Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py
-# @title Beer Game Final Version (v4.18 - UI Adjustments)
+# @title Beer Game Final Version (v4.19 - Fixed TypeError, Removed Costs, Highlighted Player)
 
 # -----------------------------------------------------------------------------
 # 1. Import Libraries
@@ -22,6 +22,7 @@ from huggingface_hub import HfApi
 # -----------------------------------------------------------------------------
 st.set_page_config(page_title="Beer Game: Human-AI Collaboration", layout="wide")
 
+
 # -----------------------------------------------------------------------------
 # 2. Game Parameters & API Configuration
 # -----------------------------------------------------------------------------
@@ -54,14 +55,14 @@ except Exception as e:
 else:
     st.session_state.initialization_error = None
 
+
 # -----------------------------------------------------------------------------
-# 3. Core Game Logic Functions
+# 3. Core Game Logic Functions (Using stable v4.17 logic)
 # -----------------------------------------------------------------------------
 
 def get_customer_demand(week: int) -> int:
     return 4 if week <= 4 else 8
 
-# =============== CORRECTED Initialization ===============
 def init_game_state(llm_personality: str, info_sharing: str):
     roles = ["Retailer", "Wholesaler", "Distributor", "Factory"]
     human_role = "Distributor" # Role is fixed
@@ -74,7 +75,6 @@ def init_game_state(llm_personality: str, info_sharing: str):
         'factory_production_pipeline': deque([0] * FACTORY_LEAD_TIME, maxlen=FACTORY_LEAD_TIME),
         'decision_step': 'initial_order',
         'human_initial_order': None,
-        # Initialize last week's orders to 0, representing the state before week 1
         'last_week_orders': {name: 0 for name in roles}
     }
 
@@ -83,21 +83,17 @@ def init_game_state(llm_personality: str, info_sharing: str):
         downstream = roles[i - 1] if i - 1 >= 0 else None
 
         if name == "Distributor": shipping_weeks = FACTORY_SHIPPING_DELAY
-        elif name == "Factory": shipping_weeks = 0 # Factory produces, doesn't receive shipments
-        else: shipping_weeks = SHIPPING_DELAY # Retailer/Wholesaler use general delay
+        elif name == "Factory": shipping_weeks = 0
+        else: shipping_weeks = SHIPPING_DELAY
 
         st.session_state.game_state['echelons'][name] = {
             'name': name,
-            'inventory': INITIAL_INVENTORY, # End-of-week state
-            'backlog': INITIAL_BACKLOG,     # End-of-week state
-            'incoming_shipments': deque([0] * shipping_weeks, maxlen=shipping_weeks), # Represents only SHIPPING delay
-            'incoming_order': 0, # Order received THIS week
-            'order_placed': 0,   # Order placed THIS week
-            'shipment_sent': 0,  # Shipment sent THIS week
+            'inventory': INITIAL_INVENTORY, 'backlog': INITIAL_BACKLOG,
+            'incoming_shipments': deque([0] * shipping_weeks, maxlen=shipping_weeks),
+            'incoming_order': 0, 'order_placed': 0, 'shipment_sent': 0,
             'weekly_cost': 0, 'total_cost': 0, 'upstream_name': upstream, 'downstream_name': downstream,
         }
     st.info(f"New game started! AI Mode: **{llm_personality} / {info_sharing}**. You are playing as the: **{human_role}**.")
-# ==============================================================================
 
 def get_llm_order_decision(prompt: str, echelon_name: str) -> (int, str):
     # This function remains correct.
@@ -117,78 +113,54 @@ def get_llm_order_decision(prompt: str, echelon_name: str) -> (int, str):
             match = re.search(r'\d+', raw_text)
             if match: return int(match.group(0)), raw_text
             st.warning(f"LLM for {echelon_name} did not return a valid number. Defaulting to 4. Raw Response: '{raw_text}'")
-            return 4, raw_text # Default to 4 if no number found
+            return 4, raw_text # Default to 4
         except Exception as e:
-            st.error(f"API call failed for {echelons[human_role]}: {e}. Defaulting to 4.") # Fixed typo: echelon_name -> echelons[human_role]
+            st.error(f"API call failed for {echelon_name}: {e}. Defaulting to 4.")
             return 4, f"API_ERROR: {e}"
 
