Badil System

Badil System is an intelligent web-based platform designed to enhance criminal justice procedures by leveraging Natural Language Processing (NLP) for automated, transparent, and fair handling of crime-related complaints and the recommendation of correctional alternatives. The system enables citizens to submit complaints, automates crime type classification, and recommends suitable non-custodial measures—streamlining case processing for judicial and executive authorities.

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Features

• Centralized Complaint Portal: Citizens can submit detailed narratives about crimes or violations.

• Automated Crime Classification: NLP algorithms analyze complaint text to classify crime types.

• Correctional Alternative Recommendations: Suggests rehabilitation-focused alternatives to imprisonment, tailored to the offense.

• Role-Based Access & Workflow:

  • Citizens: Register, submit complaints, track their cases.
  • Judges: Review cases, approve or reject recommended alternatives, set ruling durations.
  • Executive Authorities: Enforce approved alternatives, update status, manage implementation.

• Case Tracking & Analytics: Real-time dashboard and statistics for all user roles.

• User Authentication & Security: Robust login and access control for all users.

• Modern, Responsive Interface: Built with HTML5, CSS3, Bootstrap, and AJAX for an interactive user experience.

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System Architecture Overview

The Badil System consists of three core layers:

• Frontend: Built using HTML5, CSS3, JavaScript, Bootstrap, and AJAX for responsive, real-time interfaces.
• Backend: Powered by PHP, interfacing with a MySQL database for user management, workflow logic, and data storage.
• NLP Engine: Implemented in Python, the NLP engine processes textual complaint data to classify crimes and recommend alternatives. It integrates with the backend via direct script calls or RESTful API.

Workflow Summary:

1. Citizen submits complaint through the web portal.
2. NLP engine analyzes complaint, classifies crime, and recommends correctional alternatives.
3. Judge reviews the case and system recommendations, then approves/rejects the alternatives.
4. Executive authority implements or logs the approved alternative, updating case status.

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Installation & Setup

Prerequisites

• Windows 10 (recommended; also works with Linux/macOS with LAMP stack)
• XAMPP (includes Apache, PHP, MySQL)
• Python 3.8+ (for NLP module)
• pip (Python package manager)
• Node.js/NPM (optional, for frontend asset management)

1. Clone the Repository

git clone https://github.com/sa1ah-ai/badil_system.git
cd badil_system

2. Backend Setup

• Install and run XAMPP.
• Use phpMyAdmin to create a MySQL database (e.g., badil_db).
• Import the provided SQL schema (/db/schema.sql or equivalent).
• Update database connection settings in /backend/config.php.
• Start Apache and MySQL services in XAMPP.

3. NLP Engine Setup

• Navigate to the Python/NLP directory (e.g., /ml or /python).

• Install Python dependencies:

  pip install -r requirements.txt

• Run the training script or load the pre-trained model as described in /ml/README.md.

• If the NLP service is provided via REST API, launch it and ensure the backend can reach the API.

4. Frontend Setup

• Serve the frontend files via Apache (/frontend or /public directory).
• Ensure AJAX requests correctly target backend PHP endpoints.

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Usage

For Citizens

• Register an account and log in.
• Submit a complaint by providing details about the crime/violation.
• Track the status of complaints and view recommended correctional alternatives.

For Judges

• Log in through the judge portal.
• Access and review new cases and system recommendations.
• Approve or reject alternatives, set durations, and monitor statistics.

For Executive Authorities

• Log in through the executive portal.
• View approved cases, implement correctional measures, and update case statuses.

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Technology Stack

• Frontend: HTML5, CSS3, JavaScript, Bootstrap, AJAX
• Backend: PHP 7+, MySQL
• Machine Learning/NLP: Python (scikit-learn, pandas, numpy, etc.)
• Environment: XAMPP (for local development), compatible with any LAMP stack

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Screenshots

Below are sample screenshots showcasing key interfaces of the Badil System. All images are stored in the sys_screenshots folder. Thumbnails are displayed here for quick reference:

Image Descriptions

Screenshot	File Name	Description
Login	login.png	User authentication page
Create Account	create_account.png	Registration form for new users
Profile	profile.png	User profile with personal/case statistics
Case Filing Questionnaire	case_filing_questionnaire.png	Initial complaint/case submission form
Upload Case Page	upload_case_page.png	Interface for filing/uploading a new case
Text of Crime Details	text_of_crime_detalis.png	Input for narrative details of the crime
Case Details	case_details.png	Case data and decision workflow
Crime Details (Judge View)	crime_details_Judge.png	In-depth view for judges, including decision/approval
Change Password	change_password.png	User password reset form

For the complete user experience, see all files in the sys_screenshots folder. Images can be expanded for detail when viewed in your code hosting platform.

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Extensibility & Customization

• NLP Model: Upgrade to advanced deep learning models or retrain with new datasets.
• Workflow: Add new user roles or process steps to fit evolving judicial or administrative needs.
• Analytics: Expand dashboards and reporting features for deeper insights.
• Localization: Adapt the interface and NLP processing for additional languages or jurisdictions.

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Limitations

• Optimized for Windows environments via XAMPP; for Linux/macOS, a standard LAMP setup is required.
• NLP accuracy is dependent on training data quality; for best results, use domain-specific datasets.
• Integration between backend and NLP engine may require configuration based on deployment context.

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License

This project is provided for research and non-commercial use. For commercial or large-scale deployment, please contact the project maintainer.