Text2Code for Jupyter notebook project is a proof-of-concept extension for Jupyter Notebook that allows users to generate Python code directly from natural language queries written in English. The tool is designed to simplify data analysis workflows by enabling users to describe their intended operation in plain language instead of manually writing code. When a user enters a textual command, the extension interprets the request and generates a corresponding Python code snippet that can be inserted into the notebook and executed automatically. The system uses natural language processing techniques to identify the intent of the query, extract relevant variables, and map the request to predefined code templates. Technologies such as sentence embeddings and named entity recognition are used to interpret user instructions and construct appropriate code outputs.
Features
- Natural language interface that converts English queries into Python code
- Integration with Jupyter Notebook through frontend and server extensions
- Intent detection using sentence embeddings and similarity matching
- Named entity recognition for identifying variables and parameters in queries
- Automatic insertion and execution of generated code within notebook cells
- Designed to simplify data analysis and programming workflows in notebooks