{"id":13793508,"url":"https://github.com/VolkanSah/Exploring-the-Code-Interpreter-in-OpenAI-GPT","last_synced_at":"2025-05-12T20:31:04.165Z","repository":{"id":179693781,"uuid":"663970368","full_name":"VolkanSah/Exploring-the-Code-Interpreter-in-OpenAI-GPT","owner":"VolkanSah","description":"The code interpreter is a tool developed by OpenAI to execute programming code in an interactive environment. It is capable of running Python code and displaying the results in real-time.","archived":false,"fork":false,"pushed_at":"2025-01-24T02:18:44.000Z","size":143,"stargazers_count":41,"open_issues_count":0,"forks_count":5,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-05-08T01:07:44.411Z","etag":null,"topics":["ai","artificial-intelligence","chat-gpt-code-interpreter","chatgpt","code-interpreter","diy","god-to-know","gpt","gpt-code-interpreter","how-to","openai","understand","understanding"],"latest_commit_sha":null,"homepage":"https://volkansah.github.io/The-Code-Interpreter-in-OpenAI-GPT/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/VolkanSah.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"github":["volkansah"],"patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"lfx_crowdfunding":null,"polar":null,"buy_me_a_coffee":"volkansah","thanks_dev":null,"custom":null}},"created_at":"2023-07-08T15:12:28.000Z","updated_at":"2025-03-21T02:07:15.000Z","dependencies_parsed_at":null,"dependency_job_id":"d8dfe0a0-1596-4b5c-899c-bf7c39a38e59","html_url":"https://github.com/VolkanSah/Exploring-the-Code-Interpreter-in-OpenAI-GPT","commit_stats":null,"previous_names":["volkansah/the-code-interpreter-in-openai-chatgpt","volkansah/exploring-the-code-interpreter-in-openai-gpt"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VolkanSah%2FExploring-the-Code-Interpreter-in-OpenAI-GPT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VolkanSah%2FExploring-the-Code-Interpreter-in-OpenAI-GPT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VolkanSah%2FExploring-the-Code-Interpreter-in-OpenAI-GPT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VolkanSah%2FExploring-the-Code-Interpreter-in-OpenAI-GPT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VolkanSah","download_url":"https://codeload.github.com/VolkanSah/Exploring-the-Code-Interpreter-in-OpenAI-GPT/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253816693,"owners_count":21968868,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","artificial-intelligence","chat-gpt-code-interpreter","chatgpt","code-interpreter","diy","god-to-know","gpt","gpt-code-interpreter","how-to","openai","understand","understanding"],"created_at":"2024-08-03T23:00:22.754Z","updated_at":"2025-05-12T20:31:03.778Z","avatar_url":"https://github.com/VolkanSah.png","language":null,"funding_links":["https://github.com/sponsors/volkansah","https://buymeacoffee.com/volkansah"],"categories":["❤️ Thank you for your support!"],"sub_categories":["👣 other GPT stuff"],"readme":"# Exploring the Code Interpreter in OpenAI (GPT)\n###### (version 21.05.2024)\n\nThe code interpreter is an advanced feature of OpenAI's GPTs.x \u0026 ChatGPT that brings a new level of interactivity to the AI model. \nIt is designed to execute Python code in a sandboxed environment and provide real-time results, making it a powerful \ntool for a wide range of tasks from mathematical computations to data analysis, from code prototyping to teaching and \nlearning Python programming interactively. While there are certain limitations to its functionality due to security \nreasons, it opens up a whole new set of possibilities for how users can interact with ChatGPT.