This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
What I Built
My friend is a freelance lawyer who handles sensitive client contracts. She wanted to use AI to quickly find information in long documents, but she couldn't upload them to ChatGPT or other cloud services because of confidentiality rules.
I built LocalDoc Assistant — a private AI tool that reads her documents and answers questions about them, completely offline. Her data never leaves her laptop.
Demo
The screenshot below shows the app correctly answering "When is my birthday?" from an uploaded text file — with zero internet access.
Code
https://github.com/vaibhav7549/localdoc-assistant
How I Built It
I used Ollama to run Google's Gemma 2 (2B) open-weight model locally on my laptop. The interface is built with Streamlit, an open-source Python framework. For PDF reading, I used PyPDF2.
The entire AI inference runs on the local machine. No API keys, no cloud calls, no data leaving the computer.
Why Does Open Innovation Matter?
This project would be impossible with a closed API. The entire point is privacy — my friend cannot upload client documents to a third-party server. Open-source AI made this possible because:
- Privacy: The model runs locally. No data is transmitted anywhere.
- Zero cost: No API subscriptions or per-token fees.
- Customizable: She can swap in a larger model later if she needs better accuracy, without changing any code.
Prize Categories
- Best Use of Gemma
Thanks for reading! Built with ❤️ for a friend who values privacy.
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