Skip to main content

ProMoAI: Process Modeling with Generative AI

Project description

ProMoAI

ProMoAI leverages Large Language Models for the automatic generation of process models. ProMoAI transforms textual descriptions of processes into process models that can be exported in the BPMN and PNML formats. It also supports user interaction by providing feedback on the generated model to refine it. ProMoAI supports three input types:

  • Text: Provide the initial process description in natural language.
  • Process Model: Start with an already existing semi-block-structured BPMN or Petri net and use ProMoAI to refine it.
  • Event Log: Start with an event log in the XES format and the initial process model will be derived using process discovery.

Launching as a Streamlit App

You have two options for running ProMoAI.

  • On the cloud: under https://promoai.streamlit.app/.
  • Locally: by cloning this repository, installing the required environment and packages, and then running 'streamlit run app.py'.

Installing as a Python Library

Run pip install promoai.

Requirements

  • Environment: the app is tested on both Python 3.9 and 3.10.
  • Dependencies: all required dependencies are listed in the file 'requirements.txt'.
  • Packages: all required packages are listed in the file 'packages.txt'.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

promoai-1.4.0.tar.gz (80.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

promoai-1.4.0-py3-none-any.whl (76.3 kB view details)

Uploaded Python 3

File details

Details for the file promoai-1.4.0.tar.gz.

File metadata

  • Download URL: promoai-1.4.0.tar.gz
  • Upload date:
  • Size: 80.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for promoai-1.4.0.tar.gz
Algorithm Hash digest
SHA256 ec7217937cf64b0507ee6398479a9effd4151773ec538001d5ddb438a12f46c0
MD5 9fdf9005252978763335075454002638
BLAKE2b-256 6991055a0c30bd181d5b3ab97326903421d85da2a2d1597e8f8788eefac66419

See more details on using hashes here.

Provenance

The following attestation bundles were made for promoai-1.4.0.tar.gz:

Publisher: publish.yml on humam-kourani/ProMoAI

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file promoai-1.4.0-py3-none-any.whl.

File metadata

  • Download URL: promoai-1.4.0-py3-none-any.whl
  • Upload date:
  • Size: 76.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for promoai-1.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 6fa7c89bc383a415f9b29e610700b0b6b6a862453991053d6c551b65c93b9d1b
MD5 1231472fda61ae67c43ba0ab2cc728a6
BLAKE2b-256 bf00b9012e5ec4eef8f3bb386da9b8cacf6269b69eb5e4942b4280fbec2f25e4

See more details on using hashes here.

Provenance

The following attestation bundles were made for promoai-1.4.0-py3-none-any.whl:

Publisher: publish.yml on humam-kourani/ProMoAI

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page