Update main.py

main.py

@@ -1035,23 +1035,28 @@ def get_player_playoff_stats():
         logging.error(f"Error in /api/nba/player_playoff_stats: {e}")
         return jsonify({'error': str(e)}), 500
 
+# In your Flask script (app.py or main.py on Hugging Face)
+
 @app.route('/api/nba/team_stats', methods=['POST'])
 @cross_origin()
 def get_team_stats():
-    """Retrieves team standings for selected NBA teams and a specific season."""
+    # --- ADD THIS LINE AT THE VERY TOP OF THE FUNCTION ---
+    logging.info("DEBUG: Request successfully entered get_team_stats function!")
+    # --- END ADDITION ---
     try:
         data = request.get_json()
         selected_teams = data.get('teams')
         selected_season_str = data.get('season')
 
         if not selected_teams or not selected_season_str:
+            # This would return a 400, not a 404
             return jsonify({'error': 'Teams and season are required'}), 400
 
         year_for_team_stats = int(selected_season_str.split('–')[1])
-        # Use the new BRScraper-based function for team standings
         tm_df = get_team_standings_brscraper(year_for_team_stats)
 
         if tm_df.empty:
+            # This would return a 404 with a specific message
             return jsonify({'error': f'No team data available for {selected_season_str}'}), 404
 
         stats = []
@@ -1067,17 +1072,17 @@ def get_team_stats():
                 teams_with_no_data.append(t)
 
         if not stats:
+            # This would return a 404 with a specific message
             return jsonify({
                 'error': 'No data available for selected teams.',
                 'teams_with_no_data': teams_with_no_data
             }), 404
 
         comp = pd.DataFrame(stats)
-        # Ensure numeric columns are correctly typed for JSON output
-        for col in ['WINS', 'LOSSES', 'WIN_LOSS_PCT', 'RANK']: # Adjust columns based on standings data
+        for col in ['WINS', 'LOSSES', 'WIN_LOSS_PCT', 'RANK']:
             if col in comp.columns:
                 comp[col] = pd.to_numeric(comp[col], errors='coerce')
-        comp = comp.replace({np.nan: None}) # Ensure NaN are None for JSON compliance
+        comp = comp.replace({np.nan: None})
 
         return jsonify({
             'team_stats': comp.to_dict(orient='records'),