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from llm_sandbox import SandboxSession

# Create a new sandbox session
with SandboxSession(image="python:3.9.19-bullseye", keep_template=True, lang="python") as session:
    result = session.run("print('Hello, World!')")
    print(result)

# With custom Dockerfile
with SandboxSession(dockerfile="Dockerfile", keep_template=True, lang="python") as session:
    result = session.run("print('Hello, World!')")
    print(result)

# Or default image
with SandboxSession(lang="python", keep_template=True) as session:
    result = session.run("print('Hello, World!')")
    print(result)

from llm_sandbox import SandboxSession

with SandboxSession(lang="python", keep_template=True) as session:
    # Copy a file from the host to the sandbox
    session.copy_to_runtime("test.py", "/sandbox/test.py")

    # Run the copied Python code in the sandbox
    result = session.run("python /sandbox/test.py")
    print(result)

    # Copy a file from the sandbox to the host
    session.copy_from_runtime("/sandbox/output.txt", "output.txt")

import docker
from llm_sandbox import SandboxSession

tls_config = docker.tls.TLSConfig(
    client_cert=("path/to/cert.pem", "path/to/key.pem"),
    ca_cert="path/to/ca.pem",
    verify=True
)
docker_client = docker.DockerClient(base_url="tcp://<your_host>:<port>", tls=tls_config)

with SandboxSession(
    client=docker_client,
    image="python:3.9.19-bullseye",
    keep_template=True,
    lang="python",
) as session:
    result = session.run("print('Hello, World!')")
    print(result)

from typing import Optional, List
from llm_sandbox import SandboxSession
from langchain import hub
from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langchain.agents import AgentExecutor, create_tool_calling_agent


@tool
def run_code(lang: str, code: str, libraries: Optional[List] = None) -> str:
    """
    Run code in a sandboxed environment.
    :param lang: The language of the code.
    :param code: The code to run.
    :param libraries: The libraries to use, it is optional.
    :return: The output of the code.
    """
    with SandboxSession(lang=lang, verbose=False) as session:  # type: ignore[attr-defined]
        return session.run(code, libraries).text


if __name__ == "__main__":
    llm = ChatOpenAI(model="gpt-4o", temperature=0)
    prompt = hub.pull("hwchase17/openai-functions-agent")
    tools = [run_code]

    agent = create_tool_calling_agent(llm, tools, prompt)
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
    output = agent_executor.invoke(
        {
            "input": "Write python code to calculate Pi number by Monte Carlo method then run it."
        }
    )
    print(output)

    output = agent_executor.invoke(
        {
            "input": "Write python code to calculate the factorial of a number then run it."
        }
    )
    print(output)

    output = agent_executor.invoke(
        {"input": "Write python code to calculate the Fibonacci sequence then run it."}
    )
    print(output)

from typing import Optional, List
from llm_sandbox import SandboxSession

from llama_index.llms.openai import OpenAI
from llama_index.core.tools import FunctionTool
from llama_index.core.agent import FunctionCallingAgentWorker


import nest_asyncio

nest_asyncio.apply()


def run_code(lang: str, code: str, libraries: Optional[List] = None) -> str:
    """
    Run code in a sandboxed environment.
    :param lang: The language of the code, must be one of ['python', 'java', 'javascript', 'cpp', 'go', 'ruby'].
    :param code: The code to run.
    :param libraries: The libraries to use, it is optional.
    :return: The output of the code.
    """
    with SandboxSession(lang=lang, verbose=False) as session:  # type: ignore[attr-defined]
        return session.run(code, libraries).text


if __name__ == "__main__":
    llm = OpenAI(model="gpt-4o", temperature=0)
    code_execution_tool = FunctionTool.from_defaults(fn=run_code)

    agent_worker = FunctionCallingAgentWorker.from_tools(
        [code_execution_tool],
        llm=llm,
        verbose=True,
        allow_parallel_tool_calls=False,
    )
    agent = agent_worker.as_agent()

    response = agent.chat(
        "Write python code to calculate Pi number by Monte Carlo method then run it."
    )
    print(response)

    response = agent.chat(
        "Write python code to calculate the factorial of a number then run it."
    )
    print(response)

    response = agent.chat(
        "Write python code to calculate the Fibonacci sequence then run it."
    )
    print(response)

    response = agent.chat("Calculate the sum of the first 10000 numbers.")
    print(response)