The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
ContractorHub is a Django 5 service marketplace connecting clients with skilled contractors — plumbers, electricians, painters — across Pakistan. It features AI-powered contractor matching using Groq's LLaMA 3.3 70B model, role-based dashboards for clients and contractors, and real-time messaging with proposal/impression analytics. Authentication runs through Google OAuth, backed by CSRF protection and role-based access control. Built with a PostgreSQL-ready architecture, the platform is designed to scale from local service discovery into a full contractor ecosystem.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
AI Home Services App — Smart Technician Booking & Repair
A premium AI-powered home services app designed to simplify how people diagnose home problems, find the right service, and book qualified technicians.
The product uses AI to turn a simple problem description into a clear service recommendation — helping users move from “What’s wrong?” → “What do I need?” → “Who can fix it?”
Key Experience
→ AI-powered problem diagnosis
→ Text, voice & photo input
→ Smart service recommendations
→ Technician discovery & booking
→ Transparent service information
→ Real-time technician tracking
→ Premium, intuitive mobile experience
The concept is designed for founders building AI home-service platforms, technician marketplaces, repair apps, and SaaS products where reducing friction and improving the booking experience can create a stronger customer journey.
Role: Product Designer
Focus: AI Product Design · UX/UI · Mobile App Design · Service Marketplace
I built this face detection app to run machine learning models directly in the browser inside a clean interactive UI.
Users can upload any photo and get an instant scan. The system draws targeting brackets over the face, maps 68 facial points, estimates age and gender, and shows percentage bars for seven basic emotions. If a photo has more than one person, the app counts them and lets you click through each face to update the readings on screen.
Everything runs on the client side using JavaScript and HTML5. The photos stay on the user's computer and never get uploaded to an external server. The model detects the face position, facial points, age, and emotion expression in one pass. If someone uploads a photo without a visible face or any pics of living creatures other than humans, the app handles the error and updates the status message instead of freezing.
The ragebaiting part was to format and structure out the data so it felt clean and fast. Putting the emotion stats beside the face box makes it easy to read the numbers while looking at the person. No distractions, no drama... Showing the total face count in the corner also helps users know if the model missed anyone in the photo.😊
Looks very futuristic💯
Shipped an editorial SaaS landing page delivering 100/100 Lighthouse scores, built strictly with semantic HTML5, vanilla CSS3, ES6 JavaScript, local WOFF2 typography, custom design tokens, CSS Grid, and zero external build tooling. Flux | Run Your business on Autopilot