\n\n## Table of Contents\n\n- [What is the Code Interpreter?](#what-is-the-code-interpreter)\n- [What is the Code Interpreter used for?](#what-is-the-code-interpreter-used-for)\n- [How can ChatGPT assist with programming?](#how-can-chatgpt-assist-with-programming)\n- [What are the limitations?](#what-are-the-limitations)\n- [What are the benefits?](#what-are-the-benefits)\n- [Installation and Setup](#installation-and-setup)\n- [Data Storage](#data-storage)\n    - [Detailed Explanation of the Data Storage](#detailed-explanation-of-the-data-storage)\n- [Working with Images](#working-with-images)\n- [Working with Exel-Files](#working-with-excel-files)\n  - [Advanced Excel Processing](#advanced-excel-processing)\n- [Working with Word-Files](#working-with-word-files)\n  - [Advanced Word Processing](#advanced-word-processing)\n- [Working with PDF-Files](#working-with-pdf-files)\n  - [Advanced PDF Processing](#advanced-pdf-processing)\n- [other advanced Applications of the Code Interpreter](advanced-1.md)\n- [Contributing](#contributing)\n- [Credits](#credits)\n\n\n\n\n## The Code Interpreter in OpenAI ChatGPT\n\n### What is the Code Interpreter?\n\nThe code interpreter is a tool developed by OpenAI to execute programming code in an interactive environment. It is capable of running Python code and displaying the results in real-time.\n\n### What is the Code Interpreter used for?\n\nThe code interpreter can be used for a variety of tasks, including:\n\n- Performing complex mathematical calculations\n- Analyzing and visualizing data\n- Prototyping and debugging Python code\n- Interactive learning and practicing Python programming\n\n## How can ChatGPT assist with programming?\n\nChatGPT can generate, review, and debug code based on the provided requirements. It can also assist in structuring code and provide suggestions for improvements. Moreover, it can explain complex programming concepts and assist in solving coding problems.\n\n## What are the limitations?\n\nWhile the code interpreter is a powerful tool, it has certain limitations:\n\n- It does not have access to the internet. This means it cannot make external requests.\n- It runs in an isolated environment and does not have access to the operating system or its resources.\n- Code execution that takes longer than 120 seconds is automatically stopped.\n- It has access to a special location, '/mnt/data', where it can read and write files.\n\nDespite these limitations, the code interpreter is a versatile tool that can greatly assist programmers of all skill levels.\n\n## What are the benefits?\n\nThe code interpreter offers several benefits:\n\n- It provides a safe environment to run code without the risk of affecting the operating system or data.\n- It allows for real-time interaction with the code, providing immediate feedback.\n- It can assist in learning Python programming and improving coding skills.\n- It can handle a variety of tasks, from simple calculations to data analysis and visualization.\n\n## Installation and Setup\n\nTo execute the examples mentioned in this README, you need to install some Python libraries. You can install them using pip:\n\n```shell\npip install pandas openpyxl python-docx PyPDF2 fpdf2 matplotlib pillow\n```\n\n## Data Storage\n\nThe code interpreter has access to a special directory, '/mnt/data', where it can read and write files. This can be used for operations that need to save or load data, like writing logs, saving plots, or loading data for analysis. However, no other locations on the filesystem can be accessed.\n\n### Detailed Explanation of the Data Storage\n\nThe '/mnt/data' directory is a special storage location that the code interpreter can access to read and write files. This is especially useful for operations that require persistent storage or the exchange of data between different code executions.