import numpy as np
from PIL import Image
from transformers import pipeline

_router = None


def init_router():
    global _router
    if _router is None:
        _router = pipeline("image-classification", model="Matthijs/mobilevit-small")


def detect_modality(image_path: str) -> str:
    """Hybrid modality detection: filename hints + visual + lightweight model"""
    name = image_path.lower()
    # 1️⃣ تحليل الاسم
    if any(k in name for k in ["xray", "chest", "lung"]):
        return "xray"
    if any(k in name for k in ["ct", "abdomen", "liver"]):
        return "ct"
    if any(k in name for k in ["ultrasound", "us", "sonogram"]):
        return "ultrasound"
    if any(k in name for k in ["mri", "brain", "spine"]):
        return "mri"
    if any(k in name for k in ["histopath", "slide", "micro"]):
        return "pathology"
    if any(k in name for k in ["skin", "derma"]):
        return "skin"
    if any(k in name for k in ["eye", "retina", "fundus"]):
        return "eye"
    if any(k in name for k in ["cardio", "echo", "heart"]):
        return "cardio"
    if any(k in name for k in ["msk", "musculoskeletal", "orthopedic"]):
        return "musculoskeletal"

    # 2️⃣ تحليل بصري بسيط
    with Image.open(image_path).convert("RGB") as img:
        arr = np.array(img)
    mean_sat = np.std(arr, axis=(0, 1)).mean()
    if mean_sat < 15:
        return "xray"
    elif mean_sat < 25:
        return "mri"
    elif mean_sat > 45:
        return "skin"

    # 3️⃣ نموذج صغير fallback
    if _router is None:
        init_router()
    pred = _router(image_path)[0]["label"].lower()
    valid = {
        "xray",
        "ct",
        "ultrasound",
        "mri",
        "pathology",
        "skin",
        "eye",
        "cardio",
        "musculoskeletal",
    }
    return "general" if pred not in valid else pred