"""
XGBoost Busy Detector - Hugging Face Inference Endpoint Handler
Custom handler for HF Inference Endpoints.

Loads XGBoost model, applies normalization, runs evidence accumulation scoring,
and returns busy_score + confidence + recommendation.

Derived from: src/normalization.py, src/scoring_engine.py, src/model.py
"""

from typing import Dict, Any, Tuple
import json
import math
import numpy as np
import pickle
from pathlib import Path


class EndpointHandler:
    """HF Inference Endpoint handler for XGBoost busy detection."""

    def __init__(self, path: str = "."):
        model_dir = Path(path)

        # --- Load XGBoost model ---
        model_path = None
        for candidate in [
            model_dir / "model.pkl",
            model_dir / "busy_detector_v1.pkl",
            model_dir / "busy_detector_5k.pkl",
        ]:
            if candidate.exists():
                model_path = candidate
                break

        if model_path is None:
            raise FileNotFoundError(
                f"No model file found in {model_dir}. "
                "Expected model.pkl, busy_detector_v1.pkl, or busy_detector_5k.pkl"
            )

        with open(model_path, "rb") as f:
            saved = pickle.load(f)

        # Handle both raw model and dict-wrapped model
        if isinstance(saved, dict):
            self.model = saved.get("model") or saved.get("booster")
            self.feature_names = saved.get("feature_names")
        else:
            self.model = saved
            self.feature_names = None

        print(f"✓ XGBoost model loaded from {model_path}")

        # --- Load feature ranges ---
        ranges_path = model_dir / "feature_ranges.json"
        with open(ranges_path) as f:
            ranges_data = json.load(f)

        self.voice_ranges = ranges_data["voice_ranges"]
        self.text_ranges = ranges_data["text_ranges"]
        self.voice_order = ranges_data["voice_feature_order"]
        self.text_order = ranges_data["text_feature_order"]

        # --- Load scoring rules ---
        rules_path = model_dir / "scoring_rules.json"
        with open(rules_path) as f:
            self.scoring = json.load(f)

        self.weights = self.scoring["weights"]
        self.thresholds = self.scoring["thresholds"]
        print("✓ Feature ranges and scoring rules loaded")

    # --------------------------------------------------------------------- #
    # Public interface
    # --------------------------------------------------------------------- #

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Entrypoint for HF Inference Endpoints.

        Expected input (JSON):
        {
            "inputs": {
                "audio_features": { "v1_snr": 15.0, ... },
                "text_features":  { "t1_explicit_busy": 0.8, ... }
            }
        }

        Returns:
        {
            "busy_score": 0.72,
            "confidence": 0.85,
            "recommendation": "EXIT",
            "ml_probability": 0.65,
            "evidence_details": [...]
        }
        """
        # HF wraps payload in "inputs"
        inputs = data.get("inputs", data)
        audio_features = inputs.get("audio_features", {})
        text_features = inputs.get("text_features", {})

        # 1. Normalize
        normalized = self._normalize_features(audio_features, text_features)

        # 2. XGBoost inference
        import xgboost as xgb

        dmatrix = xgb.DMatrix(normalized.reshape(1, -1))
        ml_prob = float(self.model.predict(dmatrix)[0])

        # 3. Evidence accumulation scoring
        final_score, confidence, details = self._score_with_evidence(
            ml_prob, audio_features, text_features
        )

        # 4. Recommendation
        recommendation = self._get_recommendation(final_score)

        return {
            "busy_score": round(final_score, 4),
            "confidence": round(confidence, 4),
            "recommendation": recommendation,
            "ml_probability": round(ml_prob, 4),
            "evidence_details": details,
        }

