from __future__ import annotations

from typing import Any

from sqlalchemy import select
from sqlalchemy.orm import Session

from app.db.base import Base
from app.models.agent_feedback import AgentOperationFeedback
from app.schemas.agent_feedback import (
    AgentFeedbackCreate,
    AgentFeedbackRead,
    AgentFeedbackSummaryRead,
)

LOW_RATING_MAX = 3


class AgentFeedbackService:
    def __init__(self, db: Session) -> None:
        self.db = db

    def ensure_storage_ready(self) -> None:
        Base.metadata.create_all(bind=self.db.get_bind(), tables=[AgentOperationFeedback.__table__])

    def create_feedback(self, payload: AgentFeedbackCreate) -> AgentFeedbackRead:
        self.ensure_storage_ready()
        feedback = AgentOperationFeedback(
            run_id=payload.run_id,
            conversation_id=payload.conversation_id,
            user_id=payload.user_id,
            agent=payload.agent or "",
            source=payload.source or "",
            session_type=payload.session_type or "",
            operation_type=payload.operation_type or "assistant_round",
            operation_status=payload.operation_status or "",
            rating=int(payload.rating),
            reason=self._normalize_reason(payload.reason),
            context_json=self._normalize_context(payload.context_json),
        )
        self.db.add(feedback)
        self.db.commit()
        self.db.refresh(feedback)
        return AgentFeedbackRead.model_validate(feedback)

    def summarize_feedback(
        self,
        *,
        agent: str | None = None,
        session_type: str | None = None,
        limit: int = 200,
    ) -> AgentFeedbackSummaryRead:
        self.ensure_storage_ready()
        stmt = select(AgentOperationFeedback).order_by(AgentOperationFeedback.created_at.desc()).limit(limit)
        if agent:
            stmt = stmt.where(AgentOperationFeedback.agent == agent)
        if session_type:
            stmt = stmt.where(AgentOperationFeedback.session_type == session_type)

        feedback_items = list(self.db.scalars(stmt).all())
        rating_distribution = {str(score): 0 for score in range(1, 6)}
        agents: dict[str, int] = {}
        session_types: dict[str, int] = {}
        low_feedback: list[dict[str, Any]] = []
        total_rating = 0

        for item in feedback_items:
            rating = max(1, min(int(item.rating or 0), 5))
            total_rating += rating
            rating_distribution[str(rating)] = rating_distribution.get(str(rating), 0) + 1
            if item.agent:
                agents[item.agent] = agents.get(item.agent, 0) + 1
            if item.session_type:
                session_types[item.session_type] = session_types.get(item.session_type, 0) + 1
            if rating <= LOW_RATING_MAX:
                low_feedback.append(
                    {
                        "feedback_id": item.feedback_id,
                        "run_id": item.run_id,
                        "conversation_id": item.conversation_id,
                        "user_id": item.user_id,
                        "agent": item.agent,
                        "session_type": item.session_type,
                        "rating": rating,
                        "reason": item.reason,
                        "created_at": item.created_at,
                    }
                )

        total_feedback = len(feedback_items)
        average_rating = round(total_rating / total_feedback, 2) if total_feedback else 0.0
        return AgentFeedbackSummaryRead(
            window_limit=limit,
            total_feedback=total_feedback,
            average_rating=average_rating,
            low_rating_count=len(low_feedback),
            rating_distribution=rating_distribution,
            agents=agents,
            session_types=session_types,
            recent_low_feedback=low_feedback[:10],
        )

    @staticmethod
    def _normalize_reason(value: str | None) -> str | None:
        normalized = str(value or "").strip()
        return normalized[:1000] if normalized else None

    @staticmethod
    def _normalize_context(value: dict[str, Any] | None) -> dict[str, Any]:
        if not isinstance(value, dict):
            return {}
        return value