import logging
from typing import Dict, Any
import os

logger = logging.getLogger(__name__)

class AINode:
    def __init__(self):
        logger.info("Initializing AINode")
        # Initialize any AI-related configurations or models here

    def execute(self, node_data: Dict[str, Any]) -> Dict[str, Any]:
        operation = node_data.get('operation')
        if operation == 'decision':
            return self.make_decision(node_data)
        elif operation == 'end':
            return self.end_workflow(node_data)
        else:
            raise ValueError(f"Unsupported operation: {operation}")

    def make_decision(self, node_data: Dict[str, Any]) -> Dict[str, Any]:
        condition = node_data.get('condition', '')
        # Implement your decision logic based on the condition
        # For example, evaluate the condition string safely
        try:
            # WARNING: Using eval can be dangerous. Use with caution.
            is_true = eval(condition)
            return {"status": "success", "output": {"decision": is_true}}
        except Exception as e:
            logger.error(f"Decision evaluation error: {e}")
            return {"status": "error", "message": str(e), "output": None}

    def end_workflow(self, node_data: Dict[str, Any]) -> Dict[str, Any]:
        logger.info("Workflow has ended.")
        return {"status": "success", "message": "Workflow completed successfully.", "output": None}
