Survival LLM
A free AI assistant and survival library (1.5 million offline articles) that runs on your own computer, with no internet. Shows the source of every answer. Mac and Windows.
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一分鐘完成驗證Football transfer fee predictor with SHAP-based stat-improvement counterfactuals (side project)
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取自儲存庫 README
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檢視完整 README ↗A sports valuation & prediction platform. Two data/modeling domains — soccer (transfer-fee model, squad-strength sim engine, and the apps built on it) and KBO baseball (sabermetric season/postseason sim) — feed a Next.js web app backed by Supabase.
The project began as a single soccer transfer-fee notebook and grew into several Python modules plus a production web app. It was reorganized into the layout below; see ARCHITECTURE.md for the full system + data-flow map.
azz3/
├── python/ # all Python (installed as one editable package: `valuetrack`)
│ ├── pyproject.toml
│ ├── core/ # soccer transfer-fee model → import core.*
│ │ ├── config.py # REPO_ROOT / DATA_DIR / WEB_* paths
│ │ └── cli/ # train · predict · explain · export_for_web · …
│ ├── soccer/
│ │ ├── engine/ # shared squad-strength / synergy / match sim engine
│ │ └── apps/ # worldcup (winner) · premier_league · worldcup_stars · destination
│ ├── kbo/ # self-contained KBO pipeline (src/ + scripts/)
│ ├── data/ # local model cache + trained model (gitignored)
│ ├── predictions/ # committed demo run artifacts
│ └── tests/ # pytest (covers core.* + kbo.src.*)
├── web/ # Next.js 14 app (Vercel) — reads Supabase + exported JSON
├── supabase/ # Postgres migrations (auth, payments, kbo_*, soccer_*, api_tokens)
└── docs/ # design notes, model report, Open News API spec, assets/
The web app lives at the repo root (Vercel roots there and bundles an embedded Python
serverless function under web/api/). Python packages resolve web/ via
core.config.REPO_ROOT (walks up to .git), so python/ can move without breaking exports.
# Python (CPython 3.9)
python3 -m venv .venv
.venv/bin/pip install -e "python/[dev]" # installs core, soccer.*, kbo.* + dev tools
brew install libomp # macOS only — xgboost needs it
# Web
cd web && yarn install # Yarn 4 (Berry), Node >= 20
cp .env.example .env.local # fill in Supabase + KakaoPay keys
There is no more root requirements.txt; Python deps live in python/pyproject.toml.
web/requirements.txt is separate (it pins the Vercel Python serverless runtime).
# --- soccer core model (writes into web/public + web/api/model) ---
.venv/bin/python -m core.cli.train
.venv/bin/python -m core.cli.export_for_web
.venv/bin/python -m core.cli.export_real_players
# --- soccer sim apps ---
.venv/bin/python -m soccer.apps.worldcup.run_prediction --sims 1000000
.venv/bin/python -m soccer.apps.premier_league.run_prediction --sims 1000000
.venv/bin/python -m soccer.apps.worldcup_stars.run_stars
.venv/bin/python -m soccer.apps.destination.run_recommender
# --- KBO ---
.venv/bin/python -m kbo.scripts.run_prediction
# --- web (Next.js + local Python inference bridge) ---
cd web && yarn dev # web only
cd web && yarn dev:full # web + local Python model server
.venv/bin/python -m pytest python/tests -v # Python
cd web && yarn test # web (Vitest)
cd web && yarn typecheck && yarn build # web type + build check
CLAUDE.md — repo conventions for AI-assisted work (gstack).