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User-friendly home screen to begin a new secure session and access all features of the application
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Camera calibration ensures accurate face and eye detection before running cognitive tests
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Includes Pro-saccade, Pursuit, and Voice analysis tests — measuring reaction time, smooth eye tracking, and speech stability
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Automated dashboard showing test outcomes, combined risk score (Green/Amber/Red), and exportable report options
Inspiration
Mental health is one of the most important aspects of overall well-being, yet it is often overlooked due to lack of awareness or stigma. We wanted to create a solution that leverages AI and technology to provide individuals with quick, private, and insightful mental health assessments. The idea was to empower people to understand their emotional state better and encourage them to take the next step toward self-care.
What it does
NeuroHealth Insight is a web-based platform that allows users to input their thoughts, moods, or concerns. Using an AI/ML-powered API, the system analyzes the input and provides personalized feedback about emotional tone, potential stress levels, and well-being indicators. It also suggests resources and coping strategies for healthier mental balance.
How we built it
Framework: Built entirely with Streamlit, which allowed us to combine the interface and backend in one place using Python. Users can input text and immediately see real-time analysis results in a clean UI.
AI/ML Integration: Integrated a sentiment and emotion analysis model using Hugging Face APIs/models to process and analyze mental health–related text.
Deployment: Hosted on Hugging Face Spaces, making the app publicly accessible without complex server setup.
Challenges we ran into
Integrating the AI/ML API with Flask/Node.js and ensuring smooth communication with the frontend.
Handling model response time to provide a seamless user experience.
Designing a simple but effective UI/UX that feels approachable for users discussing sensitive topics.
Accomplishments that we're proud of
Successfully creating a working prototype within the hackathon timeline.
Building an app that merges AI, psychology, and web development to address real-life challenges.
Deploying the solution online so that anyone can try it instantly.
What we learned
How to build and deploy interactive applications using Streamlit, without needing a separate frontend like React.js.
Gained practical experience in integrating Hugging Face AI/ML models into real-world projects.
Learned the importance of creating a simple and user-friendly interface, especially when dealing with sensitive topics like mental health.
What's next for NeuroHealth Insight
Adding multilingual support to make the tool accessible globally.
Improving the AI model for more accurate and empathetic insights.
Building a mobile version for easier daily use.
Partnering with mental health professionals to integrate credible resources and helpline connections.
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