iBIS: Chatbot for Learning Algorithms

This chatbot is designed to help users learn about different search and sort algorithms in computer science.

Getting Started With Rasa

To get started with this chatbot, follow these instructions:

1. Clone the repository to your local machine:

   git clone https://github.com/NIGHTMARE-NIGHTMARE-NIGHTMARE/Senior-Capstone.git

2. Install necessary dependencies

   cd Senior-Capstone/rasa
   pip install -r requirements.txt

3. Make a virtual environment with the necessary depenencies and activate it

   [path to python 3.8] -m venv [virtual environment name]
   [virtual environment name]/Scripts/activate

4. Train the NLU

   rasa train nlu

5. Train the chatbot model

   rasa train

6. Run in terminal to test

   rasa shell

Conversation Flow

The chatbot is designed to guide users through learning about search and sort algorithms. It supports the following conversation flow:

1. User selects a category: search algorithms or sort algorithms.
2. User chooses a specific algorithm within the selected category.
3. The chatbot provides information, definitions, pseudocode, visualizations, and additional resources about the selected algorithm.

Supported Algorithms

Search Algorithms

1. Linear Search

Sort Algorithms

1. Quicksort
2. Bubble Sort

To support additional algorithms, data for them needs to be added to domain.yml and stories needs to be modified to include that data. Additionally, if the algorithms are not recognized by NLU, it will need to be trained on those entities.

Getting Started With Djagno

To get started with Django, follow these instructions:

1. Clone the repository to your local machine:

   git clone https://github.com/NIGHTMARE-NIGHTMARE-NIGHTMARE/Senior-Capstone.git

2. Install necessary dependencies

   cd Senior-Capstone/django
   pip install -r requirements.txt

3. Make a virtual environment with the necessary depenencies and activate it

   [path to python 3.12] -m venv [virtual environment name]
   [virtual environment name]/Scripts/activate

4. Start the Django server

   py manage.py runserver

Why we are using Django

We chose to use Django that way we can easily connect our front-end with the Rasa server. We talk to it through API calls. Each response is cleaned up and then returned to our front-end.