# backend\ai_server.py

import json
import os
import secrets
from pathlib import Path
from typing import List, Optional

from fastapi import Depends, FastAPI, HTTPException, Request, status
from pydantic import BaseModel
from ultralytics import YOLO


BASE_DIR = Path(__file__).resolve().parent
KEYPOINT_NAMES = [
    "nose",
    "left_eye", "right_eye",
    "left_ear", "right_ear",
    "left_shoulder", "right_shoulder",
    "left_elbow", "right_elbow",
    "left_wrist", "right_wrist",
    "left_hip", "right_hip",
    "left_knee", "right_knee",
    "left_ankle", "right_ankle",
]

NO_SEX_POSITION_LABEL = "keine"
NO_SEX_POSITION_ALIASES = {
    "",
    NO_SEX_POSITION_LABEL,
}


def normalize_sex_position_label(value) -> str:
    clean = str(value or "").strip().lower()
    if clean in NO_SEX_POSITION_ALIASES:
        return NO_SEX_POSITION_LABEL
    return clean


def is_no_sex_position_label(value) -> bool:
    return normalize_sex_position_label(value) == NO_SEX_POSITION_LABEL


def normalize_sex_position_labels(values) -> set[str]:
    labels = set()
    has_no_position = False

    for value in values or []:
        clean = normalize_sex_position_label(value)
        if is_no_sex_position_label(clean):
            has_no_position = True
            continue
        if clean:
            labels.add(clean)

    if has_no_position:
        labels.add(NO_SEX_POSITION_LABEL)

    return labels


def existing_file(path: Path) -> Optional[Path]:
    try:
        if path.exists() and path.is_file() and path.stat().st_size > 0:
            return path
    except OSError:
        pass
    return None


def resolve_training_root() -> Path:
    env_root = os.environ.get("TRAINING_ROOT", "").strip()
    if env_root:
        root = Path(env_root).expanduser().resolve()
        root.mkdir(parents=True, exist_ok=True)
        return root

    candidates = [
        # Wenn ai_server.py aus backend/ läuft:
        BASE_DIR / "generated" / "training",

        # Wenn ai_server.py aus backend/ml/ laufen würde:
        BASE_DIR.parent / "generated" / "training",

        # Wenn ai_server.py embedded aus Temp läuft, aber backendRoot als cwd gesetzt wurde:
        Path.cwd() / "generated" / "training",

        # Wenn Working Directory Projektroot ist:
        Path.cwd() / "backend" / "generated" / "training",
    ]

    for root in candidates:
        if (
            existing_file(root / "detection_labels.json")
            or existing_file(root / "detector" / "model" / "best.pt")
        ):
            root.mkdir(parents=True, exist_ok=True)
            return root.resolve()

    # Fallback: Server soll trotzdem starten.
    root = (Path.cwd() / "generated" / "training").resolve()
    root.mkdir(parents=True, exist_ok=True)
    return root


TRAINING_ROOT = resolve_training_root()
DEFAULT_MODEL_PATH = TRAINING_ROOT / "detector" / "model" / "best.pt"
DEFAULT_POSE_MODEL_PATH = TRAINING_ROOT / "pose" / "model" / "best.pt"


def resolve_detection_labels_path() -> Path:
    env_path = os.environ.get("DETECTION_LABELS_PATH", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_file(p):
            return p
        raise RuntimeError(f"DETECTION_LABELS_PATH not found: {p}")

    p = TRAINING_ROOT / "detection_labels.json"
    if existing_file(p):
        return p.resolve()

    raise RuntimeError(f"detection_labels.json not found: {p}")


def resolve_model_path() -> str:
    env_path = os.environ.get("YOLO_MODEL", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_file(p):
            return str(p)
        raise RuntimeError(f"YOLO_MODEL not found: {p}")

    if existing_file(DEFAULT_MODEL_PATH):
        return str(DEFAULT_MODEL_PATH)

    raise RuntimeError(f"YOLO model not found: {DEFAULT_MODEL_PATH}")


def resolve_pose_model_path() -> Optional[Path]:
    env_path = os.environ.get("YOLO_POSE_MODEL", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_file(p):
            return p
        raise RuntimeError(f"YOLO_POSE_MODEL not found: {p}")

    if existing_file(DEFAULT_POSE_MODEL_PATH):
        return DEFAULT_POSE_MODEL_PATH.resolve()

    return None


# Server darf auch ohne Labels/Model starten.
DETECTION_LABELS_PATH: Optional[Path] = None

LABEL_GROUPS = {
    "people": set(),
    "sexPositions": {NO_SEX_POSITION_LABEL},
    "bodyParts": set(),
    "objects": set(),
    "clothing": set(),
}

