Generate relevant Python code from English queries using Jupyter Notebook | iNNovationMerge

Generate relevant Python code from English queries using Jupyter Notebook



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What?

  • Advanced technologies are making the way of writing code easy.
  • Natural Language Processing(NLP), Named Entity Recognition(NER) are base to understand developer queries and extract intent out of it.

Why?

  • There will be normal queries which Developers use frequently during Exploratory data analysis.
  • Instead of writing the syntax for each query, Developers can now use normal English similar to a comment and generate a Python code.

How?

Related Article:

Software’s Required:

  • Python 3.6
  • Browser

Network Requirements

  • Internet to download packages

Implementation

  • Blog post explains the implementation using open source libraries.
  • Relevant python code is generated by extracting the entities from user input text, identifying, matching with predefined intents and filling the template.
  • Text2Code Jupyter extension is packaged with required frontend which loads with Jupyter notebook for user interaction and created with below process
    • Generating training data
    • Intent Matching
    • NER(Named Entity Recognition)
    • Fill Template

Github Source Code Repository

Demo from DeepKlarity


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