SlabUploader/docs/IMPL-PLAN.md

Implementation Plan

Status: SPEC · 2026-08-24 · SlabUploader The build plan a professional (or a fresh Hermes session) executes against. Each phase has: goal, deliverables, acceptance criteria (testable), and exit gate. Ordering is by dependency — do not skip ahead. Definition of done for EVERY phase: code + tests + real execution output in the PR/commit message + docs updated.

Ground rules (from AGENTS.md):

• Deterministic core is unit-tested before any UI consumes it.
• No feature without a passing test and shown output.
• Inference stays toggleable; the full suite must pass with it disabled.
• One phase = a reviewable, shippable increment.

---

Phase 0 — Skeleton & deterministic core (server)

Goal: repo scaffold + the tested deterministic pipeline, no UI, no Woo.

Deliverables:

• backend/ FastAPI skeleton: app factory, health endpoint, config, logging.
• pipeline/ implementing TECH-SPEC: constants.py, ruler→scale, geometry, width sampler, bdft math, pricing, green-removal, crop/normalize, merch filter.
• db/: schema.sql + alembic migration for all tables (DATA-MODEL).
• tests/: port every TECH-SPEC test vector (TV-1…TV-9) to pytest; add property tests (bdft monotonic in inputs; width sampler returns 3–5 points; crop fill ∈ [0.75,0.85]).
• Dockerfile for backend; runs pytest in CI/local.

Acceptance:

• pytest green, covering all 9 TVs + property tests; coverage of pipeline/ ≥ 95%.
• GET /api/health works on the dev container.
• A fixture photo set (3–5 real slab photos) runs end-to-end through the pipeline CLI (python -m app.pipeline.run <dir>) and prints bdft/widths/normalized outputs matching hand-computed expectations.

Exit gate: Ty reviews the pipeline CLI output on real photos; math confirmed.

Phase 1 — API surface (no Woo, no inference)

Goal: full slab CRUD + photo upload + settings, backed by the Phase-0 core.

Deliverables:

• Routers per OPENAPI: slabs (CRUD + upload + poll), photos (serve), pricing-rules, settings (with AES-GCM encryption + test stubs), taxonomy (cache read/write).
• Optimistic concurrency (client_rev/server_rev, 409 paths).
• Status state machine + in-process job queue (processing→ready).
• Integration tests (httpx TestClient): create→upload→poll→ready; revision conflict; duplicate SKU; settings encryption round-trip (secret never returned).

Acceptance:

• OPENAPI.md endpoints implemented; a generated openapi.yaml matches the doc.
• Integration tests green; secrets never leak into any GET response (asserted).
• docker compose up (backend + a throwaway frontend stub) serves the API.

Exit gate: Ty drives the API with curl through a full draft→ready cycle.

Phase 2 — Frontend: capture & offline PWA

Goal: mobile-first capture flow, fully offline, with the local light pass.

Deliverables:

• SvelteKit + Tailwind app (SPA/SSG), Workbox service worker, IndexedDB stores.
• Capture flow: SKU close-up (Tesseract.js OCR + manual fallback), ruler shot (OpenCV.js hint + manual length fallback), 1–5 inventory photos, thickness entry.
• Draft autosave to IndexedDB (rev management), offline banner, upload queue.
• Photo review UI (reorder/delete/retake; mark top-down photo).

Acceptance:

• On a real phone (LAN/Tailscale), the entire capture flow works with airplane mode on (photos + OCR + draft persist offline).
• On reconnect, queued draft uploads and the enriched draft merges.
• Lighthouse mobile ≥ 90 on the capture screens; one-hand usable (manual check).
• SW precache verified (reload with network off after first load).

Exit gate: Ty captures a real slab fully offline on his phone; draft survives a browser restart.

Phase 3 — Frontend: enrich, review, publish UI

Goal: the enriched review screen and publish action, wired to Phase-1 API.

Deliverables:

• Review screen: all fields editable (dimensions, species/character with confidence
  • manual override, price with recommendation, content with "Refine with AI" toggle, normalized photos).
• Species/character inference UI (calls server; shows confidence; manual confirm below threshold).
• Publish button (online-only), status polling, success/failure states, error detail.
• Settings UI (FR32): Woo creds + test, inference endpoint + test (vision check), pricing rules editor, brand voice/GEO. Prompts are server files, not UI settings.

Acceptance:

• Full round-trip in browser: capture→upload→review→edit→publish→published (against a Woo sandbox/mock in tests; against real store in Phase 6).
• Inference disabled → review screen fully functional with manual species/character.
• Every field is editable and the override persists (asserted in a test).

