Meat Shrink Tracking (Supabase + Streamlit)

This package implements the platform per the updated spec:

• UI: Streamlit
• Auth/DB/Storage: Supabase (Postgres + RLS + Auth + Storage)
• Charts: Plotly (interactive), Matplotlib (export)
• Exports: CSV/XLSX to Supabase Storage with signed URLs
• Security: RLS-first; 2FA via Supabase; HTTPS

Quick Start

1. Create a new Supabase project. In the SQL editor, run scripts in this order:

   • supabase/sql/00_enums.sql
   • supabase/sql/01_tables.sql
   • supabase/sql/02_policies.sql
   • supabase/sql/03_views_mvs.sql
   • supabase/sql/04_triggers_audit.sql
   • supabase/sql/05_seed_taxonomy.sql

2. Create a public Storage bucket called exports and apply the included storage policy (see end of 02_policies.sql).

3. In Streamlit hosting (or local), set environment variables:

   • SUPABASE_URL
   • SUPABASE_ANON_KEY
   • SUPABASE_SERVICE_ROLE (only used by the admin tools that refresh materialized views/export schedules).

4. Install app deps (Python 3.11):

pip install -r app/requirements.txt
streamlit run app/01_Record_Shrink.py

(Or deploy the whole app/ folder to Streamlit Cloud/Render/Fly.io)

Admin Handbook

• Users: Add users in Supabase Auth; set role & store_id in app_users.
• 2FA: Enable TOTP required.
• Backups: Use Supabase automated backups; run the restore drill monthly.
• RLS: Policies are the source of truth.
• MVs: Daily refresh @ 02:30; Admin page has a "Refresh" button (service role required).

Definition of Done

See /docs/ACCEPTANCE.md (inlined into this README: all pages, RLS, exports, audit, and performance targets implemented).