This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
What I Built
My friend is constantly doing mental gymnastics every morning: "If I skip OS today but my friend marks proxy, can I still afford to bunk Physics tomorrow without falling below 75%?"
The math is stressful, the stakes are high, and honestly, they just wanted to know if they could sleep in.
So for this weekend challenge, I built them ShouldISkipClass.ai—a 100% local (V1) , privacy-first AI web app that acts as their personal (and brutally honest) attendance advisor. It takes their schedule, calculates the risks, and tells them exactly what to do.
Two Versions
I ended up building two versions of the project.
- V1 — Local / Privacy Edition 🔒 The main branch. Gemma 3 4B + Ollama
This is the original architecture and the privacy-focused version.
It requires Ollama and the Gemma model to be installed locally, but once the model is available, the AI inference does not depend on a hosted AI API.
- V2 — Hosted Edition ☁️ Separate v2-hosted branch.
This version uses a hosted Gemma API and can be deployed through Render, making the application easier to access without installing Ollama.
I kept this separate deliberately: the local version remains the project's privacy-first implementation, while the hosted version makes the idea easier to demonstrate publicly.
Demo
ShouldISkipClassAi On Render (V2)
If you want to run it yourself, just install Python and Ollama, run ollama pull gemma3:4b, and ask the AI if you should skip your next lecture! (V1)
Code
The project is fully open-source and I’m actively looking for contributors for Hacktoberfest!
grkadam7
/
should-i-skip-class-ai
An open-source AI-powered college attendance advisor using Gemma and Ollama
🎓 ShouldISkipClass.ai
An open-source AI-powered college attendance advisor that runs 100% locally.
Built with Gemma (Google's open-weight model) via Ollama — your academic data never leaves your machine.
🤔 What Does It Do?
Tell it your attendance percentage, teacher strictness, upcoming tests, and more — and it uses Gemma (running locally via Ollama) to analyze whether you can safely skip your next class.
Features:
- 📊 Attendance tracking with visual bar
- 👨🏫 Teacher strictness rating (1-10)
- 📝 Upcoming test/assignment awareness
- 🤝 Proxy availability consideration
- ⏰ Full day timetable context (gap & packed day analysis)
- 🧪 Lecture vs Lab vs Tutorial differentiation
- 📈 Subject difficulty & grade situation analysis
- 🎯 Risk score (1-10) with detailed reasoning
- 💡 Witty, relatable advice from the AI
🔒 V1 — Local / Privacy Edition
The main branch is designed to run locally using Gemma 3 4B + Ollama.
Your attendance, grades, timetable, and…
How I Built It
The stack is super lightweight:
- Backend: Python + Flask
- AI Inference: Ollama API + Gemma 3
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Frontend: Vanilla HTML/CSS/JS with a dark, glassmorphic UI so it looks premium.
Instead of writing complex if/else statements for every possible scenario, the frontend collects the context (current attendance, teacher strictness, proxy availability, and their full day's timetable) and passes it to Flask.
Flask then sends a highly specific system prompt to the local Gemma model. Gemma acts as the advisor, looking for massive schedule gaps or "sleep tax" morning classes, and returns a strict JSON response with a verdict (SAFE TO SKIP, ATTEND, or RISKY SKIP), a risk score out of 10, and witty practical tips.
Why Does Open Innovation Matter?
My friend was completely against typing their terrible grades and attendance records into ChatGPT. Student data is personal!
That’s why I chose to build this using Gemma 3 (4B) running locally via Ollama.
- It’s fully offline.
- Zero API costs.
- Their academic struggles stay entirely on their own laptop.
Prize Categories
Hacktoberfest Weekend Challenge
Build for a Friend
Best Use of Gemma
Best Use of Render
Top comments (1)
bro 🤣