## Inspiration

We set out to create a tool that unleashes dancers' creativity by syncing their moves with AI-generated music that matches perfectly. Inspired by the vibrant dance scenes on TikTok and Instagram, where beats and moves are inseparable, we wanted to take it to the next level. Imagine dancing to music made just for your style, effortlessly turning your moves into shareable, jaw-dropping videos with custom soundtracks. With our tool, dancers don’t just follow the beat—they create it! It's like having your bb
 DJ that grooves with you.

## What it does

KhakiAI allows users to upload or record short dance 6-second videos analyzed by our AI-powered system. The AI tracks the dancers' movements, tempo, and style, generating a custom music track that perfectly matches the rhythm and energy of the performance. Users can further customize the music by selecting different genres or adding sound effects. The tool then syncs the music with the video, creating a seamless, high-quality dance video that can be shared directly on social media.

## How we built it

We built this project with a complex tech stack involving several APIs, LLMs, and programming languages. Throughout our programming process, we broke up the task into various parts and pieced them together as we went. To begin, we focused on a key functionality of dance movement recognition with OpenPose/OpenCV. This recognition outputs a JSON that gets put into a MongoDB Database. Then, we use Llama, Tune AI, and Cerebras to pass the JSON through an LLM quickly to create a low latency, so that the user generates the prompt quickly. SunoAPI then uses the generated prompt to create music for the video. Then, we attach it with Python and output it.

## Challenges we ran into

There were many challenges involved in the creation of this project. The Suno AI API doesn't have an official API, so we had to rely on an unofficial version API that uses cookies, which lengthened our ability to complete this project, instead of getting the actual API Key.

## Accomplishments that we're proud of

We are proud of the way we made a way for computer vision to detect a way to make dance moves generate a prompt for music to text.

## What we learned

We learned about computer vision, flask/next.js implementation, and react. We made proper use of version control. I was experienced and used new AI technologies like Cerebras.
## Inspiration

Parker was riding his bike down Commonwealth Avenue on his way to work this summer when a car pulled out of nowhere and hit his front tire. Lucky, he wasn't hurt, he saw his life flash before his eyes in that moment and it really left an impression on him. (His bike made it out okay as well, other than a bit of tire misalignment!)

As bikes become more and more ubiquitous as a mode of transportation in big cities with the growth of rental services and bike lanes, bike safety is more prevalent than ever.

## What it does

We designed \_ Bikeable \_ - a Boston directions app for bicyclists that uses machine learning to generate directions for users based on prior bike accidents in police reports. You simply enter your origin and destination, and Bikeable creates a path for you to follow that balances efficiency with safety. While it's comforting to know that you're on a safe path, we also incorporated heat maps, so you can see where the hotspots of bicycle theft and accidents occur, so you can be more well-informed in the future!

## How we built it

Bikeable is built in Google Cloud Platform's App Engine (GAE) and utilizes the best features three mapping apis: Google Maps, Here.com, and Leaflet to deliver directions in one seamless experience. Being build in GAE, Flask served as a solid bridge between a Python backend with machine learning algorithms and a HTML/JS frontend. Domain.com allowed us to get a cool domain name for our site and GCP allowed us to connect many small features quickly as well as host our database.

## Challenges we ran into

We ran into several challenges.

Right off the bat we were incredibly productive, and got a snappy UI up and running immediately through the accessible Google Maps API. We were off to an incredible start, but soon realized that the only effective way to best account for safety while maintaining maximum efficiency in travel time would be by highlighting clusters to steer waypoints away from. We realized that the Google Maps API would not be ideal for the ML in the back-end, simply because our avoidance algorithm did not work well with how the API is set up. We then decided on the HERE Maps API because of its unique ability to avoid areas in the algorithm. Once the front end for HERE Maps was developed, we soon attempted to deploy to Flask, only to find that JQuery somehow hindered our ability to view the physical map on our website. After hours of working through App Engine and Flask, we found a third map API/JS library called Leaflet that had much of the visual features we wanted. We ended up combining the best components of all three APIs to develop Bikeable over the past two days.

The second large challenge we ran into was the Cross-Origin Resource Sharing (CORS) errors that seemed to never end. In the final stretch of the hackathon we were getting ready to link our front and back end with json files, but we kept getting blocked by the CORS errors. After several hours of troubleshooting we realized our mistake of crossing through localhost and public domain, and kept deploying to test rather than running locally through flask.

## Accomplishments that we're proud of

We are incredibly proud of two things in particular.

Primarily, all of us worked on technologies and languages we had never touched before. This was an insanely productive hackathon, in that we honestly got to experience things that we never would have the confidence to even consider if we were not in such an environment. We're proud that we all stepped out of our comfort zone and developed something worthy of a pin on github.

We also were pretty impressed with what we were able to accomplish in the 36 hours. We set up multiple front ends, developed a full ML model complete with incredible data visualizations, and hosted on multiple different services. We also did not all know each other and the team chemistry that we had off the bat was astounding given that fact!

## What we learned

We learned BigQuery, NumPy, Scikit-learn, Google App Engine, Firebase, and Flask.

## What's next for Bikeable

Stay tuned! Or invest in us that works too :)

**Features that are to be implemented shortly and fairly easily given the current framework:**

* User reported incidents - like Waze for safe biking!
* Bike parking recommendations based on theft reports
* Large altitude increase avoidance to balance comfort with safety and efficiency.
## Inspiration

We wanted this project to be able to help us share our love of music with others who may not be able to enjoy it through sound alone. Inspired by our love of music as well as technology, we hoped to make things more accessible for people with our passion for technology.

## What it does

Our ASL Music Generator takes in the user's choice of song/artist and returns the lyrics, translating it to American Sign Language

## How we built it.

We started by setting up our workspace, working on **Replit** at the start to make collaboration easier for our team, and in the later stage - GitHub and VSCode. Essentially, we prompt engineered a prompt to call using the Groq API so that we could return song lyrics with the user\_inputted variables **song\_name** and **artist**. We used javascript to connect the front-end and back-end in receiving information. We used Figma to brainstorm the format and structure of our app.

## Challenges we ran into

We ran into a lot of challenges with the setup of the project because we had spent a lot of time downloading a lot of packages as we wanted to use a large and complicated model that was able to translate text to pose/animation (American Sign Language). One of the more difficult processes was combining the front end with the back end in terms getting the song lyrics and inputting into the LLM that translates the lyrics into HamNoSys and animation/pose format. We also ran into some challenges when using Replit, it was super helpful when collaborating with a team but we also ran into a lot of efficiency issues when downloading the packages we needed to get the model to run. At the end, we had to transfer all of our code we had made on Replit into VSCode and still had to use GitHub the model we were implementing was too large.

## Accomplishments that we're proud of

We're really proud of the fact that we were able to condense such a large and complicated model to fit our project.

## What we learned

We learned that we can sleep in any position when we're tired. We also learned how to sign language "We are cooked" (No but we actually had a fun time learning a couple of sign language phrases). Additionally, we gained more experience in full-stack web development and creating a project centered around an AI model.

## What's next for ASL Lyric Generator

Our next plan is to successfully deploy the ASL Lyric Generator is to maybe implement some learning and educational features. Right now it's a lyric generator to make music more accessible to people, but we want to make this a learning experience, helping people learn sign-language through music!

`Credit for the sign.mt LLM`
[Translate Model GitHub Repo](https://github.com/sign/translate.git)