Exploring OpenAI’s ChatGPT Code Interpreter: A Deep Dive into its Capabilities

OpenAI advancements in Natural Language Processing (NLP) are marked by the rise of Large Language Models (LLMs), which underpin products utilized by millions, including the coding assistant GitHub Copilot and the Bing search engine. These models, through their unique ability to memorize and amalgamate information, have set unparalleled benchmarks in tasks like code and text generation.

Understanding ChatGPT’s Code Interpreter

To understand the significance of the ChatGPT Code Interpreter, it’s essential to first grasp what it is and how it was built.

In essence, the ChatGPT Code Interpreter leverages the capabilities of ChatGPT but introduces an enhanced proficiency in understanding, interpreting, and even generating code across a myriad of programming languages. This feature transforms ChatGPT from a text generator into an invaluable tool for developers, aiding in code understanding, debugging, and even code generation.

Training GPT for Coding: The Codex Approach

Both GitHub Copilot and ChatGPT’s Code Interpreter utilize the Codex model developed by OpenAI.

Codex, a specialized GPT language model, is engineered to have proficient Python code-writing capabilities. Trained on publicly sourced code from GitHub, Codex showcases its potential by powering features in GitHub Copilot. When assessed for its ability to synthesize programs from docstrings, a measure of functional correctness, Codex outperforms both GPT-3 and GPT-J.

A striking observation is that repeated sampling enhances Codex’s proficiency. When up to 100 samples per problem are utilized, the model’s success rate increases to 70.2%. Such efficiency suggests the possibility of using heuristic ranking to pick accurate code samples, without necessitating full evaluation for each.

To evaluate its capabilities, the model was tasked with creating standalone Python functions based solely on docstrings. The generated code’s accuracy was then gauged using unit tests. In a dataset comprising 164 original programming problems, which includes language comprehension, algorithms, and basic math tests, Codex with 12B parameters solved 28.8% of them on a single attempt.

By honing the model further through fine-tuning correctly implemented standalone functions, its efficiency was boosted, resulting in Codex-S solving 37.7% of challenges on the first try. However, in the practical realm of programming, a trial-and-error approach is common. Mimicking this real-world scenario, the Codex-S model, when given 100 chances, successfully addressed 77.5% of the challenges.

Generative models like ChatGPT that produce code are usually assessed by comparing generated samples to a reference solution. This comparison can either be exact or employ a similarity measure like the BLEU score. However, these match-based metrics often fail to grasp the nuances of coding. A key critique of BLEU is its inefficacy in capturing code’s semantic characteristics.

Instead of relying solely on matching, a more relevant measure has been proposed: functional correctness. This means that the code produced by the model should pass a given set of unit tests. The idea is aligned with standard coding practices, as developers often use unit tests to gauge the efficacy and accuracy of their code.

This metric evaluates not just the accuracy, but the functional usability of the generated code.

The [email protected] metric is introduced as a measure of functional correctness. It essentially means that if any one of the “k” generated code samples passes the unit tests, the problem is deemed solved. However, instead of just using this in its direct form, an unbiased estimator is used to calculate [email protected] to avoid high variance.

To assess ChatGPT’s code capabilities, the research utilized the HumanEval dataset. This dataset consists of hand-written Python problems, each accompanied by unit tests.

Given the risks associated with running unknown or untrusted code, a sandbox environment was designed to test the generated code safely. This environment used gVisor to emulate resources and create a barrier between the host system and the running code. Thus, even if the model produces malicious code, it remains contained and cannot harm the host or network.

Using ChatGPT Code Interpreter

OpenAI’s ChatGPT has undergone numerous evolutions, with the Code Interpreter standing out as a revolutionary feature in the GPT-4 model. Unlike traditional chat interfaces, the Code Interpreter allows users to dive deeper into computational tasks, seamlessly blending the lines between human-AI conversations and computational processes.

