[Feature] Enviformer fine tuning and evaluation

## 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>

tests/test_enviformer.py

@@ -0,0 +1,31 @@
+from tempfile import TemporaryDirectory
+from django.test import TestCase
+from epdb.logic import PackageManager
+from epdb.models import User, EnviFormer, Package
+
+
+class EnviFormerTest(TestCase):
+    fixtures = ["test_fixtures.jsonl.gz"]
+
+    @classmethod
+    def setUpClass(cls):
+        super(EnviFormerTest, cls).setUpClass()
+        cls.user = User.objects.get(username='anonymous')
+        cls.package = PackageManager.create_package(cls.user, 'Anon Test Package', 'No Desc')
+        cls.BBD_SUBSET = Package.objects.get(name='Fixtures')
+
+    def test_model_flow(self):
+        """Test the full flow of EnviFormer, dataset build -> model finetune -> model evaluate -> model inference"""
+        with TemporaryDirectory() as tmpdir:
+            with self.settings(MODEL_DIR=tmpdir):
+                threshold = float(0.5)
+                data_package_objs = [self.BBD_SUBSET]
+                eval_packages_objs = []
+                mod = EnviFormer.create(self.package, data_package_objs, eval_packages_objs, threshold=threshold)
+
+                mod.build_dataset()
+                mod.build_model()
+                mod.multigen_eval = True
+                mod.save()
+                mod.evaluate_model()
+                results = mod.predict('CCN(CC)C(=O)C1=CC(=CC=C1)C')