The problem SmileSync AI solves

SmileSync AI streamlines the process of developing user profiles, establishes connections between users based on their responses, and fosters the growth of a vibrant and engaging community.
It achieves it by:

• Building User Profiles: Smile Sync AI allows users to develop personalized profiles based on their responses to a questionnaire of personality-based questions. This helps in creating a detailed understanding of each user's preferences, traits, and characteristics.

• Relating Different Users: By analyzing the responses from various users, Smile Sync AI can identify similarities and differences between users. This enables the system to relate different users based on their responses, interests, and personalities.

• Building a Community: With the ability to relate users and understand their commonalities, Smile Sync AI facilitates the creation of a community. Users who share similar traits or interests can be connected, fostering interaction, collaboration, and a sense of belonging within the community.

Challenges we ran into

Some of the challenges we ran into include:

• Developing an Appealing and Engaging Frontend: Designing a visually appealing and user-friendly frontend can be challenging. It requires expertise in UI/UX design principles, responsive design for different devices, and ensuring a seamless user experience.

• Integrating ML Model with the Web App: Integrating machine learning (ML) models into a web application involves technical complexities. You need to ensure compatibility between the ML model and the web framework, handle data preprocessing and feature engineering, manage model inference efficiently, and handle model updates and versioning.

• Deploying ML Models: Deploying ML models for production involves considerations such as scalability, performance optimization, security, and monitoring. You need to choose the right deployment platform, set up infrastructure for model hosting, manage dependencies, handle versioning and model updates, and implement monitoring for model performance and data drift.