AI Taste App: Learning from

AI Taste App: Learning from Personal Preferences

Personal taste is hard to describe as a set of rules. I built AI Taste App to explore a personal dating question: could a small application learn which faces I find attractive from simple yes/no feedback?

I split the experience into Teach and Predict. In Teach, I upload a portrait and choose “Yay” or “Nay,” saving another training example. In Predict, I submit a new portrait and get a score based on the earlier feedback, without changing the training examples.

Teach mode saves positive and negative feedback for the personal classifier.

Predict mode scores a new portrait against earlier feedback. This is an illustrative result, not an accuracy measurement.

Laravel handles the web application, while a separate FastAPI service handles face detection, image processing, and machine learning. A pretrained vision model extracts visual features, and a small personal classifier learns from the saved ratings. The practical work was connecting those steps to a straightforward mobile interface.

This is a private personal prototype: its predictions reflect the feedback it learns from.

Read about the implementation and see illustrative screenshots on NikoCodes.