SoftSight-Web

This repository contains the web interface and backend services for SoftSight, a tool that predicts the success of software projects and provides AI-driven advice for improvement. It serves as the user-facing part of the SoftSight project, interacting with a separate machine learning model repository.

🌟 Key Features

• Frontend: A modern, responsive user interface built with Next.js and TypeScript.
• Backend: A powerful backend API developed with Python and Flask.
• Predictive Insights: Utilizes a machine learning model to forecast project success scores.
• AI-Powered Advice: Employs a Retrieval Augmented Generation (RAG) system with a Large Language Model (LLM) to provide actionable suggestions.
• Visual Analytics: Presents prediction results and insights in a user-friendly and intuitive way.
• Modular Architecture: Designed for easy maintenance and scalability.

🏛️ Architecture

The SoftSight web application is composed of two main parts:

1. Frontend: A Next.js application responsible for the user interface. It provides a form for users to input project details and displays the results.
2. Backend: A Flask application that exposes a REST API. It receives data from the frontend, processes it using a machine learning model, and generates advice using an LLM.

The frontend and backend communicate via HTTP requests. The backend also interacts with a separate machine learning model and a vector database for the RAG system.

🚀 Getting Started

To get the full application running, you will need to set up both the frontend and the backend.

Prerequisites

• Node.js (v18 or later)
• Python (v3.9 or later)
• pip

Installation and Running

1. Clone the repository:

   git clone https://github.com/your-username/softsight-web.git
   cd softsight-web

2. Set up and run the backend:

   Navigate to the backend directory and follow the instructions in its README.md file.

   cd backend
   # Follow backend setup instructions
   cd ..

3. Set up and run the frontend:

   Navigate to the frontend directory and follow the instructions in its README.md file.

   cd frontend
   # Follow frontend setup instructions

Once both the frontend and backend servers are running, you can access the application in your browser at http://localhost:3000.

🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue if you have any suggestions or find any bugs.