- Add streaming schemas and events
- Implement run_review_stream in ReviewerAgent
- Update task_worker to broadcast streaming events via WebSocket
- Create ReviewStream component for real-time progress visualization
- Integrate ReviewStream into ReviewDetail page
- Show agent steps, LLM messages, and progress in real-time
- Changed the method name from `review_pull_request` to `run_review` in the `ReviewerAgent` class.
- Adjusted the parameters passed to the new method for consistency with the updated implementation.
- Introduced `fix-service-simple.sh` to automate the creation and management of a systemd service for the AI Review Agent.
- The script sets up the service with appropriate configurations, including logging and restart policies.
- Added user feedback for service status checks and troubleshooting steps to enhance usability.
- Modified `diagnose-and-fix.sh` to determine the installation directory based on the script's location.
- Added output to display the working directory for improved user feedback during diagnostics.
- Introduced `diagnose-and-fix.sh` to automate the diagnosis and repair of the AI Review Agent environment.
- Implemented checks for the existence of backend and frontend directories, virtual environment, and required files.
- Added functionality to create a new virtual environment, install dependencies, and apply database migrations.
- Included user feedback and error handling throughout the script for improved usability and troubleshooting.
- Updated `deploy-ubuntu.sh` to allow users to choose the installation directory dynamically.
- Added checks for required files (`package.json`, `requirements.txt`, and `migrate.py`) with appropriate error messages.
- Improved feedback during the installation process, including detailed logging for service status and troubleshooting steps.
- Enhanced user experience with clearer prompts and success/error messages throughout the deployment process.
- Modified `fix-installation.sh` to determine the installation directory based on the script's location.
- Added output to display the working directory for better user feedback during installation.
- Improved `redeploy-ubuntu.sh` with additional error handling and user feedback.
- Updated `REDEPLOY_GUIDE.md` to clarify the redeployment process and include new troubleshooting tips.
- Made structural enhancements to deployment documentation for better accessibility and navigation.
- Adjusted `redeploy-hint.md` to align with recent automation changes.
- Updated `redeploy-ubuntu.sh` to enhance error handling and user feedback during the redeployment process.
- Revised `REDEPLOY_GUIDE.md` for clarity and added troubleshooting tips based on user feedback.
- Improved structure and navigation of deployment documentation for better accessibility.
- Minor adjustments to `redeploy-hint.md` to reflect recent changes in automation examples.
- Updated `README.md` to include instructions for the new `redeploy-ubuntu.sh` script and added a link to the new `REDEPLOY_GUIDE.md`.
- Created `REDEPLOY_GUIDE.md` detailing the redeployment process, including backup creation, code updates, and troubleshooting steps.
- Introduced `redeploy-hint.md` for GitHub Actions automation example, outlining setup for automatic redeployment on push events.
- Improved documentation structure for better navigation and clarity.
- Introduced `redeploy-ubuntu.sh` for automated redeployment of the AI Review Agent.
- Implemented steps for service management, backup creation, code updates, dependency installation, database migrations, and frontend build.
- Added user-friendly output messages and error handling for better user experience.
- Ensured proper permissions and checks for running the script as a non-root user.
- Included instructions for rollback in case of deployment issues.
- Introduced new models for `Organization` and `ReviewTask` to manage organizations and review tasks.
- Implemented API endpoints for CRUD operations on organizations and tasks, including scanning organizations for repositories and PRs.
- Developed a background worker for sequential processing of review tasks with priority handling and automatic retries.
- Created frontend components for managing organizations and monitoring task queues, including real-time updates and filtering options.
- Added comprehensive documentation for organization features and quick start guides.
- Fixed UI issues and improved navigation for better user experience.
- Created `.env.production` file to define relative paths for production deployment.
- Updated frontend API and WebSocket client to use environment variables for dynamic URL handling.
- Enhanced deployment scripts (`deploy-ubuntu.sh`, `start.sh`, `start.bat`) to generate `.env.production` automatically.
- Added `PRODUCTION_URLS.md` for documentation on production URL configuration and troubleshooting.
- Introduced `deploy-ubuntu.sh` for automated deployment of the AI Code Review Platform on Ubuntu with systemd.
- Added `UBUNTU_DEPLOYMENT.md` for detailed deployment instructions and requirements.
- Updated `README.md` to include quick deployment instructions and features of the new script.
- Created `uninstall-ubuntu.sh` for easy removal of the platform and its components.
- Enhanced logging and configuration setup within the deployment script.
- Introduced `DEPLOYMENT.md` for comprehensive deployment instructions.
- Added `start.bat` and `start.sh` scripts for unified project startup on Windows and Linux/Mac.
- Updated `.gitignore` to exclude additional files and directories.
- Enhanced `main.py` to serve static frontend files and handle SPA routing.
- Configured Vite to output the frontend build to the backend public directory.
- Minor adjustments in `ReviewDetail.tsx` for improved data handling.
