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Mealie-backed AI meal planner + shopping list for the family
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Kayos cc6222139d v0.3 step 2: density-table aggregator engine — the killer math 
Pure-Python module + 14 unit tests proving the centerpiece works:

  test_rice_mixed:
    in:  [(2 cup, rice), (1.25 lb, rice)]
    out: 2.25 lb rice  (one line, properly mass+volume combined via density)

  test_butter_mixed:
    in:  [(0.5 cup, butter), (4 oz, butter)]
    out: ~227g butter (~8oz / 0.5 lb)

  test_three_recipes:
    feeds 9 ingredients across 3 recipes through the aggregator;
    rice (cup + lb) collapses, garlic (cloves) sums, eggs count, salt as 'pinch'
    bucketed as to-taste. All on one shopping list.

Algorithm in cauldron/aggregator.py:
  1. Bucket ingredients by canonical food (foods_lookup callable injected — no DB coupling)
  2. Within each food, classify each unit (mass / volume / count / vague / unknown)
  3. CASE 1: only one unit class present → simple sum, display in canonical store-friendly unit
  4. CASE 2: mass + volume (the killer) → use density_g_per_ml to combine to grams
  5. CASE 3: count + (mass | volume) → use common_size_g to convert count to grams
  6. CASE 4: anything that can't reconcile (no density, mixed unknown) → split into 1 line per class with is_split=True
  7. vague (pinch, dash, to taste) → annotate as 'plus to-taste'
  8. unknown units → emit verbatim with the original text

Display: store-friendly unit picker:
  <30g  → grams
  <500g → ounces (nearest 0.5)
  <2kg  → pounds (nearest 0.25)
  >2kg  → big pounds

The aggregator is dependency-injection-friendly — foods_lookup(name) is
the only external call. Tests pass a stub dict; production will pass
foods.search_food(db, name). Decouples math from data quality.

Tests run via:
  python3 -m unittest discover -s tests -v
2026-04-28 22:14:01 -07:00
cauldron v0.3 step 2: density-table aggregator engine — the killer math 2026-04-28 22:14:01 -07:00
scripts v0.3 step 1: foods schema + USDA SR Legacy density seed 2026-04-28 22:03:17 -07:00
tests v0.3 step 2: density-table aggregator engine — the killer math 2026-04-28 22:14:01 -07:00
.env.example v0.2 foundation — Authentik OIDC + sulkta-mariadb DB + Fernet crypto 2026-04-28 19:47:47 -07:00
.gitignore v0.1 — backend bones + ingredient sterilizer 2026-04-28 16:59:11 -07:00
compose.yml compose: also join sulkta-db-net so cauldron can reach sulkta-mariadb 2026-04-28 19:48:59 -07:00
Dockerfile v0.1 — backend bones + ingredient sterilizer 2026-04-28 16:59:11 -07:00
LICENSE Initial commit 2026-04-28 16:35:30 -07:00
README.md v0.1 — backend bones + ingredient sterilizer 2026-04-28 16:59:11 -07:00
requirements.txt search: local fuzzy recipe index — way smarter than Mealie's lexical default 2026-04-28 21:37:12 -07:00

README.md

cauldron

Mealie-backed AI meal planner + shopping list for the family. LAN-only, internal tool. Mealie at recipes.sulkta.com is the source of truth for recipes / meal plans / shopping lists; cauldron is the AI layer + Abby's branded UI on top.

Status

v0.1 — backend bones (current). Ingredient sterilizer endpoint working. No UI yet; bearer-auth API only. Frontend + Authentik OIDC arrives in v0.2. Native Kotlin Android in v0.5.

Surface (v0.1)

GET  /healthz                              liveness + clawdforge upstream
GET  /api/recipes                          list Mealie recipes (paginated)
POST /api/sterilize/preview/<slug>         dry-run AI parse, return proposals
POST /api/sterilize/apply/<slug>           write parses back to Mealie

All routes except /healthz require Authorization: Bearer <ADMIN_BEARER>.

Architecture

Abby's phone (later: Kotlin app)
        │
        ▼
  cauldron (Flask, port 7790, LAN-only)
   ├─ Mealie API client  ─── recipes.sulkta.com  (source of truth)
   ├─ clawdforge client  ─── 192.168.0.5:8800   (claude -p runner)
   └─ Authentik OIDC (v0.2)

cauldron does NOT hold its own database in v0.1 — all state lives in Mealie. A small Postgres/MariaDB schema lands in v0.2 for Abby-specific prefs + chat history.

Ingredient sterilizer

Mealie's CRF parser is mediocre. Cobb's hand-typed recipes have lots of free-form quantity strings ("about 2 cups cooked white rice", "1 small handful kale", "a pinch of salt") that don't aggregate cleanly into a shopping list.

The sterilizer batches all ingredients of one recipe into a single Sonnet call (via clawdforge), gets back parallel structured parses, then on apply links each parse to existing Mealie food/unit records (creating any missing by name) and PUTs the recipe back.

Preview is non-destructive — review proposals before apply.

# Dry-run preview
curl -sS -X POST -H "Authorization: Bearer $ADMIN_BEARER" \
  http://192.168.0.5:7790/api/sterilize/preview/spaghetti-bolognese | jq .

# Apply (creates missing foods/units by default)
curl -sS -X POST -H "Authorization: Bearer $ADMIN_BEARER" \
  http://192.168.0.5:7790/api/sterilize/apply/spaghetti-bolognese | jq .

Deploy

  1. ssh lucy
  2. cd /mnt/user/appdata && git clone <gitea-url> cauldron && cd cauldron/build (or wherever the deploy convention lands)
  3. Drop .env at /mnt/cache/appdata/secrets/cauldron.env (chmod 600 root:root)
    • CLAWDFORGE_TOKEN is already populated by the bootstrap (see memory/2026-04-28.md)
    • MEALIE_API_TOKEN — mint at recipes.sulkta.com → user → API tokens
    • ADMIN_BEARER — pick 32 bytes of entropy
    • SECRET_KEY — 32 bytes for Flask sessions
  4. docker compose up -d --build
  5. Smoke: curl http://192.168.0.5:7790/healthz

Roadmap

  • v0.1 ✓ — sterilizer backend + Flask shell
  • v0.2 — Authentik OIDC, Abby-branded web UI, palette CSS, postgres for prefs
  • v0.3 — meal plan generator (week → Mealie meal plan write)
  • v0.4 — shopping list aggregator (read meal plan → consolidated grocery list)
  • v0.5 — native Kotlin + Compose Android app (read-only shopping list + plan view)

Repo layout

cauldron/
├─ cauldron/
│  ├─ config.py            env-driven config
│  ├─ forge.py             clawdforge HTTP client
│  ├─ mealie.py            Mealie API client
│  ├─ sterilizer.py        ingredient parse + apply pipeline
│  └─ server.py            Flask app
├─ Dockerfile
├─ compose.yml
├─ requirements.txt
└─ .env.example