ClownLearn

Inspiration:

The growing need for financial literacy, especially among working professionals. What it does:

ClownLearn is designed to teach working professionals about the financial world through a game-like experience. Users log in to the app and progress through levels by completing various financial games and tasks, such as the EBIDTA Speed Run Challenge. The app also features an AI-powered chatbot that allows users to ask finance-related questions and receive quick, informative responses. How we built it:

We used StreamLit for the frontend, Python for backend logic, and MongoDB for data storage. We also integrated Auth0 for authentication and Google Gemini for AI-powered features. Challenges we ran into:

StreamLit was tricky to work with, especially when compared to traditional JavaScript/TypeScript frameworks. We encountered many issues during the frontend development, but with persistence and trial-and-error, we created a satisfactory final product. Additionally, coming up with a concrete idea for the hackathon was challenging, but once we saw the TRUIST Challenges, we recognized a significant gap in financial literacy among working professionals. Accomplishments that we're proud of:

We successfully integrated various tools and technologies into a seamless experience. The AI Chatbot feature was a standout, allowing users to get quick answers to their financial questions. The completion of the EBIDTA Speed Run Challenge also turned out to be an engaging and educational experience for users. What we learned:

We gained a deeper understanding of how to integrate AI features, deal with databases, and build a user-friendly frontend using StreamLit. We also learned the importance of persistence when dealing with complex frameworks and problem-solving in real time. What's next for ClownLearn:

We plan to further enhance the game by adding more levels, challenges, and interactive features to improve the learning experience for working professionals. We also intend to refine the AI chatbot for even more accurate and in-depth financial advice. Team Members:

Vedaant Mohta Samarjeet Purba Aryan Dhabale Tools Utilized:

StreamLit Python CSS MongoDB Auth0 Google Gemini Public Frameworks Utilized:

Pandas PyMongo Google Gemini Fitz Plotly.express PyPDF2 yfinance