# Image Categorizer — Design (M1: Foundation + Trip-Cluster)

**Date:** 2026-06-27
**Status:** Approved design, pending implementation plan
**Scope of this spec:** Milestone M1 only (shared foundation + the `trip-cluster` app). Later milestones are documented as a roadmap, each to get its own spec→plan→build cycle.

## Purpose

A monorepo of small, independent tools that clean up and structure a ~40k-image, 15-year [Immich](https://immich.app) library so it can become travel "memories." This is the **upstream** stage of an existing pipeline:

```
[NEW categorizer: trips + tags]  →  image-rater (pick best → 4+ album)  →  travel-memories (album → Grav blog)
```

Every handoff is through **Immich itself** (tags, albums, ratings) — no app talks to another directly. The categorizer's job is to leave clean **trip tags** (and explicit **non-trip** marks) in Immich for `image-rater` to consume by tag.

### Division of labor (the core principle)

AI/algorithms do what they do well; the human does QA and (later, downstream) content creation.

- The machine **proposes** groupings with a confidence score.
- The human **reviews at the cluster level** (a few dozen candidate trips), not photo-by-photo, mostly in bulk, with attention concentrated where confidence is low.
- Nothing is silently auto-applied to the library. Trust matters with 15 years of memories.

### What M1 explicitly does NOT do

- It does **not** decide which photos *within* a trip are keepers — that is `image-rater`'s job downstream.
- It does **not** fully categorize everyday/non-trip photos beyond marking them as non-trip (deferred to a future milestone).
- It does **not** draft any narrative text (the user owns content creation).

## Context & constraints (from the existing ecosystem)

The two existing apps (`claude-image-rater`, `claude-travel-memories`, both under `/home/mischa/Projects/`) establish strong, consistent conventions this project follows and builds upon:

- **Stack:** Python 3.12 + Flask (factory `create_app()`) + an argparse CLI sharing one package.
- **No-build frontend:** Jinja templates extending `base.html`, **DaisyUI 4 + Tailwind + Alpine.js** (CDN), HTMX for navigation, one `static/app.js`.
- **Single-responsibility modules:** one module is the *only* thing that talks to Immich; one is the *only* thing that talks to Anthropic.
- **Testing:** pytest + `pytest-httpserver` (mock Immich) + Playwright UI tests, **TDD** ("failing test first").
- **Docker:** one container per app, distinct port, `user: ${UID}:${GID}`, state on a mounted volume.
- **Proven UI:** travel-memories' triage screen (grid + ring-selection + keyboard navigation + fullscreen lightbox) was reused by image-rater. This becomes the canonical shared component (see §UI).

Both apps currently **duplicate** near-identical code (`immich.py`, `config.py`, `slugify`, `base.html`, thumbnail proxy route, JSON-state pattern). That duplication is the evidence that extracting a shared foundation is high-value, and that migrating those apps onto it later (M4/M5) is largely mechanical.

### Decisions locked during brainstorming

- **State store: SQLite** (not the JSON-file pattern of the existing apps). A single DB makes the cross-trip filtering this project needs ("all assets in a trip window but missing the trip tag", "everything still unreviewed") far easier. `shared/core` is designed cleanly enough that the other apps *could* migrate onto it later.
- **Immich is the source of truth.** SQLite is the working/review layer; all durable results are written back to Immich as tags.
- **Monorepo with shared packages + per-app containers** (Approach A on a shared foundation). Build the categorizer first as the POC that proves the foundation, then migrate the other apps onto it in later milestones (Option 2 end-state), in milestones — not all at once.
- **GPS is a minority signal.** Most old camera photos lack GPS, so trip detection is **timestamp-first**, anchored by existing tags, refined by GPS where present.
- **Everyday/non-trip photos:** assign trips AND positively mark non-trip so they stop resurfacing as "unreviewed." Full non-trip categorization is deferred.

## Roadmap (sequenced; each milestone independently shippable)

**End state:** one monorepo — `shared/{immich, core, ai, ui}` + `apps/{trip-cluster, tag-verify, enrich, image-rater, travel-memories}`, each its own container.

- **M1 — Foundation + trip-cluster (THIS SPEC).** Stand up the monorepo, shared packages, SQLite store, Immich ingest, and the `trip-cluster` app end-to-end. Validate on the last trip's photos. This is the POC that proves the foundation.
- **M2 — tag-verify** app on the proven foundation (verify/normalize existing tags, dedupe vocabulary, find outliers).
- **M3 — enrich** app (geocode location tags like "Kiev" → coordinates and optionally backfill GPS into Immich to improve future trip detection; AI captioning; content tags; noise detection).
- **M4 — migrate image-rater** onto shared packages (+ optional SQLite).
- **M5 — migrate travel-memories** onto shared packages (+ optional SQLite).
- **M6 (future/optional) — non-trip categorization** (the deferred full-library taxonomy).

