Tristan Leduc Claude Opus 4.8 (1M context) commited on
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2ab78dc
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1 Parent(s): 6881784

Docs/UI: reflect multi-city + Off the Grid; Thousand Token Wood track

Browse files

- README/Space card: cities (Paris/London/Barcelona/NYC), offline no-cloud-API
framing, track tag → thousand-token-wood, badge tags → off-the-grid/off-brand/
tiny-titan/field-notes/openbmb.
- Start-field help now names the supported cities so judges find multi-city.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Files changed (2) hide show
  1. README.md +18 -13
  2. src/discoverroute/ui/shell.py +2 -1
README.md CHANGED
@@ -11,11 +11,11 @@ pinned: true
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  license: apache-2.0
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  short_description: A-to-B routes through places you'll love
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  tags:
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- - backyard-ai-track
 
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  - badge-off-brand
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  - badge-tiny-titan
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- - badge-best-agent
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- - badge-best-demo
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  - openbmb
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  ---
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@@ -31,7 +31,8 @@ going and the kind of moment you're after — "a slow Sunday-morning kind of wal
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  one is on your path. Same destination. A walk you'll remember instead of one you'll forget.
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  > One-liner: **WanderLust turns any walk from A to B into a personal discovery — routing
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- > you through places that match your taste, not just the fastest path.** Single city today: **Paris**.
 
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  ---
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@@ -64,8 +65,9 @@ the experience feel like it read your mind.)
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  weights (JSON), and route → first-person itinerary narration.
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  - **ZeroGPU:** the model runs **inside the Space** on HF ZeroGPU via `@spaces.GPU`,
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  weights pulled from the Hub — no external inference API, nothing leaves the Space.
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- - **OpenStreetMap + OSMnx:** the Paris walking/biking graph and ~30k POIs, pre-built and
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- cached offline (Git LFS) so the demo city is instant.
 
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  - **Routing — classical, exact:** a `networkx` + SciPy multi-source Dijkstra travel-time
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  matrix, solved by a custom **orienteering** (prize-collecting TSP) heuristic with
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  submodular diversity — so you get a park + a viewpoint + a bookshop, not five cafés.
@@ -117,13 +119,16 @@ story, `PROGRESS.md` for the per-feature log.
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  ## Architecture
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- **Offline (built once for Paris, cached):** OSM extract → walk/bike routing graph →
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- POIs with feature priors + confidence.
 
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- **Runtime (per request):** interpret vibe → score corridor POIs → solve the detour
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- (orienteering) → trace a real polyline → narrate + overlay on the map.
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  The model is load-bearing only in **interpretation and narration**; routing is pure
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- classical algorithms. Geocoding is local-first — named Paris places resolve against the
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- cached POI table with no network call (`DISCOVERROUTE_OFFLINE=1` forbids the Nominatim
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- fallback entirely). Map data © OpenStreetMap contributors (ODbL).
 
 
 
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  license: apache-2.0
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  short_description: A-to-B routes through places you'll love
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  tags:
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+ - thousand-token-wood-track
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+ - badge-off-the-grid
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  - badge-off-brand
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  - badge-tiny-titan
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+ - badge-field-notes
 
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  - openbmb
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  ---
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  one is on your path. Same destination. A walk you'll remember instead of one you'll forget.
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  > One-liner: **WanderLust turns any walk from A to B into a personal discovery — routing
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+ > you through places that match your taste, not just the fastest path.** Cities: **Paris,
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+ > London, Barcelona, New York** — all routed **fully offline** (no cloud APIs at request time).
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  ---
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  weights (JSON), and route → first-person itinerary narration.
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  - **ZeroGPU:** the model runs **inside the Space** on HF ZeroGPU via `@spaces.GPU`,
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  weights pulled from the Hub — no external inference API, nothing leaves the Space.
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+ - **OpenStreetMap + OSMnx:** walking/biking graphs and POIs for **Paris, London,
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+ Barcelona and New York**, pre-built and cached offline (Git LFS) so every city is
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+ instant — and routes with **no cloud API calls at request time** (Off the Grid).
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  - **Routing — classical, exact:** a `networkx` + SciPy multi-source Dijkstra travel-time
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  matrix, solved by a custom **orienteering** (prize-collecting TSP) heuristic with
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  submodular diversity — so you get a park + a viewpoint + a bookshop, not five cafés.
 
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  ## Architecture
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+ **Offline (built once per city, cached):** OSM extract → walk/bike routing graph →
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+ POIs with feature priors + confidence. Paris ships full-city; London, Barcelona and
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+ New York are baked as walkable cores (`python -m discoverroute.data.build_city`).
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+ **Runtime (per request):** pick the city → interpret vibe → score corridor POIs → solve
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+ the detour (orienteering) → trace a real polyline → narrate + overlay on the map.
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  The model is load-bearing only in **interpretation and narration**; routing is pure
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+ classical algorithms. Geocoding is local-first — named places resolve against the cached
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+ POI index (Paris + every pre-baked city) with no network call. With
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+ `DISCOVERROUTE_OFFLINE=1` (the deployed config) there are **zero cloud API calls at
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+ request time** — routing is limited to the pre-baked cities. Map data © OpenStreetMap
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+ contributors (ODbL).
src/discoverroute/ui/shell.py CHANGED
@@ -344,7 +344,8 @@ def _left_panel() -> str:
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  value="Place de la République, Paris">
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  </div>
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  <div class="combo-list" id="dr-start-list"></div>
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- <div class="dr-help">Try a landmark or place namenot a street address.</div>
 
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  </div>
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  <div class="dr-control combo">
 
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  value="Place de la République, Paris">
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  </div>
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  <div class="combo-list" id="dr-start-list"></div>
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+ <div class="dr-help">📍 Paris · London · Barcelona · New York try a landmark
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+ (e.g. "British Museum, London"), not a street address.</div>
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  </div>
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  <div class="dr-control combo">