用 Python 对接 Azure 的 chatGPT4 接口实现聊天功能_azure chatgpt gpt 4

今天拿到了 Azure 授权的 chatGPT4  的接口，内心很激动，终于可以安心使用chatGPT4了。赶紧采用 gradio 组件封装了一个简单的对话窗口来体验一下 GPT4 的超能力，看似简单的功能在显示打字机效果时，还是花了一些时间思考。

代码如下：

1）先安装 python 的 gradio 组件

pip install gradio

2）运行如下代码，尽情享受超能力吧

import time
import gradio as gr
import mdtex2html
 
import os
import openai
openai.api_type = "azure"
openai.api_base = "https://xxxx.openai.azure.com/"
openai.api_version = "2023-05-15"
openai.api_key = "API_KEY"
 
# This will correspond to the custom name you chose for your deployment when you deployed a model.
deployment_name = 'XXXXXXX'
 
 
prompt = """
我想让你充当数学历史老师，提供有关数学概念的历史发展和不同数学家的贡献的信息。
你应该只提供信息而不是解决数学问题。使用以下格式回答：“{数学家/概念} - {他们的贡献/发展的简要总结}。
我的第一个问题是“毕达哥拉斯对数学的贡献是什么？”
"""
prompt = ""
 
 
def predict(chatbot, input, history, temperature, max_tokens, top_p, chat_turns=0):
    chat_turns = chat_turns +1
    chatbot.append((input, ""))
    history.append(input)
    messages = []
    messages.append({"role": "system", "content": prompt})
 
    try:
        # print("==========request======:", question)
        messages.append({"role": "user", "content": input})
        response = openai.ChatCompletion.create(
            engine=deployment_name,
            messages=messages,
            temperature=temperature,
            max_tokens=max_tokens,
            top_p=top_p,
            frequency_penalty=0,
            presence_penalty=0,
            stop=None,
            stream=True,
        )
    except Exception as e:
        print(e)
        return e
 
 
    content = {'role': '', 'content': ''}
    for event in response:
        if event['choices'][0]['finish_reason'] == 'stop':
            print(f'收到的完成数据: {content}')
            history.append(content["content"])
            break
        for delta_k, delta_v in event['choices'][0]['delta'].items():
            print(f'流响应数据: {delta_k} = {delta_v}')
            content[delta_k] += delta_v
            responses = [(u, b) for u, b in zip(history[::2], history[1::2])]
            # responses = [(input, content[delta_k])]
            responses.append((input, content[delta_k]))
            yield responses, history, chat_turns
 
 
def reset_user_input():
    return gr.update(value='')
 
 
def reset_state():
    return [], [],0
 
 
with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">悦高软件-人工智能数字助手 GPT-4</h1>""")
    # radio = gr.Radio(value='gpt-3.5-turbo',
    #                  choices=['gpt-3.5-turbo', 'gpt-4'], label='models')
    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(show_label=False, placeholder="请输入内容...", lines=12).style(
                    container=False)
            with gr.Column(min_width=32, scale=1):
                submitBtn = gr.Button("提交", variant="primary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("重新对话")
            max_length = gr.Slider(
                0, 4096, value=800, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.95, step=0.01,
                              label="Top P", interactive=True)
            temperature = gr.Slider(
                0, 1, value=0.7, step=0.01, label="Temperature", interactive=True)
            chat_turns = gr.Slider(1,20,value=0,step=1,label="对话轮数",info="记录历史对话轮数",interactive=False)
        state = gr.State([])
 
        submitBtn.click(predict, inputs=[chatbot, user_input, state, temperature, max_length, top_p, chat_turns], outputs=[
                        chatbot, state, chat_turns], queue=True, show_progress=True)
        submitBtn.click(reset_user_input, [], [user_input])
        emptyBtn.click(reset_state, outputs=[
                       chatbot, state, chat_turns], show_progress=True)
 
 
demo.title = "Yogoo AI"
demo.queue()
demo.launch(share=False, inbrowser=True)