-# =============== MODIFIED FUNCTION (Prompt uses simplified state) ===============
 def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personality: str, info_sharing: str, all_echelons_state_decision_point: dict) -> str:
-    """Generates the prompt for the LLM based on the game scenario.
-       Uses the state AFTER arrivals and new orders are processed (decision point)."""
-
+    # This function's logic remains correct (from v4.17).
     e_state = echelon_state_decision_point
-
-    # Base Info reflects state before shipping
     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"
-
     if e_state['name'] == 'Factory':
         task_word = "production quantity"
         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 (arriving next week onwards): {list(e_state['incoming_shipments'])}" # This queue length matches shipping delay
-
-    # --- 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
-        # Lead time calculation remains the same for target inventory
         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
-
-        # Calculate Inventory Position based on decision point state + relevant pipelines
         if e_state['name'] == 'Factory':
-            # IP = Inv - Backlog + In Production
             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 + Order Placed Last Week (in transit to supplier)
             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'])
             supply_line_desc = "In Production"
         else:
-            # Supply line includes shipments AND the order placed last week
             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():
             if name != e_state['name']: full_info_str += f"- {name}: Inv={other_e_state['inventory']}, Backlog={other_e_state['backlog']}\n"
-
         return f"""
         **You are a supply chain manager ({e_state['name']}) with full system visibility.**
         You can see everyone's current inventory and backlog before shipping, and the real customer demand.
@@ -199,7 +171,6 @@ def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personalit
         You are still human and might get anxious about your own stock levels.
         What {task_word} should you decide on this week? Respond with a single integer.
         """
-
     elif llm_personality == 'human_like' and info_sharing == 'local':
         return f"""
         **You are a reactive supply chain manager for the {e_state['name']}.** You have a limited view and tend to over-correct based on fear.
@@ -209,225 +180,107 @@ 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 (Simplified Order Logic) ===============
 def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: int):
+    # This function's logic remains correct (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"] # Defined here
+    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} # Track arrivals for logging
-
-    # --- Game Simulation Steps ---
-
-    # Step 1a & 1b: Shipments/Production Arrive & Update Temp Inventory
-    inventory_after_arrival = {} # Store intermediate inventory state
-    # Factory production completion
+    arrived_this_week = {name: 0 for name in echelon_order}
+    inventory_after_arrival = {}
     factory_state = echelons["Factory"]
     produced_units = 0
     if state['factory_production_pipeline']:
-        produced_units = state['factory_production_pipeline'].popleft() # Pop completed production
+        produced_units = state['factory_production_pipeline'].popleft()
         arrived_this_week["Factory"] = produced_units
     inventory_after_arrival["Factory"] = factory_state['inventory'] + produced_units
-
-    # Downstream shipment arrivals
     for name in ["Retailer", "Wholesaler", "Distributor"]:
         arrived_shipment = 0
         if echelons[name]['incoming_shipments']:
-            arrived_shipment = echelons[name]['incoming_shipments'].popleft() # Pop arrived shipment
+            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 Downstream & Update Temp Backlog
-    # Orders arrive based on LAST WEEK's placed order (Delay = 1)
-    total_backlog_before_shipping = {} # Store intermediate backlog state
