ml4ai / askem-ta1-dockervm Goto Github PK

@Free-Quarks , as a step towards simplifying and consolidating our examples (#39), can you work on migrating whatever MORAE examples in https://github.com/ml4ai/ASKEM-TA1-DockerVM you’re committing to maintaining to the end-to-end-rest subdir and modify such that they use the docker-compose file included there?

There are a few notebooks we have with examples of using our endpoints. However I think they are probably outdated at this point. We should make sure they get updated before the program ends so they are still valid documentation. This issue is to remind us and track that work.

• eq2amr endpoints updated
• #53
• image2mml endpoints updated
• isa endpoints updated
• text-reading endpoints updated

We should also make sure the README is up to date as well with respect to these endpoints.

• eq2amr endpoints updated
• code2amr endpoints updated
• image2mml endpoints updated
• isa endpoints updated
• text-reading endpoints updated

Create a REST API using fastapi to serve as the entry point that will orchestrate our annotation workflows and route requests to the appropriate web service (MIT or AZ)

This endpoint will receive one or more text files in its request's body and pass them along to the reading pipelines.
After both results are ready, it will run the canonical format unifier

Enrich the jupyter notebook associated with code2fn-rest with an example illustrating how to process a zip archive of code.

In coordination with @vincentraymond-ua , add add code2FN notebooks to code2fn-rest example.

In coordination with @Free-Quarks , enhance or supplement end-to-end-rest example with AMR output.

We have a snippet we expect to work (to be added to code2amr.ipynb), but MORAE doesn't yet support our typical gromet:

from IPython.display import display, HTML, Image
from pathlib import Path
import requests
import json
import os

pp = lambda x: print(json.dumps(x, indent=2))

SKEMA_PA_SERVICE = os.environ.get("SKEMA_PA_ADDRESS", "http://skema-py:8000")
SKEMA_RS_SERVICE = os.environ.get("SKEMA_RS_ADDRESS", "http://skema-rs:8080")


filename = "CHIME_SIR.py"
with open(Path("/data") / "skema" / "code" / filename, "r") as infile:
    code = infile.read()

# display file contents
display(HTML(f"<code>{code}</code>"))

# API call and response

response = requests.post(f"{SKEMA_RS_SERVICE}/extract-comments", json={"language" : "Python", "code" : code})

r = requests.put(f"{BASE_URL}/models/PN", json=response.json())
r.json()

# NOTE: the put request cleans up after itself (from @Free-Quarks)
#requests.delete(f"{BASE_URL}/models/{MODEL_ID}").text

@enoriega , as a step towards simplifying and consolidating our examples (#39), can you work on migrating whatever text reading examples in https://github.com/ml4ai/ASKEM-TA1-DockerVM you’re committing to maintaining to the end-to-end-rest subdir and modify such that they use the docker-compose file included there?

Now that we have demo notebooks, we should package them nice and tidy for the hackaton

Docker image with notebooks

Relates to ml4ai/skema#213

<text>

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

</text>

@vincentraymond-ua :

• Can you verify that my list of supported Fortran extensions is accurate and complete?
• Does my description of the input types look correct?

Provide a jupyter notebook for the code2fn-rest example illustrating how to call program analysis endpoints to generate function networks from code.

To reduce confusion, we move away from the subdir-based examples to a unified approach that uses a single docker-compose file.

Depends on ml4ai/skema#239

Currently set to 20 GB by default, perhaps this is too much and too restrictive. Find if a lower amount works and set it default

Consulting with @adarshp , add an example notebook to code2fn-rest subdir that demonstrates how to call the comment extraction service from lumai/askem-skema-rs.

In coordination with @Free-Quarks , add AMR service for output to equations-rest example.