This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Tallyroll turns a pile of paper bills into one clean spreadsheet. You drop in photos of a restaurant's supplier bills and receipts, and it reads each one, sorts it into a kitchen category (vegetables, meat, dairy, gas, rent and so on), and flags anything it is unsure about.

Restaurant owners spend hours at month end sorting a shoebox of bills for the accountant. Cloud tools mean uploading who they buy from and what they pay. With Tallyroll, the owner drops in the photos and gets a spreadsheet with date, vendor, category, total and tax, ready for the accountant.

Who would pay: small restaurants and cafes, [your pricing idea, e.g. a one-time setup fee].

Demo

Code

browser (static/)  --raw image-->  server.py  --/api/chat + JSON schema-->  Ollama (Gemma, local)
                                      |
                                      +--> normalise: numbers, dates, currency, category, sanity checks
                                      +--> data/receipts.json + data/images/   (your computer only)

How I Built It

• Model: Gemma (open-weight, vision-capable), running locally through Ollama. It reads the bill photo directly, so there is no separate OCR step. I used [gemma3:4b or gemma4:e4b].
• Backend: a small Python server with no packages to install. It sends the photo and a strict prompt to Gemma with a JSON schema, so the answer comes back in a fixed shape (vendor, date, total, tax, category, line items, confidence).
• Clean-up layer: it fixes number and date formats and marks a bill "Check" when the date or total is missing, the items don't add up, the photo is blurry, or it looks like a duplicate. Unit tests for this run in GitHub Actions.
• Frontend: one web page with a receipt-printer animation, a ledger with a side panel to correct any bill next to its photo, a category breakdown, and CSV export for Excel.
• Demo mode: a built-in mode replays 8 made-up sample bills without a model, and tags them "demo".

I built this with AI assistance (Claude) and tested it on real bills on my own laptop.

Why Does Open Innovation Matter?

• Privacy: a restaurant's bills show who they buy from and what they pay. They never leave the owner's computer.
• Cost: there's no per-bill fee, so a small business pays only for electricity.
• Offline: it works with no internet, and my demo is recorded with Wi-Fi off.
• No lock-in: the model is one setting, so the owner can switch to a better Gemma later by changing one line.
• Room to grow: because the weights are open, the model could later be fine-tuned on local suppliers' bills. I haven't done that yet.

My First User

I sat with Meena, who runs a 12-table family restaurant, and ran 14 of last month's real supplier bills through Tallyroll on my laptop while she watched.

• Result: 11 bills were read correctly. 2 were flagged "Check" (one with a smudged date, one that looked like a duplicate). 1 had a wrong total that was not flagged. It took about 40 seconds per bill.
• What she said: "I usually lose a whole Sunday on this. If I only have to fix three bills instead of typing fourteen, that's worth something."
• What went wrong: the wrong total was on a crumpled butcher bill, where Gemma read an 8 as a 3. She spotted it only because the photo was right beside the numbers.
• What she asked for next: a Hindi version of the screen, and a way to send the month's sheet straight to her accountant.
• Would she pay? "Maybe, if it never mixes up numbers." She said ₹500 a month felt fair, but she'd want to try it for a month first.

Prize Categories

• Best Use of Gemma