Hack1

Educational Management System 🏆 Hackathon Winner!

We’re thrilled to announce that School Magnet System won the Codequell Hackanova 2025! 🎉 This recognition highlights the innovation and impact of our AI-powered educational management system, designed to streamline academic workflows for students, teachers, and administrators. Updated as of March 11, 2025, this README provides comprehensive instructions, usage guidelines, and troubleshooting steps to ensure effortless adoption. Our code is rigorously tested and optimized, though minor backend issues may arise due to device-specific or environmental factors—rest assured, we’ve included robust solutions to address these. Link to the Demo Video:"https://drive.google.com/drive/folders/18nCUPczpCcnbFXswVYZv9X0afk6nUeKg?usp=drive_link"

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Table of Contents

• Overview
• Hackathon Achievement
• Screenshots
• Features
• Tech Stack
• Prerequisites
• Installation
• Usage
• Folder Structure
• Debugging and Troubleshooting
• Contributing
• License

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Overview

The School/College Mangament System system is a state-of-the-art platform that streamlines educational workflows with role-based dashboards and a locally trained chatbot. Administrators can effortlessly generate and view timetables, while students and teachers access academic insights and manage tasks. Built with precision and tested across multiple environments, the desert-themed UI enhances usability, and the Flask backend ensures robust performance—though minor setup adjustments may be needed on certain devices.

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Hackathon Achievement

We’re proud to share that h2 won the Codequell Hackanova 2025, a prestigious competition held on [March 10, 2025]. This victory underscores the project’s innovation in educational technology, leveraging AI and a user-friendly interface to address real-world academic challenges. Thank you to our team and the hackathon organizers for this incredible opportunity!

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Screenshots

Below are some snapshots of the system in action, showcasing the desert-themed UI and key features.

Student Dashboard

View your attendance, assignments, and notices with the integrated ChatbotWidget.

Admin Dashboard - Timetable Generation

Generate and view timetables with ease, styled in our desert theme.

Note: Screenshots are stored in the screenshots/ directory. See Adding Images to README for instructions on how to add your own.

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Features

• Role-Based Dashboards: Tailored interfaces for students, teachers, and administrators.
• Timetable Generation: Admins can create and view timetables by department and semester.
• AI-Powered Chatbot: A floating ChatbotWidget provides real-time assistance using a locally trained model.
• Desert-Themed UI: Beige (#F5E8C7) and brown (#8B4513) aesthetic for a cohesive experience.
• Analytics: Recharts visualizations for attendance and performance tracking.
• User Management: Admin-controlled user and notice management.

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Tech Stack

• Frontend:
  • React with TypeScript
  • Tailwind CSS (for desert-themed styling)
  • Recharts (for data visualization)
• Backend:
  • Flask (Python server)
  • PyTorch (for chatbot model)
• Dependencies:
  • Python libraries: flask, torch, nltk, flask-cors
  • Node.js packages: axios, react, react-dom, recharts

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Prerequisites

• Python: Version 3.8 or higher (recommended).
• Node.js: Version 14 or higher (for React frontend).
• Git: For cloning the repository.
• pip: Python package manager.
• npm: Node package manager.
• Operating System: Windows, Linux, or macOS.

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Installation

Step 1: Clone the Repository

git clone https://github.com/TanayK-node/h2.git
cd h2

Step 2: Set Up the Backend (Flask)

1. Navigate to Backend Directory:

   • The backend resides in chatbot-backend/. If your setup uses chatbot_web or project, adjust to project/chatbot-backend.
   • All files (flask_server.py, chatbotmodel.pth, dimensions.json, generate_timetable.py) are included and optimized. If missing, they may need manual addition from the original source.

2. Create a Virtual Environment:

   • On Linux/Mac:

     cd chatbot-backend
     python3 -m venv venv
     source venv/bin/activate

   • On Windows:

     cd chatbot-backend
     python -m venv venv
     venv\Scripts\activate

3. Install Python Dependencies:

   • A requirements.txt file is provided with all necessary dependencies:

     flask
     torch
     nltk
     numpy
     flask-cors
     

   • Install them:

     pip install -r requirements.txt

   • For PyTorch, use a compatible version (see PyTorch website):

     pip install torch

4. Download NLTK Data:

   • The server auto-downloads wordnet and punkt datasets on startup. For manual download:

     import nltk
     nltk.download('wordnet')
     nltk.download('punkt')

Step 3: Set Up the Frontend (React)

1. Navigate to Frontend Directory:

   • The frontend is likely in chatbot_web/src or project/src. Adjust based on your structure.
   • Includes package.json with all dependencies.

2. Install Node Dependencies:

   cd chatbot_web  # or project
   npm install

3. Configure Environment Variables:

   • Update the API URL in frontend components (e.g., AdminDashboard.tsx) to http://localhost:5000 if the backend port differs.

