Completed PythonPyQt5Desktop AppAPI IntegrationVibe-Coded

Local-Flix — Personal Video Player

A Netflix-style desktop video player for your local collection — auto-fetches cover art, descriptions, and genres from TMDB API with a polished PyQt5 interface.

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Overview

I built a polished desktop video player that transforms how you browse a local video collection. Instead of raw folder navigation, Local-Flix presents movies and series in a Netflix-style interface with live metadata, cover art, and genre filtering — all pulled automatically from TMDB.

This was built in close collaboration with ChatGPT, where I handled the vision, direction, and refinement while ChatGPT provided the architecture and implementation.

Image of Functioning Application with Example Videos

What I Built

The Problem

Local video collections are scattered across folders with no metadata. Playing a film requires hunting through directories. There’s no browsing experience, no discovery, no polish.

My Solution

A desktop app that:

  • Scans your local library
  • Auto-fetches cover art, plot summaries, genres from TMDB
  • Displays everything in a Netflix-style grid
  • Filters by genre, media type (movie vs. series)
  • Plays videos with seamless integration

Key Features

Intelligent Metadata Loading: I directed Claude to implement threaded background loading so the UI never freezes while fetching data from TMDB. You see the app respond instantly while metadata streams in.

Genre Filtering & Sorting: Instead of browsing 100 titles, filter by genre instantly. The app pulls real genre tags from TMDB, not guesses.

Local Playback: PyQt5’s media integration handles playback. No external player needed.

Responsive Design: The UI stays snappy because heavy operations (API calls, image loading) happen on separate threads.

The Collaboration

My Role:

  • Defined the feature set and user experience
  • Iterated on the design (feedback on layout, colors, usability)
  • Directed ChatGPT toward specific implementations
  • Tested functionality and requested refinements

ChatGPT’s Role:

  • Architected the PyQt5 application structure
  • Implemented TMDB API integration
  • Built the threading system for responsive loading
  • Wrote image fetching and caching logic

This demonstrates effective AI collaboration — not having the AI do everything, but using it as a tool to accelerate development while maintaining creative direction.

Technical Highlights

API Integration: Uses tmdbv3api to fetch real movie/series metadata. The app handles failures gracefully — if TMDB is down, the app still works (just without metadata).

Threading: Metadata loading happens on background threads. Your clicks are never delayed by network requests.

Caching: Downloaded images are cached locally so the app doesn’t re-fetch the same poster 10 times.

Why This Matters

This project demonstrates:

  • AI collaboration skills — working with AI to build polished applications
  • Product thinking — I could define what “good” looks like and direct toward it
  • Technical judgment — I knew when ChatGPT’s solution was right and when to push back
  • Full-stack thinking — APIs, threading, UI, playback all integrated

Tech Stack

Python · PyQt5 · tmdbv3api · requests · threading · JSON

Built in collaboration with ChatGPT AI. Code by ChatGPT, vision and direction by me.