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A snapshot of the platform in response to a situation
Inspiration
Our team understands how challenging it can be to comfort a friend who is going through a tough time. In fact, our inspiration for creating this platform came from our own personal experiences. As a team, we've all had friends who were struggling, and we wanted to do something to help them feel better. However, we often found ourselves at a loss for words, unsure of what to say or how to express our support. This is what inspired us to create EmpathEase - a platform that helps friends console and uplift each other during difficult times. Our goal is to make it easier for people to express empathy and show they care.
What it does
empathEase takes in user input of a hard situation the user feels of somebody else through a website and gives a response to what they should do in that scenario to assist their troubled friend, giving suggested prompts and the rationale for each one.
How we built it
Using flask, we designed a web page to take a user input about their situation. Then, we formatted the string into a format so that when we sent it to OpenAI, it would return a specific format detailing both suggested responses and rationales (we also included sample responses to further encourage OpenAI to do the format we desired), and we displayed the end result to the user through Flask again
Challenges we ran into
Since we had no prior experience with frontend development, learning how to format the website properly was a massive difficulty and strapped us for time in the end. We also had a period where openAI went down which went we were unable to test our code in that time, which further constricted our time.
Accomplishments that we're proud of
We're proud of finishing our first Hackathon ever, even if we went over time by a few minutes. It was a great experience generating ideas and discovering new ways to implement the technology that we were learning to our project as we continued to build skills through the workshops.
What we learned
We learned a lot about front end development regarding HTML and Flask, and we also learned to how to implement API's like openAI. Also, the workshops we attended showed us about the usage of Flask and Hugging Face and how those models can be used in the real world.
What's next for empathEase
We wanted to format the response string into just a list of suggested responses with their justification separate, but due to time constraint we were forced to only output the entire string. That would make the output much more readable for the user.
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