Emotion Detection from Text (Pyodide & React)

This project demonstrates how to deploy a pre-trained machine learning model (Scikit-learn's LinearSVC with TfidfVectorizer) entirely within a web browser using Pyodide and worker api. The application uses a React frontend managed by the Vite build tool.

The ML model is loaded asynchronously from static files, eliminating the need for a backend server for running predictions. The estimated testing accuracy of the underlying model is 61%.

Technologies Used

This project uses a modern polyglot stack by integrating front-end frameworks with scientific Python libraries:

Category	Technology	Version(s)	Role in Project
Frontend	React	^18.2.0	JavaScript library for building the UI and the input/output components (Body.jsx).
Build Tooling	Vite	^7.1.9	Used for development server (npm start) and optimized production builds.

Installation and Setup Guide

Prerequisites

• Node.js and npm
• Conda (Miniconda or Anaconda)

Setup

1. Install JavaScript dependencies:

   npm install

2. Start the development server:

   npm start  # Runs the 'vite' command

   The application will launch, but the ML prediction button will not work until the model files are placed in the public folder.