adminandClaude Sonnet 4.6 2373883fe7 fix: exact-name fast path in matcher + save priceless produce in scraper
Scraper: remove price guard in map_product so produce items without a
catalog price (e.g. Fresh Garlic, Lime sold by weight) are saved to
grocery_item with current_price=NULL rather than skipped.

Matcher:
- Add exact-name fast path: build a lowercase-trimmed name→index map
  and skip fuzzy search entirely when the ingredient name matches a
  grocery item exactly. Lime → Lime (confidence 1.0), Garlic → Fresh
  Garlic from fuzzy (confidence 1.0).
- Add exclusion words: juice, gelatin to prevent beverage/dessert
  products from matching cooking ingredients.
- Increase fuzzy candidate limit 20→100 so exact-name items buried in
  large tie groups are not missed.
- Add 'juice' to exclusion: prevents '100% Lime Juice' from winning
  over plain 'Lime'.

Result: all recipe ingredients now match correct Lucky CA products or
show '—' (no match); zero category cross-contamination remaining.

Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-05-10 20:17:03 -07:00
2026-05-09 11:46:31 -07:00
2026-05-08 06:12:10 -07:00

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
  • Pantry Integration: Specify home items to incorporate into suggestions
  • Web UI: Modern interface for the whole family
  • Learning: Feedback-based meal recommendations
  • Recipe Images: Scraped from public recipe sites, AI fallback available

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

  1. System generates 7-day meal plan based on sales, dietary constraints, budget, variety
  2. Email sent to both adults with meal previews
  3. One denial = meal swapped; no denials = auto-approved
  4. 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
Email SendGrid
Hosting Docker Compose, nginx
S
Description
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Readme
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Python 72.5%
TypeScript 25.9%
JavaScript 0.5%
PLpgSQL 0.4%
CSS 0.3%
Other 0.2%