BODY_LABELS = LABEL_GROUPS["bodyParts"]
OBJECT_LABELS = LABEL_GROUPS["objects"]
CLOTHING_LABELS = LABEL_GROUPS["clothing"]
POSITION_LABELS = set()

PERSON_LABELS = {
    "person_male",
    "person_female",
}

_MODEL_PATH = ""
_MODEL_ERROR = ""
_POSE_MODEL_PATH = ""
_POSE_MODEL_ERROR = ""
_LABEL_ERROR = ""

_DEVICE = os.environ.get("YOLO_DEVICE", "")
_CONF = float(os.environ.get("YOLO_CONF", "0.25"))
_POSE_CONF = float(os.environ.get("YOLO_POSE_CONF", "0.30"))
_BATCH = int(os.environ.get("YOLO_BATCH", "16"))
_IMGSZ = int(os.environ.get("YOLO_IMGSZ", "640"))
_HALF = os.environ.get("YOLO_HALF", "0").lower() in {"1", "true", "yes", "on"}

model = None
pose_model = None

app = FastAPI()

AI_SERVER_TOKEN = os.environ.get("AI_SERVER_TOKEN", "").strip()
AI_SERVER_AUTH_REQUIRED = os.environ.get("AI_SERVER_AUTH_REQUIRED", "1").strip().lower() not in {
    "0",
    "false",
    "no",
    "off",
}


def require_ai_server_auth(request: Request) -> None:
    if not AI_SERVER_AUTH_REQUIRED:
        return

    if not AI_SERVER_TOKEN:
        raise HTTPException(
            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
            detail="AI server auth token not configured",
        )

    auth = request.headers.get("authorization", "").strip()
    header_token = request.headers.get("x-ai-server-token", "").strip()

    provided = ""

    if auth.lower().startswith("bearer "):
        provided = auth[7:].strip()
    elif header_token:
        provided = header_token

    if not provided or not secrets.compare_digest(provided, AI_SERVER_TOKEN):
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="unauthorized",
            headers={"WWW-Authenticate": "Bearer"},
        )


class PredictBatchRequest(BaseModel):
    paths: List[str]
    detectorOnly: bool = False
    imageSize: int = 640
    model: Optional[str] = None


def empty_prediction(source: str = "model_missing") -> dict:
    return {
        "modelAvailable": False,
        "source": source,
        "sexPosition": NO_SEX_POSITION_LABEL,
        "sexPositionScore": 0.0,
        "peoplePresent": [],
        "bodyPartsPresent": [],
        "objectsPresent": [],
        "clothingPresent": [],
        "boxes": [],
        "persons": [],
    }


def load_label_groups_safe() -> None:
    global DETECTION_LABELS_PATH
    global LABEL_GROUPS
    global BODY_LABELS
    global OBJECT_LABELS
    global CLOTHING_LABELS
    global POSITION_LABELS
    global PERSON_LABELS
    global _LABEL_ERROR

    try:
        path = resolve_detection_labels_path()
        DETECTION_LABELS_PATH = path

        with path.open("r", encoding="utf-8") as f:
            data = json.load(f)

        LABEL_GROUPS = {
            "people": set(
                str(x).strip().lower()
                for x in data.get("people", [])
                if str(x).strip()
            ),
            "sexPositions": normalize_sex_position_labels(data.get("sexPositions", [])),
            "bodyParts": set(
                str(x).strip().lower()
                for x in data.get("bodyParts", [])
                if str(x).strip()
            ),
            "objects": set(
                str(x).strip().lower()
                for x in data.get("objects", [])
                if str(x).strip()
            ),
            "clothing": set(
                str(x).strip().lower()
                for x in data.get("clothing", [])
                if str(x).strip()
            ),
        }

        if not LABEL_GROUPS["sexPositions"]:
            LABEL_GROUPS["sexPositions"] = {NO_SEX_POSITION_LABEL}

