Long Notice? Take It Slow. 🦥 Building NoticeNap with Open-Weight AI

说明与用途

NoticeNap 将冗长的 PDF 通知转换为结构化摘要和操作清单，减少手动搜索时间。

问题

Applicants struggle to locate deadlines, eligibility, fees, and instructions scattered across long PDF notices, wasting time searching manually.

结果

NoticeNap transforms a complex PDF notice into a concise, structured summary and actionable checklist, reducing manual search time and improving workflow efficiency.

工作流

• Upload PDF
• Extract text page‑by‑page with PyMuPDF
• Send extracted text to local Gemma 2 via Ollama
• Generate structured output (deadlines, eligibility, fees, etc.)
• Validate output with Pydantic, marking missing info as "Not found" or "Needs verification"
• Create action checklist and export to Markdown/iCalendar
• Allow user to review source references and track progress

证据

NoticeNap is a local‑first AI assistant designed to make reading long, information‑heavy PDF notices less tedious.

来源

NoticeNap combines PDF text extraction, a Python backend, and an open‑weight language model served locally through Ollama.

来源

The backend uses Pydantic to validate structured data returned through the analysis workflow.

来源

NoticeNap organizes findings into a checklist, supports tracking task progress, and provides Markdown and calendar export functionality where implemented.

来源