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PySymBench

Infrastructure for model comparison and evaluation in symbolic execution workflows.

This project is a local web application designed to compare symbolic execution results of an uploaded trained model (in .onnx format) on a selected dataset with a baseline symbolic execution approach (non-AI).

The system uses PySymGym tools to run symbolic execution on the dataset and evaluate the results. After execution completes, the results are sent to the email address you provide.

Installation

The repository contains both frontend and backend components, and both must be launched for the application to work.

Email Communication (Gmail)

To enable email delivery of results, create a .env file containing your Gmail credentials:

EMAIL=your_email@gmail.com
APP_PASSWORD=your_app_password

EMAIL — your Gmail address
APP_PASSWORD — your Gmail App Password (not your regular account password)

Backend Setup

  1. Install Python 3.14 and Docker, then install the project dependencies:
pip install -r requirements.txt
  1. Run the application setup script (this builds a Docker container with the PySymGym repository and downloads the required dataset):
python -m backend.launch_service.app_setup
  1. Start the Celery broker (Redis):
docker run --name redis-for-celery -p 6379:6379 -d redis
  1. Start the Celery worker and the application server:
celery -A backend.utils.task worker --loglevel=info && uvicorn backend.main:app

Frontend Setup

  1. Install Node.js with npm.

  2. Install frontend dependencies:

cd frontend
npm install
  1. Start the frontend development server:
npm run build