Skip to content

Adi-3108/naija-nutri-hub

 
 

Repository files navigation

Hacktoberfest Open Source Challenge 3.0

Welcome to the Hacktoberfest Open Source Challenge 3.0 organized by the Microsoft Learn Student Ambassadors Community in collaboration with the GitHub Campus Experts, Unilag Community! We're thrilled to have you on board for this exciting journey of collaboration and open-source contributions.

About the Challenge

This year the Hacktoberfest ML/AI Open-Source Challenge focuses on one flagship project — Naija Nutri Hub.

Naija Nutri Hub is an end-to-end, AI-powered food platform focused on Nigerian cuisine.
Its goal is to create a production-ready tool that combines:

  • 🍽️ Computer vision for food recognition
  • 🧮 Nutrition knowledge & calorie analysis
  • 📜 Recipe generation
  • 📍 Local restaurant discovery

Participants can contribute across:

  • 🤖 ML/AI Engineering
  • 💻 Backend (FastAPI) & Frontend
  • 🎨 UI/UX Design
  • ⚙️ DevOps
  • 📄 Non-technical roles (Documentation, Testing, Community, etc.)

Project overview

Naija Nutri Hub allows a user to take or upload a photo of a meal and receive:

  • Food Classification — Identify dishes (single or multiple) in an image
  • Nutritional & Calorie Estimates — Fetch accurate nutrition data from external APIs
  • Recipe Suggestions — Generate step-by-step recipes with ingredient and portion guidance
  • Nearby Restaurants — Recommend local restaurants serving similar dishes

How to Participate

1. Register for Hacktoberfest

Visit the Hacktoberfest website to register.
Your contributions to this repository will count toward your Hacktoberfest progress.

2. Fork the Repository

Fork this repository to your GitHub account.

3. Contribute to Naija Nutri Hub

This repository focuses on ML/AI Engineering and Backend Development for Naija Nutri Hub.
You can contribute by:

  • Fixing bugs 🐞
  • Adding new features ⚙️
  • Improving documentation 📝
  • Addressing open issues 💡

Refer to the Contribution Guidelines before starting.

4. Submit a Pull Request

After committing your changes, open a Pull Request and link it to the issue you’re solving.
Be sure to follow the project’s PR format.


Still Confused About How to Get Started? 🤔

Watch the recording of our info session on YouTube 🎥
👉 Hacktoberfest Challenge 3.0 Session


🏆 Leaderboard

We maintain a leaderboard to recognize your contributions and their impact on our projects. The more you contribute, the higher you'll climb on the leaderboard.

Top 10 Contributors

Thank you to all our fantastic contributors for their hard work and dedication! Here are our top 10 contributors:

S/N Rank Contributor Merged PRs
1 🥇 Avatar Adi-3108 4
2 🥇 Avatar Pritesh-30 4
3 🥈 Avatar Omoytom 3
4 🥉 Avatar Algebra101 2
5 🥉 Avatar GboyeStack-Robotics-ML-Engineer 2
6 🥉 Avatar ishanpeshkar 2
7 🥉 Avatar KingDavid2908 2
8 🥉 Avatar Nishchal-29 2
9 4 Avatar aneeshsunganahalli 1
10 4 Avatar G26karthik 1

Thank you to all our fantastic contributors for their hard work and dedication!

Check out the full leaderboard here.

🥇 Prizes

Exciting prizes await our top contributors 🎁
Including LinkedIn Premium vouchers and more!
Your efforts can earn you recognition and rewards within our community. 🌟

💬 Get in Touch

If you have questions, need assistance, or want to discuss the projects, feel free to reach out to us:

📣 Help Us Spread the Word

Share your journey on social media with #Hacktoberfest and inspire others to join!
Together, let’s code, collaborate, and celebrate open source! 💫

Happy Hacking! 🎉

Thank you for being part of the Hacktoberfest Open Source Challenge.
Your contributions truly make a difference and together, we can create amazing open-source solutions.

Let's code, collaborate, and celebrate open source! 🚀

About

GenAI and Image-based food classifier that returns nutritional estimates, recipe suggestions, and nearby restaurants — built with Azure AI Services.

Resources

License

Code of conduct

Contributing

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Jupyter Notebook 86.6%
  • Python 10.4%
  • Shell 2.3%
  • HTML 0.7%