This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
LocalLedger is a local-first invoice inbox for freelancers. It turns invoice text into a reviewable table of vendor, dates, amounts, category, and payment status, then exports the results to CSV. It is aimed at solo freelancers who want to spend less time copying invoice details into a spreadsheet without sending client documents to a third-party AI API.
Demo
Run it locally by following the setup in the README. A public demo link or video can be added here when one is available.
Code
View the Challenge 3 source on GitHub.
How I Built It
The interface uses Streamlit. The app extracts text from text-based PDFs and text files, then asks the open-weight qwen2.5:3b model through a local Ollama instance to return structured invoice fields as JSON. Users can review the results and download a CSV. Invoice content is sent only to the user's local Ollama service; this version does not implement scanned-document OCR.
Why Does Open Innovation Matter?
Invoices contain sensitive business information. Running an open-weight model locally means invoice text does not have to go to a hosted model provider, and there is no per-document inference charge after downloading the model. Users can inspect or change the extraction prompt and swap to another Ollama-compatible model rather than being locked to one provider's API and pricing.
My First User
I have not yet tested this with a first user. This section should be updated after showing the app to a freelancer and recording their actual feedback.
Prize Categories
Main Hacktoberfest Open-Source AI Challenge: Side Hustle.
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