Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import pandas as pd
 import numpy as np
+from functools import partial
 
 custom_css = """
 .tab-nav button {
@@ -62,7 +63,7 @@ def load_leaderboard_data(csv_file_path):
         return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS)
 
 # Update the leaderboard table based on the search query and parameter range filters
-def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list) -> pd.DataFrame:
+def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list, w10_min: float, w10_max: float) -> pd.DataFrame:
     filtered_df = df.copy()
     if param_ranges:
         param_mask = pd.Series(False, index=filtered_df.index)
@@ -88,6 +89,10 @@ def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list
     if query:
         filtered_df = filtered_df[filtered_df['Model'].str.contains(query, case=False, na=False)]
     
+    # Apply W/10 filtering
+    if 'W/10 👍' in filtered_df.columns:
+        filtered_df = filtered_df[(filtered_df['W/10 👍'] >= w10_min) & (filtered_df['W/10 👍'] <= w10_max)]
+    
     return filtered_df[columns]
 
 # Define the Gradio interface
@@ -120,6 +125,9 @@ with GraInter:
                 interactive=True,
                 elem_id="filter-columns-size",
             )
+        with gr.Row():
+            w10_min = gr.Slider(minimum=0, maximum=10, value=0, step=0.1, label="Min W/10")
+            w10_max = gr.Slider(minimum=0, maximum=10, value=10, step=0.1, label="Max W/10")
     
     # Load the initial leaderboard data
     leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
@@ -239,30 +247,42 @@ with GraInter:
             **NA:** When models either reply with one number for every anime, give ratings not between 1 and 10, or don't give every anime in the list a rating.
             """)
 
-    def update_all_tables(query, param_ranges):
-        ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS)
+    def update_all_tables(query, param_ranges, w10_min, w10_max):
+        ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS, w10_min, w10_max)
         
         ws_df = leaderboard_df.sort_values(by='Reg+MyScore 🏆', ascending=False)
-        ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS)
+        ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS, w10_min, w10_max)
         
         arp_df = leaderboard_df.sort_values(by='Score 🏆', ascending=False)
         arp_df_na = arp_df[arp_df[['Dif', 'Cor']].isna().any(axis=1)]
         arp_df = arp_df[~arp_df[['Dif', 'Cor']].isna().any(axis=1)]
         
-        arp_table = update_table(arp_df, query, param_ranges, ANIME_RATING_COLS)
-        arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS).fillna('NA')
+        arp_table = update_table(arp_df, query, param_ranges, ANIME_RATING_COLS, w10_min, w10_max)
+        arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS, w10_min, w10_max).fillna('NA')
         
         return ugi_table, ws_table, arp_table, arp_na_table
 
     search_bar.change(
         fn=update_all_tables,
-        inputs=[search_bar, filter_columns_size],
+        inputs=[search_bar, filter_columns_size, w10_min, w10_max],
         outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
     )
     
     filter_columns_size.change(
         fn=update_all_tables,
-        inputs=[search_bar, filter_columns_size],
+        inputs=[search_bar, filter_columns_size, w10_min, w10_max],
+        outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
+    )
+
+    w10_min.change(
+        fn=update_all_tables,
+        inputs=[search_bar, filter_columns_size, w10_min, w10_max],
+        outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
+    )
+
+    w10_max.change(
+        fn=update_all_tables,
+        inputs=[search_bar, filter_columns_size, w10_min, w10_max],
         outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
     )