card-grader/CLAUDE.md

Card Grader

Self-hosted web app that estimates PSA trading card grades from photos, using vision-capable LLMs (Claude and/or GPT) as the grading engine. Runs as a single Docker container on the user's Unraid home server, exposed at hippofam.com/cards through Nginx Proxy Manager.

No framework: the whole backend is Python stdlib (http.server.ThreadingHTTPServer) plus sqlite3. Optional third-party packages are anthropic, openai, and Pillow (image processing only — never required for the server to boot).

Architecture

• app.py — HTTP routing/handlers. Reads Basic Auth username forwarded by the reverse proxy for attribution only (who graded what), never for access control — auth itself is enforced by Nginx Proxy Manager in front of the container via htpasswd. Admin-only settings are gated by ADMIN_USERNAME (CARD_GRADER_ADMIN_USER env var). Request bodies are capped (MAX_BODY_BYTES); oversized/malformed bodies get a 413 and the connection is closed rather than trusting a client-supplied length.
• vision.py — Provider abstraction over Anthropic and OpenAI vision APIs (_call_anthropic / _call_openai, dispatched from a common entrypoint). MODELS holds per-model pricing/capabilities. GRADING_SYSTEM is the large system prompt encoding PSA's actual grading rubric (centering tolerances, corner/edge/surface criteria, qualifiers — MC/OC/PD/ST/MK, half-grades, Altered Authentic) — verified against psacard.com, beckett.com, and cgccards.com, not guessed. Self-reported "high" confidence is capped to "medium" when neither centering nor edge whitening was independently measurable from the image geometry.
• cardimage.py — Pillow-based image pipeline: card boundary detection, corner/edge/surface crop generation, centering/edge-whitening/aspect-ratio measurement from pixels (not just left to the model's eye). Includes holder/toploader-aware detection (detect_card_box): a combinatorial search over candidate edge positions constrained to standard card aspect ratios, so the app can locate the actual card inside a slab/toploader rather than just detecting the holder's outline. When a holder is detected, edge-whitening measurement is refused (the holder's own inner edge corrupts that specific measurement) but centering still runs (border-width geometry, unaffected by clear plastic), and aspect-ratio measurement deliberately uses the unrefined box to avoid circularity (refinement selects for standard-ratio rectangles, so measuring the refined box's ratio would trivially always look "normal").
• store.py — SQLite (WAL mode) persistence. grades and grade_events tables, JSON columns for structured data, schema migrations via idempotent ALTER TABLE in store.init(). List/detail API responses explicitly exclude source_images_json to keep payloads small. DB path overridable via CARD_GRADER_DB_PATH.
• static/ — Vanilla JS/CSS/HTML frontend, PWA-enabled (manifest, service worker). "PSA slab" visual theme. __BASE__ is templated at request time so the app works correctly when served from a sub-path (/cards) behind the proxy.

Local development

python3 app.py

Needs ANTHROPIC_API_KEY and/or OPENAI_API_KEY in the environment to actually grade; the server itself has no required third-party deps. A local grades.db is gitignored and is scratch/test data only — it is not the production database (that lives on the server, see below).

Deployment

Target: Unraid server, container path /mnt/user/appdata/card-grader, built from Dockerfile / docker-compose.yml in this repo. Non-root container user (99:100 / nobody:users), healthcheck, log rotation, init: true. Reverse-proxied by Nginx Proxy Manager at hippofam.com/cards with per-user HTTP Basic Auth.

Current deploy mechanism (as of 2026-08-25): manual. Changes are copied to the server by hand (scp) and the container is rebuilt over SSH; the two copies of the repo (local Mac, server) are kept in sync by committing on both sides. There is currently no automated push-to-deploy path connected — see below.

Pending: restricted git-push deploy. A setup script (setup-card-grader-deploy.sh, delivered to the user, not yet run) creates a scoped carddeploy user on the Unraid box for exactly this purpose:

• Login shell is git-shell — accepts git push/pull only, nothing else (no interactive shell, no arbitrary commands, no SFTP).
• A post-receive hook (root-owned, mode 755 — not writable by the carddeploy account, and not something git push can overwrite via the protocol regardless) triggers a single root-owned deploy script via one narrowly-scoped sudo rule (NOPASSWD for that exact script path only — deliberately never raw docker/the docker group, both of which are root-equivalent on the whole box via the daemon socket).
• The deploy script does git checkout -f main into the live app directory and docker compose up -d --build.
• State (the bare repo, the deploy script, the sudoers source file) lives under /mnt/user/appdata/card-grader-deploy/ on the array — Unraid boots from USB into RAM, so anything living only under / would vanish on reboot. Setup is reinstalled idempotently via /boot/config/go (Unraid's official boot-time persistence hook) so it survives reboots.

Once the user runs that script and confirms, the local SSH config alias unraid-cardgrader (in ~/.ssh/config, already pointed at the new carddeploy user and a fresh dedicated keypair ~/.ssh/id_ed25519_cardgrader_deploy) will be usable, and the remaining step is adding the bare repo as a git remote here and doing a first push to verify the pipeline end-to-end. Do not assume this pipeline is live — confirm with the user before relying on it.

A prior root SSH key/access to the whole box was explicitly revoked by the user; the replacement above is intentionally scoped to only updating this one container, not general Unraid/Docker access.

Conventions and constraints established for this project

• PSA grading rules must be verified against a real source (PSA's own site, Beckett, CGC) before being encoded into GRADING_SYSTEM — this rubric has had real accuracy bugs (e.g. a self-contradictory centering tolerance, a missing 5% front-centering leeway rule for grades 7+) found and fixed through actual verification, not assumption.
• The vision prompt's cannot_assess escape hatch is deliberately narrow: surface condition genuinely can't be separated from dust/scratches on a toploader/slab's plastic, so it keeps the escape hatch. Corner and edge geometry (sharpness, whitening, chipping) does read through clear plastic and must be judged normally — an earlier prompt version gave the model an escape hatch for corners/edges too, which caused false "cannot tell from photo" results on cards that were plainly visible through the holder. Don't reintroduce that.
• No pre-flight/"precheck" step before a paid grading call. This was tried and explicitly rejected by the user — their workflow is screenshots that can't be pulled out of the toploader, so a pre-check step doesn't fit and was reverted.
• No browser automation (Playwright or otherwise) to scrape eBay listings. Tried a plain curl-based fetch once (not Playwright) and got an immediate 403; iterating on headers/fingerprinting to get past that would be bot-detection evasion regardless of which tool performs it, and is out of scope. This feature is paused; if revisited, the legitimate path is a client-side bookmarklet/extension that uses the user's own authenticated browser session rather than a server-side fetch.
• Never trust the reverse proxy's forwarded auth header for anything beyond attribution (whose name to log against a grade) — access control is the proxy's job, not the app's.