"""Redis consumer — listens for chat requests and dispatches to deep_agent.

Subscribes to a Redis pattern channel chat:request:* so it receives
requests for ALL users. Each request is processed in a separate asyncio task.
"""

from __future__ import annotations

import asyncio
import json
import logging
from uuid import uuid4

from shared.db import async_session
from shared.redis import redis_client, ws_out_channel

from app.deep_agent import run_floating_stream, run_home_stream
from app.memory_middleware import MemoryMiddleware
from app.output_formatter import StreamFormatter
from shared.ws_context import clear_current_user, set_current_user
from app import tracing

logger = logging.getLogger(__name__)


def start_consumer() -> asyncio.Task:
    """Start the Redis consumer as a background asyncio task."""
    return asyncio.create_task(_consumer_loop())


async def _consumer_loop() -> None:
    """Subscribe to chat:request:* and dispatch incoming frames."""
    pubsub = redis_client.pubsub()
    await pubsub.psubscribe("chat:request:*")
    logger.info("redis_consumer: subscribed to chat:request:*")

    try:
        while True:
            message = await pubsub.get_message(
                ignore_subscribe_messages=True, timeout=1.0
            )
            if message is not None and message["type"] == "pmessage":
                frame = json.loads(message["data"])
                asyncio.create_task(_dispatch(frame))
            else:
                await asyncio.sleep(0.01)
    except asyncio.CancelledError:
        logger.info("redis_consumer: shutting down")
    finally:
        await pubsub.punsubscribe()
        await pubsub.aclose()


async def _dispatch(frame: dict) -> None:
    """Route a chat request frame to the appropriate handler."""
    frame_type = frame.get("type")
    user_id = frame.get("user_id")

    if not user_id:
        logger.warning("redis_consumer: frame missing user_id: %s", frame.get("type"))
        return

    if frame_type == "home_request":
        await _handle_home_request(user_id, frame)
    elif frame_type == "floating_request":
        await _handle_floating_request(user_id, frame)
    else:
        logger.debug("redis_consumer: unknown frame type %r", frame_type)


async def _publish_frame(user_id: str, frame_data: str) -> None:
    """Publish a frame to ws:out:{user_id} for the WS Gateway to forward."""
    channel = ws_out_channel(user_id)
    await redis_client.publish(channel, frame_data)


async def _handle_home_request(user_id: str, frame: dict) -> None:
    """Process a home_request — enrich with memory, run deep_agent, stream results."""
    request_id = frame.get("request_id") or str(uuid4())
    message: str = frame.get("message", "")
    session_id: str = frame.get("session_id") or str(uuid4())

    logger.info(
        "redis_consumer: home_request user=%s req=%s msg=%s",
        user_id, request_id, message[:200],
    )

    response_chunks: list[str] = []

    with tracing.trace_span(
        name="home_request",
        user_id=user_id,
        session_id=session_id,
        trace_id=request_id,
        input=message,
        metadata={"message_preview": message[:200]},
        tags=["home"],
    ) as span:
        langfuse_handler = tracing.get_langfuse_callback()

        # Enrich with memory context
        async with async_session() as db:
            memory = MemoryMiddleware(db)
            memory_context = await memory.enrich_context(
                user_id, message,
                trace_id=request_id, session_id=session_id,
            )

        context: dict = {
            "conversation_history": frame.get("conversation_history", []),
            "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
            **memory_context,
        }

        set_current_user(user_id)
        try:
            event_stream = run_home_stream(user_id, message, context, langfuse_handler=langfuse_handler)
            formatter = StreamFormatter(request_id=request_id)
            async for ws_frame in formatter.format(event_stream):
                await _publish_frame(user_id, ws_frame.model_dump_json())
                if hasattr(ws_frame, "chunk"):
                    response_chunks.append(ws_frame.chunk)
        except Exception as exc:
            logger.error("redis_consumer: home_request failed user=%s req=%s: %s", user_id, request_id, exc)
        finally:
            clear_current_user()

        # Link prompt and attach output preview
        tracing.link_prompt_to_trace(span, "home_system")
        response_text = "".join(response_chunks)
        span.update(output=response_text[:500] if response_text else None)

    tracing.flush()

    # Store episode
    async with async_session() as db:
        memory = MemoryMiddleware(db)
        await memory.store_episode(
            user_id, session_id, message, "".join(response_chunks),
            trace_id=request_id,
        )


async def _handle_floating_request(user_id: str, frame: dict) -> None:
    """Process a floating_request — enrich with memory, run deep_agent, stream results."""
    request_id = frame.get("request_id") or str(uuid4())
    message: str = frame.get("message", "")
    session_id: str = frame.get("session_id") or str(uuid4())
    scope: dict = frame.get("scope", {})

    logger.info(
        "redis_consumer: floating_request user=%s req=%s scope=%s msg=%s",
        user_id, request_id, json.dumps(scope)[:200], message[:200],
    )

    response_chunks: list[str] = []

    with tracing.trace_span(
        name="floating_request",
        user_id=user_id,
        session_id=session_id,
        trace_id=request_id,
        input=message,
        metadata={"message_preview": message[:200], "scope": scope},
        tags=["floating"],
    ) as span:
        langfuse_handler = tracing.get_langfuse_callback()

        # Enrich with memory context
        async with async_session() as db:
            memory = MemoryMiddleware(db)
            memory_context = await memory.enrich_context(
                user_id, message,
                trace_id=request_id, session_id=session_id,
            )

        context: dict = {
            "scope": scope,
            "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
            **memory_context,
        }

        set_current_user(user_id)
        try:
            event_stream = run_floating_stream(user_id, message, context, langfuse_handler=langfuse_handler)
            formatter = StreamFormatter(request_id=request_id)
            async for ws_frame in formatter.format(event_stream):
                await _publish_frame(user_id, ws_frame.model_dump_json())
                if hasattr(ws_frame, "chunk"):
                    response_chunks.append(ws_frame.chunk)
        except Exception as exc:
            logger.error("redis_consumer: floating_request failed user=%s req=%s: %s", user_id, request_id, exc)
        finally:
            clear_current_user()

        # Link prompt and attach output preview
        tracing.link_prompt_to_trace(span, "floating_system")
        response_text = "".join(response_chunks)
        span.update(output=response_text[:500] if response_text else None)

    tracing.flush()

    # Store episode
    async with async_session() as db:
        memory = MemoryMiddleware(db)
        await memory.store_episode(
            user_id, session_id, message, "".join(response_chunks),
            trace_id=request_id,
        )