And these same full-stack generators were the base of the [**FastAPI** Project Generators](project-generation.md){.internal-link target=_blank}.

!!! info
    Flask-apispec was created by the same Marshmallow developers.

!!! check "Inspired **FastAPI** to"
    Generate the OpenAPI schema automatically, from the same code that defines serialization and validation.

## Client and SDK Generators - Sponsor

There are also some **company-backed** Client and SDK generators based on OpenAPI (FastAPI), in some cases they can offer you **additional features** on top of high-quality generated SDKs/clients.

    response = client.post(
        "/items/", json={"name": "Foo", "price": "50.5", "description": "Some Foo"}
    )
    assert response.status_code == 200
    assert response.json() == {
        "name": "Foo",
        "price": 50.5,
        "description": "Some Foo",
        "tax": None,
    }


@needs_py310
def test_post_with_str_float_description_tax(client: TestClient):

    response = client.post(
        "/items/", json={"name": "Foo", "price": "50.5", "description": "Some Foo"}
    )
    assert response.status_code == 200
    assert response.json() == {
        "name": "Foo",
        "price": 50.5,
        "description": "Some Foo",
        "tax": None,
    }


def test_post_with_str_float_description_tax(client: TestClient):
    response = client.post(

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

    {!> ../../../docs_src/response_model/tutorial002.py!}
    ```

Now, whenever a browser is creating a user with a password, the API will return the same password in the response.

In this case, it might not be a problem, because it's the same user sending the password.

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

!!! danger

* Each application that you have running on your computer has some process behind it, each running program, each window, etc. And there are normally many processes running **at the same time** while a computer is on.
* There can be **multiple processes** of the **same program** running at the same time.

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.

```

### Testing file

Then you could have a file `test_main.py` with your tests. It could live on the same Python package (the same directory with a `__init__.py` file):

``` hl_lines="5"
.
├── app
│   ├── __init__.py
│   ├── main.py
│   └── test_main.py
```

Because this file is in the same package, you can use relative imports to import the object `app` from the `main` module (`main.py`):

```Python hl_lines="3"

**Spoiler alert**: il tutorial - Guida Utente include:

* Dichiarazione di **parameters** da altri posti diversi come: **headers**, **cookies**, **form fields** e **files**.
* Come stabilire **vincoli di validazione** come `maximum_length` o `regex`.