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Text2Code for Jupyter notebook

A proof-of-concept jupyter extension which converts english queries into relevant python code.

Blog post with more details:

Data analysis made easy: Text2Code for Jupyter notebook

Demo Video:

Text2Code for Jupyter notebook

Supported Operating Systems:

  • Ubuntu
  • macOS

Installation

NOTE: We have renamed the plugin from mopp to jupyter-text2code. Uninstall mopp before installing new jupyter-text2code version.

pip uninstall mopp

CPU-only install:

For Mac and other Ubuntu installations not having a nvidia GPU, we need to explicitly set an environment variable at time of install.

export JUPYTER_TEXT2CODE_MODE="cpu"

GPU install dependencies:

sudo apt-get install libopenblas-dev libomp-dev

Installation commands:

git clone https://github.com/deepklarity/jupyter-text2code.git
cd jupyter-text2code
pip install .
jupyter nbextension enable jupyter-text2code/main

Uninstallation:

pip uninstall jupyter-text2code

Usage Instructions:

  • Start Jupyter notebook server by running the following command: jupyter notebook
  • If you don't see Nbextensions tab in Jupyter notebook run the following command:jupyter contrib nbextension install --user
  • You can open the sample notebooks/ctds.ipynb notebook for testing
  • If installation happened successfully, then for the first time, Universal Sentence Encoder model will be downloaded from tensorflow_hub
  • Click on the Terminal Icon which appears on the menu (to activate the extension)
  • Type "help" to see a list of currently supported commands in the repo
  • Watch Demo video for some examples

Docker containers for jupyter-text2code

We have published CPU and GPU images to docker hub with all dependencies pre-installed.

Visit https://hub.docker.com/r/deepklarity/jupyter-text2code/ to download the images and usage instructions.
CPU image size: 1.51 GB
GPU image size: 2.56 GB

Model training:

Generate training data:

From a list of templates present at jupyter_text2code/jupyter_text2code_serverextension/data/ner_templates.csv, generate training data by running the following command:

cd scripts && python generate_training_data.py

This command will generate data for intent matching and NER(Named Entity Recognition).

Create intent index faiss

Use the generated data to create a intent-matcher using faiss.

cd scripts && python create_intent_index.py

Train NER model

cd scripts && python train_spacy_ner.py

Steps to add more intents:

  • Add more templates in ner_templates with a new intent_id
  • Generate training data. Modify generate_training_data.py if different generation techniques are needed or if introducing a new entity.
  • Train intent index
  • Train NER model
  • modify jupyter_text2code/jupyter_text2code_serverextension/__init__.py with new intent's condition and add actual code for the intent
  • Reinstall plugin by running: pip install .

TODO:

  • Publish Docker image
  • Refactor code and make it mode modular, remove duplicate code, etc
  • Add support for Windows
  • Add support for more commands
  • Improve intent detection and NER
  • Explore sentence Paraphrasing to generate higher-quality training data
  • Gather real-world variable names, library names as opposed to randomly generating them
  • Try NER with a transformer-based model
  • With enough data, train a language model to directly do English->code like GPT-3 does, instead of having separate stages in the pipeline
  • Create a survey to collect linguistic data
  • Add Speech2Code support

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