AI Assistant with Expense Tracking

A production-style Python project that combines a conversational AI assistant with automatic expense detection, SQLite storage, and secure API key handling.

Overview

This project simulates a personal AI assistant that can chat naturally while also acting as a lightweight expense tracker.
It detects expense-related messages, stores them in a local SQLite database, and uses conversation memory to respond with better context over time.

Key Features

• Natural language chatbot conversations
• Automatic detection of expense-related inputs
• Persistent storage using SQLite database
• Conversation memory (context-aware responses)
• Secure API key management via .env file (no hard-coded secrets)
• Clean, minimal codebase suitable for beginners and reviewers

Tech Stack

• Language: Python
• Database: SQLite
• APIs: OpenAI / LLM-based API (configurable)
• Environment Management: python-dotenv
• Version Control: Git & GitHub

Architecture

• app.py – main application logic and conversation loop
• database.db – SQLite database for storing expenses
• models/ (if present) – helpers for database or message handling
• .env – stores API keys and secrets (excluded from Git)
• requirements.txt – Python dependencies

ai-assistant/
│── app.py
│── database.db
│── requirements.txt
│── .env            # not committed to Git
│── README.md
└── (any helper modules)

How It Works

1. The user sends a message to the assistant.
2. The assistant generates a natural reply using the configured AI model.
3. The app checks if the message contains expense-related information (amounts, categories, etc.).
4. If yes, the expense is parsed and stored in the SQLite database.
5. Previous conversation history is used to maintain context and improve responses.

##output

Setup & Installation

1. Clone the repository

git clone https://github.com/harshitha1106/ai-assistant.git
cd ai-assistant

2. Create and activate a virtual environment

python -m venv venv
# Windows
venv\Scripts\activate
# Mac/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Create a .env file in the project root:

API_KEY=your_api_key_here

5. Run the application

python app.py

Example Use Cases

• Log daily expenses through natural conversation
• Build a starting point for a personal finance assistant
• Learn how to combine AI APIs with a local database
• Practice secure secret handling in real projects

What I Learned

Working on this project helped me practice:

• Structuring a small but realistic Python application
• Integrating an AI/LLM API into a backend
• Using SQLite for simple persistent storage
• Handling environment variables securely with .env
• Writing cleaner, more readable code for open source and portfolios

Author

Harshitha

• GitHub: @harshitha1106
• Role: 2nd-year Electronics & Communication Engineering student exploring AI and web development