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Add final DMP '25 report with RAG workflow and AI-assisted translation
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‎src/constants/MarkdownFiles/posts/dmp-25-AmanChadha-FinalReport.md‎

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---
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title: "DMP '25 Final Report by Aman Chadha"
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excerpt: "Final Report for the project Modernizing Music Blocks’ i18n with AI-Assisted Translation"
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category: "DEVELOPER NEWS"
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date: "2025-09-17"
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slug: "2025-09-17-dmp-25-aman-chadha-final-report"
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author: "@/constants/MarkdownFiles/authors/aman-chadha.md"
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tags: "dmp25,sugarlabs,finalreport,aman-chadha"
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image: "assets/Images/c4gt_DMP.png"
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---
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<!-- markdownlint-disable -->
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# DMP '25 Final Report by Aman Chadha
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## Contributor Details
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**Name:** Aman Chadha
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**Email:** [aman.chadha.mmi@gmail.com](mailto:aman.chadha.mmi@gmail.com)
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**GitHub:** [AmanChadha](https://github.com/ac-mmi)
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**Organization:** [Sugar Labs](https://www.sugarlabs.org/)
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**Project:** [JS Internationalization with AI Translation Support](https://github.com/sugarlabs/musicblocks/pull/4731)
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**Mentors:** [Walter Bender](https://github.com/walterbender), [Devin Ulibarri](https://github.com/pikurasa)
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---
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## Project Overview
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Music Blocks is a learning platform for children worldwide. Currently, it primarily supports **English, Japanese, and Spanish**, leaving many learners struggling when the platform is not in their native language. The goal of this project was to **modernize Music Blocks’ i18n system** and introduce an **AI-assisted translation workflow** to improve accessibility and engagement globally.
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Key problems addressed:
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- The legacy `webL10n.js` system lacked **modern i18n features**, including fallback strategies and JSON-based translation support.
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- UI strings often lacked context, leading to ambiguous or inaccurate translations.
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- Translators faced difficulty translating terms with multiple meanings, like "duck" (pitch vs. volume).
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---
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## Project Objectives
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- Migrate from **webL10n.js to i18next** for modern, modular, and maintainable i18n.
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- Automate translation of missing strings using **AI with contextual awareness**.
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- Ensure a **contributor-friendly workflow**, where human translators can review AI suggestions.
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- Expand accessibility for new languages and improve adoption by educators worldwide.
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---
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## Technical Approach
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### Framework Migration
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- **Why migration was needed:**
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- `webL10n.js` was outdated and lacked support for modern i18n features.
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- i18next supports **language-specific formatting**, flexible fallbacks, and **JSON-based translation files**.
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- **Process:**
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- Replaced `webL10n.js` references in the codebase with i18next API calls.
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- Added **fallback strategies**: cleaned text, lowercase, title case, hyphenated strings.
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- Incrementally tested migration to ensure existing UI remained functional.
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### Context-Aware Translation (RAG Model)
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- Extracted **code context** for each `msgid` by taking **5 lines above and below** and any developer comments.
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- Stored context snippets in **context_ui_full.json** with metadata: source file, line numbers, and snippet.
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- Indexed the JSON in **ChromaDB**, a vector database optimized for semantic search.
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- Built a **RAG model** to retrieve and analyze context, generating clear explanations for each string.
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---
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## AI-Assisted Translation Workflow
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### Workflow Steps
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1. **.PO to JSON Automation:**
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- Converted `.po` files to JSON using a Python script, enabling AI integration.
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2. **Translation with Context:**
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- Retrieved context using the RAG model.
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- Sent `msgid + context` to translation API for accurate translations.
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3. **Google Translate API Integration:**
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- Chose Google Translate for its **robustness, contextual translation quality, and reliability**.
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- Open-source alternatives like LibreTranslate produce **word-by-word translations** and fail to use surrounding context.
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4. **Automated QA:**
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- Developed a **Selenium + GPT script** to validate translations automatically.
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- Detected inaccuracies and flagged strings for manual review by a human translator.
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5. **PO File Generation:**
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- Generated complete Arabic, Japanese, and Hindi `.po` files using the automated pipeline.
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---
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### Key Python Translation Script
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```python
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from google.cloud import translate_v2 as translate
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translate_client = translate.Client()
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def translate_prompt(msgid, context, target_lang="ar"):
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prompt = f"{msgid}: {context}"
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result = translate_client.translate(prompt, target_language=target_lang)
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translated = html.unescape(result["translatedText"]).strip()
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return translated.split(':')[0].strip() if ':' in translated else translated
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```
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---
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## Challenges & Solutions
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| Challenge | Solution |
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|-----------|---------|
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| Ambiguous UI strings (e.g., "duck","pitch","minor" etc) | Implemented **RAG model** for context-aware translations |
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| Legacy i18n system | Migrated from `webL10n.js` → i18next with JSON support |
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| Automated translation validation | Built **Selenium + GPT-based QA system** to mark errors for review |
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| Open-source translation drawbacks | Used **Google Translate API** for higher quality and context handling |
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---
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## Achievements
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- Successfully **migrated Music Blocks to i18next**.
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- Developed a **context-aware AI translation workflow**.
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- Generated **Arabic, Japanese, and Hindi `.po` files**.
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- Built an **automation pipeline** for `.po → JSON → AI translation → validation → .po` cycle.
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- Created QA tooling to **check translation accuracy** before human review.
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---
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## Key Learnings
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- Extracting and using **context** drastically improves translation accuracy.
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- Clean migration and testing are crucial when replacing legacy infrastructure.
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- Combining **AI automation with human review** ensures high-quality localization.
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- Open-source translation tools can be limited; commercial APIs may be necessary for production quality.
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---
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## Future Work
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- Add support for more AI translation models (e.g., DeepL, OpenAI).
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- Extend automated QA to **more languages**.
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- Build a **web-based UI** for translators to review flagged translations.
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- Integrate GitHub Actions for automatic updates of `.po` files on new/modified strings.
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---
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## Resources & References
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- **Music Blocks Repository:** [github.com/sugarlabs/musicblocks](https://github.com/sugarlabs/musicblocks)
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- **Migration PR:** [#4731](https://github.com/sugarlabs/musicblocks/pull/4731)
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- **i18next Documentation:** [i18next.com](https://www.i18next.com/)
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- **ChromaDB:** [chromadb.com](https://www.chromadb.com/)
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---
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## Conclusion
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This project modernized Music Blocks’ localization infrastructure, introduced **AI-assisted, context-aware translations**, and enabled **scalable multilingual support**. By combining **framework migration, RAG-based context generation, automated translation, and QA tooling**, Music Blocks is now better equipped to serve children worldwide in their **native languages**, improving engagement, accessibility, and global adoption.
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I am deeply grateful to my mentors, the Sugar Labs community, and C4GT for their guidance and support throughout this journey.
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