"""Akosha admin shell adapter for distributed intelligence.

This module extends the Oneiric AdminShell with Akosha-specific functionality
for distributed intelligence operations including aggregation, search,
anomaly detection, knowledge graph queries, and trend analysis.
"""

from __future__ import annotations

import asyncio
import importlib.metadata
import logging
from typing import TYPE_CHECKING, Any

from oneiric.shell import AdminShell
from oneiric.shell.session_tracker import SessionEventEmitter

if TYPE_CHECKING:
    from akosha.main import AkoshaApplication

logger = logging.getLogger(__name__)


class AkoshaShell(AdminShell):
    """Akosha admin shell for distributed intelligence and pattern recognition.

    This shell provides an interactive IPython environment with Akosha-specific
    commands for managing distributed memory, knowledge graphs, and pattern
    detection across systems.

    Features:
    - aggregate() - Aggregate across systems
    - search() - Search distributed memory
    - detect() - Detect anomalies
    - graph() - Query knowledge graph
    - trends() - Analyze trends
    - Session tracking via Session-Buddy MCP

    Example:
        >>> from akosha.shell import AkoshaShell
        >>> from akosha.main import AkoshaApplication
        >>> app = AkoshaApplication()
        >>> shell = AkoshaShell(app)
        >>> shell.start()
    """

    def __init__(self, app: AkoshaApplication, config: Any | None = None) -> None:
        """Initialize Akosha admin shell.

        Args:
            app: Akosha application instance
            config: Optional shell configuration
        """
        super().__init__(app, config)
        self.session_tracker = SessionEventEmitter(
            component_name="akosha",
        )
        self._add_akasha_namespace()

    def _get_component_name(self) -> str:
        """Get component name for session tracking.

        Returns:
            Component name identifier
        """
        return "akosha"

    def _get_component_type(self) -> str:
        """Get component type for role identification.

        Returns:
            Component type (soothsayer = reveals hidden patterns)
        """
        return "soothsayer"

    def _get_component_version(self) -> str:
        """Get Akosha component version.

        Returns:
            Version string from package metadata
        """
        try:
            return importlib.metadata.version("akosha")
        except Exception:
            return "unknown"

    def _get_adapters_info(self) -> list[str]:
        """Get Akosha adapter information.

        Returns:
            List of adapter names
        """
        return ["vector_db", "graph_db", "analytics", "alerting"]

    def _add_akasha_namespace(self) -> None:
        """Add Akosha-specific helpers to shell namespace.

        Adds async command wrappers that automatically run coroutines
        in the event loop for convenient shell usage.
        """
        self.namespace.update(
            {
                # Intelligence commands
                "aggregate": lambda *args, **kwargs: asyncio.run(self._aggregate(*args, **kwargs)),
                "search": lambda *args, **kwargs: asyncio.run(self._search(*args, **kwargs)),
                "detect": lambda *args, **kwargs: asyncio.run(self._detect(*args, **kwargs)),
                "graph": lambda *args, **kwargs: asyncio.run(self._graph(*args, **kwargs)),
                "trends": lambda *args, **kwargs: asyncio.run(self._trends(*args, **kwargs)),
                # Adapters
                "adapters": self._get_adapters_info,
                "version": self._get_component_version,
            }
        )

    async def _aggregate(
        self,
        query: str = "*",
        filters: dict[str, Any] | None = None,
        limit: int = 100,
    ) -> dict[str, Any]:
        """Aggregate across distributed systems.

        Args:
            query: Aggregation query pattern
            filters: Optional filter criteria
            limit: Maximum results to return

        Returns:
            Aggregation results with metadata
        """
        logger.info(f"Aggregating data: query={query}, filters={filters}, limit={limit}")

        # TODO: Implement actual aggregation logic
        return {
            "status": "success",
            "query": query,
            "filters": filters,
            "limit": limit,
            "results": [],
            "count": 0,
            "message": "Aggregation endpoint - implement distributed query logic",
        }

    async def _search(
        self,
        query: str,
        index: str = "all",
        limit: int = 10,
    ) -> dict[str, Any]:
        """Search distributed memory.

        Args:
            query: Search query string
            index: Index to search (default: all)
            limit: Maximum results to return

        Returns:
            Search results with relevance scores
        """
        logger.info(f"Searching memory: query={query}, index={index}, limit={limit}")

        # TODO: Implement actual search logic
        return {
            "status": "success",
            "query": query,
            "index": index,
            "limit": limit,
            "results": [],
            "count": 0,
            "message": "Search endpoint - implement vector similarity search",
        }

    async def _detect(
        self,
        metric: str = "all",
        threshold: float = 0.8,
        window: int = 300,
    ) -> dict[str, Any]:
        """Detect anomalies in distributed systems.

