The caching is now finished. The cache is created in `settings.py` giving us the most flexibility for using it in the future.
The cache is currently updated/accessed by `tasks.py/get_ml_model` which can be called from whatever task needs to access ml models in this way (currently, `predict` and `predict_simple`).
This implementation currently caches all ml models including the relative reasoning. If we don't want this and only want to cache enviFormer, i can change it to that. However, I don't think there is a harm in having the other models be cached as well.
Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#156
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
