Update app.py

app.py

@@ -3,6 +3,7 @@ import pandas as pd
 import numpy as np
 from functools import partial
 from gradio_rangeslider import RangeSlider
+from datetime import datetime, timedelta
 
 custom_css = """
 .tab-nav button {
@@ -42,12 +43,32 @@ custom_css = """
 UGI_COLS = ['#P', 'Model', 'UGI 🏆', 'W/10 👍', 'Unruly', 'Internet', 'Stats', 'Writing', 'PolContro']
 WRITING_STYLE_COLS = ['#P', 'Model', 'Reg+MyScore 🏆', 'Reg+Int 🏆', 'MyScore 🏆', 'ASSS⬇️', 'SMOG⬆️', 'Yule⬇️']
 ANIME_RATING_COLS = ['#P', 'Model', 'Score 🏆', 'Dif', 'Cor', 'Std']
+ADDITIONAL_COLS = ['Release Date', 'Date Added', 'Active Params', 'Total Params']
 
 # Load the leaderboard data from a CSV file
 def load_leaderboard_data(csv_file_path):
     try:
         df = pd.read_csv(csv_file_path)
-        df['Model'] = df.apply(lambda row: f'<a href="{row["Link"]}" target="_blank" style="color: blue; text-decoration: none;">{row["Model"]}</a>' if pd.notna(row["Link"]) else row["Model"], axis=1)
+        
+        # Convert date columns to datetime
+        for col in ['Release Date', 'Date Added']:
+            df[col] = pd.to_datetime(df[col], errors='coerce')
+        
+        # Calculate the date two weeks ago from today
+        two_weeks_ago = datetime.now() - timedelta(days=9)
+        
+        # Add 🆕 to the model name if Date Added is within the last two weeks
+        df['Model'] = df.apply(
+            lambda row: f'🆕 {row["Model"]}' if pd.notna(row["Date Added"]) and row["Date Added"] >= two_weeks_ago else row["Model"],
+            axis=1
+        )
+        
+        # Add hyperlink to the model name
+        df['Model'] = df.apply(
+            lambda row: f'<a href="{row["Link"]}" target="_blank" style="color: blue; text-decoration: none;">{row["Model"]}</a>' if pd.notna(row["Link"]) else row["Model"],
+            axis=1
+        )
+        
         df.drop(columns=['Link'], inplace=True)
         
         # Round numeric columns to 3 decimal places
@@ -61,30 +82,30 @@ def load_leaderboard_data(csv_file_path):
         return df
     except Exception as e:
         print(f"Error loading CSV file: {e}")
-        return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS)
+        return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS + ADDITIONAL_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, w10_range: tuple) -> pd.DataFrame:
+def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list, w10_range: tuple, additional_cols: list) -> pd.DataFrame:
     filtered_df = df.copy()
     if param_ranges:
         param_mask = pd.Series(False, index=filtered_df.index)
         for param_range in param_ranges:
             if param_range == '~2':
-                param_mask |= (filtered_df['Params'] < 2.5)
+                param_mask |= (filtered_df['Total Params'] < 2.5)
             elif param_range == '~4':
-                param_mask |= ((filtered_df['Params'] >= 2.5) & (filtered_df['Params'] < 6))
+                param_mask |= ((filtered_df['Total Params'] >= 2.5) & (filtered_df['Total Params'] < 6))
             elif param_range == '~8':
-                param_mask |= ((filtered_df['Params'] >= 6) & (filtered_df['Params'] < 9.5))
+                param_mask |= ((filtered_df['Total Params'] >= 6) & (filtered_df['Total Params'] < 9.5))
             elif param_range == '~13':
-                param_mask |= ((filtered_df['Params'] >= 9.5) & (filtered_df['Params'] < 16))
+                param_mask |= ((filtered_df['Total Params'] >= 9.5) & (filtered_df['Total Params'] < 16))
             elif param_range == '~20':
-                param_mask |= ((filtered_df['Params'] >= 16) & (filtered_df['Params'] < 28))
+                param_mask |= ((filtered_df['Total Params'] >= 16) & (filtered_df['Total Params'] < 28))
             elif param_range == '~34':
-                param_mask |= ((filtered_df['Params'] >= 28) & (filtered_df['Params'] < 40))
+                param_mask |= ((filtered_df['Total Params'] >= 28) & (filtered_df['Total Params'] < 40))
             elif param_range == '~50':
-                param_mask |= ((filtered_df['Params'] >= 40) & (filtered_df['Params'] < 65))
+                param_mask |= ((filtered_df['Total Params'] >= 40) & (filtered_df['Total Params'] < 65))
             elif param_range == '~70+':
-                param_mask |= (filtered_df['Params'] >= 65)
+                param_mask |= (filtered_df['Total Params'] >= 65)
         filtered_df = filtered_df[param_mask]
     
     if query:
@@ -94,6 +115,17 @@ def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list
     if 'W/10 👍' in filtered_df.columns:
         filtered_df = filtered_df[(filtered_df['W/10 👍'] >= w10_range[0]) & (filtered_df['W/10 👍'] <= w10_range[1])]
     
