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| 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 | |
| ```bash | |
| 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/`: | |
| ```bash | |
| 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). | |