WealthWise 💰

Plan Smart, Retire Confident.

An AI-assisted, privacy-first platform that helps retirees and near-retirees maximize their lifetime pension benefits through smart scenario modeling, optimization, and plain-language guidance.

🌟 Features

📊 Financial Analytics

Data-Driven Modeling: Verified formulas, not AI guesses.
Scenario Generation: Compares various payout options.
Portfolio Analysis: Track and analyze investment portfolios
Risk Assessment: Evaluate risk tolerance and investment preferences
Retirement Planning: Calculate retirement needs and projections
Stock Market Integration: Real-time financial data integration

🤖 AI-Powered Chatbot

Intelligent Conversations: Context-aware chatbot with conversation history
Document Analysis: Upload PDFs for information extraction or Q&A
RAG (Retrieval-Augmented Generation): Semantic search through uploaded documents using Pinecone
Multi-Intent Processing: Handles questions, document extraction, and general queries

🔮 Predictive Modeling

Life Expectancy Prediction: ML models for health-based life expectancy estimation
Retirement Optimization: Predictive algorithms for optimal retirement strategies
Risk Profiling: Advanced risk assessment using machine learning

📱 Modern Web Interface

Responsive Design: Built with React and Tailwind CSS
Interactive Charts: Data visualization with Chart.js
Real-time Updates: Live data synchronization
User-friendly UX: Intuitive interface for all user types

🏗️ Architecture

WealthWise/
├── frontend/          # React.js web application
├── backend/           # Django REST API
├── predictions/       # FastAPI ML service
└── README.md

Backend (Django)

Users App: User management, authentication, profile data
Chatbot App: AI conversation engine with RAG capabilities
Financial Data App: Market data and portfolio management
RESTful APIs: Comprehensive API endpoints

Frontend (React)

Modern Stack: React 19, Redux Toolkit, React Router
Styling: Tailwind CSS with responsive design
Charts: Interactive visualizations with Chart.js
State Management: Redux for global state

ML Service (FastAPI)

Prediction Models: XGBoost, Scikit-learn models
Health Analytics: Life expectancy and risk assessment
Fast API: High-performance ML inference endpoints

🚀 Quick Start

Prerequisites

Python 3.8+
Node.js 16+
Git

1. Clone Repository

git clone <repository-url>
cd WealthWise

2. Backend Setup (Django)

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
pip install -r requirements_rag.txt

# Environment setup
cp .env.example .env
# Edit .env with your API keys (see Configuration section)

# Database setup
python manage.py makemigrations
python manage.py migrate
python manage.py createsuperuser

# Run server
python manage.py runserver

3. Frontend Setup (React)

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

4. ML Service Setup (FastAPI)

cd predictions

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run FastAPI server
fastapi run app.py

⚙️ Configuration

Environment Variables

Create .env file in the backend/ directory:

# ===========================================
# ENVIRONMENT MODE (Change this to switch between dev/prod)
# ===========================================
ENV_MODE=dev # dev or prod

GOOGLE_CLIENT_ID=
GOOGLE_CLIENT_SECRET=

# Google AI Configuration
GOOGLE_API_KEY=your_google_api_key_here

# ===========================================
# DEVELOPMENT ENVIRONMENT CONFIGS
# ===========================================
SITE_URL_DEV=http://localhost:3000
FASTAPI_URL_DEV=http://localhost:5000
KITE_API_KEY_DEV=
KITE_API_SECRET_DEV=

# ===========================================
# PRODUCTION ENVIRONMENT CONFIGS
# ===========================================
SITE_URL_PROD=
FASTAPI_URL_PROD=
KITE_API_KEY_PROD=
KITE_API_SECRET_PROD=

# Pinecone Configuration for RAG
PINECONE_API_KEY=your_pinecone_api_key_here
PINECONE_ENVIRONMENT=us-east-1
PINECONE_INDEX_NAME=pension-chatbot

API Keys Required

Google AI API Key
- Visit Google AI Studio
- Create new API key
- Add to GOOGLE_API_KEY in .env
Pinecone API Key (for RAG functionality)
- Sign up at Pinecone
- Create new project and get API key
- Add to PINECONE_API_KEY in .env

🔧 Usage

Chatbot Features

1. Document Information Extraction

User: "Extract my personal information from this PDF"
System: Processes PDF and returns structured JSON which updates global user states:
- Personal details (name, age, location)
- Financial information (salary, investments)
- Health data (BMI, medical conditions)
- Retirement plans

2. Document Q&A (RAG)

User: "What does my pension document say about early retirement?"
System: 
1. Processes PDF into chunks
2. Stores embeddings in Pinecone
3. Retrieves relevant sections
4. Provides contextual answers

3. Financial Advice

User: "Should I increase my pension contribution?"
System: Analyzes user profile and provides personalized advice

