Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py
-# @title Beer Game Final Version (v4.24 - Corrected 3-Week Lead Time Logic & UI)
+# @title Beer Game Final Version (v4.25 - Based on v4.21 Logic + UI Fixes)
 
 # -----------------------------------------------------------------------------
 # 1. Import Libraries
@@ -32,10 +32,10 @@ st.set_page_config(page_title="Beer Game: Human-AI Collaboration", layout="wide"
 WEEKS = 24
 INITIAL_INVENTORY = 12
 INITIAL_BACKLOG = 0
-ORDER_PASSING_DELAY = 1 # 订单传递延迟
-SHIPPING_DELAY = 2 # 通用运输延迟 (R->W, W->D)
-FACTORY_LEAD_TIME = 1 # 工厂生产延迟
-FACTORY_SHIPPING_DELAY = 1 # 工厂到分销商的运输延迟
+ORDER_PASSING_DELAY = 1 # Handled by last_week_orders
+SHIPPING_DELAY = 2 # General shipping delay (R->W, W->D)
+FACTORY_LEAD_TIME = 1
+FACTORY_SHIPPING_DELAY = 1 # Specific delay from Factory to Distributor
 HOLDING_COST = 0.5
 BACKLOG_COST = 1.0
 
@@ -65,42 +65,30 @@ else:
 def get_customer_demand(week: int) -> int:
     return 4 if week <= 4 else 8
 
-# =============== MODIFIED Initialization (Corrected Queues) ===============
+# =============== MODIFIED Initialization (v4.21 logic + v4.23 bugfix) ===============
 def init_game_state(llm_personality: str, info_sharing: str, participant_id: str):
     roles = ["Retailer", "Wholesaler", "Distributor", "Factory"]
-    human_role = "Distributor"
+    human_role = "Distributor" # Role is fixed
     
     st.session_state.game_state = {
-        'game_running': True, 'participant_id': participant_id, 'week': 1,
+        'game_running': True,
+        'participant_id': participant_id,
+        'week': 1,
         'human_role': human_role, 'llm_personality': llm_personality,
         'info_sharing': info_sharing, 'logs': [], 'echelons': {},
-        
-        # 管道现在必须模拟总延迟
-        # Factory: 1 (订单) + 1 (生产) = 2 周延迟
-        # 我们需要一个队列来处理订单 (1周)，一个队列处理生产 (1周)
-        'factory_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
         'factory_production_pipeline': deque([0] * FACTORY_LEAD_TIME, maxlen=FACTORY_LEAD_TIME),
-        
-        # Distributor -> Factory: 1(订单) + 1(生产) + 1(运输) = 3 周
-        # Wholesaler -> Distributor: 1(订单) + 2(运输) = 3 周
-        # Retailer -> Wholesaler: 1(订单) + 2(运输) = 3 周
-        'distributor_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
-        'wholesaler_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
-        'retailer_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
-        
         'decision_step': 'initial_order',
         'human_initial_order': None,
-        'current_ai_suggestion': None,
+        'current_ai_suggestion': None, # v4.23 Bugfix: 用于存储AI建议
+        'last_week_orders': {name: 0 for name in roles} # v4.21 Logic: 初始化为0
     }
 
     for i, name in enumerate(roles):
         upstream = roles[i + 1] if i + 1 < len(roles) else None
         downstream = roles[i - 1] if i - 1 >= 0 else None
-
         if name == "Distributor": shipping_weeks = FACTORY_SHIPPING_DELAY
-        elif name == "Factory": shipping_weeks = 0 # 工厂不收货
-        else: shipping_weeks = SHIPPING_DELAY # R和W是2周
-
+        elif name == "Factory": shipping_weeks = 0
+        else: shipping_weeks = SHIPPING_DELAY
         st.session_state.game_state['echelons'][name] = {
             'name': name, 'inventory': INITIAL_INVENTORY, 'backlog': INITIAL_BACKLOG,
             'incoming_shipments': deque([0] * shipping_weeks, maxlen=shipping_weeks),
@@ -133,70 +121,45 @@ def get_llm_order_decision(prompt: str, echelon_name: str) -> (int, str):
             st.error(f"API call failed for {echelon_name}: {e}. Defaulting to 4.")
             return 4, f"API_ERROR: {e}"
 
