mblanke/Gov_Travel_App
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3 Commits

Author SHA1 Message Date
mblanke
ae1f13d69e feat: add scripts for database inspection and migration 
- Implemented multiple scripts to check and inspect meal plans, scraped data, and accommodations for Munich and Riga.
- Added a migration script to convert scraped data into the application's database format.
- Introduced new database service methods for querying and updating travel rates.
- Enhanced server configuration for serving static files in production.
- Updated PostCSS configuration for consistency.
 2026-01-13 11:32:49 -05:00
mblanke
4d915aa3ea Build premium data portal with React + Tailwind CSS 
Frontend Features:
- Landing page with glassmorphism, animated counters, hero section
- Interactive data tables with search, sort, filter, CSV export
- Premium dark theme (navy + gold accents)
- Framer Motion animations and micro-interactions
- Responsive design with Inter + Playfair Display typography
- DataTable component with pagination and live search

Backend Updates:
- New API endpoints: /api/rates/per-diem, /api/rates/accommodations, /api/stats
- Database service methods for bulk data retrieval
- Production mode serves built React app from /dist/client
- Fallback to legacy HTML for development

Tech Stack:
- React 18 + TypeScript
- Vite 7 build tool
- Tailwind CSS 4 with @tailwindcss/postcss
- Framer Motion for animations
- Lucide React icons
- SQLite3 backend

Build Output:
- 351KB optimized JavaScript bundle
- 29KB CSS bundle
- Fully tree-shaken and minified
 2026-01-13 11:05:54 -05:00
mblanke
15094ac94b Add Python web scraper for NJC travel rates with currency extraction 
- Implemented Python scraper using BeautifulSoup and pandas to automatically collect travel rates from official NJC website
- Added currency extraction from table titles (supports EUR, USD, AUD, CAD, ARS, etc.)
- Added country extraction from table titles for international rates
- Flatten pandas MultiIndex columns for cleaner data structure
- Default to CAD for domestic Canadian sources (accommodations and domestic tables)
- Created SQLite database schema (raw_tables, rate_entries, exchange_rates, accommodations)
- Successfully scraped 92 tables with 17,205 rate entries covering 25 international cities
- Added migration script to convert scraped data to Node.js database format
- Updated .gitignore for Python files (.venv/, __pycache__, *.pyc, *.sqlite3)
- Fixed city validation and currency conversion in main app
- Added comprehensive debug and verification scripts

This replaces manual JSON maintenance with automated data collection from official government source.
 2026-01-13 09:21:43 -05:00