"""Author RAG Chatbot SaaS — Upsell Strategy Engine.

Selects and injects the appropriate upsell strategy into every response.
RULE: Strategy drives LLM behavioral instructions — not canned append-only hooks.
RULE: Buy Book button shown when engagement and intent warrant it.
"""

import structlog

from app.services.prompter import UPSELL_HOOKS, get_upsell_strategy_instruction
from app.services.session_core.manager import SessionContext

logger = structlog.get_logger(__name__)

_INTRIGUE_SIGNALS: tuple[str, ...] = (
    "?",
    "you'll",
    "you will",
    "what happens",
    "want to know",
    "curious",
    "find out",
    "waiting",
    "won't see",
    "imagine",
    "worth",
    "surprise",
)


class UpsellEngine:
    """Selects and injects upsell strategy based on user context."""

    _STRATEGY_MATRIX: dict[tuple[str, str], str] = {
        ("purchase_intent", "low"):    "DIRECT_CTA",
        ("purchase_intent", "medium"): "DIRECT_CTA",
        ("purchase_intent", "high"):   "DIRECT_CTA",
        ("full_story_request", "low"):    "DIRECT_CTA",
        ("full_story_request", "medium"): "DIRECT_CTA",
        ("full_story_request", "high"):   "DIRECT_CTA",
        ("question", "low"):           "RECIPROCITY",
        ("question", "medium"):        "CURIOSITY_GAP",
        ("question", "high"):          "DIRECT_CTA",
        ("comparison", "low"):         "SOCIAL_PROOF",
        ("comparison", "medium"):      "SOCIAL_PROOF",
        ("comparison", "high"):        "FUTURE_PACING",
        ("complaint", "low"):          "PAIN_SOLUTION",
        ("complaint", "medium"):       "PAIN_SOLUTION",
        ("complaint", "high"):         "STORY_BRIDGE",
        ("greeting", "low"):           "RECIPROCITY",
        ("greeting", "medium"):        "CURIOSITY_GAP",
        ("greeting", "high"):          "DIRECT_CTA",
    }

    @staticmethod
    def normalize_intent(intent: str) -> str:
        """Map classifier intents to upsell matrix keys."""
        if intent == "book_comparison":
            return "comparison"
        return intent

    def select_strategy(self, intent: str, context: SessionContext) -> str:
        """Select the optimal upsell strategy for this turn."""
        intent = self.normalize_intent(intent)

        if intent in ("purchase_intent", "full_story_request"):
            return "DIRECT_CTA"

        if context.turn_count >= 3:
            interest_tier = self._get_interest_tier(context.interest_score)
            return self._STRATEGY_MATRIX.get((intent, interest_tier), "CURIOSITY_GAP")

        if context.turn_count >= 1:
            return "CURIOSITY_GAP"

        return "RECIPROCITY"

    def get_strategy_instruction(self, strategy: str) -> str:
        """Return prompt-ready behavioral instruction for the given strategy."""
        return get_upsell_strategy_instruction(strategy)

    def build_hook(
        self,
        strategy: str,
        purchase_url: str | None = None,
        chapter_ref: str | None = None,
        author_name: str = "the author",
    ) -> str:
        """Build the upsell hook text for the given strategy (fallback use only)."""
        template = UPSELL_HOOKS.get(strategy, UPSELL_HOOKS["RECIPROCITY"])
        return template.format(
            purchase_url=purchase_url or "#",
            chapter_ref=chapter_ref or "a key chapter",
            author_name=author_name,
        )

    def should_include_link(
        self,
        intent: str,
        context: SessionContext,
        strategy: str,
    ) -> bool:
        """Determine if a purchase link button should be shown.

        Rules (ordered by priority):
        1. Complaint or off_topic → never
        2. purchase_intent → always (even turn 0 — they're asking to buy)
        3. full_story_request → always (they want it badly enough to ask for the full thing)
        4. Turn 0 (very first message) → never (too early for anything else)
        5. Turn 1+ with book selected + URL → show
        6. Turn 2+ with sufficient engagement → show
        """
        intent = self.normalize_intent(intent)

        if intent in ("complaint", "off_topic"):
            return False

        if intent in ("purchase_intent", "full_story_request"):
            return True

        if context.turn_count < 1:
            return False

        if strategy == "DIRECT_CTA":
            return True

        if context.selected_book_id and context.turn_count >= 1:
            return True

        if context.interest_score >= 0.40 and context.turn_count >= 2:
            return True

        return False

    def fallback_hook_if_needed(
        self,
        response_text: str,
        strategy: str,
        interest_score: float,
    ) -> str | None:
        """Return a canned hook only when the LLM omitted an intrigue close."""
        if strategy == "DIRECT_CTA":
            return None
        if interest_score < 0.4:
            return None
        if response_has_intrigue_hook(response_text):
            return None
        return self.build_hook(strategy)

    @staticmethod
    def _get_interest_tier(score: float) -> str:
        if score < 0.3:
            return "low"
        if score < 0.7:
            return "medium"
        return "high"


def response_has_intrigue_hook(text: str) -> bool:
    """Heuristic: did the response end with curiosity or engagement?"""
    cleaned = text.strip()
    if not cleaned:
        return False
    if cleaned.endswith("?"):
        return True
    lower = cleaned.lower()
    return any(signal in lower for signal in _INTRIGUE_SIGNALS)