def test_index():
    client = TestClient(app)
    with client.websocket_connect("/") as websocket:
        data = json.loads(websocket.receive_text())
        assert data == ["app", "index"]


def test_routerindex():
    client = TestClient(app)
    with client.websocket_connect("/router") as websocket:
        data = json.loads(websocket.receive_text())
        assert data == ["app", "router2", "router", "routerindex"]

            "required": True,
        },
    },
)
async def create_item(request: Request):
    raw_body = await request.body()
    try:
        data = yaml.safe_load(raw_body)
    except yaml.YAMLError:
        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:
        item = Item.model_validate(data)
    except ValidationError as e:

* **PGAdmin** für die PostgreSQL-Datenbank, können Sie problemlos ändern, sodass PHPMyAdmin und MySQL verwendet wird.
* **Flower** für die Überwachung von Celery-Jobs.
* Load Balancing zwischen Frontend und Backend mit **Traefik**, sodass Sie beide unter derselben Domain haben können, getrennt durch den Pfad, aber von unterschiedlichen Containern ausgeliefert.

                    "type": "type_error.dict",
                }
            ]
        }
    )


@needs_py310
def test_other_exceptions(client: TestClient):
    with patch("json.loads", side_effect=Exception):
        response = client.post("/items/", json={"test": "test2"})
        assert response.status_code == 400, response.text


@needs_py310
def test_openapi_schema(client: TestClient):

                    "type": "type_error.dict",
                }
            ]
        }
    )


def test_other_exceptions(client: TestClient):
    with patch("json.loads", side_effect=Exception):
        response = client.post("/items/", json={"test": "test2"})
        assert response.status_code == 400, response.text


def test_openapi_schema(client: TestClient):

### Server Memory

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    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()

        Doc(
            """
            Load and use Google Fonts.
            """
        ),
    ] = True,
) -> HTMLResponse:
    """
    Generate and return the HTML response that loads ReDoc for the alternative
    API docs (normally served at `/redoc`).

    You would only call this function yourself if you needed to override some parts,
    for example the URLs to use to load ReDoc's JavaScript and CSS.

            "required": True,
        },
    },
)
async def create_item(request: Request):
    raw_body = await request.body()
    try:
        data = yaml.safe_load(raw_body)
    except yaml.YAMLError:
        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:
        item = Item.parse_obj(data)
    except ValidationError as e:

- 🔒 Secure password hashing by default.
- 🔑 JWT token authentication.
- 📫 Email based password recovery.
- ✅ Tests with [Pytest](https://pytest.org).
- 📞 [Traefik](https://traefik.io) as a reverse proxy / load balancer.
- 🚢 Deployment instructions using Docker Compose, including how to set up a frontend Traefik proxy to handle automatic HTTPS certificates.