In a real application, you probably would have your tests in a different file.

And your **FastAPI** application might also be composed of several files/modules, etc.

### **FastAPI** app file

Let's say you have a file structure as described in [Bigger Applications](bigger-applications.md){.internal-link target=_blank}:

```
.
├── app
│   ├── __init__.py
│   └── main.py
```

In the file `main.py` you have your **FastAPI** app:

**FastAPI** provides a convenience tool to structure your application while keeping all the flexibility.

!!! info
    If you come from Flask, this would be the equivalent of Flask's Blueprints.

## An example file structure

Let's say you have a file structure like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
│   ├── dependencies.py

<font color="#3465A4">INFO    </font> Resolved absolute path <font color="#75507B">/home/user/code/awesomeapp/</font><font color="#AD7FA8">main.py</font>
<font color="#3465A4">INFO    </font> Searching for package file structure from directories with <font color="#3465A4">__init__.py</font> files
<font color="#3465A4">INFO    </font> Importing from <font color="#75507B">/home/user/code/</font><font color="#AD7FA8">awesomeapp</font>

<font color="#3465A4">INFO    </font> Resolved absolute path <font color="#75507B">/home/user/code/awesomeapp/</font><font color="#AD7FA8">main.py</font>
<font color="#3465A4">INFO    </font> Searching for package file structure from directories with <font color="#3465A4">__init__.py</font> files
<font color="#3465A4">INFO    </font> Importing from <font color="#75507B">/home/user/code/</font><font color="#AD7FA8">awesomeapp</font>

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

<font color="#3465A4">INFO    </font> Resolved absolute path <font color="#75507B">/home/user/code/awesomeapp/</font><font color="#AD7FA8">main.py</font>
<font color="#3465A4">INFO    </font> Searching for package file structure from directories with <font color="#3465A4">__init__.py</font> files
<font color="#3465A4">INFO    </font> Importing from <font color="#75507B">/home/user/code/</font><font color="#AD7FA8">awesomeapp</font>

# Codes HTTP supplémentaires

Par défaut, **FastAPI** renverra les réponses à l'aide d'une structure de données `JSONResponse`, en plaçant la réponse de votre  *chemin d'accès* à l'intérieur de cette `JSONResponse`.

Il utilisera le code HTTP par défaut ou celui que vous avez défini dans votre *chemin d'accès*.

## Codes HTTP supplémentaires

    ```Python hl_lines="8-11"
    {!> ../../../docs_src/dependencies/tutorial001.py!}
    ```

That's it.

**2 lines**.

And it has the same shape and structure that all your *path operation functions* have.

You can think of it as a *path operation function* without the "decorator" (without the `@app.get("/some-path")`).

And it can return anything you want.

This way, its schema will show up in the API docs user interface:

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

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

    ```Python hl_lines="3"
    {!> ../../../docs_src/cookie_params/tutorial001.py!}
    ```

## Declarar parámetros de `Cookie`

Luego declara los parámetros de cookie usando la misma estructura que con `Path` y `Query`.

El primer valor es el valor por defecto, puedes pasar todos los parámetros adicionales de validación o anotación:

=== "Python 3.10+"

    ```Python hl_lines="9"