AI-powered symptom tracker built in 72-hour sprint

Hey r/FlutterDev,

I’m a 17-year-old student from Türkiye. I’m sharing the **full source code** for a project I built in a solo 72-hour sprint for a national tech competition (TEKNOFEST).

**GitHub (Source Code):** https://github.com/op-synthesis/YAKATS

**Technical Insights from a 3-Day Sprint:**
Building a multi-service app this quickly required a specific approach to Flutter architecture:

1. **Service-Based Architecture:** Instead of putting logic in the UI or State classes, I decoupled everything into standalone services (Location, Weather, Analytics, RiskPrediction). This allowed me to "hot-swap" logic during the sprint without breaking the UI.
2. **Local-First with SQLite:** I avoided Firebase to keep the app functional offline and privacy-focused. Managing SQLite (sqflite) migrations and relational symptom data under a 72-hour deadline was the biggest hurdle.
3. **Background Monitoring:** I implemented a background service to passively monitor environmental conditions (via weather API) and trigger `Awesome Notifications` based on a local risk-scoring algorithm.
4. **AI-Driven Logic:** I used AI tools as a "Compiler for Vision." I provided the architecture and system flows, and used AI to generate the boilerplate and specific service implementations, allowing me to focus entirely on the system integration.

**Why I'm sharing this:**
I want to move this project toward a privacy-secure medical research platform. I'm seeking specific technical feedback on:
* My **Provider/GoRouter** implementation for a multi-screen app.
* How to best architect a **Supabase anonymization layer** for medical data.
* The efficiency of my **Background Service** implementation.

Source code is available under MIT license. I'm happy to discuss the architecture or the "sprint" workflow in the comments!