+    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:
             downstream_name = echelons[name]['downstream_name']
-            if downstream_name:
-                # Use the order placed by the downstream partner LAST week
-                incoming_order_for_this_week = state['last_week_orders'].get(downstream_name, 0)
-
-        echelons[name]['incoming_order'] = incoming_order_for_this_week # Store for logging/UI this week
+            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
         total_backlog_before_shipping[name] = echelons[name]['backlog'] + incoming_order_for_this_week
-
-    # --- Create State Snapshot for AI/Human Decision Point ---
-    # This reflects the state AFTER arrivals and new orders, BEFORE shipping
     decision_point_states = {}
     for name in echelon_order:
-         # Need a copy, including DEQUEUES for prompt generation
          decision_point_states[name] = {
-             'name': name,
-             'inventory': inventory_after_arrival[name], # Inventory available
-             'backlog': total_backlog_before_shipping[name], # Total demand to meet
-             'incoming_order': echelons[name]['incoming_order'], # Order received this week
-             # Pass the current state of queues for prompt generation
+             '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) ---
-    # Agents make decisions based on the decision_point_states
     current_week_orders = {}
     for name in echelon_order:
-        e = echelons[name] # Get the main state dict to store results
-        prompt_state = decision_point_states[name] # Use the snapshot for the prompt
-
-        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) # Store the decision in the main state dict
-        current_week_orders[name] = e['order_placed'] # Store for NEXT week's Step 2
-
-    # Factory schedules production based on its 'order_placed' decision
+        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'])
-
-
-    # --- Step 3: Fulfill orders (Ship Beer) ---
-    # Uses inventory_after_arrival and total_backlog_before_shipping
     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'] # Store temporarily
-
-        # 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 3b: Place shipped items into the *end* of the downstream partner's incoming shipment queue
-    # Factory -> Distributor (uses FACTORY_SHIPPING_DELAY)
-    if units_shipped["Factory"] > 0:
-        echelons['Distributor']['incoming_shipments'].append(units_shipped["Factory"])
-    # Distributor -> Wholesaler (uses SHIPPING_DELAY)
-    if units_shipped['Distributor'] > 0:
-         echelons['Wholesaler']['incoming_shipments'].append(units_shipped['Distributor'])
-    # Wholesaler -> Retailer (uses SHIPPING_DELAY)
-    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 not suitable for direct logging
     del log_entry['echelons'], log_entry['factory_production_pipeline'], log_entry['logs'], log_entry['last_week_orders']
-
     for name in echelon_order:
-        e = echelons[name]
-        # Costs are based on the END OF WEEK state
-        e['weekly_cost'] = (e['inventory'] * HOLDING_COST) + (e['backlog'] * BACKLOG_COST)
-        e['total_cost'] += e['weekly_cost']
-
-        # Log end-of-week internal state and decisions/events of the week
-        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]
-
-        # Log prediction for next week's arrival/completion (based on queues AFTER this week's processing)
-        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 human-specific decisions
-    log_entry[f'{human_role}.initial_order'] = human_initial_order
-    log_entry[f'{human_role}.ai_suggestion'] = ai_suggestion
-
+        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'
-    state['last_week_orders'] = current_week_orders # Store current decisions for next week's Step 2
-
+    state['week'] += 1; state['decision_step'] = 'initial_order'; state['last_week_orders'] = current_week_orders
     if state['week'] > WEEKS: state['game_running'] = False
-# ==============================================================================
-
 
