- ai.md: Added comprehensive documentation for TUI v1.3 conversion layer with 64+ supported components, CSS styling, and JavaScript interaction capabilities
- oss/tui/: Created complete TUI module with converter.py implementing HTML/CSS/JS to terminal conversion engine, supporting 40+ component types and advanced styling
- oss/tui/plugin.py: Implemented TUI plugin with dual startup architecture accessing WebUI's /tui interface for HTML conversion and terminal rendering
- store/@{NebulaShell}/webui/tui/: Added TUI package with converter, configuration files, and index.html for terminal interface
- store/@{NebulaShell}/webui/core/server.py: Enhanced WebUI server with TUI interface endpoints (/tui/*) for providing special-marked HTML to conversion layer
- store/@{NebulaShell}/webui/main.py: Updated WebUI plugin to support TUI dual launch with automatic homepage redirection and navigation integration
- .gitignore: Updated ignore patterns for better project cleanliness
The update provides a sophisticated terminal interface that automatically converts WebUI content through a powerful transformation layer, enabling seamless dual-mode operation.
Key features implemented:
- Updated package metadata and dependencies in PKG-INFO, setup files
- Added main.py entry point for backward compatibility with README launch method
- Enhanced CLI with config options, system info command, and proper signal handling
- Implemented minimal PluginManager loading only plugin-loader core plugin
- Refactored PluginLoader to follow minimal core design, removed sandbox/isolation complexity
- Updated auto-dependency plugin with safer PL injection mechanism and disabled pl_injection
- Removed legacy plugin files (firewall, frp_proxy, ftp_server, multi_lang_deploy, ops_toolbox, security_gateway) as functionality moved to core plugin system
- Improved gitignore with comprehensive ignore patterns
The changes implement a minimal core framework design where only the plugin-loader is directly loaded by the core, with all other plugins managed through the PL injection mechanism, significantly simplifying the architecture.
- Added performance-optimizer plugin with FastCache LRU caching, ObjectPool for object reuse, BatchProcessor for bulk operations, MemoryArena for pre-allocated memory, PerfProfiler for low-overhead timing, and StringIntern for deduplication
- Implemented high-performance routing optimizations in router.py using @lru_cache decorators for path matching and parameter extraction functions
- Created comprehensive plugin infrastructure with manifest.json configuration and unified access interface through PerformanceOptimizerPlugin class
- Enhanced system performance through multiple optimization strategies targeting different bottlenecks with measurable performance gains from 2x to 100x improvement ratios
Key features implemented:
- Updated README.md to prominently display MIT license badge and include full license text in expandable section
- Enhanced .gitignore with comprehensive file patterns for Python, dependencies, logs, and build artifacts
- Added detailed plugin manifest example showing license declaration in plugin configuration
- Included license information in plugin development documentation section
The updates provide clear license visibility and improved development workflow configuration.
