Parikshak AI Examiner 🌟

Description

This "Parikshak AI Examiner," allows teachers to analyze student answers using AI. It provides a user-friendly interface to upload images of student answers, input the extracted text, compare it with the teacher's answer, and evaluate the result.

Usage

  1. Upload Student Answer Photo: Upload an image of the student's answer.
  2. Enter Student Answer Text: Enter the extracted text from the uploaded image.
  3. Enter Teacher's Answer: Input the correct answer from the teacher.
  4. Total Marks of Question: Enter the total marks for the question.

Getting Started

Installation

  1. Clone the repository:

    git clone https://github.com/om-ashish-soni/ai_examiner_parikshak.git
    cd ai_examiner_parikshak
    
  2. Environment Setup:

    • Create .env file in /ai_search_ui directory of project
    • Replace .env content with
    HF_TOKEN=YOUR_HF_API_TOKEN
    
    • How to get your hf token for free
      • Log in to huggingface
      • Go to Profie > then go to Setttings > then go to Access Tokens tab
      • Access Tokens Page
      • If there exists Access Token then copy it and paste it as HF_TOKEN in .env file of project
      • If Access Token does not exist then click on new token Write the "Name of Token" and Select the "Type of Token" (Read / Write) Access.
      • After creating copy the token and paste it as HF_TOKEN in .env file of project.
  3. Running the AI Examiner:

    • Create Virtual Enviornment & Install Dependencies
    • For windows ( git bash )
      python -m venv .venv
      source .venv/Scripts/activate
      pip install -r requirements.txt
    • For windows ( cmd )
      python -m venv .venv
      .venv\Scripts\activate
      pip install -r requirements.txt
    • For Linux & Mac
      python -m venv .venv
      souce .venv/bin/activate
      pip install -r requirements.txt
    • Run the Streamlit app:
      streamlit run app.py
    • Access the UI in your browser at localhost:8501 (default Streamlit address).

App Structure

  • app.py: Contains the main Streamlit application.
  • LLM.py: Module for performing inference on student and teacher answers.
  • util.py: Utility functions for writing evaluation results.
  • trans.py: Module for text-to-speech functionality.