demoTestApplication

Wildcat Lost & Found

Elevator Pitch: Wildcat Lost & Found: Quickly locate lost items on UA campus with a smart, student-friendly web app.

Inspiration

As a University of Arizona student, I’ve seen how easy it is to misplace something on our massive campus—whether it’s a phone, a backpack, or even a water bottle. I’ve experienced the frustration of losing items myself and heard friends complain about the hassle of tracking them down. That sparked the idea for Wildcat Lost & Found: a tool to simplify the process and save students time and stress. Working solo, I was motivated to create something practical and useful for my fellow Wildcats.

What it does

Wildcat Lost & Found is a web app designed to help UA students find their lost items quickly. Here’s how it works:

You enter a description of your lost item (like "I lost my blue backpack near the library") and the area where you last saw it.
The app uses natural language processing (NLP) to analyze your input and identify the lost item.
It then matches the item to relevant lost-and-found locations on campus and returns a list of suggested places to check.

It’s a simple, student-focused solution to a common problem, built entirely by me.

How we built it

Since I worked solo, I handled every part of the project myself, from planning to coding. Here’s the tech stack I used:

Backend: I built the server with Flask, a Python web framework, to manage the app’s logic. For the NLP part, I integrated NLTK (Natural Language Toolkit) to extract the lost item from user input—like pulling "blue backpack" out of a sentence.
Frontend: I designed a clean, user-friendly interface using HTML, CSS, and JavaScript. It’s just a simple form, but it gets the job done.
Deployment Attempt: I aimed to deploy the app using AWS but faced some deployment issue and switched to Google Cloud Run using GitHub Actions for a seamless, cloud-hosted solution. I set up the project structure and workflow, but I didn’t finish the deployment due to time constraints.

I coded everything locally, tested it, and tried to push it to the cloud—all as a one-person team.

Challenges we ran into

Working alone meant I had to tackle every hurdle myself. Here are the biggest ones:

Deployment Struggles: Getting the app onto Google Cloud Run via GitHub Actions was tougher than expected. The workflow wouldn’t trigger properly—probably because of a misconfiguration or missing details like the project number. I spent a lot of time troubleshooting but ran out of time to fix it.
Time Crunch: Balancing frontend, backend, and deployment setup solo was overwhelming. I had to prioritize features over polish.
Integration: Making sure the NLP output from the backend worked smoothly with the frontend took extra effort. Debugging it all by myself was a challenge.

Despite these roadblocks, I pushed through and learned a ton in the process.

Accomplishments that I am proud of

Even though I didn’t get everything perfect, I’m proud of what I pulled off on my own:

NLP That Works: I got NLTK to successfully extract items from messy, natural input—like "my red jacket" from "I lost my red jacket somewhere by the union." That felt like a big win.
Full-Stack Solo Build: I built a functional Flask app that ties together a frontend and backend, all by myself. It runs locally and does what it’s supposed to.
Deployment Prep: I set up the project for Google Cloud Run and wrote a GitHub Actions workflow. It didn’t deploy, but the foundation is there, and that’s progress I’m happy with.

What we learned

This project was a crash course in a lot of things since I was the only one working on it:

Tech Skills: I leveled up my knowledge of Flask, NLTK, and how to combine frontend and backend code. Writing everything solo forced me to understand each piece deeply.
Cloud Basics: I dipped my toes into cloud deployment with Google Cloud Run and GitHub Actions. Even though it didn’t work yet, I learned how crucial configs are.
Solo Workflow: I figured out how to manage a project alone—planning, coding, testing, and debugging without anyone to lean on. It was tough but rewarding.

What's next for wildcatLostnFound

I’ve got big plans to keep improving this app:

Finish Deployment: Fix the GitHub Actions issues and get it live on Google Cloud Run so anyone can use it.
Smarter NLP: Tweak the NLP to handle trickier inputs and improve accuracy.
More Locations: Add more UA lost-and-found spots to make it even more helpful.
New Features: Maybe add a database to store lost item reports or user logins for personalized tracking.

This is just the start—I’m excited to keep building and making it a go-to tool for UA students.