Build AI Expense Tracker with Python, Streamlit, and Function Calling

This article details how to construct an AI-powered expense tracker using Python. The project leverages Streamlit for the user interface, FastAPI for the backend, and LLM function calling to interpret natural language inputs. The tracker can automatically understand expenses described in plain text, perform create, read, update, and delete operations, and store data in Google Sheets, ultimately generating spending analytics and financial insights. AI

IMPACT Provides a practical guide for developers to integrate LLM function calling into applications for natural language data entry and analysis.

RANK_REASON Article describes a tutorial for building a specific application using existing tools and techniques.

Read on Towards AI →

• FastAPI
• Google Sheets
• LLM function calling
• Python
• streamlit

• product
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →