app.py

import gradio as gr
import csv
import os
from pathlib import Path

import cv2
import numpy as np
from PIL import Image
import onnxruntime as ort
from huggingface_hub import hf_hub_download
import spaces

# 画像のサイズ設定
IMAGE_SIZE = 448

def preprocess_image(image):
    image = np.array(image)
    image = image[:, :, ::-1]  # BGRからRGBへ変換

    # 画像を正方形にするためのパディングを追加
    size = max(image.shape[0:2])
    pad_x = size - image.shape[1]
    pad_y = size - image.shape[0]
    pad_l = pad_x // 2
    pad_t = pad_y // 2
    image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)

    # サイズに合わせた補間方法を選択
    interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
    image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
    image = image.astype(np.float32)
    return image

@spaces.GPU
def process_image(image_path, input_name, ort_sess, rating_tags, character_tags, general_tags):
    thresh = 0.35
    try:
        image = Image.open(image_path)
        image = image.convert("RGB") if image.mode != "RGB" else image
        image = preprocess_image(image)
    except Exception as e:
        print(f"画像を読み込めません: {image_path}, エラー: {e}")
        return

    img = np.array([image])
    prob = ort_sess.run(None, {input_name: img})[0][0]  # ONNXモデルからの出力

    # NSFW/SFW判定
    tag_confidences = {tag: prob[i] for i, tag in enumerate(rating_tags)}
    max_nsfw_score = max(tag_confidences.get("questionable", 0), tag_confidences.get("explicit", 0))
    max_sfw_score = tag_confidences.get("general", 0)
    NSFW_flag = None

    if max_nsfw_score > max_sfw_score:
        NSFW_flag = "NSFWの可能性が高いです"
    else:
        NSFW_flag = "SFWの可能性が高いです"

    # 版権キャラクターの可能性を評価
    character_tags_with_probs = []
    for i, p in enumerate(prob[4:]):
        if p >= thresh and i >= len(general_tags):
            tag_index = i - len(general_tags)
            if tag_index < len(character_tags):
                tag_name = character_tags[tag_index]
                prob_percent = round(p * 100, 2)  # 確率をパーセンテージに変換
                character_tags_with_probs.append((tag_name, f"{prob_percent}%"))

    IP_flag = None
    if character_tags_with_probs:
        IP_flag = f"版権キャラクター: {character_tags_with_probs}の可能性があります"
    else:
        IP_flag = "版権キャラクターの可能性が低いと思われます"

     # タグを生成
    tag_freq = {}
    undesired_tags = []     
    combined_tags = []
    general_tag_text = ""
    character_tag_text = ""
    remove_underscore = True
    caption_separator = ", "
    general_threshold = 0.35
    character_threshold = 0.35

    for i, p in enumerate(prob[4:]):
        if i < len(general_tags) and p >= general_threshold:
            tag_name = general_tags[i]
            if remove_underscore and len(tag_name) > 3:  # ignore emoji tags like >_< and ^_^
                tag_name = tag_name.replace("_", " ")

            if tag_name not in undesired_tags:
                tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1
                general_tag_text += caption_separator + tag_name
                combined_tags.append(tag_name)
        elif i >= len(general_tags) and p >= character_threshold:
            tag_name = character_tags[i - len(general_tags)]
            if remove_underscore and len(tag_name) > 3:
                tag_name = tag_name.replace("_", " ")

            if tag_name not in undesired_tags:
                tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1
                character_tag_text += caption_separator + tag_name
                combined_tags.append(tag_name)

    # 先頭のカンマを取る
    if len(general_tag_text) > 0:
        general_tag_text = general_tag_text[len(caption_separator) :]
    if len(character_tag_text) > 0:
        character_tag_text = character_tag_text[len(caption_separator) :]
    tag_text = caption_separator.join(combined_tags)
 
    return NSFW_flag, IP_flag, tag_text


class webui:
    def __init__(self):
        self.demo = gr.Blocks()

    @spaces.GPU
    def main(self, image_path, model_id):
        print("Hugging Faceからモデルをダウンロード中")
        onnx_path = hf_hub_download(model_id, "model.onnx")
        csv_path = hf_hub_download(model_id, "selected_tags.csv")

        print("ONNXモデルを実行中")
        print(f"ONNXモデルのパス: {onnx_path}")

        ort_sess = ort.InferenceSession(onnx_path)

        with open(csv_path, "r", encoding="utf-8") as f:
            reader = csv.reader(f)
            header = next(reader)
            rows = list(reader)
        assert header == ["tag_id", "name", "category", "count"], f"CSVフォーマットが期待と異なります: {header}"

        rating_tags = [row[1] for row in rows if row[2] == "9"]
        character_tags = [row[1] for row in rows if row[2] == "4"]
        general_tags = [row[1] for row in rows[1:] if row[2] == "0"]

        NSFW_flag, IP_flag, tag_text = process_image(image_path, ort_sess.get_inputs()[0].name, ort_sess, rating_tags, character_tags, general_tags)
        return NSFW_flag, IP_flag, tag_text


    def launch(self):
        with self.demo:
            with gr.Row():
                with gr.Column():
                    input_image = gr.Image(type='filepath', label="Analysis Image")
                    model_id = gr.Textbox(label="NSFW Flag", value="SmilingWolf/wd-vit-tagger-v3")
                    output_0 = gr.Textbox(label="NSFW Flag")
                    output_1 = gr.Textbox(label="IP Flag")
                    output_2 = gr.Textbox(label="Tags")
                    submit = gr.Button(value="Start Analysis")
                    
                    submit.click(
                        self.main, 
                        inputs=[input_image, model_id], 
                        outputs=[output_0, output_1, output_2]
                    )

        self.demo.launch()

if __name__ == "__main__":
    ui = webui()
    ui.launch()