"""Sentiment API methods for ChainShiftClient.

Mixin class extracted from api_client.py.
Methods access self._get, self._post, self._session, self.base_url, self.headers from the parent class.
"""
from typing import Any


class SentimentApiMixin:
    """Gen3 Nudge, Brand Mentions, LLM Verification, Feedback, Keyword APIs."""

    # ========================================================================
    # Gen3 APIs - Nudge Detection
    # ========================================================================

    def get_nudge_candidates(
        self,
        campaign_id: int,
        page: int = 1,
        page_size: int = 50,
        bit_quadrant: str | None = None,
        platform: str | None = None,
    ) -> dict:
        """Get answers flagged for nudge (in-house brand negative mentions).

        Returns:
            - total_nudge_candidates: int
            - by_confidence_tier: {HIGH: n, MEDIUM: n, LOW: n}
            - by_platform: {CHATGPT: n, GOOGLE_AI: n, ...}
            - by_cej: {AWARENESS_COMPARISON: n, PURCHASE: n, ...}
            - by_bit_quadrant: {neutral: n, ...}
            - candidates: list of nudge items
        """
        params = {"page": page, "page_size": page_size}
        if bit_quadrant:
            params["bit_quadrant"] = bit_quadrant
        if platform:
            params["platform"] = platform
        return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/nudge-candidates", params)

    def get_brand_mentions(
        self,
        campaign_id: int,
        page: int = 1,
        page_size: int = 50,
        brand_type: str | None = None,
        polarity: str | None = None,
        llm_verified: str | None = None,
        brand_name: str | None = None,
        sort_by: str | None = "analyzed_at",
        sort_desc: bool = True,
    ) -> dict:
        """Get brand mention analysis."""
        params = {"page": page, "page_size": page_size, "sort_desc": sort_desc}
        if brand_type:
            params["brand_type"] = brand_type
        if polarity:
            params["polarity"] = polarity
        if llm_verified:
            params["llm_verified"] = llm_verified
        if brand_name:
            params["brand_name"] = brand_name
        if sort_by:
            params["sort_by"] = sort_by
        return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/brand-mentions", params)

    def export_brand_mentions(
        self,
        campaign_id: int,
        brand_type: str | None = None,
        polarity: str | None = None,
        llm_verified: str | None = None,
        brand_name: str | None = None,
        include_full_answers: bool = False,
    ) -> bytes:
        """Export brand mentions to Excel."""
        params = {}
        if brand_type:
            params["brand_type"] = brand_type
        if polarity:
            params["polarity"] = polarity
        if llm_verified:
            params["llm_verified"] = llm_verified
        if brand_name:
            params["brand_name"] = brand_name
        if include_full_answers:
            params["include_full_answers"] = "true"

        url = f"{self.base_url}/api/v1/sentiment/campaigns/{campaign_id}/brand-mentions/export"
        response = self._session.get(url, params=params, headers=self.headers, timeout=120)
        response.raise_for_status()
        return response.content

    # ========================================================================
    # LLM Verification & Human Feedback APIs
    # ========================================================================

    def verify_answer(self, answer_id: int, force: bool = False) -> dict:
        """Request LLM 2차 검증 for an answer."""
        return self._post("/api/v1/sentiment/verify", {
            "answer_id": answer_id,
            "force": force,
        })

    def submit_feedback(
        self,
        answer_id: int,
        campaign_id: int,
        feedback_type: str,
        corrected_polarity: str | None = None,
        wrong_reason: str | None = None,
        comment: str | None = None,
    ) -> dict:
        """Submit human feedback for an answer sentiment."""
        data = {
            "answer_id": answer_id,
            "campaign_id": campaign_id,
            "feedback_type": feedback_type,
        }
        if corrected_polarity:
            data["corrected_polarity"] = corrected_polarity
        if wrong_reason:
            data["wrong_reason"] = wrong_reason
        if comment:
            data["comment"] = comment
        return self._post("/api/v1/sentiment/feedback", data)

    def get_feedback_stats(self, campaign_id: int) -> dict:
        """Get feedback statistics for a campaign."""
        return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/feedback/stats")

    def get_full_answers(self, answer_ids: list[int]) -> dict:
        """Get full answer text for multiple answers."""
        return self._post("/api/v1/sentiment/answers/full", {"answer_ids": answer_ids})

