TechEdu Hub - AI-Powered Educational Chatbot 🎓

A comprehensive web platform that combines IT support, educational resources, and an intelligent AI chatbot to help students with technical issues and academic questions.

🌟 Features

• 🤖 AI-Powered Chatbot: Intelligent assistant using sentence transformers and FAISS for semantic search
• 📚 Educational Resources: Comprehensive learning materials covering networking, databases, programming, and cybersecurity
• 🎨 Modern UI/UX: Responsive design with smooth animations and intuitive navigation
• ⚡ Real-time Chat: Interactive chatbot with typing indicators and quick question buttons
• 📱 Mobile Responsive: Optimized for all devices and screen sizes
• 🔍 Semantic Search: Advanced FAQ matching using machine learning embeddings
• 🎯 Student-Focused: Designed specifically for educational environments

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• Git

Installation

1. Clone the repository

   git clone https://github.com/yourusername/techedu-hub.git
   cd techedu-hub

2. Create a virtual environment

   python -m venv venv

3. Activate the virtual environment

   Windows:

   venv\Scripts\activate

   macOS/Linux:

   source venv/bin/activate

4. Install dependencies

   pip install -r requirements.txt

5. Set up environment variables

   # Copy the example environment file
   cp .env.example .env
   
   # Edit .env file and add your OpenAI API key (optional for demo)
   OPENAI_API_KEY=your_openai_api_key_here

6. Run the application

   python run.py

7. Access the application

   Open your browser and navigate to: http://localhost:5000

📁 Project Structure

techedu-hub/
├── app.py                 # Main Flask application
├── run.py                 # Application runner
├── config.py              # Configuration settings
├── requirements.txt       # Python dependencies
├── .env.example          # Environment variables template
├── .gitignore            # Git ignore rules
├── README.md             # Project documentation
├── templates/            # HTML templates
│   ├── index.html        # Homepage
│   ├── about.html        # About page
│   ├── services.html     # Services page
│   ├── resources.html    # Resources page
│   └── contact.html      # Contact page
├── .cache/               # Cached embeddings and data
│   ├── faq_embeddings.npy
│   └── faq_questions.json
└── ICT Cell Common problems - Hardware issues.csv  # FAQ dataset

🛠️ Technology Stack

• Backend: Flask (Python web framework)
• AI/ML:
  • Sentence Transformers (for text embeddings)
  • FAISS (for similarity search)
  • OpenAI API (optional integration)
• Frontend:
  • HTML5, CSS3, JavaScript
  • Font Awesome icons
  • Google Fonts
• Data Processing: Pandas, NumPy
• Environment: Python-dotenv for configuration

🔧 Configuration

Environment Variables

Create a .env file in the root directory:

# OpenAI API Key (optional - for enhanced AI responses)
OPENAI_API_KEY=your_api_key_here

# Flask Configuration
FLASK_ENV=development
FLASK_DEBUG=True

FAQ Dataset

The application uses ICT Cell Common problems - Hardware issues.csv for the knowledge base. You can:

• Replace this file with your own FAQ dataset
• Ensure the CSV has columns for questions and answers
• The system will automatically generate embeddings for semantic search

🎯 Usage

For Students

1. Homepage: Browse educational content and features
2. AI Assistant: Click "Try AI Assistant" to start chatting
3. Quick Questions: Use pre-defined buttons for common issues
4. Learning Resources: Explore theories and educational materials
5. Navigation: Use the top menu to access different sections

For Developers

1. API Endpoints:

   • POST /api/chat - Send messages to the chatbot
   • GET / - Homepage
   • GET /about - About page
   • GET /services - Services page
   • GET /resources - Resources page
   • GET /contact - Contact page

2. Customization:

   • Modify templates in the templates/ directory
   • Update FAQ data in the CSV file
   • Customize styling in the HTML templates
   • Add new routes in app.py

🚀 Deployment

Local Development

# Run with debug mode
python run.py

# Or use Flask CLI
flask --app app run --debug

Production Deployment

1. Using Gunicorn (recommended for production):

   pip install gunicorn
   gunicorn -w 4 -b 0.0.0.0:5000 app:app

2. Using Docker (create Dockerfile):

   FROM python:3.9-slim
   WORKDIR /app
   COPY requirements.txt .
   RUN pip install -r requirements.txt
   COPY . .
   EXPOSE 5000
   CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "app:app"]

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

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

🆘 Troubleshooting

Common Issues

1. Import Errors:

   • Ensure virtual environment is activated
   • Install all dependencies: pip install -r requirements.txt

2. Port Already in Use:

   • Change port in run.py or kill the process using port 5000

3. Missing Dependencies:

   • Update pip: python -m pip install --upgrade pip
   • Reinstall requirements: pip install -r requirements.txt --force-reinstall

4. Slow Initial Load:

   • First run downloads ML models (sentence transformers)
   • Subsequent runs will be faster due to caching

Performance Optimization

• The application caches embeddings in .cache/ directory
• First-time setup may take longer due to model downloads
• Consider using a production WSGI server for better performance

📞 Support

For support and questions:

• Create an issue on GitHub
• Check the troubleshooting section
• Review the documentation

🙏 Acknowledgments

• Sentence Transformers for semantic search capabilities
• FAISS for efficient similarity search
• Flask community for the excellent web framework
• Font Awesome for beautiful icons
• Google Fonts for typography

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Made with ❤️ for students and educators

TechEdu Hub - Empowering learning through AI and technology