MindScape 🧠

MindScape is an AI-powered mental wellness platform that analyzes user interactions and emotional patterns to provide personalized wellness recommendations and motivational guidance inspired by ancient wisdom texts like the Bhagavad Gita.

The platform combines AI-driven conversational support, mood analysis, and Retrieval-Augmented Generation (RAG) to deliver meaningful and context-aware experiences for users.

Chat Interface

Dashboard

✨ Features

• AI-based conversational support
• Retrieval-Augmented Generation (RAG) for contextual responses
• Semantic search using FAISS vector database
• Mood and emotion analysis using DeepFace
• Facial analysis with OpenCV
• User authentication using Supabase
• Personalized dashboard experience
• Fallback retrieval mechanism for reliable responses
• Responsive and user-friendly interface

🛠️ Tech Stack

Frontend

• React.js
• Vite
• JavaScript

Backend

• Flask
• Python
• REST API

AI/ML

• LangChain
• HuggingFace Embeddings
• FAISS Vector Database
• DeepFace
• OpenCV

Database & Services

• Supabase

🚀 Installation & Setup

Clone Repository

git clone <repository-url>
cd MindScape

Frontend Setup

cd frontend
npm install
npm run dev

Backend Setup

cd backend
pip install -r requirements.txt
python app.py

🔐 Environment Variables

Create a .env file inside the frontend folder:

VITE_SUPABASE_URL=your-supabase-url
VITE_SUPABASE_ANON_KEY=your-anon-key
VITE_API_BASE_URL=http://127.0.0.1:5000

Create a .env file inside the backend folder:

SUPABASE_URL=your-supabase-url
SUPABASE_SERVICE_ROLE_KEY=your-service-role-key

Note: Never expose your Supabase service role key in frontend code.

🧠 AI Workflow

User Input
    ↓
Flask Backend API
    ↓
LangChain RAG Pipeline
    ↓
Text Retrieval using FAISS
    ↓
Relevant Context Generation
    ↓
AI Response

Camera/Image Input
    ↓
OpenCV Processing
    ↓
DeepFace Emotion Analysis
    ↓
Mood Detection
    ↓
Personalized Experience

📂 Folder Structure

MindScape/
│
├── frontend/
│   ├── src/
│   ├── public/
│   └── package.json
│
├── backend/
│   ├── app.py
│   ├── routes/
│   ├── models/
│   └── requirements.txt
│
├── gita_data/
│   └── gita.txt
│
└── README.md

🔮 Future Improvements

• Browser extension integration for analyzing digital wellness patterns
• Social media usage insights from platforms like Facebook and WhatsApp
• Long-term mood trend analysis based on user activity patterns
• More personalized recommendations using behavioral insights

👨‍💻 Author

Shriom Pal

GitHub:
https://github.com/shriom17