-# =============== MODIFIED FUNCTION (Prompt uses new pipeline view) ===============
 def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personality: str, info_sharing: str, all_echelons_state_decision_point: dict) -> str:
+    # This function's logic remains correct (from v4.21).
     e_state = echelon_state_decision_point
     base_info = f"Your Current Status at the **{e_state['name']}** for **Week {week}** (Before Shipping):\n- On-hand inventory: {e_state['inventory']} units.\n- Backlog (total unfilled orders): {e_state['backlog']} units.\n- Incoming order this week (just received): {e_state['incoming_order']} units.\n"
-    
-    # 查找正确的订单队列
-    order_pipeline_to_show = deque()
-    if e_state['name'] == 'Distributor':
-        order_pipeline_to_show = st.session_state.game_state['distributor_order_pipeline']
-    elif e_state['name'] == 'Wholesaler':
-        order_pipeline_to_show = st.session_state.game_state['wholesaler_order_pipeline']
-    elif e_state['name'] == 'Retailer':
-        order_pipeline_to_show = st.session_state.game_state['retailer_order_pipeline']
-
     if e_state['name'] == 'Factory':
         task_word = "production quantity"
-        base_info += f"- Your Production Pipeline (In Production): {list(st.session_state.game_state['factory_production_pipeline'])}\n"
-        base_info += f"- Orders waiting for production (Just Arrived): {list(st.session_state.game_state['factory_order_pipeline'])}"
+        base_info += f"- Your Production Pipeline (completing next week onwards): {list(st.session_state.game_state['factory_production_pipeline'])}"
     else:
         task_word = "order quantity"
-        base_info += f"- Shipments In Transit To You (On the way): {list(e_state['incoming_shipments'])}\n"
-        base_info += f"- Orders You Placed (In transit to supplier): {list(order_pipeline_to_show)}"
-
-    # --- Perfect Rational ---
+        base_info += f"- Shipments In Transit To You (arriving next week onwards): {list(e_state['incoming_shipments'])}"
     if llm_personality == 'perfect_rational' and info_sharing == 'full':
         stable_demand = 8
-        if e_state['name'] == 'Factory': total_lead_time = FACTORY_LEAD_TIME + ORDER_PASSING_DELAY
+        if e_state['name'] == 'Factory': total_lead_time = FACTORY_LEAD_TIME
         elif e_state['name'] == 'Distributor': total_lead_time = ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY
         else: total_lead_time = ORDER_PASSING_DELAY + SHIPPING_DELAY
         safety_stock = 4
         target_inventory_level = (stable_demand * total_lead_time) + safety_stock
-        
         if e_state['name'] == 'Factory':
-            # IP = Inv - Backlog + In Production + Orders Waiting
-            inventory_position = (e_state['inventory'] - e_state['backlog'] 
-                                  + sum(st.session_state.game_state['factory_production_pipeline']) 
-                                  + sum(st.session_state.game_state['factory_order_pipeline']))
-            inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InProd={sum(st.session_state.game_state['factory_production_pipeline'])} + Waiting={sum(st.session_state.game_state['factory_order_pipeline'])})"
+            inventory_position = (e_state['inventory'] - e_state['backlog'] + sum(st.session_state.game_state['factory_production_pipeline']))
+            inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InProd={sum(st.session_state.game_state['factory_production_pipeline'])})"
         else:
-            # IP = Inv - Backlog + In Transit Shipments + Orders In Transit to Supplier
-            inventory_position = (e_state['inventory'] - e_state['backlog'] 
-                                  + sum(e_state['incoming_shipments']) 
-                                  + sum(order_pipeline_to_show))
-            inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={sum(e_state['incoming_shipments'])} + InTransitOrder={sum(order_pipeline_to_show)})"
-
+            order_in_transit_to_supplier = st.session_state.game_state['last_week_orders'].get(e_state['name'], 0)
