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How to build scalable web apps with OpenAI's Privacy Filter

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How to build scalable web apps with OpenAI's Privacy Filter

All three are built on gradio.Server, which lets you pair custom HTML/JS frontends with Gradio's queueing, ZeroGPU allocation, and gradio_client SDK. In all these apps, gradio.Server plays the same backend role, and that consistency is exactly what makes it really powerful.

Privacy Filter is a 1.5B-parameter model with 50M active parameters, permissively licensed under Apache 2.0. PII categories are private_person, private_address, private_email, private_phone, private_url, private_date, account_number, secret. Context is 128,000 tokens. Achieves state-of-the-art performance on the PII-Masking-300k benchmark. Full numbers and methodology are in the official release blog.

User problem. You want to read a PII-heavy document (a contract, a resume, an exported chat log) with every detected span highlighted by category, a filter in the sidebar, and a summary dashboard up top. The reading experience should feel like a normal document, not a form.

What Privacy Filter does here. The whole file goes through in a single 128k-context forward pass, so there's no chunking, no stitching, and span offsets line up directly with the rendered text. BIOES decoding keeps span boundaries clean through long ambiguous runs.

Server does here. HighlightedText and a sidebar, and it would work. The reading experience we wanted (serif body, category filters that toggle CSS classes client-side instead of re-running the model, a summary dashboard that doesn't force a page re-render) was easier to hand-author than to compose.



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