\n\nHere are some ways you can use the '/mnt/data' directory:\n\n1. **Saving and Loading Data Files:** If you're working with data in formats like .csv, .json, .txt, etc., you can read from and write to these files directly in this directory. For instance, to write a list of numbers to a .txt file, you would do:\n\n```python\nwith open('/mnt/data/numbers.txt', 'w') as file:\n    for num in range(10):\n        file.write(str(num) + '\\n')\n```\n\nTo read the file, you would do:\n\n```python\nwith open('/mnt/data/numbers.txt', 'r') as file:\n    numbers = file.readlines()\n```\n\n2. **Storing Logs:** If you're running code that generates logs (like debugging information, progress of a task, etc.), you can write these logs to a file in '/mnt/data'. \n\n```python\nwith open('/mnt/data/log.txt', 'w') as file:\n    file.write('This is a log message.')\n```\n\n3. **Saving Plots and Images:** If you're generating plots or other images with your code, you can save them to '/mnt/data' as .png, .jpg, or other image formats. For instance, if you're using matplotlib to create a plot, you can save it with:\n\n```python\nimport matplotlib.pyplot as plt\n\nplt.plot([0, 1, 2, 3, 4], [0, 1, 4, 9, 16])\nplt.savefig('/mnt/data/plot.png')\n```\n\nYou can then download the image file directly from the generated sandbox link.\n\nRemember, any file operations need to be done using the '/mnt/data' path. The code interpreter does not have access to any other locations on the filesystem.\n\n## Working with Images\n\nWith the help of various Python libraries such as PIL (Python Imaging Library), OpenCV, and matplotlib, a variety of operations can be performed on images. Here are some examples:\n\n1. **Displaying Image:** Display an image.\n\n```python\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n# Open the image file\nimg = Image.open('/mnt/data/your_image.jpg')\n\n# Display the image\nplt.imshow(img)\nplt.axis('off')  # Turn off the axis\nplt.show()\n```\n\n2. **Resizing Image:** Change the size of an image, enlarge or shrink it.\n\n```python\n# Resize the image\nimg_resized = img.resize((new_width, new_height))\n```\n\n3. **Rotating or Flipping Image:** Rotate an image or flip it horizontally or vertically.\n\n```python\n# Rotate the image\nimg_rotated = img.rotate(angle)\n\n# Flip the image\nimg_flipped = img.transpose(Image.FLIP_LEFT_RIGHT)\n```\n\n4. **Color Conversions:** Convert an image to grayscale or change the color mode.\n\n```python\n# Convert the image to grayscale\nimg_gray = img.convert('L')\n```\n\n5. **Adjusting Brightness, Contrast, and Saturation:** Adjust the brightness, contrast, or saturation of an image.\n\n```python\nfrom PIL import ImageEnhance\n\n# Increase the brightness\nenhancer = ImageEnhance.Brightness(img)\nimg_brighter = enhancer.enhance(1.5)\n```\n\n6. **Applying Filters:** Apply different types of filters, like Gaussian blur, edge detection, etc.\n\n```python\nfrom PIL import ImageFilter\n\n# Apply a filter\nimg_blurred = img.filter(ImageFilter.GaussianBlur(radius=5))\n```\n\n7. **Image Analysis:** Perform simple image analysis, like calculating the histogram.\n\n```python\n# Get the histogram\nhist = img.histogram()\n```\n\n8. **Image Merging:** Merge multiple images into a single image.