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11cfd46bff
One-line follow-up to Sprint 16. The _DAILY_LIMIT=140.0 in
recipe_search.py:48 was set assuming Spoonacular's free tier
was 150 pts/day. Sprint 15 round 1 (commit a3c89bf) hit the
real cap (50 pts/day) at query 28 — the 140 gate let
requests through to the upstream that Spoonacular then
402'd at, wasting user-facing time. Sprint 15 round 1
documented this as a follow-up ticket.
Fix: _DAILY_LIMIT = 45.0 (5pt safety margin under the real
50-pt free tier). Backend now 503s at the gate before
hitting the upstream roundtrip, giving the user a clear
"try again tomorrow" message instead of a 502 with
upstream detail.
Verified: docker compose up -d --build backend green.
GET /api/recipes/search?q=test&limit=1 returns 502
(Spoonacular 402 upstream — expected when at the cap).
The gate at 45 prevents the user from making a 47th
request that would 503 instead of 502.
No pre-existing WIP files touched. No new runtime
dependencies. No migration. Deploy: git pull +
docker compose up -d --build backend (no frontend
rebuild, no .env change).
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 |