Question Paper Analyzing Tool (QPAT-01)

Overview

QPAT-01 is an AI-powered tool designed to analyze past question papers, identify recurring patterns, and generate insightful question banks. It helps students and educators focus on high-priority topics, predict potential exam questions, and save valuable preparation time.

Key Capabilities

• Automated PDF Extraction: Automatically extracts questions from PDF files.
• Intelligent Question Analysis: Uses AI to understand question context and categorize them.
• Pattern Identification: Identifies frequently repeated questions across multiple years.
• Question Bank Generation: Creates organized question banks, categorized by subject, unit, and year.
• Likely Question Prediction: Predicts potential questions for future exams based on historical data.
• Flexible Export Options: Supports downloads in both Markdown and PDF formats.
• User-Friendly Interface: Provides an intuitive web interface via Streamlit.

Getting Started

Prerequisites

• Python 3.7+
• A valid Google API key (required for AI functionalities)
• Internet connectivity (for accessing Google AI services)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd EH_01

2. Install the required Python packages:

   pip install -r requirements.txt

Configuration

1. API Key: Obtain a Google API key and enter it in the application's sidebar. The application will automatically save it to .env file.

Usage

1. Run the Streamlit application:

   streamlit run main.py

2. Open the URL displayed in the terminal in your web browser (usually http://localhost:8501).

3. Upload your PDF question papers using the file uploader.

4. Click the "Process Files" button.

5. Explore the generated question bank, predicted questions, and download options in the provided tabs.

Project Structure

/Users/kumarswamikallimath/Desktop/QPAT/
├── .streamlit/             # Streamlit configuration files
├── data/                  # Data storage (JSON, Markdown, PDFs)
│   ├── json_data/
│   ├── md/
│   └── output/
├── pdf/                   # Directory for uploaded PDF files
├── src/                   # Python source code
│   ├── json_to_markdown.py
│   ├── markdown_to_pdf.py
│   ├── most_likely_questions.py
│   └── pdf_to_json.py
├── .env                    # Stores the Google API key
├── .gitignore              # Specifies intentionally untracked files that Git should ignore
└── main.py                 # Streamlit application entry point

Contributing

Feel free to contribute to the project by submitting pull requests, reporting issues, or suggesting new features.

License

This project is licensed under the MIT License - see the LICENSE file for details.