"""
ImpactEvaluator

Evaluates the breadth of impact a memory has based on associated topics.
"""

from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from app.models.memory import UserMemory


class ImpactEvaluator:
    """Evaluate the impact breadth of a memory"""

    # Threshold for maximum impact score
    IMPACT_THRESHOLD = 5  # Number of associated topics for max impact

    def evaluate(self, memory: "UserMemory") -> float:
        """Calculate impact score (0.0 - 1.0)

        The more associated topics a memory has, the higher its impact.
        Topics represent "what this memory is about" — if it touches
        many aspects of the user's life, it has high impact.
        """
        associated_topics = memory.associated_topics or []
        if not associated_topics:
            return 0.0

        # Normalize: IMPACT_THRESHOLD topics = full impact (1.0)
        raw_score = len(associated_topics) / self.IMPACT_THRESHOLD
        return min(1.0, raw_score)

    def get_topic_overlap(self, memory_a: "UserMemory", memory_b: "UserMemory") -> float:
        """Calculate topic overlap between two memories (0.0 - 1.0)

        Used for finding related memories.
        """
        topics_a = set(memory_a.associated_topics or [])
        topics_b = set(memory_b.associated_topics or [])

        if not topics_a or not topics_b:
            return 0.0

        intersection = topics_a & topics_b
        union = topics_a | topics_b

        return len(intersection) / len(union) if union else 0.0

    def rank_by_impact(self, memories: list["UserMemory"]) -> list["UserMemory"]:
        """Rank memories by impact score (descending)"""
        return sorted(memories, key=lambda m: self.evaluate(m), reverse=True)