Contextify: ZERO SHOT CLASSIFICATION MODEL

Overview

This project provides a web-based application to predict the probability of a given text belonging to a set of provided candidate labels. The algorithm also offers a novel candidate label suggestion when none of the provided labels adequately fit the text. It supports both English and Italian entries.

Prerequisites

• Node.js (v18 or later recommended)
• npm (comes with Node.js)
• Angular CLI (v19 or later)
• Python 3.12 (recommended)

Features

• Text Classification: Users can get the context of the text they enter.
• Multilingual Support: Application supports both English and Italian inputs, offering a variety to the user.
• User-Friendly Interface: A web-based UI built using Flask and Angular for seamless interaction.

Setup and Installation

Step 1: Clone the Repository

git clone https://github.com/femito1/Contextify.git 

cd Contextify

Step 2: Create a Virtual Environment for backend

To create a virtual environment using Conda, run the following commands:

cd backend
conda create --name contextify_app python=3.12 -y
conda activate contextify_app

Step 3: Activate the virtual Environment & Install Dependencies

Once inside the Conda environment, install the required dependencies from requirements.txt:

pip install -r requirements.txt

Step 4: Run the Flask Application

To start the Flask application, run the following command:

python application.py

The backend will be available at:

http://0.0.0.0:5000/

Step 5: Create the Frontend server

To install the dependencies, create a new terminal and change the directory to the zero-shot-classification. Then, run the next commands:

cd frontend
npm install

Step 6: Running the Frontend

To start the Angular development server, run:

ng serve

The backend will be available at:

http://0.0.0.0:4200/

Proxy Configuration

The frontend is configured to proxy API requests to the backend server. This is handled through the proxy.conf.json file in the project root.