ScalpClinic

ScalpClinic: Combat hair loss with AI scalp analysis and peer accountability. Map your progress and connect with a "Scalp Squad" to turn hair health into a shared journey!

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Inspiration

Hair loss is a deeply personal and often isolating experience for millions of men. The journey from noticing thinning to taking action is typically filled with anxiety, misinformation, and "doom-scrolling" through anonymous forums. We wanted to transform this passive, stress-inducing process into a proactive, supportive, and data-backed journey. By combining AI precision with peer accountability, we’ve created a space where users can face hair health head-on without the stigma.

What it does

ScalpClinic is a mobile-responsive platform that helps users track and manage hair health through three core pillars:

  • AI-Powered Analysis: Users upload scalp images for a non-clinical analysis using Gemini 2.5 Flash, which maps their current stage on the Norwood-Hamilton Scale.
  • Scalp Squads: Users are matched into 4-person peer groups at the same hair-loss stage to share a collective "Health Bar" and progress through daily missions together.
  • The Skill Lab: A community-driven repository where users share and vote on hair health modules, which are then verified for safety by an autonomous AI agent.

How we built it

We utilized a high-performance Python-centric stack to ensure rapid deployment and robust data handling:

  • Backend: A FastAPI server managing 15+ asynchronous endpoints and JWT-based authentication.
  • Frontend: A React + Vite PWA styled with a "Dark Gaming" aesthetic for a sticky, high-fidelity user experience.
  • Database: MongoDB Atlas for managing unstructured user signals, squad matching, and real-time chat history.
  • AI: Gemini 2.5 Flash integrated with strict JSON Schema outputs for both image classification and the autonomous "Skill Lab" auditor.

We also used Antigravity for its Agentic Approach to optimize development, the screenshot is in the file attached!

Challenges we ran into

Our biggest hurdle was State Management Syncing. Transitioning from in-memory prototyping to a live MongoDB Atlas cluster required a total rewrite of our data lifecycle. We had to ensure that individual "Daily Mission" completions updated a shared "Squad HP" in real-time without creating race conditions or database lag. Additionally, prompting Gemini to provide consistent, structured anatomical analysis required significant iteration on our Pydantic models.

Accomplishments that we're proud of

We successfully built a fully functional PWA that feels like a native mobile app without the overhead of traditional app stores. We are particularly proud of our "Norwood Reaper" mechanic—a time-locked daily deadline that creates genuine peer-to-peer accountability. Seeing our AI sub-agent autonomously vet community-submitted content was a major "aha" moment for our team's technical implementation.

What we learned

We learned the power of Agentic Workflows using Google Antigravity to accelerate our development cycle. More importantly, we discovered how to balance "gamification" with sensitive topics; we learned that providing users with a representative avatar and a shared goal significantly reduces the "shame" often associated with hair loss treatment.

What's next for ScalpClinic

We plan to expand our AI model to include longitudinal tracking, allowing users to see a "Time-Lapse" of their progress over months. We also aim to integrate with wearable health data to automatically verify missions like sleep quality and stress management, which are key factors in hair health.

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