A guide to building an AI model using TensorFlow, Python, and matplotlib, and packaging it for the web using Ruby on Rails. In this article, I will cover the functionality and architecture of this app from a practical, code-first approach.
I was recently tasked with building a web API for fruit classification. I realized that such a product would need to have the ability to detect general objects to weed out images of non-fruits. So I decided that I would start the project by getting my hands dirty by exposing a TensorFlow model to the web.
In this article, I will cover the functionality and architecture of this app from a practical, code-first approach. At a high level, the project will be split between its AI and web layers, and I will give you a guided tour of how I glued them together.
In case you want to jump straight to the code, check out the GitHub repository. An installation guide is included in the repo so you can take the app for a spin locally (tested on Mac and Ubuntu). Alternatively, you can view the live app here.
So without further ado, here is a general object classification model I hacked together with TensorFlow, Python, and Ruby on Rails.
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David Heinemeier Hansson is the creator of Ruby on Rails, co-founder & CTO of Basecamp, best-selling author, Le Mans class-winning race car driver, family man, frequent podcast guest, and inspirational conference speaker.