    def export_nudge_candidates(
        self,
        campaign_id: int,
        include_full_answers: bool = False,
        include_evidence: bool = True,
        llm_verified_only: bool = False,
        platform: str | None = None,
        llm_is_negative: bool | None = None,
    ) -> bytes:
        """Export nudge candidates to Excel."""
        url = f"{self.base_url}/api/v1/sentiment/campaigns/{campaign_id}/nudge-candidates/export"
        params = {
            "include_full_answers": str(include_full_answers).lower(),
            "include_evidence": str(include_evidence).lower(),
            "llm_verified_only": str(llm_verified_only).lower(),
        }
        if platform:
            params["platform"] = platform
        if llm_is_negative is not None:
            params["llm_is_negative"] = str(llm_is_negative).lower()
        response = self._session.get(url, headers=self.headers, params=params, timeout=120)
        response.raise_for_status()
        return response.content

    # ========================================================================
    # Keyword Sentiment APIs
    # ========================================================================

    def start_keyword_analysis(self, campaign_id: int, keywords: list[str]) -> dict:
        """Start a keyword sentiment analysis job."""
        return self._post(
            f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-analysis",
            {"keywords": keywords},
        )

    def get_keyword_summary(
        self,
        campaign_id: int,
        keyword: str | None = None,
        page: int = 1,
        page_size: int = 50,
    ) -> dict:
        """Get keyword sentiment summary with optional filter and pagination."""
        params: dict[str, Any] = {"page": page, "page_size": page_size}
        if keyword:
            params["keyword"] = keyword
        return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-summary", params)

    def get_keyword_results(
        self,
        campaign_id: int,
        keyword: str | None = None,
        sentiment: str | None = None,
        brand_name: str | None = None,
        competitor: bool = False,
        llm_verified: str | None = None,
        llm_is_negative: bool | None = None,
        platform: str | None = None,
        brand_only: bool = False,
        no_brand: bool = False,
        page: int = 1,
        page_size: int = 50,
    ) -> dict:
        """Get keyword results with filters."""
        params: dict[str, Any] = {"page": page, "page_size": page_size}
        if keyword:
            params["keyword"] = keyword
        if sentiment:
            params["sentiment"] = sentiment
        if brand_name:
            params["brand_name"] = brand_name
        if competitor:
            params["competitor"] = "true"
        if llm_verified:
            params["llm_verified"] = llm_verified
        if llm_is_negative is not None:
            params["llm_is_negative"] = str(llm_is_negative).lower()
        if platform:
            params["platform"] = platform
        if brand_only:
            params["brand_only"] = "true"
        if no_brand:
            params["no_brand"] = "true"
        return self._get(
            f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-results", params
        )

    def get_keyword_brand_analysis(
        self,
        campaign_id: int,
        keyword: str | None = None,
        brand_name: str | None = None,
        competitor: bool = False,
    ) -> dict:
        """Get keyword x brand cross analysis."""
        params: dict[str, Any] = {}
        if keyword:
            params["keyword"] = keyword
        if brand_name:
            params["brand_name"] = brand_name
        if competitor:
            params["competitor"] = "true"
        return self._get(
            f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-brand-analysis", params
        )

    def export_keyword_results(
        self,
        campaign_id: int,
        keyword: str | None = None,
        sentiment: str | None = None,
        brand_name: str | None = None,
        competitor: bool = False,
        llm_verified: str | None = None,
        llm_is_negative: bool | None = None,
        platform: str | None = None,
        brand_only: bool = False,
        no_brand: bool = False,
        format: str = "xlsx",
    ) -> bytes:
        """Export keyword results to Excel or CSV with same filters as list endpoint."""
        params: dict[str, Any] = {"format": format}
        if keyword:
            params["keyword"] = keyword
        if sentiment:
            params["sentiment"] = sentiment
        if brand_name:
            params["brand_name"] = brand_name
        if competitor:
            params["competitor"] = "true"
        if llm_verified:
            params["llm_verified"] = llm_verified
        if llm_is_negative is not None:
            params["llm_is_negative"] = str(llm_is_negative).lower()
        if platform:
            params["platform"] = platform
        if brand_only:
            params["brand_only"] = "true"
        if no_brand:
            params["no_brand"] = "true"
        url = f"{self.base_url}/api/v1/sentiment/campaigns/{campaign_id}/keyword-results/export"
        response = self._session.get(url, params=params, headers=self.headers, timeout=120)
        response.raise_for_status()
        return response.content