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Update app.py
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app.py
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@@ -8,11 +8,16 @@ from PIL import Image
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# number convert to label
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labels = ["Drawings", "Hentai", "Neutral", "Porn", "Sexy"]
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description = """### NSFW 图片分类器 🚀
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# Define CNN model
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class Classifier(nn.Module):
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def __init__(self):
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@@ -54,18 +59,57 @@ def predict(image_path):
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return result
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# 美化 Gradio 界面
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gr.Markdown("# 🌟 NSFW 图片分类器")
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gr.Markdown(description)
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(type="filepath", label="📷 上传图片")
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submit_btn = gr.Button("🚀 运行检测", variant="primary")
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with gr.Column():
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output_label = gr.Label(num_top_classes=2, label="📊 预测结果")
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submit_btn.click(predict, inputs=image_input, outputs=output_label)
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# 启动 Web 界面
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# number convert to label
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labels = ["Drawings", "Hentai", "Neutral", "Porn", "Sexy"]
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description = """### NSFW 图片分类器 🚀
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该模型可以分类NSFW(非工作安全)图片,支持以下类别:
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- **Drawings**:绘画图像
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- **Hentai**:色情漫画
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- **Neutral**:中立图像
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- **Porn**:成人内容
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- **Sexy**:性感内容
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上传一张图片,我们将为您预测其类别。
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更多信息请访问 [GitHub](https://github.com/csuer411/nsfw_classify)"""
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# Define CNN model
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class Classifier(nn.Module):
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def __init__(self):
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return result
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# 美化 Gradio 界面
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css = """
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.gradio-container {
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font-family: 'Arial', sans-serif;
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background-color: #f0f4f8;
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}
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.gr-button {
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background-color: #4CAF50;
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color: white;
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font-weight: bold;
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}
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.gr-button:hover {
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background-color: #45a049;
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}
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.gr-label {
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font-size: 1.2em;
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font-weight: bold;
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}
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.gr-image {
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border: 2px solid #4CAF50;
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border-radius: 8px;
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}
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.gr-row {
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margin-bottom: 20px;
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}
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.gr-markdown {
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color: #333;
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}
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.gr-input {
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border-radius: 5px;
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}
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footer {visibility: hidden}
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"""
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with gr.Blocks(theme="huggingface", css=css) as demo:
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gr.Markdown("# 🌟 NSFW 图片分类器")
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gr.Markdown(description)
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(type="filepath", label="📷 上传图片", elem_id="input-image")
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submit_btn = gr.Button("🚀 运行检测", variant="primary")
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with gr.Column():
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output_label = gr.Label(num_top_classes=2, label="📊 预测结果")
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gr.Markdown("#### 示例图片")
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examples = [
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"./example/anime.jpg",
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"./example/real.jpg"
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]
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gr.Image(examples=examples, type="filepath", label="点击上传示例图片", elem_id="example-images")
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submit_btn.click(predict, inputs=image_input, outputs=output_label)
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# 启动 Web 界面
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