This is a submission for the GitHub Copilot CLI Challenge
What I Built
I built Document Localization Studio — a terminal-first + UI-powered app that localizes documents beyond basic translation.
Instead of treating localization as “just translate text,” this project tackles the real-world complexity teams hit in enterprise docs:
- 🌐 Language + terminology adaptation (custom glossary + reusable term memory)
- 🗓️ Date/time + timezone conversion (e.g., America/New_York → Europe/Berlin)
- 💱 Currency + FX conversion (USD → EUR/JPY/BRL/… with locale defaults you can edit)
- 📏 Unit conversion (mi→km, lb→kg, °F→°C)
- 📬 Address/phone/postal tweaks (locale labels + phone formatting)
- 🧾 Tax label adaptation (Sales Tax → VAT/GST-style labels)
- 🔒 Legal clause lock/protection (
[[LOCK]]...[[/LOCK]]+ auto-protect legal-ish sentences) - ✅ Structure-aware QA (placeholders preserved, length-change warnings, cross-ref/TOC flags, workflow gating)
Supported file types 🧩
.txt.docx-
.pdf- layout-preserving mode for editable PDFs (when available)
- screenshots/images:
.png,.jpg,.jpegvia OCR
Supported locales 🗺️
de_de, es_es, fr_fr, it_it, ja_jp, ko_kr, pt_br, zh_cn, zh_tw
Run locally 🧪
cd "/Users/swatigoyal/Documents/New project/document_localizer_challenge"
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
streamlit run app.py
CLI example 🧰
python -m localizer.cli input.pdf output.pdf \ --locale de_de \ --source-timezone America/New_York \ --tone legal
Demo
- Repo: https://github.com/swatigoyal911/document_localizer_challenge
- Live demo: https://youtu.be/yX4bfBfwlMk
Walkthrough idea (video/screenshots) 🎬
- Upload a real invoice/contract PDF (or a DOCX).
- Pick a target locale (ex:
de_de) and watch the default FX rate auto-load (editable). - Toggle components (units, tax labels, legal lock, term memory).
- Run localization.
- Show the outputs:
- 📊 Before/After scorecards
- 🔎 Side-by-side visual diff
- 🌡️ Layout risk heatmap
- 🧾 QA report (JSON)
- Download the localized file + QA report.
Stack / Libraries 🧱🐍
Built with a “free stack”:
-
streamlit(UI dashboard) -
python-docx(DOCX read/write) -
pypdf(PDF text extraction) -
pymupdf(PyMuPDF — layout-preserving PDF localization mode) -
reportlab(PDF re-render fallback when layout mode isn’t available) -
pillow+pytesseract(OCR pipeline for screenshots/images)
OCR note: screenshot localization requires a local Tesseract binary in addition to
pytesseract(ex: macOSbrew install tesseract).
My Experience with GitHub Copilot CLI 🤝⚡
I used GitHub Copilot CLI as a coding partner directly in the terminal to:
- 🏗️ scaffold modules quickly (pipeline, PDF/DOCX/image IO, CLI wiring)
- 🧠 iterate on regex-heavy transformations (dates, currency, units, placeholders)
- 🧩 design locale profiles/defaults and keep the logic consistent
- 🎛️ wire Streamlit controls to the backend config without breaking flow
- 🧪 add QA heuristics + sensible fallback paths for PDFs/OCR
- 🧹 speed up refactors while keeping the project clean and extensible
The biggest win: fast iteration on non-trivial logic (PDF handling + transformation rules + feature toggles) without leaving the terminal.
What’s Next / Improvements 🚀🤖
This is a strong prototype — and there’s a lot we can level-up with AI integration later:
- 🧠 LLM-backed translation (while keeping deterministic transforms + locks)
- 📚 smarter terminology alignment (context-aware term choice + consistency scoring)
- 🧾 stronger compliance checks (policy packs per industry/locale)
- 🧩 plug-in architecture for new transforms + QA rules
- 🖼️ better OCR layout reconstruction (tables, columns, headers/footers)
If you’ve worked on localization, I’d love your feedback: what transformations or QA checks would you trust most in production?




Top comments (2)
Impressive work 👏
Document Localization Studio goes far beyond basic translation by handling terminology, legal locks, FX/unit conversion, and structure-aware QA — that’s real enterprise-level thinking. Clean execution, strong feature depth, and great use of GitHub Copilot CLI for rapid iteration. 🚀
Wow, that’s a great set of functionalities you’ve provided — really well thought out and comprehensive! 👏