This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
Disclaimer: Halal Scanner is still under active development. Features, ingredient data, and classification accuracy may change as development continues. The app is not a substitute for authoritative halal certification or guidance from qualified authorities.
What I Built
Halal Scanner is a mobile application designed to help Muslim consumers check ingredient labels on packaged food.
Reading ingredient lists isn't always straightforward. Ingredients can have unfamiliar names, manufacturers may use different terms for the same substance, and some ingredients require further verification before their halal status can be determined.
The idea behind Halal Scanner is simple: take a picture of an ingredient label, extract the text, and check the recognized ingredients against a local database.
The app categorizes results as Halal, Haram, or Uncertain. Instead of automatically treating unknown ingredients as permissible, it flags them for further verification.
The potential audience includes Muslim consumers who regularly shop for packaged food and halal-conscious retailers looking for a convenient ingredient-checking tool.
For the side-hustle angle, a potential business model could offer a free consumer app with optional premium features, such as an expanded ingredient database and personalized product lists. This is a proposed direction, not an existing paid service.
Demo
Project repository: Halal Scanner on GitHub
A demo video or installation link can be added here when available.
Code
Explore the source code and project implementation:
github.com/CodewithTanzeel/HF-Dev2-challenge
The application connects image capture, on-device text recognition, ingredient matching, classification, and local scan history into one mobile workflow.
How I Built It
I built Halal Scanner using React Native and Expo.
The scanning workflow consists of four main stages:
- Capture: The user takes a picture of an ingredient label.
- Recognize: On-device text recognition extracts the printed text using Google ML Kit.
- Classify: A local rules engine matches recognized ingredients and their synonyms against the available ingredient data.
- Save: Scan results can be stored locally for later reference.
Rather than relying on a generative model to make religious judgments, the current classification approach uses explicit rules. When an ingredient cannot be confidently matched, the app can return an uncertain result.
This approach makes the decision process easier to inspect and provides a clear way to handle missing information.
The project is still evolving, and the ingredient database, matching logic, and overall user experience will need continued testing and refinement.
Why Does Open Innovation Matter?
A food-label scanner shouldn't necessarily need to send every photograph to a remote service just to recognize its text.
On-device processing can reduce dependence on network connectivity, avoid per-request cloud inference costs, and help keep ingredient-label images on the device during text recognition.
Open tooling also makes it easier for developers to inspect the implementation, improve ingredient matching, expand the database, and contribute to the project.
There is an important limitation, though: a local database is only as reliable as its sources, coverage, and maintenance. Recognizing an ingredient doesn't automatically establish its halal status, and an uncertain result should encourage further verification rather than provide false confidence.
My goal is to make ingredient checking more accessible while keeping the technical decisions understandable and open to improvement.
My Agent Session
Optional: Add your DevRelay agent-session link here if you have one.
Prize Categories
Add the eligible partner categories whose technologies your project actually uses, based on the challenge requirements.
