Hello Everyone 😃

🙌 Creating a Web Application for Docker using Java Script 👇

👍 Here, we have created a simple UI using HTML AND CSS with our async function in JS file which will render the div component when the response is returned without stopping other functionalities.

GITHUB URL :- https://github.com/Mjking18/Docker-cgi-using-JS

function lw() {
  var i = document.getElementById("input").value;
  var xhr = new XMLHttpRequest();
xhr.open("GET", "http://IP ADDRESS /cgi-bin/docker.py?cmd=" + i, true);
  xhr.send();
  xhr.onload = function () {
    var output = xhr.responseText;
    document.getElementById("d1").innerHTML = output;
};
}

The XMLHttpRequest object can be used to request data from a web server.

The XMLHttpRequest object is a developers work, because its functions are:

• Update a web page without reloading the page.
• Request data from a server — after the page has loaded.
• Receive data from a server — after the page has loaded.
• Send data to a server — in the background.

All modern browsers have a built-in XMLHttpRequest object to request data from a server.

XML stands for eXtensible Markup Language.

XML was designed to store and transport data.

XML was designed to be both human- and machine-readable.

Also, give sudo permissions in server to apache and disable selinux.

Docker APP UI

👍 Blog explaining the usecase of javascript in one of my favorite ongoing industries Technology

🙌 TensorFlow.js: The Javascript library for Machine Learning in the browser

TensorFlow.js is a JavaScript library created by Google as an open-source framework for training and using machine learning models in the browser. In short, the framework helps JavaScript developers build and deploy ML models within client-side applications.

Developers have swarmed to using TensorFlow.js as they can use it to both:

• Create new machine-learning models from scratch
• As well as run — or retrain — existing, pre-trained models

The language is also a companion to its namesake TensorFlow (the ML library used with Python), meaning any machine learning model built using TensorFlow can be converted to run in the browser using TensorFlow.js.

👉 why do we need a Javascript machine learning library?

The fact that TensorFlow.js runs within the browser opens up a range of exciting possibilities for businesses and developers alike.

As browsers are an interactive space: one that offers access to various sensors — including webcams and microphones — which can provide visuals and sounds as an input into any machine learning model.

👨 developers are moving from handling ML on back-end servers to front-end applications.

And thanks to TensorFlow.js, teams can now create and run ML models in static HTML documents without ever setting up a server or even database — enabling the following services, hosted entirely client-side.

• Automatic Picture Manipulation: auto-adjust images based on a predefined rule-set using a browser-based application — even generate art using convolutional neural networks, as Google has done.
• Offline Game Opponents: play against an AI-operated adversary, even when a video game is offline — as you can do against Chrome’s built-in Trex opponent.
• Content Recommendation Engine: build and train an ML algorithm in the browser, identifying what users like to look at and surfacing more relevant content — just as Twitter have done to rank tweets.
• Activity Monitoring: install a client-side application that learns usage patterns on a local network or device — to monitor and flag unusual activity.
• Object Detection: use a client-side application to detect documents or objects in pictures — such as Airbnb uses to alert users to the presence of sensitive information when they upload a passport or driving license photo.

😊 THANK YOU FOR READING 🙌