https://github.com/jalpa015/ml-webapp

Practicing Real time ML model prediction in Web app using Tensorflow.js
https://github.com/jalpa015/ml-webapp

deep-learning deploy machine-learning neural-network python tensorflow tensorflowjs

Last synced: 2 months ago
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Practicing Real time ML model prediction in Web app using Tensorflow.js

• Host: GitHub
• URL: https://github.com/jalpa015/ml-webapp
• Owner: jalpa015
• Created: 2022-08-23T04:11:59.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-08-27T13:55:32.000Z (almost 4 years ago)
• Last Synced: 2025-03-27T10:24:13.010Z (over 1 year ago)
• Topics: deep-learning, deploy, machine-learning, neural-network, python, tensorflow, tensorflowjs
• Language: Jupyter Notebook
• Homepage: https://digiibot.herokuapp.com/
• Size: 16.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

          
## Machine Learning predicitons in Web app

![Heroku](https://pyheroku-badge.herokuapp.com/?app=mnist-ml-web-app)

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

Working on approach to use Tensorflow.js library for real time predictions in an web app.

MNIST is a popular dataset of images of handwritten digits [link](http://yann.lecun.com/exdb/mnist/)

There are various models to predict Handwritten Digits. This project uses a Convolution 2D model to predict the digits. 

The dataset has various images to train the model. If this model is deployed on a web application then the trained ML model can be used to predict digits written on browser. 

### Key Features about the project -

    

    * Uses Conv2D model to predict handwritten digits

    * Deployed ML model to Node.js WebApp

    * Integrated model predictions in JavaScript code

    * Developed CI pipeline for Master branch

### Getting Started - 

To get started with the project follow the below metioned steps - 

    * This project requires Node.js.

    * In the terminal of project folder, type npm install to install the dependencies.

    * Once the dependencies are installed, type npm start to run the project locally.

    * You can see the app on localhost:4545/ in your browser.

#### Editing the ML model

    * To edit the ML model you will need pip to install the Python packages. 

    * Once pip is installed in your computer, install the libraries.

    * Now the Jupyter Notebook can be edited.