🎓 SnapClass.AI - AI-Powered Educational Assistant

SnapClass.AI is a fully Offline intelligent educational platform that combines multiple AI models to process, analyze, and generate educational content from various input sources including audio, PDFs, and text.

🏗️ Project Architecture

SnapClass/
├── 📁 server/                    # Main application backend
│   ├── 🚀 desktop_app.py        # Main GUI application (PyQt5/Tkinter)
│   ├── 🌐 app.py                # Flask web server
|   ├── 🌐 desktop_app.py
|   ├── 🌐 chat.py
│   ├── 🔄 trans.py              # Main processing orchestrator
│   ├── 🎤 stt.py                # Speech-to-Text (Whisper)
│   ├── 📄 pdf_reader.py         # PDF processing (Nougat + BLIP)
│   ├── ❓ question_gen.py        # Question generation (LLaMA)
│   ├── 📊 slm_analyse.py        # Student analysis (LLaMA)
│   ├── 🛠️ utils.py              # Utility functions
│   ├── ⚙️ setup.py              # Model downloader
│   ├── 📁 templates/            # HTML templates
│   ├── 🎨 static/               # CSS/JS assets
│   ├── 📁 uploads/              # User file uploads
│   └── 📁 output/               # Processed results
│   ├── 📁 llama3/               # LLaMA 3.2 3B model
│   │   ├── genie-t2t-run.exe    # Genie inference engine
│   │   ├── genie_config.json    # Model configuration
│   │   ├── *.bin                # Model weights (3 parts)
│   │   └── *.dll                # Windows dependencies
│   │
│   ├── 📁 whisper/              # OpenAI Whisper model
│   │   ├── model.safetensors    # Speech recognition model
│   │   ├── tokenizer.json       # Tokenizer
│   │   └── config.json          # Model configuration
│   │
│   └── 📁 poppler/              # PDF processing utilities
│
└── 📋 requirements.txt           # Python dependencies

🚀 Key Features

🎯 Multi-Modal AI Processing

• Audio Processing: Speech-to-text conversion using Whisper
• Document Processing: PDF text extraction module
• Text Generation: Question generation and analysis using LLaMA 3.2

🖥️ Dual Interface

• Desktop Application: Native GUI built with customtkinter
• Web Interface: Flask-based web server with React frontend

🔄 Workflow Pipeline

1. Input Processing: Audio files, PDFs, or text input
2. AI Analysis: Multi-model AI processing pipeline
3. Content Generation: Questions, summaries, and insights
4. Output Delivery: Structured results via GUI or web interface

🛠️ Technology Stack

Backend (Python)

• Framework: Flask (web server)
• GUI: customtkinter (desktop app)
• AI/ML: PyTorch, Transformers, Whisper, Llama3.2
• Audio: librosa, soundfile
• PDF: pytesseract, pypdf
• Data: numpy, PIL, PyYAML

AI Models

• LLaMA 3.2 3B: Text generation and analysis
• Whisper: Speech-to-text transcription

📋 Prerequisites

System Requirements

• OS: Windows 10/11, macOS, or Linux
• Processor: Snapdragon X Elite
• RAM: Minimum 8GB, Recommended 16GB+
• Storage: 10GB+ free space for models
• GPU: Optional but recommended for faster inference

Software Requirements

• Python: 3.8 - 3.11
• Git: For version control

🚀 Installation & Setup

1. Clone Repository

git clone https://github.com/YOUR_USERNAME/SnapClass.git
cd SnapClass

2. Python Environment Setup

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

3. Model Setup

# Navigate to server directory
cd server

# Download and setup AI models
python setup.py

Dwonload NPU optimised Llama3.2 model Click here Download poppler from here Note: This will download ~5GB of AI models. Ensure stable internet connection.

🎯 Usage

Desktop Application

cd server
python desktop_app.py

🔧 Configuration

Model Configuration

• LLaMA: Edit server/llama3/genie_config.json
• Whisper: Configure in server/whisper/config.json
• Nougat: Settings in server/nougat/config.json
• BLIP: Options in server/blip/config.json

📦 Building Executable and Msix

Using PyInstaller

# Use MSIX build script
.\build.ps1 -Version "1.0.0.0" -Publisher "CN=SnapClass.A"

🐛 Troubleshooting

Model Loading Errors

# Verify model files exist
ls -la server/llama3/
ls -la server/whisper/

Memory Issues

• Reduce batch sizes in model configurations
• Use CPU-only inference for lower memory usage
• Close other applications to free RAM
• Check the availability of Snapdragon NPU

Import Errors

# Reinstall dependencies
pip uninstall -r requirements.txt
pip install -r requirements.txt

Performance Optimization

• GPU Acceleration: Install CUDA-enabled PyTorch
• Model Quantization: Use quantized models for faster inference
• Batch Processing: Process multiple files simultaneously

🤝 Contributing

Development Setup

1. Fork the repository
2. Create feature branch: git checkout -b feature-name
3. Make changes and test thoroughly
4. Commit: git commit -m 'Add feature'
5. Push: git push origin feature-name
6. Create Pull Request

Code Style

• Python: Follow PEP 8 guidelines
• Documentation: Update README for new features

📄 License

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

🙏 Acknowledgments

• Meta AI: LLaMA 3.2 model
• OpenAI: Whisper speech recognition
• Open Source Community: Various libraries and tools

📞 Support

• A T Abbilaash (abbilaashat@gmail.com)

Made with ❤️ for the educational community