M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

#!/usr/bin/env python3 """Apply translations from JSON back into a DOCX file.""" import json import re import sys from pathlib import Path from docx import Document BODY_RE = re.compile(r"^body:p(\d+):r(\d+)$") HEADER_RE = re.compile(r"^header:s(\d+):p(\d+):r(\d+)$") FOOTER_RE = re.compile(r"^footer:s(\d+):p(\d+):r(\d+)$") def apply_translations(docx_path: str, translations_path: str, output_path: str): doc = Document(docx_path) translations = json.loads(Path(translations_path).read_text(encoding="utf-8")) applied = 0 for item in translations: unit_id = item["id"] target = item.get("target", item.get("source", "")) run = None m = BODY_RE.match(unit_id) if m: p_i, r_i = map(int, m.groups()) try: run = doc.paragraphs[p_i].runs[r_i] except IndexError: continue m = HEADER_RE.match(unit_id) if m: s_i, p_i, r_i = map(int, m.groups()) try: run = doc.sections[s_i].header.paragraphs[p_i].runs[r_i] except IndexError: continue m = FOOTER_RE.match(unit_id) if m: s_i, p_i, r_i = map(int, m.groups()) try: run = doc.sections[s_i].footer.paragraphs[p_i].runs[r_i] except IndexError: continue if run: run.text = target applied += 1 doc.save(output_path) print(f"Applied {applied} translations -> {output_path}") if __name__ == "__main__": if len(sys.argv) < 4: print("Usage: python apply_translations.py <input.docx> <translations.json> <output.docx>") sys.exit(1) apply_translations(sys.argv[1], sys.argv[2], sys.argv[3])