Nomus AI - A Technology Powered Autocompletion Tool

Unlock the power of AI and Big Data to complete documentation faster and more efficiently.

Table of Contents 🗓

1. About The Project
2. Libraries Used
3. Getting Started
   • Prerequisites
   • Installation
4. Roadmap
5. Contact
6. Acknowledgments

About The Project 🚀

Nomus AI is designed to revolutionize the way documentation is created by leveraging the latest advancements in Natural Language Processing (NLP) and machine learning. This tool helps users unlock the power of AI and Big Data by providing customized and intelligently optimized autocompletion solutions, significantly enhancing the efficiency and speed of documentation tasks.

The key features of Nomus AI include:

• Intelligent Autocompletion: Provides context-aware suggestions to help users complete their documentation faster.
• Customizable Templates: Allows users to define templates tailored to their specific needs.
• Machine Learning Integration: Utilizes advanced algorithms such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy.
• User-Friendly Interface: Designed with ease of use in mind, ensuring a seamless user experience.

(back to top)

Libraries Used

Check requirements.txt for a complete list of libraries used in this project.

(back to top)

Getting Started

Prerequisites

Ensure that the following libraries are installed for the code to run properly. Install them using the specified version with the following commands:

• npm

  npm install npm@latest -g

• library

  npm install library_name_here -g

Installation

After downloading the project, install the required libraries. In the console, run the command:

streamlit run nomus.py

(back to top)

Roadmap

• Identify possible datasets
  • Balance the data
  • Create meta.csv (labels)
• Create a preprocessing pipeline
  • Data cleaning and preparation
  • Feature extraction and selection
• Develop the Nomus AI model
  • Implement RNN
  • Develop and train LSTM
  • Optimize performance
• Build the autocompletion engine
  • Integrate machine learning models
  • Develop context-aware suggestions
• Design the user interface
  • Create a user-friendly layout
  • Implement customization options
• Deploy the application
  • Cloud deployment
  • Scalability enhancements
• Future improvements
  • Advanced NLP features
  • Multi-language support

Contact

Your Name - @rishitsaraf - rishitsaraf24@gmail.com

(back to top)