At its core, the Code Interpreter is akin to having a computer embedded within the chatbot. This dynamic feature offers users temporary disk space to upload a plethora of file formats ranging from common types like TXT, PDF, and JPEG to more specialized ones such as CPP, PY, and SQLite. This breadth of support amplifies its versatility across various tasks, be it document processing or image manipulation.

Operating within a robust and secure framework, the Code Interpreter is equipped with over 300 pre-installed libraries. This sandboxed environment ensures security while still providing substantial computational power. Interestingly, when tasked, it crafts a Python script in real-time to execute the user’s request. Take, for example, converting an image-based PDF to a searchable format using OCR; all a user needs to do is upload the document, and ChatGPT handles the rest.

A point of intrigue has been the file size limit for uploads. While definitive specifications are yet to be announced, user experiments suggest the system can effectively process files considerably larger than 100MB. Regardless of the size, it’s crucial to note that these files are transient, being discarded after the chat session concludes.

The brilliance of Code Interpreter isn’t just its technical prowess, but its accessibility. OpenAI offers this feature to subscribers of ChatGPT Plus, which comes with the GPT-4 model. Thus, this transformative tool isn’t just for the tech elite but is gradually becoming accessible to a broader audience.

The distinction between the standard ChatGPT model and the Code Interpreter lies in their interaction paradigms. While the former primarily generates textual responses, the latter comprehends and executes code, offering direct results. This not only makes it a valuable asset for tech professionals but also empowers those without coding knowledge to perform complex computational tasks.

The ChatGPT Code Interpreter’s capabilities can revolutionize several aspects of Software Development and Data Science:

• Automated Code Generation: For both software applications and data analytics scripts, given a high-level description, the system can produce boilerplate structures or intricate code snippets, accelerating the development and data analysis processes.
• Code Reviews and Data Validation: AI-driven tools like ChatGPT can aid in enhancing the quality and security of software codebases. Additionally, in the realm of data science, such tools can be instrumental in reviewing and validating data processing and transformation scripts, ensuring accuracy and efficiency.
• Data Analysis Assistance: For data scientists, the ChatGPT Code Interpreter can help in generating code for preliminary data exploration, visualization, and even basic statistical tests, thus easing the data analysis workflow.

If you’re keen on finding more on the intricacies of ChatGPT and prompt engineering, Unite AI offers a comprehensive breakdown in ‘ChatGPT: Advanced Prompt Engineering‘.

Setting Up ChatGPT Code Interpreter

Code Interpreter integration allows the platform to interpret user queries, execute them as Python code, and display results in an interactive chat format. To access this feature, users can navigate to the ChatGPT settings, explore the Beta features section, and activate the Code Interpreter.

What sets it apart is its transparent mechanism. As users prompt a task, the platform unveils each step of the processing journey, offering clarity on how commands are interpreted and executed. Importantly, for privacy and security reasons, the Code Interpreter operates without any internet connectivity.

Exploring the Benefits of the ChatGPT Code Interpreter

Data Visualization & Analysis

ChatGPT goes beyond the scope of traditional charts, offering both conventional and innovative graphical representations. This ensures users can view their data in formats that provide the most meaningful insights.

However, it’s not just about visualizing raw data. The ChatGPT model is adept at processing and refining data. Though powerful, users should exercise caution.

Financial analysts will find the Code Interpreter’s ability to analyze and visualize stock rates particularly useful. Through seamless integration, users can upload data sets and visualize them in various formats. This function’s significance is evident when individuals can conduct complex data analyses.

The video below demonstrates how ChatGPT’s code interpreter created a comprehensive TSLA stock analysis.

Key Takeaways:

• Tesla’s stock has faced volatility but has also shown resilience with periods of growth.
• High trading volumes on specific days indicate significant market interest or reactions to key events.
• The downward Year-to-Date (YTD) return suggests that investors should analyze both internal company factors and external market conditions when considering future investments.

Implementing Computer Vision and OCR

Face detection, an essential function in computer vision, was approached with a classical technique: the Haar Cascade classifier from OpenCV.

