Refactor constants management to use Pydantic BaseModel for configuration

- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.

tests/test_training_e2e.py

@@ -0,0 +1,113 @@
+import sys
+import types
+import importlib
+import shutil
+from os import path as osp
+from pathlib import Path
+
+import pytest
+
+for _n in ("boto3", "netron", "requests"):
+    if _n not in sys.modules:
+        sys.modules[_n] = types.ModuleType(_n)
+
+for _k in [k for k in sys.modules if k == "ultralytics" or k.startswith("ultralytics.")]:
+    del sys.modules[_k]
+from ultralytics import YOLO
+
+for _m in ("exports", "train"):
+    if _m in sys.modules:
+        importlib.reload(sys.modules[_m])
+
+import constants as c
+import train as train_mod
+import exports as exports_mod
+
+_PROJECT_ROOT = Path(__file__).resolve().parent.parent
+_DATASET_IMAGES = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/images"
+_DATASET_LABELS = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/labels"
+_CONFIG_TEST = _PROJECT_ROOT / "config.test.yaml"
+
+
+@pytest.fixture(scope="module")
+def e2e_result(tmp_path_factory):
+    base = tmp_path_factory.mktemp("e2e")
+
+    old_config = c.config
+    c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(base / "azaion"))
+
+    data_img = Path(c.config.data_images_dir)
+    data_lbl = Path(c.config.data_labels_dir)
+    data_img.mkdir(parents=True)
+    data_lbl.mkdir(parents=True)
+    Path(c.config.models_dir).mkdir(parents=True)
+
+    for img in sorted(_DATASET_IMAGES.glob("*.jpg")):
+        shutil.copy2(img, data_img / img.name)
+        lbl = _DATASET_LABELS / f"{img.stem}.txt"
+        if lbl.exists():
+            shutil.copy2(lbl, data_lbl / lbl.name)
+
+    from augmentation import Augmentator
+    Augmentator().augment_annotations()
+
+    train_mod.train_dataset()
+
+    exports_mod.export_onnx(c.config.current_pt_model)
+    exports_mod.export_coreml(c.config.current_pt_model)
+
+    today_ds = osp.join(c.config.datasets_dir, train_mod.today_folder)
+
+    yield {
+        "today_dataset": today_ds,
+    }
+
+    c.config = old_config
+
+
+@pytest.mark.e2e
+class TestTrainingPipeline:
+    def test_augmentation_produced_output(self, e2e_result):
+        proc = Path(c.config.processed_images_dir)
+        assert len(list(proc.glob("*.jpg"))) == 800
+
+    def test_dataset_formed(self, e2e_result):
+        base = Path(e2e_result["today_dataset"])
+        for split in ("train", "valid", "test"):
+            assert (base / split / "images").is_dir()
+            assert (base / split / "labels").is_dir()
+        total = sum(
+            len(list((base / s / "images").glob("*.jpg")))
+            for s in ("train", "valid", "test")
+        )
+        assert total == 800
+
+    def test_data_yaml_created(self, e2e_result):
+        yaml_path = Path(e2e_result["today_dataset"]) / "data.yaml"
+        assert yaml_path.exists()
+        content = yaml_path.read_text()
+        assert "nc: 80" in content
+        assert "train:" in content
+        assert "val:" in content
+
+    def test_training_produces_pt(self, e2e_result):
+        pt = Path(c.config.current_pt_model)
+        assert pt.exists()
+        assert pt.stat().st_size > 0
+
+    def test_export_onnx(self, e2e_result):
+        p = Path(c.config.current_onnx_model)
+        assert p.exists()
+        assert p.suffix == ".onnx"
+        assert p.stat().st_size > 0
+
+    def test_export_coreml(self, e2e_result):
+        pkgs = list(Path(c.config.models_dir).glob("*.mlpackage"))
+        assert len(pkgs) >= 1
+
+    def test_onnx_inference(self, e2e_result):
+        onnx_model = YOLO(c.config.current_onnx_model)
+        img = sorted(_DATASET_IMAGES.glob("*.jpg"))[0]
+        results = onnx_model.predict(source=str(img), imgsz=c.config.export.onnx_imgsz, verbose=False)
+        assert len(results) == 1
+        assert results[0].boxes is not None