+            inventory_position = (e_state['inventory'] - e_state['backlog'] + sum(e_state['incoming_shipments']) + order_in_transit_to_supplier)
+            inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={sum(e_state['incoming_shipments'])} + OrderToSupplier={order_in_transit_to_supplier})"
         optimal_order = max(0, int(target_inventory_level - inventory_position))
         return f"**You are a perfectly rational supply chain AI with full system visibility.**\nYour only goal is to maintain stability and minimize costs based on mathematical optimization.\n**System Analysis:**\n* **Known Stable End-Customer Demand:** {stable_demand} units/week.\n* **Your Current Total Inventory Position:** {inventory_position} units. {inv_pos_components}\n* **Optimal Target Inventory Level:** {target_inventory_level} units (Target for {total_lead_time} weeks lead time).\n* **Mathematically Optimal {task_word.title()}:** The optimal decision is **{optimal_order} units**.\n**Your Task:** Confirm this optimal {task_word}. Respond with a single integer."
-
     elif llm_personality == 'perfect_rational' and info_sharing == 'local':
         safety_stock = 4; anchor_demand = e_state['incoming_order']
         inventory_correction = safety_stock - (e_state['inventory'] - e_state['backlog'])
-
         if e_state['name'] == 'Factory':
-            supply_line = sum(st.session_state.game_state['factory_production_pipeline']) + sum(st.session_state.game_state['factory_order_pipeline'])
-            supply_line_desc = "In Production / Waiting"
+            supply_line = sum(st.session_state.game_state['factory_production_pipeline'])
+            supply_line_desc = "In Production"
         else:
-            supply_line = sum(e_state['incoming_shipments']) + sum(order_pipeline_to_show)
-            supply_line_desc = "Supply Line (In Transit Shipments + Orders)"
-
+            order_in_transit_to_supplier = st.session_state.game_state['last_week_orders'].get(e_state['name'], 0)
+            supply_line = sum(e_state['incoming_shipments']) + order_in_transit_to_supplier
+            supply_line_desc = "Supply Line (In Transit Shipments + Order To Supplier)"
         calculated_order = anchor_demand + inventory_correction - supply_line
         rational_local_order = max(0, int(calculated_order))
         return f"**You are a perfectly rational supply chain AI with ONLY LOCAL information.**\nYou must use a logical heuristic to make a stable decision. A proven method is \"Anchoring and Adjustment\".\n\n{base_info}\n\n**Rational Calculation (Anchoring & Adjustment):**\n1.  **Anchor on Demand:** Your best guess for future demand is your last incoming order: **{anchor_demand} units**.\n2.  **Adjust for Inventory:** You want to hold a safety stock of {safety_stock} units. Your current stock (before shipping) is {e_state['inventory'] - e_state['backlog']}. You need to order an extra **{inventory_correction} units** to correct this.\n3.  **Account for {supply_line_desc}:** You already have **{supply_line} units** being processed. These should be subtracted from your new decision.\n\n**Final Calculation:**\n* Decision = (Anchor Demand) + (Inventory Adjustment) - ({supply_line_desc})\n* Decision = {anchor_demand} + {inventory_correction} - {supply_line} = **{rational_local_order} units**.\n**Your Task:** Confirm this locally rational {task_word}. Respond with a single integer."
-    
-    # --- Human-like ---
     elif llm_personality == 'human_like' and info_sharing == 'full':
         full_info_str = f"\n**Full Supply Chain Information (State Before Shipping):**\n- End-Customer Demand this week: {get_customer_demand(week)} units.\n"
         for name, other_e_state in all_echelons_state_decision_point.items():
@@ -220,167 +183,79 @@ def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personalit
         Your gut instinct is to panic and {task_word.split(' ')[0]} enough to ensure you are never caught with a backlog again, considering your current inventory.
         **React emotionally.** What is your knee-jerk {task_word}? Respond with a single integer.
         """
-# ==============================================================================
 
