I Built an AI That Reads Your Pet's Mood — Here's the Python Behind It

We've all been there. Your dog is acting weird . Is she anxious? Bored? Sick? Or just being a dog? I spent three months building an AI system that tries to answer that question — and along the way I learned some wild things about multimodal models, behavior classification, and why pet data is surprisingly hard to get right. Here's what I built, what broke, and the actual code. The Problem Pets can't talk. Owners miss behavioral signals all the time — not because they don't care, but because subtle changes in posture, vocalization patterns, or eating habits are easy to overlook. Vets see animals for maybe 20 minutes a year. That gap? It's enormous. The idea: use a combination of image classification + behavioral pattern analysis to give pet owners an early-warning system. The Stack Python 3.11 — backbone OpenAI Vision API — image-based posture analysis scikit-learn — behavioral pattern classification FastAPI — REST interface PostgreSQL + pgvector — storing and querying behavioral embedd