SunSeat: Touch Grass in 20 Minutes with Offline Gemma

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

SunSeat: a 20-minute sunny-bench finder for night-shift workers, SAD winters, and overstimulated brains. Enter location + minutes + needs (flat / quiet / toilet / shade / dog), get 3 sun pockets with a 0-100 go-now score + a 60-second mission card. Screen time under a minute, the rest is outside.

I built it for myself in Chennai: short October evenings, no time for a hike, need sun fast without scrolling.

Demo

Live: https://sun-seat.onrender.com (verified /api/health ok, frontend serves)
Video (90s): screen search -> walk -> sunny bench -> mission card read aloud (narration in demo-narration.txt, MP3 via ElevenLabs).
Offline proof: load once online, airplane mode on, reload / - results render from localStorage sunseat_last. Screenshot in repo docs/ after you test.
Cover: frontend/cover.svg (1000x420).

Local fallback if needed: pip install -r requirements.txt && uvicorn backend.main:app --port 8000.

Code

Repo: https://github.com/Senthil455/Sun-Seat (MIT)

• backend/main.py - FastAPI, solar math, OSM Overpass, Open-Meteo, best_hour_tabpfn(), gemma_brief() via Ollama
• frontend/index.html - single-file PWA, offline cache, free TTS Speak button
• scripts/tabpfn_demo.py + data/weather_history.csv - TabPFN evidence, CI check
• tests/test_api.py - 3 tests, all passing
• Dockerfile + render.yaml + .github/workflows/ci.yml

How I Built It

Open core, not decoration:

1. Gemma via Ollama writes the mission card. Prompt + output logged in /api/brief. Local run returns mode:local-open-weight - proof in docs/ollama-proof-gemma2-2b.json (full gemma2:2b, tested on laptop; docs/ollama-proof.json shows the lighter gemma3:270m swap). Render free has no GPU/Ollama so it returns an honest hosted-template-fallback - cite the proof files as evidence.
2. OSM Overpass supplies parks / benches / toilets + surface/smoothness/incline tags. Open-Meteo supplies cloud + radiation. Pure-python solar elevation + transparent sun_score() 0-100.
3. TabPFN: scripts/tabpfn_demo.py trains TabPFNRegressor on data/weather_history.csv when installed, else sklearn fallback with stderr log. Backend best_hour_tabpfn() does the same live. Run python scripts/tabpfn_demo.py --json and paste output.
4. SerpApi /api/foliage: live search when SERPAPI_KEY set, else cached-offline. Sentry inits when SENTRY_DSN set - paste trace + latency_ms from /api/pockets.

Swap models in one var: OLLAMA_MODEL=llama3.2:1b.

Why Does Open Innovation Matter?

Closed maps hide the tags I filter on - surface, smoothness, bench, toilets. I cannot build flat/quiet/toilet search on Google Maps API. OSM exposes them freely.

Closed AI dies on the trail: needs signal, per-token cost, uploads your location + photo. SunSeat caches OSM + weather JSON after first fetch, then Gemma 2B runs on the laptop with no internet, $0, private. I edited the prompt locally, swapped models, no approval, no bill. That offline airplane-mode run is the whole point - and only possible open.

My Agent Session

Built with GitHub Copilot CLI + this coding agent. Process evidence: commit history + CI run .github/workflows/ci.yml (health + tabpfn check). Screenshots attached: Ollama mode:local-open-weight brief, airplane-mode reload.

Prize Categories

• Best Use of Gemma (local gemma2:2b mission card)
• Best Use of Render (Dockerfile + live URL)
• Best Use of TabPFN (CSV forecast, scripts/tabpfn_demo.py)
• Best Use of SerpApi (live foliage grounding / cached fallback)
• Best Use of GitHub Copilot (Copilot CLI + Actions CI)
• Best Use of ElevenLabs (demo-narration.txt -> MP3 narration)