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Modes of offering
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- Webapp - drop the jupyter notebook and forge the project you want
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- CLI
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- VS-Code extension
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Milestones :
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- convert notebook to python project -- done
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- stream lit -- done
- to provide stream lit desc create a markdown with the header
NBForge_Streamlit - if there are multiple such markdown blocks we pick the latest one
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- fast api support -- DONE
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- proper structure - DONE
- add description for each function -- DONE
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- update toml file -- DONE
- optimze (remove unnecessary dependencies)
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- setup logging -- DONE
- add proper logging to each file -- ADDED TO converter.py , rest not adding
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- proper packaging (toml) -- DONE
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- format the files -- DONE
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- convert to package [ IMP ] -- DONE
- understand difference between
- a. publishing a package that one can import
- b. publishing a pakcage that people install and use in cli
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- cli -- DONE optimze (remove unnecessary dependencies)
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- publish to pypi
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- webapp
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- vs code extension
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MAYBE : we will provide an option to create test cases for the modules
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add anotations :
- through comments probably
- user can state whether to add this markdown as comment or as as main readme
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if we are offering as cli then how to use the groq api key?
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revamp the prompt
- make sure llm does not add anything of its own
- it just goes over the code and separates the code and comments
- should we allow it to revamp the comments and readme?
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check the streamlit app . check whether it is correctly picking the csv file .
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test the demo MSE calculation program with all 3
- default modules
- stream lit
- fast api
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Sample image from streamlit app generated using this package
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Sample endpoint from fastAPI app generated using this package
- Run the following command to covnert the jupyter notebook file at
NOTEBOOK_PATHinto a python module created in directoryOUTPUT_DIRECTORY.
$ nbforge convert <NOTEBOOK_PATH> <OUTPUT_DIRECTORY>

