import gradio as gr import pandas as pd from constants import * # ... 其他导入 ... # 定义自定义 CSS custom_css = """ h1 { /* 根据需要选择正确的标题标签 */ background-color: blue; /* 蓝色背景 */ color: white; /* 白色文字 */ padding: 10px; /* 内边距 */ text-align: center; /* 文本居中 */ } h2 { /* 根据需要选择正确的标题标签 */ color: white; /* 白色文字 */ padding: 10px; /* 内边距 */ text-align: center; /* 文本居中 */ } """ def get_preview_data(): df = pd.read_json(DATA_DIR) df=df.head(4) return df def get_result_data(): df={ "DataSet": ["WikiData_recent", "WikiData_recent", "WikiData_recent", "WikiData_recent", "ZsRE", "ZsRE", "ZsRE", "ZsRE", "WikiBio", "WikiBio", "WikiBio", "WikiData_counterfact", "WikiData_counterfact", "WikiData_counterfact", "WikiData_counterfact", "ConvSent", "ConvSent", "ConvSent", "Sanitation", "Sanitation", "Sanitation"], "Metric": ["Edit Succ. ↑", "Portability ↑", "Locality ↑", "Fluency ↑", "Edit Succ. ↑", "Portability ↑", "Locality ↑", "Fluency ↑", "Edit Succ. ↑", "Locality ↑", "Fluency ↑", "Edit Succ. ↑", "Portability ↑", "Locality ↑", "Fluency ↑", "Edit Succ. ↑", "Locality ↓", "Fluency ↑", "Edit Succ. ↑", "Locality ↑", "Fluency ↑"], "SERAC": [98.68, 63.52, 100.00, 553.19, 99.67, 56.48, 30.23, 410.89, 99.69, 69.79, 606.95, 99.99, 76.07, 98.96, 549.91, 62.75, 0.26, 458.21, 0.00, 100.00, 416.29], "ICE": [60.74, 36.93, 33.34, 531.01, 66.01, 63.94, 23.14, 541.14, 95.53, 47.90, 632.92, 69.83, 45.32, 32.38, 547.22, 52.78, 49.73, 621.45, 72.50, 56.58, 794.15], "AdaLoRA": [65.61, 47.22, 55.78, 537.51, 69.86, 52.95, 72.21, 532.82, 97.02, 57.87, 615.86, 72.14, 55.17, 66.78, 553.85, 44.89, 0.18, 606.42, 2.50, 65.50, 330.44], "MEND": [76.88, 50.11, 92.87, 586.34, 96.74, 60.41, 92.79, 524.33, 93.66, 69.51, 609.39, 78.82, 57.53, 94.16, 588.94, 50.76, 3.42, 379.43, 0.00, 5.29, 407.18], "ROME": [85.08, 37.45, 66.2, 574.28, 96.57, 52.20, 27.14, 570.47, 95.05, 46.96, 617.25, 83.21, 38.69, 65.4, 578.84, 45.79, 0.00, 606.32, 85.00, 50.31, 465.12], "MEMIT": [85.32, 37.94, 64.78, 566.66, 83.07, 51.43, 25.46, 559.72, 94.29, 51.56, 616.65, 83.41, 40.09, 63.68, 568.58, 44.75, 0.00, 602.62, 48.75, 67.47, 466.10], "FT-L": [71.18, 48.71, 63.7, 549.35, 54.65, 45.02, 71.12, 474.18, 83.41, 40.09, 63.68, 568.58, 66.27, 60.14, 604.00, 51.12, 39.07, 62.51, 48.75, 67.47, 466.10] } df=pd.DataFrame(df) return df block = gr.Blocks(css=custom_css) # 应用自定义 CSS with block: gr.Markdown(TITLE) gr.Markdown("## BACKGROUND") gr.Markdown( BACKGROUND ) gr.Image('./img/demo.gif') gr.Markdown("## DATA PREVIEW") gr.Markdown(LEADERBORAD_INTRODUCTION) with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("🏅 Data preview ", elem_id="ke-benchmark-tab-table", id=0): # 创建数据帧组件 ke_data_component = gr.components.Dataframe( value=get_preview_data(), headers=DATA_COLUMN_NAMES, type="pandas", ) with gr.TabItem("data Structure", elem_id="about-struct-tab-table", id=3): gr.Markdown(DATA_STRUCT, elem_classes="markdown-text") with gr.TabItem("📝 data schema", elem_id="about-benchmark-tab-table", id=4): gr.Markdown(DATA_SCHEMA, elem_classes="markdown-text") gr.Markdown("## EXPERIMENT RESULTS") gr.Markdown("We list the results of current knowledge editing methods on Llama2-7b-chat in Table") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("🏅 result", elem_id="ke-benchmark-tab-table", id=0): # 创建数据帧组件 ke_data_component = gr.components.Dataframe( value=get_result_data(), headers=RESULT_COLUMN_NAMES, type="pandas", ) # About tab with gr.TabItem("📝 About", elem_id="about-benchmark-tab-table", id=4): gr.Markdown("Results of existing knowledge edit methods on the constructed benchmark. The symbol indicates that higher numbers correspond to better performance, while ↓ denotes the opposite, with lower numbers indicating better performance. For WikiBio and Convsent, we do not test the portability as they are about specific topics. ", elem_classes="markdown-text") with gr.Row(): with gr.Accordion("Citation", open=False): citation_button = gr.Textbox( value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, elem_id="citation-button", ).style(show_copy_button=True) block.launch(share=True)