ReliaPy is an open-source Python/Gradio workbench designed to make reliability analysis accessible, transparent, reproducible, and publicationoriented. It integrates four major workflows: life-data analysis, reliability-growth modeling, repairable-system analysis, and accelerated life testing. Users can upload CSV or Excel data, apply Weibull, lognormal, Crow–AMSAA, power-law, log-linear, piecewise nonhomogeneous Poisson process, Arrhenius, and power-law stress models, and obtain parameter tables, diagnostic plots, engineering interpretations, and downloadable CSV and high-resolution PNG outputs. The platform supports censored data, automatic column inference, change-point detection, recurrent-event visualization, and use-stress life prediction. Its modular architecture separates data processing, computation, presentation, and export, enabling deployment in Google Colab, Hugging Face Spaces, or local environments. Validation against a NIST censored life-test dataset produced Weibull estimates differing by less than 0.005% from reference values. ReliaPy supports students, researchers, and small engineering laboratories. It is intended for education, rapid analysis, reproducible demonstrations, and early-stage reliability method development rather than replacing expert engineering judgment.
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