 def plot_results(df: pd.DataFrame, title: str, human_role: str):
     # This function remains correct.
     fig, axes = plt.subplots(4, 1, figsize=(12, 22))
     fig.suptitle(title, fontsize=16)
-
     echelons = ['Retailer', 'Wholesaler', 'Distributor', 'Factory']
     plot_data = []
     for _, row in df.iterrows():
         for e in echelons:
-            # Safely access keys, provide default if missing (e.g., first few weeks)
             plot_data.append({'week': row.get('week', 0), 'echelon': e,
-                'inventory': row.get(f'{e}.inventory', 0), # Use end-of-week inventory for plots
-                'order_placed': row.get(f'{e}.order_placed', 0),
+                'inventory': row.get(f'{e}.inventory', 0), 'order_placed': row.get(f'{e}.order_placed', 0),
                 'total_cost': row.get(f'{e}.total_cost', 0)})
     plot_df = pd.DataFrame(plot_data)
-
     inventory_pivot = plot_df.pivot(index='week', columns='echelon', values='inventory').reindex(columns=echelons)
-    inventory_pivot.plot(ax=axes[0], kind='line', marker='o', markersize=4)
-    axes[0].set_title('Inventory Levels (End of Week)')
-    axes[0].grid(True, linestyle='--')
-    axes[0].set_ylabel('Stock (Units)')
-
+    inventory_pivot.plot(ax=axes[0], kind='line', marker='o', markersize=4); axes[0].set_title('Inventory Levels (End of Week)'); axes[0].grid(True, linestyle='--'); axes[0].set_ylabel('Stock (Units)')
     order_pivot = plot_df.pivot(index='week', columns='echelon', values='order_placed').reindex(columns=echelons)
-    order_pivot.plot(ax=axes[1], style='--')
-    axes[1].plot(range(1, WEEKS + 1), [get_customer_demand(w) for w in range(1, WEEKS + 1)], label='Customer Demand', color='black', lw=2.5)
-    axes[1].set_title('Order Quantities / Production Decisions')
-    axes[1].grid(True, linestyle='--')
-    axes[1].legend()
-    axes[1].set_ylabel('Ordered/Produced (Units)')
-
-    # Ensure total_cost calculation handles potential missing data gracefully
-    total_costs = plot_df.loc[plot_df.groupby('echelon')['week'].idxmax()] # Get row with max week for each echelon
+    order_pivot.plot(ax=axes[1], style='--'); axes[1].plot(range(1, WEEKS + 1), [get_customer_demand(w) for w in range(1, WEEKS + 1)], label='Customer Demand', color='black', lw=2.5); axes[1].set_title('Order Quantities / Production Decisions'); axes[1].grid(True, linestyle='--'); axes[1].legend(); axes[1].set_ylabel('Ordered/Produced (Units)')
+    total_costs = plot_df.loc[plot_df.groupby('echelon')['week'].idxmax()]
     total_costs = total_costs.set_index('echelon')['total_cost'].reindex(echelons, fill_value=0)
-    total_costs.plot(kind='bar', ax=axes[2], rot=0)
-    axes[2].set_title('Total Cumulative Cost')
-    axes[2].set_ylabel('Cost ($)')
-
-    # Safely access human decision columns
+    total_costs.plot(kind='bar', ax=axes[2], rot=0); axes[2].set_title('Total Cumulative Cost'); axes[2].set_ylabel('Cost ($)')
     human_cols = [f'{human_role}.initial_order', f'{human_role}.ai_suggestion', f'{human_role}.order_placed']
     human_df_cols = ['week'] + [col for col in human_cols if col in df.columns]
-
-    # Add try-except for robust plotting if columns are missing
     try:
         human_df = df[human_df_cols].copy()
-        human_df.rename(columns={
-            f'{human_role}.initial_order': 'Your Initial Order',
-            f'{human_role}.ai_suggestion': 'AI Suggestion',
-            f'{human_role}.order_placed': 'Your Final Order'
-        }, inplace=True)
-
-        if len(human_df.columns) > 1: # Check if there's data to plot
-            human_df.plot(x='week', ax=axes[3], marker='o', linestyle='-')
-            axes[3].set_title(f'Analysis of Your ({human_role}) Decisions')
-            axes[3].set_ylabel('Order Quantity')
-            axes[3].grid(True, linestyle='--')
-            axes[3].set_xlabel('Week')
+        human_df.rename(columns={ f'{human_role}.initial_order': 'Your Initial Order', f'{human_role}.ai_suggestion': 'AI Suggestion', f'{human_role}.order_placed': 'Your Final Order'}, inplace=True)
+        if len(human_df.columns) > 1: human_df.plot(x='week', ax=axes[3], marker='o', linestyle='-'); axes[3].set_title(f'Analysis of Your ({human_role}) Decisions'); axes[3].set_ylabel('Order Quantity'); axes[3].grid(True, linestyle='--'); axes[3].set_xlabel('Week')
         else: raise ValueError("No human decision data columns found.")
     except (KeyError, ValueError) as plot_err:
-        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
+        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
 
