This repo is no longer maintained.
Flask api and the code to retrain the model, which requires data, both extracted out of SIA and some dumps out of old systems. For data, contact: m.sukel@amsterdam.nl
pip install -r requirements-train.txt
Use the requirements.txt to run (flask) endpoint locally. This step can be skipped if you are using the docker container.
pip install -r requirements.txt
csv input file with at least the following columns:
| column | description |
|---|---|
| Main | Main category |
| Middle | Middle category |
| Sub | Sub category |
| Text | message |
navigate to app folder See python train.py for all options.
To train Middle and Sub categoeries use:
python train.py --csv file.csv --columns Middle,Sub
This step will generate a categories json file. Use this file to load the categories in the backend.
python manage.py load_categories <file.json>
To train Middle category use:
python train.py --csv file.csv --columns Middle
Rename resulting files to "main_model.pkl, sub_model.pkl, main_slugs.pkl, sub_slugs.pkl" and copy the pkl files into the classification endpoint.
To load new model into flask (copy into app folder)
| file | description |
|---|---|
| main_model.pkl | model for main category |
| sub_model.pkl | model for sub category |
| main_slugs.pkl | slugs for main category |
| sub_slugs.pkl | slugs for sub category |
run docker-compose build
To activate the flask api run:
docker-compose up -d
To test the current loaded model, open web_pages/index.html or POST "text" to http://localhost:8140/signals_mltool/predict with the flask app running.