-# =============== CORRECTED step_game FUNCTION (Fixed Lead Time Logic v4.21) ===============
 def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: int):
+    # This is the correct logic from v4.17
     state = st.session_state.game_state
     week, echelons, human_role = state['week'], state['echelons'], state['human_role']
     llm_personality, info_sharing = state['llm_personality'], state['info_sharing']
     echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"]
     llm_raw_responses = {}
-
-    # Store state at the very beginning of the week (End of last week)
     opening_inventories = {name: e['inventory'] for name, e in echelons.items()}
     opening_backlogs = {name: e['backlog'] for name, e in echelons.items()}
     arrived_this_week = {name: 0 for name in echelon_order}
-
-    # --- Game Simulation Steps ---
-
-    # Step 1: Shipments Arrive (from incoming_shipments queue)
     inventory_after_arrival = {}
+    factory_state = echelons["Factory"]
+    produced_units = 0
+    if state['factory_production_pipeline']:
+        produced_units = state['factory_production_pipeline'].popleft()
+        arrived_this_week["Factory"] = produced_units
+    inventory_after_arrival["Factory"] = factory_state['inventory'] + produced_units
     for name in ["Retailer", "Wholesaler", "Distributor"]:
         arrived_shipment = 0
         if echelons[name]['incoming_shipments']:
             arrived_shipment = echelons[name]['incoming_shipments'].popleft()
             arrived_this_week[name] = arrived_shipment
         inventory_after_arrival[name] = echelons[name]['inventory'] + arrived_shipment
-
-    # Step 2: Orders Arrive (from order_pipeline queue)
     total_backlog_before_shipping = {}
     for name in echelon_order:
         incoming_order_for_this_week = 0
-        if name == "Retailer":
-            incoming_order_for_this_week = get_customer_demand(week)
+        if name == "Retailer": incoming_order_for_this_week = get_customer_demand(week)
         else:
-            # Check the correct order pipeline based on the downstream partner
             downstream_name = echelons[name]['downstream_name']
-            if downstream_name == 'Distributor':
-                if state['distributor_order_pipeline']: incoming_order_for_this_week = state['distributor_order_pipeline'].popleft()
-            elif downstream_name == 'Wholesaler':
-                 if state['wholesaler_order_pipeline']: incoming_order_for_this_week = state['wholesaler_order_pipeline'].popleft()
-            elif downstream_name == 'Retailer':
-                 if state['retailer_order_pipeline']: incoming_order_for_this_week = state['retailer_order_pipeline'].popleft()
-        
+            if downstream_name: incoming_order_for_this_week = state['last_week_orders'].get(downstream_name, 0)
         echelons[name]['incoming_order'] = incoming_order_for_this_week
-        # Factory's 'incoming_order' is now set (from distributor_order_pipeline)
-        
-        # Calculate intermediate state for Factory (production completion)
-        if name == "Factory":
-            produced_units = 0
-            if state['factory_production_pipeline']:
-                produced_units = state['factory_production_pipeline'].popleft()
-                arrived_this_week["Factory"] = produced_units
-            inventory_after_arrival["Factory"] = factory_state['inventory'] + produced_units
-
         total_backlog_before_shipping[name] = echelons[name]['backlog'] + incoming_order_for_this_week
-
-    # --- Create State Snapshot for AI/Human Decision Point ---
     decision_point_states = {}
     for name in echelon_order:
          decision_point_states[name] = {
-             'name': name,
-             'inventory': inventory_after_arrival[name],
-             'backlog': total_backlog_before_shipping[name],
-             'incoming_order': echelons[name]['incoming_order'],
+             'name': name, 'inventory': inventory_after_arrival[name],
+             'backlog': total_backlog_before_shipping[name], 'incoming_order': echelons[name]['incoming_order'],
              'incoming_shipments': echelons[name]['incoming_shipments'].copy() if name != "Factory" else deque(),
          }
-
-    # --- Step 4: Agent Decisions (Place Orders / Schedule Production) ---
+    current_week_orders = {}
     for name in echelon_order:
-        e = echelons[name]
-        prompt_state = decision_point_states[name]
-
-        if name == human_role:
-            order_amount, raw_resp = human_final_order, "HUMAN_FINAL_INPUT"
+        e = echelons[name]; prompt_state = decision_point_states[name]
+        if name == human_role: order_amount, raw_resp = human_final_order, "HUMAN_FINAL_INPUT"
         else:
             prompt = get_llm_prompt(prompt_state, week, llm_personality, info_sharing, decision_point_states)
             order_amount, raw_resp = get_llm_order_decision(prompt, name)
-
-        llm_raw_responses[name] = raw_resp
-        e['order_placed'] = max(0, order_amount)
-
-        # Put the order into the correct pipeline to simulate ORDER_PASSING_DELAY
-        if name == 'Distributor': state['distributor_order_pipeline'].append(e['order_placed'])
-        elif name == 'Wholesaler': state['wholesaler_order_pipeline'].append(e['order_placed'])
-        elif name == 'Retailer': state['retailer_order_pipeline'].append(e['order_placed'])
-        # Factory's 'order_placed' is its production decision
-
-
-    # --- Step 3 (Logic): Fulfill orders (Ship Beer) ---
+        llm_raw_responses[name] = raw_resp; e['order_placed'] = max(0, order_amount); current_week_orders[name] = e['order_placed']
+    state['factory_production_pipeline'].append(echelons["Factory"]['order_placed'])
