Inspiration
Mental health issues such as stress, anxiety, and emotional burnout are becoming increasingly common, especially among students and young professionals. Many people hesitate to seek help due to stigma, lack of awareness, or limited access to mental health resources. We were inspired to create this project to provide a safe, private, and accessible platform where users can track their mental health regularly and gain meaningful insights using AI. Our aim was to encourage early awareness and self-care before problems become severe.
What it does
The Mental Health Predictor Project allows users to:
Log their daily mood, emotions, and thoughts
Analyze mental health patterns over time
Receive AI-generated emotional insights and supportive suggestions
Understand trends related to stress, anxiety, and emotional well-being
The application does not provide medical diagnoses but acts as a mental wellness companion that promotes self-reflection and awareness.
How we built it
Frontend: A simple and user-friendly interface for mood tracking and journaling
Backend: Handles user authentication, data processing, and API communication
Gemini API: Used for analyzing user inputs, understanding emotional context, and generating empathetic, personalized responses
MongoDB: Used to store user data, mood logs, journal entries, and timestamps securely and efficiently
This combination allows real-time analysis, scalability, and personalized mental health insights.
Challenges we ran into
Understanding and structuring emotional data for meaningful AI analysis
Integrating the Gemini API to generate empathetic and relevant responses
Designing the system to ensure user privacy and data security
Handling unstructured text data efficiently in the database
Balancing accuracy while ensuring the app does not act as a medical diagnostic tool
Accomplishments that we're proud of
Successfully integrating AI-driven emotional analysis using Gemini API
Building a scalable and flexible data model using MongoDB
Creating a user-friendly and judgment-free experience
Delivering a complete, functional mental health tracking system within hackathon time constraints
What we learned
Practical implementation of AI APIs in real-world applications
How to handle and analyze unstructured data using MongoDB
The importance of ethical considerations in mental health technology
Team collaboration, problem-solving, and time management during rapid development
What's next for Mental Health Predictor Project
Adding visual mood analytics and dashboards
Integrating therapist or counselor support features
Implementing real-time alerts for severe emotional distress
Multi-language support for wider accessibility
Improving AI personalization with long-term user data
Mobile app and wearable device integration
Log in or sign up for Devpost to join the conversation.