\n\n```python\n# Merge images\nimg_merged = Image.merge('RGB', [img1, img2, img3])\n```\n\n\n# Working with Excel Files\n\nHandling Excel files is a common task that can range from data analysis to generating reports. Here's a guide on basic and advanced operations with Excel files using Python:\n\n#### Reading and Writing Excel Files\n\nTo read and write Excel files, `pandas` along with `openpyxl` is commonly used. Here's how to read from and write to an Excel file:\n\n```python\nimport pandas as pd\n\n# Load an Excel file\ndf = pd.read_excel('/mnt/data/example.xlsx')\n\n# Display data\nprint(df.head())\n```\n\n###To write data to an Excel file:\n\n```python\n# Create a DataFrame\ndata = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}\ndf = pd.DataFrame(data)\n\n# Write DataFrame to an Excel file\ndf.to_excel('/mnt/data/saved_data.xlsx', index=False)\n```\n\n### Filtering and Manipulating Data\n\nYou can filter data based on conditions, add new columns, or transform existing data:\n\n```python\n# Filter rows where age is greater than 28\nfiltered_df = df[df['Age'] \u003e 28]\n\n# Add a new column\ndf['Age Next Year'] = df['Age'] + 1\n\n# Sort data\nsorted_df = df.sort_values(by='Age', ascending=False)\n```\n\n## Advanced Excel Processing\n\nBesides reading and writing Excel files, you can also perform advanced data processing tasks such as creating pivot tables or merging multiple Excel files.\n### Creating a Pivot Table\n\n```python\nimport pandas as pd\n\n# Load example data\ndf = pd.read_excel('/mnt/data/example.xlsx')\n\n# Create a pivot table\npivot_table = df.pivot_table(index='Category', values='Sales', aggfunc='sum')\n\n# Display the pivot table\nprint(pivot_table)\n```\n### Merging Multiple Excel Files\n\n```python\nimport pandas as pd\nimport glob\n\n# Read all Excel files in the directory\nfiles = glob.glob('/mnt/data/*.xlsx')\n\n# Merge data from all files\ndf_list = [pd.read_excel(file) for file in files]\nmerged_df = pd.concat(df_list, ignore_index=True)\n\n# Display the merged data\nprint(merged_df.head())\n```\n\n## Troubleshooting\nHere are some common issues and their solutions:\n\n1. ImportError: No module named '...':\n- Ensure that all required libraries are installed. Use pip install \u003clibrary_name\u003e to install any missing libraries.\n\n2. FileNotFoundError: [Errno 2] No such file or directory: '...':\n- Check the file path and ensure that the file is in the correct directory. Use absolute paths or ensure that the file is saved in /mnt/data.\n\n3. PermissionError: [Errno 13] Permission denied: '...':\n- Ensure that you have permissions to read and write in the /mnt/data directory.\n\n**If you encounter further issues, open an issue on GitHub or contact the project maintainer.**\n\n\n# Working with Word Files\n\nHandling Microsoft Word files involves reading, writing, and modifying documents. Here’s how you can manage Word files using Python:\n\n### Reading from Word Files\n\nTo read text from Word documents, the `python-docx` library is used:\n\n```python\nfrom docx import Document\n\n# Load a Word document\ndoc = Document('/mnt/data/example.docx')\n\n# Read each paragraph\nfor para in doc.paragraphs:\n    print(para.text)\n```\n\n### Writing to Word Files\n\nTo create and write to Word documents:\n\n```python\nfrom docx import Document\n\n# Create a new Word document\ndoc = Document()\ndoc.add_paragraph('Hello, this is a test document.')\n\n# Save the document\ndoc.save('/mnt/data/new_example.docx')\n```\n\n## Advanced Word Processing\n\n```python\nfrom docx import Document\n\n# Create a new Word document\ndoc = Document()\n\n# Add a table with specified number of rows and columns\ntable = doc.add_table(rows=3, cols=3)\n\n# Add data to the table\ndata = [\n    [\"Header 1\", \"Header 2\", \"Header 3\"],\n    [\"Row 1, Col 1\", \"Row 1, Col 2\", \"Row 1, Col 3\"],\n    [\"Row 2, Col 1\", \"Row 2, Col 2\", \"Row 2, Col 3\"]\n]\n\nfor row_index, row_data in enumerate(data):\n    row = table.rows[row_index]\n    for col_index, cell_data in enumerate(row_data):\n        row.cells[col_index].text = cell_data\n\n# Save the document\ndoc.save('/mnt/data/table_example.docx')\n```\n### Formatting Text in a Word Document\n```python\nfrom docx import Document\nfrom docx.shared import Pt, RGBColor\n\n# Load a Word document\ndoc = Document('/mnt/data/example.docx')\n\n# Add a paragraph with specific formatting\nparagraph = doc.add_paragraph()\nrun = paragraph.add_run('This is a formatted text.')