How To Use ChatGPT Code Interpreter For Free?

Let’s see how you can use ChatGPT code interpreter for free. Analysis and coding have become easier since the advent of AI and with OpenAI releasing its Code Interpreter for ChatGPT, things have become even more stress-free. The hitch here is that this Code Interpreter has been made available in ChatGPT only for paying users. The fee for this Interpreter is $20 per month, which may not be possible for everyone to spend on analysis. If you have been looking for a way to avoid having to pay this fee and still be able to use the Interpreter, then like every problem has its solution, here’s a way for you to use ChatGPT’s Code Interpreter without having to pay for it.

A developer known as Shroominic has made this possible by developing an open-source implementation of ChatGPT’s Code Interpreter. Especially for a small dataset, this solution works beautifully and in a manner close to ChatGPT. Using this solution for large datasets will cause the imposition of OpenAI’s rate limits that they have in place for their free users. Hence, remember to work with smaller datasets if you apply this solution to use the Code Interpreter for free.

Steps to use the ChatGPT Code Interpreter for free:

Step 1:  Code Interpreter Setup

• On your computer, begin by installing Python and Pip.

• During the installation process, do not forget to check the box for the option to ‘Add python.exe to PATH’.
• After the completion of the installation process, check whether the setup has been done correctly. For this, open Terminal and enter the following commands:

python --version

pip --version

The output for these commands should be the versions of Python and Pip that have been installed on your device.

• Next, you are ready to install the Code Interpreter API with this command:

pip install "codeinterpreterapi[all]"

•  After this, you can acquire a key for the API from the website of OpenAI. Look for the option to ‘Create new secret key’ and copy the key generated after clicking on it.

Step 2:  Run Code Interpreter

• Bring up your code editor. You can use Notepad++ or Sublime Text depending on your preference.

• Copy the following code and paste it onto the code editor.

import os
os.environ["OPENAI_API_KEY"] = "PASTE THE OPENAI API KEY HERE"

from codeinterpreterapi import CodeInterpreterSession


async def main():
    # create a session
    session = CodeInterpreterSession(model="gpt-3.5-turbo")
    await session.astart()

    # generate a response based on user input
    response = await session.generate_response(
        "Plot the Apple stock price chart from 2007 to 2023 june"
    )

    # output the response (text + image)
    print("AI: ", response.content)
    for file in response.files:
        file.show_image()

    # terminate the session
    await session.astop()


if __name__ == "__main__":
    import asyncio
    # run the async function
    asyncio.run(main())

Make the following changes to the text marked in yellow:

• In Line 2, enter the API Key generated and copied from OpenAI.
• In Line 9, edit the version of the ChatGPT as per the one you have access to. For instance, if you have access to the GPT-4 API, define the model as “gpt-4” in the 9^th line. Do not forget to make the necessary changes here.

• Lastly, the 14^th Line is where you can type in your query.
• Once the necessary edits have been made, you can save this file on the Desktop of your device. Do not forget to add the extension “.py” to the file name while saving. For instance, if you are saving the file under the name “chart” then save it as “chart.py”

• You can now head to the Terminal and carry out the following commands to run this file saved on the Desktop of your device:

cd Desktop
python chart.py

• Upon execution, the Code Interpreter API will give you a chart as the output.

• In case you are interested in seeing everything that is being used to come up with the output, you can edit the code & include the following:

os.environ["VERBOSE"] = "True"

• You can now simply make necessary changes in the query of the code and run your file again for new charts.

Step 3:  Data Analysis

In case you have your own dataset, an analysis can be done using that as well. Simply create a folder named “Analysis” on the Desktop of your device and follow these steps ahead:

• Save a copy of your dataset in this folder on the Desktop. This file could be in any one of the CSV, XSL, or XLSX formats. Consider for instance that we are using the “globaltemperatures.csv” file in the “Analysis” folder.

• Once you have your dataset saved in the right place, you can then go ahead with the coding process. Copy the following code and paste it onto the editor of your preference:

import os
os.environ["OPENAI_API_KEY"] = "PASTE THE OPENAI API KEY HERE"

from codeinterpreterapi import CodeInterpreterSession, File

async def main():
    # context manager for auto start/stop of the session
    async with CodeInterpreterSession(model="gpt-3.5-turbo") as session:
        # define the user request
        user_request = "Analyze this dataset and plot global temperature from the year 1950 to 2016. Consider the GCAG system."
        files = [
            File.from_path("globaltemperature.csv"),
        ]

        # generate the response
        response = await session.generate_response(
            user_request, files=files
        )

        # output to the user
        print("AI: ", response.content)
        for file in response.files:
            file.show_image()


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

Just like before, make the required changes in the text marked in yellow:

• Enter your OpenAI API key in the second line and the version of ChatGPT API you have access to in the eighth line.

• Edit the file name in the code as per the file name of your dataset.
• Do note that you can easily edit the query as well as the model used for analysis based on what you hope to find in your dataset.

• In the “Analysis” folder on the Desktop of your device, save this file as “data.py”

• You can now bring up the Terminal and run this file as we did earlier:

cd Desktop/analysis
python data.py

• The chart generated now after these steps will be based on your local dataset.

For larger datasets that cannot be analyzed in the ways described above, you might just have to become a paying user of ChatGPT for flawless and quick operations. On the other hand, smaller datasets can freely be used through this open-sourced alternative to the Code Interpreter of ChatGPT. Two important things to remember to edit in the code would be the key from OpenAI and the version of your ChatGPT API. Once these two are set, you can go about with your analysis. Now you know how to use ChatGPT code interpreter for free.