Symptomfy

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  One-pager explaining Symtomfy's Startup Proposal

Inspiration

Your spark for Symptomfy came from watching how easily people—yourself included—get anxious and confused by online searches or generic symptom checkers, only to end up waiting hours in an ER (or worse, delaying care when it really mattered). You wanted a fast, question-driven flow that delivers clear, personalized next steps—in minutes—so users can decide whether to treat at home, book a doctor, or head to urgent care, all while cutting down unnecessary costs and easing the strain on health systems.

What it does

Personalized Care Advice Uses a medical-grade decision tree (with room to plug in ML models later) to recommend one of three paths:

1. Home care resources
2. Book a nearby provider
3. Seek urgent/emergency care

How we built it

rontend

React + TypeScript with Tailwind CSS for rapid, responsive layouts

Headless UI & Framer Motion for accessible, animated dialogs and transitions

Recharts for nice-looking wait-time and cost comparison charts

Backend

Python Flask hosted in a Docker container

Decision‐tree logic encoded in a service layer, with clear interfaces to swap in ML models later

Infrastructure

Containerized on Google Cloud Run for auto-scaling and zero-maintenance operations

Appointment-booking integration via secure RESTful APIs (OAuth2)

Logging & monitoring with Cloud Logging and Cloud Monitoring

Challenges we ran into

SO MANY CORS ISSUES. SO MANY BUGS.

Accomplishments that we're proud of

That we finally connected it all together at 4am.

What we learned

It’s better to ask one well-phrased question than three overlapping ones.

What's next for Symptomfy

Better ML models.

Built With

• flask
• python
• react
• tailwind
• typescript