     units_shipped = {name: 0 for name in echelon_order}
     for name in echelon_order:
-        e = echelons[name]
-        demand_to_meet = total_backlog_before_shipping[name]
-        available_inv = inventory_after_arrival[name]
-
-        e['shipment_sent'] = min(available_inv, demand_to_meet)
-        units_shipped[name] = e['shipment_sent']
-
-        # Update the main state dict's inventory and backlog to reflect END OF WEEK state
-        e['inventory'] = available_inv - e['shipment_sent']
-        e['backlog'] = demand_to_meet - e['shipment_sent']
-
-    # --- Step 5: Advance Pipelines (New Logic) ---
-    
-    # Factory: takes its incoming order (from distributor_order_pipeline)
-    # and schedules it for production (adds to factory_production_pipeline)
-    # This simulates FACTORY_LEAD_TIME
-    # *** BUG FIX: Factory's order_placed IS its production decision ***
-    # *** Factory's incoming_order is what drives its decision ***
-    
-    # Factory's decision ('order_placed') from Step 4 enters the production pipeline
-    state['factory_production_pipeline'].append(echelons["Factory"]['order_placed'])
-    
-    # What the Factory *shipped* in Step 3 (units_shipped["Factory"])
-    # now enters the Distributor's shipping queue
-    if units_shipped["Factory"] > 0:
-        echelons['Distributor']['incoming_shipments'].append(units_shipped["Factory"])
-    
-    # What the Distributor *shipped* in Step 3...
-    if units_shipped['Distributor'] > 0:
-         echelons['Wholesaler']['incoming_shipments'].append(units_shipped['Distributor'])
-    
-    # What the Wholesaler *shipped* in Step 3...
-    if units_shipped['Wholesaler'] > 0:
-         echelons['Retailer']['incoming_shipments'].append(units_shipped['Wholesaler'])
-
-
-    # --- Calculate Costs & Log (End of Week) ---
+        e = echelons[name]; demand_to_meet = total_backlog_before_shipping[name]; available_inv = inventory_after_arrival[name]
+        e['shipment_sent'] = min(available_inv, demand_to_meet); units_shipped[name] = e['shipment_sent']
+        e['inventory'] = available_inv - e['shipment_sent']; e['backlog'] = demand_to_meet - e['shipment_sent']
+    if units_shipped["Factory"] > 0: echelons['Distributor']['incoming_shipments'].append(units_shipped["Factory"])
+    if units_shipped['Distributor'] > 0: echelons['Wholesaler']['incoming_shipments'].append(units_shipped['Distributor'])
+    if units_shipped['Wholesaler'] > 0: echelons['Retailer']['incoming_shipments'].append(units_shipped['Wholesaler'])
     log_entry = {'timestamp': datetime.utcnow().isoformat() + "Z", 'week': week, **state}
-    # Clean up fields
-    del log_entry['echelons'], log_entry['factory_production_pipeline'], log_entry['logs']
-    for key in ['distributor_order_pipeline', 'wholesaler_order_pipeline', 'retailer_order_pipeline', 'factory_order_pipeline']:
-        if key in log_entry: del log_entry[key]
-
-
+    del log_entry['echelons'], log_entry['factory_production_pipeline'], log_entry['logs'], log_entry['last_week_orders']
+    if 'current_ai_suggestion' in log_entry: del log_entry['current_ai_suggestion']
     for name in echelon_order:
-        e = echelons[name]
-        e['weekly_cost'] = (e['inventory'] * HOLDING_COST) + (e['backlog'] * BACKLOG_COST)
-        e['total_cost'] += e['weekly_cost']
-
-        log_entry[f'{name}.inventory'] = e['inventory']; log_entry[f'{name}.backlog'] = e['backlog']
-        log_entry[f'{name}.incoming_order'] = e['incoming_order']; log_entry[f'{name}.order_placed'] = e['order_placed']
-        log_entry[f'{name}.shipment_sent'] = e['shipment_sent']; log_entry[f'{name}.weekly_cost'] = e['weekly_cost']
-        log_entry[f'{name}.total_cost'] = e['total_cost']; log_entry[f'{name}.llm_raw_response'] = llm_raw_responses.get(name, "")
+        e = echelons[name]; e['weekly_cost'] = (e['inventory'] * HOLDING_COST) + (e['backlog'] * BACKLOG_COST); e['total_cost'] += e['weekly_cost']
+        for key in ['inventory', 'backlog', 'incoming_order', 'order_placed', 'shipment_sent', 'weekly_cost', 'total_cost']: log_entry[f'{name}.{key}'] = e[key]
+        log_entry[f'{name}.llm_raw_response'] = llm_raw_responses.get(name, "")
         log_entry[f'{name}.opening_inventory'] = opening_inventories[name]; log_entry[f'{name}.opening_backlog'] = opening_backlogs[name]
         log_entry[f'{name}.arrived_this_week'] = arrived_this_week[name]
-
-        if name != 'Factory':
-             log_entry[f'{name}.arriving_next_week'] = list(e['incoming_shipments'])[0] if e['incoming_shipments'] else 0
-        else:
-             log_entry[f'{name}.production_completing_next_week'] = list(state['factory_production_pipeline'])[0] if state['factory_production_pipeline'] else 0
-
+        if name != 'Factory': log_entry[f'{name}.arriving_next_week'] = list(e['incoming_shipments'])[0] if e['incoming_shipments'] else 0
+        else: log_entry[f'{name}.production_completing_next_week'] = list(state['factory_production_pipeline'])[0] if state['factory_production_pipeline'] else 0
     log_entry[f'{human_role}.initial_order'] = human_initial_order; log_entry[f'{human_role}.ai_suggestion'] = ai_suggestion
     state['logs'].append(log_entry)
-
-    # --- Advance Week ---
-    state['week'] += 1; state['decision_step'] = 'initial_order'
-    # 'last_week_orders' is no longer needed with this pipeline logic
-    # We rely on the order pipelines
+    state['week'] += 1; state['decision_step'] = 'initial_order'; state['last_week_orders'] = current_week_orders
+    state['current_ai_suggestion'] = None # Clean up
     if state['week'] > WEEKS: state['game_running'] = False
-# ==============================================================================
-
 
