Diagram Detector

Overview

The Diagram Detector project identifies and classifies diagrams in questions and images. It uses machine learning models for text and image detection to help detect diagram-related content in educational and scientific material.

Project structure

• data/ - datasets

  • raw/ - original datasets
    • text_dataset.csv
    • images/
      • diagram/ (subfolders: biology, botany, chemistry, mathematics, physics, zoology)
      • no_diagram/
  • processed/ - processed datasets
    • processed_text_dataset.csv
    • processed_images/

• models/ - trained models

  • text_detector/
  • image_detector.pt

• src/ - training / preprocessing / utilities

  • preprocess.py
  • text_detector.py
  • image_detector.py

• api/ - FastAPI application

  • main.py

• tests/ - unit tests

  • test_api.py

Other files: Dockerfile, requirements.txt, setup.sh, start.py.

Features

• Text detection using transformer-based models (BERT or similar)
• Image detection using a ResNet-based classifier
• FastAPI REST endpoints with automatic docs
• Health checks and basic monitoring endpoints
• Configurable via environment variables
• Tests with pytest and included example scripts

Requirements

• Python 3.8+
• Recommended: create and use a virtual environment (instructions below)

Virtual environment (recommended)

Use a virtual environment to isolate project dependencies.

1. Verify Python is available (use python3 on many Linux/macOS systems):

   python3 --version

2. Create the virtual environment in the project root:

   python3 -m venv .venv

3. Activate the virtual environment:

   • Linux / macOS (bash/zsh):

     source .venv/bin/activate

   • Windows (PowerShell):

     .\.venv\Scripts\Activate.ps1

4. Upgrade packaging tools and install requirements:

   pip install --upgrade pip setuptools wheel
   pip install -r requirements.txt

Notes:

• If you need CUDA-enabled PyTorch, use the selector at https://pytorch.org/get-started/locally/ to obtain the correct install command (it may use a custom --index-url). Example CPU-only command:

  pip install torch --index-url https://download.pytorch.org/whl/cpu

Quick start

1. Clone the repository:

   git clone https://github.com/ankitrajsh/ML-Detect-Diagram-in-Question-convert-into-Mathjax.git
   cd ML-Detect-Diagram-in-Question-convert-into-Mathjax

2. Create and activate a virtual environment (see the "Virtual environment" section above for commands).

3. Install dependencies:

   pip install -r requirements.txt

4. (Optional) Copy environment example and edit values if present:

   cp .env.example .env  # only if .env.example exists

5. Start the API (two equivalent options):

   # recommended: helper script
   python start.py
   
   # or run the FastAPI app directly
   python api/main.py

6. Open the API docs in your browser: http://localhost:8000/docs

Using Docker

1. Build the image:

   docker build -t diagram-detector .

2. Run the container (map the port):

   docker run -p 8000:8000 diagram-detector

API endpoints

• Documentation: GET /docs
• Health: GET /health
• Info: GET /

Example: Text detection

curl -X POST "http://localhost:8000/detect_text" \
     -H "Content-Type: application/json" \
     -d '{"question": "Draw the structure of benzene"}'

Example: Image detection

curl -X POST "http://localhost:8000/detect_image" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@your_image.jpg"

Configuration

Configuration is controlled via environment variables. If a .env.example is provided, copy it to .env and update values. Important variables (defaults shown where applicable):

• API_HOST (default: 0.0.0.0)
• API_PORT (default: 8000)
• LOG_LEVEL (default: info)
• MAX_FILE_SIZE_MB (default: 10)
• TEXT_MODEL_PATH - path to a saved text model (if required)
• IMAGE_MODEL_PATH - path to the image model (e.g., models/image_detector.pt)

Development

• Run tests:

  pytest

• Run a single test file:

  pytest tests/test_api.py

• Run basic tests without pytest (if the file is executable as a script):

  python tests/test_api.py

• Create sample data (if preprocess.py supports it):

  python src/preprocess.py --create-sample

• Train models (if training scripts are implemented):

  python src/text_detector.py
  python src/image_detector.py

Troubleshooting

• If the API fails to start, check that dependencies from requirements.txt are installed and that the configured model paths exist.
• Check logs (the application respects LOG_LEVEL).

Contributing

Contributions are welcome. Please open issues or submit pull requests.

License

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