🎓 GRADE PILOT AI A multi-modal automated grading system designed to drastically reduce teacher workload by intelligently evaluating assignments. By automating the assessment pipeline, Grade Pilot AI enables educators to focus more on student development, mentorship, and overall academic growth.

✨ Key Features Multi-Modal Evaluation: Processes both text-based and handwritten assignments seamlessly.

Intelligent Document Parsing: Utilizes Google Cloud Vision OCR for highly accurate extraction of student submissions.

Advanced LLM Orchestration: Powered by Gemini for complex reasoning and Mistral (SFR-embeddings) for precise semantic search and context retrieval.

Automated Feedback Loop: Generates constructive, standardized feedback for students based on grading rubrics.

🚀 Tech Stack

Based on the run commands, this project uses:

• Backend: Python (FastAPI / Starlette) - Served with uvicorn.
• Frontend: JavaScript/TypeScript - (e.g., React, Vue, Svelte) - Managed with npm.

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🏁 Getting Started

Follow these instructions to get a copy of the project up and running on your local machine for development and testing.

Prerequisites

You will need the following software installed on your system:

• Python 3.8+
• Node.js & npm

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⚙️ Installation

1. Clone the repository:

   git clone [YOUR_REPOSITORY_URL]
   cd [YOUR_PROJECT_NAME]

2. Set up the Backend:

   cd backend
   
   # (Recommended) Create and activate a virtual environment
   python -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate
   
   # Install Python dependencies
   pip install -r requirements.txt 
   cd ..

3. Set up the Frontend:

   cd frontend
   
   # Install JavaScript dependencies
   npm install
   cd ..

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🖥️ Running the Project (Local Development)

You will need to open two separate terminals to run both the backend and frontend servers simultaneously.

Terminal 1: Start the Backend Server

cd backend

# Activate the virtual environment (if you created one)
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Run the Uvicorn server
python -m uvicorn app:app --reload --port 8000

ℹ️ Your backend API will now be running at http://localhost:8000

Terminal 2: Start the Frontend Server

cd frontend
# Run the development server
npm run dev

ℹ️ Your frontend will now be running at http://localhost:3000 (or 5173, etc. — check your terminal for the exact URL).