## Inspiration

Tourists are usually overwhelmed by the culture of any big city they visit. Getting to know the best food in town, navigating world class museums and most importantly getting the hang of local music can be tough. So through this app, we try to simplify the process of local music recommendations via text through a process that takes 10-15 seconds.

## What it does

Recommends Local Music to Tourists via Text in a matter of 10 seconds. Built using Python-Flask framework and Twilio Messaging API; deployed on Google Cloud Platform(GCP).
## Inspiration 🧠

Have you ever been in a new city, sifting through thousands of highly-rated restaurants?


How do you know the difference between an overhyped tourist trap over a high-quality local treasure?




**Little Gems** provides a way to find the "iykyk" spots among locals that fit your dietary needs and preferences. Culinary tourism is a fast-growing billion-dollar industry. With over 195 million solo travellers each year, tourists are constantly searching for unique dining experiences that are not only highly rated but also authentic and immersive in local communities.




By connecting locals with tourists through a time-flexible matching/pairing system, **visitors can eat like locals, with locals**, for a flat fee of $5. Through this convenient and transparent transaction, locals can share their knowledge of their community to provide flavourful recommendations and dish out their favourite spots alongside curious tourists.


## What it does 📱

**As a tourist**, customize your profile based on dietary restrictions and other information. Then set a location where you plan to be at a certain time and find a local who knows the best eats in that area! There will be a small fee given towards the local for their service.

**As a local**, you get paid for your help in introducing your local culture to tourists. This is also a great way to meet new people and teach them about what your local area has to offer that many don't know about online.

To ensure that both parties are satisfied, we implement a photo verification of either the receipt or a selfie with the other party. We would also be acting as an "escrow" to prevent tourists from taking advantage of locals.

## How we built it ⚙️

The entire frontend uses *React Native* and the backend is fully powered by *Convex*. The **real-time databases** and **web-socket connections** from *Convex* allowed **real-time messaging** and **lag-free synchronization** between frontend and backend.

## Challenges we ran into 🔧

* **Authentication**: was a big pain in the beginning. Thankfully Convex's authentication features with Clerk helped make things go much smoother.
* **Accessibility**: Another big hurdle we had was making the app more accessible and available on both iOS and Android. This required extensive user testing and research as well as meticulous UI designs.
* **Location**: Since our app filters by location, we needed to safely acquire the user's location (mainly for locals) as we allow them to pick a range. This was somewhat difficult to do on both Android and iOS through Expo. For tourists, they were allowed to pick a city where they would like to make their request. This was surprisingly difficult to do so we implemented a "hacky" way of searching cities through [link](https://nominatim.openstreetmap.org/ui/search.html).

## Accomplishments that we're proud of 🦾

Live chat feature, payment feature, nice UI
## Inspiration

Partially inspired by the Smart Cities track, we wanted our app to have the direct utility of ordering food, while still being fun to interact with. We aimed to combine convenience with entertainment, making the experience more enjoyable than your typical drive-through order.

## What it does

You interact using only your voice. The app automatically detects when you start and stop talking, uses AI to transcribe what you say, figures out the food items (with modifications) you want to order, and adds them to your current order. It even handles details like size and flavor preferences. The AI then generates text-to-speech audio, which is played back to confirm your order in a humorous, engaging way. There is absolutely zero set-up or management necessary, as the program will completely ignore all background noises and conversation. Even then, it will still take your order with staggering precision.

## How we built it

The frontend of the app is built with React and TypeScript, while the backend uses Flask and Python. We containerized the app using Docker and deployed it using Defang. The design of the menu is also done in Canva with a dash of Harvard colors.

## Challenges we ran into

One major challenge was getting the different parts of the app—frontend, backend, and AI—to communicate effectively. From media file conversions to AI prompt engineering, we worked through each of the problems together. We struggled particularly with maintaining smooth communication once the app was deployed. Additionally, fine-tuning the AI to accurately extract order information from voice inputs while keeping the interaction natural was a big hurdle.

## Accomplishments that we're proud of

We're proud of building a fully functioning product that successfully integrates all the features we envisioned. We also managed to deploy the app, which was a huge achievement given the complexity of the project. Completing our initial feature set within the hackathon timeframe was a key success for us. Trying to work with Python data type was difficult to manage, and we were proud to navigate around that. We are also extremely proud to meet a bunch of new people and tackle new challenges that we were not previously comfortable with.

## What we learned

We honed our skills in React, TypeScript, Flask, and Python, especially in how to make these technologies work together. We also learned how to containerize and deploy applications using Docker and Docker Compose, as well as how to use Defang for cloud deployment.

## What's next for Harvard Burger

Moving forward, we want to add a business-facing interface, where restaurant staff would be able to view and fulfill customer orders. There will also be individual kiosk devices to handle order inputs. These features would allow *Harvard Burger* to move from a demo to a fully functional app that restaurants could actually use. Lastly, we can sell the product by designing marketing strategies for fast food chains.