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midnightoatmeal/
42-ML_reproducibility_checker-hf

Name: 42-ML_reproducibility_checker-hf
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42: ML Reproducibility Checker

42 is an AI-assisted reproducibility audit tool designed to assess the alignment between a research paper and its corresponding Python code. Upload your PDF and code files, and get a quick reproducibility check with a downloadable audit report.

PDF + Code Analysis: Upload a research paper and one or more .py files.
Keyword Extraction: Extract key ML terms from the paper (e.g., attention, optimizer, token).
Code Parsing: Detect Python functions, imports, and random seed usage using AST and regex.
Cross-Validation: Compare extracted paper keywords with function names to find missing alignment.
Reproducibility Report: Generate and download a plaintext audit of code-paper consistency.

git clone https://github.com/midnightoatmeal/42-ML_reproducibility_checker pip install -r requirements.txt streamlit run app1.py

Sample Use Case

You're reviewing a NeurIPS or ICLR submission. With 42, you can:

Upload the PDF paper and its codebase.
Instantly check if:
- Random seeds are set (for reproducibility).
- Key terms in the paper appear in the code.
- All functions and imports are clearly defined.
Download an audit report summarizing the alignment.

Live Demo

Try it live: https://whatis42.streamlit.app

License

MIT

midnightoatmeal/42-ML_reproducibility_checker-hf

42: ML Reproducibility Checker

Sample Use Case

Live Demo

License

midnightoatmeal/
42-ML_reproducibility_checker-hf