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)
    return {"result": result}
```

## Future

By this point, it's already clear that **FastAPI** with its ideas is being useful for many people.

It is being chosen over previous alternatives for suiting many use cases better.

@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)

    ```Python hl_lines="20"
    {!> ../../../docs_src/dependencies/tutorial011.py!}
    ```

!!! tip
    All this might seem contrived. And it might not be very clear how is it useful yet.

    These examples are intentionally simple, but show how it all works.

    In the chapters about security, there are utility functions that are implemented in this same way.

        restart.style.visibility = 'hidden'
        this.container.appendChild(restart)
        this.container.setAttribute('data-termynal', '');
    }

    /**
     * Initialise the widget, get lines, clear container and start animation.
     */
    init() {
        /**
         * Calculates width and height of Termynal container.

FastAPI will now:

* **Validate** the data making sure that the max length is 50 characters
* Show a **clear error** for the client when the data is not valid
* **Document** the parameter in the OpenAPI schema *path operation* (so it will show up in the **automatic docs UI**)

## Alternative (old) `Query` as the default value

All the framework was designed to be easy and intuitive to use, all the decisions were tested on multiple editors even before starting development, to ensure the best development experience.

In the Python developer surveys, it's clear <a href="https://www.jetbrains.com/research/python-developers-survey-2017/#tools-and-features" class="external-link" target="_blank">that one of the most used features is "autocompletion"</a>.

### Understand the question

* Check if you can understand what is the **purpose** and use case of the person asking.

* Then check if the question (the vast majority are questions) is **clear**.

    assert "/async_raise" in errors
    errors.clear()


def test_async_raise_server_error():
    client = TestClient(app, raise_server_exceptions=False)
    response = client.get("/async_raise")
    assert response.status_code == 500, response.text
    assert state["/async_raise"] == "asyncgen raise finalized"
    assert "/async_raise" in errors
    errors.clear()


def test_context_b():

```Python
item: Item
```

...and with that single declaration you get:

* Editor support, including:
    * Completion.
    * Type checks.
* Validation of data:
    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.