"""
Image Emotion Classifier — ML-based, for Deep Archive Scan.
Uses a pre-trained ViT (Vision Transformer) model from HuggingFace
that was specifically trained on facial expression datasets.
Model: trpakov/vit-face-expression (7-class: angry, disgust, fear, happy, neutral, sad, surprise)

Falls back to DeepFace, then to a lightweight CNN heuristic if HuggingFace is unavailable.
"""

import numpy as np

_vit_pipe = None       # HuggingFace ViT pipeline
_deepface_ready = False

# Label maps (HF model uses these exact label names)
_HF_LABEL_MAP = {
    "angry":    "Angry",
    "disgust":  "Disgust",
    "fear":     "Fear",
    "happy":    "Happy",
    "neutral":  "Neutral",
    "sad":      "Sad",
    "surprise": "Surprise",
}

_ENGAGEMENT_MAP = {
    "Happy":    90,
    "Surprise": 75,
    "Neutral":  50,
    "Angry":    30,
    "Fear":     25,
    "Sad":      15,
    "Disgust":  10,
}


def _load_vit():
    global _vit_pipe
    if _vit_pipe is not None:
        return _vit_pipe

    try:
        from transformers import pipeline as hf_pipeline
        import torch
        device = 0 if torch.cuda.is_available() else -1
        _vit_pipe = hf_pipeline(
            "image-classification",
            model="trpakov/vit-face-expression",
            top_k=None,
            device=device
        )
        print("[ImageEmotion] HuggingFace ViT face expression model loaded.")
        return _vit_pipe
    except Exception as e:
        print(f"[ImageEmotion] HuggingFace ViT unavailable: {e}")
        _vit_pipe = "UNAVAILABLE"
        return _vit_pipe


def predict_from_image(img_rgb: np.ndarray) -> dict:
    """
    Main entry point. Takes an RGB numpy image array.
    Returns emotion, confidence, probabilities, engagement_score.
    """
    # ── Strategy 1: HuggingFace ViT ─────────────────────────
    pipe = _load_vit()
    if pipe and pipe != "UNAVAILABLE":
        try:
            from PIL import Image
            pil_img = Image.fromarray(img_rgb)
            results = pipe(pil_img)

            # Flatten list-of-list if needed
            if isinstance(results[0], list):
                results = results[0]

            # Build score map
            scores = {}
            for r in results:
                label = _HF_LABEL_MAP.get(r["label"].lower(), r["label"].capitalize())
                scores[label] = r["score"]

            dominant = max(scores, key=scores.get)
            raw_conf = scores[dominant]

            # Map to 58%→99.99% scale (same as live model)
            confidence = round(58.0 + raw_conf * (99.99 - 58.0), 2)

            probs = {k: round(v * 100, 1) for k, v in scores.items()}

            return {
                "emotion":          dominant,
                "confidence":       confidence,
                "probabilities":    probs,
                "engagement_score": _ENGAGEMENT_MAP.get(dominant, 50),
                "provider":         "ViT-Face-Expression",
                "face_detected":    True,
            }
        except Exception as e:
            print(f"[ImageEmotion] ViT inference error: {e}")

    # ── Strategy 2: DeepFace ────────────────────────────────
    try:
        from deepface import DeepFace
        analysis = DeepFace.analyze(
            img_rgb,
            actions=["emotion"],
            enforce_detection=False,
            detector_backend="opencv"
        )
        if isinstance(analysis, list):
            analysis = analysis[0]

        emotions_raw = analysis.get("emotion", {})
        dominant = analysis.get("dominant_emotion", "neutral").capitalize()
        dominant = _HF_LABEL_MAP.get(dominant.lower(), dominant)

        # Normalize to 0-1
        total = sum(emotions_raw.values()) or 1
        probs = {
            _HF_LABEL_MAP.get(k.lower(), k.capitalize()): round((v / total) * 100, 1)
            for k, v in emotions_raw.items()
        }

        raw_conf = emotions_raw.get(dominant.lower(), 0) / total
        confidence = round(58.0 + raw_conf * (99.99 - 58.0), 2)

        return {
            "emotion":          dominant,
            "confidence":       confidence,
            "probabilities":    probs,
            "engagement_score": _ENGAGEMENT_MAP.get(dominant, 50),
            "provider":         "DeepFace",
            "face_detected":    True,
        }
    except Exception as e:
        print(f"[ImageEmotion] DeepFace unavailable: {e}")

    # ── Strategy 3: Fallback — pixel intensity heuristic ────
    return _pixel_heuristic(img_rgb)


def _pixel_heuristic(img_rgb: np.ndarray) -> dict:
    """
    Lightweight fallback: reads face region brightness, eye/mouth contrast
    to make a basic guess. Better than always returning Neutral 50%.
    """
    import cv2
    gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
    h, w = gray.shape

    # Rough facial region splits
    eye_region  = gray[int(h*0.25):int(h*0.50), int(w*0.1):int(w*0.9)]
    mouth_region= gray[int(h*0.65):int(h*0.90), int(w*0.2):int(w*0.8)]
    forehead    = gray[int(h*0.05):int(h*0.25), int(w*0.1):int(w*0.9)]

    eye_std     = float(np.std(eye_region))
    mouth_std   = float(np.std(mouth_region))
    forehead_std= float(np.std(forehead))
    overall_mean= float(np.mean(gray))

    # Very crude heuristics — better than fixed Neutral
    scores = {
        "Happy":    max(0, mouth_std - 30) * 2,
        "Surprise": max(0, eye_std - 20) * 1.5 + max(0, mouth_std - 35) * 1.5,
        "Angry":    max(0, forehead_std - 15) * 2,
        "Sad":      max(0, 50 - overall_mean) * 0.5,
        "Fear":     max(0, eye_std - 25) * 1.0,
        "Disgust":  max(0, forehead_std - 20) * 1.0,
        "Neutral":  20.0,
    }

    dominant = max(scores, key=scores.get)
    total = sum(scores.values()) or 1
    probs = {k: round((v / total) * 100, 1) for k, v in scores.items()}
    raw_conf = scores[dominant] / total
    confidence = round(58.0 + min(raw_conf, 1.0) * 30, 2)

    return {
        "emotion":          dominant,
        "confidence":       confidence,
        "probabilities":    probs,
        "engagement_score": _ENGAGEMENT_MAP.get(dominant, 50),
        "provider":         "Pixel-Heuristic",
        "face_detected":    True,
    }