https://github.com/pythonhealthdatascience/llm_simpy_models
The SimPy models and apps generated by LLMs, deployed as a single app.
https://github.com/pythonhealthdatascience/llm_simpy_models
Last synced: 3 months ago
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The SimPy models and apps generated by LLMs, deployed as a single app.
- Host: GitHub
- URL: https://github.com/pythonhealthdatascience/llm_simpy_models
- Owner: pythonhealthdatascience
- License: mit
- Created: 2025-03-20T11:00:34.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-25T10:44:06.000Z (3 months ago)
- Last Synced: 2025-03-25T11:23:49.258Z (3 months ago)
- Language: Python
- Homepage: https://pythonhealthdatascience.github.io/llm_simpy_models/
- Size: 67.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
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# Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models
This repository is complementary to:
> Thomas Monks, Alison Harper, and Amy Heather. **Research Compendium: Replicating Simulations in Python using Generative AI**. https://github.com/pythonhealthdatascience/llm_simpy.
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 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**.
**Link to the deployed app:** https://pythonhealthdatascience.github.io/llm_simpy_models/
**Code:** The final formatted code from each stage are stored in 📁`pages\`:
* `CCU_Stage_1.py`
* `CCU_Stage_2.py`
* `Stroke_Stage_1.py`
* `Stroke_Stage_2.py`
The stroke `.py` files combine the seperate model and app .py files from the [llm_simpy](https://github.com/pythonhealthdatascience/llm_simpy) repository.
For a full record of the generation of these models, please refer to: https://github.com/pythonhealthdatascience/llm_simpy.
## 👥 Authors
* Thomas Monks [](https://orcid.org/0000-0003-2631-4481)
* Alison Harper [](https://orcid.org/0000-0001-5274-5037)
* Amy Heather [](https://orcid.org/0000-0002-6596-3479)
## 🌐 Creating the environment
The project uses `conda` to manage dependencies. Navigate your terminal to the directory containing the code and run:
```
conda env create -f binder/environment.yml
```
This will create a conda environment called `gen_simpy_apps`. To activate:
```
conda activate gen_simpy_apps
```
This environment is a simplified version of that from the [llm_simpy](https://github.com/pythonhealthdatascience/llm_simpy) repository, containing only the dependencies required for running the apps.
## 🖥️ Viewing the apps locally
For deployment, we have brought the LLM-generated apps together into a single app, which can be deployed by running:
```
streamlit run Home.py
```
However, you can also run the individual original apps generated by the LLMs by calling on a specific file - for example:
```
streamlit run pages/CCU_Stage_1.py
```
To test the stlite app locally, run the following command, and then open on your web browser:
```
python3 -m http.server
```
## 📝 Citation
Please cite the archived repository:
> Thomas Monks, Alison Harper, and Amy Heather. **Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models**. .
You can also cite this GitHub repository as:
> Thomas Monks, Alison Harper, and Amy Heather. **Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models**. .
A `CITATION.cff` file is also provided.
## 💰 Funding
This project was developed as part of the project STARS: Sharing Tools and Artefacts for Reproducible Simulations. It is supported by the Medical Research Council [grant number [MR/Z503915/1](https://gtr.ukri.org/projects?ref=MR%2FZ503915%2F1)].