/grade.ai

grade.ai is an AI-powered student answer evaluation tool for educators. It uses advanced NLP models to automate grading, providing insightful analytics and saving time for teachers. Built with React, Flask, and OpenAI API, backed by a PostgreSQL database.

Primary LanguagePython

grade.ai

grade.ai is an AI-assisted evaluator tool designed to simplify the grading process for educators. This tool leverages advanced AI models to validate student answers, making the assessment process more efficient and accurate. Teachers can use this platform to upload question papers, schema, and relevant batch information. Once student answer PDFs are uploaded, they are converted into text, evaluated against the schema, and presented in a dashboard with detailed scores and analytics.

Project Overview

The platform includes the following features:

  • Teacher Interface: Allows educators to add question papers, grading schema, and batch-specific information.
  • Student Answer Evaluation: Automatically converts uploaded PDFs of student answers into text for AI-based evaluation.
  • Dashboard: Displays detailed scoring and insightful analytics on student performance.

Tech Stack

  • Front-end: React.js
  • Back-end: Flask and Python
  • API Service: OpenAI
  • Database: PostgreSQL

How It Works

  1. Input Questions and Schema: Teachers can input questions and define the evaluation schema for each question set.
  2. Upload Student Answers: PDFs containing student answers are uploaded, converted to text, and stored in the database.
  3. AI-Powered Evaluation: The tool evaluates student responses based on the schema using OpenAI's language model.
  4. Visualization and Analytics: Scores are displayed on a dashboard, offering insights and visual representation for easy comprehension.

Getting Started

To get started with grade.ai:

  1. Clone the repository.
  2. Set up the backend by following the detailed instructions below.
  3. Install the dependencies for the front-end.
  4. Set up the database and environment variables.
  5. Run the React front-end and Flask back-end.
  6. Access the dashboard to view evaluation results.

Backend Setup

  1. Clone the repository:

    git clone https://github.com/your-username/grade.ai.git

  2. Navigate into the backend directory:

    cd grade.ai/backend

  3. Create a .env file:

    • Create a file named .env in the backend directory.
    • Add your OpenAI API key to this file: 
      OPENAI_API_KEY=your_openai_api_key_here

  4. Create a virtual environment:

    python3 -m venv venv

  5. Activate the virtual environment:

    source venv/bin/activate

  6. Install the backend dependencies:

    pip install -r requirements.txt

  7. Run the backend server:

    python app.py

  8. Testing with Postman:

    • Use Postman to send requests to the backend and test the API.
    • Configure and use sample data and responses to see how the system works.

Usage

  1. Start the front-end server:

    cd frontend
npm start

  2. Access the application at http://localhost:3000.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.