from __future__ import annotations

from typing import Any, Dict


def compute_importance(cluster: Dict[str, Any]) -> float:
    """Compute an importance score for an already-enriched cluster.

    Preference: use LLM-derived signals when available.

    Heuristic blend:
    - consensus/coverage: sources + number of articles
    - signal strength: |sentimentScore| (LLM-derived)
    """

    sources = len(set(cluster.get("sources", [])))
    article_count = len(cluster.get("articles", []))
    sentiment_score = cluster.get("sentimentScore")
    if sentiment_score is None:
        sentiment_score = 0.0

    # Coverage term (kept conservative)
    coverage = 0.10 * sources + 0.01 * article_count
    # LLM signal term: higher magnitude sentiment => higher importance.
    signal = 0.60 * min(1.0, abs(float(sentiment_score)))

    score = coverage + signal
    return min(0.99, round(score, 2))