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Snowmass Pod Recommendation MVP

Production-style MVP for recommending Snowmass ski pods using strict user constraints and Open-Meteo hourly forecasts.

For the simplest local launch on this computer, start with START_HERE.md.

Project Structure

  • backend/: FastAPI API, terrain dataset, scoring logic, tests.
  • frontend/: Static HTML/CSS/JS single-page UI.

Run locally

This project has two small parts:

  • The backend is the brain. It picks the best Snowmass pod based on your settings and weather.
  • The frontend is the page you open in your browser.

On Windows:

  1. Install Python 3.11 from python.org.
  2. Open PowerShell in this folder.
  3. Install backend packages:
python -m pip install -r .\backend\requirements.txt
  1. Start the backend:
python -m uvicorn app.main:app --host 127.0.0.1 --port 8000 --app-dir .\backend
  1. In a second PowerShell window, start the frontend:
node .\frontend\server.js
  1. Open http://127.0.0.1:3000
  2. Quick checks:

Quickstart

1) Setup backend

make setup-backend

2) Run backend

make run-backend

Backend URL: http://127.0.0.1:8000

3) Run tests

make test-backend

4) Open frontend

Open frontend/index.html in your browser. The page calls http://127.0.0.1:8000/recommend.

API

  • GET /health -> {"ok": true}
  • POST /recommend -> top 3 pod recommendations (with best 2-hour window, reasons, and excluded pods)
  • GET /live/snowmass -> live runs and lifts, with each mapped run including official pod metadata and a canonical difficulty label when the trail can be confidently linked

Recommendation Design

  1. Apply strict hard constraints first.
  2. Pull forecast hours from Open-Meteo.
  3. Score each pod per hour and select best contiguous 2-hour window.
  4. Return top pods with explainability bullets (2-5).
  5. If weather unavailable, use neutral assumptions and reduce confidence.

Future Work

  • Resort operations feed integration (lifts, closures, grooming reports).
  • Better crowd modeling and dynamic congestion penalties.
  • Run-level route suggestions beyond pod-level.
  • Feedback-driven personalization from user labels.