HackStack

Project Info

Mood Snap

Devpost

This project did not submit a demo video on Devpost.

Inspiration

Our inspiration for the Mood App stemmed from a genuine concern for the emotional well-being of individuals in today's fast-paced world. We noticed the lack of platforms that prioritize emotional expression and connection in a simple and engaging way. We wanted to create a space where people could freely share their thoughts and feelings, fostering a sense of community and support.

What it does

The Mood App is a unique platform designed to encourage users to share their emotions and current moods. It's a safe and supportive space where users are prompted to post their thoughts and feelings once a day. The app features a user-friendly feed where people can comment and react to these posts, allowing for interaction and emotional support among users. It's like "BeReal" but specifically geared towards encouraging emotional expression and connection.

How we built it

We built the Mood App for the CalHacks 10 hackathon using a combination of technologies. We employed Reflex, a Python-based React Frontend, for the web application, and utilized Convex, a real-time backend framework based on Node.js, to ensure seamless and immediate interactions. For the mobile version, we used Flutter to create an intuitive and responsive user experience. This combination of technologies enabled us to provide a multi-platform solution, reaching a wider audience and ensuring that anyone can use the Mood App regardless of their device preferences.

Challenges we ran into

Developing the Mood App presented its own set of challenges. Integrating the frontend and backend seamlessly was a complex task. Ensuring real-time updates, user authentication, and maintaining a clean and responsive user interface on both web and mobile platforms required a significant amount of effort and problem-solving. In addition, building an engaging feed while safeguarding user privacy was a delicate balance we needed to strike. We also had to contend with the technical intricacies of deploying and maintaining our app for a hackathon setting.

Accomplishments we're proud of

We are incredibly proud of what we've achieved with the Mood App. We successfully created a platform that encourages users to open up about their emotions and mood, fostering a sense of community and support. The combination of Python-based Reflex, Convex for real-time updates, and Flutter for a mobile app resulted in a versatile and user-friendly experience. The integration of these technologies allowed us to provide a seamless and accessible solution for our users.

What we learned

Throughout the development process, we gained valuable insights into real-time backend frameworks, multi-platform development with Flutter, and the intricacies of creating a user-friendly and engaging feed. We also learned about the importance of prioritizing user privacy and data security while maintaining a transparent and supportive atmosphere for emotional expression.

What's next

for Mood App Our journey doesn't end with the hackathon; we see great potential for the Mood App beyond this event. In the future, we plan to refine the app's features and user experience based on user feedback. We aim to enhance our real-time features, implement user customization, and potentially introduce sentiment analysis to provide even more valuable insights to users about their emotional well-being. Additionally, we intend to expand our user base and community, promoting the Mood App as a valuable resource for emotional expression and support. We're also exploring possibilities for partnerships and collaborations that can help us grow and improve the app even further. The Mood App's journey is just beginning, and we're excited about the potential it holds for making emotional well-being a top priority for people everywhere.

Analysis

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Technology

Found in codeClaimed only
  • JavaScriptIn code
  • PythonIn code
  • TypeScriptIn code

3 of 3 appear in the indexed code.

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Codebase size

Source size

34 KB

Source files

32

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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