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Description:

codeinterpreterapiwarren 0.1.0

Code Interpreter API
A LangChain implementation of the ChatGPT Code Interpreter.
Using CodeBoxes as backend for sandboxed python code execution.
CodeBox is the simplest cloud infrastructure for your LLM Apps.
You can run everything local except the LLM using your own OpenAI API Key.
Features

Dataset Analysis, Stock Charting, Image Manipulation, ....
Internet access and auto Python package installation
Input text + files -> Receive text + files
Conversation Memory: respond based on previous inputs
Run everything local except the OpenAI API (OpenOrca or others maybe soon)
Use CodeBox API for easy scaling in production (coming soon)

Installation
Get your OpenAI API Key here and install the package.
pip install "codeinterpreterapi[all]"

Everything for local experiments are installed with the all extra.
For deployments, you can use pip install codeinterpreterapi instead which does not install the additional dependencies.
Usage
To configure OpenAI and Azure OpenAI, ensure that you set the appropriate environment variables (or use a .env file):
For OpenAI, set the OPENAI_API_KEY environment variable:
export OPENAI_API_KEY=your_openai_api_key

For Azure OpenAI, set the following environment variables:
export OPENAI_API_TYPE=azure
export OPENAI_API_VERSION=your_api_version
export OPENAI_API_BASE=your_api_base
export OPENAI_API_KEY=your_azure_openai_api_key
export DEPLOYMENT_NAME=your_deployment_name

Remember to replace the placeholders with your actual API keys and other required information.
from codeinterpreterapi import CodeInterpreterSession

async def main():
# create a session
session = CodeInterpreterSession()
await session.astart()

# generate a response based on user input
response = await session.generate_response(
"Plot the bitcoin chart of 2023 YTD"
)

# 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())

Bitcoin YTD Chart Output
Dataset Analysis
from codeinterpreterapi import CodeInterpreterSession, File

async def main():
# context manager for auto start/stop of the session
async with CodeInterpreterSession() as session:
# define the user request
user_request = "Analyze this dataset and plot something interesting about it."
files = [
File.from_path("examples/assets/iris.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())

Iris Dataset Analysis Output
Production
In case you want to deploy to production, you can utilize the CodeBox API for seamless scalability.
Please contact me if you are interested in this, as it is still in the early stages of development.
Contributing
There are some remaining TODOs in the code.
So, if you want to contribute, feel free to do so.
You can also suggest new features. Code refactoring is also welcome.
Just open an issue or pull request and I will review it.
Please also submit any bugs you find as an issue with a minimal code example or screenshot.
This helps me a lot in improving the code.
Thanks!
Streamlit WebApp
To start the web application created with streamlit:
streamlit run frontend/app.py

License
MIT
Contact
You can contact me at contact@shroominic.com.
But I prefer to use Twitter or Discord DMs.
Support this project
If you would like to help this project with a donation, you can click here.
Thanks, this helps a lot! ❤️
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