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-1.2.4.tar.gz (73.0 kB view details)

Uploaded Source

Built Distribution

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

promoai-1.2.4-py3-none-any.whl (72.4 kB view details)

Uploaded Python 3

File details

Details for the file promoai-1.2.4.tar.gz.

File metadata

  • Download URL: promoai-1.2.4.tar.gz
  • Upload date:
  • Size: 73.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.7

File hashes

Hashes for promoai-1.2.4.tar.gz
Algorithm Hash digest
SHA256 101a4d6b232cb114eaaeaee2e4410c62bbbe0551de2d355f07216a66574c4575
MD5 6b4bc9762fe295e8c2bafb8d9ae4002c
BLAKE2b-256 962e0628458639bd685a8f9f0f7f6b041135000228975ae68ae58c90ca528862

See more details on using hashes here.

File details

Details for the file promoai-1.2.4-py3-none-any.whl.

File metadata

  • Download URL: promoai-1.2.4-py3-none-any.whl
  • Upload date:
  • Size: 72.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.7

File hashes

Hashes for promoai-1.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 93538c9bb36cd68e1e85592394832c6b91dfc0f8c9e920023cd5e96da25536b6
MD5 ee1ac1a04efe3e0bf0da46c22c929edc
BLAKE2b-256 5369ff7f6a8cf281f1a45169d1ea2ba3e2f8f474594631db4f5b2e2c193432df

See more details on using hashes here.

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