InkGrader 🖋️

InkGrader is a state-of-the-art, AI-powered grading system designed to automate the evaluation of handwritten documents. By combining advanced OCR (Optical Character Recognition) with intelligent multi-agent systems and RAG (Retrieval-Augmented Generation), InkGrader provides an unparalleled experience for automated grading and assessment management.

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✨ Features

• 📷 Intelligent OCR Extraction: High-accuracy extraction of handwritten text from scans and PDFs using multi-engine OCR strategies.
• 🤖 Multi-Agent AI System:
  • Extraction Agent: Seamlessly parses raw OCR text into structured question/answer formats.
  • Grading Agent: Evaluates student responses based on context, accuracy, and provided reference materials.
• 📚 RAG-Enhanced Evaluation: Powered by FAISS and OpenVINO, the system cross-references student answers with official marking schemes for highly accurate grading.
• ⚡ Real-time Updates: Track the grading process in real-time via WebSockets as the agents work through batches of documents.
• 📊 Modern Dashboard: A premium Next.js 15 dashboard for managing exams, uploading student work, and viewing detailed feedback.
• 🔐 Enterprise-Grade Auth: Secure authentication flow with JWT (Access & Refresh tokens) and MongoDB storage.

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🛠️ Technology Stack

Backend (Python/FastAPI)

• Framework: FastAPI (Asynchronous high-performance API)
• AI/LLM: PydanticAI, Groq (LLM Inference), Sentence-Transformers
• Vector Search: FAISS with OpenVINO optimization
• Database: MongoDB (Async Motor driver)
• Streaming: Redis Streams for background task coordination
• OCR: OCR.Space API / Pypdf

Frontend (Next.js/React)

• Framework: Next.js 15 (App Router)
• Styling: Tailwind CSS 4, Framer Motion
• Components: Radix UI, Lucide Icons
• Fetching: Axios with interceptors for auth management
• Design: Premium aesthetics with Geist Sans & Geist Mono

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🚀 Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+
• MongoDB Instance (Atlas)
• Redis Instance
• Groq API Key
• OCR.Space API Key

1. Backend Setup

1. Navigate to the backend:

   cd backend

2. Setup environment:

   python -m venv .venv
   source .venv/bin/activate  # Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure .env (refer to Configuration):
5. Start the server:

   python app.py

2. Frontend Setup

1. Navigate to the frontend:

   cd frontend_next_js

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

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⚙️ Configuration

Create a .env file in the backend/ directory:

# Database
MONGO_DB_USERNAME=your_username
MONGO_DB_PASSWORD=your_password

# Authentication
JWT_SECRET_KEY=your_secret_key
JWT_REFRESH_SECRET_KEY=your_refresh_key

# AI & OCR
GROQ_API_KEY=your_groq_key
OCR_API_KEY=your_ocr_key

# Redis
REDIS_URL=localhost
REDIS_PORT=6379
REDIS_PASSWORD=default

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📂 Project Structure

• backend/: FastAPI application, AI agents, and file processing logic.
• frontend_next_js/: Next.js 15 dashboard and user interface.
• backend/Agents/: The "brains"—Extraction and Grading agents.
• backend/FileProcessor/: OCR and document parsing utilities.

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🤝 Contributing

Contributions make the open-source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📜 License

Distributed under the MIT License. See LICENSE for more information.

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📧 Contact

Project Lead: [Your Name/Github] Project Link: https://github.com/Geoff-Robin/InkGrader