FundSight — AI-Powered Personal Finance Assistant

Built with Python + Flask + Google Gemini AI. No database required.

🚀 Quick Setup

1. Install Dependencies

pip install -r requirements.txt

2. Set Your Gemini API Key

Create your own .env file and write this inside:

GEMINI_API_KEY = 'your_actual_gemini_api_key_here'

Or set as environment variable:

# Windows
set GEMINI_API_KEY=your_key_here

# Mac/Linux
export GEMINI_API_KEY=your_key_here

3. Run the App

python app.py

Visit: http://localhost:5000

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📁 Project Structure

FundSight/
├── app.py                   # Flask backend + all API routes
├── requirements.txt         # Python dependencies
├── mock_expenses.csv        # Test file for CSV import
└── templates/
    ├── base.html            # Base layout with sidebar nav
    ├── index.html           # Landing page
    ├── dashboard.html       # Main dashboard
    ├── budget.html          # Budget planner + CSV import
    ├── savings.html         # Savings goal planner
    ├── investment.html      # Investment guidance
    ├── chat.html            # AI chatbot interface
    └── reports.html         # Report generation + PDF download

🧠 AI Features (Gemini-Powered)

Feature	Description
Budget Analysis	Health score, 50/30/20 rule check, overspending alerts
Savings Plan	Monthly milestones, feasibility check, strategies
Investment Guidance	Risk-based Indonesian market recommendations
AI Chatbot	Context-aware assistant with your full financial profile
Quick Insights	4-point summary: positive, warning, opportunity, next step
Report Narrative	Full AI-written financial health summary

📊 Charts (Matplotlib)

• Pie chart — expense distribution
• Bar chart — category comparison
• 50/30/20 allocation — budget rule visualization
• Savings projection — line graph with goal milestone

📄 Export Options

• PDF Report — formatted with tables via ReportLab
• Text file — plain text summary
• Print — browser print dialog

💡 Tips

• Use mock_expenses.csv to test CSV import in the Budget page
• The AI chatbot remembers the last 20 messages per session
• All data is in-memory — restarting the server resets data
• Risk profile persists within a session

🔧 Tech Stack

• Backend: Python 3.12+, Flask 3.1.0
• AI: Google Gemini 3.1 Flash Lite
• Charts: Matplotlib (server-side rendering)
• PDF: ReportLab
• Frontend: Vanilla JS + CSS custom properties (no framework)
• Storage: In-memory Python dict (no database)