 def plot_results(df: pd.DataFrame, title: str, human_role: str):
     # This function remains correct.
@@ -412,7 +287,8 @@ def plot_results(df: pd.DataFrame, title: str, human_role: str):
         axes[3].set_title(f'Analysis of Your ({human_role}) Decisions - Error Plotting Data'); axes[3].text(0.5, 0.5, f"Error: {plot_err}", ha='center', va='center'); axes[3].grid(True, linestyle='--'); axes[3].set_xlabel('Week')
     plt.tight_layout(rect=[0, 0, 1, 0.96]); return fig
 
-# =============== Leaderboard Functions (Unchanged) ===============
+
+# =============== NEW: Leaderboard Functions ===============
 @st.cache_data(ttl=60)
 def load_leaderboard_data():
     if not hf_api or not HF_REPO_ID: return {}
@@ -538,7 +414,7 @@ def save_logs_and_upload(state: dict):
 # ==============================================================================
 
 # -----------------------------------------------------------------------------
-# 4. Streamlit UI (Adjusted for Custom ID, Leaderboard, and UI Fixes)
+# 4. Streamlit UI (Applying v4.22 + v4.23 fixes)
 # -----------------------------------------------------------------------------
 st.title("🍺 The Beer Game: A Human-AI Collaboration Challenge")
 
@@ -548,12 +424,12 @@ else:
     # --- Game Setup & Instructions ---
     if 'game_state' not in st.session_state or not st.session_state.game_state.get('game_running', False):
         
-        # Introduction is removed as requested
-        
         st.markdown("---")
         st.header("⚙️ Game Configuration")
         
+        # =============== NEW: Participant ID Input ===============
         participant_id = st.text_input("Enter Your Name or Team ID:", key="participant_id_input", placeholder="e.g., Team A")
+        # =======================================================
         
         c1, c2 = st.columns(2)
         with c1:
@@ -561,14 +437,16 @@ else:
         with c2:
             info_sharing = st.selectbox("Information Sharing Level", ('local', 'full'), format_func=lambda x: x.title(), help="**Local:** You and the AI agents can only see your own inventory and incoming orders. **Full:** Everyone can see the entire supply chain's status and the true end-customer demand.")
 
