AI-Powered Personal Finance Manager

A smart financial assistant web app that helps users analyze, manage, and optimize their spending habits using AI.

🚀 Features

• 📤 File Upload: Upload bank statements in CSV or Excel format
• 🤖 AI Categorization: Automatic transaction categorization using GPT or rule-based system
• 📊 Interactive Dashboard: Beautiful charts showing spending patterns and trends
• 💡 Smart Insights: AI-generated financial insights and recommendations
• 🔮 Budget Forecasting: Predict future expenses based on historical data
• 📱 Responsive Design: Works on desktop and mobile devices

🛠️ Tech Stack

• Frontend: Streamlit with Plotly for visualizations
• Backend: FastAPI for REST API
• Database: SQLite (easily upgradeable to PostgreSQL)
• AI: OpenAI GPT for insights and categorization
• Data Processing: Pandas and NumPy
• Deployment: Can be deployed on Render, Streamlit Cloud, or AWS

📦 Installation

1. Clone the repository

git clone <your-repo-url>
cd Finance_Manager

2. Create virtual environment

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

3. Install dependencies

pip install -r requirements.txt

4. Set up environment variables

cp .env.example .env
# Edit .env file with your OpenAI API key

🏃‍♂️ Quick Start

Method 1: Frontend Only (Streamlit)

# Run the Streamlit app directly (includes demo mode)
streamlit run frontend/app.py

Method 2: Full Stack (Backend + Frontend)

# Terminal 1: Start the backend API
cd backend
python main.py

# Terminal 2: Start the frontend
streamlit run frontend/app.py

The app will be available at:

• Frontend: http://localhost:8501
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

📊 Usage

1. Upload Your Bank Statement

   • Go to "Upload Transactions" page
   • Upload a CSV or Excel file with your bank transactions
   • The app will automatically categorize transactions

2. View Your Dashboard

   • Interactive charts showing spending by category
   • Spending trends over time
   • Key financial metrics

3. Get AI Insights

   • Personalized recommendations for saving money
   • Financial health score
   • Spending pattern analysis

4. Budget Forecasting

   • Predict future expenses
   • Get budget recommendations
   • Track savings potential

📁 File Format

Your bank statement should have these columns (in any order):

• Date: Transaction date
• Description: Transaction description
• Amount: Transaction amount (negative for expenses, positive for income)
• Category: (Optional) Existing category

Example CSV format:

Date,Description,Amount,Category
2024-01-15,Starbucks Coffee,-5.50,Food & Dining
2024-01-14,Salary Deposit,3500.00,Income
2024-01-13,Amazon Purchase,-89.99,Shopping

🔧 Configuration

OpenAI API Key

To enable AI-powered insights and categorization:

1. Get an API key from OpenAI
2. Add it to your .env file:

OPENAI_API_KEY=your_api_key_here

Supported Categories

• Food & Dining
• Transportation
• Shopping
• Entertainment
• Bills & Utilities
• Healthcare
• Travel
• Education
• Income
• Transfer
• Investment
• Other

🚀 Deployment

Streamlit Cloud

1. Push code to GitHub
2. Connect to Streamlit Cloud
3. Deploy the frontend/app.py file

Render (Full Stack)

1. Create two services on Render:
   • Web Service for FastAPI backend
   • Web Service for Streamlit frontend
2. Set environment variables
3. Deploy

Local Docker (Coming Soon)

docker-compose up

🧪 Development

Project Structure

Finance_Manager/
├── backend/
│   ├── api/           # FastAPI routes
│   ├── models/        # Data models
│   ├── services/      # Business logic
│   ├── utils/         # Helper functions
│   ├── config.py      # Configuration
│   └── main.py        # FastAPI app
├── frontend/
│   └── app.py         # Streamlit app
├── data/
│   └── sample/        # Sample data files
├── tests/             # Unit tests
├── requirements.txt   # Dependencies
└── README.md

Running Tests

pytest tests/

Code Formatting

black backend/ frontend/
flake8 backend/ frontend/

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔒 Privacy & Security

• All data processing happens locally or on your chosen cloud provider
• No transaction data is stored permanently without your consent
• OpenAI API calls only include transaction descriptions for categorization
• Use environment variables for sensitive configuration

🆘 Support

If you encounter any issues:

1. Check the Issues page
2. Create a new issue with detailed information
3. Contact support via email

🎯 Roadmap

• Bank API integration (Plaid, Yodlee)
• Mobile app (React Native)
• Advanced ML models for better predictions
• Multi-user support with authentication
• Goal tracking and alerts
• Investment portfolio analysis
• Export to popular accounting software

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Made with ❤️ for better financial health