[Project] 공공데이터 공통표준용어 검색 만들기 - FastAPI ResponseBody Model 만들기

FastAPI ResponseBody Model 만들기

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https://fastapi.tiangolo.com/ko/tutorial/response-model/

응답 모델 - FastAPI

 

 

1. ResponseModel

 

from typing import Optional, List

from pydantic import Field, BaseModel


class ResponseModel(BaseModel):
    msg:str = Field("", description="응답메세지")
    isOk:bool =  Field(True, description="응답 성공 여부")
    items:Optional[List[dict] | dict] = Field(None, description="응답 데이터")

 

 

2. ReponseModelPaging

 

class Paging(BaseModel):
    totalSize: int = Field(0, description="전체 갯수")
    totalPage: int = Field(0, description="전체 페이지 수")
    page: int = Field(1, description="현재 페이지")
    size: int = Field(10, description="출력 갯수")


class ResponseModelPaging(ResponseModel):
    paging: Paging = Field(Paging(), description="페이지 정보")

 

 

3. Genric Type

from typing import TypeVar, Generic
T = TypeVar("T")

class ResponseModel(BaseModel,Generic[T]):

 

 

4. Main

"""
main.py
@제목: 메인 실행 파일
@설명: 메인 실행 파일

    작성일자        작성자
-----------------------
    2024.03.14    hiio420

"""
import math
import os.path

from fastapi import FastAPI
from pydantic import BaseModel
from starlette.middleware.cors import CORSMiddleware

from libs import ExcelUtils
from models import ResponseModel, ResponseModelPaging, Paging

# 엑셀 파일 불러오기

CUR_DIR = os.path.abspath(os.path.dirname(__file__))
UPLOAD_DIR = os.path.join(CUR_DIR, 'upload')
file_path = os.path.join(UPLOAD_DIR, '(붙임)공공데이터 공통표준용어(2022.7월).xlsx')

excel_utils = ExcelUtils()
df_dict = excel_utils.read_excel(file_path)

app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


class Item(BaseModel):
    data_id: int = 0
    page: int = 1
    limit: int = 10


@app.get("/",response_model=ResponseModelPaging[dict])
def read_root(dataId: int = 0, page: int = 1, limit: int = 10, srchTxt: str = ""):
    if page < 1:
        page = 0
    if limit < 10:
        limit = 10
    df = df_dict[dataId]
    df = df[df.apply(lambda row: row.astype(str).str.contains(srchTxt, case=False).any(), axis=1)]

    first_idx = (page - 1) * limit
    last_idx = first_idx + limit - 1
    total_size = df.shape[0]
    last_page = math.ceil(total_size / limit)

    df_sliced = df[first_idx:last_idx + 1].copy()

    df_sliced["번호"] = [i for i in
                       range(total_size - (page - 1) * limit, total_size - (page - 1) * limit - df_sliced.shape[0], -1)]
    data = []
    if df_sliced.shape[0] != 0:
        data = df_sliced.to_dict("records")
    paging = Paging(page=page, size=limit, totalPage=last_page,totalSize=total_size)
    resp = ResponseModelPaging(items=data,paging=paging)
    print(resp)
    return resp