A fun, modern quiz where you guess the artist behind famous Norwegian paintings. Built for art lovers, students, and anyone curious about Norway's rich visual heritage.
- 1000s of paintings from 80+ Norwegian artists
- Multiple quiz categories: movements, genres, museums, and more
- Responsive, mobile-friendly UI
- Artist bios, tags, and painting galleries
- Data sourced from open Wikimedia and Wikidata APIs
- Diagnostics and data health checks
- Select a category (e.g., Impressionism, Women Painters, National Museum)
- View a painting and choose the correct artist from four options
- Get instant feedback and learn about each artist
- Try to build a streak and explore the gallery!
The repository supports a complete 6-step workflow:
- Finding new images →
python scripts/workflow.py --collect - Cleaning them and deleting bad ones →
python scripts/workflow.py --clean - Finding manual URLs to add →
python scripts/workflow.py --add-urls - Finding manual URLs to remove →
python scripts/workflow.py --remove-urls - Push to app →
python scripts/workflow.py --process+python scripts/workflow.py --deploy - App with working game logic → Ready to use!
# Initialize the workflow structure
python scripts/workflow.py --init
# Run complete workflow (all 6 steps)
python scripts/workflow.py --full
# Run quick workflow (collect, clean, process, deploy)
python scripts/workflow.py --quick
# Individual steps
python scripts/workflow.py --collect --url "https://commons.wikimedia.org/wiki/Category:Paintings_by_Artist"
python scripts/workflow.py --clean --quality --duplicates
python scripts/workflow.py --add-urls --file data/config/urls_to_add.txt
python scripts/workflow.py --remove-urls --file data/config/urls_to_remove.txt
python scripts/workflow.py --process
python scripts/workflow.py --deploykunstquiz/
├── scripts/ # All Python scripts
│ ├── workflow.py # Complete 6-step workflow
│ ├── collect_art.py # Main collection script
│ ├── clean.py # All cleanup operations
│ ├── stats.py # All analysis operations
│ ├── process.py # All data processing
│ ├── utils.py # All utility operations
│ └── diagnostics.py # Comprehensive diagnostics
├── data/ # Data files
│ ├── paintings.json # Web app paintings
│ ├── artists.json # Web app artists
│ ├── app_data.json # Unified app data
│ └── config/ # Configuration files
├── docs/ # Documentation
│ └── category_strategy.md # Collection strategy guide
├── assets/ # Web app assets (CSS, JS)
│ ├── css/
│ │ └── style.css
│ └── js/
│ └── script.js
├── index.html # Main web app
└── README.md # This file
The repository uses consolidated scripts for better organization:
scripts/workflow.py- Complete 6-step workflow managementscripts/collect_art.py- Main collection scriptscripts/clean.py- All cleanup operations (duplicates, quality, etc.)scripts/stats.py- All analysis and statistics operationsscripts/process.py- All data processing operationsscripts/utils.py- All utility operations (URLs, backups, health checks)scripts/diagnostics.py- Comprehensive diagnostics report generation
The scripts/stats.py --diagnostics script checks for data consistency, category coverage, and missing info. It also updates the stats below:
Latest Art Quiz Stats
- Total paintings: 3036
- Total unique artists in paintings: 59
- Total artists in bios: 91
- Categories:
- Full Collection: 3036 paintings, 59 painters
- Popular Painters: 1444 paintings, 10 painters
- Landscape: 1661 paintings, 40 painters
- Realism: 379 paintings, 3 painters
- Impressionism: 376 paintings, 8 painters
- Romantic Nationalism: 732 paintings, 7 painters
- Modernism: 73 paintings, 2 painters
- Female Artists: 240 paintings, 4 painters
- Full Collection: All paintings
- Popular Painters: Top 10 artists by painting count
- Landscape: Landscape paintings (34 artists)
- Realism: Realist movement (15 artists)
- Impressionism: Impressionist paintings (12 artists)
- Romantic Nationalism: Norwegian romantic nationalism (10 artists)
- Modernism: Modern works (10 artists)
- Female Artists: Female painters (4 artists)
Pull requests and suggestions are welcome! See issues or open a PR.
MIT. All painting images and artist data are from open Wikimedia/Wikidata sources.
Made with love for Norwegian art lovers.