"""run_python — the calculator. Agents call this for ALL arithmetic instead of doing
mental math (LLMs are unreliable at it). Executes in an isolated subprocess with a hard
timeout and a **scrubbed environment** (no account secrets), so model-generated code can't
read API keys. For heavier data/analysis work, agents use run_analysis (tools/sandbox.py).
"""

from __future__ import annotations

import subprocess
import sys
import tempfile

from langchain_core.tools import tool

_TIMEOUT_S = 5


@tool
def run_python(code: str) -> str:
    """Execute a short Python snippet for EXACT math and return its stdout.

    Use for ANY arithmetic: position sizing, percentages, P/E, returns, averages, ratios,
    option breakevens. STANDARD LIBRARY ONLY (`math`, `statistics`) — NO pandas, numpy, or
    yfinance here; for data analysis use run_analysis instead. You MUST print() the result.
    Example: `print(round(1000 / 23.55, 4))`. No access to secrets; 5s limit.
    """
    from tools.sandbox import scrubbed_env

    try:
        proc = subprocess.run(
            [sys.executable, "-I", "-c", code],
            capture_output=True,
            text=True,
            timeout=_TIMEOUT_S,
            env=scrubbed_env(tempfile.gettempdir()),
        )
    except subprocess.TimeoutExpired:
        return f"ERROR: execution timed out ({_TIMEOUT_S}s limit)."
    except Exception as e:  # pragma: no cover - defensive
        return f"ERROR: {type(e).__name__}: {e}"

    if proc.returncode != 0:
        return f"ERROR: {proc.stderr.strip() or 'non-zero exit'}"
    out = proc.stdout.strip()
    return out or "(no output — did you forget to print()?)"