Feature/dataset/annotation and autolabel

[mettafore]

Changes Since Initial Generation Branch

New Files Added (6 major components):

1. Annotation System:
   - annotation/app.py - Single-record annotation interface (521 lines)
   - annotation/app_bulk.py - Bulk annotation interface (531 lines)
   - annotation/templates/annotate.html - Professional single-record UI (793 lines)
   - annotation/templates/annotate_bulk.html - Compact multi-record UI (1104 lines)
   - annotation/README.md - Comprehensive annotation documentation
2. Documentation & Scripts:
   - CLAUDE.md - Complete project overview and development guide (194 lines)
   - run_annotation_app.sh - Launch script for single annotation tool
   - run_bulk_annotation.sh - Launch script for bulk annotation tool

Enhanced Existing Files:

• Migration System: Enhanced migrate_synthetic_data.py with partial migration support for bridged intent records.
• MLflow Reporting: Improved mlflow_runs_summary.py with git hash tracking.
• Query Deduplication: Enhanced notebook with better duplicate handling
• Shell Scripts: Updated migration and generation scripts for better workflow

Statistics:

• 16 files changed with 3,665 additions, 93 deletions
• Focus on annotation tooling and workflow enhancement
• Production-ready interfaces with comprehensive error handling

Technical Implementation

Annotation Architecture

• Flask-based web applications with clean REST API design
• Dual-mode support: CSV editing vs. database integration
• Real-time progress tracking with session management
• Keyboard shortcuts for efficient annotation workflows

Enhanced Migration System

• Partial migration support for intent generation workflows
• Git hash tracking for complete data lineage
• Improved rollback capabilities with better error handling
• Enhanced reporting with comprehensive run summaries

Usage Examples

Single Record Annotation

./run_annotation_app.sh # Opens http://localhost:5002

• Side-by-side consumable/query display
• Detailed information panels
• One-click ESCI labeling

Bulk Annotation

./run_bulk_annotation.sh # Opens http://localhost:5003

• Compact multi-record view
• Efficient batch processing
• Progress tracking across pages

Enhanced Migration

./run_migrate_synthetic_data.sh --experiment Initial_Generation --bridged

• Partial migration support for intent generation
• Enhanced git hash tracking
• Improved error handling and rollback

Quality Assurance

• ✅ Linting: All code passes Ruff formatting and checks
• ✅ Documentation: Comprehensive README and inline documentation
• ✅ Error Handling: Robust error handling in annotation tools
• ✅ User Experience: Professional interfaces with keyboard shortcuts
• ✅ Data Integrity: Enhanced migration validation and rollback capabilities

Impact & Next Steps

This PR transforms the existing data generation system into a complete annotation workflow:

1. Generate synthetic data using existing initial/intent generation
2. Migrate approved MLflow runs to database with enhanced tracking
3. Annotate using professional web tools (single or bulk mode)

The annotation tools are production-ready and can handle both CSV files and database integration.