Getting Started

Follow these steps to set up and run the project on your local machine.

Prerequisites

Make sure you have the following installed on your machine:

• Python
• Git

Installation

1. Clone the repository to your local machine using the following command:

   git clone https://github.com/your-username/project.git

2. Navigate to the project directory:

   cd project

3. Install the required Python packages:

   pip install -r requirements.txt

Usage

Run the following command to start the project:

streamlit run MLmodel/main-local.py

Screen shot of the final product.

NLP Minor Project: Bridging the Gap - Empowering Citizens with Government Welfare Schemes

Project Overview

Welcome to our Natural Language Processing (NLP) minor project that addresses a critical issue in society - the lack of awareness about government welfare schemes. Governments often introduce numerous welfare initiatives, but a significant number of eligible individuals remain unaware of these opportunities. Our project aims to bridge this gap by leveraging cutting-edge technologies in NLP and machine learning.

Problem Statement

Challenge: Government welfare schemes go unnoticed, leaving the intended beneficiaries uninformed and unable to avail themselves of the benefits.

Objective: Develop a conversational chatbot that can interact with users in a multilingual format and communicate information about various government welfare schemes in a casual or layman language.

Solution Overview

Our proposed solution involves the creation of a chatbot powered by a Large Language Model (LLM) based on advanced machine learning techniques. This chatbot will serve as an interactive medium, allowing users to inquire about different welfare schemes in a user-friendly manner. The chatbot's responses will be drawn from a vast and meticulously curated database containing information about all government initiatives.

Features

1. Multilingual Conversations: The chatbot is designed to facilitate conversations in multiple languages, ensuring that a diverse range of users can easily access information about welfare schemes.

2. Casual and Layman Language Support: The chatbot employs natural language processing to understand and respond to queries in a casual or layman language, making information more accessible to a wider audience.

3. LLM-based Machine Learning Model: The heart of our system is a Large Language Model (LLM) that has been trained on diverse datasets. This model enables the chatbot to understand and generate human-like responses, enhancing the user experience.

4. Vast Database: Our extensive database contains comprehensive information about various government welfare schemes, ensuring that the chatbot can provide accurate and up-to-date details to users.

Getting Started

To interact with our chatbot, follow these steps:

1. Access the Chatbot: Fork the repo and then clone the project, further instructions will be provided once the project is done.

2. Ask Questions: Feel free to ask the chatbot about specific welfare schemes, eligibility criteria, application processes, or any related queries.

3. Receive Informative Responses: The chatbot will provide you with relevant and user-friendly information, helping you understand and benefit from government welfare schemes.

Future Enhancements

We envision continuous improvement and expansion of our project. Future enhancements may include:

• Integration with additional communication channels (e.g., messaging apps).
• Real-time updates on changes to welfare schemes.
• Enhanced user personalization for tailored information delivery.

Feedback

We welcome your feedback to improve our project. If you encounter any issues, have suggestions, or want to collaborate, please reach out to us via [payadikishan@gmail.com] or [GitHub].

Thank you for joining us on this journey to empower citizens with information about government welfare schemes!