Inspiration
We were inspired to pursue this subject through our shared passion for food. As professional foodies, Google Maps and its review system is one way to find new eateries to try. However, there are occasions where the app promises 5 star food, but delivers a 2 star disappointment. This can be attributed to the unreliability of the review system, where bot accounts leave high reviews for undeserving restaurants. Thus, we decided to create a system to combat this.
What it does
Our website allows users to search for eateries through their current locations as well as specified ones. When chosen, our website displays the top 10 locations accompanied by an AI summary powered by GPT-4o, that has filtered out irrelevant content. This ensures that users will be obtain quality and trustworthy reviews on locations.
How we built it
We used python to code the backend of our trustworthy food review website so that we would be able to store user data for continual training of the ML model. We used Javascript to code the frontend of our website, providing a user-friendly interface for users to easily find the best food catered to their desires. To link both ends, we utilised a REST API for the transfer of data between the client and server sides.
Challenges we ran into
We faced CORS issues when utilising the "Google Places API", which prevented our code from running without errors. We also struggled with linking the backend and frontend of the system together.
Accomplishments that we're proud of
As our first hackerthon, we are grateful for the experience and being able to work on a project together.
What we learned
We learned the difficulties of full-stack programming, as well as the proper dissemination of work in a realistic project-based environment.
Built With
- css
- html
- javascript
- openai
- places
- python
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