The Tack-App

User-Interface
Database in Python

Inspiration

The idea for this project came up while we were trying to find ways in which different inconveniences in everyday life can be solved using software and programs.

What it does

The program first asks a user to log in to the app, then they are able to upload images of the items they want to track. Once the image is uploaded into the system, AI will detect what image and will store its name with a unique ID. The app will then record the time and location where the picture was taken and will notify the user when there is a change in their location and after a few minutes to remind them to check their belongings. The app will ask the user if they would like to update the location of the item they've stored, to which the user can reply no and there will be no recent updates. If they reply with yes, the location will be set to the user's current location.

How we built it

We used react native to build the front end while using SLQite3 in Python to build the database that stores users and the corresponding items that they've registered to track.

Challenges we ran into

Connecting React Native to the API to do the POST request from the frontend side was a major challenge. This was important to connect the front end to the database and Axios and React Native were having issues that didn't occur with React. Debugging the code while using these tools became a challenge as they were unfamiliar.

Accomplishments that we're proud of

Working with Flask and REST API which were new tools as well as using SQLite3 in Python. We were able to work fluidly as a team while communicating our progress and assigning roles.