## Summary
This PR improves the local development setup experience by adding Docker Compose and Makefile for streamlined setup.
## Changes
- **Added `docker-compose.yml`**: for one-command PostgreSQL database setup
- **Added `Makefile`**: Convenient shortcuts for common dev tasks (\`make setup\`, \`make dev\`, etc.)
- **Updated `README.md`**: Quick development setup instructions using Make
-
- **Added**: RDkit installation pain point documentation
- **Fixed**: Made Java feature properly dependent
## Why these changes?
The application uses PostgreSQL-specific features (\`ArrayField\`) and requires an anonymous user created by the bootstrap command. This PR makes the setup process trivial for new developers:
```bash
cp .env.local.example .env
make setup # Starts DB, runs migrations, bootstraps data
make dev # Starts development server
```
Java fix:
Moved global Java import to inline to avoid everyone having to configure the Java path.
Numerous changes to view and settings.
- Applied ruff-formatting
## Testing
Verified complete setup from scratch works with:
- PostgreSQL running in Docker
- All migrations applied
- Bootstrap data loaded successfully
- Anonymous user created
- The development server starts correctly.
Co-authored-by: Tobias O <tobias.olenyi@tum.de>
Co-authored-by: Tobias O <tobias.olenyi@envipath.com>
Co-authored-by: Liam <62733830+limmooo@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#143
Reviewed-by: jebus <lorsbach@envipath.com>
Reviewed-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-authored-by: t03i <mail+envipath@t03i.net>
Co-committed-by: t03i <mail+envipath@t03i.net>
## Changes
- I have finished the backend integration of EnviFormer (#19), this includes, dataset building, model finetuning, model evaluation and model prediction with the finetuned model.
- `PackageBasedModel` has been adjusted to be more abstract, this includes making the `_save_model` method and making `compute_averages` a static class function.
- I had to bump the python-version in `pyproject.toml` to >=3.12 from >=3.11 otherwise uv failed to install EnviFormer.
- The default EnviFormer loading during `settings.py` has been removed.
## Future Fix
I noticed you have a little bit of code in `PackageBasedModel` -> `evaluate_model` for using the `eval_packages` during evaluation instead of train/test splits on `data_packages`. It doesn't seem finished, I presume we want this for all models, so I will take care of that in a new branch/pullrequest after this request is merged.
Also, I haven't done anything for a POST request to finetune the model, I'm not sure if that is something we want now.
Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#141
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
Bump Python Version to 3.12
Make use of "epauth" optional
Cache `srs` property of rules to speed up apply
Adjust view names for use of `reverse()`
Fix Views for Scenario Attachments
Added Simply Compare View/Template to identify differences between rdkit and ambit
Make migrations consistent with tests + compare
Fixes#76
Set default year for Scenario Modal
Fix html tags for package description
Added Tests for Pathway / Rule
Added remove stereo for apply
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#132
