HackStack

Project Info

DontCrashOutAI

Devpost

This project did not submit a demo video on Devpost.

Inspiration

Our team sometimes gets tempered when using a computer, and we set out to minimize that. Our initial idea was related to video games, as they can often be causes of frustration, especially online ones. But we realized this idea could be scaled to cover emotions throughout all computer usage, not just video games. Whether that be working on homework, playing online games, or programming a project, there are a variety of actions you do on a computer that can get frustrating. This application seeks to alleviate.

What it does

Once the app is open, it starts tracking your emotions via webcam, and when anger is detected, a creative calming notification will be sent to your desktop. This calming notification utilizes Google's Gemini API to generate an infinite amount of creative messages. These messages help remind the user that despite their frustration, everything is going okay and to take a break from the computer.

How we built it

We used Python to build both the front and back end of the application. Using a model that could detect faces within a webcam, we developed a program in the backend that can predict a person's facial expression and emotion. This prediction is quite accurate with facial tracking. We then implemented Google's Gemini API as the creative source for the calming notification messages.

Challenges we ran into

The biggest challenge we came across was finding and fine-tuning a model that catered to both our facial recognition needs and our calming message needs. Initially, we tried to use Google's MediaPipe facial detection software to track and detect faces being captured in the webcam. Unfortunately, it was incompatible with the model that we were using. Instead, we used openCV to perform facial recognition within our program. We also ran into issues with the webcam capturing weird and low-resolution angles, making it hard to capture a face within the frame. Another issue we came across was complications with Google Gemini simply not working with the API key we generated as well as text not generating correctly.

Accomplishments we're proud of

Getting the facial recognition working was our first hurdle that we accomplished and are extremely proud of. The fact that we were able to get our program to somewhat accurately predict facial expressions is something that we are extremely proud of. As we are both novice programmers, getting any of the above items to work was an accomplishment in and of itself.

What we learned

We got a glimpse into the world of facial recognition and AI fine-tuning. We also learned how to utilize the Google Gemini API and integrate it into our project.

What's next

for DontClashOutAI In the future, we hope to implement Google's Mediapipe for more advanced facial and emotion recognition. We would also like to retrain the model for more accurate and consistent results when predicting facial expressions. We're also working hard to clean up the GUI of the application allowing for a more streamlined and efficient experience. This is where we sourced the model from: link

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • PythonIn code
  • Google GeminiClaimed

1 of 2 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

917 KB

Source files

4

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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