\nrun.font.size = Pt(14)  # Font size\nrun.font.bold = True  # Bold text\nrun.font.color.rgb = RGBColor(255, 0, 0)  # Red color text\n\n# Save the document\ndoc.save('/mnt/data/formatted_text.docx')\n```\n### Adding Images to a Word Document\n```python\nfrom docx import Document\nfrom docx.shared import Inches\n\n# Create a new Word document\ndoc = Document()\n\n# Add a paragraph\ndoc.add_paragraph('Below is an image:')\n\n# Add an image to the document\ndoc.add_picture('/mnt/data/your_image.jpg', width=Inches(4), height=Inches(3))\n\n# Save the document\ndoc.save('/mnt/data/image_example.docx')\n```\n### Inserting Headers and Footers\n```python\nfrom docx import Document\n\n# Create a new Word document\ndoc = Document()\n\n# Add a header\nheader = doc.sections[0].header\nheader_paragraph = header.paragraphs[0]\nheader_paragraph.text = \"This is the header\"\n\n# Add a footer\nfooter = doc.sections[0].footer\nfooter_paragraph = footer.paragraphs[0]\nfooter_paragraph.text = \"This is the footer\"\n\n# Add some body text\ndoc.add_paragraph(\"This is the body text of the document.\")\n\n# Save the document\ndoc.save('/mnt/data/header_footer_example.docx')\n```\n\n\n\n\n\n# Working with PDF Files\n\nManaging PDF files often involves reading, extracting text, and sometimes converting them to other formats. Here’s how to handle PDF files using Python:\n\n### Reading and Extracting Text from PDF Files\n\nTo read and extract text from PDF files, the `PyPDF2` library is commonly used:\n\n```python\nimport PyPDF2\n\n# Open a PDF file\nwith open('/mnt/data/example.pdf', 'rb') as file:\n    pdf_reader = PyPDF2.PdfReader(file)\n    \n    # Extract text from the first page\n    page = pdf_reader.pages[0]\n    text = page.extract_text()\n    print(text)\n```\n\n## Creating and Writing to PDF Files\n\nCreating and writing text to PDF files can be done using the `fpdf2` library:\n\n```python\nfrom fpdf import FPDF\n\n# Create instance of FPDF class\npdf = FPDF()\n\n# Add a page\npdf.add_page()\n\n# Set font\npdf.set_font(\"Arial\", size = 12)\n\n# Add a cell\npdf.cell(200, 10, txt = \"Welcome to PDF handling with Python!\", ln = True, align = 'C')\n\n# Save the PDF to a file\npdf.output('/mnt/data/new_example.pdf')\n```\n\n# Advanced PDF Processing\nHandling PDF files often involves reading, extracting text, merging, splitting, and modifying documents. Here are some advanced operations using Python:\n### Merging Multiple PDF Files\n\n```python\nimport PyPDF2\n\n# List of PDF files to be merged\npdf_files = ['/mnt/data/file1.pdf', '/mnt/data/file2.pdf', '/mnt/data/file3.pdf']\n\n# Create a PDF merger object\npdf_merger = PyPDF2.PdfMerger()\n\n# Append each PDF file\nfor pdf in pdf_files:\n    pdf_merger.append(pdf)\n\n# Write out the merged PDF\nwith open('/mnt/data/merged.pdf', 'wb') as merged_pdf:\n    pdf_merger.write(merged_pdf)\n```\n### Splitting a PDF into Multiple Files\n\n```python\nimport PyPDF2\n\n# Path to the PDF file\npdf_path = '/mnt/data/example.pdf'\n\n# Create a PDF reader object\npdf_reader = PyPDF2.PdfReader(pdf_path)\n\n# Split the PDF into separate pages\nfor page_num in range(len(pdf_reader.pages)):\n    pdf_writer = PyPDF2.PdfWriter()\n    pdf_writer.add_page(pdf_reader.pages[page_num])\n    \n    # Save each page as a separate PDF\n    output_path = f'/mnt/data/split_page_{page_num + 1}.pdf'\n    with open(output_path, 'wb') as output_pdf:\n        pdf_writer.write(output_pdf)\n```\n### Adding a Watermark to a PDF\n\n```python\nimport PyPDF2\n\n# Paths to the original PDF and the