Labelsmith

What's Changed
* Labelsmith v0.0.1b4: Bug Fixes by kosmolebryce in https://github.com/labelstream/labelsmith/pull/2


**Full Changelog**: https://github.com/labelstream/labelsmith/compare/v0.0.1b3...v0.0.1b4

This release focuses on bug fixes and improvements to enhance stability and user experience.

What's New

Bug Fixes
- **Local Data Persistence**: Resolved an issue where shift log files were not being properly persisted locally, ensuring data integrity across sessions.
- **UI Theme Accessibility**: Corrected a UI bug that previously disabled the theme menu, now allowing users to change application themes as intended.

Looking Ahead

We're continuously working to improve Labelsmith. Some features we're planning for future releases include:
- Enhanced productivity visualization tools
- Tabbed interface for reviewing task history during ongoing shifts

Feedback

Your feedback is crucial in shaping the future of Labelsmith. If you encounter any issues or have suggestions for improvements, please don't hesitate to [open an issue](https://github.com/LabelSTREAM/labelsmith/issues) on our GitHub repository.

Thank you for your continued support and for being a part of the Labelsmith community!

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For full details on changes in this release, please refer to our [CHANGELOG.md](https://github.com/LabelSTREAM/labelsmith/blob/main/CHANGELOG.md).

We're excited to announce the first beta release of Labelsmith, an open-source productivity suite designed for data annotators. This release marks a significant milestone in our journey to empower the data science and machine learning community with robust, extensible tools.

🚀 Key Features

- **Shyft**: A comprehensive shift-logging application built on the `tkinter` GUI framework.
- Automatic shift duration tracking with a configurable, always-on-top timer
- Task metadata recording and note-taking interface
- Local data persistence for shift entries and tasking history
- Full CRUD operations for shift records

- **Utility Scripts**:
- `utils.income`: Includes the `simulate` function for modeling earnings scenarios based on various workload parameters
- `utils.metrics`: Tools for analyzing productivity and earnings data

🛠 Installation

macOS Users
We're providing a DMG installer for Shyft, our main GUI application, for macOS users. You can download it directly from this release page.

All Users
Install Labelsmith using pip:


pip install labelsmith


Please note that this is a beta release and may not be fully stable. We recommend installing in a virtual environment for testing purposes.

Non-macOS Users
Currently, we don't have installers for other operating systems. However, you can run the Shyft GUI using Poetry:


poetry run shyft


⚠️ Known Limitations

- The project is in its early stages and may undergo significant changes
- Limited cross-platform testing (primary development on macOS)
- Some features may be incomplete or subject to change
- Installers are currently only available for macOS

🤝 Feedback and Contributions

We welcome your feedback and contributions! Please report any issues or suggest improvements on our [GitHub Issues page](https://github.com/kosmolebryce/labelsmith/issues).

For guidelines on contributing, please refer to our [CONTRIBUTING.md](https://github.com/kosmolebryce/labelsmith/blob/main/CONTRIBUTING.md) file.

📘 Documentation

For more detailed information on how to use Labelsmith, please refer to our [README.md](https://github.com/kosmolebryce/labelsmith/blob/main/README.md) file.

🙏 Acknowledgements

Thank you to all the early testers and contributors who have helped shape this release. Your input has been invaluable in getting Labelsmith to this stage.

We're excited to see how Labelsmith can help streamline your data annotation workflows. Happy labeling!