Update app.py

app.py

@@ -62,7 +62,7 @@ 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, w10_range: list, columns: list) -> pd.DataFrame:
     filtered_df = df.copy()
     if param_ranges:
         param_mask = pd.Series(False, index=filtered_df.index)
@@ -85,6 +85,9 @@ def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list
                 param_mask |= (filtered_df['Params'] >= 65)
         filtered_df = filtered_df[param_mask]
     
+    # Apply W/10 filter
+    filtered_df = filtered_df[(filtered_df['W/10 👍'] >= w10_range[0]) & (filtered_df['W/10 👍'] <= w10_range[1])]
+    
     if query:
         filtered_df = filtered_df[filtered_df['Model'].str.contains(query, case=False, na=False)]
     
@@ -120,6 +123,15 @@ with GraInter:
                 interactive=True,
                 elem_id="filter-columns-size",
             )
+        with gr.Row():
+            w10_slider = gr.RangeSlider(
+                minimum=0,
+                maximum=10,
+                value=[0, 10],
+                step=0.1,
+                label="W/10 Range",
+                elem_id="w10-slider"
+            )
     
     # Load the initial leaderboard data
     leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
@@ -239,30 +251,36 @@ 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_range):
+        ugi_table = update_table(leaderboard_df, query, param_ranges, w10_range, UGI_COLS)
         
         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, w10_range, WRITING_STYLE_COLS)
         
         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, w10_range, ANIME_RATING_COLS)
+        arp_na_table = update_table(arp_df_na, query, param_ranges, w10_range, ANIME_RATING_COLS).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_slider],
         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_slider],
+        outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
+    )
+    
+    w10_slider.change(
+        fn=update_all_tables,
+        inputs=[search_bar, filter_columns_size, w10_slider],
         outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
     )