Open Interpreter: A 2023 Local Code-Execution Walkthrough

ChatGPT’s Code Interpreter can work with uploaded PDFs, images and data through natural language. I introduce Open Interpreter as a way to run model-generated code on your own computer and work with local files. This is a September 2023 demonstration, and the installation steps and model options may have changed.

The project describes a terminal interface that can execute Python, JavaScript, shell commands and more. Potential uses include editing files, analyzing data, controlling a browser and changing computer settings. These capabilities depend on the environment, permissions and generated code; having local execution does not make every task reliable or remove all limitations.

The video includes a paid sponsorship for CapCut Online, whose script-to-video and editing tools Berman promotes. He then returns to the Open Interpreter installation.

I open a terminal with Anaconda installed and create a Conda environment named oi using Python 3.11.4. After activating it with `conda activate oi`, I install the package with `pip install open-interpreter`. In this setup the environment must be activated when launching the tool; these are the commands shown in 2023, not a guarantee of current compatibility.

I launch `interpreter` with the then-default GPT-4 option and supply an OpenAI API key. Although commands execute on my computer, GPT-4 inference uses a hosted API rather than running the model locally; prompts and relevant content may be sent to the provider. I say I will revoke the key shown during recording.

I ask what files are on my desktop. The tool proposes a Windows shell command, asks whether it should run, encounters an error and tries again successfully. The historical `interpreter -y` option skips approval prompts, but doing so removes the opportunity to inspect commands before they can modify or access local resources.

Next I ask what an Excel file on the desktop contains. The tool installs libraries, retries after an error and displays sample rows from a fashion-brand dataset. I then ask which company had the highest growth rate in the latest year in that dataset; it returns Nike, which I say matches the data.

I ask for a chart of the companies’ growth rates. The tool installs matplotlib and produces a graph, then I ask it to rename the spreadsheet to a simpler name. It encounters an error, tries a different command and succeeds; I verify the renamed file on the desktop.

For a PDF, I request a summary of only the first page of Attention Is All You Need. The process encounters multiple problems, including a deprecated PDF reader, package issues and missing NLTK data. After trying alternatives, installing dependencies and retrying, it produces a summary. The example demonstrates an iterative recovery, not a seamless or full-document summary.

Finally, I try the then-available `interpreter --local` path for Code Llama, selecting a small model and GPU use. Installation of its interface fails on my system, so I cannot demonstrate local inference. I remain excited about the possibility of an open-source, fully local setup and invite viewers to suggest other use cases.