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Summarize information and classify severity of incoming Emergency Department patients.

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Project Polonius

This is a sample flask app to demonstrate using Azure OpenAI with Semantic Kernel to summarize and classify the severity of incoming trauma patients.

Azure Architecture

azure architecture diagram

Pre-requisites

  • Azure CLI (az)
  • Azure Developer CLI (azd)
  • Docker Desktop (for devcontainer) - OR - local python dev environment
  • Azure OpenAI (or use azd provision to create)

Getting Started

  1. Clone this repo
  2. Rename src/.env.example to .env
  3. Update .env file with your configuration values
  4. Run locally with debugger

Deploy to Azure

  1. Deploy to Azure with azd up

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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Summarize information and classify severity of incoming Emergency Department patients.

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  • CSS 54.1%
  • Bicep 34.8%
  • SCSS 5.4%
  • Python 4.2%
  • HTML 1.3%
  • Dockerfile 0.1%
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