Developers Build AI Calorie Tracker Using YOLOv10 and GPT-4o Vision

A developer tutorial published on DEV Community outlines how to build a real-time dietary analysis app by combining two AI models: YOLOv10 for food detection and GPT-4o for nutritional estimation. YOLOv10 identifies and localizes food items within an image, while GPT-4o's multimodal reasoning estimates portion weight and calculates macronutrients such as calories, protein, carbohydrates, and fats. The system uses a React Native mobile frontend, a FastAPI Python backend, and Pydantic for data validation, forming a full-stack pipeline from phone camera to cloud inference. A hybrid architecture was chosen to balance real-time performance with mobile battery efficiency, avoiding heavy on-device processing. The tutorial provides working code snippets for each component, covering the detection model, GPT-4o Vision API integration, and the backend endpoint that returns structured nutritional data as JSON.