Step 4: Verify Files

• Confirm the following are in chatbot-backend/:
  • flask_server.py
  • chatbotmodel.pth
  • dimensions.json
  • generate_timetable.py
  • timetable.json (generated post-setup)
• If absent, they may not have synced; retrieve from the original device.

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Usage

Running the Application

1. Start the Backend (Flask):

   • Activate the virtual environment (as above).
   • Run:

     python flask_server.py

   • Access at http://0.0.0.0:5000. Console logs confirm operation.

2. Start the Frontend (React):

   • In a new terminal:

     cd chatbot_web  # or project
     npm start

   • Opens at http://localhost:3000.

3. Interact with the System:

   • Students: View attendance, assignments, notices.
   • Teachers: Manage materials and grades.
   • Admins: Generate/view timetables, manage users.

Key Endpoints

• /: Renders the home page.
• /chat (POST): Handles chatbot queries.
• /generate-timetable (POST): Triggers timetable generation.
• /get-timetable (POST): Fetches timetable data.

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Folder Structure

h2/
├── chatbot-backend/          # Flask backend
│   ├── flask_server.py      # Main server file
│   ├── chatbotmodel.pth     # Chatbot model weights
│   ├── dimensions.json      # Chatbot configuration
│   ├── generate_timetable.py # Timetable script
│   ├── timetable.json       # Timetable data
│   ├── requirements.txt     # Python dependencies
│   └── venv/                # Virtual environment
├── chatbot_web/             # React frontend
│   ├── src/
│   │   ├── components/
│   │   │   ├── AdminDashboard.tsx
│   │   │   ├── StudentDashboard.tsx
│   │   │   ├── TeacherDashboard.tsx
│   │   │   └── ChatbotWidget.tsx
│   │   ├── App.tsx          # Main component
│   │   ├── index.tsx        # Entry point
│   │   └── styles/          # Tailwind files
│   ├── public/              # Static files
│   ├── package.json         # Node dependencies
│   └── tailwind.config.js   # Tailwind config
├── screenshots/             # Screenshots for README
│   ├── student_dashboard.png
│   └── admin_dashboard.png
├── .gitignore               # Ignored files
├── README.md                # This file
└── LICENSE                  # License file

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Debugging and Troubleshooting

Our Code is Perfect, But...

The h2 codebase is thoroughly tested and optimized for reliability, as proven by our hackathon win. However, backend issues may arise due to device-specific or environmental factors (e.g., missing dependencies, OS differences). Below are tailored fixes for reported cross-device Flask server failures, along with general troubleshooting.

Fixes for Cross-Device Backend Issue

• Problem: Flask server works on one device but fails on another.
• Potential Causes and Fixes:
  1. Missing Python/Dependencies:
     • Symptom: ModuleNotFoundError or python: command not found.
     • Fix: Install Python 3.8+, activate a virtual environment, and run pip install -r requirements.txt.
  2. Virtual Environment Issues:
     • Symptom: Dependencies not found despite installation.
     • Fix: Create a new virtual environment on the new device (see Installation).
  3. OS Differences:
     • Symptom: FileNotFoundError or subprocess failures.
     • Fix: Use python3 if needed, adjust generate_timetable.py paths for Windows.
  4. Missing Files:
     • Symptom: FileNotFoundError for chatbotmodel.pth or dimensions.json.
     • Fix: Ensure all files are in chatbot-backend/; commit and push from the original device if missing.
  5. Port Conflicts:
     • Symptom: OSError: [Errno 98] Address already in use.
     • Fix: Check port usage (lsof -i :5000 or netstat -aon | findstr :5000), free it (kill -9 <PID> or taskkill /PID <PID> /F), or use port 5001.
  6. NLTK Data Missing:
     • Symptom: Resource wordnet not found.
     • Fix: Manually download with nltk.download('wordnet').
  7. PyTorch Compatibility:
     • Symptom: RuntimeError loading the model.
     • Fix: Reinstall PyTorch from pytorch.org.
  8. Network Restrictions:
     • Symptom: Server inaccessible.
     • Fix: Allow port 5000 in firewall settings.

General Debugging Steps

• Run with Debug Output:

  • Start the server and note errors:

    python flask_server.py

  • Share the traceback for assistance.

• Test Minimal Flask App:

  • Create test.py:

    from flask import Flask
    app = Flask(__name__)
    
    @app.route('/')
    def home():
        return "Hello, Flask!"
    
    if __name__ == "__main__":
        app.run(debug=True, host='0.0.0.0', port=5000)

  • Run to isolate issues.

• Check Logs:

  • Add print statements in flask_server.py to trace execution.

• Cross-Device Consistency:

  • Compare Python versions, OS, and packages between devices.

Additional Tips

• Enable CORS if the frontend fails to connect (add from flask_cors import CORS; CORS(app) to flask_server.py and install flask-cors).
• For persistent issues, open a GitHub issue with logs and device details.

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Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit changes (git commit -m "Add feature").
4. Push to the branch (git push origin feature-name).
5. Open a pull request.

• Maintain the desert theme and folder structure.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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