<font color="#4E9A06">INFO</font>:     Application startup complete.
```

</div>

In the output, there's a line with something like:

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

That line shows the URL where your app is being served, in your local machine.

### Check it

            // More stuff here
    }
}
```

In this example, the "Proxy" could be something like **Traefik**. And the server would be something like FastAPI CLI with **Uvicorn**, running your FastAPI application.

### Providing the `root_path`

To achieve this, you can use the command line option `--root-path` like:

<div class="termy">

```console
$ fastapi run main.py --root-path /api/v1

The main thing you need to run a **FastAPI** application (or any other ASGI application) in a remote server machine is an ASGI server program like **Uvicorn**, this is the one that comes by default in the `fastapi` command.

There are several alternatives, including:

```

</div>

#### Generate Client Code

To generate the client code you can use the command line application `openapi-ts` that would now be installed.

Because it is installed in the local project, you probably wouldn't be able to call that command directly, but you would put it on your `package.json` file.

It could look like this:

```JSON  hl_lines="7"
{
  "name": "frontend-app",
  "version": "1.0.0",

```

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


```Python
{!../../../docs_src/app_testing/main.py!}
```

### 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
```

It's relatively tightly coupled with relational databases (like MySQL or PostgreSQL), so, having a NoSQL database (like Couchbase, MongoDB, Cassandra, etc) as the main store engine is not very easy.

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself....

    Notice that `response_model` is a parameter of the "decorator" method (`get`, `post`, etc). Not of your *path operation function*, like all the parameters and body.

`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.

```

In your client you will get a JSON response like:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Path parameters containing paths

Let's say you have a *path operation* with a path `/files/{file_path}`.

But you need `file_path` itself to contain a *path*, like `home/johndoe/myfile.txt`.

* 따라서 이미지, 동영상, 큰 이진코드와 같은 대용량 파일들을 많은 메모리를 소모하지 않고 처리하기에 적합합니다.
* 업로드 된 파일의 메타데이터를 얻을 수 있습니다.
* <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">file-like</a>  `async` 인터페이스를 갖고 있습니다.