        _LABEL_ERROR = ""

    except Exception as exc:
        DETECTION_LABELS_PATH = None
        _LABEL_ERROR = str(exc)

        LABEL_GROUPS = {
            "people": set(),
            "sexPositions": {NO_SEX_POSITION_LABEL},
            "bodyParts": set(),
            "objects": set(),
            "clothing": set(),
        }

    BODY_LABELS = LABEL_GROUPS["bodyParts"]
    OBJECT_LABELS = LABEL_GROUPS["objects"]
    CLOTHING_LABELS = LABEL_GROUPS["clothing"]

    POSITION_LABELS = {
        label for label in LABEL_GROUPS["sexPositions"]
        if label and not is_no_sex_position_label(label)
    }

    PERSON_LABELS = {
        label for label in LABEL_GROUPS["people"]
        if label
    }


def get_model():
    global model
    global _MODEL_PATH
    global _MODEL_ERROR

    if model is not None:
        return model

    try:
        path = resolve_model_path()
        loaded = YOLO(path)

        model = loaded
        _MODEL_PATH = path
        _MODEL_ERROR = ""

        # Labels erst laden, wenn Inference wirklich gebraucht wird.
        load_label_groups_safe()

        return model

    except Exception as exc:
        model = None
        _MODEL_PATH = ""
        _MODEL_ERROR = str(exc)
        return None


def get_pose_model():
    global pose_model
    global _POSE_MODEL_PATH
    global _POSE_MODEL_ERROR

    if pose_model is not None:
        return pose_model

    try:
        path = resolve_pose_model_path()
        if path is None:
            pose_model = None
            _POSE_MODEL_PATH = ""
            _POSE_MODEL_ERROR = ""
            return None

        loaded = YOLO(str(path))

        pose_model = loaded
        _POSE_MODEL_PATH = str(path)
        _POSE_MODEL_ERROR = ""

        return pose_model

    except Exception as exc:
        pose_model = None
        _POSE_MODEL_PATH = ""
        _POSE_MODEL_ERROR = str(exc)
        return None


def scored(label: str, score: float) -> dict:
    return {
        "label": label,
        "score": float(score),
    }


def best_score(items: list[dict], label: str, score: float) -> None:
    for item in items:
        if item["label"] == label:
            if score > item["score"]:
                item["score"] = float(score)
            return

    items.append(scored(label, score))


def prediction_from_result(result) -> dict:
    names = result.names or {}

    boxes_out = []
    people_present = []
    body_parts = []
    objects = []
    clothing = []

    if result.boxes is not None:
        xywhn = result.boxes.xywhn.cpu().tolist()
        cls_values = result.boxes.cls.cpu().tolist()
        conf_values = result.boxes.conf.cpu().tolist()

        for box_xywhn, cls_id, conf in zip(xywhn, cls_values, conf_values):
            label = str(names.get(int(cls_id), int(cls_id))).strip().lower()
            score = float(conf)

            if not label:
                continue

            cx, cy, w, h = [float(v) for v in box_xywhn]

            x = max(0.0, min(1.0, cx - w / 2.0))
            y = max(0.0, min(1.0, cy - h / 2.0))
            w = max(0.0, min(1.0 - x, w))
            h = max(0.0, min(1.0 - y, h))

            is_person = label in PERSON_LABELS
            is_body = label in BODY_LABELS
            is_object = label in OBJECT_LABELS
            is_clothing = label in CLOTHING_LABELS

            if not (is_person or is_body or is_object or is_clothing):
                continue

            boxes_out.append({
                "label": label,
                "score": score,
                "x": x,
                "y": y,
                "w": w,
                "h": h,
            })

            if is_person:
                best_score(people_present, label, score)

            if is_body:
                best_score(body_parts, label, score)

            if is_object:
                best_score(objects, label, score)

