README.md

metadata

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 — 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 · Demo video (coming soon) · Blog 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

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 · 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.