---

# M1 Design

## Monorepo layout

```
claude-image-categorizer/
  pyproject.toml          # workspace; shared/* as editable path deps
  docker-compose.yml      # one service per app (just trip-cluster for M1)
  .env.example            # IMMICH_URL, IMMICH_API_KEY, ANTHROPIC_API_KEY, UID, GID
  README.md
  CLAUDE.md
  shared/
    immich/               # the one true Immich client
    core/                 # SQLite store + domain models + persistence
    ai/                   # Anthropic batch wrapper (scaffolded; light/unused in M1)
    ui/                   # base.html, Tailwind/DaisyUI/Alpine/HTMX, Jinja macros, app.js
  apps/
    trip-cluster/
      Dockerfile
      categorize.py       # CLI entry point -> app.cli.main()
      app/                # Flask factory, routes, app-specific logic
      tests/
  docs/superpowers/specs/ ...
```

- `shared/*` are local, editable Python packages so apps `import` them directly. Packaging via a workspace (`pyproject.toml` path deps; `uv` workspace optional). This stays close to the existing pip+venv practice while enabling the monorepo.
- Each app owns its Dockerfile and a distinct port. **trip-cluster serves on 8084** (8082 = travel-memories, 8083 = image-rater).
- The SQLite DB and downloaded thumbnails live on a mounted volume so state persists across container restarts and is reachable from the host. `user: ${UID}:${GID}`.

## Shared packages

### `shared/immich`
The single Immich REST client (consolidates the duplicated `immich.py` from both existing apps). `requests.Session` with `x-api-key`. Capabilities needed for M1:
- `resolve_tag_id(name)`, list tags / tag inventory.
- Search assets (paginated `POST /api/search/metadata`) by date range, by tag, and broadly — returning at least: `id`, `originalFileName`, `localDateTime`, GPS lat/lon (when present), city/country (when present), `type`, existing tags, rating.
- `download_thumbnail(asset_id)` (`…/thumbnail?size=preview`).
- `upsert_tag(name)`, `tag_assets(tag_id, asset_ids)` (write-back of trip / non-trip tags).

### `shared/core`
SQLite store + domain dataclasses + persistence (connection, schema/migrations, data-access helpers). Owns the on-disk schema. Designed as a clean data-access layer so other apps can adopt it later.

**Domain model (central unit = Cluster / candidate trip):**

- **Asset** — mirror of an Immich asset: `immich_id`, `taken_at`, `gps_lat`/`gps_lon` (nullable), `place_city`/`place_country` (nullable), `type`, `has_gps`, `thumb_path`, `ingested_at`.
- **Tag** — inventory of Immich tags: `name`, `immich_tag_id`, usage `count` (ingested now; used heavily in M2).
- **AssetTag** — existing Immich tags per asset (denormalized for filtering).
- **Cluster** — a candidate trip: `id`, `start_at`, `end_at`, `count`, `suggested_name`, `confidence`, `kind_guess` (`trip`|`everyday`), `status` (`pending`|`approved`|`non_trip`|`merged`|`split`|`skipped`), `decided_name`, `reviewed_at`, `notes`.
- **ClusterMember** — asset↔cluster link with `member_confidence` and `is_outlier`.
- **WritebackLog** — every change pushed to Immich (`asset_id`, `action`, `tag`, `result`, `applied_at`) for idempotency.
- **Meta** — last-ingest timestamp, run params, schema version.

**SQLite tables:** `assets`, `asset_tags`, `tags`, `clusters`, `cluster_members`, `writeback_log`, `meta`. Single DB file on the mounted volume.

### `shared/ai`
Anthropic **batch** Messages API wrapper using the official `anthropic` SDK, carrying over image-rater's proven conventions (per-criterion judgments, `confidence`, score floors, chunking ≤50/batch, default model `claude-haiku-4-5`). **Scaffolded but barely used in M1** — trip-cluster is algorithm-first (near-zero AI cost). It earns its keep in M3 (enrich).

### `shared/ui`
The shared visual foundation, extracted from the proven travel-memories/image-rater templates:
- `base.html` (DaisyUI 4 + Tailwind + Alpine.js + HTMX via CDN, navbar, content block).
- The canonical **grid + fullscreen lightbox** component: thumbnail grid with ring-selection, **arrow-key navigation**, **full-screen view** (arrows = prev/next, Esc = close). *(First-class requirement.)*
- Reusable Jinja macros (image grid, lightbox, confidence/status badges, approve/reject controls) and shared `app.js` Alpine components.

## The `trip-cluster` app (M1 deliverable)

### Workflow

```
categorize ingest [--from DATE --to DATE | --tag NAME | --subset N]   # 1. pull → SQLite + thumbs
categorize cluster [--gap-threshold ...]                              # 2. build candidate trips
categorize serve                                                      # 3. review UI on :8084
                                                                      #    (approve / non-trip / split / merge)
categorize apply                                                      # 4. write approved tags back to Immich
```

(`serve` may also trigger `apply` per-cluster via a UI button, plus a batch "apply all approved" action; CLI `apply` is the headless equivalent.)

### 1. Ingest (automatic, scopeable)
Pull assets from Immich via `shared/immich`, upsert metadata into SQLite, download thumbnails. **Incremental & idempotent** (fetch only changed-since-last via Immich `updatedAt`; upsert by `immich_id`).