            if is_clothing:
                best_score(clothing, label, score)

    return {
        "modelAvailable": True,
        "source": f"yolo-server:{Path(_MODEL_PATH).name}",
        "sexPosition": NO_SEX_POSITION_LABEL,
        "sexPositionScore": 0.0,
        "peoplePresent": people_present,
        "bodyPartsPresent": body_parts,
        "objectsPresent": objects,
        "clothingPresent": clothing,
        "boxes": boxes_out,
        "persons": [],
    }


def pose_persons_from_result(result) -> list[dict]:
    names = result.names or {}
    persons = []

    if result.boxes is None:
        return persons

    kpts_xyn = None
    kpts_conf = None

    if result.keypoints is not None:
        try:
            kpts_xyn = result.keypoints.xyn.cpu().tolist()
        except Exception:
            kpts_xyn = None
        try:
            kpts_conf = result.keypoints.conf.cpu().tolist()
        except Exception:
            kpts_conf = None

    xywhn_values = result.boxes.xywhn.cpu().tolist()
    cls_values = result.boxes.cls.cpu().tolist()
    conf_values = result.boxes.conf.cpu().tolist()

    for i, (box_xywhn, cls_id, conf) in enumerate(
        zip(xywhn_values, cls_values, conf_values)
    ):
        label = str(names.get(int(cls_id), int(cls_id))).strip().lower()
        score = float(conf)

        cx, cy, w, h = [float(v) for v in box_xywhn]
        x = max(0.0, min(1.0, cx - w / 2.0))
        y = max(0.0, min(1.0, cy - h / 2.0))
        w = max(0.0, min(1.0 - x, w))
        h = max(0.0, min(1.0 - y, h))

        if w <= 0 or h <= 0:
            continue

        keypoints = []
        if kpts_xyn is not None and i < len(kpts_xyn):
            for ki, point in enumerate(kpts_xyn[i]):
                if len(point) < 2:
                    continue

                kconf = 0.0
                if (
                    kpts_conf is not None
                    and i < len(kpts_conf)
                    and ki < len(kpts_conf[i])
                ):
                    kconf = float(kpts_conf[i][ki])

                keypoints.append({
                    "name": KEYPOINT_NAMES[ki] if ki < len(KEYPOINT_NAMES) else str(ki),
                    "x": float(point[0]),
                    "y": float(point[1]),
                    "conf": kconf,
                })

        persons.append({
            "label": label,
            "score": score,
            "box": {
                "x": x,
                "y": y,
                "w": w,
                "h": h,
            },
            "keypoints": keypoints,
        })

    return persons


def apply_pose_result_to_prediction(prediction: dict, result) -> dict:
    persons = pose_persons_from_result(result)
    if not persons:
        return prediction

    prediction["persons"] = persons

    best_position = ""
    best_score_value = 0.0

    for person in persons:
        label = str(person.get("label") or "").strip().lower()
        score = float(person.get("score") or 0.0)

        if is_no_sex_position_label(label) or label not in POSITION_LABELS:
            continue

        if score > best_score_value:
            best_position = label
            best_score_value = score

    if best_position:
        prediction["sexPosition"] = best_position
        prediction["sexPositionScore"] = best_score_value

    source = str(prediction.get("source") or "").strip()
    prediction["source"] = f"{source}+yolo_pose" if source else "yolo_pose"

    return prediction


def apply_pose_batch_to_predictions(paths: list[str], predictions: list[dict], imgsz: int) -> None:
    global _POSE_MODEL_ERROR

    current_pose_model = get_pose_model()
    if current_pose_model is None:
        return

    try:
        pose_results = current_pose_model.predict(
            source=paths,
            imgsz=imgsz,
            conf=_POSE_CONF,
            batch=_BATCH,
            device=_DEVICE or None,
            half=_HALF,
            verbose=False,
        )
    except Exception as exc:
        _POSE_MODEL_ERROR = str(exc)
        return

    for prediction, pose_result in zip(predictions, pose_results):
        apply_pose_result_to_prediction(prediction, pose_result)


def pose_model_status() -> dict:
    try:
        expected = resolve_pose_model_path()
        expected_text = str(expected) if expected else str(DEFAULT_POSE_MODEL_PATH)
        exists = expected is not None
        error = _POSE_MODEL_ERROR
    except Exception as exc:
        expected_text = str(DEFAULT_POSE_MODEL_PATH)
        exists = False
        error = str(exc)

    return {
        "poseModelAvailable": exists,
        "poseModelLoaded": pose_model is not None,
        "poseModel": _POSE_MODEL_PATH or (expected_text if exists else ""),
        "poseModelError": error,
        "expectedPoseModel": expected_text,
    }


