💸 E.L.I.Z.A.B.E.T.H.

Expense Ledger & Income Zone Assistant for Budgeting, Expenditure, Taxes & Househould-finance.

This is the backend for a privacy-first, LLM-enhanced expense tracking system built with Flask, Google Cloud Vision, OpenAI, and ChromaDB.

It handles:

• OCR-powered receipt parsing
• CSV ingestion
• Transaction storage
• LLM-powered monthly summaries
• Semantic search using vector embeddings

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🚀 Features

• 📸 Smart Receipt Uploads
  Snap a photo → OCR + GPT parses vendor, date, amount, category

• 📊 CSV Uploads
  Drop bank exports into the system and ingest them into your local DB

• 🧠 LLM Financial Summary
  Generate a monthly report (total spending, top categories, outliers, suggestions)

• 🔍 Vector Searchable Transactions
  All expenses are embedded and stored in a local ChromaDB vector DB — for semantic search and RAG

• 🔌 Modular Codebase

  • Flask for routing + APIs
  • OpenAI for parsing and summarization
  • ChromaDB for semantic memory
  • Google Cloud Vision or Tesseract for OCR
  • SQLite for simple local persistence

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🛠️ Setup

1. Clone the project

git clone https://github.com/yourname/expense-tracker.git
cd expense-tracker

2. Create a virtual environment

python3.10 -m venv .venv
source .venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Set up your environment variables

Create a .env file with the following:

OPENAI_API_KEY=your_openai_key
GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/google/credentials.json

📡 API Overview

Endpoint Method Description /upload_receipt POST Upload a photo of a receipt /upload_csv POST Upload a bank CSV file /add_expense POST Add an expense manually (JSON) /transactions GET List all transactions /transactions/ DELETE Delete a transaction by ID / GET View summary report (basic HTML)

🧠 LLM Summary

Run the following script to generate a GPT-powered monthly spending report (e.g. cron job):

python rag_query.py

🧰 Project Structure

├── app.py                        # Main Flask app
├── utils/
│   └── openai_util.py            # OCR + OpenAI parsing
├── scripts/
│   └── load_db_format_chunks.py  # Format transactions for vector DB
├── rag_query.py                 # Monthly summary via GPT
├── templates/
│   └── report.html               # Jinja template (optional)
├── requirements.txt
├── .env

📫 Contributing

Feel free to fork, star, or yell at me on Twitter.

Pull requests welcome!