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Sprint 15 round 3 (no new code; reused scripts/seed_recipes.py
from round 1, idempotent) added 10 more Spoonacular recipes to
the local library. 37 duplicates were skipped. Imports: 2
Asian leftovers (pho, kung pao) + 8 American comfort dishes
(chili, meatloaf, mac and cheese, BBQ chicken, pot roast,
shepherd pie, chicken pot pie, beef stew).
DB went 67 -> 77 total recipes (47 Spoonacular + 30 manual).
LLM test (Sprint 13, week 2026-08-03, prompt "comfort food,
no repeats from past 2 weeks"):
{picked_count: 0, filled_count: 21, failed_count: 0}
21/21 slots filled, 0 failed.
All 6 running docs updated: plan.md (S15R3.1-S15R3.2),
context.md (D11-D13), sprint15-verification.md (round 3
section + 3-round summary table), ui-nielsen-audit.md
(round 3 paragraph), fix-ui-audit.md (T8.7), handoff-ui-
audit.md (TL;DR + Sprint 15 section), HANDOFF.md (round 3
paragraph + Last-updated footer).
Cumulative Sprint 15 work: 46 new Spoonacular recipes across
3 rounds. Library at 77 total — well past the 4-week coverage
threshold.
Meal Planner
Self-hosted meal planning system that integrates with Lucky California grocery store, sends weekly meal proposals via email to family members, generates shopping lists, and learns from feedback.
Background
This project was born out of frustration with meal kit services (Blue Apron → EveryPlate → HungryRoot → Sunbasket) that:
- Escalate costs to 3x ingredient markup
- Fall into repetitive meal rhythms
- Force users to log into apps to manage selections
- Don't integrate with home pantry items
Features
- Grocery Integration: Scrapes Lucky California weekly ads and sales
- Family Approval Workflow: Email proposals with approve/deny; one denial swaps the meal
- Shopping List Generation: Weekly list grouped by store aisles, highlighting sales, with interactive checkboxes to track purchased items
- Pantry Integration: Specify home items to incorporate into suggestions
- Web UI: Modern interface for the whole family
- Learning: Feedback-based meal recommendations, with weekly auto-discovery of new recipes from external APIs when family preferences are signaled
- Recipe Images: Scraped from public recipe sites, AI fallback available
- Unit Conversion: Converts recipe quantities (cups, tbsp, lb) to grocery units for accurate cost estimates
Architecture
- Backend: Python/FastAPI
- Database: PostgreSQL
- Frontend: React + Tailwind CSS
- Email: SendGrid
- Hosting: Docker Compose with nginx reverse proxy
Documentation
Quick Start
# Clone and start
docker-compose up -d
# View logs
docker-compose logs -f
# Stop
docker-compose down
Family Profile
Household: 2 adults, 2 children
- One adult likes mushrooms, one child OK with them
- Three family members do NOT like mushrooms
- No allergies
- Calorie, budget, and health conscious eating
Approval Workflow
- System generates 7-day meal plan based on sales, dietary constraints, budget, variety
- Email sent to both adults with meal previews
- One denial = meal swapped; no denials = auto-approved
- Shopping list generated after approval
Tech Stack
| Component | Technology |
|---|---|
| Backend | Python 3.11, FastAPI |
| Database | PostgreSQL 15 |
| Frontend | React 18, TypeScript, Tailwind |
| Scraping | Playwright, BeautifulSoup |
| SendGrid | |
| Hosting | Docker Compose, nginx |