Chapterter, developed by a group of language modelers, is a new Jupyter plugin that integrates ChatGPT to allow you to create Python notebooks. The system can also read the results of previously executed cells.
Chapterter is a plugin for JupyterLab, which allows seamless integration of GPT-4 into the development environment. It has an interpreter that can take the natural language written description and convert it into Python code that can be executed automatically. Chapterter can increase productivity and allow you to try new things by enabling “natural language programming” in your preferred IDE.
essential features
- The process of automatically generating code from natural language and running it.
- The production of new code based on past code and the results of previous executions.
- Code fixes and bug fixes on the fly.
- Full visibility and customization options for AI setup prompts.
- Prioritize privacy when using cutting-edge artificial intelligence technology.
The library prompts and settings are made public, and the researchers are working to make it easy to customize those questions and settings. Chapterter/programs.py is where one can see this.
Please refer to your API’s data usage policies for more information on how OpenAI handles training data. Rather, every time one uses Copilot or ChatGPT, some of the data will be cached and used for training and analysis by those services. Chapterter consists of two main parts: using ipython’s magic command to handle the prompt, and using that command to call the GPT-X models. Chapterer’s cell execution monitoring user interface executes newly created cells and updates cell styles automatically.
Many programmers prefer to work in notebooks in a “chunky” fashion, writing only a few lines of code at a time before moving on to the next cell. The mission or purpose of each cell is relatively modest and autonomous from those of neighboring cells. Later work may have little in common with earlier. Adding the dataset loader, for example, when creating a neural network, requires different ways of thinking and writing code. Constantly switching between tasks is not only inefficient but also potentially exhausting. The command “Please load the dataset so that it tests the neural network” could be useful when you want to write and let the machine do the rest.
Cell-level code development and Chapterter’s stand-alone execution provide a solution to this problem. When you create a new cell, Chapterter will automatically invoke the GPT-X model to build the code and run it based on the text you type. Unlike systems like Copilot, which focus on supporting microtasks that span only a few lines of code but are highly relevant to ongoing work (such as finishing a function call), Chapterter aims to take care of entire tasks, some of which may differ from existing code.
Chapterter is a lightweight Python tool that integrates seamlessly with JupyterLab after a local installation. By default, the OpenAI API is configured to discard data and interaction code after calling GPT-X models. The library contains all standard prompts, “programs” and the option to load custom prompts. By analyzing past coding decisions and runtime data, Chapterr can make intelligent recommendations. Files can be uploaded if desired, and suggestions for further processing and analysis will be provided.
Given the limitations of today’s AI, Chapterter was built so that its generated code can be easily debugged and improved.
The three-step installation process is easy to follow. On GitHub, at https://github.com/chapyter/chapytermore information can be found.
Soon, the researchers will release major enhancements to Chapterter that will make it even more flexible and secure in code generation and execution. They can’t wait to put you to the test on some of the most demanding and complex coding tasks in the real world, like ensuring a jupyter notebook with 300 cell runs has all the help it needs. Try our tools and stay tuned for future improvements; They value your thoughts and opinions.
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Dhanshree Shenwai is a Computer Engineer and has good experience in FinTech companies covering Finance, Cards & Payments and Banking domain with strong interest in AI applications. She is enthusiastic about exploring new technologies and advancements in today’s changing world, making everyone’s life easier.
