# demo/orchestrator.py - COMPLETE FIXED VERSION
from __future__ import annotations

import logging
import asyncio
from typing import Any, Dict, Optional, List
import time

logger = logging.getLogger(__name__)

# Import mock ARF functions
try:
    from demo.mock_arf import (
        simulate_arf_analysis,
        run_rag_similarity_search,
        create_mock_healing_intent,
        calculate_pattern_confidence
    )
    MOCK_ARF_AVAILABLE = True
    logger.info("Mock ARF functions available")
except ImportError as e:
    logger.warning(f"Mock ARF functions not available: {e}")
    MOCK_ARF_AVAILABLE = False


class DemoOrchestrator:
    """
    Orchestrates demo scenarios with proper agent workflow.
    """

    def __init__(self, enable_streamlit: bool = False):
        self.enable_streamlit = enable_streamlit
        logger.info("DemoOrchestrator initialized")
    
    async def analyze_incident(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze an incident using the ARF agent workflow.
        This is the method called by app.py
        """
        logger.info(f"Analyzing incident: {scenario_name}")
        
        if not MOCK_ARF_AVAILABLE:
            logger.error("Mock ARF functions not available")
            return {
                "status": "error",
                "message": "Mock ARF functions not available",
                "scenario": scenario_name
            }
        
        try:
            # Step 1: Detection Agent
            logger.debug("Running detection agent...")
            detection_result = simulate_arf_analysis(scenario_data)
            
            # Step 2: Recall Agent  
            logger.debug("Running recall agent...")
            similar_incidents = run_rag_similarity_search(scenario_data)
            
            # Step 3: Decision Agent
            logger.debug("Running decision agent...")
            confidence = calculate_pattern_confidence(scenario_data, similar_incidents)
            healing_intent = create_mock_healing_intent(scenario_data, similar_incidents, confidence)
            
            # Simulate processing time
            await asyncio.sleep(0.5)
            
            result = {
                "status": "success",
                "scenario": scenario_name,
                "detection": detection_result,
                "recall": similar_incidents,
                "decision": healing_intent,
                "confidence": confidence,
                "processing_time_ms": 450
            }
            
            logger.info(f"Analysis complete for {scenario_name}")
            return result
            
        except Exception as e:
            logger.error(f"Error analyzing incident: {e}", exc_info=True)
            return {
                "status": "error",
                "message": str(e),
                "scenario": scenario_name
            }
    
    def run_scenario(self, scenario: Dict[str, Any]) -> Dict[str, Any]:
        """
        Run a demo scenario (legacy method).
        """
        logger.info("Running scenario: %s", scenario.get("name", "unknown"))

        return {
            "scenario": scenario.get("name"),
            "status": "completed",
            "output": scenario,
        }