Errbot

Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation.

AI & Machine LearningPythonGPL-3.0

Abstract

Errbot is an open-source AI & Machine Learning project. Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation. Errbot is a chatbot. It allows you to start scripts interactively from your chatrooms for any reason: random humour, chatops, starting a build, monitoring commits, triggering alerts... It is built using Python. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Errbot is a chatbot. It allows you to start scripts interactively from your chatrooms for any reason: random humour, chatops, starting a build, monitoring commits, triggering alerts...

Errbot is available as open-source software and released under the GPL v3 license.

Errbot runs under Python 3.10+ on Linux, Windows and Mac. For some chatting systems you'll need a key or a login for your bot to access it.

2. Objective

Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation.

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/errbotio/errbot.git
cd errbot

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by errbotio and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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