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"]

### 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. 🚨

import json
from pathlib import Path

file_path = Path("./openapi.json")
openapi_content = json.loads(file_path.read_text())

for path_data in openapi_content["paths"].values():
    for operation in path_data.values():
        tag = operation["tags"][0]
        operation_id = operation["operationId"]
        to_remove = f"{tag}-"
        new_operation_id = operation_id[len(to_remove) :]
        operation["operationId"] = new_operation_id

        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.

                    "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):

Used by FastAPI / Starlette:

* <a href="https://www.uvicorn.org" target="_blank"><code>uvicorn</code></a> - for the server that loads and serves your application.
* <a href="https://github.com/ijl/orjson" target="_blank"><code>orjson</code></a> - Required if you want to use `ORJSONResponse`.

    response = client.get("/context_b_bg")
    data = response.json()
    assert data["context_b"] == "started b"
    assert data["context_a"] == "started a"
    assert data["bg"] == "not set"
    middleware_state = json.loads(response.headers["x-state"])
    assert middleware_state["context_b"] == "finished b with a: started a"
    assert middleware_state["context_a"] == "finished a"
    assert middleware_state["bg"] == "not set"

### Traefik

<a href="https://traefik.io/" class="external-link" target="_blank">Traefik</a> é um _proxy_ reverso / _load balancer_ de alta performance. Ele pode fazer o trabalho do _"TLS Termination Proxy"_ (à parte de outros recursos).

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()