https://github.com/henry7720/aep-hack12

A Python implementation to parse and query CSV data for risk in event descriptions, for the AEP challenge in HackOHI/O 12.
https://github.com/henry7720/aep-hack12

aep csv hackathon hackohio llm ollama pandas plotly plotly-express python

Last synced: 14 days ago
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A Python implementation to parse and query CSV data for risk in event descriptions, for the AEP challenge in HackOHI/O 12.

• Host: GitHub
• URL: https://github.com/henry7720/aep-hack12
• Owner: henry7720
• License: mit
• Created: 2024-10-22T03:01:57.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-22T03:50:47.000Z (2 months ago)
• Last Synced: 2024-12-12T11:07:12.392Z (14 days ago)
• Topics: aep, csv, hackathon, hackohio, llm, ollama, pandas, plotly, plotly-express, python
• Language: Python
• Homepage:
• Size: 18.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# AEP Challenge: HackOHI/O 12

The Hackathon was a 2-day event, although we made this in probably one day for the purpose of experiential learning. This was my first time making a Python project for a real-world use case, so it was valuable in its own right! 

For the AEP challenge, then, our objective was to take in a 20,000-line CSV data source, parse it, then categorize each event listed based on its safety risk by the presence of "high energy" situations, using the power of LLMs. These events had lengthy descriptions and titles, with many being red-herrings. We used Ollama with `llama3.2:3b` running locally to maintain data privacy. Unfortunately, I can't share the source data CSV at American Electric Power's request for privacy. However, I am happy to share more about the process.

# Language and Dependencies

This was run on Python 3.12 (although the version shouldn't matter a great deal), with `ollama`, `csv`, `pandas`, and `plotly.express` libraries installed and imported.

# Code Below

Please see [`chat.py`](chat.py) to see the main processing and `llama3.2` prompting script and [`graph.py`](graph.py) to see my graphing methodology.

# License

[`MIT`](LICENSE)