How to Use ChatGPT’s Code Interpreter for Free

How to Use ChatGPT Code Interpreter for Free

Have you been curious about how you could leverage the incredible capability of ChatGPT’s Code Interpreter without having to shell out that hefty $20 per month? Well, you’re in luck! Thanks to a developer named Shroominic, a free and open-source implementation exists that replicates the core features of the ChatGPT Code Interpreter. This article is your comprehensive guide on how to set it up, along with some actionable insights that will aid you in analyzing datasets without breaking the bank.

Things to Keep in Mind Before Jumping In

Before you roll up your sleeves, here’s some crucial information to consider:

1. Free versus Paid: The implementation we’re utilizing is based on the free and open-source Code Interpreter API project available on GitHub. It combines several technologies including CodeBoxes, OpenAI’s API, LangChain Agents, as well as multiple Python packages, simulating the ChatGPT’s Code Interpreter experience.
2. Dataset Size: This tool works splendidly for smaller datasets, however, should you attempt to analyze a massive dataset, you might hit OpenAI’s rate limits imposed on free users. If you’re anticipating delving into heavier datasets, you might want to consider adding a payment method to your OpenAI account.
3. API Compatibility: If you’re equipped with access to the GPT-4 API, you shouldn’t face any issues with functionality. However, I have tailored the code to ensure compatibility with the GPT-3.5-turbo model as well.

Step 1: Set Up Code Interpreter API

Alright, let’s get this operation underway! Setting up the Code Interpreter API is your initial step:

1. First things first, ensure that you have Python and Pip installed on your computer. If you don’t, don’t sweat it; you can follow our linked tutorial to get it all sorted. Make sure to add python.exe to your PATH during the installation process.
2. Once you have Python and Pip installed, it’s time to check if everything is in order. Open the Terminal and run the following commands; both commands should return output showing their respective version numbers:

python –version pip –version

3. Now, let’s move on to installing the Code Interpreter API. Execute the command below:

pip install « codeinterpreterapi[all] »

4. After installation, fetch your API key from OpenAI’s website. Just click on “Create new secret key” and copy that snazzy key!

Step 2: Run ChatGPT Code Interpreter for Free

Now, it’s crunch time! Let’s get the Code Interpreter API running:

1. With the API key in hand, it’s time to fire up the coding editor of your choice—Sublime Text or Notepad++ are great options (you can download either one easily).
2. Copy the code snippet below into your code editor. This is a modified version I’ve tailored from the GitHub page of the Code Interpreter API:

import os os.environ[« OPENAI_API_KEY »] = « PASTE THE OPENAI API KEY HERE » from codeinterpreterapi import CodeInterpreterSession async def main(): session = CodeInterpreterSession(model= »gpt-3.5-turbo ») await session.astart() response = await session.generate_response(« Plot the Apple stock price chart from 2007 to 2023 June ») print(« AI: « , response.content) for file in response.files: file.show_image() await session.astop() if __name__ == « __main__ »: import asyncio asyncio.run(main())

3. This is where the magic happens! Remember to replace the placeholder with your actual OpenAI API key located in the second line of code.
4. If you possess access to the GPT-4 API, replace the model= »gpt-3.5-turbo » with model= »gpt-4″ in the ninth line. You will then define your query on what you want to create in the 14th line.
5. Save this file as chart.py right on your Desktop. Don’t forget that essential .py extension!
6. Next, head back to Terminal. You’ll need to navigate to the Desktop and execute the Python file you just created. Use the following commands:

cd Desktop python chart.py

7. Just give it a moment, and voila! The Code Interpreter API should work its own magic and generate the chart for you. This nifty tool employs several backend services, such as LangChain Agents, Yahoo Finance for obtaining data, and Matplotlib for plotting graphs, to provide you with the end result.
8. If you’re curious about the inner workings, add the line below in your code, so you can observe every operation taking place behind the scenes:

os.environ[« VERBOSE »] = « True »

9. From here on, simply tweak the query in your code and run the chart.py file again to generate different charts.

Step 3: Perform Data Analysis Using Code Interpreter API

Have some local datasets you’re itching to analyze? Great! Here’s how to proceed:

1. Start by creating a folder on your Desktop and naming it analysis.
2. Now, move your dataset (in formats like CSV, XSL, or XSLX) to the analysis folder. For demonstration purposes, let’s use a file named globaltemperature.csv.
3. Similar to the previous steps, open your code editor and copy the following code snippet:

import os os.environ[« OPENAI_API_KEY »] = « PASTE THE OPENAI API KEY HERE » from codeinterpreterapi import CodeInterpreterSession, File async def main(): async with CodeInterpreterSession(model= »gpt-3.5-turbo ») as session: 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 »), ] response = await session.generate_response(user_request, files=files) print(« AI: « , response.content) for file in response.files: file.show_image() if __name__ == « __main__ »: import asyncio asyncio.run(main())

4. Ah, don’t forget—you need to paste your OpenAI API key once more! Make sure your family of code friends is all in order.
5. Feel free to rename globaltemperature.csv to whatever your dataset is named! Adapt the model and user query as per your wants and needs.
6. Save this file as data.py inside the analysis folder on your Desktop.
7. Fire up the Terminal once again and execute the file, using the command:

cd Desktop/analysis python data.py

8. Done! Now watch as the chart based on your local dataset appears! This is how you can utilize the Code Interpreter API for data analysis—all without spending a dime.

Troubleshooting Common Issues

As you embark on this exciting journey of data interpretation, it’s likely you’ll encounter some hiccups along the way. Here are a few common issues and their remedies:

1. API Key Issues

One user reported an error, “ValueError: Please set the API key for the LLM you want to use.” Double-check that you’ve properly pasted your API key into the code. Ensure there are no misplaced quotes or spaces. It’s like trying to get into a club; make sure your VIP pass (API key) is valid!

2. ModuleNotFoundError

If you came across this frustrating message: “ModuleNotFoundError: No module named ‘codeinterpreterapi’,” you likely need to revisit your installation step. Ensure the installation command was executed successfully and your environment can recognize the package.

3. FileNotFound Errors

Another common issue might resemble “FileNotFoundError: [Errno 2] No such file or directory: ‘globaltemperature.csv’.“ This generally indicates your specified file isn’t where the code expects it to be. Double-check that your CSV file is indeed located in the analysis folder you created on your Desktop.

4. Rate Limits

You might face rate limit errors, such as “Retrying langchain.chat_models.openai.acompletion_with_retry.” This is an indication you’ve exceeded the allocated limits for free users. You might need to either reduce the complexity of your requests or budget for a paid plan if you intend to work extensively with larger datasets.

Conclusion

In conclusion, with Shroominic’s ingenious open-source version of the ChatGPT Code Interpreter, you can step into the world of data analysis for free. You’re equipped with all the tools and knowledge to set everything in motion. It’s an exciting realm where coding meets data; dive right in and unlock data insights with ease! Remember to tweak and personalize the code as desired, and happy coding!