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docs(README): explain stlite
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README.md

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> Thomas Monks, Alison Harper, Amy Heather, and Navonil Mustafee. **Using Large Language Models to support researchers reproduce and reuse unpublished health care discrete-event simulation computer models: a feasibility and pilot study in Python**. https://github.com/pythonhealthdatascience/llm_simpy.
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It contains the final formatted code from each of the SimPy discrete-event simulation models that were generated by Perplexity as part of that project. The web apps are deployed as a single app on Streamlit Community Cloud.
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It contains the final formatted code from each of the SimPy discrete-event simulation models that were generated by Perplexity as part of that project.
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**Link to the deployed app:** https://llm-simpy.streamlit.app/
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The web applications are deployed as a single app on GitHub pages using `stlite`. This allows the app to run directly in a user's web browser without requiring any manual installations. It achieves this by using WebAssembly technology to run a serverless version of `streamlit` (i.e. `stlite`). The entire app, along with all its dependencies, are downloaded and installed within the browser at runtime using `pyodide` and `micropip`. There will be a short wait while the app is setup. Once the setup is complete, the app runs locally in the browser, meaning that no user data leaves the local machine. **Please note that `stlite` does not currently work in Mozilla Firefox**.
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**Link to the deployed app:** <!--TODO: Add once deployed (requires public repository) -->
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**Code:** The final formatted code from each stage are stored in 📁`pages\`:
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streamlit run pages/CCU_Stage_1.py
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```
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To test the stlite app locally, run the following command, and then open <http://0.0.0.0:8000/> on your web browser:
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```
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python3 -m http.server
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```
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## 📝 Citation
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Please cite this repository as:

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