Week 6 was all about bringing CodeAtlas to life on the frontend. After stabilizing the backend and data pipelines last week, I finally started visualizing the data—turning raw Neo4j relationships into an interactive graph UI.

This week had some satisfying visual wins, but also exposed a critical lesson: data consistency between frontend state and Neo4j matters more than visuals.

From canvas rendering to graph coloring, from repo-switching bugs to wiping databases clean—this week was a full frontend + data debugging sprint.

1. Starting Frontend Graph Visualization

The main focus this week was frontend graph rendering.

Graph Canvas: Before & After

I began by integrating graph data into the canvas. The initial version was functional but messy—nodes overlapped, relationships were unclear, and everything looked… confusing.

After iteration:

• Improved node spacing

• Cleaner edge rendering

• Better zoom & pan behavior

This was the first moment CodeAtlas actually felt real.

2. Loading Neo4j Data for Graph Rendering

Once the canvas was ready, the next challenge was loading graph data correctly.

Initial Approach

• Load all nodes

• Load all relationships

• Render everything at once

The Issue

This caused:

• Performance issues

• Overloaded graphs

• Difficulty filtering meaningful connections

Fix

I changed the approach to:

• Load nodes

• Fetch only required relationships

• Render a controlled subset of the graph

🎯 Result: Faster rendering and much clearer graph exploration.

3. Visual Differentiation: Colors & Legend

Once the data was stable, I focused on visual clarity.

Node Coloring

Different colors were assigned based on node type:

• Files

• Functions

• Classes

• Dependencies

This made the graph immediately understandable—even without reading labels.

Legend Added

To avoid confusion, I added a legend in the frontend that clearly explains:

• What each color represents

• How to interpret the graph

  

🧭 This small UI addition massively improved usability.

4. The Repo Switching Bug (Major Debugging Moment ⚠️)

Everything looked good—until I switched repositories.

The Problem

• Repo A loads correctly

• Switch to Repo B

• Graph still shows Repo A relationships

• Data looked “mixed” and incorrect

At first glance, it looked like a frontend state bug.

Root Cause

After debugging, I discovered:

• When switching repos, new repo data was NOT loading into Neo4j

• The frontend was querying Neo4j correctly—but Neo4j still contained old repo data

So technically:

The frontend was right. The database was wrong.

5. Temporary Fix: Separate Branch + JSON Loader

To isolate the issue:

• I created a separate branch for loading a new repo

• Repo data was converted into a JSON file

• JSON files were stored in the public/ folder

Logic Implemented

• If JSON exists → load into Neo4j

• If not → throw an error

This confirmed the issue was data persistence, not rendering.

6. Duplicate & Multiple Entries in Neo4j

Once repo loading worked, another issue surfaced.

New Problem

• Repo 1 exists in Neo4j

• Load Repo 2

• Neo4j adds data without deleting Repo 1

• Result:

  • Duplicate nodes

  • Multiple relationships

  • Frontend filters completely break

Final Fix (The Right One ✅)

Before loading any new repo data:

• Run a wipe script on Neo4j

• Clear all existing nodes and relationships

• Load the new repo from scratch

Now:

• One repo at a time

• Clean graph

• Predictable frontend behavior

🎉 Result: Everything works correctly and consistently.

i also put the empty filter as you see some user just want to see the file it load the 30 file max

Key Takeaways

• Frontend graphs are only as good as backend data consistency

• Visual clarity (colors + legends) is not optional—it’s essential

• Repo switching must be treated as a full state reset

• Neo4j needs explicit cleanup unless multi-repo support is designed intentionally

• Debugging frontend issues often leads back to data layer mistakes

What’s Coming Next (Week 7 Roadmap 🚀)

Next week, the focus shifts to user experience & intelligence:

• 📁 File structure visualization

• 🧠 AI-powered code summaries

• 📋 One-click code copy feature

• 👉 Side panel that opens on node click

  • File details

  • Function summaries

  • Dependencies

The goal: make CodeAtlas not just visual—but useful for real code understanding.

🤝 Contributions

Ideas, improvements, and suggestions are always welcome.
You’re encouraged to submit issues or pull requests to help evolve the platform.

Github repo