 def save_logs_and_upload(state: dict):
     # This function remains correct.
@@ -436,35 +289,20 @@ def save_logs_and_upload(state: dict):
     try:
         df = pd.json_normalize(state['logs'])
         fname = LOCAL_LOG_DIR / f"log_{participant_id}_{int(time.time())}.csv"
-
-        # Convert potential object columns safely before saving
-        for col in df.select_dtypes(include=['object']).columns:
-            df[col] = df[col].astype(str)
-
+        for col in df.select_dtypes(include=['object']).columns: df[col] = df[col].astype(str)
         df.to_csv(fname, index=False)
         st.success(f"Log successfully saved locally: `{fname}`")
-        with open(fname, "rb") as f:
-            st.download_button("📥 Download Log CSV", data=f, file_name=fname.name, mime="text/csv")
-
+        with open(fname, "rb") as f: st.download_button("📥 Download Log CSV", data=f, file_name=fname.name, mime="text/csv")
         if HF_TOKEN and HF_REPO_ID and hf_api:
             with st.spinner("Uploading log to Hugging Face Hub..."):
                 try:
-                    url = hf_api.upload_file(
-                        path_or_fileobj=str(fname),
-                        path_in_repo=f"logs/{fname.name}",
-                        repo_id=HF_REPO_ID,
-                        repo_type="dataset",
-                        token=HF_TOKEN
-                    )
+                    url = hf_api.upload_file( path_or_fileobj=str(fname), path_in_repo=f"logs/{fname.name}", repo_id=HF_REPO_ID, repo_type="dataset", token=HF_TOKEN)
                     st.success(f"✅ Log successfully uploaded to Hugging Face! [View File]({url})")
-                except Exception as e_upload:
-                    st.error(f"Upload to Hugging Face failed: {e_upload}")
-    except Exception as e_save:
-        st.error(f"Error processing or saving log data: {e_save}")
-
+                except Exception as e_upload: st.error(f"Upload to Hugging Face failed: {e_upload}")
+    except Exception as e_save: st.error(f"Error processing or saving log data: {e_save}")
 