+        # =============== MODIFIED: Start Game Button ===============
         if st.button("🚀 Start Game", type="primary", disabled=(client is None)):
             if not participant_id:
                 st.error("Please enter a Name or Team ID to start!")
             else:
                 existing_data = load_leaderboard_data()
                 if participant_id in existing_data:
-                    # Check for a re-click confirmation
+                    # 如果ID已存在，添加一个session_state标志，要求再次点击
                     if st.session_state.get('last_id_warning') == participant_id:
+                        # 这是第二次点击，确认覆盖
                         st.session_state.pop('last_id_warning', None)
                         init_game_state(llm_personality, info_sharing, participant_id)
                         st.rerun()
@@ -576,18 +454,22 @@ else:
                         st.session_state['last_id_warning'] = participant_id
                         st.warning(f"ID '{participant_id}' already exists! Your score will be overwritten. Click 'Start Game' again to confirm.")
                 else:
+                    # 新ID，直接开始
                     if 'last_id_warning' in st.session_state:
                          del st.session_state['last_id_warning']
                     init_game_state(llm_personality, info_sharing, participant_id)
                     st.rerun()
+        # ===========================================================
         
+        # =============== NEW: Show Leaderboard on Start Page ===============
         show_leaderboard_ui()
+        # =================================================================
 
     # --- Main Game Interface ---
     elif 'game_state' in st.session_state and st.session_state.game_state.get('game_running'):
         state = st.session_state.game_state
         week, human_role, echelons, info_sharing = state['week'], state['human_role'], state['echelons'], state['info_sharing']
-        echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"]
+        echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"] # Define here for UI
 
 
         st.header(f"Week {week} / {WEEKS}")
@@ -595,7 +477,7 @@ else:
         st.markdown("---")
         st.subheader("Supply Chain Status (Start of Week State)") 
 
-        # =============== MODIFIED UI LOGIC (v4.21) ===============
+        # =============== MODIFIED UI LOGIC (v4.22) ===============
         if info_sharing == 'full':
             cols = st.columns(4)
             for i, name in enumerate(echelon_order):
@@ -610,21 +492,17 @@ else:
                     
                     st.metric("Inventory (Opening)", e['inventory'])
                     st.metric("Backlog (Opening)", e['backlog'])
+
+                    # 移除成本显示
                     
-                    # --- Calculate and Display This Week's Events ---
-                    # Incoming Order (arriving in Step 2)
+                    # --- NEW: Added Arriving This Week ---
                     current_incoming_order = 0
                     if name == "Retailer":
                         current_incoming_order = get_customer_demand(week)
                     else:
-                        # PEEK at the order pipeline
                         downstream_name = e['downstream_name']
-                        if downstream_name == 'Distributor': pipeline_peek = state['distributor_order_pipeline']
-                        elif downstream_name == 'Wholesaler': pipeline_peek = state['wholesaler_order_pipeline']
-                        elif downstream_name == 'Retailer': pipeline_peek = state['retailer_order_pipeline']
-                        else: pipeline_peek = deque()
-                        current_incoming_order = list(pipeline_peek)[0] if pipeline_peek else 0
-                                        
+                        if downstream_name:
+                            current_incoming_order = state['last_week_orders'].get(downstream_name, 0)
                     st.write(f"Incoming Order (This Week): **{current_incoming_order}**") 
                                         
                     if name == "Factory":
@@ -637,23 +515,23 @@ else:
                         st.write(f"Arriving This Week: **{arriving_this_week}**")
                         
                         arriving_next = 0
+                        # 检查队列长度是否大于1
                         if len(e['incoming_shipments']) > 1:
                             arriving_next = list(e['incoming_shipments'])[1]
-                        # Handle 2-week delay display for R/W
+                        # 专门处理R和W的2周延迟
                         elif name in ('Wholesaler', 'Retailer') and e['incoming_shipments'].maxlen == 2:
-                             if len(e['incoming_shipments']) == 1: # Only one item in queue
-                                 arriving_next = list(e['incoming_shipments'])[0] # This must be the item for next week
-                                 arriving_this_week = 0 # This week's arrival must have been 0
-                             # Re-calculate arriving_this_week for R/W based on maxlen
-                             arriving_this_week = list(e['incoming_shipments'])[0] if len(e['incoming_shipments']) == e['incoming_shipments'].maxlen else 0
-                             arriving_next = list(e['incoming_shipments'])[1] if len(e['incoming_shipments']) == e['incoming_shipments'].maxlen else (list(e['incoming_shipments'])[0] if len(e['incoming_shipments']) == 1 else 0)
+                             # 如果队列长度为1，说明下周的货在[0]，这周的货是0
+                             if len(e['incoming_shipments']) == 1:
+                                 arriving_next = list(e['incoming_shipments'])[0]
+                                 arriving_this_week = 0 # 覆盖
+                             else: # 队列为0
+                                 arriving_next = 0
+                                 arriving_this_week = 0 # 覆盖
                              
