LexiFIR

LexiFIR is a mobile application designed to assist the Madhya Pradesh Police by addressing the lack of legal expertise and improving operational efficiency. It leverages advanced AI-driven technologies to simplify investigations, enhance law enforcement, and provide critical tools for police personnel.

Features

1. Conversational Chatbot

• Provides quick and accurate legal advice.
• Guides personnel through procedural tasks and legal documentation.

2. Investigation Assistance

• Aids in collecting the right evidence from crime scenes.
• Offers guidelines and checklists to ensure comprehensive evidence collection.

3. Landmark Judgments and References

• Provides access to significant court judgments and legal references.
• Enhances decision-making with accurate legal precedents.

4. Crime Pattern Analysis

• Analyzes nationwide crime trends and patterns using AI.
• Helps in strategizing better law enforcement practices.

5. Training Module

• Educates police personnel on legal procedures, evidence handling, and investigation techniques.
• Offers interactive content to improve knowledge retention and practical application.

Tech Stack

Frontend

• Flutter: Cross-platform mobile application development.

Backend and Microservices

• Spring Boot: Spring Security, Spring Cloud, JWT auth, CRUD operations on DB, Text to Speech with Amazon Polly, Document upload and retrieval with Amazon S3
• AI Services: PyTorch, LangChain, Flask.
• Speech-to-Text Microservice: Flask with WebSockets using Amazon Transcribe.

Database

• MongoDB Engine: Hosted on AWS DocumentDB for secure and scalable data storage.

Analytics

• Power BI: For generating actionable insights and visualizations.

Cloud and Infrastructure

• AWS: Used for hosting backend services, database, and ensuring secure operations.

Architecture Overview

LexiFIR integrates multiple layers to provide a seamless user experience:

1. User Interface: A Flutter-based mobile app for intuitive interaction.
2. AI Services: Powered by Flask and PyTorch for intelligent chatbot responses.
3. Database: CRUD operations managed through Spring Boot, with MongoDB in AWS DocumentDB.
4. Microservices: Real-time processing via Flask WebSockets for speech-to-text.
5. Analytics: Power BI integration for dashboards and reporting.

Setup and Deployment

Prerequisites

• Flutter SDK
• Python (for Flask and PyTorch services)
• MongoDB (AWS DocumentDB credentials)
• Power BI (for analytics)
• AWS CLI for managing cloud resources

Steps to Set Up

1. Frontend:

   • Install Flutter SDK and set up your environment.
   • Clone the repository and run flutter pub get to install dependencies.
   • Launch the app with flutter run.

2. Backend Services:

   • Set up Flask for AI services and microservices.
   • Deploy the trained PyTorch model in the AI service.
   • Use AWS DocumentDB for the MongoDB database connection.

3. Database:

   • Configure Spring Boot to connect with MongoDB in AWS DocumentDB.
   • Ensure proper database indices for faster querying.

4. Analytics:

   • Connect Power BI to AWS DocumentDB for data visualization.
   • Create dashboards and publish them for integration with the app.

5. Deployment:

   • Host the backend services on AWS.
   • Use a CI/CD pipeline to streamline app updates and deployments.

Usage

• Login: Police personnel can log in securely through the app.
• Chatbot: Use the conversational interface for legal advice and assistance.
• Training: Access the training module to improve skills and knowledge.
• Analytics: View crime trends and insights through the integrated dashboards.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Support

For any queries, issues, or suggestions, please feel free to open an issue in this repository or contact the development team.

Future Enhancements

We are continuously working to improve LexiFIR. Planned features include:

• Integration with real-time crime alert systems.
• Advanced natural language processing for more nuanced chatbot responses.
• Role-based access control for enhanced security.
• Multi-language support for improved accessibility (The app supports only Hindi and English).

Acknowledgments

We would like to thank the Madhya Pradesh Police Department for their valuable insights and feedback during the development of this application. Their real-world challenges inspired us to create LexiFIR.

Smart India Hackathon 2024

LexiFIR was developed in 36 hours at the Smart India Hackathon 2024 Grand Finale in Bhopal, Madhya Pradesh. We are proud of the effort and collaboration that went into creating this app in such a short span of time.

Team Members:

• K S Prateek
• Nirupam Naragund
• Kedar Bhagat
• Venkatnarayanan B
• Mokshita Chourasia
• Mohith H M

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Let’s empower law enforcement with innovation and precision!
Explore the repository, contribute, and help us make a difference with LexiFIR. 🚀

Check out our Wiki for more details on features