Shelf-Sense

Shelf audits are still done by eye: someone walks the aisle, counts facings and writes them down. Gaps sit unnoticed between walks, counts differ between people, and nobody knows what a missing facing costs. Shelf-Sense turns one shelf photograph into a preliminary compliance report. The user picks a shelf specification, adds a photo and runs one inspection. Gemma-4-31B-IT, called through AMD Developer Cloud's Token Factory, counts each product shelf by shelf. Everything after that is plain Python: Pydantic validation with one stricter retry, invented items dropped, unreliable or contradictory counts sent to review, a verdict computed from per-product statuses, and an estimated margin at risk computed with Decimal arithmetic from demo unit margins. A second AMD call may word the restocking note, but every name, count and figure in it is inserted by code, and the app labels who wrote it. We measured it honestly on a self-taken photo with a hand-counted ground truth. A shelf-by-shelf prompt cut the counting error from 8 boxes to 1. The model still misses one half-hidden box, at 100% confidence, so the fully stocked demo shelf is reported as short. Higher resolution and a second model did not fix it; per-shelf cropping did but overcounted, so it is not in production. That is why every dollar figure is labelled "Estimated Margin at Risk (Unverified)" and per-shelf counts are shown for a quick human check. All 24 experiment records, with raw model replies, are in the repository. Built with Streamlit, Python, Pydantic and Docker; 195 offline tests. MIT licensed.