Tutorial: AI Pipeline Uses GPT-4o and SAM to Estimate Calories from Food Photos

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A new developer tutorial on DEV Community demonstrates how to build an automated food nutrition estimation system using a combination of AI tools. The pipeline uses Meta's Segment Anything Model (SAM) to isolate individual food items in an image before passing them to OpenAI's GPT-4o Vision for ingredient identification and weight estimation. To reduce the risk of AI-generated inaccuracies, the system cross-references GPT-4o outputs against a verified nutritional database stored in PostgreSQL using the pgvector extension for similarity search. The full-stack guide covers tools including FastAPI, PyTorch, and Python 3.10+, and is aimed at developers interested in multimodal AI, retrieval-augmented generation, and health-tech applications.

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