Day 4 – February 12, 2026

Date: February 12, 2026
Week: 22
Internship: AI Engineer Intern at AnanaCare
Mentor: Praveen Kulkarni Sir

Welcome to another exciting chapter in my AI/ML internship journey! Today marks Day 9 of my internship, and I’m continuing to dive deep into the AnanaCare Relabel project – a fascinating initiative that combines computer vision, user experience design, and software engineering best practices. This project represents a perfect blend of the theoretical knowledge I’ve been building throughout my internship and real-world application development.

As I reflect on the past weeks, I’ve transitioned from foundational learning to hands-on problem-solving. The AnanaCare Relabel project isn’t just about technical implementation; it’s about creating tools that make complex AI workflows more accessible and efficient. Today, I’m focusing on improving the ScatterPlot component, enhancing UI/UX, and integrating version control into the relabeling workflow.

The ScatterPlot improvements began with a critical issue: the chart appeared distorted at different browser zoom levels. This wasn’t just a visual problem—it undermined the accuracy of data interpretation. I implemented a ResizeObserver to dynamically adjust the aspect ratio, locking it at height = width 0.6 to ensure consistent proportions across all viewing conditions. The regression line and ellipses were also refined, with adjusted opacity to improve visibility without overpowering the data points.

UI/UX enhancements were equally important. I redesigned the notification system to prevent stacking, ensuring only one alert is visible at a time. Button text was improved (“Save Changes” “Save Progress”) and tooltips were added for better clarity. The entire layout was made fully responsive, enabling full-width chart display across all screen sizes.

Version control integration represented a significant leap forward. I added automatic Git commit and push functionality triggered by “Save Progress” clicks. Each commit includes a timestamp and description of landmark edits, with comprehensive error handling and detailed feedback notifications. This ensures all relabel edits are version-controlled and backed up automatically.

The Tune & Train workflow was placed in maintenance mode with clear messaging, preserving the original logic for future restoration.

Testing & validation covered all aspects: TypeScript compilation, Python backend validation, Git operations testing, and chart rendering consistency across zoom levels and screen sizes.

By day’s end, the ScatterPlot component now maintains perfect aspect ratios, automatic Git integration ensures data safety, and the UI provides a professional, responsive experience. The Tune & Train workflow is safely maintained for future use.

Project Status Report: UI/UX and Version Control Integration

1. Completed Milestone: ScatterPlot Component Enhancement

The chart rendering system has been completely overhauled with dynamic aspect ratio management and improved visual clarity. The ResizeObserver ensures consistent display across all browser conditions.

2. Implementation Plan: Version Control and UI Improvements

The integration involved implementing several key features:

Automatic Git Integration: Commit and push on save actions
Responsive Layout: Full-width charts across all devices
Notification System: Controlled, single-alert display
Maintenance Mode: Safe preservation of Tune & Train workflow

This approach transforms the application into a professional, version-controlled tool.

Feature Architecture: Git-Integrated Workflow

One of the most fascinating challenges has been integrating version control seamlessly into a web-based ML workflow. This isn’t just about technical implementation – it’s about creating tools that feel natural while ensuring data safety and auditability.

The save process now automatically creates Git commits, but what happens when users make multiple edits? Or when they want to track specific changes? A robust version control system transforms a simple save action into a comprehensive audit trail, encouraging experimentation without fear of data loss.

To implement this feature, we’re drawing from proven software engineering patterns while adapting them to the specific needs of our annotation workflow. The Automatic Commit Pattern combined with a Feedback Loop provides a solid foundation that maintains performance while ensuring reliability.

1. State Management (Version Control)

Our system now maintains a sophisticated version control workflow with carefully orchestrated operations:

Change Detection: Monitors for meaningful modifications
Automatic Commits: Timestamped saves with descriptive messages
Error Handling: Comprehensive feedback for Git operations
Audit Trail: Complete history of all data modifications

This approach ensures users can work confidently with full version control.

2. Interaction Logic

The key insight was making version control invisible yet comprehensive:

Seamless Integration: Automatic commits on save actions
Descriptive Messages: Meaningful commit descriptions
Error Recovery: Clear feedback and recovery options
Performance Optimization: Efficient Git operations

3. Proposed Workflow Structure

javascript { workflow: { detect: () => { /* check for changes */ }, commit: () => { /* create timestamped commit */ }, push: () => { /* sync with remote repository */ }, feedback: () => { /* provide user notifications */ } } }

This structure ensures reliable version control integration.

Next Steps

As I look ahead, I’m excited about the implementation phase that lies before us. The UI/UX and version control improvements we’ve completed today set a strong foundation, but the real magic will happen when these features prove their value in daily use.

User Testing: I’ll validate the new UI and Git integration through comprehensive testing.
Performance Monitoring: Ensure automatic commits don’t impact responsiveness.
Feature Documentation: Create user guides for the enhanced workflow.

The coming days promise to be a thrilling blend of technical implementation and user experience refinement. Each improvement brings us closer to creating tools that don’t just work, but delight and empower their users. The AnanaCare Relabel project is evolving from a technical exercise into a demonstration of how thoughtful design can transform complex workflows into intuitive experiences.