This is a service to trigger image analysis process.
- 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
- Python 3.9.x
- Pyenv
- Docker
for development purpose, we consider you want to run locally:
content in progress
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:
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
source .venv/bin/activate
Once clone the repo to local, run below command from terminal
./run.sh dep-install
make sure you have a .env file including above content in root folder.
./run.sh start
then you can make request vis Postman or any api tool by http://0.0.0.0:8080
./run.sh test
- 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.
pytest --junitxml=junit/test-results.xml --cov=src --cov-report=xml --cov-report=html filename