📚 AI Assignment Grading Assistant

Project Overview

This project is an AI-powered assignment grading assistant designed to streamline the evaluation process for educators. It leverages a team of specialized AI agents (built with the agno framework and powered by Google's Gemini model) to read student assignments, apply a grading rubric, provide detailed feedback, and offer specific justifications for scores. The application features a user-friendly web interface built with Streamlit, allowing users to easily upload PDF or DOCX assignment files.

Features

• File Upload: Supports PDF and DOCX assignment file uploads.
• Text Extraction: Automatically extracts clean text content from uploaded documents.
• AI-Powered Grading: Utilizes a multi-agent system to grade assignments based on a predefined rubric (Content Relevance, Accuracy, Structure/Clarity, Grammar/Presentation).
• Constructive Feedback: Generates detailed feedback highlighting strengths, weaknesses, and suggestions for improvement.
• Grade Justification: Provides specific, detailed explanations for low scores (C or below) and general encouragement for higher grades.
• Intuitive UI: A simple and clear web interface for interacting with the grading system.

How It Works

The grading pipeline involves several AI agents working in sequence:

1. AssignmentReader: Extracts and cleans text from the uploaded assignment.
2. GradingAgent: Evaluates the cleaned text against a rubric and provides a score and letter grade.
3. FeedbackAgent: Generates constructive feedback based on the assignment content and assigned grade.
4. GradeJustifier: Provides additional justification for the grade, particularly detailed for lower scores, including what the correct answer should contain and improvement suggestions.

Setup and Installation

Prerequisites

• Python 3.8+
• A Google Gemini API Key

Steps

1. Clone the repository:

   git clone [https://github.com/AnshGajera/AUTOMATED-GRADING-BOT.git](https://github.com/AnshGajera/AUTOMATED-GRADING-BOT.git)
   cd AUTOMATED-GRADING-BOT

2. Create a virtual environment (recommended):

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

3. Install dependencies:

   pip install -r requirements.txt

4. Set up your Gemini API Key: Create a file named .env in the root directory of your project (where main.py is located) and add your Gemini API key:

   GEMINI_API_KEY=YOUR_GEMINI_API_KEY_HERE
   

   Important: Do not commit your .env file to GitHub! It's already included in the .gitignore below.

Running the Application

Once setup is complete, you can run the Streamlit application:

streamlit run app.py