```

But that is still not that useful.

Let's make it give us the current user.

## Create a user model

First, let's create a Pydantic user model.

The same way we use Pydantic to declare bodies, we can use it anywhere else:

=== "Python 3.10+"

    ```Python hl_lines="5  12-16"
    {!> ../../../docs_src/security/tutorial002_an_py310.py!}
    ```

=== "Python 3.9+"

@pytest.fixture(name="client")
def get_client():
    from docs_src.schema_extra_example.tutorial004_an_py310 import app

    client = TestClient(app)
    return client


# Test required and embedded body parameters with no bodies sent
@needs_py310
def test_post_body_example(client: TestClient):
    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",

```Python hl_lines="19-24 28"
{!../../../docs_src/additional_responses/tutorial002.py!}
```

!!! note "Remarque"
    Notez que vous devez retourner l'image en utilisant directement un `FileResponse`.

!!! info
    À moins que vous ne spécifiiez explicitement un type de média différent dans votre paramètre `responses`, FastAPI supposera que la réponse a le même type de média que la classe de réponse principale (par défaut `application/json`).

It is designed to have functions that receive two parameters, one "request" and one "response". Then you "read" parts from the request, and "write" parts to the response. Because of this design, it is not possible to declare request parameters and bodies with standard Python type hints as function parameters.

## Use cases

Some use cases include:

* Converting non-JSON request bodies to JSON (e.g. <a href="https://msgpack.org/index.html" class="external-link" target="_blank">`msgpack`</a>).
* Decompressing gzip-compressed request bodies.
* Automatically logging all request bodies.

## Handling custom request body encodings

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.
* Implement and use well-known cryptographic tools, like Passlib and JWT tokens, etc.

    * A les meilleures performances, étant donné qu'il n'a pas beaucoup de code mis-à-part le serveur en lui-même.
    * On n'écrit pas une application avec uniquement Uvicorn. Cela signifie que le code devrait inclure plus ou moins, au minimum, tout le code offert par Starlette (ou **FastAPI**). Et si on fait cela, l'application finale apportera les mêmes complications que si on avait utilisé un framework et que l'on avait minimisé la quantité de code et de bugs.

Au final, dans les deux situations, il est fort probable que **FastAPI** soit tout de même [plus rapide](index.md#performance){.internal-link target=_blank} que (ou au moins de vitesse égale à) votre framework précédent.

### Dépendances

    * Composite bodies' IDs are now based on path, not only on route name, as the auto-generated name uses the function names, that can be duplicated in different modules.
    * The same new ID generation applies to response models.
    * This also changes the generated title for those models.

* **Editor support**.
* **Type checks**.

...and **FastAPI** uses the same declarations to:

* **Define requirements**: from request path parameters, query parameters, headers, bodies, dependencies, etc.
* **Convert data**: from the request to the required type.
* **Validate data**: coming from each request:
    * Generating **automatic errors** returned to the client when the data is invalid.