Discover how ChatGPT-5 helped create a Python app to track AI models effortlessly. Let’s jump into this fun coding adventure!

🤖 Vibe Coding: Creating Your Own App with ChatGPT-5!

Have you ever looked at your AI model library and thought, "Man, this is a mess?" (Yeah, me too! 🙌) As I’ve been dabbling in AI lately, downloading loads of models and add-ons, I realized I needed a way to keep track of them all.

📂 The Initial Struggle

At first, I tried documenting everything in a simple text file, which quickly became chaotic (anyone else feel that pain?). I then considered using Excel or Google Sheets, but typing data into spreadsheets? Ugh, major turn-off. So, here I was, brainstorming solutions and thinking, “Why not build a web app?” Enter ChatGPT-5! 💡

🔍 The Prompt Magic

Here’s the magic part: I decided to test the brand new ChatGPT-5 for my app-building needs.

My Prompt:

"I’d like you to create a little application for me to store info on my AI LLM model files. Please create it in Python with a user interface. Data can be stored in JSON or any other no-SQL system. For each model, I’d like to collect the following info, and I’m open to other suggestions if you have them. But here’s the list I have:

Type (checkpoint, lora, etc)

Base Model

URL

Description of what it is used for"

Within seconds, the response was almost magical:

"Heck yes, Pete — let’s spin up a tiny, no-fuss desktop app to track your model zoo. I built you a self-contained Python/Tkinter app that…"

🛠️ Features of the App

Portable Storage: Everything's stored in models.json (talk about zero setup, right?).
Clean UI: A user-friendly interface lets you add, edit, or delete entries with ease.
Search & Filter: Need to find something quickly? Search through all fields in real-time.
Export/Import JSON: Transfer your data with no hassle!
Optional Metadata: Collect additional info like tags, local path, license, and much more!

How to Get Going

Download the Files:
- Model Catalog Script
- Starter JSON File
Setup:
- Save both files in the same folder.
Run the App:
- Executing this command opens the GUI:
```
python model_catalog.py
```
(And voilà! Your new app is ready to roll!)

🧭 Quick Tour of the UI

Basic Fields: Enter Type, Base Model, URL, and Description right upfront.
Optional Fields: Want more detailed info? Toggle on additional fields under the View menu.
Search Box: This will filter your model list in real-time as you type, making it super easy to find specific models.
Open URL: With just a click, open the selected model's link (yes, this is a time-saver!).

Design Choices

Tkinter Love: We chose Tkinter for its simplicity and dependency-free setup. Because let’s be real, who needs extra complications?
JSON Editor: It's human-readable, super easy to version-control, and portable. (Future you will thank present you! Trust me!)

🌟 Results and Reflections

And guess what? The app worked like a charm! 🎉 Once I started using it, I even went back to ChatGPT for a few extra features, but I never wrote a single line of code myself.

In honor of the coding wizardry that is GPT, I named it Model Zoo.

Sure, it’s not the shiniest application on the market, but it gets the job done—quickly and efficiently. Next steps? Enhancing functionalities like auto-calculations, file drag-and-drop, and maybe even a web UI!

🤔 The Future of Coding

So, what does this mean for us future coders? It seems like the landscape is shifting from traditional coding roles to those who can effectively communicate and validate AI-generated code. Exciting? Yes! Slightly terrifying? Also, yes! 😬

If you fancy getting the Python file or need help along the way, just holler! Let’s keep this AI adventure rolling! 🚀