You ever open up a project and instantly regret your life choices? The imports are tangled, classes are calling each other like it’s a group chat gone wrong, and just finding where one thing connects to another feels like you’re decoding ancient scrolls. That was me—until I got tired of playing archaeologist in my own code. So I built a tool: An AI-powered code dependency visualizer.
Think Google Maps for Python projects. It scans your files, maps your relationships, and spits out a clean Graphviz-based visual that actually makes sense. And because I’ve gone all-in on tablet life in 2025, I built the whole thing right on my Samsung Galaxy Tab S9. Yep—no laptop needed.
This post breaks down the how, the why, the facepalms, and why this might be your next weekend project.
Environment Setup: Dev from a Tablet? Yes, Really.
Gear Check:
- Tablet: Samsung Galaxy Tab S9 in DeX mode
- Keyboard + Mouse: Essential for file navigation and sanity
- Python 3.x: Installed via Termux
- Graphviz: For rendering dot files into readable diagrams
- AI/NLP Layer: Lightweight model for labeling function/class relationships
- GitHub Integration: Cloned and updated projects directly into Termux workspace
The whole thing ran locally. No cloud servers. No bloated IDEs. Just clean, offline code mapping from the couch.
How It Works: Under the Hood
Here’s the breakdown of how this beast functions:
1. Code Parsing
The script walks through your project directory, picking up .py files. It identifies:
- Imports
- Function and class definitions
- Intra-project dependencies (calls, inheritance, usage)
2. AI Annotation
Using a basic NLP layer, the script:
- Analyzes docstrings and inline comments
- Labels relationships (“extends”, “calls”, “utility for”, etc.)
- Helps make the graph more understandable, even if the code is messy
3. Graphviz Rendering
Using the graphviz Python module, it:
- Draws files and functions as nodes
- Connects them with arrows representing relationships
- Uses colors and shapes for module types (UI, backend, utils)
- Adds tooltips and clickable links (where supported)
4. Interactivity (Optional)
In Termux or a basic local web server, I could click a node and open the file directly in a local text editor. Handy for debugging weird logic.
Real-World Wins
- Navigating legacy code became way faster
- Reviewing old freelance projects? Zero context-switch pain
- New dev onboarding? Send them the map
- Found circular dependencies I didn’t even know existed
This wasn’t just a visualization—it became a dev sanity tool.
Best Practices: From Pain to Polish
If you’re building a version of this (you should), here are some lessons I paid for in headaches:
- Keep AI lightweight: You don’t need transformer models here. Regex + keyword-based parsing + some fuzzy logic is enough for docstring labeling.
- Avoid graph overload: Don’t plot everything. Focus on key relationships.
- Modularize: Separate the parser, analyzer, and visualizer. Makes testing way easier.
- Cache
.dotoutputs: Re-parsing on every commit wastes time. Use file hashes to update only what changed. - Name your nodes well: A node labeled
helper.pyis helpful. A node labeledv23-bak.py~is a cry for help.
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Conclusion: Why Every Dev Needs a Map
This project started as a “why not” experiment on a tablet and ended up becoming part of my weekly workflow.
There’s something magical about seeing your entire codebase laid out like a subway map—dependencies visible, paths obvious, bugs easier to spot.
And no, doing it on a tablet wasn’t just to flex. It reminded me that development in 2025 is mobile-ready. With the right setup, your dev environment can live in your backpack and still punch above its weight.
So if you’re tired of wandering blind in old projects—or just want to flex your Python skills while making something useful—build a code visualizer.
Not only will it make your life easier, but your future self (and every dev who inherits your code) will silently thank you.
