Implement Project Roadmap: From Tool to Extensible Platform#30
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Implement Project Roadmap: From Tool to Extensible Platform#30google-labs-jules[bot] wants to merge 1 commit intomasterfrom
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…nalyzer from a simple client-side tool into a robust, extensible, and scalable platform. The changes are implemented in three main phases: Phase 1: Foundational Improvements - Implemented session persistence using localStorage to prevent data loss on page refresh. - Expanded the list of metric presets to include common defaults for PyTorch, Keras, and Hugging Face. - Significantly increased unit test coverage for the core data parsing logic (`ValueExtractor`). Phase 2: Platformization with a Backend - Integrated the Firebase SDK to provide backend services. - Implemented user authentication with Google Sign-In. - Added a Firestore database backend to save and load workspaces for authenticated users, while retaining localStorage for anonymous sessions. Phase 3: Extensibility with a Plugin Architecture - Refactored the data extraction logic into a modular plugin system. - Each extraction method (Keyword, Regex) is now a self-contained plugin with its own logic and UI configuration. - A central plugin registry allows for easy addition of new parsing capabilities in the future.
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This series of changes implements a multi-stage roadmap to significantly enhance the ML Log Analyzer. It adds session persistence, expands metric presets, and improves test coverage. It then introduces a Firebase backend with user authentication and cloud storage. Finally, it refactors the core data parsing logic into a highly extensible plugin-based architecture, preparing the project for future growth and community contributions.