Data Structures and Algorithms Learning System

An interactive Flask learning site for visualizing core data structures and algorithms. It combines step-by-step browser animations with an optional OpenAI-powered teaching assistant.

Learning modules

• Sorting: bubble, insertion, quick, and merge sort
• Trees: traversal, binary trees, and BST insert/search/delete
• Graphs: BFS, DFS, and Dijkstra's algorithm
• Data structures: arrays and linked lists
• Contextual chat assistant through /chat-api

Tech stack

• Python and Flask
• Vanilla JavaScript visualizations
• HTML/CSS templates
• OpenAI API for the optional assistant

Setup

git clone https://github.com/mark2146/education_system.git
cd education_system
python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
python -m pip install -r requirements.txt
cp .env.example .env

The visualizations work without an API key. To enable chat, set OPENAI_API_KEY in .env. For a shared deployment, also set APP_ACCESS_TOKEN and configure the frontend/reverse proxy to send it as a bearer token.

Run

flask --app app run --debug

Open http://127.0.0.1:5000. For a production-like process:

gunicorn app:app

Structure

app.py             Flask routes and chat endpoint
templates/         pages for individual topics
static/js/         visualization algorithms
static/css/        site styling
requirements.txt   Python runtime dependencies

Security notes

• Keep .env and OPENAI_API_KEY out of version control.
• /chat-api supports optional bearer-token protection, a 32 KB request limit, and a 2,000-character message limit. Add per-client rate limiting before public deployment.
• Internal exceptions are logged server-side and are not returned to clients.
• Set request-size limits and production security headers at the application or reverse proxy.

Status

Educational project. The visualizations prioritize clarity and are not intended as production algorithm libraries.