-                             # Overwrite for clarity
+                             # 重新显示修正后的"Arriving This Week"
                              st.write(f"Arriving This Week: **{arriving_this_week}**")
-                             st.write(f"Arriving Next Week: **{arriving_next}**")
-                        else: # Distributor case (maxlen 1)
-                            arriving_next = 0 # Peek at index 1 is correct, it doesn't exist for maxlen=1
-                            st.write(f"Arriving Next Week: **{arriving_next}**")
+                        
+                        st.write(f"Arriving Next Week: **{arriving_next}**")
         
         else: # Local Info Mode
             st.info("In Local Information mode, you can only see your own status dashboard.")
@@ -666,10 +544,10 @@ else:
                 st.metric("Backlog (Opening)", e['backlog'])
             
             with col2:
-                # Calculate Incoming Order for this week
                 current_incoming_order = 0
-                pipeline_peek = state['wholesaler_order_pipeline']
-                current_incoming_order = list(pipeline_peek)[0] if pipeline_peek else 0
+                downstream_name = e['downstream_name'] # Wholesaler
+                if downstream_name:
+                     current_incoming_order = state['last_week_orders'].get(downstream_name, 0)
                 st.write(f"**Incoming Order (This Week):**\n# {current_incoming_order}")
                 
             with col3:
@@ -677,8 +555,8 @@ else:
                 arriving_this_week = list(e['incoming_shipments'])[0] if e['incoming_shipments'] else 0
                 st.write(f"**Shipment Arriving (This Week):**\n# {arriving_this_week}")
                 
-                # Arriving NEXT week (Peek at the next item in the 1-week delay queue)
-                arriving_next = 0 # maxlen is 1, so index [1] doesn't exist
+                # Arriving NEXT week (Distributor的队列长度为1，所以[1]永远是0)
+                arriving_next = 0
                 st.write(f"**Shipment Arriving (Next Week):**\n# {arriving_next}")
 
         # =======================================================
@@ -689,7 +567,7 @@ else:
         # Prepare the state snapshot for the AI prompt (State AFTER arrivals/orders, BEFORE shipping)
         all_decision_point_states = {}
         for name in echelon_order:
-            e_curr = echelons[name]
+            e_curr = echelons[name] # This is END OF LAST WEEK state
             arrived = 0
             if name == "Factory":
                  if state['factory_production_pipeline']: arrived = list(state['factory_production_pipeline'])[0]
@@ -700,11 +578,7 @@ else:
             if name == "Retailer": inc_order_this_week = get_customer_demand(week)
             else:
                 ds_name = e_curr['downstream_name']
-                pipeline_peek = deque()
-                if ds_name == 'Distributor': pipeline_peek = state['distributor_order_pipeline']
-                elif ds_name == 'Wholesaler': pipeline_peek = state['wholesaler_order_pipeline']
-                elif ds_name == 'Retailer': pipeline_peek = state['retailer_order_pipeline']
-                inc_order_this_week = list(pipeline_peek)[0] if pipeline_peek else 0
+                if ds_name: inc_order_this_week = state['last_week_orders'].get(ds_name, 0)
 
             inv_after_arrival = e_curr['inventory'] + arrived
             backlog_after_new_order = e_curr['backlog'] + inc_order_this_week
@@ -725,16 +599,20 @@ else:
                     state['human_initial_order'] = int(initial_order) if initial_order is not None else 0
                     state['decision_step'] = 'final_order'
                     
+                    # --- NEW: Calculate and store suggestion ONCE ---
                     prompt_sugg = get_llm_prompt(human_echelon_state_for_prompt, week, state['llm_personality'], state['info_sharing'], all_decision_point_states)
                     ai_suggestion, _ = get_llm_order_decision(prompt_sugg, f"{human_role} (Suggestion)")
                     state['current_ai_suggestion'] = ai_suggestion # Store it
+                    # ------------------------------------------------
                     
                     st.rerun()
 
         elif state['decision_step'] == 'final_order':
             st.success(f"Your initial order was: **{state['human_initial_order']}** units.")
             
+            # --- NEW: Read stored suggestion ---
             ai_suggestion = state.get('current_ai_suggestion', 4) # Read stored value
+            # -----------------------------------
 
             with st.form(key="final_order_form"):
                 st.markdown(f"#### **Step 4b:** The AI suggests ordering **{ai_suggestion}** units.")