Image Analysis service

This is a service to trigger image analysis process.

Concept of the project

By isolating the data in data lakes: raw, stage, and result to make the workflow more transparent with Minio.
Make prediction(analysis) with the model on demand

Dependencies

Python 3.9.x
Pyenv
Docker

for development purpose, we consider you want to run locally:

Run Service locally

1. Setting up Python

content in progress

2. Apply a virtual environment for Python

It is recommended to use a virtual environment to keep a given Python version and the project dependencies from the system Python and other projects. There are several virtual environment managers (venv, virtualenv, pyenv, pipenv, conda...) and the developer is free to choose among them.

Run the commands below in the root project folder to build a virtual environment:

a way manage and control virtual environment in the same repo folder

brew install pyenv pyenv-virtualenv
pyenv install 3.9.12
pyenv local 3.9.12
python -m venv .venv
python -m pip install --upgrade pip
source .venv/bin/activate

Activate manually if you can not see it activate on terminal by below:

source .venv/bin/activate

3. Installing dependencies

Once clone the repo to local, run below command from terminal

./run.sh dep-install

4. Prepare .env file

make sure you have a .env file including above content in root folder.

5. Running the Service locally

./run.sh start

then you can make request vis Postman or any api tool by http://0.0.0.0:8080

Other commands

1. Running tests

./run.sh test

2. Linting code

At the very minimum, any code MUST pass ./run.sh check (this is enforced during CI).
Code need to pass ./run.sh lint.
For a full linting (including style), run pylint src at the root folder.

3. generate unit tst report

pytest --junitxml=junit/test-results.xml --cov=src --cov-report=xml --cov-report=html filename

poting-lin / image-analysis