💸 Personal Finance Streamlit

An elite, multi-interface financial intelligence platform designed to uncover "leaks" in your personal economy. Whether you prefer the Modern Flagship Dashboard (Next.js) or the Original Intelligence Terminal (Streamlit), Streamlit uses advanced heuristics and AI to transform messy bank statements into actionable savings.

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📋 Submission Overview

This project is submitted as a functional prototype for [Event/Competition Name]. It addresses the problem of financial opacity and privacy in personal budgeting.

• Project Overview: Detailed breakdown of the problem, solution, and impact.
• Presentation Script: A structured pitch for the Streamlit platform.
• Prototype: Fully functional Next.js and Streamlit interfaces (see Getting Started).

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🏗️ The Architecture

Streamlit is built on a Privacy-First, Local-First philosophy. Your financial data stays in your browser (Next.js) or on your local machine (Streamlit).

1. Flagship Dashboard (Next.js)

The premium web experience designed for speed, aesthetics, and institutional-grade visualization.

• Location: /frontend
• Tech: Next.js 16.2 (App Router), Tailwind CSS v4, Recharts, PapaParse.
• AI: Secure server-side integration with OpenRouter for LLM-driven insights.

2. Intelligence Terminal (Streamlit)

The original Python-powered engine for rapid data science and detailed transaction analysis.

• Location: /app
• Tech: Python 3.11+, Streamlit, Pandas, Plotly.
• AI: Direct integration with LLM providers for heuristic analysis.

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✨ Key Features

• 🛡️ Secure Client-Side Parsing: Bank CSVs are parsed instantly using PapaParse (Next.js) or Pandas (Streamlit). No transaction data ever hits our servers.
• 🧠 AI-Powered Gaps: Advanced heuristics detect spending patterns that exceed 50/30/20 budget benchmarks or customized thresholds.
• 📊 Institutional Visualizations: High-fidelity charts showing spending distribution, income-to-expense ratios, and top transaction drains.
• 🔮 Savings Simulator: Interactive tools to project the long-term impact of plugging specific financial leaks.
• 🧹 Auto-Normalization: Intelligent header mapping that handles "dirty" CSV data from different financial institutions.

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🚀 Getting Started

Flagship Dashboard (Next.js)

1. Navigate to the frontend:

   cd frontend

2. Install dependencies:

   npm install

3. Configure Environment: Create a .env.local file:

   OPENROUTER_API_KEY="your_api_key_here"

4. Launch:

   npm run dev

Intelligence Terminal (Streamlit)

1. Install requirements:

   pip install -r requirements.txt

2. Launch Terminal:

   streamlit run app/main.py

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📊 Data Preparation

The system expects a CSV file with at least three columns:

• Date: Transaction date.
• Description: Merchant or transaction details.
• Amount: Negative for expenses, positive for income.

Sample data is provided in app/sample_data.csv for immediate testing.

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🏆 Development Principles

This project demonstrates the evolution from a Python prototype to a production-ready SaaS interface:

• Scalable UI: Tailwind v4 for a cutting-edge design system.
• Type Safety: Full TypeScript implementation in the modern dashboard.
• Modular Logic: Shared categorization heuristics across both platforms.

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Designed with 💙 for financial freedom.