Update app.py

app.py

@@ -3,6 +3,7 @@ os.system("pip install gradio==4.31.5 pydantic==2.7.1 gradio_modal==0.0.3")
 
 import gradio as gr
 import pandas as pd
+import re
 from apscheduler.schedulers.background import BackgroundScheduler
 from huggingface_hub import snapshot_download
 from gradio_space_ci import enable_space_ci
@@ -49,6 +50,7 @@ from gradio_modal import Modal
 import plotly.graph_objects as go
 
 selected_indices = []
+selected_values = {}
 
 # Start ephemeral Spaces on PRs (see config in README.md)
 #enable_space_ci()
@@ -111,6 +113,7 @@ def restart_space():
 
 
 def init_space(full_init: bool = True):
+    
     if full_init:
         try:
             branch = REPO.active_branch.name
@@ -284,19 +287,44 @@ def filter_models(
 
 def select(df, data: gr.SelectData):
     global selected_indices
-    selected_index = data.index[0]  
+    global selected_values
     
+    selected_index = data.index[0]
     if selected_index in selected_indices:
-        selected_indices.remove(selected_index) 
+        selected_indices.remove(selected_index)
+        
+        value = df.iloc[selected_index].iloc[1]
+        pattern = r'<a[^>]+>([^<]+)</a>'
+        match = re.search(pattern, value)
+        if match:
+            text_content = match.group(1)
+            if text_content in selected_values:
+                del selected_values[text_content]
     else:
-        selected_indices.append(selected_index)  
+        selected_indices.append(selected_index)
+
+        value = df.iloc[selected_index].iloc[1]
+        pattern = r'<a[^>]+>([^<]+)</a>'
+        match = re.search(pattern, value)
+        if match:
+            text_content = match.group(1)
+            selected_values[text_content] = value
+
+    print('selected_values', selected_values, selected_indices)
+    return gr.CheckboxGroup(list(selected_values.keys()))
+
+def generate_spider_chart(df, selected_keys):
+    global selected_values
+    print('generate_spider_chart', selected_values, selected_keys)
+    current_selected_values = [selected_values[key] for key in selected_keys if key in selected_values]
+    selected_rows = df[df.iloc[:, 1].isin(current_selected_values)]
+
 
     fig = go.Figure()
-    for i in selected_indices:
-        row = df.iloc[i, :]
+    for _, row in selected_rows.iterrows():
         fig.add_trace(go.Scatterpolar(
             r=[row['Average ⬆️'], row['ARC-c'], row['ARC-e'], row['Boolq'], row['HellaSwag'], row['Lambada'], row['MMLU'], row['Openbookqa'], row['Piqa'], row['Truthfulqa'], row['Winogrande']],
-            theta=['Average ⬆️', 'ARC-c', 'ARC-e', 'Boolq', 'HellaSwag', 'Lambada', 'MMLU', 'Openbookqa', 'Piqa', 'Truthfulqa', 'Winogrande',],
+            theta=['Average ⬆️', 'ARC-c', 'ARC-e', 'Boolq', 'HellaSwag', 'Lambada', 'MMLU', 'Openbookqa', 'Piqa', 'Truthfulqa', 'Winogrande'],
             fill='toself',
             name=str(row['Model'])  
         ))
@@ -308,7 +336,6 @@ def select(df, data: gr.SelectData):
         showlegend=True
     )
     
-
     return fig    
 
 leaderboard_df = filter_models(
@@ -396,7 +423,11 @@ with demo:
                             filter_columns_weightDtype = gr.Dropdown(choices=[i.value.name for i in WeightDtype], label="Weight Dtype", multiselect=False, value="All", interactive=True,)
                             filter_columns_doubleQuant = gr.Dropdown(choices=["True", "False"], label="Double Quant", multiselect=False, value="False", interactive=True)
                             filter_columns_groupDtype = gr.Dropdown(choices=[i.value.name for i in GroupDtype], label="Group Size", multiselect=False, value="All", interactive=True,)
-                    big_block = gr.HTML("""<h2 style='text-align:center; padding:1.5rem 0; width: 100%; background-color: pink;'>Display Visualization Upon Clicking Table</h2>""")
+
+                    with gr.Row():
+                        model_comparison = gr.CheckboxGroup(label="Spider Chart Model Comparison(Select a model from the table)", choices=list(selected_values.keys()), value=[], interactive=True, elem_id="model_comparison")
+                    with gr.Row():                        
+                        spider_btn = gr.Button("Model Comparison")
                     
             leaderboard_table = gr.components.Dataframe(
                 value=leaderboard_df[
@@ -415,8 +446,9 @@ with demo:
             with Modal(visible=False) as modal:
                 map = gr.Plot()
             
-            leaderboard_table.select(select, leaderboard_table, map)
-            leaderboard_table.select(lambda: Modal(visible=True), None, modal)
+            leaderboard_table.select(select, leaderboard_table, model_comparison)
+            spider_btn.click(generate_spider_chart, [leaderboard_table, model_comparison], map)
+            spider_btn.click(lambda: Modal(visible=True), None, modal)
             
             # Dummy leaderboard for handling the case when the user uses backspace key
             hidden_leaderboard_table_for_search = gr.components.Dataframe(
@@ -455,9 +487,7 @@ with demo:
             )
 
             """
-
            
-
             # Define a hidden component that will trigger a reload only if a query parameter has been set
             hidden_search_bar = gr.Textbox(value="", visible=False)
             hidden_search_bar.change(
@@ -473,7 +503,6 @@ with demo:
                 ],
                 leaderboard_table,
             )
-
             # Check query parameter once at startup and update search bar + hidden component
             demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])