Moss Results scraper with powerful insights & analysis 💡
Install using pip from PyPI
pip install plagcheck
or directly from GitHub if you cannot wait to test new features
pip install git+https://github.com/codeclassroom/PlagCheck.git
"""Usage example""" import os import pprint from plagcheck.plagcheck import check, insights, share_scores from dotenv import load_dotenv load_dotenv() language = "java" userid = os.environ["USER_ID"] moss = check(language, userid) moss.addFilesByWildCard("testfiles/test_java*.java") # or moss.addFile("testfiles/test_python.py") moss.submit() print(moss.getHomePage()) result = moss.getResults() pprint.pprint(result) # print potential distributor-culprit relationships pprint.pprint(insights(result)) # print frequency of each shared solution pprint.pprint(share_scores(result))
- Python 3.6+
- virtualenv
- Create virtual environment.
virtualenv -p python3 venv && cd venv && source bin/activate
- Clone the repository.
git https://github.com/codeclassroom/PlagCheck.git
- Install Dependencies.
pip install -r requirements-dev.txt
- Run tests.
pytest plagcheck
- Lint the project with
flake8 plagcheck --max-line-length=88 --ignore=F401 black --check --diff plagcheck
See the CHANGELOG.md file for details.
👥 Bhupesh Varshney
- Twitter: @bhupeshimself
- DEV: bhupesh
This project is licensed under the MIT License. See the LICENSE file for details.
Please read the CONTRIBUTING guidelines for the process of submitting pull requests to us.
Thanks goes to these wonderful people (emoji key):
Alexey Dubrov 💻 🐛 |
This project follows the all-contributors specification. Contributions of any kind welcome!