This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
WhatsApp Order Ledger turns a messy order chat into a clean, usable sheet.
Plenty of small businesses run on WhatsApp: home bakers, tiffin and meal services, boutique and handmade sellers. Customers send orders as casual text, half-sentences, edits ("make that 3, not 2"), mixed-language messages, and voice notes. The seller then spends an hour every evening scrolling through it all, copying orders into a notebook or spreadsheet, and working out who owes what.
You export the chat from WhatsApp, drop the file into the app, and it produces:
- A clean order sheet: customer, items, quantities, notes, delivery date
- A delivery list, grouped by date and area, ready to print
- Daily totals: how many of each item to make or pack
- A payment tracker: bills per customer and who hasn't paid yet
- Voice notes, transcribed and read like any other message
The seller sets up their menu and prices once in a simple list, including the nicknames customers actually use for items and the way their customers actually write. After that, every day's chat becomes a sheet in minutes.
Who would pay for it: a solo home baker, caterer, or small seller who takes 10 to 50 orders a day over WhatsApp. It replaces the daily hour of copying and tallying, and it catches mistakes that cost real money, like a missed order or a forgotten payment. It's priced as a small monthly fee, which is a tiny cost against an hour saved every day. [Add the price you would actually charge.]
How I Built It
- Language model: Gemma, an open-weight model, running locally through [Ollama / your runtime]. It reads each message in context and extracts items, quantities, corrections, delivery dates, and payment notes as structured JSON.
- Voice notes: an open-source Whisper model transcribes WhatsApp voice notes locally, so they flow into the same pipeline as text.
- Chat parsing: the standard WhatsApp "Export chat" text file is parsed into messages with timestamps and senders. The app never connects to WhatsApp itself, so there is no risk to the seller's account and nothing to authorize.
- Menu and slang config: a plain editable file holds the seller's items, prices, and the nicknames customers use for them. The model is prompted with this list, so it learns the shop's own vocabulary without any retraining.
- Structured output and validation: the model's JSON output is validated against the menu. If an item or quantity looks wrong or ambiguous, it is flagged for the seller to check instead of silently guessed.
- Totals are computed in plain code, not by the model. The model only reads messages, and normal code does the arithmetic. This keeps bills and totals exact.
- Outputs: a printable delivery list and a CSV export that opens in any spreadsheet app.
- Interface: [describe: a simple local web app, with a drag-and-drop file upload and an editable review table].
Why Does Open Innovation Matter?
- The customers' data stays on the seller's laptop. Order chats are full of customer names, phone numbers, and home addresses. Sending that to a third-party API is a real privacy problem for a small business that depends on its customers' trust. Running an open-weight model locally means none of it leaves the machine.
- The business actually works at this price. Each day's chat can be thousands of messages, plus voice notes. With a paid API, the cost per seller grows with every message. With a local model, the marginal cost is zero, so a small monthly fee can be real profit.
- It handles how people actually write. Order chats mix languages, abbreviations, and local item names. Because the model and the prompts are open and under my control, I can tune them to the seller's own menu and vocabulary, add a new language, or swap in a better model later, without waiting on a provider.
- No lock-in. If a provider changes prices or terms, a seller on a closed API is stuck. Here, the model is a file on their machine, and the pieces can be swapped.
- It works offline. The tool does not depend on a stable connection, which matters for small sellers who often work from home on unreliable internet.
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