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| title: PII Detection | |
| emoji: 🕵️ | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: docker | |
| app_port: 7860 | |
| header: mini | |
| pinned: false | |
| private: true | |
| short_description: Multilingual PII detection with LFM2.5, server-side CPU | |
| # PII Detection | |
| Server-side PII detection powered by [`LiquidAI/LFM2.5-Encoder-350M-PII-Detector`](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector): a bidirectional LFM2.5 encoder (354M) with a token-classification head, covering 40 PII types across 16 languages. It runs on the Space's CPU; the browser only sends text to `/api/detect` and highlights the returned spans, so nothing is downloaded to the visitor. | |
| ## How it works | |
| - `pii.py` — loads the model via its custom modeling code (`trust_remote_code`) and runs the model's own inference-time **hybrid regex decode** (`pii_hybrid_decode.py` + `context_cued.py`, fetched from the model repo): the token classifier locates PII, a validator-gated regex layer owns format-bound types (email/IBAN/credit-card/JWT/…), and cue-gated rules recover structure-less IDs. Spans are enriched with a domain (color), a pretty label, and a redaction tag. | |
| - `app.py` — Flask server: loads the model once, serves the page, and answers `POST /api/detect {text}` with `{spans, redacted, count, ms}`. | |
| - `index.html` / `style.css` — the UI: highlight or redacted view, a per-domain legend, and detection stats. | |
| f32 on CPU (not fp16: x86 has no fast half path; not int8: token-classification boundaries are precision-sensitive) — the same reasoning as the spellchecker / policy-linting Spaces. Threads are matched to the container's CPU quota. | |
| ## Setup | |
| The model repo is private, so the Space needs an **`HF_TOKEN`** secret with read access to `LiquidAI/LFM2.5-Encoder-350M-PII-Detector`. | |