API服务的快速搭建和测试

API服务的快速搭建和测试

使用Python的FastAPI迅速搭建一个简单API

from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
import uvicorn, json, datetime
import torch

# 设置CUDA设备信息
DEVICE = "cuda"
DEVICE_ID = "0"
CUDA_DEVICE = f"{
     DEVICE}:{
     DEVICE_ID}" if DEVICE_ID else DEVICE

# 清理CUDA缓存的函数
def torch_gc():
    if torch.cuda.is_available():
        with torch.cuda.device(CUDA_DEVICE):
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()

# 创建FastAPI应用
app = FastAPI()

# 定义POST请求的处理函数
@app.post("/")
async def create_item(request: Request):
    global model, tokenizer

    # 从请求中获取JSON数据
    json_post_raw = await request.json()
    json_post = json.dumps(json_post_raw)
    json_post_list = json.loads(json_post)
    
    # 从JSON数据中提取必要的参数
    prompt = json_post_list.get('prompt')
    history = json_post_list.get('history')
    max_length = json_post_list.get('max_length')
    top_p = json_post_list.get('top_p')
    temperature = json_post_list.get('temperature')
    
    # 调用模型生成聊天响应
    response, history = model.chat(tokenizer,
                                   prompt,
                                   history=history,
                                   max_length=max_length if max_length else 2048,
                                   top_p=top_p if top_p else 0.8,
                                   temperature=temperature if temperature else 0.8)
    
    # 获取当前时间
    now = datetime.datetime.now()
    time = now.strftime("%Y-%m-%d %H:%M:%S")
    
    # 构建响应对象
    answer = {
   
        "response": response,
        "history": history,
        "status": 200,
        "time": time
    }
    
    # 构建日志信息
    log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
    print(log)
    
    # 调用函数清理CUDA缓存
    torch_gc()
    
    # 返回响应
    return answer

# 主程序入口
if __name__ == '__main__':
    # 加载模型和分词器
    tokenizer = AutoTokenizer.from_pretrained("../base_model/chatglm3-6b", trust_remote_code=True)
    model = AutoModel.from_pretrained("../base_model/chatglm3-6b", trust_remote_code=True).cuda()
    model.eval()
    
    # 启动FastAPI应用
    uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)

使用Python调用API

import requests

# 定义请求URL
url = "http://实际API服务地址:8000"

# 定义请求头
headers = {
   
    "Content-Type": "application/json"
}

# 定义请求体数据
data = {
   
    "prompt": "你好",
    "history": []
}

# 发送POST请求
response = requests.post(url, headers=headers, json=data)

# 打印响应
print(response.text)