Our AI-Powered Real-Time Finance Tracker is a cutting-edge financial management system that enables users to:
- Seamlessly upload transactions π
- Link bank accounts securely π¦
- Gain AI-driven insights and personalized financial advice π€
- Visualize earnings, investments, and expenses π
- Future Finance Advisor for our benefits
- Interactive Pie Charts, Line Graphs, Bar Plots for our personal finance data
This project leverages Pathway Vector Store, Fetch.AI Agents (User Agent & Finance Advisor Agent), and integrates Next.js (frontend), TypeScript, and Flask (backend) to create a fully functional and intelligent financial tracking system.
| Technology | Purpose |
|---|---|
| Next.js | Frontend framework for a responsive UI |
| TypeScript | Type-safe JavaScript for better maintainability |
| Flask | Lightweight backend for API interactions |
| Pathway Vector Store | Efficient transaction storage and retrieval |
| Fetch.AI Agents | Autonomous AI agents for financial recommendations |
graph TD
A[User Uploads Transactions / Links Bank Account] -->|Data Stored| B[Pathway Vector Store]
B -->|Fetch.AI User Agent Processes Transactions| C[Fetch.AI Finance Advisor Agent]
C -->|Analyzes Data & Generates Insights| D[AI-Driven Financial Advice]
D -->|Displays Recommendations & Visualizations| E[Next.js Frontend Dashboard]
E -->|Triggers Email Alerts If Budget Exceeded| F[Real-Time Email Notifications]
The RAG Pipeline ensures that financial insights are generated in real-time using both stored transaction history and external data sources. The following interactive flowchart visualizes the RAG process:
# Clone the repository
git clone https://github.com/Aman071106/FrostHack2025.git
cd FrostHack2025
# Set up virtual environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate - Create a .env file in the root directory and add the following:
GEMINI_API_KEY = AIzaSyD59g**************************52L2BAWKc
DB_USER = ****
DB_PASSWORD = ********
DB_HOST = ******
DB_PORT = ****
DB_NAME = ******
pip install -r requirements.txtcd aibackend/agents
python rag.py
python user.py
cd ..
cd update_transactions
python csv_uploader.pycd aibackend/app
streamlit run app.py# Setup Auth Backend
cd auth_backend
# Create a .env file with your MongoDB URI
# MONGODB_URI=mongodb+srv://<your-uri>
npm install dotenv mongodb express cors mongoose bcryptjs
node server.js
# Setup Frontend
cd ../../frontend
npm install next react react-dom
cd app
npm run dev-
- Streamlit app
- Create a .env file in the root directory and add the following:(or use -e tags)
GEMINI_API_KEY = AIzaSyD59g**************************52L2BAWKc
DB_USER = ****
DB_PASSWORD = ********
DB_HOST = ****** (ip address if machine localhost as docker localhost is diff or use `host.docker.internal`)
DB_PORT = ****
DB_NAME = ******
docker build -t st_app .OR
docker pull deadlyharbor/st_app docker run -p 8080:8080 -p 8000:8000 -p 8501:8501 -p 5001:5001 --env-file .env st_app- π¦ Multi-Bank Support (Integrate Plaid API for wider banking compatibility)
- π‘ Blockchain Integration (For secure financial transactions & tracking)
- Aman Gupta - Backend & AI Development
- Harsh Yadav - Backend & AI Development
- Kunal Mittal - UI/UX, Frontend Development and Backend Development
- Added RAG Pipeline Flowchart π
- Flowcharts are now interactive (GitHub renders MermaidJS)
- Updated Local Hosting Instructions for better clarity
Let me know if you want any more modifications! π