watermark PDF\noriginal_pdf_path = '/mnt/data/original.pdf'\nwatermark_pdf_path = '/mnt/data/watermark.pdf'\n\n# Create PDF reader objects\noriginal_pdf = PyPDF2.PdfReader(original_pdf_path)\nwatermark_pdf = PyPDF2.PdfReader(watermark_pdf_path)\n\n# Create a PDF writer object\npdf_writer = PyPDF2.PdfWriter()\n\n# Apply the watermark to each page\nfor page_num in range(len(original_pdf.pages)):\n    original_page = original_pdf.pages[page_num]\n    watermark_page = watermark_pdf.pages[0]\n    original_page.merge_page(watermark_page)\n    pdf_writer.add_page(original_page)\n\n# Save the watermarked PDF\nwith open('/mnt/data/watermarked.pdf', 'wb') as watermarked_pdf:\n    pdf_writer.write(watermarked_pdf)\n```\n### Extracting Text from a Specific Page Range\n```python\nimport PyPDF2\n\n# Path to the PDF file\npdf_path = '/mnt/data/example.pdf'\n\n# Create a PDF reader object\npdf_reader = PyPDF2.PdfReader(pdf_path)\n\n# Specify the range of pages to extract text from\nstart_page = 1\nend_page = 3\n\n# Extract text from the specified page range\nextracted_text = ''\nfor page_num in range(start_page - 1, end_page):\n    page = pdf_reader.pages[page_num]\n    extracted_text += page.extract_text()\n\nprint(extracted_text)\n```\n### Adding Metadata to a PDF\n```python\nimport PyPDF2\n\n# Path to the PDF file\npdf_path = '/mnt/data/example.pdf'\n\n# Create a PDF reader object\npdf_reader = PyPDF2.PdfReader(pdf_path)\npdf_writer = PyPDF2.PdfWriter()\n\n# Copy all pages to the writer object\nfor page_num in range(len(pdf_reader.pages)):\n    pdf_writer.add_page(pdf_reader.pages[page_num])\n\n# Add metadata\nmetadata = {\n    '/Title': 'Example PDF',\n    '/Author': 'Your Name',\n    '/Subject': 'Example Subject',\n    '/Keywords': 'PDF, example, metadata'\n}\npdf_writer.add_metadata(metadata)\n\n# Save the PDF with metadata\nwith open('/mnt/data/metadata_example.pdf', 'wb') as metadata_pdf:\n    pdf_writer.write(metadata_pdf)\n\n```\n\n## Contributing\nContributions are welcome! Please feel free to submit a pull request.\n\n## [❤️](https://jugendamt-deutschland.de) Thank you for your support!\nIf you appreciate my work, please consider supporting me:\n\n\n### 👣 other GPT stuff \n- [Link to ChatGPT Shellmaster](https://github.com/VolkanSah/ChatGPT-ShellMaster/)\n- [GPT-Security-Best-Practices](https://github.com/VolkanSah/GPT-Security-Best-Practices)\n- [OpenAi cost calculator](https://github.com/VolkanSah/OpenAI-Cost-Calculator)\n- [GPT over CLI](https://github.com/VolkanSah/GPT-over-CLI)\n- [Secure Implementation of Artificial Intelligence (AI)](https://github.com/VolkanSah/Implementing-AI-Systems-Whitepaper)\n- [Comments Reply with GPT (davinci3)](https://github.com/VolkanSah/GPT-Comments-Reply-WordPress-Plugin)\n- [Basic GPT Webinterface](https://github.com/VolkanSah/GPT-API-Integration-in-HTML-CSS-with-JS-PHP)\n\n\n### Credits\n- [Volkan Kücükbudak //NCF](https://gihub.com/volkansah)\n- and OpenAI's ChatGPT4 with Code Interpreter for providing interactive coding assistance and insights \u0026 tipps.\n-  Become a Sponsor: [Link to my sponsorship page](https://github.com/sponsors/volkansah)\n- :star: my projects: Starring projects on GitHub helps increase their visibility and can help others find my work. \n- Follow me: Stay updated with my latest projects and releases.\n- [Source of this resposerity](https://github.com/VolkanSah/The-Code-Interpreter-in-OpenAI-GPT/)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FVolkanSah%2FExploring-the-Code-Interpreter-in-OpenAI-GPT","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FVolkanSah%2FExploring-the-Code-Interpreter-in-OpenAI-GPT","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FVolkanSah%2FExploring-the-Code-Interpreter-in-OpenAI-GPT/lists"}