1# Project Oppenheimer

Description

Project Oppenheimer is an ambitious initiative aimed at integrating public data from CERN into enhanced AI capabilities. This project aims to leverage the vast scientific data and resources of CERN to enrich AI functionalities in the realms of particle physics, data analysis, and more.

                  +-------------------+
                  |                   |
                  |   Project GPT     |
                  | (Data Processing  |
                  |   & Interaction)  |
                  |                   |
                  +---+-----------^---+
                      |           |
                      | API Calls |
                      |           |
                      v           |
        +-------------+---+   +---+-------------+
        | Gcloud Ubuntu   |   |  Flask App     |
        | Machine         |   |  (API Server)  |
        |                 |   |                 |
        | - Fetch .root   |   | - Serve API     |
        |   data from     |   |   endpoints     |
        |   CERN          |   | - Process       |
        | - Process data  |   |   CERN data     |
        |                 |   | - Visualization |
        +-----------------+   +-----------------+

Getting Started

Dependencies

• List any libraries, frameworks, or tools that need to be installed for this project.
• Specify if this project is platform-specific (Windows, MacOS, Linux).

Installation

• Instructions on how to install and get the project running.
• Include steps to install any required dependencies.

Usage

• How to use the project after installation.
• Examples of commands or scripts to run.

Structure

• src: Contains the source code of the project.
• data: Stores datasets used by the project.
• docs: Documentation for the project.
• tests: Test scripts and test cases.
• scripts: Utility scripts for various tasks.
• lib: External libraries not managed by package managers.
• bin: Compiled or executable files.
• config: Configuration files for different environments.
• examples: Example code and usage demonstrations.
• notebooks: Jupyter notebooks for data analysis and prototyping.
• assets: Non-code assets like images or media.
• contrib: Contributions from the community or external sources.

Contributing

• Guidelines for how to contribute to the project.
• Information on the process for submitting pull requests.

Authors

• List of the main authors and contributors to the project.

License

• Information about the project's license.

Acknowledgments

• Credits to any resources or individuals that helped in the development of the project.