Restaurant Filters
Aggie Review Login Page
Aggie Review gui
Restaurant indepth gui
Monthly Themes Page (See-food)
Top Recommended Restaurants Page
Interactive Map/Main Page

Inspiration

We were inspired by the lack of adequate information on restaurants in the College Station area. Compounded with the fact that TAMU dining hall food isn't consistent, we realized that a restaurant database was a need.

What it does

Our site is primarily a restaurant database allowing Aggies to find restaurants within a 5 mile radius of the College Station Texas A&M campus. This is done through an interactive map with toggleable filters such as price, distance, cuisine type, dietary restrictions, etc. All restaurants have a calculated review score using google and yelp reviews. Each restaurant also has a custom GUI detailing information such as address, exact average price, menu, and phone number. Aggies can also leave their own reviews on the website by logging in using a Tamu email and leaving reviews. These reviews are shown on a specific section of the restaurant GUI's titled "Aggie Reviews." Some other quality of life features are a top recommended list composed of the top 10 restaurants with the highest ratings and a monthly theme featuring restaurants of a monthly theme.

How we built it

We started by compiling a list of every idea and feature that we wanted the website to have. Then, we made a next.js framework and asked Codex to generate a frame for what we wanted the website to look like. We then split up work between each of us to implement each of the features, such as the login and ratings.

Challenges we ran into

Codex made our page.tsx file mirror the discover.tsx file, which was confusing and caused everything to break when bringing the code from chatGPT to Github. Yelp blocked Tamu wifi, so we could not access their api directly and had to use SerpAPI. Parts of the login pointed to empty variables a lot. We ran out of API key tokens so we generated new ones.

Accomplishments that we're proud of

Getting the map interactive and working with star ratings, the login system being functional.

What we learned

We learned that there are levels to prompt engineering and optimizing a prompt can save a lot of time and confusion. We learned how to deploy a website through Vercel, and how to use APIs.

What's next for Gig 'eats

We plan to aggregate more reviews, add an option for catering, make the GUI more customizable through different colors and maybe preset themes, put a "featured food" for both the monthly themes and individual restaurants, and we plan to add AI overviews of human reviews, grading them by recency, history of reviewer, and if it was service or food that led to the review.