adminandClaude Opus 4.7 ccfb38a34e feat: scripts/refresh_swiftly_token.py - capture fresh Swiftly token via seleniumbase
Drives luckysupermarkets.com in stealth CDP mode: opens the store
locator, types the zip code, clicks the target store, then triggers a
category page navigation. A fetch + XHR interceptor (installed via JS)
captures the first Authorization header sent to a Swiftly host. The
captured JWT is validated (iss + exp), then written into the env file.

Runs on the host (not docker) since seleniumbase needs a real Chrome.
Defaults to .env.test, headless, zip 94806, store 757. Flags:
  --debug          visible Chrome window
  --restart-backend  rerun docker compose to pick up the new token
  --env-file PATH  override target env file
  --zip / --store  override location

Selectors are intentionally JS-based and tolerant of UI changes
(querySelectorAll fallthrough by attribute heuristics + textContent
substring match) so first-attempt failures degrade to clear errors
rather than silent breakage.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-06 10:42:48 -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%