🤖 CustomGPT - Chat with your Data 📚

CustomGPT is a sophisticated, multilingual chatbot designed to streamline the extraction, processing, and interaction with text data from PDF documents.
Leveraging advanced NLP and machine learning models, it enables rich, interactive communication across multiple languages, making it ideal for businesses, educational institutions or individuals dealing with diverse document formats.

📚 Table of Contents

• Introduction
• Features
• Getting Started
  • Installation
• Usage
• Contributing
• Credits
• License

📖 Introduction

CustomGPT harnesses the power of conversational AI to enhance the way organizations or individuals handle document-based information.
By automatically extracting and analyzing text from PDFs and facilitating dynamic interactions through its chatbot interface, CustomGPT transforms static data into actionable insights.
This integration of document processing with advanced dialogue systems offers a unique solution that significantly boosts productivity and user engagement.

Screenshot

✨ Features

• PDF Text Extraction: Utilizes PyPDF2 for efficient text extraction from PDFs, handling multiple layouts and formats.
• Advanced Text Processing: Integrates tokenizers and Spacy text splitters for text segmentation, and employs Spacy Language Detection module for robust language detection, ensuring precise text analysis.
• Multilingual Support: Powered by multiple instances of the transformer-based large language models Mistral-7B-Instruct-v0.2, supports interactions in multiple languages using Hugging Face API:
  • English 🇬🇧
  • Spanish 🇪🇸
  • French 🇫🇷
  • German 🇩🇪
  • Italian 🇮🇹
  • Ukrainian 🇺🇦
  • Russian 🇷🇺
  • Chinese 🇨🇳
  • Japanese 🇯🇵
• Interactive User Interface: Offers a user-friendly command-line interface that may evolve into a more graphical interface.

🚀 Getting Started

⚙️ Installation

• Step 1: clone the repo

git clone https://github.com/wayzeek/CustomGPT.git

• Step 2: navigate to the directory

cd CustomGPT

• Step 3: install dependencies

bash install.sh

• Step 4: move to virtual environment

source .venv/bin/activate

• Step 5: start application

python3 main.py 

🔍 Usage

Process PDFs

• Step 1: add your PDFs to the data directory
• Step 2: launch application

python3 main.py

• Step 3: select if your PDFs is structured by Markdowns (Chapters, Titles, ...) or not
• Step 4: Choose the chunk size aka the average sizes of your paragraph
• Step 5: Wait & enjoy chating with your data !

🤝 Contributing

1. Fork the repo
2. Create your feature branch (git checkout -b feature/amazingFeature)
3. Commit your changes (git commit -am 'Add some amazingFeature')
4. Push to the branch (git push origin feature/amazingFeature)
5. Open a pull request

🏆 Credits

This is a solo project made by myself

⚖️ License

MIT License - see the LICENSE file for details