Inspiration
Accessibility is not something that most software developers think about when building solutions. Our team wants to change that by programming more mindful solutions. For our project, we wanted to focus on helping those who struggle with visual impairments just because technology is such a visual experience. In particular, we wanted to create an inclusive community that supports those who are visually impaired by providing them information about their surrounding environments with just the press of a button.
What it does
Iris is a web application for those who are visually impaired. Our tool allows users to upload an image of anything they are uncertain about and get more clarity. Answers are crowdsourced from other members of the Iris community - which can be anyone and everyone. Our answers are verified, as well, and we use machine learning technology to help with classification.
How I built it
We used Computer Vision Client and Cognitive Services API to build out our tech. We used Flask in the backend.
Challenges I ran into
- [ ] Deployment of the application to Flask
- [ ] Dealing with ever-changing database schema since we were all collaborating at the same time
- [ ] Integrating the additional BQA ML model
Accomplishments that I'm proud of
What I learned
- How to collaborate effectively on git/ better team delegation
- Fleshing out the core priorities for the application
- Building a robust backend
- Prioritizing sleep
What's next for Iris
Integration in large communities such as Facebook.
Built With
- azure
- computervisionclient
- flask
- jinja
- machine-learning
- python
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