Public Access
adminandClaude Sonnet 4.6
dbc26bcc30
Matcher improvements (matcher.py): - Plural normalization: 'tortillas'→'tortilla', 'thighs'→'thigh' so subset recall check works without stemmer - Precision floor lowered 0.45→0.30: allows 'Bacon'→'Wright Brand Bacon' (1/3=0.33) while exclusion words still block category contaminants - _EXCLUSION_WORDS now normalized through same singularizer for consistency LLM second-pass (llm_matcher.py): - run_llm_match_job(): for each still-unmatched ingredient, collects top-12 candidates from grocery catalog ranked by fuzzy×precision (same metric as AUTO matcher), then asks Ollama to pick the best match - Candidate scoring: combined = (partial_token_sort_ratio/100) × precision ensures "McCormick Black Pepper" outranks "Dr Pepper" for 'Black Pepper' - Stores picks as source='auto_llm' (confidence=0.750) - Ollama Cloud endpoint: https://ollama.com/v1, model: kimi-k2.6:cloud Migration 0010: adds 'auto_llm' to ingredient_match_source_enum Config: OLLAMA_BASE_URL / OLLAMA_API_KEY / OLLAMA_MODEL settings Docker-compose: wires all three Ollama + Spoonacular env vars to backend/scheduler Scraper service: calls run_llm_match_job after run_match_job on every scrape Results: AUTO matcher went from 36→25 unmatched (plural normalization fix), LLM added 3 more (Black Pepper, Zucchini, Chicken Thighs). Remaining 22 are genuine Lucky CA catalog gaps (standalone olive oil, dried spices, etc. not in Swiftly weekly ad). Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
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
- 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 |