ksanyok/TextHumanize v0.28.4

TextHumanize v0.28.4

on GitHub

Added

• Explainable AI audit — added detect_ai_explain() with metric contributions, highlighted spans, sentence-level reports, mixed-content shares, calibration, confidence intervals, and suggested actions.
• Unified watermark forensics — added watermark_report(), watermark_report_batch(), clean_safe(), and neutralise_aggressive() for Unicode, homoglyph, invisible-character, and statistical watermark risk reporting.
• Promopilot-ready audit API — added audit_report() plus CLI/reporting paths for AI and watermark audit flows.
• Strict and minimal humanization controls — added quality_gate="strict", minimal=True, --minimal, --only-flagged, and intent aliases for seo_article, landing_page, product_description, support_reply, academic, legal, and social_post.
• Humanize explain metadata — humanize() now returns lightweight metrics_after["humanize_explain"] with top change reasons, remaining risks, sentence report, score delta, and quality summary.
• Short commercial copy golden set — added regression coverage for landing, product, and support-copy flows used by Promopilot-style integrations.

Changed

• GitHub CI stability — Python 3.12 now uses one parallel test run and keeps coverage as a local release check, avoiding hosted-runner coverage hangs while preserving full matrix validation.
• Release verification baseline — local release checks now include full pytest (2105 passed), mypy, ruff, version sync, and coverage (80.09%).

Fixed

• NumPy dtype stability in training v2 — NumpyMLP.forward() preserves float32 for sigmoid/tanh activations, fixing Python 3.12 mypy failures in CI.
• Neural inference warnings — stabilized NumPy matmul paths in neural engine/LM code and covered the cleanup with regression tests.