Update app.py

app.py

@@ -72,6 +72,10 @@ def update_va_table(category, venue, action, search):
     display_df['action'] = display_df['action'].map(ACTION_LABELS)
     return display_df, f"**📊 Statistics:** Total {len(df)} models | Categories: {df['category'].nunique()}"
 
+# 初始化VA表格数据
+def init_va_table():
+    return update_va_table("All", "", "All", "")
+
 def create_va_plot(category):
     df = df_va.copy()
     if category != "All":
@@ -133,6 +137,10 @@ def update_vla_table(category, venue, language, search):
     display_df['action'] = display_df['action'].map(ACTION_LABELS)
     return display_df, f"**📊 Statistics:** Total {len(df)} models | Categories: {df['category'].nunique()}"
 
+# 初始化VLA表格数据
+def init_vla_table():
+    return update_vla_table("All", "", "", "")
+
 def create_vla_plot(category):
     df = df_vla.copy()
     if category != "All":
@@ -191,6 +199,10 @@ def update_datasets_table(category, year, search):
         df = df[mask]
     return df, f"**📊 Statistics:** Total {len(df)} datasets | Years: {df['year'].nunique()}"
 
+# 初始化Datasets表格数据
+def init_datasets_table():
+    return update_datasets_table("All", "", "")
+
 # Benchmark tables update functions
 def update_nuscenes_table(category, search):
     df = df_nuscenes.copy()
@@ -201,6 +213,10 @@ def update_nuscenes_table(category, search):
         df = df[mask]
     return df, f"**📊 Statistics:** Total {len(df)} models"
 
+# 初始化nuScenes表格数据
+def init_nuscenes_table():
+    return update_nuscenes_table("All", "")
+
 def create_nuscenes_plot(category):
     """创建nuScenes统计图表"""
     df = df_nuscenes.copy()
@@ -259,6 +275,10 @@ def update_wod_table(category, search):
         df = df[mask]
     return df, f"**📊 Statistics:** Total {len(df)} models"
 
+# 初始化WOD表格数据
+def init_wod_table():
+    return update_wod_table("All", "")
+
 def create_wod_plot(category):
     """创建WOD统计图表"""
     df = df_wod.copy()
@@ -323,6 +343,10 @@ def update_navsim_table(category, search):
         df = df[mask]
     return df, f"**📊 Statistics:** Total {len(df)} models"
 
+# 初始化NAVSIM表格数据
+def init_navsim_table():
+    return update_navsim_table("All", "")
+
 def create_navsim_plot(category):
     """创建NAVSIM统计图表"""
     df = df_navsim.copy()
@@ -381,6 +405,10 @@ def update_bench2drive_table(category, search):
         df = df[mask]
     return df, f"**📊 Statistics:** Total {len(df)} models"
 
+# 初始化Bench2Drive表格数据
+def init_bench2drive_table():
+    return update_bench2drive_table("All", "")
+
 def create_bench2drive_plot(category):
     """创建Bench2Drive统计图表"""
     df = df_bench2drive.copy()
@@ -430,7 +458,15 @@ def create_bench2drive_plot(category):
     plt.close('all')
     return result
 
