Adhikar

Adhikar is a powerful, AI-driven legal research tool designed to help streamline the process of categorizing and analyzing Indian Commercial Law data. This project leverages modern web technologies like React, FastAPI, and PostgreSQL to create an intuitive interface and scalable backend, enabling users to explore legal documents, predict case outcomes, and categorize data efficiently.

Features

• Legal Document Categorization: Automatically categorize over 4,000 rows of Indian Commercial Law data using machine learning models.
• Predictive Analytics: Apply machine learning algorithms to predict the outcomes of legal cases based on historical patterns.
• Scalable Backend: Built with FastAPI and PostgreSQL, ensuring real-time data processing and storage.
• User-Friendly Interface: Frontend powered by React and Tailwind CSS, delivering a seamless user experience.

Tech Stack

Frontend

• React.js: JavaScript library for building the user interface.
• Tailwind CSS: Utility-first CSS framework for fast UI development.

Backend

• FastAPI: Fast, modern, and asynchronous Python web framework.
• PostgreSQL: Open-source relational database for secure and scalable data management.

Machine Learning

• Scikit-learn: Used for applying machine learning algorithms to predict legal case outcomes.

Setup & Installation

Prerequisites

• Node.js and npm (for frontend)
• Python 3.8+ (for backend)
• PostgreSQL (for database)

Steps

1. Clone the repository

   git clone https://github.com/sushilpandeyy/Adhikar.git

2. Frontend Setup

   cd adhikar/frontend
   npm install
   npm start

   This will start the React development server.

3. Backend Setup

   cd adhikar/backend
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

4. Database Setup

   • Create a PostgreSQL database and configure the connection in backend/config.py.

5. Run the Backend

   uvicorn main:app --reload

   This will start the FastAPI server at http://localhost:8000.

Usage

1. Open the frontend by navigating to http://localhost:3000 in your browser.
2. Use the platform to upload legal documents, explore existing categorized data, and view predictive case outcomes.
3. The backend API will process the data and respond with real-time insights.

Contributors

• Team Lead & Data Analyst: Prakashita Singh
• Frontend Developer: BJ Gridhar
• UI/UX Designer: Sahil Sharma
• Backend Engineer: Sushil Pandey
• ML Model Trainer: Anand Kumar Sharma
• ML Model Trainer: Sachin Kumar Yadav