**Scopeable from day one** — the mechanism for the vet-first plan. Instead of forcing a full 40k pull, `ingest` accepts a bounded scope:
- `--from / --to` (date window) → e.g. the last trip plus surrounding everyday photos;
- `--tag NAME` → a single already-tagged trip;
- `--subset N` → a cap.

This lets the whole tool be validated on the last trip for near-zero cost before widening to the backlog over time.

### 2. Cluster (automatic) — signal hierarchy
Pure, algorithmic, **no API calls** (keeps the POC nearly free). Produces candidate trips, each with a `suggested_name`, `confidence`, and `kind_guess`:

1. **Existing trip tag** → authoritative seed; its assets form a confirmed cluster (still shown, to verify *completeness*). Respects the user's existing trip-tag convention.
2. **Timestamp gap clustering** → primary structure: sort by `taken_at`, split where the inter-photo gap exceeds a tunable threshold.
3. **Location anchors** (existing location tags like "Kiev" + GPS when present) → refine boundaries, propose names.
4. **Coverage detection** → assets *inside* a confirmed trip's time window but *missing* its tag are flagged "likely belongs here" (the completeness gap); assets carrying a trip tag but *outside* their cluster are flagged as outliers.
5. **Visual similarity** (Immich CLIP / local pHash) → *optional* tie-breaker for ambiguous boundaries; marked optional so it can never block the slice.

**Confidence & kind_guess** (echoing image-rater's confidence/floor approach): tight time window + existing trip tag + consistent location → high confidence; sparse, untagged, no GPS → low ("needs your eye"). Low-volume scattered clusters → `kind_guess = everyday` (suggested non-trip).

### 3. Review (human, cluster-level)
A review screen lists **candidate trips sorted by "needs attention"** (low confidence first). Per cluster: thumbnail grid (with the shared grid+lightbox / arrow-key / full-screen component), editable suggested name, and actions:
- **Approve trip** (confirm + tweak name)
- **Mark non-trip**
- **Split** (break one cluster into two)
- **Merge adjacent** (combine with a neighbor)
- **Skip**

To keep it fast: high-confidence clusters arrive **pre-filled**, and an **"approve all high-confidence"** bulk action lets the user rubber-stamp the obvious cases, concentrating attention on the fuzzy ones. Every decision **persists immediately** to SQLite (resumable: closing and reopening resumes exactly where the user left off).

### 4. Write-back (to Immich, idempotent)
On approval (per-cluster or batch `apply`):
- **Approve** → write the trip tag (via `upsert_tag` + `tag_assets`) to all member assets, respecting the existing trip-tag convention.
- **Mark non-trip** → apply a `non-trip` tag (named to fit the user's existing scheme) so those assets are filtered out and never resurface as unreviewed.
- **Idempotent**: `writeback_log` records applied changes; re-runs skip what's already done. **Explicit confirmation before any write** (mirrors image-rater's export safety).

All durable state lands in **Immich**; SQLite remains the working/review layer.

### Tag-scheme reconciliation (important)
Before introducing any tag convention (trip names, the `non-trip` mark, or any future `ai-*` scheme), **inspect the live Immich instance and reconcile with what already exists** rather than inventing a parallel scheme. The user's trips already follow a convention — trip-cluster must read and respect it.

## App shape & conventions
- `categorize` CLI (argparse subcommands: `ingest` · `cluster` · `serve` · `apply`) + Flask factory `create_app()`.
- Jinja + DaisyUI/Tailwind/Alpine/HTMX via `shared/ui`, **no build step**, Immich thumbnail proxy route, serves on **8084**.
- Env vars (same names as existing apps): `IMMICH_URL`, `IMMICH_API_KEY`, `ANTHROPIC_API_KEY` (Anthropic optional for M1), loaded from `.env`.

## Testing strategy
TDD throughout, mirroring the existing apps:
- **Unit:** clustering algorithm as a pure function (timestamps/tags/GPS → clusters) — deterministic and easy to assert; SQLite store; config; Immich client (mocked via `pytest-httpserver`).
- **Route/UI:** the review flow (approve / non-trip / split / merge, immediate persistence, idempotent write-back) with Immich mocked.
- **Playwright UI tests:** grid + arrow-key navigation + full-screen lightbox behavior, mirroring travel-memories/image-rater.
- Shared packages tested independently of the apps.

## Cost notes
M1 is algorithm-first, so trip-cluster makes **essentially no Anthropic calls** — clustering is local timestamp/tag/GPS math. AI cost becomes relevant only in M3 (enrich), where image-input tokens dominate and the batch API (~50% cheaper, chunked ≤50) plus the Haiku default keep it modest (image-rater reference: ~700 images on Haiku ≈ $1).

## Out of scope for M1
- Choosing keepers within a trip (image-rater).
- Full non-trip/everyday categorization (M6).
- Migrating the existing apps onto the shared foundation (M4/M5).
- Geocoding / GPS backfill / AI captioning (M3).
- Narrative text drafting (human-owned; downstream).