This application is designed as a “real app” playground: multiple screens, persistent data, and repeatable workflows around images and ML projects, while keeping the codebase modular enough to evolve over time.

It includes common UI flows (navigation, CRUD-like interactions, image browsing) plus ML-focused flows such as project selection, dataset preparation, training runs, and evaluation/detection.

• Security: password hashing/verification and encryption utilities for app data.
• Image workflows: browsing, selection, pagination/lazy loading, and dataset-related operations.
• ML tooling: classification training pipelines and YOLO-based detection workflows.
• Projects: project-oriented folder structure under app/training/ to keep datasets/configs/models grouped.
• Testing: Kivy-aware pytest fixtures + integration tests that cover navigation/auth/CRUD/images/DB/ML flows.

pip install -r requirements.txt

python main.py

Integration tests live under app/tests/ and include a short guide at app/tests/README_TESTS.md.

Quick run:

pytest --timeout 20 -v -s
pytest --timeout 20 -v -s .\app\tests\integration\

app/
  screens/        # UI screens + helpers
  ui/             # .kv files
  resources/      # assets (icons/images)
  training/       # ML projects/datasets/models
  tests/          # pytest + integration tests

• Improve multi-project UX and config portability (import/export).
• Packaging/deployment helpers (build scripts, Docker).
• Expand ML evaluation utilities and make more parameters configurable.

This project is licensed under the MIT License — see LICENSE.

• README structure inspired by Best-README-Template.
• Built with assistance from generative AI tools for ideation and code suggestions; all changes were reviewed and tested by the author.