    # --------------------------------------------------------------------- #
    # Normalization (mirrors src/normalization.py FeatureNormalizer)
    # --------------------------------------------------------------------- #

    def _normalize_value(self, value: float, min_val: float, max_val: float) -> float:
        if max_val == min_val:
            return 0.0
        value = max(min_val, min(max_val, value))
        return (value - min_val) / (max_val - min_val)

    def _normalize_features(
        self,
        audio_features: Dict[str, float],
        text_features: Dict[str, float],
    ) -> np.ndarray:
        """Min-max normalize all 26 features and concatenate."""
        voice_norm = []
        for feat in self.voice_order:
            val = audio_features.get(feat, 0.0)
            lo, hi = self.voice_ranges[feat]
            voice_norm.append(self._normalize_value(val, lo, hi))

        text_norm = []
        for feat in self.text_order:
            val = text_features.get(feat, 0.0)
            lo, hi = self.text_ranges[feat]
            text_norm.append(self._normalize_value(val, lo, hi))

        return np.array(voice_norm + text_norm, dtype=np.float32)

    # --------------------------------------------------------------------- #
    # Evidence scoring (mirrors src/scoring_engine.py ScoringEngine)
    # --------------------------------------------------------------------- #

    @staticmethod
    def _sigmoid(x: float) -> float:
        return 1.0 / (1.0 + math.exp(-x))

    @staticmethod
    def _logit(p: float) -> float:
        p = max(0.01, min(0.99, p))
        return math.log(p / (1.0 - p))

    def _score_with_evidence(
        self,
        ml_prob: float,
        audio_features: Dict[str, float],
        text_features: Dict[str, float],
    ) -> Tuple[float, float, list]:
        """Evidence accumulation scoring exactly matching ScoringEngine.calculate_score."""
        evidence = 0.0
        details = []

        # --- Text evidence ---
        explicit = text_features.get("t1_explicit_busy", 0.0)
        if explicit > 0.5:
            pts = self.weights["explicit_busy"] * explicit
            evidence += pts
            details.append(f"Explicit Intent (+{pts:.1f})")

        explicit_free = text_features.get("t0_explicit_free", 0.0)
        if explicit_free > 0.5:
            pts = self.weights["explicit_free"] * explicit_free
            evidence += pts
            details.append(f"Explicit Free ({pts:.1f})")

        short_ratio = text_features.get("t3_short_ratio", 0.0)
        if short_ratio > 0.3:
            pts = self.weights["short_answers"] * short_ratio
            evidence += pts
            details.append(f"Brief Responses (+{pts:.1f})")

        deflection = text_features.get("t6_deflection", 0.0)
        if deflection > 0.1:
            pts = self.weights["deflection"] * deflection
            evidence += pts
            details.append(f"Deflection (+{pts:.1f})")

        # --- Audio evidence ---
        traffic = audio_features.get("v2_noise_traffic", 0.0)
        if traffic > 0.5:
            pts = self.weights["traffic_noise"] * traffic
            evidence += pts
            details.append(f"Traffic Context (+{pts:.1f})")

        rate = audio_features.get("v3_speech_rate", 0.0)
        if rate > 3.5:
            pts = self.weights["rushed_speech"]
            evidence += pts
            details.append(f"Rushed Speech (+{pts:.1f})")

        pitch_std = audio_features.get("v5_pitch_std", 0.0)
        if pitch_std > 80.0:
            evidence += 0.5
            details.append("Voice Stress (+0.5)")

        emotion_stress = audio_features.get("v11_emotion_stress", 0.0)
        if emotion_stress > 0.6:
            pts = self.weights["emotion_stress"] * emotion_stress
            evidence += pts
            details.append(f"Emotional Stress (+{pts:.1f})")

        emotion_energy = audio_features.get("v12_emotion_energy", 0.0)
        if emotion_energy > 0.7:
            pts = self.weights["emotion_energy"] * emotion_energy
            evidence += pts
            details.append(f"High Energy (+{pts:.1f})")

        # --- ML baseline ---
        ml_evidence = self._logit(ml_prob) * self.weights["ml_model_factor"]
        evidence += ml_evidence
        details.append(f"ML Baseline ({ml_evidence:+.1f})")

        # --- Final ---
        final_score = self._sigmoid(evidence)
        confidence = float(math.tanh(abs(evidence) / 2.0))

        return final_score, confidence, details

    def _get_recommendation(self, score: float) -> str:
        if score < self.thresholds["continue"]:
            return "CONTINUE"
        elif score < self.thresholds["check_in"]:
            return "CHECK_IN"
        else:
            return "EXIT"