CreditX

This repository contains the CreditX loan approval prediction service. It's built with Node.js and Express, serving as an interface to a Flask-based machine learning backend.

Table of Contents

• CreditX Frontend
  • Table of Contents
  • Description
  • Features
  • Prerequisites
  • Installation
  • Configuration
  • Running the Application
  • Usage
  • Project Structure
  • System Architecture
  • Prediction Process Flow
  • Contributing
  • License

Description

The CreditX frontend provides a user-friendly web interface for submitting loan application details and receiving credit prediction results (approval status and confidence score) from a backend machine learning service. It handles form data, communicates with the Flask backend, and renders the results using EJS templates.

Features

• Express.js Server: A robust web server for handling requests.
• EJS Templating: Dynamic rendering of web pages.
• Backend Integration: Seamlessly communicates with a Flask ML service for predictions.
• Form Handling: Processes user input from a web form.
• Error Handling: Provides user-friendly messages for backend service unavailability.
• ML Service Warm-up: Attempts to wake up the Flask backend on application start.

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (LTS version recommended)
• npm (comes with Node.js) or Yarn
• A running Flask backend for the credit prediction service (e.g., on http://localhost:5000).

Installation

1. Clone the repository:

   git clone <repository-url>
   cd creditX/frontend

2. Install dependencies:

   npm install
   # or
   yarn install

Configuration

The application can be configured using environment variables:

• FLASK_URL: The URL of your Flask backend service. Defaults to http://localhost:5000. Example: FLASK_URL=http://your-flask-backend.com:5000
• PORT: The port on which the Express frontend server will run. Defaults to 3000. Example: PORT=8080

Running the Application

To start the frontend server:

npm start
# or
node app.js

The application will typically be accessible at http://localhost:3000 (or the port you configured).

Usage

1. Ensure your Flask backend service is running and accessible at the configured FLASK_URL.
2. Start the frontend application as described above.
3. Open your web browser and navigate to http://localhost:3000.
4. Fill out the loan application form with the required details.
5. Submit the form to get a credit prediction (approved/not approved) and a confidence score.

Contributing

Contributions are welcome! Please feel free to open issues or submit pull requests.

License

This project is open-sourced under the MIT License.