@app.post("/predict-batch", dependencies=[Depends(require_ai_server_auth)])
def predict_batch(req: PredictBatchRequest):
    paths = [str(path).strip() for path in req.paths if str(path).strip()]
    if not paths:
        return {
            "ok": False,
            "predictions": [],
            "error": "no paths supplied",
        }

    current_model = get_model()
    if current_model is None:
        return {
            "ok": True,
            "predictions": [empty_prediction("model_missing") for _ in paths],
            "error": _MODEL_ERROR or f"YOLO model not found: {DEFAULT_MODEL_PATH}",
        }

    if DETECTION_LABELS_PATH is None or _LABEL_ERROR:
        return {
            "ok": False,
            "predictions": [],
            "error": f"detection labels missing: {_LABEL_ERROR}",
        }

    imgsz = int(req.imageSize or _IMGSZ or 640)

    try:
        results = current_model.predict(
            source=paths,
            imgsz=imgsz,
            conf=_CONF,
            batch=_BATCH,
            device=_DEVICE or None,
            half=_HALF,
            verbose=False,
        )

        predictions = [prediction_from_result(result) for result in results]

        if not req.detectorOnly:
            apply_pose_batch_to_predictions(paths, predictions, imgsz)

        return {
            "ok": True,
            "predictions": predictions,
        }

    except Exception as exc:
        return {
            "ok": False,
            "predictions": [],
            "error": str(exc),
        }


@app.get("/health", dependencies=[Depends(require_ai_server_auth)])
def health():
    current_model = get_model()
    names = getattr(current_model, "names", {}) or {} if current_model is not None else {}

    status_payload = {
        "ok": True,
        "ready": current_model is not None,
        "modelAvailable": current_model is not None,
        "model": _MODEL_PATH,
        "modelError": _MODEL_ERROR,
        "expectedModel": str(DEFAULT_MODEL_PATH),
        "trainingRoot": str(TRAINING_ROOT),
        "classCount": len(names),
        "classes": list(names.values())[:80] if isinstance(names, dict) else names,
        "labelConfig": str(DETECTION_LABELS_PATH) if DETECTION_LABELS_PATH else "",
        "labelError": _LABEL_ERROR,
    }

    status_payload.update(pose_model_status())
    return status_payload

@app.post("/reload", dependencies=[Depends(require_ai_server_auth)])
def reload_model():
    global model
    global pose_model
    global _MODEL_PATH
    global _MODEL_ERROR
    global _POSE_MODEL_PATH
    global _POSE_MODEL_ERROR
    global DETECTION_LABELS_PATH

    model = None
    pose_model = None
    _MODEL_PATH = ""
    _MODEL_ERROR = ""
    _POSE_MODEL_PATH = ""
    _POSE_MODEL_ERROR = ""
    DETECTION_LABELS_PATH = None

    current_model = get_model()
    names = getattr(current_model, "names", {}) or {} if current_model is not None else {}

    status_payload = {
        "ok": current_model is not None,
        "ready": current_model is not None,
        "modelAvailable": current_model is not None,
        "model": _MODEL_PATH,
        "modelError": _MODEL_ERROR,
        "expectedModel": str(DEFAULT_MODEL_PATH),
        "trainingRoot": str(TRAINING_ROOT),
        "classCount": len(names),
        "classes": list(names.values())[:80] if isinstance(names, dict) else names,
        "labelConfig": str(DETECTION_LABELS_PATH) if DETECTION_LABELS_PATH else "",
        "labelError": _LABEL_ERROR,
    }

    status_payload.update(pose_model_status())
    return status_payload