Update app.py

app.py

@@ -449,7 +449,7 @@ def plot_results(df: pd.DataFrame, title: str, human_role: str):
     plt.tight_layout(rect=[0, 0, 1, 0.96]); return fig
 
 
-# =============== NEW: Leaderboard Functions ===============
+# =============== NEW: Leaderboard Functions (MODIFIED) ===============
 @st.cache_data(ttl=60)
 def load_leaderboard_data():
     if not hf_api or not HF_REPO_ID: return {}
@@ -474,38 +474,57 @@ def save_leaderboard_data(data):
     except Exception as e:
         st.sidebar.error(f"Failed to upload leaderboard: {e}")
 
+# ---------- MODIFIED FUNCTION (v2) ----------
 def display_rankings(df, top_n=10):
     if df.empty:
         st.info("No completed games for this category yet. Be the first!")
         return
-    df['total_cost'] = pd.to_numeric(df['total_cost'], errors='coerce')
-    df['order_std_dev'] = pd.to_numeric(df['order_std_dev'], errors='coerce')
-    df = df.dropna(subset=['total_cost', 'order_std_dev'])
-    if df.empty:
-        st.info("No valid completed games for this category yet.")
-        return
+    
+    # 为新旧数据列进行数值转换
+    df['distributor_cost'] = pd.to_numeric(df.get('total_cost'), errors='coerce') # 'total_cost' 是旧的 distributor_cost
+    df['total_chain_cost'] = pd.to_numeric(df.get('total_chain_cost'), errors='coerce')
+    df['order_std_dev'] = pd.to_numeric(df.get('order_std_dev'), errors='coerce')
+
     c1, c2, c3 = st.columns(3)
+
+    # 排行榜 1: 总供应链成本 (新)
     with c1:
         st.subheader("🏆 Supply Chain Champions")
-        st.caption(f"Top {top_n} - Lowest Total Cost")
-        champs_df = df.sort_values('total_cost', ascending=True).head(top_n).copy()
-        champs_df['total_cost'] = champs_df['total_cost'].map('${:,.2f}'.format)
-        champs_df.rename(columns={'id': 'Participant', 'total_cost': 'Total Cost'}, inplace=True)
-        st.dataframe(champs_df[['Participant', 'Total Cost']], use_container_width=True, hide_index=True)
+        st.caption(f"Top {top_n} - Lowest **Total Chain** Cost")
+        # .dropna() 确保只对有该数据的条目进行排序 (兼容旧数据)
+        champs_df = df.dropna(subset=['total_chain_cost']).sort_values('total_chain_cost', ascending=True).head(top_n).copy()
+        if champs_df.empty:
+            st.info("No data for this category yet.")
+        else:
+            champs_df['total_chain_cost'] = champs_df['total_chain_cost'].map('${:,.2f}'.format)
+            champs_df.rename(columns={'id': 'Participant', 'total_chain_cost': 'Total Chain Cost'}, inplace=True)
+            st.dataframe(champs_df[['Participant', 'Total Chain Cost']], use_container_width=True, hide_index=True)
+
+    # 排行榜 2: 你的 (Distributor) 成本 (修改)
     with c2:
-        st.subheader("👑 Bullwhip Kings")
-        st.caption(f"Top {top_n} - Highest Total Cost")
-        kings_df = df.sort_values('total_cost', ascending=False).head(top_n).copy()
-        kings_df['total_cost'] = kings_df['total_cost'].map('${:,.2f}'.format)
-        kings_df.rename(columns={'id': 'Participant', 'total_cost': 'Total Cost'}, inplace=True)
-        st.dataframe(kings_df[['Participant', 'Total Cost']], use_container_width=True, hide_index=True)
+        st.subheader("👤 Distributor Champions")
+        st.caption(f"Top {top_n} - Lowest **Your** (Distributor) Cost")
+        dist_df = df.dropna(subset=['distributor_cost']).sort_values('distributor_cost', ascending=True).head(top_n).copy()
+        
+        if dist_df.empty:
+            st.info("No data for this category yet.")
+        else:
+            dist_df['distributor_cost'] = dist_df['distributor_cost'].map('${:,.2f}'.format)
+            dist_df.rename(columns={'id': 'Participant', 'distributor_cost': 'Your Cost'}, inplace=True)
+            st.dataframe(dist_df[['Participant', 'Your Cost']], use_container_width=True, hide_index=True)
+
+    # 排行榜 3: 订单平滑度 (不变)
     with c3:
         st.subheader("🧘 Mr. Smooth")
         st.caption(f"Top {top_n} - Lowest Order Variation (Std. Dev.)")
-        smooth_df = df.sort_values('order_std_dev', ascending=True).head(top_n).copy()
-        smooth_df['order_std_dev'] = smooth_df['order_std_dev'].map('{:,.2f}'.format)
-        smooth_df.rename(columns={'id': 'Participant', 'order_std_dev': 'Order Std. Dev.'}, inplace=True)
-        st.dataframe(smooth_df[['Participant', 'Order Std. Dev.']], use_container_width=True, hide_index=True)
+        smooth_df = df.dropna(subset=['order_std_dev']).sort_values('order_std_dev', ascending=True).head(top_n).copy()
+        
+        if smooth_df.empty:
+            st.info("No data for this category yet.")
+        else:
+            smooth_df['order_std_dev'] = smooth_df['order_std_dev'].map('{:,.2f}'.format)
+            smooth_df.rename(columns={'id': 'Participant', 'order_std_dev': 'Order Std. Dev.'}, inplace=True)
+            st.dataframe(smooth_df[['Participant', 'Order Std. Dev.']], use_container_width=True, hide_index=True)
 
