///

## **FastAPI** Tools

FastAPI stellt für jedes dieser Sicherheitsschemas im Modul `fastapi.security` verschiedene Tools bereit, die die Verwendung dieser Sicherheitsmechanismen vereinfachen.

```

</div>

<details markdown="1">
<summary>A parancsról <code>uvicorn main:app --reload</code>...</summary>

A `uvicorn main:app` parancs a következőre utal:

* `main`: fájl `main.py` (a Python "modul").
* `app`: a `main.py`-ban a `app = FastAPI()` sorral létrehozott objektum.
* `--reload`: kód változtatás esetén újra indítja a szervert. Csak fejlesztés közben használandó.

</details>

### Ellenőrizd

```

</div>

<details markdown="1">
<summary>O komendzie <code>uvicorn main:app --reload</code>...</summary>
Komenda `uvicorn main:app` odnosi się do:

* `main`: plik `main.py` ("moduł" w Pythonie).
* `app`: obiekt stworzony w `main.py` w lini `app = FastAPI()`.
* `--reload`: spraw by serwer resetował się po każdej zmianie w kodzie. Używaj tego tylko w środowisku deweloperskim.

</details>

</div>

<details markdown="1">
<summary>Was macht der Befehl <code>uvicorn main:app --reload</code> ...</summary>

Der Befehl `uvicorn main:app` bezieht sich auf:

* `main`: die Datei `main.py` (das Python-„Modul“).
* `app`: das Objekt, das innerhalb von `main.py` mit der Zeile `app = FastAPI()` erzeugt wurde.
* `--reload`: lässt den Server nach Codeänderungen neu starten. Tun Sie das nur während der Entwicklung.

</details>

series title: Building simple E-Commerce with NuxtJS and FastAPI - author: Rodrigo Arenas author_link: https://rodrigo-arenas.medium.com/ link: https://medium.com/analytics-vidhya/serve-a-machine-learning-model-using-sklearn-fastapi-and-docker-85aabf96729b title: "Serve a machine learning model using Sklearn, FastAPI and Docker" - author: Yashasvi Singh author_link: https://hashnode.com/@aUnicornDev link: https://aunicorndev.hashnode.dev/series/supafast-api title: "Building an API with FastAPI and...

			<url url="${repo.url}/${module.groupId}/${module.name.prefix}${module.name}/${module.version}/${module.name.prefix}${module.name}-${module.zip.version}.zip" />
		</get>
		<unzip dest="${modules.dir}/${module.name}" src="${target.dir}/${module.name.prefix}${module.name}-${module.zip.version}.zip">
			<patternset>
				<include name="**" />
			</patternset>
			<cutdirsmapper dirs="1" />
		</unzip>
	</target>

	<target name="remove.jars" if="with.fess">

But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

/// danger

Never store the plain password of a user or send it in a response like this, unless you know all the caveats and you know what you are doing.

///

## Add an output model { #add-an-output-model }

You could load it at the top level of the module/file, but that would also mean that it would **load the model** even if you are just running a simple automated test, then that test would be **slow** because it would have to wait for the model to load before being able to run an independent part of the code.

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

  <ciManagement>
    <system>GitHub Actions</system>
    <url>https://github.com/google/guava/actions</url>
  </ciManagement>
  <modules>
    <module>guava</module>
    <module>guava-bom</module>
    <module>guava-gwt</module>
    <module>guava-testlib</module>
    <module>guava-tests</module>
  </modules>
  <build>
    <!-- Handle where Guava deviates from Maven defaults -->
    <sourceDirectory>src</sourceDirectory>