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

Text2Code for Jupyter notebook

Jupyter plugin Installation:

pip install .

Open Jupyter notebook
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

From a list of templates present at mopp/mopp_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).

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

cd scripts && python create_intent_index.py

cd scripts && python train_spacy_ner.py

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 mopp/mopp_serverextension/__init__.py with new intent's condition and add actual code for the intent
Reinstall plugin by running: pip install .

Refactor code and make it mode modular, remove duplicate code, etc
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