开源模型应用落地-chatglm3-6b-gradio-入门篇（七）

一、前言

早前的文章，我们都是通过输入命令的方式来使用Chatglm3-6b模型。现在，我们可以通过使用gradio，通过一个界面与模型进行交互。这样做可以减少重复加载模型和修改代码的麻烦，

让我们更方便地体验模型的效果。

二、术语

2.1、Gradio

是一个用于构建交互式界面的Python库。它使得在Python中创建快速原型、构建和共享机器学习模型变得更加容易。

Gradio的主要功能是为机器学习模型提供一个即时的Web界面，使用户能够与模型进行交互，输入数据并查看结果，而无需编写复杂的前端代码。它提供了一个简单的API，可以将输入和输出绑定到模型的函数或方法，并自动生成用户界面。

三、前置条件

3.1. windows or linux操作系统均可

3.2. 下载chatglm3-6b模型

从huggingface下载：https://huggingface.co/THUDM/chatglm3-6b/tree/main

​

从魔搭下载：魔搭社区汇聚各领域最先进的机器学习模型，提供模型探索体验、推理、训练、部署和应用的一站式服务。https://www.modelscope.cn/models/ZhipuAI/chatglm3-6b/fileshttps://www.modelscope.cn/models/ZhipuAI/chatglm3-6b/files​

** 3.3. 创建虚拟环境&安装依赖**

conda create --name chatglm3 python=3.10
conda activate chatglm3
pip install protobuf transformers==4.39.3 cpm_kernels torch>=2.0 sentencepiece accelerate
pip install gradio

四、技术实现

# -*-  coding = utf-8 -*-
import gradio as gr
import torch
from threading import Thread

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer
)

modelPath = "/model/chatglm3-6b"

def loadTokenizer():
    tokenizer = AutoTokenizer.from_pretrained(modelPath, use_fast=False, trust_remote_code=True)
    return tokenizer

def loadModel():
    model = AutoModelForCausalLM.from_pretrained(modelPath, device_map="auto",  trust_remote_code=True).cuda()
    model = model.eval()
    return model

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [0, 2]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

def parse_text(text):
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split('`')
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f'<br></code></pre>'
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>" + line
    text = "".join(lines)
    return text

def predict(history, max_length, top_p, temperature):
    stop = StopOnTokens()
    messages = []
    for idx, (user_msg, model_msg) in enumerate(history):
        if idx == len(history) - 1 and not model_msg:
            messages.append({"role": "user", "content": user_msg})
            break
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if model_msg:
            messages.append({"role": "assistant", "content": model_msg})

    model_inputs = tokenizer.apply_chat_template(messages,
                                                 add_generation_prompt=True,
                                                 tokenize=True,
                                                 return_tensors="pt").to(next(model.parameters()).device)
    streamer = TextIteratorStreamer(tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = {
        "input_ids": model_inputs,
        "streamer": streamer,
        "max_new_tokens": max_length,
        "do_sample": True,
        "top_p": top_p,
        "temperature": temperature,
        "stopping_criteria": StoppingCriteriaList([stop]),
        "repetition_penalty": 1.2,
    }
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    for new_token in streamer:
        if new_token != '':
            history[-1][1] += new_token
            yield history

with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">ChatGLM3-6B Gradio Simple Demo</h1>""")
    chatbot = gr.Chatbot()

    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10, container=False)
            with gr.Column(min_width=32, scale=1):
                submitBtn = gr.Button("Submit")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_length = gr.Slider(0, 32768, value=8192, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0.01, 1, value=0.6, step=0.01, label="Temperature", interactive=True)

    def user(query, history):
        return "", history + [[parse_text(query), ""]]

    submitBtn.click(user, [user_input, chatbot], [user_input, chatbot], queue=False).then(
        predict, [chatbot, max_length, top_p, temperature], chatbot
    )
    emptyBtn.click(lambda: None, None, chatbot, queue=False)

if __name__ == '__main__':
    model = loadModel()
    tokenizer = loadTokenizer()

    demo.queue()
    demo.launch(server_name="0.0.0.0", server_port=8989, inbrowser=True, share=False)

调用结果：

启动成功：

GPU使用情况：

浏览器访问：

推理：

五、附带说明

5.1. 问题：AttributeError: 'ChatGLMTokenizer' object has no attribute 'apply_chat_template'

1. transformers的版本太低，需要升级

pip install --upgrade transformers==4.39.3

5.2. 界面无法打开

1. 服务监听地址不能是127.0.0.1

1. 检查服务器的安全策略或防火墙配置

服务端：lsof -i:8989 查看端口是否正常监听

客户端：telnet ip 8989 查看是否可以正常连接