from typing import Annotated

from fastapi import Cookie, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Cookies(BaseModel):
    class Config:
        extra = "forbid"

    session_id: str
    fatebook_tracker: str | None = None
    googall_tracker: str | None = None


@app.get("/items/")
async def read_items(cookies: Annotated[Cookies, Cookie()]):

from fastapi import FastAPI, Header
from pydantic import BaseModel

app = FastAPI()


class CommonHeaders(BaseModel):
    class Config:
        extra = "forbid"

    host: str
    save_data: bool
    if_modified_since: str | None = None
    traceparent: str | None = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: CommonHeaders = Header()):

from fastapi import FastAPI, Path
from typing_extensions import Annotated

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    item_id: Annotated[int, Path(title="The ID of the item to get", ge=1)], q: str
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})

# `UploadFile` class

You can define *path operation function* parameters to be of the type `UploadFile` to receive files from the request.

You can import it directly from `fastapi`:

```python
from fastapi import UploadFile
```

::: fastapi.UploadFile
    options:
        members:
            - file
            - filename
            - size
            - headers
            - content_type
            - read

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item = Body(embed=True)):
    results = {"item_id": item_id, "item": item}

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: list[str] = []


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

from fastapi import FastAPI, Path
from typing_extensions import Annotated

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    item_id: Annotated[int, Path(title="The ID of the item to get", gt=0, le=1000)],
    q: str,
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})

```

/// note | "기술적 세부 사항"

`from starlette.responses import JSONResponse`를 사용할 수도 있습니다.

**FastAPI**는 개발자의 편의를 위해 `starlette.responses`를 `fastapi.responses`로 제공합니다. 그러나 대부분의 가능한 응답은 Starlette에서 직접 제공합니다.

///

## 사용자 정의 `Response` 반환하기
위 예제는 필요한 모든 부분을 보여주지만, 아직 유용하지는 않습니다. 사실 데이터를 직접 반환하면 **FastAPI**가 이를 `JSONResponse`에 넣고 `dict`로 변환하는 등 모든 작업을 자동으로 처리합니다.

이제, 사용자 정의 응답을 반환하는 방법을 알아보겠습니다.

from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel

app = FastAPI()


class CommonHeaders(BaseModel):
    model_config = {"extra": "forbid"}

    host: str
    save_data: bool
    if_modified_since: str | None = None
    traceparent: str | None = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: Annotated[CommonHeaders, Header()]):