exec_repl.py

import os
import docker

client = docker.from_env()

FILENAME = "frankenstein.txt"

def execute_repl_code(code_string: str) -> str:
    """Executes the LLM's code inside the persistent Docker container."""

# Context injection: Read the file from the mounted volume on every call
    script_content = f"""
from llm_query import llm_query
import os
import re

# Ensure context is loaded into memory for the LLM's code to use
try:
    with open("/workspace/{FILENAME}", "r", encoding="utf-8") as f:
        context = f.read()
except FileNotFoundError:
    context = "Error: Could not find {FILENAME} in /workspace"

# --- LLM GENERATED CODE BELOW ---
{code_string}
"""

    # Save the injected context + LLM generated code to a temporary .py file in the shared mounted workspace
    # which is immediately accessible to the running container. This allows us to execute arbitrary code generated by the LLM
    temp_filename = "tmp_execution.py"
    with open(temp_filename, "w", encoding="utf-8") as f:
        f.write(script_content)

    try:
        # Get the running container
        container = client.containers.get("rlm_worker")

        # Execute the script
        exit_code, output = container.exec_run(f"python /workspace/{temp_filename}")

        # Clean up the file
        os.remove(temp_filename)

        result = output.decode('utf-8')

        if exit_code != 0:
            return f"Execution Error:\n{result}"

        return result

    except docker.errors.NotFound:
        return "System Error: Container 'rlm_worker' is not running."
    except Exception as e:
        if os.path.exists(temp_filename):
            os.remove(temp_filename)
        return f"System Error:\n{str(e)}"