Update src/streamlit_app.py

src/streamlit_app.py

@@ -283,60 +283,57 @@ def get_player_index_brscraper():
         return pd.DataFrame({'name': fallback_players})
 
 @st.cache_data(ttl=300)
-def get_player_career_stats_brscraper(player_name):
+def get_player_career_stats_brscraper(player_name, seasons_to_check=10):
     """
-    Uses BRScraper to get a player's career stats.
-    Applies column renaming and numeric conversion.
+    Build a player's career stats by fetching per-game data season-by-season
+    via nba.get_stats, instead of nba.get_player_stats (which was returning empty).
     """
     if not BRSCRAPER_AVAILABLE:
         return pd.DataFrame()
-    
-    try:
-        # BRScraper's nba.get_player_stats returns a DataFrame with career stats
-        df = nba.get_player_stats(player_name)
-        
-        if df.empty:
-            return pd.DataFrame()
-
-        # Standardize column names from BRScraper output to app's expected format
-        # BRScraper's nba.get_player_stats uses column names like 'FG%', 'TRB', 'Tm', 'Age'
-        column_mapping = {
-            'Season': 'Season', # BRScraper returns 'Season'
-            'G': 'GP', 'GS': 'GS', 'MP': 'MIN',
-            'FG%': 'FG_PCT', '3P%': 'FG3_PCT', 'FT%': 'FT_PCT',
-            'TRB': 'REB', 'AST': 'AST', 'STL': 'STL', 'BLK': 'BLK', 'TOV': 'TO',
-            'PF': 'PF', 'PTS': 'PTS',
-            'Age': 'AGE', 'Tm': 'TEAM_ABBREVIATION', 'Lg': 'LEAGUE_ID', 'Pos': 'POSITION',
-            'FG': 'FGM', 'FGA': 'FGA', '3P': 'FG3M', '3PA': 'FG3A',
-            '2P': 'FGM2', '2PA': 'FGA2', '2P%': 'FG2_PCT', 'eFG%': 'EFG_PCT',
-            'FT': 'FTM', 'FTA': 'FTA', 'ORB': 'OREB', 'DRB': 'DREB'
-        }
-        
-        # Apply column mapping only for columns that exist
-        for old_col, new_col in column_mapping.items():
-            if old_col in df.columns:
-                df = df.rename(columns={old_col: new_col})
-
-        # Ensure 'Season' column is string and uses en-dash
-        if 'Season' in df.columns:
-            df['Season'] = df['Season'].astype(str).str.replace('-', '–')
-        
-        # Convert numeric columns
-        # Exclude 'Season', 'TEAM_ABBREVIATION', 'LEAGUE_ID', 'POSITION' as they are strings
-        non_numeric_cols = {'Season', 'TEAM_ABBREVIATION', 'LEAGUE_ID', 'POSITION'}
-        for col in df.columns:
-            if col not in non_numeric_cols:
-                df[col] = pd.to_numeric(df[col], errors="coerce")
-        
-        # Add 'Player' column for consistency with app logic
-        df['Player'] = player_name
 
-        return df
-        
-    except Exception as e:
-        st.error(f"Error fetching stats for {player_name} with BRScraper: {e}")
+    all_rows = []
+    seasons = get_available_seasons(seasons_to_check)
+    for season_str in seasons:
+        end_year = int(season_str.split('–')[1])
+        try:
+            df_season = nba.get_stats(end_year, info='per_game', playoffs=False, rename=False)
+            if 'Player' in df_season.columns:
+                row = df_season[df_season['Player'] == player_name]
+                if not row.empty:
+                    row = row.copy()
+                    # normalize the season label
+                    row['Season'] = season_str
+                    all_rows.append(row)
+        except Exception as e:
+            # skip any failures
+            st.warning(f"Could not fetch {season_str} for {player_name}: {e}")
+
+    if not all_rows:
         return pd.DataFrame()
 
+    df = pd.concat(all_rows, ignore_index=True)
+
+    # Standardize columns
+    mapping = {
+        'G':'GP','GS':'GS','MP':'MIN',
+        'FG%':'FG_PCT','3P%':'FG3_PCT','FT%':'FT_PCT',
+        'TRB':'REB','AST':'AST','STL':'STL','BLK':'BLK','TOV':'TO',
+        'PF':'PF','PTS':'PTS','ORB':'OREB','DRB':'DREB',
+        'FG':'FGM','FGA':'FGA','3P':'FG3M','3PA':'FG3A',
+        '2P':'FGM2','2PA':'FGA2','2P%':'FG2_PCT','eFG%':'EFG_PCT',
+        'FT':'FTM','FTA':'FTA'
+    }
+    df = df.rename(columns={o:n for o,n in mapping.items() if o in df.columns})
+
+    # Convert numeric columns
+    non_num = {'Season','Player','Tm','Lg','Pos'}
+    for col in df.columns:
+        if col not in non_num:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+
+    df['Player'] = player_name
+    return df
+
 # ——————————————————————————————————————————————
 # Perplexity API
 PERP_KEY = os.getenv("PERPLEXITY_API_KEY")