{"id":26699656,"url":"https://github.com/pythonhealthdatascience/llm_simpy_models","last_synced_at":"2025-10-08T21:13:53.804Z","repository":{"id":284331204,"uuid":"951858699","full_name":"pythonhealthdatascience/llm_simpy_models","owner":"pythonhealthdatascience","description":"The SimPy models and apps generated by LLMs, deployed as a single app.","archived":false,"fork":false,"pushed_at":"2025-03-25T15:30:40.000Z","size":74,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-05T02:48:11.099Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://pythonhealthdatascience.github.io/llm_simpy_models/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pythonhealthdatascience.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-03-20T11:00:34.000Z","updated_at":"2025-04-01T12:34:44.000Z","dependencies_parsed_at":"2025-04-13T10:44:34.617Z","dependency_job_id":null,"html_url":"https://github.com/pythonhealthdatascience/llm_simpy_models","commit_stats":null,"previous_names":["pythonhealthdatascience/llm_simpy_models"],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/pythonhealthdatascience/llm_simpy_models","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonhealthdatascience%2Fllm_simpy_models","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonhealthdatascience%2Fllm_simpy_models/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonhealthdatascience%2Fllm_simpy_models/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonhealthdatascience%2Fllm_simpy_models/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pythonhealthdatascience","download_url":"https://codeload.github.com/pythonhealthdatascience/llm_simpy_models/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonhealthdatascience%2Fllm_simpy_models/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279000709,"owners_count":26082837,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-26T23:15:57.789Z","updated_at":"2025-10-08T21:13:53.749Z","avatar_url":"https://github.com/pythonhealthdatascience.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Licence: MIT](https://img.shields.io/badge/Licence-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Python 3.10+](https://img.shields.io/badge/-Python_≥_3.10-306998?logo=python\u0026logoColor=white)](https://www.python.org/downloads/release/python-360+/)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15082494.svg)](https://doi.org/10.5281/zenodo.15082494)\n\n# Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models\n\nThis repository is complementary to:\n\n\u003e Thomas Monks, Alison Harper, and Amy Heather. **Research Compendium: Replicating Simulations in Python using Generative AI**. https://github.com/pythonhealthdatascience/llm_simpy.\n\nIt contains the final formatted code from each of the SimPy discrete-event simulation models that were generated by Perplexity as part of that project.\n\nThe 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**.\n\n**Link to the deployed app:** https://pythonhealthdatascience.github.io/llm_simpy_models/\n\n**Code:** The final formatted code from each stage are stored in 📁`pages\\`:\n\n* `CCU_Stage_1.py`\n* `CCU_Stage_2.py`\n* `Stroke_Stage_1.py`\n* `Stroke_Stage_2.py`\n\nThe stroke `.py` files combine the seperate model and app .py files from the [llm_simpy](https://github.com/pythonhealthdatascience/llm_simpy) repository.\n\nFor a full record of the generation of these models, please refer to: https://github.com/pythonhealthdatascience/llm_simpy.\n\n\u003cbr\u003e\n\n## 👥 Authors\n\n* Thomas Monks \u0026nbsp;\u0026nbsp; [![ORCID: Monks](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481)\n\n* Alison Harper \u0026nbsp;\u0026nbsp; [![ORCID: Harper](https://img.shields.io/badge/ORCID-0000--0001--5274--5037-brightgreen)](https://orcid.org/0000-0001-5274-5037)\n\n* Amy Heather \u0026nbsp;\u0026nbsp; [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479)\n\n\u003cbr\u003e\n\n## 🌐 Creating the environment\n\nThe project uses `conda` to manage dependencies. Navigate your terminal to the directory containing the code and run:\n\n```\nconda env create -f binder/environment.yml\n```\n\nThis will create a conda environment called `gen_simpy_apps`. To activate:\n\n```\nconda activate gen_simpy_apps\n```\n\nThis 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.\n\n\u003cbr\u003e\n\n## 🖥️ Viewing the apps locally\n\nFor deployment, we have brought the LLM-generated apps together into a single app, which can be deployed by running:\n\n```\nstreamlit run Home.py\n```\n\nHowever, you can also run the individual original apps generated by the LLMs by calling on a specific file - for example:\n\n```\nstreamlit run pages/CCU_Stage_1.py\n```\n\nTo test the stlite app locally, run the following command, and then open \u003chttp://0.0.0.0:8000/\u003e on your web browser:\n\n```\npython3 -m http.server\n```\n\n\u003cbr\u003e\n\n## 📝 Citation\n\nPlease cite the archived repository:\n\n\u003e Thomas Monks, Alison Harper, and Amy Heather. **Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models**. \u003chttps://doi.org/10.5281/zenodo.15082494\u003e.\n\nYou can also cite this GitHub repository as:\n\n\u003e Thomas Monks, Alison Harper, and Amy Heather. **Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models**. \u003chttps://github.com/pythonhealthdatascience/llm_simpy_models\u003e.\n\nA `CITATION.cff` file is also provided.\n\n\u003cbr\u003e\n\n## 💰 Funding\n\nThis 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)].","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpythonhealthdatascience%2Fllm_simpy_models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpythonhealthdatascience%2Fllm_simpy_models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpythonhealthdatascience%2Fllm_simpy_models/lists"}