Welcome to Codeinterpreter-Codebox, a cutting-edge cloud service tailored for executing Code Interpreter code.

• 🎯 Key Features
• 💡 What Can You Do?
• 📋 Related Projects & Their Differences
• 💻 Sample Codes
• 📦 Deployment
• 📧 Contact

1. 🐍 Independent Jupyter Sandbox Environment
   A completely isolated Jupyter sandbox environment that supports state-preserving code execution. Supports file uploads and downloads.
2. 🐳 Dockerized One-click Deployment
3. 🌐 Completely Free and Open Source
4. 🛡️ Information Security
   Supports completely private deployments without the need to upload files to external servers.
5. 🚀 More Flexible
   Supports the opening of more ports to achieve more custom functionality requirements, such as: connecting to databases, connecting to the internet, connecting to other servers.

1. Execute Python code.
2. Supports reading of uploaded files (Upload and Analyze Files).

if name=='main':
session=CodeinterpreterSession()
try:
session.upload_files(['./../data/test_data.csv'])
session.chat('How many columns are in the uploaded file?')
finally:
session.close()

3. Supports downloading files from the sandbox.
4. Supports dynamic scaling for commercial deployments.
5. Supports custom feature modifications, open ports, network connections, etc., supports web crawlers.

1. Code Interpreter api
2. open-interpreter
3. E2B

★ Indicates our project

1. Server-Client Interaction: jupyter server communicate showcases the details of interaction between the server and the Jupyter container.
2. Remote Execution: jupyter call demonstrates how to invoke a web service via an HTTP request, execute code, and retrieve results.

test_code="""
import docker
print(docker.version)

"""
execute(test_code)

#output
Execute Result= {"output_type":"text/plain","content":"6.1.3\n","files":null}

3. Client Session Demonstration: client session invocation shows how to allow LLM to invoke and execute code in a project through session and HTTP request.

session=CodeinterpreterSession()
try:
session.upload_files(['./../data/test_data.csv'])
session.chat('How many columns are in the uploaded file?')
finally:
session.close()

1. Install Docker
   Linux: Install Docker by terminal
   Mac os: Install Docker desktop for mac
   Windows: Install Docker desktop for windows
2. Modify Docker config file
   Head over to docker compose and substitute 'CODEBOX_ROOT_PATH' and 'YOUR_MNT_PATH:/codebox' with your file path to mount 'YOUR_MNT_PATH'.
3. Launch server

• Build custom jupyter image

cd docker
docker build -t scipy-notebook:custom -f Dockerfile .

• Build web server image

cd app
docker-compose -f docker_dev.yml build

• Launch server

docker-compose -f docker_dev.yml up

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Email: zhangzhehian@gmail.com