---
title: Paperain Studio — Sticker Restock Manager
emoji: 📝
colorFrom: yellow
colorTo: pink
sdk: gradio
sdk_version: "6.14.0"
app_file: app.py
pinned: false
short_description: Piper turns messy store POs into restock workflows.
tags:
- track:backyard
- sponsor:openai
- sponsor:nvidia
- sponsor:modal
- achievement:offgrid
- achievement:offbrand
- achievement:llama
- achievement:sharing
- achievement:fieldnotes
- track:backyard
- achievement:offbrand
- achievement:bestdemo
- gradio
- build-small-hackathon
- backyard ai
- backyard-ai
- off brand
- off-brand
- best demo
- best-demo
- judges wildcard
- judges-wildcard
models:
- Qwen/Qwen2.5-7B-Instruct
---

# Paperain Studio — Sticker Restock Manager

**Piper** is an AI desk assistant for [Paperain Studio](https://paperainstudio.com) — a real sticker business in Yogyakarta, Indonesia selling through **25 partner stores** and **150 designs**.

Every month, stores send restock orders as messy Excel copy-pastes, PDFs, or WhatsApp screenshots (Indonesian, English, or both). My family used to spend **3+ hours** manually typing data into spreadsheets. Piper fixes that.

**Try it:** [Live Space](https://huggingface.co/spaces/build-small-hackathon/piper-assistant) · [Demo video](#demo-video) *(coming soon)* · [Blog post](https://www.paperainstudio.com/blog/how-we-built-piper-ai-build-small-hackathon) · [Social post](#social-post) *(coming soon)*

## TL;DR for Judges

- **Track — Backyard AI:** Built for our own family sticker business. 25 real partner stores across Java send monthly restock orders in 25 different formats — Excel, WhatsApp, mixed Indonesian/English. Piper turns that chaos into structured workflows my family can actually use.
- **Idea:** Paste any store PO → review parsed products → aggregate demand vs home stock → calculate A3 print sheets → generate bilingual delivery docs. Hours of spreadsheet work, compressed into a form-like Gradio app.
- **Tech:** Python 3.11 · Gradio 6.x · pandas · **Qwen2.5-7B-Instruct** via Hugging Face Inference API (7B params, well under the 32B cap) · 3-stage parsing pipeline (normalize → JSON extract → catalog anchor) · rule-based fallback when the API is cold.
- **Off Brand:** Custom light cream UI matching paperainstudio.com — not stock Gradio defaults.
- **Best Demo:** Demo video and social post linked below *(placeholders until published)*.
- **Judges' Wildcard:** Real small-business ops tool that doesn't fit a neat category — part parser, part print calculator, part bilingual doc generator.

## Submission Links

| Item | Link |
|------|------|
| Live Space | https://huggingface.co/spaces/build-small-hackathon/piper-assistant |
| Demo video | https://youtu.be/ivkLCgZEw20 |
| Blog post (Field Notes) | https://www.paperainstudio.com/blog/how-we-built-piper-ai-build-small-hackathon |
| Social post | https://www.instagram.com/reel/DZlcRlSB52J/?igsh=N244aTVvOHZpMGt6 |

### Demo Video

https://youtu.be/ivkLCgZEw20

### Social Post

we’re building Piper, a small AI assistant specially built to automate some of our tasks in our small business. with Piper, we can save 3-4 hours per week and ease the workload of our small team. 
https://www.instagram.com/reel/DZlcRlSB52J/?igsh=N244aTVvOHZpMGt6

## The Problem

- 25 stores, each with their own PO format and best sellers
- Mixed-language, informal orders ("stiker kucing hologram 20 pcs")
- Manual spreadsheet work that's hard for non-tech-savvy family members

## The Solution

Powered by **Qwen2.5-7B** (7 billion parameters — well under the 32B hackathon limit):

| Tool | What it does |
|------|-------------|
| **PO Intake** | Paste any PO format → structured product/qty table |
| **Stock & Demand** | Aggregate orders vs home inventory, show shortages |
| **Print Calculator** | A3 sheet math (8 A5 stickers per sheet) |
| **Delivery Docs** | Bilingual packing lists (EN / ID) |
| **Best Sellers** | Demand-based recommendations for partner stores |

## Why a Small Model?

Parsing `"stiker kucing 20 pcs"` into `{product, quantity}` is **structured extraction** — not creative writing. A 7B model handles this perfectly:

- Runs on a laptop (no GPT-4 API needed)
- Zero cost per call
- Store data stays private
- Fast enough for monthly restock workflows

**7 billion parameters. 25 real stores. 3 hours saved every month.**

## How to Run

```bash
pip install -r requirements.txt
python app.py
```

Set `HF_TOKEN` for Hugging Face Inference API, or run locally with Ollama (`FORCE_HF=0`).

Built for the [Build Small Hackathon 2026](https://huggingface.co/build-small-hackathon) · **Backyard AI** track.

## Hackathon Tags

| Prize / Badge | Status | Why we hope to qualify |
| --- | --- | --- |
| Backyard AI | **Entered** | Real problem for a real family business — 25 stores, actual monthly PO chaos. |
| Off Brand | **Targeted** | Custom Paperain-branded Gradio UI with warm cream palette, not default components. |
| Best Demo | **Targeted** | Demo video and social post placeholders above — full package once published. |
| Judges' Wildcard | **Hopeful** | Practical ops tool spanning parsing, inventory, print math, and bilingual docs. |

Submission checklist:

- **REQ-01 / Stay under 32B:** complete — Qwen2.5-7B-Instruct (7B params).
- **REQ-02 / Ship a Gradio app:** complete — Gradio Space deployed.
- **REQ-03 / Record a demo:** complete — https://youtu.be/ivkLCgZEw20.
- **REQ-04 / Post it:** complete — https://www.instagram.com/reel/DZlcRlSB52J/?igsh=N244aTVvOHZpMGt6.
- **REQ-06 / Tag your README:** complete — track and badge tags in frontmatter.