Serenify

Your AI Personalized Mental Health Coach

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Inspiration

Mental health is a critical yet often overlooked aspect of student life. Academic pressure, deadlines, and personal challenges can take a toll on mental well-being. We wanted to create a platform that provides consistent mental health support, seamlessly integrated into students' daily routines. This inspired us to build Serenify, a website designed to promote mental well-being proactively.

What it does

Serenify is a platform designed to assess, track, and improve student mental health through a simple and engaging process:

  1. Emotional Analysis: Students upload a photo, and our system, powered by Computer Vision and DeepFace, determines their emotional state (e.g., happy, neutral, or unhappy).
  2. Chatbot Support: Using the Gemini API, Serenify’s chatbot engages students in meaningful conversations, offering personalized interactions and support.
  3. Daily Tips: At the end of the interaction, students receive five actionable tips tailored to their emotional state to help improve their mood or stay motivated.
  4. Rewards System: Students earn points for daily check-ins, which they can redeem for perks like food court discounts.
  5. Proactive Escalation: For students showing consistent signs of distress, the system can escalate their data to campus mental health teams or peer mentors for timely intervention.

How we built it

  • Languages and Frameworks: Python, Flask, HTML, CSS, and JavaScript.
  • APIs: Gemini API for the chatbot, DeepFace for photo analysis.
  • Authentication: OAuth with Google for secure and easy login.
  • Database: SQL for storing user data, streaks, and emotional history.
  • Features:
    • Emotion detection integrated with DeepFace and Computer Vision tools.
    • Chatbot developed using the Gemini API for personalized conversations.
    • Points and streak system to encourage daily engagement.

Challenges we ran into

  • Photo Analysis Integration: Ensuring accurate emotional detection using Computer Vision and DeepFace while maintaining performance was challenging.
  • Seamless User Experience: Balancing the technical features with a simple and engaging design.
  • Privacy Concerns: Addressing data security and ethical handling of sensitive user information.
  • Scalability: Conceptualizing future features, such as report generation, while focusing on building a functional MVP.

Accomplishments that we're proud of

  • Successfully integrating Computer Vision tools like DeepFace for emotional analysis.
  • Creating a chatbot using the Gemini API that provides meaningful interactions.
  • Developing a rewards system that encourages consistent mental health check-ins.
  • Building a functional website accessible across devices.

What we learned

  • How to integrate AI tools like DeepFace and Gemini API into a functional web application.
  • The importance of prioritizing privacy and ethical considerations in mental health-focused solutions.
  • Techniques for managing time and collaborating effectively under tight deadlines.

What's next for Serenify

  1. Scalable Features: Forwarding the reports to on-campus Health and Wellness dept to get in-depth insights and approach as they find it feasible.
  2. Expanded Rewards System: Partnering with more campus services and local businesses to enhance rewards.
  3. Campus-wide Integration: Collaborating with Sheridan to integrate Serenify into SLATE as a course or resource.
  4. Advanced Analytics: Enhancing emotional detection with real-time feedback and detailed insights.
  5. Awareness Campaigns: Partnering with student organizations to promote the platform and normalize proactive mental health care.

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