import numpy as np
import shap

# -------------------------------
# GLOBAL EXPLAINER CACHE
# -------------------------------
_explainer = None


def _get_explainer(model):
    global _explainer

    # create only once
    if _explainer is None:
        _explainer = shap.TreeExplainer(model)

    return _explainer


# -------------------------------
# MAIN FUNCTION
# -------------------------------
def shap_explain(model, sample, top_k=5):
    """
    model  : trained XGBoost model
    sample : numpy array shape (1, n_features)
    """

    # use cached explainer
    explainer = _get_explainer(model)

    # Compute SHAP values
    shap_values = explainer.shap_values(sample)

    # Handle binary/multi output
    if isinstance(shap_values, list):
        shap_vals = shap_values[0]
    else:
        shap_vals = shap_values

    # ⭐ IMPORTANT FIX
    shap_vals = np.array(shap_vals).flatten()

    # Top important features
    top_indices = np.argsort(np.abs(shap_vals))[::-1][:top_k]

    explanation = []

    for i in top_indices:
        impact = shap_vals[i]
        direction = "AI" if impact > 0 else "Human"

        explanation.append({
            "feature_index": int(i),
            "impact": float(round(impact, 4)),
            "pushes_toward": direction
        })

    return explanation