ASKEM-TA1-DockerVM

Place to create docker recepies that will make our pipelines easy to use

Usage

The current directory must contain subdirectories inputs and outputs. Inputs will have plain text files to be processed by TA 1 reading pipelines Edit docker-compose.yml to add your OpenAI API key.

Run docker compose up and after both pipelines have finished, the outputs directory will contain files with the following prefiexes:

extractions_ Arizona output artifacts
mit_ MIT output artifacts
canonical_ merged outputs using canonical data format

SKEMA Service Components

Text Reading

The client code for both SKEMA and MIT text reading pipelines is available in end-to-end-rest/notebooks/text_reading/text_reading_pipeline.ipynb

To call the unified pipeline use integrated_text_extractions.

Example

# Read a paper from the example directory
test_text = open("1-s2.0-S2211379721005490-main.txt").read()

# Run the integrated pipeline
extractions = integrated_text_extractions(test_text)

# Print the result
for attribute in extractions[0].attributes:
    print(attribute)

Eqn2AMR

{Img,LaTeX}2pMML

There are two endpoints available for this part of the workflow, which are demonstrated in the equations.ipynb notebook located in the end-to-end-rest/notebooks directory.

get("/latex/mml")

This endpoint handles a GET request and expects a LaTeX string representing an equation as input. It then returns the corresponding presentation MathML for that equation.

post("/image/mml")

This endpoint handles a POST request and expects a PNG image of an equation as input. It then processes the image and returns the corresponding presentation MathML for that equation.

Please refer to the equations.ipynb notebook for a detailed demonstration of how to use these endpoints.

pMML2AMR

There are several endpoints that can be used for this aspect of the workflow. They are demonstrated in the MORAE_demo.ipynb notebook in the morae-demo/ directory.

put("/mathml/petrinet")

Is a put request takes in a vector of mathml strings and returns an AMR of the PetriNet variety.
put("/mathml/regnet")

Is a put request takes in a vector of mathml strings and returns an AMR of the RegNet variety.
put("/mathml/amr")

Is a put request that takes in a JSON like object which includes a vector of mathml strings and a model keyword to indicate the type of AMR wanted out, as below for an example for a regnet: { "mathml": [ "<math><mrow><mfrac><mrow><mi>d</mi><mi>x</mi></mrow><mrow><mi>d</mi><mi>t</mi></mrow></mfrac><mo>=</mo><mi>alpha</mi><mi>x</mi><mo>-</mo><mi>beta</mi><mi>x</mi><mi>y</mi></mrow></math>", "<math><mrow><mfrac><mrow><mi>d</mi><mi>y</mi></mrow><mrow><mi>d</mi><mi>t</mi></mrow></mfrac><mo>=</mo><mi>delta</mi><mi>x</mi><mi>y</mi><mo>-</mo><mi>gamma</mi><mi>y</mi></mrow></math>" ], "model": "regnet" }

Code2AMR

code2FN

The Code2FN service take code as input (in multiple different forms), runs the program analysis pipeline to parse the files into CAST and translate the CAST into a Function Network (FN) and returns Gromet Function Network Module Collection (GrometFNModuleCollection) JSON.

The service currently accepts Python and Fortran (family) source code. The language type is determined by the filename extensions:

Python: .py
Fortran: .f, .for, .f95

The service can accept the following four types of code forms:

string containing code
single file
multi-file - array of text-blobs and corresponding filenames
.zip archive containing a directory tree of source code files

TODO: Tito and/or Vincent: Please provide pointer to example of calling the endpoint(s).

FN2AMR

This is demonstrated in the MORAE_demo.ipynb notebook in the morae-demo/ directory.

This part of the workflow currently has two endpoints, one for PertriNets and one for RegNets, however only PetriNet's is supported right now. It is accessed through the following endpoint:

put("/models/PN")

This endpoint takes in a gromet function network JSON (the output of code2FN) and attempts to infer the core dynamics of a PetriNet in it and export them as an AMR.

In the near future there will be support for RegNets as well via another endpoint.

AMR Refinement