sgeboers
fbf92c82cf
Extremity Scorer (U4 enhanced): - Now scores BOTH original motion text AND layman explanation separately - Schema: text_score, text_explanation, layman_score, layman_explanation - Text scores: 1→7, 2→33, 3→5, 4→5 (mild-to-moderate) - Layman scores: 1→12, 2→20, 3→17, 4→1 (slightly milder) Sentiment Analysis (U5 enhanced): - Now scores BOTH original motion text AND layman explanation separately - Schema: text_score, text_explanation, layman_score, layman_explanation - Text sentiment avg: 0.294 (slightly positive) - Layman sentiment avg: 0.416 (more positive - summaries tone down hostility) Category Derivation (new): - Two-phase LLM approach: derive taxonomy from sample, then apply to all - Discovered 7 categories from 30-motion sample: veiligheid/justitie, corona/pandemie, economie/belasting, klimaat/milieu, defensie/buitenland, asiel/vreemdelingen, overig - Applied to 50 motions with distribution shown in DB - Adds category + category_explanation columns to right_wing_motions
Stemwijzer
A Dutch parliamentary voting compass that lets you vote on real Tweede Kamer motions and see which parties match your positions.
What is Stemwijzer?
Stemwijzer ingests motions and voting records from the Dutch House of Representatives (Tweede Kamer), stores them in DuckDB, generates AI-powered explanations with an LLM, and presents a Streamlit UI where users can vote on real motions and explore party positions through SVD visualizations, trajectory analysis, and embedding-based similarity search.
Features
- Voting Compass — Vote on real parliamentary motions and see which parties align with your choices
- Explorer — Interactive SVD visualizations, party trajectories over time, motion browser, and semantic search
- Analytics — SVD decomposition of voting patterns, UMAP projections, clustering, and drift analysis
- LLM Enrichment — Automatic generation of layman-friendly motion explanations using QWEN via OpenRouter
Prerequisites
- Python >= 3.13
- uv for dependency management
- (Optional)
OPENROUTER_API_KEYfor LLM enrichment
Quickstart
# Clone and enter the repository
git clone <your-gitea-url>/sgeboers/stemwijzer.git
cd stemwijzer
# Install dependencies
uv sync
# Run the Streamlit app
uv run streamlit run Home.py
# Run the data pipeline (fetch motions, compute embeddings, etc.)
uv run python pipeline/run_pipeline.py
# Run tests
uv run pytest tests/ -q
The app will be available at http://localhost:8501.
Project Structure
├── app.py # Streamlit UI entrypoint
├── database.py # DuckDB schema and queries
├── api_client.py # Tweede Kamer OData API client
├── explorer.py # Explorer page with SVD visualizations
├── pipeline/ # Data ingestion and analysis pipelines
├── analysis/ # SVD, clustering, trajectory modules
├── tests/ # pytest test suite
├── docs/ # Documentation, research, and plans
└── data/motions.db # DuckDB database (~18 GB)
Documentation
- ARCHITECTURE.md — Comprehensive architecture overview, tech stack, and contributor guidance
- CODE_STYLE.md — Coding conventions, naming, typing, and testing standards
- docs/solutions/ — Documented solutions to past bugs and best practices
Tech Stack
- Language: Python 3.13+
- Data: DuckDB via ibis-framework
- UI: Streamlit + Plotly
- ML/Analysis: scipy, scikit-learn, umap-learn
- LLM: QWEN via OpenRouter (OpenAI-compatible)
- Package Manager: uv
License
[Your license here]