AI Classroom Manager Agent

AI Classroom Manager Agent is an AI-powered educational automation tool built with Python, Flask, Streamlit, and OpenRouter/Gemini-compatible APIs.

The project helps teachers generate classroom resources such as timetables, lesson plans, worksheets, parent messages, behaviour notes, emergency lesson plans, and progress summaries. It includes multiple interfaces that share the same core AI logic.

Live Demo

View Streamlit App

Project Overview

AI Classroom Manager Agent was created to support teachers with common classroom planning and communication tasks.

The project is designed as a multi-interface AI application with:

• Command-line interface
• Flask web interface
• Streamlit web interface
• Shared AI feature logic
• OpenRouter / OpenAI-compatible API configuration
• Modular Python structure

The main goal of this project was to practice AI application development, prompt-based automation, API integration, and multi-interface software design.

Learning Goals

The goal of this project was to explore how AI can support teachers by automating repetitive classroom planning and communication tasks.

This project combines prompt-based generation, Python application structure, multi-interface design, and API-based AI integration into one practical education-focused tool.

Key Features

Timetable Generator

Generates a weekly timetable for multiple classes and periods.

Lesson Plan Generator

Creates structured lesson plans with learning objectives, warm-up activities, main activities, assessment ideas, and homework.

Test / Worksheet Generator

Generates classroom test or worksheet content with different question types.

Parent Message Generator

Creates professional parent messages in different tones such as polite, strict, or friendly.

Behaviour Note Generator

Generates formal behaviour or observation notes for student discipline or academic performance.

Emergency Lesson Plan Generator

Creates quick backup lesson plans for emergency or surprise classes.

Progress Heatmap

Generates text-based progress summaries and percentage-style heatmaps.

Interfaces

This project includes three interface options:

1. CLI Version

A terminal-based interface for running classroom automation features from the command line.

2. Flask Web App

A web-based interface using Flask, HTML templates, and CSS.

3. Streamlit App

A lightweight interactive web interface deployed with Streamlit.

Tech Stack

• Python
• Flask
• Streamlit
• Jinja2
• HTML
• CSS
• OpenRouter / OpenAI-compatible client
• Gemini-compatible model access
• python-dotenv

Project Structure

ai-classroom-manager/
├── app.py
├── main.py
├── streamlit_app.py
├── config.py
├── features.py
├── requirements.txt
├── templates/
│   ├── base.html
│   └── index.html
├── static/
│   └── style.css
├── screenshots/
│   ├── cli world.png
│   ├── flask world screen A.png
│   ├── flask world screen B.png
│   └── streamlit screenshot.png
└── README.md

Installation and Setup

Clone the repository:

git clone https://github.com/Shoaibstat876/ai-classroom-manager-agent.git

Go to the project folder:

cd ai-classroom-manager-agent/ai-classroom-manager

Create a virtual environment:

python -m venv .venv

Activate the virtual environment on Windows:

.\.venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Create a .env file inside the project folder:

AI_PROVIDER=openrouter
OPENROUTER_API_KEY=your_openrouter_api_key_here

For OpenAI instead of OpenRouter, use:

AI_PROVIDER=openai
OPENAI_API_KEY=your_openai_api_key_here

Running the Project

Run the CLI Version

python main.py

Run the Flask Web App

python app.py

Then open:

http://127.0.0.1:5000/

Run the Streamlit App

streamlit run streamlit_app.py

Then open:

http://localhost:8501/

Screenshots

CLI Version


Flask Web Interface



Streamlit Interface


What I Practiced

• Python application structure
• AI API integration
• Prompt-based automation
• Flask web development
• Streamlit app development
• CLI application design
• Shared business logic across multiple interfaces
• Environment variable handling
• Modular project organization
• Classroom workflow automation

Security Notes

API keys are not stored directly in the code.

The project uses environment variables through a .env file. The .env file should stay local and should not be committed to GitHub.

Example:

OPENROUTER_API_KEY=your_api_key_here

Current Status

This is a functional AI classroom automation project with CLI, Flask, and Streamlit interfaces.

It is suitable as a portfolio project to demonstrate Python, AI API integration, prompt engineering, and educational workflow automation. It is not a production school management system yet.

Future Improvements

• Add user authentication
• Add database storage
• Add saved classroom profiles
• Add export to PDF or DOCX
• Improve timetable conflict handling
• Add role-based teacher/admin views
• Add better UI design for the Flask version
• Add automated tests for core features
• Add deployment documentation for Flask
• Add structured logging and error handling

Author

Muhammad Shoaib Abdul Shakoor

Focused on AI automation, full-stack development, backend APIs, frontend interfaces, and practical AI-native applications.

License

This project is open for learning, demonstration, and portfolio purposes.