# Static Files - `StaticFiles`

You can use the `StaticFiles` class to serve static files, like JavaScript, CSS, images, etc.

Read more about it in the [FastAPI docs for Static Files](https://fastapi.tiangolo.com/tutorial/static-files/).

You can import it directly from `fastapi.staticfiles`:

```python
from fastapi.staticfiles import StaticFiles
```

E esses modelos estão compartilhando muitos dos dados e duplicando nomes e tipos de atributos.

Nós poderíamos fazer melhor.

Podemos declarar um modelo `UserBase` que serve como base para nossos outros modelos. E então podemos fazer subclasses desse modelo que herdam seus atributos (declarações de tipo, validação, etc.).

For example "**Python 3.6+**" means it's compatible with Python 3.6 or above (including 3.7, 3.8, 3.9, 3.10, etc). And "**Python 3.9+**" means it's compatible with Python 3.9 or above (including 3.10, etc).

If you can use the **latest versions of Python**, use the examples for the latest version, those will have the **best and simplest syntax**, for example, "**Python 3.10+**".

There are multiple tools that you can use to create those dependables, and they get integrated into OpenAPI so they are shown in the automatic docs UI, they can be used by automatically generated clients and SDKs, etc.

You can import them from `fastapi.security`:

```python
from fastapi.security import (
    APIKeyCookie,
    APIKeyHeader,
    APIKeyQuery,
    HTTPAuthorizationCredentials,
    HTTPBasic,

# Properties used to build to the DEB package
#

# Environment file
packaging.env.file=/etc/default/fess

# Simple marker to check that properties are correctly overridden
packaging.type=deb

# Custom header for package scripts
packaging.scripts.header=#!/bin/sh${line.separator}set -e

- Lancez le <abbr title="En anglais: debugger">débogueur</abbr> avec l'option "`Python: Current File (Integrated Terminal)`".

Il démarrera alors le serveur avec votre code **FastAPI**, s'arrêtera à vos points d'arrêt, etc.

Voici à quoi cela pourrait ressembler :

<img src="/img/tutorial/debugging/image01.png">

---

Si vous utilisez Pycharm, vous pouvez :

- Ouvrir le menu "Run".
- Sélectionnez l'option "Debug...".

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

```sh
aws s3api put-object --bucket testbucket --key lockme --object-lock-mode GOVERNANCE --object-lock-retain-until-date "2019-11-20"  --body /etc/issue
```

See <https://docs.aws.amazon.com/AmazonS3/latest/dev/object-lock-overview.html> for AWS S3 spec on object locking and permissions required for object retention and governance bypass overrides.

For that, you can use the `WSGIMiddleware` and use it to wrap your WSGI application, for example, Flask, Django, etc.

## Using `WSGIMiddleware` { #using-wsgimiddleware }

You need to import `WSGIMiddleware`.

Then wrap the WSGI (e.g. Flask) app with the middleware.

And then mount that under a path.

=================

Thank you so much for wanting to contribute to Guava! Here are a few important
things you should know about contributing:

1.  API changes require discussion, use cases, etc. Code comes later.
2.  Pull requests are great for small fixes for bugs, documentation, etc.
3.  Pull requests are not merged directly into the master branch.
4.  Code contributions require signing a Google CLA.

API changes
-----------