week 05 update by Om Santosh Suneri

Author: omsuneri

src/constants/MarkdownFiles/posts/2025-07-06-gsoc-25-omsuneri-week05.md

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+---
+title: "GSoC’25 Week 05 Update by Om Santosh Suneri"
+excerpt: "AI-powered Debugger for Music Blocks"
+category: "DEVELOPER NEWS"
+date: "2025-07-06"
+slug: "2025-07-06-gsoc-25-omsuneri-week05"
+author: "@/constants/MarkdownFiles/authors/om-santosh-suneri.md"
+tags: "gsoc25,sugarlabs,week05,Debugger,AI,Music Blocks"
+image: "assets/Images/GSOC.png"
+---
+
+<!-- markdownlint-disable -->
+
+# Week 05 Progress Report by Om Santosh Suneri
+
+**Project:** [AI-powered Debugger for Music Blocks](https://github.com/omsuneri/AI-powered-Debugger-for-Music-Blocks)  
+**Mentors:** [Walter Bender](https://github.com/walterbender/) [Sumit Srivastava](https://github.com/sum2it)  
+**Assisting Mentors:** [Devin Ulibarri](https://github.com/pikurasa/)  
+**Reporting Period:** 2025-06-29 - 2025-07-05
+
+---
+
+## Goal for This Week
+
+**Migrating Music Blocks JSON-to-Text Converter to Streamlit App**
+
+---
+
+## This Week’s Achievements
+
+### Introduction
+
+This week, I focused on one of the key deliverables of the project — **integrating the JSON-to-Text Representation Converter into a Streamlit application**. This marks a major step in our plan to create a seamless debugging experience for Music Blocks users. The converter, which was originally built using JavaScript, is now fully functional in Python via Streamlit and ready for integration with the AI-powered debugger.
+
+### What I Did
+
+#### Goal
+
+The previous tool was a **client-side JavaScript converter** that parsed JSON representations of Music Blocks projects and produced a structured, readable **tree-view text format**. The aim this week was to **translate this logic into Python** and build a **Streamlit interface** to make the tool backend-friendly and easily integrable with the AI Debugger app.
+
+#### Migration from JavaScript to Python
+
+Converting the JavaScript-based logic to Python wasn’t a simple one-to-one translation. It involved rethinking data structures, managing recursion differently, and carefully ensuring that **each Music Blocks "block" type was accurately represented** in the output.
+
+Key technical components of the migration included:
+
+* **Parsing the block structure**:
+
+  * Each block in the JSON is structured like `[block_id, block_type, ..., connections]`
+  * The Python version uses dictionaries (`block_map`) and recursion to follow nested or sequential connections (`clamp` and `stack` logic).
+
+* **Handling specific block types**:
+
+  * Each block type (like `start`, `setmasterbpm2`, `newnote`, `repeat`, etc.) has a distinct logic for representation.
+  * For example, the `setmasterbpm2` block may include a divider block to represent beat values (like `4/4 = 1.00`), which must be parsed recursively.
+
+* **Redacting base64-encoded content**:
+
+  * Just like in the JS version, the Python converter checks for base64 strings (e.g., audio/image data) and replaces them with `"data"` to keep the output clean and safe.
+
+* **Maintaining tree formatting**:
+
+  * I replicated the `├──` and `│` styled tree structure for visual clarity.
+  * Indentation is handled dynamically based on the depth of recursion.
+
+#### Enhancements Added
+
+While rewriting, I also took the opportunity to **extend the support to more block types** that weren’t handled earlier — for example:
+
+* `arc`
+* `incrementOne`
+* `pitch`
+* `text`
+* `settransposition`
+
+This ensures that even **newer or more complex Music Blocks projects** are parsed correctly and comprehensively.
+
+#### The Streamlit App
+
+Once the backend logic was ready, I integrated it with a user-friendly Streamlit interface. The app consists of:
+
+* A **text area** for JSON input.
+* A **convert button** to trigger the parsing.
+* A cleanly formatted **output section** with scrollable, monospaced text.
+* Error handling for invalid or empty JSON.
+
+---
+
+### Why It Matters
+
+Music Blocks is used in educational environments. One of the biggest challenges new users face is **understanding how blocks connect and function** under the hood. By converting the visual block code into a readable text format, this tool:
+
+* Makes debugging more accessible for students.
+* Helps educators explain project logic in class.
+* Provides an exportable, printable format of block logic.
+
+#### Foundational Component for the Debugger
+
+This converter will play a **crucial role** in the **AI-powered Music Blocks Debugger**. By giving a structured, simplified text representation of the project:
+
+* The LLM (Large Language Model) will better understand the project logic.
+* It enables **embedding**, **chunk retrieval**, and **semantic search** for debugging.
+* Users will be able to see both their visual project and a clean text summary on the same platform.
+
+#### Seamless Integration Ahead
+
+Now that the converter is in Streamlit (and Python), integrating it into the AI Debugger system becomes straightforward:
+
+* No need to mix JavaScript and Python — the backend stays unified.
+* Users can input JSON and debug in the **same interface**.
+* It aligns with the vector database and LLM pipeline we’re building.
+
+### 📸 Preview
+
+Here’s a quick preview of the app:
+
+<a href=""><img src="https://i.ibb.co/XZt6MF9k/Screenshot-2025-07-05-at-3-09-15-PM.png" alt="Convertor Streamlit interface"></a>
+
+
+### Final Thoughts
+
+Rewriting an entire logic-heavy JavaScript app into Python was both challenging and rewarding. It made me deeply understand how each block works in Music Blocks and how a simple but well-structured parser can bring clarity to even the most complex visual projects.
+
+--- 
+
+## Next Week’s Roadmap
+
+* Integrate the Streamlit-based converter directly into the AI Debugger interface.
+* Improve the understanding of actions in the project by enhancing the LLM prompts.
+
+## Resources & References
+
+- **Repository:** [JSON to Text representation](https://github.com/omsuneri/JSON-to-Text-representation)
+- **Repository:** [AI-powered Debugger for Music Blocks](https://github.com/omsuneri/AI-powered-Debugger-for-Music-Blocks)
+- **Debugger Streamlit App:** [Music Blocks Debugger](https://debuggmb.streamlit.app/)
+- **Directory for Projects:** [Embedding Project Set](https://github.com/omsuneri/AI-powered-Debugger-for-Music-Blocks/tree/main/data/docs)
+
+
+## Acknowledgments
+
+Thank you to my mentors, the Sugar Labs community, and fellow GSoC contributors for ongoing support.
+
+---