 def show_leaderboard_ui():
     st.markdown("---")
@@ -518,9 +537,12 @@ def show_leaderboard_ui():
         try:
             df = pd.DataFrame(leaderboard_data.values())
             if 'id' not in df.columns and not df.empty: df['id'] = list(leaderboard_data.keys())
+            
+            # 检查旧列是否存在即可
             if 'total_cost' not in df.columns or 'order_std_dev' not in df.columns or 'setting' not in df.columns:
                 st.error("Leaderboard data is corrupted or incomplete.")
                 return
+            
             groups = sorted(df.setting.unique())
             tabs = st.tabs(["**Overall**"] + groups)
             with tabs[0]: display_rankings(df)
@@ -532,6 +554,7 @@ def show_leaderboard_ui():
             st.error(f"Error displaying leaderboard: {e}")
             st.dataframe(leaderboard_data)
 
+# ---------- MODIFIED FUNCTION (v2) ----------
 def save_logs_and_upload(state: dict):
     if not state.get('logs'):
         st.warning("No log data to save.")
@@ -559,17 +582,34 @@ def save_logs_and_upload(state: dict):
     st.subheader("Updating Leaderboard...")
     try:
         human_role = state['human_role']
-        total_cost = logs_df[f'{human_role}.total_cost'].iloc[-1]
+        
+        # 1. 计算你的 (Distributor) 成本
+        distributor_cost = logs_df[f'{human_role}.total_cost'].iloc[-1]
+        
+        # 2. 计算总供应链成本
+        r_cost = logs_df['Retailer.total_cost'].iloc[-1]
+        w_cost = logs_df['Wholesaler.total_cost'].iloc[-1]
+        f_cost = logs_df['Factory.total_cost'].iloc[-1]
+        total_chain_cost = r_cost + w_cost + distributor_cost + f_cost
+        
+        # 3. 计算订单标准差
         order_std_dev = logs_df[f'{human_role}.order_placed'].std()
+        
         setting_name = f"{state['llm_personality']} / {state['info_sharing']}"
+        
+        # 4. 创建新的条目
         new_entry = {
-            'id': participant_id, 'setting': setting_name,
-            'total_cost': float(total_cost),
+            'id': participant_id, 
+            'setting': setting_name,
+            'total_cost': float(distributor_cost), # 'total_cost' 现在明确是 distributor_cost
+            'total_chain_cost': float(total_chain_cost), # 新增: 总成本
             'order_std_dev': float(order_std_dev) if pd.notna(order_std_dev) else 0.0
         }
+        
         leaderboard_data = load_leaderboard_data()
         leaderboard_data[participant_id] = new_entry 
         save_leaderboard_data(leaderboard_data)
+        
     except Exception as e_board:
         st.error(f"Error calculating or saving leaderboard score: {e_board}")
 # ==============================================================================