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OldBailey AI

OldBailey AI is a project developed to assist researchers and historians by providing insights into historical trends, facilitating data analysis, and offering an immersive experience through role-play. This AI leverages OpenAI to analyze the Old Bailey dataset, utilizing prompt engineering to create an AI capable of analyzing historic trends, generating tables, and role-playing as people from that era.

Table of Contents

About

OldBailey AI is designed to enhance historical research by offering advanced data analysis and immersive role-play capabilities. By analyzing the Old Bailey dataset, this AI provides valuable insights into historical trends and helps users explore history in a more interactive and engaging way.

Technologies Used

  • Frontend:

    • Next.js
    • TypeScript
    • SCSS/SASS

  • Backend:

    • Flask
    • Python
    • Docker

  • Database:

    • MongoDB

  • AI Integration:

    • OpenAI

Features

  • Historical Data Analysis: Provides insights into historical trends by analyzing the Old Bailey dataset.
  • Interactive Role-Play: Allows users to role-play as people from the historical era, enhancing the immersive experience.
  • Data Visualization: Generates tables and visualizations to facilitate data analysis.
  • Prompt Engineering: Utilizes advanced prompt engineering to maximize the capabilities of OpenAI.

Installation

To run this project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/HassanChowdhry/OldBailey-AI.git

  2. Navigate to the project directory:

    cd app

  3. Install the dependencies:

    npm install

  4. Create a virtual environment:

    python3 -m venv venv

  5. Activate the virtual environment:

    source venv/bin/activate

  6. Install the Python dependencies:

    pip install -r requirements.txt

  7. Start the development server:

    python3 run.py

Usage

You can access the application by navigating to http://localhost:3000 in your web browser. Explore the various features and capabilities offered by OldBailey AI.

Contact

Feel free to reach out to me for any professional inquiries or questions:

References

Tim Hitchcock, Robert Shoemaker, Clive Emsley, Sharon Howard and Jamie McLaughlin, et al., The Old Bailey Proceedings Online, 1674-1913 (www.oldbaileyonline.org, version 9.0, Autumn 2023).

Thank you for exploring OldBailey AI!

Ideas

Future Features:

  • 2FA
  • Localization
  • IAM ; User Roles (to support paid plans & permissions)
  • User preferences
  • Email verification
  • User notifications (newsletters, updates, etc.)

Feedback:

  • Use Poetry
  • Dockerize the App (Backend, Frontend & MongoDB)
  • Have a local vs production setup (local should run in debug mode)
  • Look into DB ORM.
  • Introduce Checkstyles (Pylint)
  • Intoduce static-type checking (MyPy)
  • Introduce test coverage
  • Introduce Unit Testing (PyTest)
  • Introduce Integration Testing (PyTest)
  • Introduce CI/CD
  • Introduce proper logging (info, warning, error) & metrics
  • Introduce monitoring (alert system)
  • Do the token authentication & refresh in interceptor/middleware