"""ADK Execution Runner for GCP Solution Architecture Agent.

Uses google.adk.runners.Runner integrated with PostgresSessionService,
PostgresArtifactRepository, and PostgreSQL database state updates.
"""

import logging
import uuid
from typing import Any, Dict, Optional

from app.adk.agents import build_adk_multi_agent_system
from app.adk.artifacts import PostgresArtifactRepository
from app.adk.compat import Runner
from app.adk.sessions import PostgresSessionService
from app.config import get_settings
from app.database import get_db_manager
from app.skills.loader import SkillLoader

logger = logging.getLogger(__name__)


class ADKAgentRunner:
    """Production-ready ADK Execution Runner."""

    def __init__(self, skill_loader: SkillLoader | None = None) -> None:
        settings = get_settings()
        self.skill_loader = skill_loader or SkillLoader(settings.SKILLS_DIR)
        self.skill_loader.load_skills()

        self.session_service = PostgresSessionService()
        self.artifact_repo = PostgresArtifactRepository(settings.BASE_DIR)
        self.db_manager = get_db_manager()

        self.orchestrator = build_adk_multi_agent_system(
            self.skill_loader,
            max_iterations=settings.ADK_MAX_LOOP_ITERATIONS,
        )
        self.runner = Runner(agent=self.orchestrator, session_service=self.session_service)

    def run_execution(self, session_id: Optional[str] = None, request_summary: str = "", target_dir: str = ".") -> Dict[str, Any]:
        """Execute multi-agent workflow using ADK runner with PostgreSQL state persistence."""
        sid = session_id or str(uuid.uuid4())
        execution_id = str(uuid.uuid4())

        initial_input = {
            "execution_id": execution_id,
            "workflow_request": request_summary or "Event-driven regional HTTP application reference architecture",
            "target_dir": target_dir,
            "active_skills": [],
        }

        logger.info("Starting ADK runner execution %s for session %s", execution_id, sid)

        # Run through ADK Runner
        result_state = self.runner.run(session_id=sid, input_state=initial_input)

        # Save artifacts to disk & PostgreSQL database
        saved_artifacts = self.artifact_repo.save_solution_artifacts(execution_id, result_state)

        # Record execution in PostgreSQL database
        self.db_manager.save_workflow_execution(
            execution_id=execution_id,
            session_id=sid,
            status="completed" if result_state.get("validation_passed") else "failed",
            current_phase=result_state.get("current_phase", "package"),
            loop_count=result_state.get("total_loop_iterations", 1),
            request_summary=request_summary,
            results={
                "validation_passed": result_state.get("validation_passed", False),
                "active_skills": result_state.get("active_skills", []),
                "artifacts_saved": len(saved_artifacts),
            },
        )

        return {
            "execution_id": execution_id,
            "session_id": sid,
            "status": "success" if result_state.get("validation_passed") else "completed_with_warnings",
            "validation_passed": result_state.get("validation_passed", False),
            "total_loop_iterations": result_state.get("total_loop_iterations", 1),
            "current_phase": result_state.get("current_phase", "package"),
            "artifacts": {
                "source_discovery_doc": result_state.get("source_discovery_doc"),
                "source_mermaid_diagram": result_state.get("source_mermaid_diagram"),
                "requirements_doc": result_state.get("requirements_doc"),
                "architecture_doc": result_state.get("architecture_doc"),
                "mermaid_diagram": result_state.get("mermaid_diagram"),
                "terraform_code": result_state.get("terraform_code"),
                "validation_results": result_state.get("validation_results"),
                "solution_guide": result_state.get("solution_guide"),
            },
            "active_skills": result_state.get("active_skills", []),
        }