In the new age of machine learning and AI, Python is undoubtedly the go-to language for any budding engineer. Clean, pseudocode looking syntax — and the biggest scientific computing and machine learning communities in the world have produced the perfect language for developers looking to make their machines a little smarter.

However, deploying anything built with Python to the masses is not easy. The most effective route in building something that anyone can use is web development.

Almost everyone with a computer has access to the internet and a web browser. Even those without will often have access via their phones.

Web development with Python is possible. The two biggest frameworks being Django and Flask — both very good, but neither compare to those available to JavaScript.

Used by 96% of all websites worldwide [1] — JavaScript dominates in client-side web browser languages. Naturally, this is the space to be for web development.

Fortunately, frameworks like Angular and Node make building web apps a much easier experience. With very little time we can build dynamic, interactive, and fast apps.

We will build this.

In this article, we will take a text generation model built-in Python and integrate it into an interactive web app using Angular. There’s a lot of detail here, so for reference:

1\. Our Model - brief description of the model and download links

2\. Tensorflow.js - conversion of model from Python to JS
3\. Getting Started With Angular - installation and set-up
4\. Bootstrap - how to implement Bootstrap CSS styles in Angular
5\. Model Component - setting up the model component
6\. Loading Our Model - how to load a model using TypeScript
7\. Making Predictions - setup of prediction function in Typescript
8\. TextGenComponent - implementation of all this code with the app

#python #angular #tensorflow #data science