feat: add storage and training modules for snore detection

- Implemented `storage.py` for managing metadata storage, including sample addition, retrieval, and review state management.
- Created `training.py` for training a local model using Random Forest, including functions for training and predicting samples.
- Developed a web interface in `app.js` for capturing audio samples, managing labels, and training the model.
- Added HTML structure in `index.html` for the SnoreStopper control room with sections for sample capture, overnight gathering, training, and status display.
- Styled the application with `styles.css` to enhance user experience and interface aesthetics.

pyproject.toml

@@ -0,0 +1,28 @@
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "snorestopper"
+version = "0.1.0"
+description = "Self-hosted snore tracking and snore event detection"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+  "fastapi>=0.115,<1.0",
+  "uvicorn[standard]>=0.30,<1.0",
+  "numpy>=2.0,<3.0",
+  "sounddevice>=0.5,<1.0",
+  "soundfile>=0.12,<1.0",
+  "scipy>=1.13,<2.0",
+  "matplotlib>=3.9,<4.0",
+  "scikit-learn>=1.5,<2.0",
+  "joblib>=1.4,<2.0",
+  "python-multipart>=0.0.9,<1.0",
+]
+
+[tool.setuptools]
+package-dir = {"" = "src"}
+
+[tool.setuptools.packages.find]
+where = ["src"]