The image below showcases the utilization of the classical Haar Cascade classifier.

The process of extracting text from images, known as optical character recognition (OCR), was seamlessly achieved using Tesseract, with the text subsequently structured by GPT-4, enhancing comprehensibility.

In the following video, see how Tesseract (OCR) extracts text from a certificate image.

The Code Interpreter excels in the realm of video, audio, and image manipulation. With straightforward commands, users can achieve detailed edits, such as converting GIFs into MP4s with specific enhancements. Simply upload your file, input your desired modifications, and watch the magic happen.

Python External Library inside your ChatGPT Code Interpreter

The ChatGPT Code Interpreter is a dynamic programming platform equipped with an extensive set of Python libraries. These cover everything from data visualization with Seaborn to advanced machine learning via Torch. But it’s more than a static toolkit.

Inspired by this Chatgpt page from Korakot Chaovavanich.

Starting with the latest nltk release, we uploaded a .whl file to the Interpreter. We then instructed ChatGPT to locate the suitable site-packages directory by analyzing an existing package’s location. The next step involved unpacking the wheel file to a temporary spot and moving the files to the identified site-packages directory. However, this hit a snag.

Seeking a workaround, we prompted: “Kindly ensure NLTK gets installed in the Python environment and is accessible post-installation.”

ChatGPT responded, providing a solution. It suggested adding the temporary directory to sys.path, allowing Python to identify and pull modules from the unpacked nltk package within that location. This tactic worked wonders, leading to the successful installation of NLTK.

Through the use of .whl files, the installation displayed a blend of ingenuity and adaptability. The ChatGPT Code Interpreter, despite the initial challenges, manifested its versatility and commitment to accommodating coders’ needs, assuring both novices and veterans of a refined coding experience.

In a fascinating showcase of the interpreter’s capabilities, a recent tweet by @DominikPeters highlighted a unique demonstration. Peters requested GPT-4 to generate a quiz on Paris arrondissements, and the model delivered a well-functioning website. The working quiz is available for a hands-on experience at dominik-peters.de/gpt-arrondissement-quiz/.

Asked GPT-4 to code me a Paris arrondissements quiz, it worked out of the box. https://t.co/Uhv7tPLspq pic.twitter.com/3agFZllEEQ

— Dominik Peters (@DominikPeters) April 30, 2023

Wrapping Up

OpenAI’s breakthrough with the ChatGPT Code Interpreter is nothing short of transformational for coders and non-coders alike. Its versatility in handling a wide range of tasks—from aiding developers in debugging to effortlessly churning out Parisian quizzes—is a testament to the limitless potential of AI in enhancing our digital experiences. Here’s a distilled essence of our deep dive:

Understand Your Tool: Just as you’d befriend a colleague, get acquainted with the Code Interpreter. It’s designed on top of Codex, which is fine-tuned from GPT-4. Its proficiency spans across multiple programming languages, making it an ideal companion for all your coding adventures.

Embrace the AI Revolution: Traditional coding practices are about to see a seismic shift. With AI-driven tools like the ChatGPT Code Interpreter, tasks such as bug identification, code generation, and even code reviews can be expedited.

Beyond Just Code: The Interpreter’s foray isn’t restricted to text or code. Its capability to deal with multiple file formats, from simple TXT files to complex PY scripts, underscores its utility across various domains.

Never Stop Experimenting: Our exploration with the NLTK library installation reflects the importance of persistence and adaptability, values that the Code Interpreter embodies. If there’s a hitch, there’s often a way around it.

Join the AI Conversation: Real-world applications, as showcased by the Paris arrondissements quiz, underline the immense real-world utility of the tool. Embrace it, explore it, and let it amplify your projects.

The above video is made using Gen-2 and Midjourney.

To sum it up, the ChatGPT Code Interpreter is more than just a tool; it’s changing the way we connect with tech. For both innovators and enthusiasts, it promises a world full of coding potential.