from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from transformers import CLIPFeatureExtractor
import numpy as np
import torch
from PIL import Image 
from typing import Optional, Tuple, Union

device = None
torch_device = None
torch_dtype = None
safety_checker = None
feature_extractor = None

def load_model():
    global device, torch_device, torch_dtype, safety_checker, feature_extractor

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    torch_device = device
    torch_dtype = torch.float16

    safety_checker = StableDiffusionSafetyChecker.from_pretrained(
        "CompVis/stable-diffusion-safety-checker"
    ).to(device)
    feature_extractor = CLIPFeatureExtractor.from_pretrained(
        "openai/clip-vit-base-patch32"
    )

def check(image):
    if not image:
        return None

    images = [image]
    safety_checker_input = feature_extractor(images, return_tensors="pt").to(device)
    images_np = [np.array(img) for img in images]

    _, has_nsfw_concepts = safety_checker(
        images=images_np,
        clip_input=safety_checker_input.pixel_values.to(torch_device),
    )

    return has_nsfw_concepts[0]