API Endpoints

Authentication

POST /api/auth/register/ - User registration
POST /api/auth/login/ - User login
POST /api/auth/logout/ - User logout

User Management

GET /api/users/profile/ - Get user profile
PUT /api/users/profile/ - Update user profile
POST /api/users/income-status/ - Add income information
POST /api/users/retirement-info/ - Add retirement plans

Chatbot

POST /api/chatbot/chat/ - Send message to chatbot
GET /api/chatbot/history/ - Get conversation history
DELETE /api/chatbot/history/ - Clear conversation history

Financial Data

GET /api/financial/stocks/ - Get stock data
POST /api/financial/portfolio/ - Add portfolio data

ML Predictions

POST /predictions/life-expectancy/ - Predict life expectancy
POST /predictions/risk-profile/ - Calculate risk profile

🧠 AI & ML Features

Conversation Intelligence

Intent Classification: Automatically categorizes user requests
Context Awareness: Maintains conversation history
Multi-turn Conversations: Handles complex, ongoing discussions

Document Processing

PDF Text Extraction: Extracts text from uploaded documents
Information Extraction: Uses LLM to extract structured data
Semantic Search: Vector-based document search with Pinecone

Predictive Analytics

Health-based Life Expectancy: ML model considering lifestyle factors
Risk Assessment: Portfolio risk analysis
Retirement Projections: Financial planning algorithms

📊 Data Models

User Data Structure

{
  "personal": {
    "name": "string",
    "age": "number",
    "dateOfBirth": "date",
    "gender": "string",
    "location": "string",
    "maritalStatus": "string",
    "numberOfDependants": "number"
  },
  "financial": {
    "currentSalary": "number",
    "yearsOfService": "number",
    "employerType": "string",
    "pensionScheme": "string",
    "pensionBalance": "number",
    "portfolioValue": "number"
  },
  "health": {
    "height": "number",
    "weight": "number",
    "bmi": "number",
    "physicalActivity": "string",
    "smokingStatus": "string",
    "medicalConditions": "object"
  }
}

🔒 Security Features

JWT Authentication: Secure token-based authentication
User Isolation: Each user's data and documents are isolated
API Rate Limiting: Protection against abuse
Input Validation: Comprehensive data validation
CORS Configuration: Secure cross-origin requests

🚀 Deployment

Production Setup

Backend (Django)

# Set production environment variables
DEBUG=False
ALLOWED_HOSTS=your-domain.com

# Use production database (PostgreSQL recommended)
# Configure static files serving
python manage.py collectstatic

# Use production WSGI server (Gunicorn)
pip install gunicorn
gunicorn wealthwise.wsgi:application

Frontend (React)

# Build for production
npm run build

# Serve with nginx or deploy to Vercel/Netlify

ML Service (FastAPI)

# Use production ASGI server (Uvicorn)
pip install uvicorn
uvicorn app:app --host 0.0.0.0 --port 8001

🆘 Troubleshooting

Common Issues

Pinecone Connection Error

Solution: Check PINECONE_API_KEY and PINECONE_ENVIRONMENT in .env

Google AI API Error

Solution: Verify GOOGLE_API_KEY and check API quotas

CORS Issues

Solution: Update CORS_ALLOWED_ORIGINS in Django settings

Database Migration Errors

python manage.py makemigrations --empty appname
python manage.py migrate --fake-initial

📞 Support

For support and questions:

Create an issue in the repository
Contact: mailto:miranfirdausi027@gmail.com

Team Prestige

Onkar Mendhapurkar
Miran Firdausi
Janmejay Pandya
Parimal Kulkarni Built with ❤️ for better financial planning and retirement security.

Name		Name	Last commit message	Last commit date
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backend		backend
frontend		frontend
predictions		predictions
.gitignore		.gitignore
README.md		README.md

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WealthWise 💰

🌟 Features

📊 Financial Analytics

🤖 AI-Powered Chatbot

🔮 Predictive Modeling

📱 Modern Web Interface

🏗️ Architecture

Backend (Django)

Frontend (React)

ML Service (FastAPI)

🚀 Quick Start

Prerequisites

1. Clone Repository

2. Backend Setup (Django)

3. Frontend Setup (React)

4. ML Service Setup (FastAPI)

⚙️ Configuration

Environment Variables

API Keys Required

🔧 Usage

Chatbot Features

1. Document Information Extraction

2. Document Q&A (RAG)

3. Financial Advice

API Endpoints

Authentication

User Management

Chatbot

Financial Data

ML Predictions

🧠 AI & ML Features

Conversation Intelligence

Document Processing

Predictive Analytics

📊 Data Models

User Data Structure

🔒 Security Features

🚀 Deployment

Production Setup

🆘 Troubleshooting

Common Issues

📞 Support

Team Prestige

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