Tutorial: Using GPT-4o and Meta's SAM to Build an AI Nutrition Tracker

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A developer tutorial published on DEV Community outlines how to build an automated nutrition logging system using two AI models: OpenAI's GPT-4o Vision and Meta's Segment Anything Model (SAM). The system works by first using SAM to segment individual food items in a photo, then passing those isolated crops to GPT-4o for calorie and macronutrient estimation. FastAPI serves as the backend web layer, while Pydantic is used to enforce structured JSON output containing calories, protein, carbs, fat, and a confidence score. The tutorial targets developers with Python 3.10+, an OpenAI API key, and access to SAM model weights. The project aims to replace manual diet logging by allowing users to simply photograph a meal and receive a structured nutrition report.

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