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
