from sqlalchemy import func, or_, select
from sqlalchemy.ext.asyncio import AsyncSession

from app.models.brain import BrainCandidate, BrainEvent, BrainMemory, BrainTag
from app.services.graph_service import GraphService


class BrainService:
    def __init__(self, db: AsyncSession):
        self.db = db

    async def create_event(
        self,
        user_id: str,
        *,
        source_type: str,
        source_id: str,
        event_type: str,
        title: str | None = None,
        content_summary: str | None = None,
        raw_excerpt: str | None = None,
        metadata_: dict | None = None,
        importance_signal: float = 0.0,
    ) -> BrainEvent:
        event = BrainEvent(
            user_id=user_id,
            source_type=source_type,
            source_id=source_id,
            event_type=event_type,
            title=title,
            content_summary=content_summary,
            raw_excerpt=raw_excerpt,
            metadata_=metadata_,
            importance_signal=importance_signal,
            status="pending",
        )
        self.db.add(event)
        await self.db.flush()
        return event

    async def recall_memories(self, user_id: str, current_query: str, top_k: int = 3) -> list[BrainMemory]:
        query_tokens = [token.strip().lower() for token in current_query.split() if token.strip()]
        statement = select(BrainMemory).where(
            BrainMemory.user_id == user_id,
            BrainMemory.status == "active",
        )
        if query_tokens:
            statement = statement.where(
                or_(
                    *[
                        or_(
                            BrainMemory.title.ilike(f"%{token}%"),
                            BrainMemory.content.ilike(f"%{token}%"),
                        )
                        for token in query_tokens
                    ]
                )
            )
        result = await self.db.execute(
            statement.order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc()).limit(top_k)
        )
        memories = list(result.scalars().all())
        if memories or query_tokens:
            return memories

        fallback_result = await self.db.execute(
            select(BrainMemory)
            .where(BrainMemory.user_id == user_id, BrainMemory.status == "active")
            .order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc())
            .limit(top_k)
        )
        return list(fallback_result.scalars().all())

    async def get_overview(self, user_id: str) -> dict:
        active_memory_count = (
            await self.db.execute(
                select(func.count()).select_from(BrainMemory).where(
                    BrainMemory.user_id == user_id,
                    BrainMemory.status == "active",
                )
            )
        ).scalar() or 0

        important_tag_count = (
            await self.db.execute(
                select(func.count()).select_from(BrainTag).where(
                    BrainTag.user_id == user_id,
                    BrainTag.priority == "important",
                )
            )
        ).scalar() or 0

        secondary_tag_count = (
            await self.db.execute(
                select(func.count()).select_from(BrainTag).where(
                    BrainTag.user_id == user_id,
                    BrainTag.priority == "secondary",
                )
            )
        ).scalar() or 0

        recent_memory_result = await self.db.execute(
            select(BrainMemory.title)
            .where(BrainMemory.user_id == user_id, BrainMemory.status == "active")
            .order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc())
            .limit(5)
        )
        recent_memory_titles = list(recent_memory_result.scalars().all())

        return {
            "active_memory_count": active_memory_count,
            "important_tag_count": important_tag_count,
            "secondary_tag_count": secondary_tag_count,
            "recent_memory_titles": recent_memory_titles,
        }

    async def list_memories(self, user_id: str) -> list[BrainMemory]:
        result = await self.db.execute(
            select(BrainMemory)
            .where(BrainMemory.user_id == user_id, BrainMemory.status == "active")
            .order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc())
        )
        return list(result.scalars().all())

    async def list_tags(self, user_id: str) -> dict:
        important_result = await self.db.execute(
            select(BrainTag)
            .where(BrainTag.user_id == user_id, BrainTag.priority == "important")
            .order_by(BrainTag.score.desc(), BrainTag.created_at.desc())
        )
        secondary_result = await self.db.execute(
            select(BrainTag)
            .where(BrainTag.user_id == user_id, BrainTag.priority == "secondary")
            .order_by(BrainTag.score.desc(), BrainTag.created_at.desc())
        )
        return {
            "important": list(important_result.scalars().all()),
            "secondary": list(secondary_result.scalars().all()),
        }

    async def list_events(self, user_id: str) -> list[BrainEvent]:
        result = await self.db.execute(
            select(BrainEvent)
            .where(BrainEvent.user_id == user_id)
            .order_by(BrainEvent.created_at.desc())
        )
        return list(result.scalars().all())

    async def run_learning(self, user_id: str) -> dict:
        pending_events_result = await self.db.execute(
            select(BrainEvent)
            .where(BrainEvent.user_id == user_id, BrainEvent.status == "pending")
            .order_by(BrainEvent.created_at.asc())
        )
        pending_events = list(pending_events_result.scalars().all())
        pending_count = len(pending_events)

        candidates_created = 0
        memories_promoted = 0

        if pending_events:
            candidate = BrainCandidate(
                user_id=user_id,
                candidate_type="daily_learning",
                title="Daily learning synthesis",
                summary=f"Processed {pending_count} pending brain events.",
                importance_score=float(pending_count),
                confidence_score=1.0,
                status="promoted",
                source_event_ids=[event.id for event in pending_events],
            )
            self.db.add(candidate)
            await self.db.flush()
            candidates_created = 1

            memory = BrainMemory(
                user_id=user_id,
                memory_type="daily_learning",
                title="Daily learning synthesis",
                content=f"Processed {pending_count} pending brain events.",
                importance=max(pending_count, 1),
                confidence=1.0,
                status="active",
                origin_candidate_id=candidate.id,
                origin_source_types=sorted({event.source_type for event in pending_events}),
            )
            self.db.add(memory)
            memories_promoted = 1

            for event in pending_events:
                event.status = "processed"
                event.processed_at = memory.created_at

            await self.db.commit()
        else:
            await self.db.commit()

        await GraphService(self.db).build_graph(user_id)

        return {
            "events_considered": pending_count,
            "candidates_created": candidates_created,
            "memories_promoted": memories_promoted,
        }