Example of POG
As big fans of video games and twitch streamers, we all loved the passion and fun that streamers have when they go live. This shared enjoyment in the platform lead us to the inspiration to develop an app to make one of our favorite platforms even better.
What it does
Our app takes the exciting emotional experience of a twitch streamer and makes it interactive and reactive live in the twitch live stream. The streamer's emotions will be constantly reported on screen and visual queues will register on screen to get the audience involved. Along with this there is also an interactive stream deck technology that is built in with the twitch chat bot we built for this. This allows the streamer to send specific messages and control the interaction our app has on their stream in real time!
How we built it
The majority of our logic for this program was made in Python but this required communication with multiple scripts in order to get the end result that we desired. The breakdown of how each language was used goes as follows:
p5.js: All of the animations that are viewed on the stream for our live emotion tracker are built using the processing libraries.
Batch: This is how we were able to integrate the Elgato Stream Deck to run the node.js server that will interact with the IRC server to send messages to the chat with our chat bot.
Challenges we ran into
Running low on sleep, motivation, and awake teammates we were really struggling around 2AM of this hackathon to work out our system for updating the .gif files that show the live emotion tracking on screen. It took a lot of research, growth, and perseverance, but one particularly driven teammate who wasn't willing to give up pushed through and got us to the outstanding result we ended up with. This was the challenge that took us the longest and considering that we didn't sleep all night and we were working on it from 2AM to 6AM it was certainly the hardest time frame for us to focus.
Accomplishments that we're proud of
We are really proud of the amount of areas we covered in the integration of this project. Our implementation meant working with Google API, Twitch API, and many other new libraries and frameworks that previously nobody on our team had any experience in. This stepping out of our comfort zone was a big risk that looking back we can all feel accomplished in our growth from the start of this event.
What we learned
As a group of all freshmen, we came from almost no experience with any of the frameworks that we ended up working with, this whole thing was one big learning experience. But along with that this was one of the biggest projects that any of us have ever collaborated with a group on. We all learned a lot about the communication and collaboration skills required to have success as a group.
What's next for Reactive Twitch.tv Feedback
Reactive Twitch.tv Feedback will most likely become a side project for all group members that will gain future implementation as ideas come.