A lightweight markup language for interacting with machine learning models
Project description
FrameML
This is meant to be a markup language for interacting with machine learning models. This is written from the ground up with simplicity, minimalism, and power usage in mind.
Core features currently include:
- Variables!
- These can be from a user supplied dictionary or special variables like:
{{1}}
, previous inputs
- These can be from a user supplied dictionary or special variables like:
- Control flow!
- Extremely basic if statementst are provided
- Check TODO for more info
Included in this repo is a python library for compiling this script and using it with your own programs. Here is a simple example script:
# You can write comments like this! All blank lines are ignored.
> Create a fictional title for the {{MOVIE_ADJECTIVE}} movie ever! This should not be a real movie; only supply me with the title.
# blank lines and comments are all deleted
> Write a comprehensive anlaysis of {{1}}, discussing the following details: {{ANALYSIS_DETAILS}}. Make sure to sound as pedantic as possible.
! if 'masterpiece' in {{2}}
> Someone approaches you and says {{1}} is the worst movie ever made. How do you respond?
! elif 'worst' in {{2}} # regex supported here!
> Some subhuman piece of garbage online actually thinks that {{1}} is the best movie made. How do you respond?
! else
> Someone feels passionately about {{1}}. They are frustrated with your apathy. What do you respond with?
! endif
The script could be called like so:
FrameML
is our class that wraps this functionalitymodel_call
is aCallable
that takes a string and returns a string from your modelFrameML.compile()
takes a**kwargs
, named parameters
TODO:
- Implement other cooler control flow
- Again, may or may not be necessary but it might be cool
- Allow user to implement their own functions in the script
- This may be out of the scope for the project
Project details
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frameml-0.1.4.tar.gz
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