app.py

import gradio as gr
import torch
import torch.nn as nn
from torchvision import transforms
from torchvision.models import resnet18, ResNet18_Weights
from PIL import Image

# 配置参数
labels = ["Drawings", "Hentai", "Neutral", "Porn", "Sexy"]
theme_color = "#6C5B7B"  # 主色调改为优雅的紫色
description = """<div style="padding: 20px; background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%); border-radius: 10px;">
<h2 style="color: {color}; margin-bottom: 15px;">🎨 NSFW 图片分类器</h2>
<p>该模型使用深度神经网络对图片内容进行分类，支持以下类别：</p>
<ul style="list-style-type: circle; padding-left: 25px;">
  <li><span style="color: #4B4453;">Drawings</span> - 艺术绘画作品</li>
  <li><span style="color: #845EC2;">Hentai</span> - 二次元成人内容</li>
  <li><span style="color: #008F7A;">Neutral</span> - 日常安全内容</li>
  <li><span style="color: #D65DB1;">Porn</span> - 露骨成人内容</li>
  <li><span style="color: #FF9671;">Sexy</span> - 性感但不露骨内容</li>
</ul>
<p style="margin-top: 15px;">🖼️ 请上传图片或点击下方示例体验</p>
</div>""".format(color=theme_color)

# 模型定义和预处理（保持不变）
# ... [保持原有模型代码不变] ...

# 高级 CSS 样式
advanced_css = f"""
.gradio-container {{
    background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%);
    min-height: 100vh;
}}
.header-section {{
    background: white;
    padding: 2rem;
    border-radius: 15px;
    box-shadow: 0 4px 6px rgba(0,0,0,0.05);
    margin-bottom: 2rem;
}}
.result-card {{
    background: white !important;
    padding: 1.5rem !important;
    border-radius: 12px !important;
    box-shadow: 0 2px 8px rgba(108,91,123,0.1) !important;
}}
.custom-button {{
    background: {theme_color} !important;
    color: white !important;
    border: none !important;
    padding: 12px 28px !important;
    border-radius: 25px !important;
    transition: all 0.3s ease !important;
}}
.custom-button:hover {{
    transform: translateY(-2px);
    box-shadow: 0 4px 12px rgba(108,91,123,0.3) !important;
}}
.upload-box {{
    border: 2px dashed {theme_color} !important;
    border-radius: 15px !important;
    background: rgba(255,255,255,0.9) !important;
}}
.example-card {{
    cursor: pointer;
    transition: all 0.3s ease;
    border-radius: 12px;
    overflow: hidden;
}}
.example-card:hover {{
    transform: scale(1.02);
    box-shadow: 0 4px 12px rgba(108,91,123,0.2);
}}
.prob-bar {{
    height: 8px;
    border-radius: 4px;
    background: linear-gradient(90deg, {theme_color} 0%, #C06C84 100%);
}}
"""
# Define CNN model
class Classifier(nn.Module):
    def __init__(self):
        super(Classifier, self).__init__()
        self.cnn_layers = resnet18(weights=ResNet18_Weights.DEFAULT)
        self.fc_layers = nn.Sequential(
            nn.Linear(1000, 512),
            nn.Dropout(0.3),
            nn.Linear(512, 128),
            nn.ReLU(),
            nn.Linear(128, 5),
        )

    def forward(self, x):
        x = self.cnn_layers(x)
        x = self.fc_layers(x)
        return x

# Pre-process
preprocess = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Load model
model = Classifier()
model.load_state_dict(torch.load("classify_nsfw_v3.0.pth", map_location="cpu"))
model.eval()

def predict(image_path):
    img = Image.open(image_path).convert("RGB")
    img = preprocess(img).unsqueeze(0)

    with torch.no_grad():
        prediction = torch.nn.functional.softmax(model(img)[0], dim=0)

    result = {labels[i]: float(prediction[i]) for i in range(5)}
    return result
with gr.Blocks(theme=gr.themes.Soft(), css=advanced_css) as demo:
    # 标题区
    with gr.Column(elem_classes="header-section"):
        gr.Markdown("# 🎭 智能内容识别系统", elem_id="main-title")
        gr.HTML(description)
    
    # 主功能区
    with gr.Row():
        # 输入列
        with gr.Column(scale=2):
            upload_box = gr.Image(
                type="filepath",
                label="📤 上传图片",
                elem_id="upload-box",
                elem_classes="upload-box",
                height=400
            )
            with gr.Row():
                submit_btn = gr.Button(
                    "✨ 开始分析",
                    elem_classes="custom-button",
                    size="lg"
                )
                clear_btn = gr.Button(
                    "🔄 重新上传",
                    variant="secondary",
                    size="lg"
                )

        # 输出列
        with gr.Column(scale=1):
            with gr.Column(elem_classes="result-card"):
                gr.Markdown("### 🔍 分析结果")
                result_display = gr.Label(
                    label="分类概率分布",
                    num_top_classes=3,
                    show_label=False
                )
                gr.Markdown("**最高概率类别**: <span id='top-class'></span>", elem_id="dynamic-text")
            
            # 示例区
            with gr.Column():
                gr.Markdown("### 🖼️ 示例图片")
                examples = gr.Examples(
                    examples=["./example/anime.jpg", "./example/real.jpg"],
                    inputs=upload_box,
                    examples_per_page=2,
                    label="点击使用示例",
                    elem_id="example-gallery"
                )

    # 交互逻辑
    clear_btn.click(fn=lambda: None, inputs=None, outputs=upload_box)
    submit_btn.click(
        fn=predict,
        inputs=upload_box,
        outputs=result_display,
        api_name="predict"
    )

# 启动界面
demo.launch()