # 转换成DataFrame
        df = pd.DataFrame.from_dict(word_counts, orient='index', columns=['count'])

        # 计算TF-IDF值
        tfidf = TfidfVectorizer()
        tfidf_matrix = tfidf.fit_transform([reply_text])
        weights = np.asarray(tfidf_matrix.mean(axis=0)).ravel().tolist()
        weights_df = pd.DataFrame({'term': tfidf.get_feature_names(), 'weight': weights})
        weights_df = weights_df.sort_values('weight', ascending=False)

        # 绘制条形图
        top_n = 10  # 取前10个高频词
        top_words = df.sort_values('count', ascending=False).head(top_n)
        top_words.plot(kind='bar', x=top_words.index, y='count', color='blue', legend=False)
        plt.xlabel('Words')
        plt.ylabel('Frequency')
        plt.title('Top {} Words'.format(top_n))
        plt.show()

        # 输出回复
        print("Chatbot:", reply_text)

这段代码将使用OpenAI进行对话，并在每次回复后绘制回复中出现的高频词汇的条形图，您可以通过修改`top_n`的值来控制绘制的高频词汇数量。