Color Theory App
A toy project/demo on how to structure and develop micro-service driven application powered by machine learning service.
Since 2019-04 till 2019-06, only 5 out of over 630 articles about ML services deployment were published on towardsdatascience.com. This figures reflects the situation in the data science community; only a small fraction of engineers/data scientist are capable to deliver ML product into production.
Built a web app to answer the question "What's the color?" Assuming that it's a point in RGB, or HEX space with a name and type.
Display the color name, RGB and HEX codes on the user input.
The app objective is to define binary category of a color selected by user. Two possible categories being warm and cool are described as following:
Color theory has described perceptual and psychological effects to this contrast. Warm colors are said to advance or appear more active in a painting, while cool colors tend to recede; used in interior design or fashion, warm colors are said to arouse or stimulate the viewer, while cool colors calm and relax.
color_theory_app
├── server
├── client
├── docker-compose.yaml
└── launch_services.sh
The app has two service sides, frontend and backend:
- client/frontend side can be generalised as the product with developers + DevOps maintaining and developing it
- server/backend side can be generalised as the micro-service with data scientist/engineers + machine learning engineers + developers + Dev-/DataOps maintaining and developing it
Frontend/product communicates to the backend (e.g. machine-learning service) to provide users unique feature, or improve users experience.
Backend has the interface(s) to communicate with other services (fronted service in our case) with a set of end-points.
server
├── common_libs
├── color_name
└── color_typeThe app's server side has two micro-services with HTTP interfaces (API end-points):
- color_name - provides color name
- color_type - provides color binary class/type
Both services API's have similar code structure:
service
└── api
├── sub-mobules
│ ├── __init__.py
│ ├── ...
│ └── module.py
├── Dockerfile
├── requirements.txt
└── run_server.pyWith service runner/executable, run_server.py.
The service requires an input color as the HEX, or RGB code and returns the color name. The color name of a given color is identified as the reference color name based on the Euclidian distance between an input color and the reference in-memory colors data set:
- Array of euclidian distances between input color and reference colors is calculated in RGB space
- Minimum value of the distance is identified
- The Reference color name which corresponds to the found reference color is assigned to the input color:
The service delivers the product feature F2 (see infrastructure).
color_type
├── Dockerfile
├── api
└── ml
├── model
└── trainThe machine learning service consumes the model from ./color_type/ml/model/ and predicts a color class/type based on the input color code:
model.predict(pandas.DataFrame({'r': [r_in],
'g': [g_in],
'b': [b_in]}))The service delivers the product feature F3 (see infrastructure).
ml
├── model
│ ├── v1
│ │ └── model_v1.sav
│ └── v2
│ └── model_v2.sav
└── train
├── data
│ └── warm_cold_colors.csv
└── ml_steps.ipynbThe models can be iteratively developed by the data science team according to the flow:
consume data from data dir -> train and evaluate the model -> model export into model dir -> model quality monitoring -> model re-train| Back-end Service | Endpoint | Response: 200 | Response: 500 |
|---|---|---|---|
|
Base Service/ Color Name |
/name/hex?hexcode=5C77FA
/name/rgb?r=92&g=119&b=250
|
{
"data": {
"color": {
"r": 92,
"g": 119,
"b": 250
},
"name": "Blueberry"
}
}
|
{ "data": null }
|
|
ML Service/ Color Type |
/type/hex?hexcode=5C77FA
/type/rgb?r=92&g=119&b=250
|
{
"data": {
"color": {
"r": 92,
"g": 119,
"b": 250
},
"is_warm": 1
}
}
|
Frontend service of the app gives web interface for a user to select a color of interest and define its properties by communicating with the backend via its API end-point(s). It also delivers feature F1 (see infrastructure).
docker ver. >= 18.09.2
docker-compose ver. >= 1.23.2Docker:
Docker-compose:
To launch the app, clone the repo
git clone [email protected]:kislerdm/color_theory_app.git && cd color_theory_appand build&run docker images with services and the client app
sh launch_services.shUpon docker images build completion and when docker containers are up and running, you can access UI by going to http://localhost:10000 in your browser.