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-1.2.2.tar.gz (14.6 kB view details)

Uploaded Source

Built Distribution

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

promoai-1.2.2-py3-none-any.whl (15.0 kB view details)

Uploaded Python 3

File details

Details for the file promoai-1.2.2.tar.gz.

File metadata

  • Download URL: promoai-1.2.2.tar.gz
  • Upload date:
  • Size: 14.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.7

File hashes

Hashes for promoai-1.2.2.tar.gz
Algorithm Hash digest
SHA256 1aa268222d75c2713ec5cb0486b4fab106ff156cdb6f634db402a5648be7a35a
MD5 44a6c7f00241ee8521be7f7fe891659e
BLAKE2b-256 a8ae279da37723ea3fd6d5aa7c89ee8090ee30c6bedbe3c9d40b68ac4f890429

See more details on using hashes here.

File details

Details for the file promoai-1.2.2-py3-none-any.whl.

File metadata

  • Download URL: promoai-1.2.2-py3-none-any.whl
  • Upload date:
  • Size: 15.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.7

File hashes

Hashes for promoai-1.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 2abf21b59e003c1bc80d5bb3de1879b3d1001d279fb1abcfc5eaea37897f4a09
MD5 765769381170c01e9d1c4e049a554311
BLAKE2b-256 ff3ab23f332d933ccfbf9ff9c4417fffadf430041a1bf198922358e3486e689e

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