Introduction to Awesome ChatGPT Code Interpreter Experiments
The "awesome-chatgpt-code-interpreter-experiments" project is a captivating endeavor that explores the cutting-edge capabilities of combining ChatGPT with a sophisticated Code Interpreter. This project aims to stretch the limits of these technologies, showcasing their potential and inspiring user creativity, all while ensuring an enjoyable experience for enthusiasts and developers alike.
Code Interpreter: An Overview
The Code Interpreter is an official ChatGPT plugin designed for data analysis, image conversions, code editing, and other tasks. Available to all ChatGPT Plus users since July 6th, 2023, the Code Interpreter provides OpenAI models with a Python interpreter that operates within a sandbox, a secure execution environment that enables file uploads and downloads. This allows for diverse experimentation and application possibilities.
How to Activate the Code Interpreter
To start using the Code Interpreter, users should navigate to ChatGPT's settings, activate the feature under the "Beta features" tab, and select the "GPT-4 + Code Interpreter" environment.
Understanding the Limitations
While the Code Interpreter is powerful, it does have some limitations. It does not allow internet access, is restricted to Python code execution, cannot install external Python packages, and caps file uploads at 100 MB. Additionally, when the environment session ends, the state is lost, and file download links become inactive. However, there are ways to work around some of these constraints.
Pro Tips for Effective Use
- Always check that imports and variables are defined, as they may disappear unexpectedly.
- Avoid generating too many logs and outputs, as they can quickly exhaust the context window.
- Verify file existence within the environment before proceeding with operations.
- Use the prompt "notalk;justgo" to streamline interactions.
Exploring Potential Jailbreaks
Installing External Python Packages
Even though the Code Interpreter is pre-installed with several Python packages, it lacks internet access which prevents installing additional packages through standard means. However, users can upload .whl files and, with creativity and polite requests, navigate this limitation.
Accessing the Code Interpreter System Prompt
By setting the system message wisely, users can influence the tone and types of responses generated by the model, tailoring the assistant's behavior to their specific needs.
Experimental Projects and Use Cases
Running JavaScript Applications
Although the Code Interpreter primarily executes Python code, it can run JavaScript applications with Deno by uploading and executing the Deno binary, demonstrating the system's flexibility.
YOLOv8 Object Detection
Though challenging, it is feasible to run the YOLOv8 object detection model in the Code Interpreter by preparing files offline and creatively bypassing restrictions.
Video Face Detection and Tracking
Using traditional techniques like Haar Cascades, the project illustrates how face detection and tracking can be achieved even without access to deep learning models.
MNIST Image Classification
This experiment trains a classifier on a subset of the MNIST dataset, showcasing how limited resources can still serve educational and experimental purposes in machine learning.
Object Tracking and Counting
By exploiting the unique colors of objects, this project tracks and counts objects in a video, highlighting innovative ways to leverage the Code Interpreter's capabilities.
OCR Text Extraction
Utilizing the Tesseract OCR engine, the project enables text extraction from uploaded documents, combining OCR capabilities with ChatGPT’s advanced language processing to structure extracted data.
The "awesome-chatgpt-code-interpreter-experiments" project is a testament to the potential of combining AI models and programming tools, inviting developers to explore, innovate, and expand the boundaries of technology. The only limit is the user's creativity and willingness to experiment.