OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Coding Tools

Jupyter Text2Code

Jupyter Text2Code is an MIT-licensed Jupyter Notebook extension that interprets supported English queries and produces Python code, including pandas commands and insertable notebook snippets. Its documented workflow uses intent detection and named-entity recognition.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Jupyter Text2Code — OpenViST project profile in AI Coding Tools.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when working in Jupyter on Ubuntu or macOS and a supported prompt, pandas command, or available integration snippet can help start a notebook task.

Who it is for

  • Jupyter users who want to turn supported natural-language requests into Python starting points.
  • Data analysts who want pandas commands or quick notebook snippets for supported tasks and integrations.
  • Developers prepared to extend intent templates, training data, models, and intent-specific code.

Documented uses

  • Request Python code in English from within a notebook.
  • Insert available integration-related notebook snippets, such as examples for Twitter follower counts or Instagram story statistics.
  • Add a supported query type by extending templates, generating data, training intent and NER components, and implementing its code.
Before you choose

Requirements & limits

The project is available as a public GitHub repository under the MIT license. The README lists Ubuntu and macOS as supported operating systems.

  • It operates inside Jupyter Notebook rather than as a standalone hosted coding assistant.
  • Its prompts map to supported intents and code paths; adding new intent types requires development and model-training steps.
  • Installation enables a Jupyter notebook extension and downloads a Universal Sentence Encoder model from TensorFlow Hub on first use.
  • The README marks Windows support as a TODO; Windows use requires modification.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install and open the Jupyter extension

Install Jupyter Text2Code and reach its documented notebook prompt interface.

Before you start

  • Ubuntu or macOS
  • Python and Jupyter Notebook
  • For CPU-only installations, set JUPYTER_TEXT2CODE_MODE="cpu" at install time.
  • For GPU installation, the README lists libopenblas-dev and libomp-dev as dependencies.

Steps

  1. Clone https://github.com/deepklarity/jupyter-text2code.git and change into the repository directory.

  2. Run pip install .

  3. Enable the extension with jupyter nbextension enable jupyter-text2code/main.

  4. Start Jupyter with jupyter notebook; if the Nbextensions tab is absent, run jupyter contrib nbextension install --user.

  5. Use the Terminal icon in the notebook menu to activate the extension, then type help to view supported commands.

Expected outcome

The extension is installed and its documented interface can display the commands supported by the repository.

Guide limitations

  • On first use, the Universal Sentence Encoder model is downloaded from TensorFlow Hub.
  • The README lists Ubuntu and macOS support; Windows support is a TODO.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
deepklarity/jupyter-text2code · Official homepage title · retained in its source language
How we review sourcesSuggest a correction