| --- |
| title: Redac |
| emoji: 🖍️ |
| colorFrom: gray |
| colorTo: red |
| sdk: gradio |
| sdk_version: 5.44.1 |
| python_version: '3.13' |
| app_file: app.py |
| pinned: false |
| license: other |
| short_description: Redac |
| tags: |
| - track:backyard |
| - sponsor:openai |
| - sponsor:openbmb |
| - achievement:offgrid |
| models: |
| - openbmb/MiniCPM-V-4_5 |
| - urchade/gliner_multi_pii-v1 |
| --- |
| |
| # Redac |
|
|
| Redac is a local privacy gateway: paste sensitive text or upload a document/ID |
| image, extract and redact PII locally, then copy the safe output into any LLM |
| without exposing raw personal data. The local placeholder map lets you rehydrate |
| answers afterward. |
|
|
| ## Submission |
|
|
| - **Demo video:** https://youtu.be/as7GmNYO83s |
| - **Social post:** https://x.com/barathishere/status/2066882507497242782 |
|
|
| ## Build Small Hackathon Badges |
|
|
| | Badge | Why Redac qualifies | |
| |---|---| |
| | 🏡 **Backyard AI** (`track:backyard`) | Redac solves a practical privacy problem for people who want to use LLMs with medical, ID, banking, or personal documents without leaking raw PII. | |
| | 🌀 **OpenAI Track** (`sponsor:openai`) | The Space history includes Codex-attributed commits, and the app is designed around a copy-safe workflow for downstream LLM use. | |
| | 🏮 **OpenBMB Awards** (`sponsor:openbmb`) | Image extraction uses `openbmb/MiniCPM-V-4_5` in-Space via ZeroGPU before the extracted fields flow into the redaction core. | |
| | 🔌 **Off the Grid** (`achievement:offgrid`) | Extraction, detection, redaction, and rehydration run inside the Space process. No external AI API receives raw PII. | |
|
|
| ## Models |
|
|
| - `openbmb/MiniCPM-V-4_5` — local document/ID image extraction. |
| - `urchade/gliner_multi_pii-v1` — local PII-tuned zero-shot NER. |
|
|
| ## Flow |
|
|
| 1. Paste text or upload a document image. |
| 2. Redac extracts fields locally when needed. |
| 3. Redac replaces detected PII with stable placeholders. |
| 4. Copy the safe output into your LLM. |
| 5. Rehydrate the LLM answer locally when you need the original values back. |
|
|