Exit gate: Ty publishes a test slab to the real store as a pending product.

Phase 4 — WooCommerce integration (real)

Goal: production Woo sync, taxonomy, dedupe, logging (FR24–28).

Deliverables:

• Woo client (httpx, Basic auth, timeouts), taxonomy sync, auto-assign, media upload, product create (status=pending default), dedupe, sync_log.
• POST /api/admin/taxonomy/sync against the real store.
• Integration tests against a Woo mock (respx) for success + error + duplicate paths.
• Partial-failure handling (orphaned media noted, per CONTENT-WOO §2.6).

Acceptance:

• Real publish: product appears in store admin as pending, correct SKU/price/images/ category/tags; woo_product_id stored; sync_log written with payload_hash.
• Duplicate SKU publish → 409, no second product created.
• Woo error (bad creds / 500) → slab failed with detail; retry path works.
• All Woo integration tests green (mock + one live smoke).

Exit gate: Ty verifies a real pending product in the store is correct.

Phase 5 — Inference (vision + optional content)

Goal: scoped, toggleable inference with guardrails.

Deliverables:

• Vision wrapper: species/character detection from the top-down photo via the configurable OpenAI-compatible endpoint; model constrained to Woo species list for accuracy; returns confidence (threshold 0.7, configurable); logs request/response.
• Content wrapper: Call 2 reads prompts/call2.txt; LLM writes prose only, dimensions injected by deterministic templates. No numeric guardrail needed. See docs/PROMPTS.md.
• test-inference endpoint (vision capability check).
• All inference code behind feature flags; suite passes with everything OFF.

Acceptance:

• With a local vision model configured: species/character suggestion appears with confidence; low confidence → manual confirm required.
• With inference OFF: full app works, tests green (explicit test asserts no network calls to the inference endpoint).
• Inference payloads stored for audit (asserted present after a call).
• Call 1 prompt file edits survive server restart (read fresh each invocation).

Exit gate: Ty runs species detection on 3 real slabs; results acceptable or manually corrected (that's the design).

Phase 6 — Deployment, UAT, hardening

Goal: production on .201 + user acceptance tests (PRD deliverable: UATs after prototype).

Deliverables:

• .201 inventory pass (DEPLOYMENT §1) → final compose + Caddyfile.
• Deploy to slab.tyubumini.local; backup/restore runbook executed once (restore tested).
• UAT suite built from PRD acceptance criteria: a scripted set of real slabs (≥3 species, ≥1 cathedral, ≥1 manual-width fallback, ≥1 offline capture, ≥1 duplicate-SKU attempt) with expected bdft/price/image-fill asserted.
• Performance check: capture OCR 1–2s, server normalization 3–5s per slab (PRD NFR).
• Docs: README, runbook, key-rotation verified.

Acceptance (PRD success metrics, measured):

• E2E capture→publish on iPhone AND Android browsers works.
• Manual length override recalculates widths/sqft/bdft correctly (UAT asserts).
• Normalization: slab ~80% frame in ≥95% of UAT photos.
• Woo product created with normalized images + correct taxonomy.
• Deterministic functions unit-tested; inference calls logged for audit.
• Backup restore demonstrated once.

Exit gate: Ty signs off on UAT results; v1 declared done.

---

Risks & mitigations

Risk	Impact	Mitigation
Ruler detection unreliable in field lighting	bad scale → bad widths	manual length is authoritative fallback (always available); ruler confidence surfaced; UAT gates it
Green-sheet color varies / U²Net CPU latency > budget	slow/failed normalization	chroma-key primary (fast); U²Net fallback; output_px can drop to 1000 min; GPU path documented
No vision model available at deploy	species inference off	inference is toggleable; manual species/character fully functional — v1 not blocked
Woo partial publish (media up, product POST fails)	orphaned images, confusing state	sync_log + retained images for retry; manual cleanup note; no auto-delete
Local llama.cpp vision endpoint not up on .202	inference off	configurable endpoint; deploy-time config; app works without it
SQLite contention under concurrent polls	slowdown	single owner, low volume; Postgres path documented
.201 port/Caddy collision with bolt.diy	deploy conflict	Phase-0/6 inventory pass before any port bind

Definition of Done (v1, project-level)

• All 6 phases exit-gated by Ty.
• PRD acceptance criteria met (measured in Phase 6 UAT).
• pytest + integration + UAT green; inference-off path green.
• Deployed on .201, HTTPS, backed up, restore tested, runbook + key-rotation verified.
• Repo on gitea-atd (Ty_Tech/SlabUploader) is source of truth; docs current.