app.py

from transformers import pipeline
import gradio as gr
import os
import requests
import timm
import torch
import json

nsfw_tf = pipeline(model="carbon225/vit-base-patch16-224-hentai")

if not os.path.exists("timm.ckpt"):
    open("timm.ckpt", "wb").write(
        requests.get(
            "https://huggingface.co/deepghs/anime_rating/resolve/main/caformer_s36_plus/model.ckpt"
        ).content
    )
    open("timmcfg.json", "wb").write(
        requests.get(
            "https://huggingface.co/deepghs/anime_rating/resolve/main/caformer_s36_plus/meta.json"
        ).content
    )
else:
    print("Model already exists, skipping redownload")

with open("timmcfg.json") as file:
    tm_cfg = json.load(file)

nsfw_tm = timm.create_model(
    "caformer_s36.sail_in22k_ft_in1k_384",
    checkpoint_path="./timm.ckpt",
    model_config=tm_cfg,
    pretrained=True
).eval()
tm_config = timm.data.resolve_model_data_config(nsfw_tm)
tm_trans = timm.data.create_transform(**tm_config)


def launch(img):
    weight = 0
    img = Image.open(img).convert('RGB')
    tm_output = model.pretrained_cfg['labels'][
        torch.argmax(
            torch.nn.functional.softmax(
                nsfw_tm(transforms(img).unsqueeze(0))[0], dim=0
            )
        )
    ]
    
    match tm_output:
        case "safe":
            weight -= 2
        case "r15":
            weight += 1
        case "r18":
            weight += 2


    tf_output = nsfw_tf(img)[0]["label"]

    match tf_output:
        case "safe":
            weight -= 2
        case "suggestive":
            weight += 1
        case "r18":
            weight += 2

    return weight > 0

app = gr.Interface(fn=generate, inputs="image", outputs="text")
app.launch()