 # -----------------------------------------------------------------------------
-# 4. Streamlit UI (Adjusted Dashboard Labels)
+# 4. Streamlit UI (Removed Costs, Highlighted Player)
 # -----------------------------------------------------------------------------
 st.title("🍺 The Beer Game: A Human-AI Collaboration Challenge")
 
@@ -474,89 +312,32 @@ else:
     # --- Game Setup & Instructions ---
     if 'game_state' not in st.session_state or not st.session_state.game_state.get('game_running', False):
 
+        # --- Introduction Section (Remains Correct) ---
         st.markdown("---")
         st.header("📖 Welcome to the Beer Game!")
         st.markdown("This is a simulation of a supply chain. You will play against 3 AI agents. **You do not need any prior knowledge to play.** Please read these instructions carefully.")
-
         st.subheader("1. Your Goal: Minimize Costs")
         st.success("**Your single, most important goal is to: Minimize the total cost for your position in the supply chain.**")
         st.markdown("You get costs from two things every week:")
-        st.markdown(f"""
-        - **Holding Inventory:** **${HOLDING_COST:,.2f} per unit per week.** (Cost applies to inventory left *after* shipping)
-        - **Backlog (Unfilled Orders):** **${BACKLOG_COST:,.2f} per unit per week.** (Cost applies to orders you couldn't fill *after* shipping)
-        """)
+        st.markdown(f"- **Holding Inventory:** **${HOLDING_COST:,.2f} per unit per week.** (Cost applies to inventory left *after* shipping)\n- **Backlog (Unfilled Orders):** **${BACKLOG_COST:,.2f} per unit per week.** (Cost applies to orders you couldn't fill *after* shipping)")
         with st.expander("Click to see a cost calculation example"):
-            st.markdown(f"""
-            Imagine at the **end** of Week 5, *after* you shipped beer to the Wholesaler, your final state is:
-            - Inventory: 10 units
-            - Backlog: 0 units
-            Your cost for Week 5 would be calculated *at this point*:
-            - `(10 units of Inventory * ${HOLDING_COST:,.2f})` = $5.00
-            - `(0 units of Backlog * ${BACKLOG_COST:,.2f})` = $0.00
-            - **Total Weekly Cost:** = **$5.00**
-            This cost is added to your cumulative total.
-            """)
-
+            st.markdown(f"Imagine at the **end** of Week 5, *after* you shipped beer to the Wholesaler, your final state is:\n- Inventory: 10 units\n- Backlog: 0 units\nYour cost for Week 5 would be calculated *at this point*:\n- `(10 units of Inventory * ${HOLDING_COST:,.2f})` = $5.00\n- `(0 units of Backlog * ${BACKLOG_COST:,.2f})` = $0.00\n- **Total Weekly Cost:** = **$5.00**\nThis cost is added to your cumulative total.")
         st.subheader("2. Your Role: The Distributor")
-        st.markdown("""
-        You will always play as the **Distributor**. The other 3 roles are played by AI.
-        - **Retailer (AI):** Sells to the final customer.
-        - **Wholesaler (AI):** Sells to the Retailer.
-        - **Distributor (You):** You sell to the Wholesaler.
-        - **Factory (AI):** You order from the Factory.
-        """)
+        st.markdown("You will always play as the **Distributor**. The other 3 roles are played by AI.\n- **Retailer (AI):** Sells to the final customer.\n- **Wholesaler (AI):** Sells to the Retailer.\n- **Distributor (You):** You sell to the Wholesaler.\n- **Factory (AI):** You order from the Factory.")
         try: st.image(IMAGE_PATH, caption="You are the Distributor. You get orders from the Wholesaler and place orders to the Factory.")
         except FileNotFoundError: st.warning("Image file not found.")
-
         st.subheader("3. The Core Challenge: Delays!")
         st.warning(f"It takes **{ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY} weeks** for an order you place to arrive in your inventory.")
         with st.expander("Click to see a detailed example of the 3-week delay"):
-            st.markdown(f"""
-            * **Week 10 (You):** You place an order for **50**.
-            * **Week 11 (System):** Your order arrives at the Factory (**{ORDER_PASSING_DELAY}w Order Delay**). Factory AI decides to produce 50.
-            * **Week 12 (System):** Factory finishes producing 50 (**{FACTORY_LEAD_TIME}w Production Delay**) & ships it.
-            * **Week 13 (System):** The 50 units arrive at your warehouse (**{FACTORY_SHIPPING_DELAY}w Shipping Delay**).
-            **Conclusion:** Think 3 weeks ahead! Your order in Week 10 arrives at the start of Week 13.
-            """)
-
+            st.markdown(f"* **Week 10 (You):** You place an order for **50**.\n* **Week 11 (System):** Your order arrives at the Factory (**{ORDER_PASSING_DELAY}w Order Delay**). Factory AI decides to produce 50.\n* **Week 12 (System):** Factory finishes producing 50 (**{FACTORY_LEAD_TIME}w Production Delay**) & ships it.\n* **Week 13 (System):** The 50 units arrive at your warehouse (**{FACTORY_SHIPPING_DELAY}w Shipping Delay**).\n**Conclusion:** Think 3 weeks ahead! Your order in Week 10 arrives at the start of Week 13.")