        Args:
            metric: Metric to analyze (default: all)
            threshold: Anomaly detection threshold (0-1)
            window: Time window in seconds

        Returns:
            Anomaly detection results
        """
        logger.info(f"Detecting anomalies: metric={metric}, threshold={threshold}, window={window}")

        # TODO: Implement actual anomaly detection
        return {
            "status": "success",
            "metric": metric,
            "threshold": threshold,
            "window": window,
            "anomalies": [],
            "count": 0,
            "message": "Anomaly detection endpoint - implement ML-based detection",
        }

    async def _graph(
        self,
        query: str,
        node_type: str | None = None,
        depth: int = 2,
    ) -> dict[str, Any]:
        """Query knowledge graph.

        Args:
            query: Graph query pattern
            node_type: Optional node type filter
            depth: Maximum traversal depth

        Returns:
            Knowledge graph query results
        """
        logger.info(
            f"Querying knowledge graph: query={query}, node_type={node_type}, depth={depth}"
        )

        # TODO: Implement actual graph queries
        return {
            "status": "success",
            "query": query,
            "node_type": node_type,
            "depth": depth,
            "nodes": [],
            "edges": [],
            "message": "Graph query endpoint - implement graph traversal",
        }

    async def _trends(
        self,
        metric: str = "all",
        window: int = 3600,
        granularity: int = 60,
    ) -> dict[str, Any]:
        """Analyze trends in distributed systems.

        Args:
            metric: Metric to analyze (default: all)
            window: Time window in seconds
            granularity: Data granularity in seconds

        Returns:
            Trend analysis results
        """
        logger.info(
            f"Analyzing trends: metric={metric}, window={window}, granularity={granularity}"
        )

        # TODO: Implement actual trend analysis
        return {
            "status": "success",
            "metric": metric,
            "window": window,
            "granularity": granularity,
            "trends": [],
            "message": "Trend analysis endpoint - implement time-series analysis",
        }

    def _get_banner(self) -> str:
        """Generate shell banner with component information.

        Returns:
            Formatted banner string
        """
        version = self._get_component_version()
        adapters = ", ".join(self._get_adapters_info())

        return f"""
╔══════════════════════════════════════════════════════════════════════╗
║                    Akosha Admin Shell                                ║
╚══════════════════════════════════════════════════════════════════════╝

Distributed Intelligence & Pattern Recognition
Version: {version}
Component Type: {self._get_component_type()}

Adapters: {adapters}
Session Tracking: ✓ Enabled

Intelligence Commands:
  aggregate(query, filters, limit)   Aggregate across systems
  search(query, index, limit)        Search distributed memory
  detect(metric, threshold, window)  Detect anomalies
  graph(query, node_type, depth)     Query knowledge graph
  trends(metric, window, granularity) Analyze trends

Utility:
  version()                           Show component version
  adapters()                          List available adapters
  app                                 Access application instance
  help()                              Python help
  %help_shell                         Shell magic commands

Type 'help()' for Python help or '%help_shell' for shell commands
{"=" * 70}
"""

    async def start(self) -> None:
        """Start the admin shell with session tracking.

        Emits session start event to Session-Buddy MCP before launching
        the interactive shell.
        """
        # Emit session start event
        if await self.session_tracker._check_availability():
            try:
                await self.session_tracker.emit_session_start(
                    shell_type="ipython",
                    metadata={
                        "component_name": self._get_component_name(),
                        "component_type": self._get_component_type(),
                        "version": self._get_component_version(),
                        "adapters": self._get_adapters_info(),
                    },
                )
                logger.info("✅ Session tracking enabled via Session-Buddy MCP")
            except Exception as e:
                logger.warning(f"⚠️ Failed to emit session start event: {e}")
        else:
            logger.info("ℹ️ Session-Buddy MCP unavailable - session tracking disabled")

        # Call parent start method
        super().start()

    async def stop(self) -> None:
        """Stop the admin shell with session tracking.

        Emits session end event to Session-Buddy MCP before cleanup.
        """
        # Emit session end event
        if await self.session_tracker._check_availability():
            try:
                await self.session_tracker.emit_session_end(
                    shell_type="ipython",
                    metadata={"component_name": self._get_component_name()},
                )
                logger.info("✅ Session end event emitted")
            except Exception as e:
                logger.warning(f"⚠️ Failed to emit session end event: {e}")

        # Call parent stop method
        super().stop()
