Spaces:
Running on Zero
Running on Zero
| title: Redac | |
| emoji: ๐๏ธ | |
| 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 | | |
| |---|---| | |
| | ๐ก **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. | | |
| | ๐ **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. | | |
| | ๐ฎ **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. | | |
| | ๐ **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` โ local document/ID image extraction. | |
| - `urchade/gliner_multi_pii-v1` โ 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. | |