Check out the projects by Cohort 6 apprentices
Project details listed below according to appearance in the demo video above.
Morgan Hale & Megan Schneider
Closet App
Challenge: “I have nothing to wear!”
Solution: Implementing an app that facilitates wardrobe inventory management and outfit planning will lead to increased efficiency in personal styling, thereby reducing the frequency of unnecessary clothing purchases among users.
Result: Our app is a wardrobe organizer/outfit planner app - heavily inspired by Cher’s closet from Clueless. We used the MERN stack and took a mobile-first design approach. Our three features are the individual item catalog, filter and sort by organizational tags, and mix-and-match outfit creation.
MongoDB, Express, React, Node.js
Other Tech: Cloudinary, Google AI tagging, AI Background Removal , Figma - Wireframes, Draw.io - user flows and planning
Ann Clawson & Roderick “Rance” White
Crumbs
Crumbs is a comprehensive and dynamic Girl Scouts© cookie sales management app. It's a full-stack app that streamlines the cookie-selling process and empowers sellers with an all-in-one solution for organizing orders, monitoring payments, and maintaining real-time inventory insights.
React, Material UI, Flask SQLAlchemy
Note: Ann Clawson focused on the front end, and Rance White focused on the back end.
Check out screenshots of the dashboard and log-in page.
Ann’s Frontend GitHub
Rance’s Backend GitHub
Taariq Elliott
Friquency Radio
Provides a platform where users can host streaming parties or run radio-station-style sessions with friends, featuring Twitch integration and a real-time chat room that fully supports images and GIFs.
Next.JS, TypeScript, Supabase, Mantine, React Tabler Icons
Watch a short demo here.
Sumi Nia Means & Jamie Neff
Eye Q: Diabetic Retinopathy Risk Assessment Tool
A comprehensive Full-Stack web application designed for clinics to efficiently manage patient data, upload retinal images, and utilize Convolutional Neural Networks (CNN) for machine learning to analyze the images. This application aims to assist healthcare professionals in the early detection and assessment of diabetic retinopathy, thereby improving patient outcomes through timely intervention.
Backend: Python, Django, FastAPI
Frontend: Tailwind CSS
Machine Learning: TensorFlow, Keras
Database: SQLite
Cloud Storage: Cloudinary API
Sumi’s GitHub (CNN Model)
Jamie’s GitHub (Web Application)