"""EmoSphere Emotion Engine — Real ML inference for emotion detection.

Integrates three modality detectors with weighted fusion.
All models run locally. No data leaves the device.
No medical screening. No anger detection. No surveillance.
"""

from __future__ import annotations

import time
from typing import Optional

import numpy as np

from models import (
    EmotionLabel, EMOTION_LABELS, EmotionScore,
    EmotionDetectionResult, FusedDetectionResult,
    CulturalRegion,
)
from face_detector import FaceEmotionDetector
from voice_detector import VoiceEmotionDetector
from text_detector import TextEmotionDetector
from posture_detector import PostureEmotionDetector


class EmotionFusionEngine:
    """Weighted average fusion of face + voice + text + posture modalities.

    Weights adapt based on modality confidence:
      face:    0.35 (most informative for basic emotions)
      voice:   0.25 (prosody reveals emotion intensity)
      text:    0.20 (semantic content)
      posture: 0.20 (body language and gestures)
    """

    BASE_WEIGHTS = {
        "face": 0.35,
        "voice": 0.25,
        "text": 0.20,
        "posture": 0.20,
    }

    def fuse(
        self,
        face: Optional[EmotionDetectionResult] = None,
        voice: Optional[EmotionDetectionResult] = None,
        text: Optional[EmotionDetectionResult] = None,
        posture: Optional[EmotionDetectionResult] = None,
    ) -> FusedDetectionResult:
        """Fuse available modality results."""
        start = time.time()

        available: list[tuple[str, EmotionDetectionResult]] = []
        if face: available.append(("face", face))
        if voice: available.append(("voice", voice))
        if text: available.append(("text", text))
        if posture: available.append(("posture", posture))

        if not available:
            neutral_scores = [
                EmotionScore(label=label, score=1.0 if label == EmotionLabel.NEUTRAL else 0.0, confidence=0.0)
                for label in EMOTION_LABELS
            ]
            return FusedDetectionResult(
                dominant=EmotionLabel.NEUTRAL,
                dominant_score=1.0,
                scores=neutral_scores,
                modality_weights={},
                confidence=0.0,
                processing_time_ms=0.0,
            )

        # Confidence-adjusted weights
        weights: dict[str, float] = {}
        for mod_name, result in available:
            base = self.BASE_WEIGHTS.get(mod_name, 0.2)
            weights[mod_name] = base * max(result.confidence, 0.01)

        total_w = sum(weights.values())
        if total_w > 0:
            weights = {k: v / total_w for k, v in weights.items()}

        # Weighted blend
        fused: dict[EmotionLabel, float] = {label: 0.0 for label in EMOTION_LABELS}
        for mod_name, result in available:
            w = weights.get(mod_name, 0.0)
            for score in result.scores:
                fused[score.label] += score.score * w

        scores = [
            EmotionScore(label=label, score=fused[label], confidence=fused[label])
            for label in EMOTION_LABELS
        ]
        dominant = max(fused, key=fused.get)  # type: ignore

        return FusedDetectionResult(
            dominant=dominant,
            dominant_score=fused[dominant],
            scores=scores,
            face_result=face,
            voice_result=voice,
            text_result=text,
            posture_result=posture,
            modality_weights=weights,
            confidence=max(r.confidence for _, r in available) * 0.95,
            processing_time_ms=(time.time() - start) * 1000,
        )


class EmotionEngine:
    """Main EmoSphere engine combining all detectors + fusion."""

    def __init__(self, device: str = "cpu"):
        self.device = device
        self.face = FaceEmotionDetector(device=device)
        self.voice = VoiceEmotionDetector(device=device)
        self.text = TextEmotionDetector(device=device)
        self.posture = PostureEmotionDetector(device=device)
        self.fusion = EmotionFusionEngine()
        self._ready = False

    def initialize(self) -> None:
        """Load all models."""
        print("=" * 50)
        print("  EmoSphere Engine — Loading models...")
        print("=" * 50)
        self.face.load()
        self.voice.load()
        self.text.load()
        self.posture.load()
        self._ready = True
        print("=" * 50)
        print("  All models loaded and ready!")
        print(f"  Face: {'transformer' if self.face.pipe else 'simulation'}")
        print(f"  Voice: {'transformer' if self.voice.pipe else 'prosodic'}")
        print(f"  Text: {self.text.model_type}")
        print(f"  Posture: {'mediapipe' if self.posture.pose else 'heuristic'}")
        print("=" * 50)

    @property
    def is_ready(self) -> bool:
        return self._ready

    @property
    def models_status(self) -> dict[str, bool]:
        return {
            "face": self.face.loaded,
            "voice": self.voice.loaded,
            "text": self.text.loaded,
            "posture": self.posture.loaded,
        }


# Singleton
engine = EmotionEngine()