"""Model loading + ensemble inference.

Loads the v2 ensemble from Builder-Neekhil/relationship-longevity-predictor
at startup, caches in memory. Exposes a single predict() function that
returns probability + per-model contributions.
"""
from __future__ import annotations

import json
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any

import joblib
import numpy as np
from huggingface_hub import hf_hub_download

MODEL_REPO = "Builder-Neekhil/relationship-longevity-predictor"

# v2 artifact paths inside the model repo
V2_XGB = "v2_enhanced/enhanced_xgb.joblib"
V2_LGB = "v2_enhanced/enhanced_lgb.joblib"
V2_CAT = "v2_enhanced/enhanced_cat.cbm"
V2_CONFIG = "v2_enhanced/enhanced_config.json"
V2_FEATURE_COLS = "v2_enhanced/enhanced_feature_columns.joblib"
GOTTMAN_RECIPE = "phase1_divorce_model/gottman_recipe.json"
SURVIVAL_RECIPE = "phase2_survival_model/survival_recipe.json"
LONGEVITY_PRIORS = "phase2_survival_model/longevity_priors.json"


@dataclass
class LoadedModel:
    xgb: Any
    lgb: Any
    cat: Any
    feature_columns: list[str]
    config: dict
    gottman_recipe: dict
    survival_recipe: dict
    longevity_priors: dict
    weights: dict[str, float]


_CACHE: LoadedModel | None = None


def _download(filename: str) -> str:
    """Download a file from the model repo, cached by HF hub."""
    return hf_hub_download(repo_id=MODEL_REPO, filename=filename)


def _load_json(path: str) -> dict:
    with open(path) as f:
        return json.load(f)


def load() -> LoadedModel:
    """Load the full ensemble + recipes. Cached after first call."""
    global _CACHE
    if _CACHE is not None:
        return _CACHE

    xgb_path = _download(V2_XGB)
    lgb_path = _download(V2_LGB)
    cat_path = _download(V2_CAT)
    config_path = _download(V2_CONFIG)
    cols_path = _download(V2_FEATURE_COLS)

    xgb = joblib.load(xgb_path)
    lgb = joblib.load(lgb_path)

    # CatBoost uses its own format
    from catboost import CatBoostClassifier
    cat = CatBoostClassifier()
    cat.load_model(cat_path)

    feature_columns = joblib.load(cols_path)
    config = _load_json(config_path)

    # Recipes are small JSONs that drive feature engineering at runtime
    try:
        gottman_recipe = _load_json(_download(GOTTMAN_RECIPE))
    except Exception:
        gottman_recipe = {}
    try:
        survival_recipe = _load_json(_download(SURVIVAL_RECIPE))
    except Exception:
        survival_recipe = {}
    try:
        longevity_priors = _load_json(_download(LONGEVITY_PRIORS))
    except Exception:
        longevity_priors = {}

    # Ensemble weights — normalize whatever key convention the config uses
    # to our canonical xgb/lgb/cat keys. This is defensive against the many
    # possible naming schemes (xgboost, enhanced_xgb, lgbm, etc.)
    raw_weights = config.get("weights", config)  # fall back to top-level keys
    weights = _normalize_weights(raw_weights)
    import logging as _log
    _log.getLogger(__name__).info("Resolved ensemble weights: %s", weights)

    _CACHE = LoadedModel(
        xgb=xgb,
        lgb=lgb,
        cat=cat,
        feature_columns=feature_columns,
        config=config,
        gottman_recipe=gottman_recipe,
        survival_recipe=survival_recipe,
        longevity_priors=longevity_priors,
        weights=weights,
    )
    return _CACHE


def predict(feature_vector: np.ndarray) -> dict:
    """Run weighted ensemble prediction.

    Args:
        feature_vector: shape (n_features,) or (1, n_features)
    Returns:
        {
          "probability": float in [0, 1],
          "per_model": {"xgb": float, "lgb": float, "cat": float},
          "band": str,   # "Low" | "Moderate" | "Moderate-High" | "High"
        }
    """
    model = load()

    x = np.asarray(feature_vector, dtype=np.float32)
    if x.ndim == 1:
        x = x.reshape(1, -1)

    expected = len(model.feature_columns)
    if x.shape[1] != expected:
        raise ValueError(
            f"Feature shape mismatch: got {x.shape[1]}, model expects {expected}. "
            f"Check feature_builder output against the feature_columns list."
        )

    p_xgb = float(model.xgb.predict_proba(x)[0, 1])
    p_lgb = float(model.lgb.predict_proba(x)[0, 1])
    p_cat = float(model.cat.predict_proba(x)[0, 1])

    w = model.weights
    p = w["xgb"] * p_xgb + w["lgb"] * p_lgb + w["cat"] * p_cat

    return {
        "probability": p,
        "per_model": {"xgb": p_xgb, "lgb": p_lgb, "cat": p_cat},
        "band": _band(p),
    }


def _band(p: float) -> str:
    """Map probability to a human-readable band.

    Bands are intentionally wide — avoids false precision.
    """
    if p < 0.35:
        return "Low"
    if p < 0.55:
        return "Moderate"
    if p < 0.75:
        return "Moderate-High"
    return "High"

def _normalize_weights(raw: dict) -> dict[str, float]:
    """Map any known naming convention to canonical xgb / lgb / cat keys.

    Handles: xgboost/xgb/enhanced_xgb, lightgbm/lgb/lgbm/enhanced_lgb,
    catboost/cat/enhanced_cat, plus any _weight suffix variant.
    """
    import logging as _log
    log = _log.getLogger(__name__)

    aliases = {
        "xgb": ["xgb", "xgboost", "enhanced_xgb", "xgb_weight", "xgboost_weight"],
        "lgb": ["lgb", "lgbm", "lightgbm", "enhanced_lgb", "lgb_weight", "lightgbm_weight"],
        "cat": ["cat", "catboost", "enhanced_cat", "cat_weight", "catboost_weight"],
    }
    defaults = {"xgb": 0.40, "lgb": 0.35, "cat": 0.25}

    out: dict[str, float] = {}
    for canonical, keys in aliases.items():
        for k in keys:
            if isinstance(raw, dict) and k in raw and isinstance(raw[k], (int, float)):
                out[canonical] = float(raw[k])
                break
        if canonical not in out:
            log.warning(
                "No weight found for %s in config (tried %s). Using default %.2f. "
                "Available top-level keys: %s",
                canonical, keys, defaults[canonical],
                list(raw.keys()) if isinstance(raw, dict) else "N/A",
            )
            out[canonical] = defaults[canonical]

    # Renormalize in case the resolved weights don't sum to 1.0
    total = sum(out.values())
    if total > 0:
        out = {k: v / total for k, v in out.items()}
    return out