SoulSpace

SoulSpace is a web-chat app using ML via scikit-learn to pair users with shared interests and mental health concerns, fostering supportive connections and conversations tailored to individual needs.

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Inspiration

25% of college students feel isolated. Students of color principally are less likely to get treatment for their mental health issues. Counseling centers are overwhelmed: waiting lists are long. Therapists and counselors are burned out. We need to find a solution. That's the idea behind SoulSpace: a web application that matches ML, chats, and trackers to improve mental health.

What it does

  • Users can log in with their email and are authenticated through Firebase;
  • Dashboard contains buttons to chat, survey, and mood tracker. Contains resources that people can use while in need.
  • First-time users fill in a survey stored in by pandas and built completely in Streamlit, data are booleans (interests, mental health concerns) and int (age) and are used for pairing in ML powered by clustering in scikit-learn. When number of users is odd, one person stays in the waitlist.
  • Mood Tracker: A tool to monitor one's mental health; includes a dropdown menu where users can say their mood and allows a note as well. Moods are graphed in a line graph and data can be exported as a CSV.
  • Chat: Users can chat with their pairs through Streamlit. Feature in progress.

Challenges we ran into

  • Connecting ML and pairing with chat

Accomplishments that we're proud of

  • Chat can show and send messages
  • ML works and pairs all users

What we learned

  • How to use Streamlit and all its amazing features!
  • Time-management is key

What's next for SoulSpace

  • Integrate ML portion and chat-pairing.

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