Project Info
This project did not submit a demo video on Devpost.
Inspiration
The video game industry is an industry that continues to grow. If you ever played video games, I'm sure you have been in this position before, you are stuck in the same rank losing to the same strategies. You could go down a never ending youtube rabbit hole, or you could get instant curated feedback and criticism on your gameplay. With Large Language Model's (LLMs) the latter becomes a reality.
What it does
Using demoparser, awpy, and redis, our application takes in a Counter Strike .Dem file, which contains the entire history of the match. It then analyzes the entire game, giving a round by round summary on what you actually did and what you could have done better. It also creates a general summary of advice along with drills that you could follow to better your gameplay. Using Redis the application keeps track of all of the bad habits that you have as a player, and tells you what percentage of games you are making this bad habit.
How we built it
Frontend: The front end was created using react, our application initially opens up to a general home page giving information on what it is. Clicking try now leads you to the main page which offers a modern interface that allows the user to input a .dem file of their own. It then breaks the game down to them in an easy to digest way. Backend:
Challenges we ran into
AI Hallucinated callout names - to combat this we implemented a framework to use a mesh map to define callout regions Deploying on the cloud - upgrade redis plan (redis blocked on university wifi), implement error message if redis is not working correctly Generating visual display - implemented a map to recreate moments from the game in 2d
Accomplishments we're proud of
We are proud to have a unique and functional demo to show after our first hackathon. We had a lot of fun building our app and experiencing the hackathon.
What we learned
We learned how to use technologies like Redis. We also deepened our understanding of technologies like React, Python, Claude, and Docker.
What's next
In the future, we could expand functionality for Counter Strike, support other games with .dem files, and eventually support games without .dem files. For that, we could potentially use computer vision to analyze replies or live gameplay.
This project did not link a GitHub repository.
Analysis
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Technology
- DockerUnchecked
- PythonUnchecked
- ReactUnchecked
- RedisUnchecked
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AI coding agents
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Codebase size
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