Mindfulness - Mental Health Assessment

Mindfulness is a web app designed for students to assess their mental health status. It utilizes a machine learning model, powered by ML libraries like Scikit-learn/Numpy/Pandas etc, to provide personalized mental health suggestions based on user responses to a 20-question MCQ assessment. The app also integrates with the OpenAI API for real-time recommendation generation using ChatGPT. Users can save their assessment data, view their mental health status, and access personalized recommendations in the profile section.

Tech Stack

• React: Frontend framework for building user interfaces.
• Auth0: Authentication and authorization management.
• TailwindCSS: For styling the components.
• OpenAI: Integration for real-time recommendation generation.
• PostgreSQL: Database for storing user data.
• Django: Backend framework for building the server side.
• Scikit-learn/Pandas/Numpy: Used for training the machine learning model.

Getting Started

Prerequisite

• Node.js (https://nodejs.org/) installed on your machine.
• Python and Django installed for the backend.

Installation

0. Clone the repository:

git clone https://github.com/singodiyashubham87/Mindfulness.git
cd Mindfulness

Frontend

1. Navigate to client directory:

cd client

3. Install dependencies:

npm install

3. Edit the .env file and add your Auth0 & OpenAi Credentials in the placeholders:

VITE_AUTH0_DOMAIN="AUTH0_DOMAIN"
VITE_AUTH0_CLIENT_ID="AUTH0_CLIENT_ID"
VITE_AUTH0_REDIRECT_URL="AUTH0_REDIRECT_URL"
VITE_OPENAI_API_KEY="OPENAI_API_KEY"

4. Start the frontend app server:

npm run dev

Backend

1. Navigate to server directory:

cd server

2. Create and source a virtual environment:

python -m venv venv
source venv/bin/activate

3. Install required modules:

pip install -r requirements.txt

4. Start the backend app server:

python manage.py runserver

Features

• Mental Health Assessment: Conduct a comprehensive 20-question MCQ test for mental health evaluation.
• Personalized Suggestions: Receive machine learning-driven, personalized mental health suggestions based on assessment responses.
• Real-time Recommendations: Access real-time, dynamic recommendations using ChatGPT API through OpenAI.
• Data Storage: Securely store assessment data in the PostgreSQL database for future reference.
• User Profile: View mental health status and personalized recommendations in the user profile section.
• User Authentication: Implement secure user authentication and authorization using Auth0.

Contributors

License

This project is licensed under the MIT License.

Support

If you find this project useful, please consider supporting it by starring the repository.