from fastapi.testclient import TestClient
from sqlalchemy.sql.elements import quoted_name

app = FastAPI(default_response_class=ORJSONResponse)


@app.get("/orjson_non_str_keys")
def get_orjson_non_str_keys():
    key = quoted_name(value="msg", quote=False)
    return {key: "Hello World", 1: 1}


client = TestClient(app)


def test_orjson_non_str_keys():
    with client:

### Archivo de aplicación **FastAPI** { #fastapi-app-file }

Digamos que tienes una estructura de archivos como se describe en [Aplicaciones Más Grandes](bigger-applications.md){.internal-link target=_blank}:

```
.
├── app
│   ├── __init__.py
│   └── main.py
```

En el archivo `main.py` tienes tu aplicación de **FastAPI**:

COPY ./requirements.txt /code/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

CMD ["fastapi", "run", "app/main.py", "--port", "80"]

# Если запускаете за прокси, например Nginx или Traefik, добавьте --proxy-headers
# CMD ["fastapi", "run", "app/main.py", "--port", "80", "--proxy-headers"]
```

</details>

## Что такое контейнер { #what-is-a-container }

    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)

from typing import Annotated

from fastapi import FastAPI, File, UploadFile

app = FastAPI()


@app.post("/files/")
async def create_file(file: Annotated[bytes | None, File()] = None):
    if not file:
        return {"message": "No file sent"}
    else:
        return {"file_size": len(file)}


@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile | None = None):
    if not file:

    separate_input_output_schemas = request.param
    app = FastAPI(separate_input_output_schemas=separate_input_output_schemas)

    from pydantic import BaseModel, computed_field

    class Rectangle(BaseModel):
        width: int
        length: int

        @computed_field
        @property
        def area(self) -> int:
            return self.width * self.length

    @app.get("/")
    def read_root() -> Rectangle:

## Приклад

### Створіть

* Створіть файл `main.py` з:

```Python
from typing import Union

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

<details markdown="1">

from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class Items(BaseModel):
    items: dict[str, int]


@app.post("/foo")
def foo(items: Items):
    return items.items


client = TestClient(app)


def test_additional_properties_post():
    response = client.post("/foo", json={"items": {"foo": 1, "bar": 2}})

from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()

oid = OpenIdConnect(
    openIdConnectUrl="/openid", description="OpenIdConnect security scheme"
)


class User(BaseModel):
    username: str


def get_current_user(oauth_header: str = Security(oid)):
    user = User(username=oauth_header)
    return user


@app.get("/users/me")

É o "**decorador de operação de rota**".

///

Você também pode usar as outras operações:

* `@app.post()`
* `@app.put()`
* `@app.delete()`

E os mais exóticos:

* `@app.options()`
* `@app.head()`
* `@app.patch()`
* `@app.trace()`

/// tip | Dica

Você está livre para usar cada operação (método HTTP) como desejar.