SUPER ASSIST

• 

  AI

• 

  DATA FLOW CHART

Inspiration

SuperMate was inspired by the growing Valorant Esports community seeking better team-building tools and player insights to enhance gameplay.

What it does Build

Teams: Suggests players based on roles and performance metrics.

How we built it

Backend: Python with Flask for the API. Database: SQLite with SQLAlchemy for player data. LLM Integration: OpenAI API for natural language processing. Data Sources: Valorant Esports API or web scraping for stats.

Challenges we ran into

Data Sourcing: Finding accurate, up-to-date player statistics. NLP Tuning: Making the LLM understand esports terminology. Performance: Ensuring fast response times for user queries.

Accomplishments that we're proud of

Successfully integrated an LLM for intelligent query responses. Created a user-friendly interface for easy interaction. Built a solid database of players and teams.

What we learned

The importance of accurate data for user trust. Effective API design and documentation are crucial. User feedback is key to improving the assistant.

What's next for SUPER ASSIST

Expand data sources for more detailed statistics. Implement machine learning for personalized recommendations. Develop a mobile app for broader access. Add community features like user profiles and team sharing.

Built With

• css3
• html5
• openal
• php
• sql