[DMP 2025]: Proposal for AI-powered Debugger Prototype for Music Blocks | GSoC 2025 #4618
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I'm curious to know what kind of issue ticket is this? |
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This is a proposal for an AI-powered debugger feature for Music Blocks, submitted under the GSoC 2025 DMP project category. |
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Hi *sai-charan1* ,
Thanks for your interest in this issue! I’m really excited that you'd like
to work on the AI-powered debugger for Music Blocks. Feel free to explore
the repo and share any thoughts, suggestions, or improvements.
Let me know if you need any help understanding the prototype or setup —
happy to collaborate!
…On Sun, Apr 13, 2025 at 10:35 AM Clyte ***@***.***> wrote:
@HasinTaj <https://github.com/HasinTaj> I would like to work on this issue
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@HasinTaj <https://github.com/HasinTaj> I would like to work on this issue
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@HasinTaj since the project is mentioned oin both Dmp 2025 and GSOC 2025 so i am curious to know how it will be assigned? |
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@HasinTaj I'm excited to explore the Music Blocks repo and start working on the AI-powered debugger. I've gone through the prototype and setup instructions, and I'm eager to begin contributing. Could you please assign this issue to me so I can start tracking my progress and development? Looking forward to collaborating! |
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@HasinTaj I will do this |
i am too looking for the answer of this question |
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kindly please wait !! |
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and regarding this issue i think this need to be closed cause this is not the right place to put your proposal reach out to us on matrix for such doubts |
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Ticket Contents
Description:
Hello Sugar Labs team,
I am submitting a proposal for an AI-powered debugger prototype for the Music Blocks project as part of GSOC 2025. The goal of this project is to develop an AI-driven debugger that helps users (especially beginners) troubleshoot and improve their block-based programs in Music Blocks. This tool will provide intelligent suggestions, detect common errors, and offer real-time feedback on their code.
Objectives:
Build an AI-based debugging system integrated into Music Blocks.
Detect and suggest fixes for common programming errors in the user’s blocks.
Offer real-time feedback, helping users to identify mistakes instantly.
Improve the overall user experience and accessibility for beginners.
Proposed Milestones:
Phase 1 – Research and data collection: Analyze user errors in Music Blocks and gather common error patterns.
Phase 2 – AI Model Development: Build the AI-powered error detection and suggestion system.
Phase 3 – Integration: Integrate the AI model into Music Blocks and create an easy-to-use interface.
Phase 4 – Testing & User Feedback: Conduct extensive testing and gather feedback to improve the system.
Expected Outcome:
The outcome of this project is to deliver an intelligent, real-time debugger tool that improves the learning and debugging experience for users of Music Blocks. This tool will be essential for helping beginners understand their mistakes and improve their programming skills.
Thank you for considering my proposal. I am excited about the opportunity to contribute to Music Blocks through GSoC 2025! a brief description of the feature, including why it is needed and what it will accomplish.]
Goals & Mid-Point Milestone
Research user errors in Music Blocks and gather data on common debugging challenges.
Develop an AI model that can detect common errors in Music Blocks code (such as incorrect block combinations or misplacements).
Integrate the AI model into Music Blocks, providing real-time feedback and suggestions to users.
Create a user-friendly interface for the debugger that makes it easy for beginners to use.
Goals Achieved by Mid-point Milestone: By the midpoint (1.5 months), the AI model should be developed and integrated with basic error detection capabilities. The debugger interface should also be partially implemented for feedback.
Setup/Installation
Setup/Installation Guide
Clone the Music Blocks Repository:
First, clone the Music Blocks repository to your local machine using the following command:
git clone https://github.com/sugarlabs/musicblocks.git
Navigate to the Music Blocks Directory:
After cloning, move into the musicblocks directory:
cd musicblocks
Install Dependencies:
Music Blocks may have some dependencies that you'll need to install. If there's a requirements.txt file, you can install Python dependencies:
pip install -r requirements.txt
Additionally, if your AI-powered debugger requires any special Python libraries (like TensorFlow, PyTorch, or others), you can list those in a new requirements.txt or install them directly:
pip install tensorflow
pip install flask # if you're using Flask for backend
Set Up AI Debugger Integration:
Clone or add your AI debugger code into the Music Blocks repository.
Make sure to include your AI model, for example, a debugger.py or ai_debug_lens_prototype.py, and any other necessary files (such as models or configuration files).
If you're using an external AI model, make sure to download or prepare the model before integrating it into the system.
Run the Application:
To test the Music Blocks environment with the debugger integrated, follow these steps:
Launch Music Blocks using:
npm start
or any other command defined in the README of Music Blocks for running the application.
Ensure that the debugger is working by triggering errors or issues within the Music Blocks interface.
Additional Setup (Optional):
If there are additional setup steps (such as setting environment variables for API keys or model configurations), include them here.
For example:
export AI_MODEL_PATH="path/to/your/model"
Expected Outcome
The expected outcome of your project would be:
A fully functional AI-powered debugger that identifies and suggests fixes for common mistakes in Music Blocks code.
A real-time error detection system providing instant feedback to users, improving the debugging experience.
The system will be designed with beginner-friendly UI so that users can easily follow and understand the debugging suggestions.
Acceptance Criteria
Here are possible acceptance criteria for your project:
The AI model detects common programming errors in Music Blocks code.
Feedback is provided in real-time, showing the user the error and suggesting possible solutions.
The debugger interface is easy to use and intuitive for Music Blocks beginners.
The solution works without errors across different Music Blocks projects.
Implementation Details
Technologies Used:
AI Model: Python, TensorFlow (for error detection using AI).
Integration: JavaScript (to integrate the AI model with Music Blocks).
Frontend: HTML, CSS, and JavaScript for the user interface.
Backend: Flask (if you need a server for running the AI model).
Version Control: Git, GitHub for repository management.
Mockups/Wireframes
Product Name
AI-powered Debugger for Music Blocks
Organisation Name
Sugar Labs
Domain
Education
Tech Skills Needed
HTML, JavaScript, Machine Learning, Python
Mentor(s)
Alberto Acquilino
Mirko d’Andrea
Keerthi Reddy Kambham
Category
AI, Other
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