使用gradio搭建服务(三)--多轮对话

使用gr.Chatbot()对象保存每轮对话中用户的提问和大语言模型的回复。

#coding:utf-8
import json
import os
from time import time
import base64
import gradio as gr
from zhipuai import ZhipuAI
'''
智谱参考文档
https://open.bigmodel.cn/dev/api#glm-4
'''
parts = ['高颅顶', '精灵耳', '下颌缘', '下颏', '颞区', '颧区', '眉弓', '重睑', '眼袋', '卧蚕', '美鼻', '眉间纹','苹果肌', '法令纹', '丰唇', '口角囊袋', '鱼尾纹', '面颊']

zhipu_client = ZhipuAI(api_key="***")

def call_zhipu(content):
    response = zhipu_client.chat.completions.create(
        model="glm-4",  # 填写需要调用的模型名称
        # messages=[
        #     {"role": "user", "content": "作为一名营销专家，请为智谱开放平台创作一个吸引人的slogan"},
        #     {"role": "assistant", "content": "当然，为了创作一个吸引人的slogan，请告诉我一些关于您产品的信息"},
        #     {"role": "user", "content": "智谱AI开放平台"},
        #     {"role": "assistant", "content": "智启未来，谱绘无限一智谱AI，让创新触手可及!"},
        #     {"role": "user", "content": "创造一个更精准、吸引人的slogan"}
        # ],
        messages = content
    )
    return response.choices[0].message.content

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox() ## 输入文本
    clear = gr.ClearButton([msg, chatbot])

    def respond(message, chat_history):
    	## 构建多轮对话的prompt. user和assistant的内容。
        if not len(chat_history):
            prompt = [{"role": "user", "content": message}]
        else:
            prompt = []
            for item in chat_history:
                prompt.append({"role": "user", "content": item[0]})
                prompt.append({"role": "assistant", "content": item[1]})
            prompt.append({"role": "user", "content": message})
        print('prompt',prompt)
        bot_message = call_zhipu(prompt)
        print('bot_message',bot_message)
		##
        chat_history.append((message, bot_message))
        # time.sleep(2)
        print('chat_history',chat_history)
        return "", chat_history
    msg.submit(respond, [msg, chatbot], [msg, chatbot])

if __name__ == "__main__":
    # demo.launch()
    demo.launch(server_name='0.0.0.0', server_port=8000)