README.md

metadata

title: Luxury Authenticator
emoji: 💎
colorFrom: gray
colorTo: yellow
sdk: gradio
sdk_version: 6.14.0
python_version: 3.11
app_file: app.py
pinned: false

Luxury Authenticator

One image. Any source. Structured signals.

A 5-layer AI pipeline (research demo) that analyses a luxury-related image and returns a structured report: image source type, item identification, a CLIP-based confidence signal, a perceptual-hash provenance check against a small demo database, and rule-based recommended actions.

Architecture

Layer	Purpose	Model / method
1 — Image type	Real photo / AI / Screenshot / Render	CLIP zero-shot (openai/clip-vit-base-patch32)
2 — Object ID	Brand, category, caption	BLIP-base + CLIP (Salesforce/blip-image-captioning-base)
3 — Confidence signal	Visual similarity vs authentic vs replica wording	CLIP zero-shot (same CLIP)
4 — Provenance	Near-duplicate check vs demo scam list	imagehash pHash + data/scam_database.csv
5 — Actions	Short recommendations	Rule engine (no LLM)

Honest limitations

• The confidence signal (Layer 3) uses CLIP visual similarity, not professional authentication.
• The provenance database contains a small set of manually seeded demo entries (hashes from data/flagged/).
• Do not use for purchase decisions over INR 50,000 without professional verification.

Local run

pip install -r requirements.txt
python app.py

First request downloads PyTorch and model weights (expect several minutes and ~2 GB RAM peak on CPU).

Rebuild scam CSV

After adding PNG/JPG files under data/flagged/:

python data/seed_phashes.py

Demo assets

Example images live in examples/. Flagged images used only for hashing live in data/flagged/. Uploading examples/fb_listing_screenshot.png should match the duplicate entry in the demo database and show Flagged in Layer 4.

Timing (reference)

End-to-end inference target is under ~25 seconds per image on Hugging Face Spaces CPU after models are loaded; cold start adds model download and load time on the first request.

Smoke tests

• Layer 4 only (no PyTorch): set PYTHONPATH=%CD% then python scripts/smoke_layer4.py (Windows) or PYTHONPATH=. python scripts/smoke_layer4.py (Unix).
• Full pipeline (requires pip install -r requirements.txt): python scripts/smoke_analyse.py from the repo root (prints timing and a short summary for examples/chanel_bag.png).

V2 ideas

GradCAM / heatmaps, TinEye-style API for provenance, larger brand lists, calibrated thresholds, optional LLM action layer.

Built by

IT portfolio project — Luxury Truth Lens PRD v2.0 (Buildable Edition).