Loginator

Inspiration

New genAI techniques

What it does

By leveraging the capabilities of transformer models, this project aims to revolutionize how we interact with and extract insights from large text datasets. Quickly and accurately point out relevant lines in a sea of information not only enhances efficiency but also opens new avenues for automating complex question-answering tasks.

How we built it

We created the user interface using the Streamlit framework and used different AI models in our backend pipeline to process the log files efficiently and produce meaningful information for the user.

Challenges we ran into

Our biggest difficulty is achieving a responsive user experience since we require exceptional performance processing very large files.

Accomplishments that we're proud of

We are proud of delivering a functioning prototype that opens the door to future improvement.

What we learned

We learned a lot about working with AI models while optimizing for speed to encourage interactivity.

What's next for the Loginator

The next steps for our project would be to further optimize the pre-processing of new and large log files to reduce waiting times.

Built With

• machine-learning
• openai
• python
• streamlit