SAMJHO

A developer built SAMJHO, an open-source, local-first AI document-understanding tool that turns confusing notices, policies and forms into structured summaries, required actions, deadlines and costs. The project runs Gemma 3 1B entirely offline via Ollama, uses PyMuPDF and Tesseract OCR for text and scanned PDFs, and adds an evidence validator that cross-references AI claims against exact document quotes to prevent hallucinations.

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend https://dev.to/challenges/hacktoberfest-weekend-2026-10-01 I built SAMJHO, a privacy-first, local AI document-understanding MVP designed to simplify confusing notices, policies, and forms into clear, actionable insights for everyday users. Core Components Built Local Document Pipeline parser.py + OCR : Extracts text page-by-page from standard text PDFs and handles scanned image-based documents using Tesseract OCR. Local AI Engine ai.py + Gemma 3 1B : Powered entirely offline via Ollama to generate structured JSON data—including summaries, required actions, deadlines, and costs—without relying on external proprietary APIs. Evidence Validator evidence.py : Powers the PROVE IT feature, cross-referencing AI-generated claims directly against original document quotes to guarantee reliability and prevent hallucinations. Streamlit Interface: A simple, user-friendly UI featuring transparent processing steps, bilingual support English and Hindi , and verifiable source inspection. By combining local open-weight models with strict evidence validation, SAMJHO bridges the gap between complex paperwork and real-world understanding while keeping all data completely offline. < -- Share a deployed link or a video demo. -- https://drive.google.com/file/d/1euuxA1SqSctYp01NZTVP9wyLy nPUnxK/view?usp=drive link https://drive.google.com/file/d/1euuxA1SqSctYp01NZTVP9wyLy nPUnxK/view?usp=drive link < -- Show us the code You can embed a GitHub repo directly into your post. -- https://github.com/ViV1-siNgh/Samjho.git https://github.com/ViV1-siNgh/Samjho.git < -- Which open-source AI did you use open-weight models, agent harnesses, frameworks, local inference , and how is your project built around it? -- I built SAMJHO using a strict local-first, modular architecture to ensure complete privacy and reliability. First, I created a robust document parser parser.py using PyMuPDF to extract text page-by-page, integrating Tesseract OCR for scanned PDFs. Next, I connected the local open-weight model Gemma 3 1B via Ollama ai.py to process the text offline, instructing it to return clean, structured JSON without hallucinating missing details. To guarantee accuracy, I built an evidence validator evidence.py that powers a "PROVE IT" feature, cross-referencing AI claims against exact document quotes. Finally, I unified the pipeline into a simple Streamlit interface offering bilingual support. < -- Why does open innovation matter for what you built? What did it make possible that a closed API wouldn't? -- Open innovation matters because complex, real-world problems—like making bureaucratic paperwork accessible to everyday people—cannot be solved in isolation. By building SAMJHO as an open-source project, open innovation drives several key benefits: Transparency and Trust: Open-source architectures allow anyone to inspect the codebase, verify that document processing happens entirely locally, and ensure that private data is never sent to external servers. Community Collaboration: It enables developers, designers, and domain experts to build upon existing foundations, share improvements, and adapt solutions to new languages, regions, or document formats. Rapid Iteration: Sharing code and methodologies openly fosters quick feedback loops, turning MVPs into reliable, robust tools much faster than closed-door development. Accessibility: Open innovation democratizes AI technology, ensuring that practical tools like evidence-backed document explainers remain free, modular, and accessible to everyone who needs them. < -- Which partner categories are you entering? List every one that applies, or remove this section. -- Best Use of Tinker Use Thinking Machines' Tinker to fine-tune a model for a specific task, and show a clear improvement in performance, latency, or cost over a baseline. ViV1-siNgh - https://github.com/ViV1-siNgh https://github.com/ViV1-siNgh Himanshu699-cyber - https://github.com/Himanshu699-cyber https://github.com/Himanshu699-cyber