-# 预生成初始图表
+# 预生成初始数据和图表
+initial_va_df, initial_va_stats = init_va_table()
+initial_vla_df, initial_vla_stats = init_vla_table()
+initial_datasets_df, initial_datasets_stats = init_datasets_table()
+initial_nuscenes_df, initial_nuscenes_stats = init_nuscenes_table()
+initial_wod_df, initial_wod_stats = init_wod_table()
+initial_navsim_df, initial_navsim_stats = init_navsim_table()
+initial_bench2drive_df, initial_bench2drive_stats = init_bench2drive_table()
+
 initial_va_plot = create_va_plot("All")
 initial_vla_plot = create_vla_plot("All")
 initial_nuscenes_plot = create_nuscenes_plot("All")
@@ -447,14 +483,14 @@ with gr.Blocks(css="#title {text-align: center;} .gradio-container {max-width: 1
         # Tab 1: VA Models
         with gr.Tab("Vision-Action Models (VA)"):
             gr.Markdown("### Table 1: Vision-Action Models in Autonomous Driving")
-            va_stats = gr.Markdown(f"**📊 Statistics:** Total {len(df_va)} models")
+            va_stats = gr.Markdown(initial_va_stats)
             with gr.Row():
                 va_category = gr.Dropdown(label="Category", choices=["All"] + sorted(df_va['category'].unique().tolist()), value="All")
                 va_venue = gr.Textbox(label="Filter by Venue", placeholder="e.g., CVPR, ICCV...")
                 va_action = gr.Dropdown(label="Action Type", choices=["All"] + sorted(df_va['action'].unique().tolist()), value="All")
                 va_search = gr.Textbox(label="Search", placeholder="e.g., TransFuser...")
             va_update_btn = gr.Button("🔍 Update", variant="primary")
-            va_table = gr.Dataframe(value=df_va[['id', 'model', 'venue', 'input', 'dataset', 'vision', 'action', 'output', 'category']], label="VA Models", interactive=False, wrap=True)
+            va_table = gr.Dataframe(value=initial_va_df, label="VA Models", interactive=False, wrap=True)
             va_plot = gr.Plot(label="Statistics", value=initial_va_plot, format="png")
             va_update_btn.click(fn=lambda cat, ven, act, search: (*update_va_table(cat, ven, act, search), create_va_plot(cat)),
                                inputs=[va_category, va_venue, va_action, va_search], outputs=[va_table, va_stats, va_plot])
@@ -462,14 +498,14 @@ with gr.Blocks(css="#title {text-align: center;} .gradio-container {max-width: 1
         # Tab 2: VLA Models
         with gr.Tab("Vision-Language-Action Models (VLA)"):
             gr.Markdown("### Table 3: Vision-Language-Action Models in Autonomous Driving")
-            vla_stats = gr.Markdown(f"**📊 Statistics:** Total {len(df_vla)} models")
+            vla_stats = gr.Markdown(initial_vla_stats)
             with gr.Row():
                 vla_category = gr.Dropdown(label="Category", choices=["All"] + sorted(df_vla['category'].unique().tolist()), value="All")
                 vla_venue = gr.Textbox(label="Filter by Venue", placeholder="e.g., CVPR...")
                 vla_language = gr.Textbox(label="Filter by Language Model", placeholder="e.g., Qwen...")
                 vla_search = gr.Textbox(label="Search", placeholder="e.g., AutoVLA...")
             vla_update_btn = gr.Button("🔍 Update", variant="primary")
-            vla_table = gr.Dataframe(value=df_vla[['id', 'model', 'venue', 'input', 'dataset', 'vision', 'language', 'action', 'output', 'category']], label="VLA Models", interactive=False, wrap=True)
+            vla_table = gr.Dataframe(value=initial_vla_df, label="VLA Models", interactive=False, wrap=True)
             vla_plot = gr.Plot(label="Statistics", value=initial_vla_plot, format="png")
             vla_update_btn.click(fn=lambda cat, ven, lang, search: (*update_vla_table(cat, ven, lang, search), create_vla_plot(cat)),
                                 inputs=[vla_category, vla_venue, vla_language, vla_search], outputs=[vla_table, vla_stats, vla_plot])
@@ -477,25 +513,25 @@ with gr.Blocks(css="#title {text-align: center;} .gradio-container {max-width: 1
         # Tab 3: Datasets
         with gr.Tab("Datasets & Benchmarks"):
             gr.Markdown("### Table 4: Summary of Datasets & Benchmarks")
-            datasets_stats = gr.Markdown(f"**📊 Statistics:** Total {len(df_datasets)} datasets")
+            datasets_stats = gr.Markdown(initial_datasets_stats)
             with gr.Row():
                 datasets_category = gr.Dropdown(label="Category", choices=["All"] + sorted(df_datasets['category'].unique().tolist()), value="All")
                 datasets_year = gr.Textbox(label="Filter by Year", placeholder="e.g., 2024...")
                 datasets_search = gr.Textbox(label="Search", placeholder="e.g., nuScenes...")
             datasets_update_btn = gr.Button("🔍 Update", variant="primary")
-            datasets_table = gr.Dataframe(value=df_datasets, label="Datasets", interactive=False, wrap=True)
+            datasets_table = gr.Dataframe(value=initial_datasets_df, label="Datasets", interactive=False, wrap=True)
             datasets_update_btn.click(fn=update_datasets_table, inputs=[datasets_category, datasets_year, datasets_search], outputs=[datasets_table, datasets_stats])
         
