Exploring the New Code Interpreter Plugin on ChatGPT

The Code Interpreter is an experimental model that generates Python code and executes it within a Jupyter notebook. This intriguing feature allows the user to upload files of any kind and request the model to analyze them or produce a new file, which can then be downloaded. The potential of this feature appears vast, and my anticipation was high as I prepared to put it to the test.

To do so, I created a folder with a few files for testing purposes, including a zip file comprising multiple CSVs from Google's search console. The Code Interpreter swiftly unzipped the file and allowed me to watch the code executing in real time, which was undeniably cool. What's more, it provided the option to copy the code for personal use, enhancing its practicality.

The model scanned the seven CSVs and employed the pandas library for data analysis, presenting a brief summary of each file without any explicit instruction on my part, which was pretty impressive. The datasets held information related to keywords, pages, dates, devices, and queries, including data on clicks, impressions, click-through rates, and average positions in search results.

Upon asking the model to identify the best ranking page in my dataset, it efficiently determined a page that ranked number one in search results, even though it had zero clicks. This led to a deeper exploration, and I posed several more queries to the model, seeking to understand which page had the most impressions and what page on a mobile device I should focus on to drive more traffic. The model provided clear and concise responses, even offering advice on how to use Google Search Console for better results.

One of the most fascinating features was the model's capability to perform visualizations. Although it initially struggled with rendering a comprehensive bar plot of all files (which is understandable given its alpha status), the model's potential was still clear. While it was working, I decided to explore the capabilities of the Code Interpreter further.

I uploaded a .m4a audio file and asked the model to convert it to an .mp3 format, and to my delight, it completed the task promptly. Venturing a step further, I requested the model to trim the first five seconds and the last ten seconds of the audio file, and then divide it into two parts. The model executed this complex prompt swiftly, providing download links for both parts of the edited file.

While I expected the Code Interpreter to be an interesting addition to ChatGPT, the level of functionality and ease of use it offers exceeded my expectations. This feature clearly has immense potential, and although it's in the early stages, it's already offering remarkable capabilities. I can't wait to see how it develops over time.

So that's my quick take on the Code Interpreter plugin. I hope you found this walkthrough helpful and intriguing. I'm excited to continue exploring its capabilities, and I promise to share anything interesting that comes up. Stay tuned, and thank you for joining me on this exciting exploration!