Operator with 481 untagged products (printableprompts.com) gets a
one-click path to a usable categorization without hand-tagging each
product. Strictly suggest-then-approve — nothing writes Tag or
ProductTag rows until the operator clicks Apply on a cluster.
- lib/tag_suggest.py: pure-function clusterer. Tokenize title (weight 3)
+ description (weight 1, capped at 100 unique tokens per product),
strip markdown / URLs / HTML, English + per-shop stopwords, simple
suffix-strip stemmer, group by stem, drop stems matching existing
tag slugs, rank by product count, label each cluster with the most
frequent original word for its stem. No new deps, no ML.
- scripts/backfill_tags.py: CLI preview + --apply for a single shop.
- views/shop.py: shop_tags gains action=apply_suggestion (creates tag +
bulk-attaches every product in cluster) and action=dismiss_suggestion
(adds the cluster's words to shop.tag_stopwords_json so it never
resurfaces). ?show_suggestions=1 triggers the cluster compute.
- templates/shop_tags.j2: "Suggest categories from titles + descriptions"
button + suggestions well with per-cluster sample titles, Apply, and
Dismiss buttons.
- 15 new tests (11 unit over tokenize / stem / cluster + 4 functional
over the suggest/apply/dismiss flow). 1064 total passing.
On a printableprompts-style sample the clusterer surfaces Math, Reading,
Literacy, Seasonal, Novel, Activities, Comprehension — matching what an
operator would manually pick.
russell@unturf.com
5dbbe697b6