[TOC]

创建、激活虚拟环境

python3 -m venv py_env
source py_env/bin/activate

导入包

使用了清华大学的镜像

// fastapi必要包
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple fastapi
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple uvicorn

// 参考了一些导入ChatterBot的方法，出现各种错误，在查找一些博客后，使用源码编译安装不报错
git clone https://gitee.com/Lamentations/ChatterBot.git
cd ChatterBot/
python setup.py build
python setup.py install
// 在使用ChatterBot报出缺乏一些包的错误，升级pip，导入以下包
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade pip
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple six
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyyaml

训练

以下内容参考ChatterBot官方文档https://chatterbot.readthedocs.io/en/stable/

自定义训练

1. 训练脚本

from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer

chatbot = ChatBot("Double mouth Teacher Lv")
conversation = [
    "你好",
    "你好",
    "你好",
    "我不好",
    "你叫什么名字",
    "名字只是一个代号，叫什么无所谓，但我不想告诉你",
    "谢谢",
    "不客气，很高兴没能帮到你",
]
trainer = ListTrainer(chatbot)
trainer.train(conversation)

2. 写个导入接口导入对话列表来训练

from fastapi import Body, FastAPI, status
from fastapi.responses import JSONResponse
import uvicorn
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
from pydantic import BaseModel
app = FastAPI()

chatbot = ChatBot("Double mouth Teacher Lv")

@app.post("/training")
async def training(uselessKey: str = None, dialogue: list=[]):
    if uselessKey != 'nnnnnnnnnnnn':
        return JSONResponse(status_code=403, content='403 Forbidden')
    conversation = dialogue
    trainer = ListTrainer(chatbot)
    trainer.train(conversation)
    return {"code": "200"}

3. 使用官方语言包来训练

from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer

chatterbot = ChatBot("Double mouth Teacher Lv")
chatterbot.set_trainer(ChatterBotCorpusTrainer)

chatterbot.train(
    "chatterbot.corpus.chinese.greetings",
    "chatterbot.corpus.chinese.conversations"
)

对话

1. 写个接口获取对话返回值

from fastapi import Body, FastAPI, status
from fastapi.responses import JSONResponse
import uvicorn
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
from pydantic import BaseModel
app = FastAPI()

chatbot = ChatBot("Double mouth Teacher Lv")

@app.get("/talking")
async def talking(uselessKey: str = None, ask: str = '你好'):
    if uselessKey != 'wszzs110':
        return JSONResponse(status_code=403, content='403 Forbidden')
    response = chatbot.get_response(ask)
    return response

启动fastapi

uvicorn main:app --reload

# 指定端口
uvicorn main:app --host '0.0.0.0' --port 8080 --reload

部署到服务器上

1. 使用gunicorn启动

# 安装依赖
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple gunicorn
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple uvloop
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple zipp
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple httptools

# 启动 (-D 守护进程 -b 指定端口)
gunicorn -D main:app -b 0.0.0.0:9090 -w 4 -k uvicorn.workers.UvicornWorker

2. 配置nginx转发

# 服务器部署
# nginx 转发
 location / {
            proxy_pass http://127.0.0.1:8000/;
}