         # Tab 4: nuScenes
         with gr.Tab("nuScenes Open-Loop"):
             gr.Markdown("### Table 5: Open-Loop Planning Results on nuScenes")
             gr.Markdown("**Metrics:** L2 Error (m) ↓ | Collision Rate ↓")
-            nuscenes_stats = gr.Markdown(f"**📊 Statistics:** Total {len(df_nuscenes)} models")
+            nuscenes_stats = gr.Markdown(initial_nuscenes_stats)
             with gr.Row():
                 nuscenes_category = gr.Dropdown(label="Category", choices=["All", "Vision-Action", "Vision-Language-Action"], value="All")
                 nuscenes_search = gr.Textbox(label="Search Model", placeholder="e.g., UniAD, EMMA...")
             nuscenes_update_btn = gr.Button("🔍 Update", variant="primary")
-            nuscenes_table = gr.Dataframe(value=df_nuscenes, label="nuScenes Results", interactive=False, wrap=True)
+            nuscenes_table = gr.Dataframe(value=initial_nuscenes_df, label="nuScenes Results", interactive=False, wrap=True)
             nuscenes_plot = gr.Plot(label="Statistics", value=initial_nuscenes_plot, format="png")
             nuscenes_update_btn.click(fn=lambda cat, search: (*update_nuscenes_table(cat, search), create_nuscenes_plot(cat)),
                                      inputs=[nuscenes_category, nuscenes_search], 
@@ -505,12 +541,12 @@ with gr.Blocks(css="#title {text-align: center;} .gradio-container {max-width: 1
         with gr.Tab("WOD-E2E"):
             gr.Markdown("### Table 6: Results on WOD-E2E Test Split")
             gr.Markdown("**Metrics:** RFS (Overall/Spotlight) ↑ | ADE (5s/3s) ↓")
-            wod_stats = gr.Markdown(f"**📊 Statistics:** Total {len(df_wod)} models")
+            wod_stats = gr.Markdown(initial_wod_stats)
             with gr.Row():
                 wod_category = gr.Dropdown(label="Category", choices=["All", "Vision-Action", "Vision-Language-Action"], value="All")
                 wod_search = gr.Textbox(label="Search Model", placeholder="e.g., AutoVLA...")
             wod_update_btn = gr.Button("🔍 Update", variant="primary")
-            wod_table = gr.Dataframe(value=df_wod, label="WOD-E2E Results", interactive=False, wrap=True)
+            wod_table = gr.Dataframe(value=initial_wod_df, label="WOD-E2E Results", interactive=False, wrap=True)
             wod_plot = gr.Plot(label="Statistics", value=initial_wod_plot, format="png")
             wod_update_btn.click(fn=lambda cat, search: (*update_wod_table(cat, search), create_wod_plot(cat)),
                                 inputs=[wod_category, wod_search], 
@@ -520,12 +556,12 @@ with gr.Blocks(css="#title {text-align: center;} .gradio-container {max-width: 1
         with gr.Tab("NAVSIM Closed-Loop"):
             gr.Markdown("### Table 7: Closed-Loop Results on NAVSIM")
             gr.Markdown("**Metrics:** NC/DAC/TTC/Comf/EP/PDMS ↑")
-            navsim_stats = gr.Markdown(f"**📊 Statistics:** Total {len(df_navsim)} models")
+            navsim_stats = gr.Markdown(initial_navsim_stats)
             with gr.Row():
                 navsim_category = gr.Dropdown(label="Category", choices=["All", "Vision-Action", "Vision-Language-Action"], value="All")
                 navsim_search = gr.Textbox(label="Search Model", placeholder="e.g., ReflectDrive...")
             navsim_update_btn = gr.Button("🔍 Update", variant="primary")
-            navsim_table = gr.Dataframe(value=df_navsim, label="NAVSIM Results", interactive=False, wrap=True)
+            navsim_table = gr.Dataframe(value=initial_navsim_df, label="NAVSIM Results", interactive=False, wrap=True)
             navsim_plot = gr.Plot(label="Statistics", value=initial_navsim_plot, format="png")
             navsim_update_btn.click(fn=lambda cat, search: (*update_navsim_table(cat, search), create_navsim_plot(cat)),
                                    inputs=[navsim_category, navsim_search], 
@@ -535,12 +571,12 @@ with gr.Blocks(css="#title {text-align: center;} .gradio-container {max-width: 1
         with gr.Tab("Bench2Drive"):
             gr.Markdown("### Table 8: Closed-Loop & Open-Loop Results on Bench2Drive")
             gr.Markdown("**Metrics:** DS/SR ↑ | L2 Avg ↓")
-            bench2drive_stats = gr.Markdown(f"**📊 Statistics:** Total {len(df_bench2drive)} models")
+            bench2drive_stats = gr.Markdown(initial_bench2drive_stats)
             with gr.Row():
                 bench2drive_category = gr.Dropdown(label="Category", choices=["All", "Vision-Action", "Vision-Language-Action"], value="All")
                 bench2drive_search = gr.Textbox(label="Search Model", placeholder="e.g., SimLingo...")
             bench2drive_update_btn = gr.Button("🔍 Update", variant="primary")
-            bench2drive_table = gr.Dataframe(value=df_bench2drive, label="Bench2Drive Results", interactive=False, wrap=True)
+            bench2drive_table = gr.Dataframe(value=initial_bench2drive_df, label="Bench2Drive Results", interactive=False, wrap=True)
             bench2drive_plot = gr.Plot(label="Statistics", value=initial_bench2drive_plot, format="png")
             bench2drive_update_btn.click(fn=lambda cat, search: (*update_bench2drive_table(cat, search), create_bench2drive_plot(cat)),
                                         inputs=[bench2drive_category, bench2drive_search],