+    # Add selected additional columns
+    columns = columns + [col for col in additional_cols if col in ADDITIONAL_COLS]
+    
+    # Ensure date columns are sorted as dates and then formatted as strings
+    if 'Release Date' in columns:
+        filtered_df['Release Date'] = pd.to_datetime(filtered_df['Release Date'], errors='coerce')
+        filtered_df['Release Date'] = filtered_df['Release Date'].dt.strftime('%Y-%m-%d')
+    if 'Date Added' in columns:
+        filtered_df['Date Added'] = pd.to_datetime(filtered_df['Date Added'], errors='coerce')
+        filtered_df['Date Added'] = filtered_df['Date Added'].dt.strftime('%Y-%m-%d')
+    
     return filtered_df[columns]
 
 # Define the Gradio interface
@@ -129,13 +161,21 @@ with GraInter:
                 )
             with gr.Column(scale=2):
                 w10_range = RangeSlider(minimum=0, maximum=10, value=(0, 10), step=0.1, label="W/10 Range")
+        with gr.Row():
+            additional_columns = gr.CheckboxGroup(
+                label="Additional Columns",
+                choices=ADDITIONAL_COLS,
+                value=[],
+                interactive=True,
+                elem_id="additional-columns",
+            )
     
     # Load the initial leaderboard data
     leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
     
     with gr.Tabs():
         with gr.TabItem("UGI-Leaderboard"):
-            datatypes_ugi = ['html' if col == 'Model' else 'str' for col in UGI_COLS]
+            datatypes_ugi = ['html' if col == 'Model' else 'str' for col in UGI_COLS + ADDITIONAL_COLS]
             leaderboard_table_ugi = gr.Dataframe(
                 value=leaderboard_df[UGI_COLS],
                 datatype=datatypes_ugi,
@@ -170,7 +210,7 @@ with GraInter:
 
         with gr.TabItem("Writing Style"):
             leaderboard_df_ws = leaderboard_df.sort_values(by='Reg+MyScore 🏆', ascending=False)
-            datatypes_ws = ['html' if col == 'Model' else 'str' for col in WRITING_STYLE_COLS]
+            datatypes_ws = ['html' if col == 'Model' else 'str' for col in WRITING_STYLE_COLS + ADDITIONAL_COLS]
             leaderboard_table_ws = gr.Dataframe(
                 value=leaderboard_df_ws[WRITING_STYLE_COLS],
                 datatype=datatypes_ws,
@@ -210,7 +250,7 @@ with GraInter:
             leaderboard_df_arp_na = leaderboard_df_arp[leaderboard_df_arp[['Dif', 'Cor']].isna().any(axis=1)]
             leaderboard_df_arp = leaderboard_df_arp[~leaderboard_df_arp[['Dif', 'Cor']].isna().any(axis=1)]
             
-            datatypes_arp = ['html' if col == 'Model' else 'str' for col in ANIME_RATING_COLS]
+            datatypes_arp = ['html' if col == 'Model' else 'str' for col in ANIME_RATING_COLS + ADDITIONAL_COLS]
             
             leaderboard_table_arp = gr.Dataframe(
                 value=leaderboard_df_arp[ANIME_RATING_COLS],
@@ -248,36 +288,42 @@ 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, w10_range):
-        ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS, w10_range)
+    def update_all_tables(query, param_ranges, w10_range, additional_cols):
+        ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS, w10_range, additional_cols)
         
         ws_df = leaderboard_df.sort_values(by='Reg+MyScore 🏆', ascending=False)
-        ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS, w10_range)
+        ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS, w10_range, additional_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, w10_range)
-        arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS, w10_range).fillna('NA')
+        arp_table = update_table(arp_df, query, param_ranges, ANIME_RATING_COLS, w10_range, additional_cols)
+        arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS, w10_range, additional_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, w10_range],
+        inputs=[search_bar, filter_columns_size, w10_range, additional_columns],
         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, w10_range],
+        inputs=[search_bar, filter_columns_size, w10_range, additional_columns],
         outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
     )
 
     w10_range.change(
         fn=update_all_tables,
-        inputs=[search_bar, filter_columns_size, w10_range],
+        inputs=[search_bar, filter_columns_size, w10_range, additional_columns],
+        outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
+    )
+
+    additional_columns.change(
+        fn=update_all_tables,
+        inputs=[search_bar, filter_columns_size, w10_range, additional_columns],
         outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
     )