AmiGo

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  Home page of our beloved product, Amigo <3

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  Can upload your driving license and there is a facial recognition model for checking purposes

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  Passenger details to confirm whether driver wants to go ahead with the passenger

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  Trip details

Inspiration

We faced commuting challenges between our Falls Church campus and the hackathon venue, and noticed many fellow students struggled with efficient carpooling. This inspired us to create a solution that helps students connect and carpool easily.

What it does

Our app allows students to find and carpool with others traveling from one location to another, streamlining the process and improving travel efficiency.

How we built it

We developed a mobile application using React Native, integrated facial recognition with Azure Custom Vision, implemented a recommendation system with K-Means Clustering in Python, and deployed it on Defang. The backend is powered by Express and the database is managed with Microsoft SQL.

Challenges we ran into

The complex design with multiple layers and tight time constraints were our biggest challenges, making the development process intense.

Accomplishments that we're proud of

We’re proud of our unique GT meter, which scores how well drivers and passengers might enjoy each other's company. Completing the project with fully integrated backend, frontend, database, and ML models in just 24 hours is also a major achievement.

What we learned

We learned effective team collaboration, dividing tasks and then integrating them seamlessly, which was crucial for our project's success.

What's next for AmiGo

We plan to enhance the app by adding features for improved security, better passenger tracking, and scaling it for broader use.

Built With

• azure
• css
• customvision
• defang
• docker
• express.js
• flask
• javascript
• mssql
• python
• react-native
• sql
• typescript