{% endfor %}
{% endfor %}
{% endfor %}

## Projets

Les projets Github avec le topic `fastapi` les plus récents :

<div class="github-topic-projects">

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import HTMLResponse

app = FastAPI()


@app.post("/files/")
async def create_files(files: list[bytes] = File()):
    return {"file_sizes": [len(file) for file in files]}


@app.post("/uploadfiles/")
async def create_upload_files(files: list[UploadFile]):
    return {"filenames": [file.filename for file in files]}


@app.get("/")
async def main():

from fastapi.responses import HTMLResponse

app = FastAPI()


@app.post("/files/")
async def create_files(
    files: list[bytes] = File(description="Multiple files as bytes"),
):
    return {"file_sizes": [len(file) for file in files]}


@app.post("/uploadfiles/")
async def create_upload_files(
    files: list[UploadFile] = File(description="Multiple files as UploadFile"),
):

* **Starlette** :
    * A les seconde meilleures performances après Uvicorn. Starlette utilise en réalité Uvicorn. De ce fait, il ne peut qu’être plus "lent" qu'Uvicorn car il requiert l'exécution de plus de code.
    * Cependant il nous apporte les outils pour construire une application web simple, avec un routage basé sur des chemins, etc.

Mais si vous suivez bien les instructions ci-dessus, alors **FastAPI** pourra effectuer quelques optimisations et ainsi améliorer les performances.

## Détails techniques

Les versions modernes de Python supportent le **code asynchrone** grâce aux **"coroutines"** avec les syntaxes **`async` et `await`**.

Analysons les différentes parties de cette phrase dans les sections suivantes :

* **Code asynchrone**

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

And then, we extend that copy `dict`, adding another key-value pair: `"id": last_record_id`:

```Python
{**note.dict(), "id": last_record_id}
```

{% endfor %}
{% endif %}

## About the data - technical details

The main intention of this page is to highlight the effort of the community to help others.

Especially including efforts that are normally less visible, and in many cases more arduous, like helping others with questions and reviewing Pull Requests with translations.

    * Normally, a token is set to expire after some time.
        * So, the user will have to log in again at some point later.
        * And if the token is stolen, the risk is less. It is not like a permanent key that will work forever (in most of the cases).
* The frontend stores that token temporarily somewhere.
* The user clicks in the frontend to go to another section of the frontend web app.

`0.5` wäre also ein gültiger Wert, aber nicht `0.0` oder `0`.

Das gleiche gilt für <abbr title="less than – kleiner als"><code>lt</code></abbr>.

=== "Python 3.9+"

    ```Python hl_lines="13"
    {!> ../../../docs_src/path_params_numeric_validations/tutorial006_an_py39.py!}
    ```

=== "Python 3.8+"

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).