# Application server
As a means of testing the API and demonstrating functionality, two
additional components are included in this repository:
a simple [swagger-based webserver](../../testing) (documented here) and
a [demo web page](../../web_demo) (documented in the [next tutorial](lesson3.md).

## Swagger API
Using a simple [flask-based connexion server](https://github.com/zalando/connexion),
an API scaffold has been built to host a serialized/dumped model.

To utilized [this example app](../../testing), an instance should first be built and downloaded
from Acumos (or dumped to disk) and then
launched locally.  Afterwards, the sample application found in
[web_demo](web_demo) (see [the next tutorial](lesson3.md))
uses a `localhost` service to transform
and visualize the results of model operation.


```
usage: app.py [-h] [-p PORT] [-d MODELDIR_DETECT] [-a MODELDIR_ANALYZE]

optional arguments:
  -h, --help            show this help message and exit
  -p PORT, --port PORT  port to launch the simple web server
  -d MODELDIR_DETECT, --modeldir_detect MODELDIR_DETECT
                        model directory for detection
  -a MODELDIR_ANALYZE, --modeldir_analyze MODELDIR_ANALYZE
                        model directory for detection
```


Example usage may be running with a detect model that was dumped to the directory `model_detect`
in the main repo source directory and a pixelate model in the
directory `model_pix` (under the same repo source directory).

```
python app.py --modeldir_detect ../model_detect --modeldir_analyze ../model_pix/
```


### Output formats
This repo provides multiple models that can be created.

* detect output - The first set, called
`detect` will analyze an image, detect face regions, and echo both the
regions and the face back to the user.  The region marked with `-1`
and a valid `mime_type` parameter will
always be the region with the original image.

```
[
    {
        "h": 143,
        "x": 0,
        "y": 0,
        "base64_data": "/9j/4AAQSkZJRgABA....",
        "w": 2048,
        "region": -1,
        "image": 0,
        "mime_type": "image/jpeg"
    },
    {
        "h": 143,
        "x": 203,
        "y": 189,
        "base64_data": "",
        "w": 143,
        "region": 0,
        "image": 0,
        "mime_type": ""
    },
    ...
    {
        "h": 212,
        "x": 886,
        "y": 409,
        "base64_data": "",
        "w": 212,
        "region": 3,
        "image": 0,
        "mime_type": ""
    }
]

```

* analyzed output - The second type of output produces processed
images as an output.  These images are base64 encoded with a decoding
mime type.
```
[
    {
        "base64_data": "/9j/4AAQSkZJRgABAQAAAQABAAD....",
        "mime_type": "image/jpeg"
    }
]

```

## Direct Evaluation

* For a graphical experience, view the swagger-generated UI at [http://localhost:8884/ui].
* Additionally, a simple command-line utility could be used to post an image
and mime type to the main interface.  Additional examples for posting base64 encoded
images from javascript can be [found on StackOverflow](https://stackoverflow.com/a/20285053).
```
curl -F base64_data=@../web_demo/images/face_renion.jpg -F mime_type="image/jpeg" "http://localhost:8884/transform"
```