         st.subheader("4. Understanding Inventory & Backlog")
-        st.markdown("""
-        Managing your inventory and backlog is key to minimizing costs. Here's how they work:
-        * **Effective "Orders to Fill":** Each week, the total demand you need to satisfy is your `Incoming Order` for the week PLUS any `Backlog` carried over from the previous week.
-        * **If you DON'T have enough inventory:**
-            * You ship **all** the inventory you have (after receiving any arrivals for the week).
-            * The remaining unfilled "Orders to Fill" becomes your **new Backlog** for next week.
-            * **Backlog is cumulative!** If you start Week 10 with a backlog of 5, get an order for 8 (total needed = 13), receive 10 units, and ship those 10 units, your new backlog for Week 11 is `13 - 10 = 3`.
-        * **If you DO have enough inventory:**
-            * You ship all the "Orders to Fill".
-            * Your Backlog becomes 0.
-            * The remaining inventory is carried over to next week (and incurs holding costs).
-        """)
-
+        st.markdown("Managing your inventory and backlog is key to minimizing costs. Here's how they work:\n* **Effective \"Orders to Fill\":** Each week, the total demand you need to satisfy is your `Incoming Order` for the week PLUS any `Backlog` carried over from the previous week.\n* **If you DON'T have enough inventory:**\n    * You ship **all** the inventory you have (after receiving any arrivals for the week).\n    * The remaining unfilled \"Orders to Fill\" becomes your **new Backlog** for next week.\n    * **Backlog is cumulative!** If you start Week 10 with a backlog of 5, get an order for 8 (total needed = 13), receive 10 units, and ship those 10 units, your new backlog for Week 11 is `13 - 10 = 3`.\n* **If you DO have enough inventory:**\n    * You ship all the \"Orders to Fill\".\n    * Your Backlog becomes 0.\n    * The remaining inventory is carried over to next week (and incurs holding costs).")
         st.subheader("5. The Bullwhip Effect (What to Avoid)")
-        st.markdown("""
-        The "Bullwhip Effect" happens when small changes in customer demand cause **amplified**, chaotic swings in orders further up the supply chain (like you and the Factory). This often leads to cycles of **panic ordering** (ordering too much when out of stock) followed by **massive inventory pile-ups** (when late orders arrive). This cycle is very expensive. Try to order smoothly.
-        """)
-
+        st.markdown("The \"Bullwhip Effect\" happens when small changes in customer demand cause **amplified**, chaotic swings in orders further up the supply chain (like you and the Factory). This often leads to cycles of **panic ordering** (ordering too much when out of stock) followed by **massive inventory pile-ups** (when late orders arrive). This cycle is very expensive. Try to order smoothly.")
         st.subheader("6. How Each Week Works & Understanding Your Dashboard")
-        st.markdown(f"""
-        Your main job is simple: place one order each week based on the dashboard presented to you.
-
-        **A) At the start of every week, BEFORE your turn:**
-        * **(Step 1) Shipments Arrive:** Beer you ordered {ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY} weeks ago arrives.
-        * **(Step 2) New Orders Arrive:** You receive a new order from the Wholesaler (their order from *last* week).
-        * **(Step 3) You Ship Beer (Automatically):** The system ships beer *immediately* based on your inventory *after* Step 1 and the total demand *after* Step 2.
-
-        **B) Your Dashboard (What You See for Your Turn):**
-        The dashboard shows your status **at the start of the week, BEFORE Steps 1, 2, and 3 happen**:
-        * `Inventory (Opening)`: Your stock **at the beginning of the week**.
-        * `Backlog (Opening)`: Unfilled orders **carried over from the end of last week**.
-        * `Incoming Order (This Week)`: The specific order quantity that **will arrive** from the Wholesaler *during* this week (Step 2). Use this for your planning.
-        * `Arriving Next Week`: The quantity scheduled to arrive at the start of the **next week**. Use this for your planning.
-        * `Your Total Cumulative Cost`: Sum of all weekly costs up to the **end of last week**.
-        * `Cost Last Week`: The specific cost incurred just **last week**.
-        """)
+        st.markdown(f"Your main job is simple: place one order each week based on the dashboard presented to you.\n\n**A) At the start of every week, BEFORE your turn:**\n* **(Step 1) Shipments Arrive:** Beer you ordered {ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY} weeks ago arrives.\n* **(Step 2) New Orders Arrive:** You receive a new order from the Wholesaler (their order from *last* week).\n* **(Step 3) You Ship Beer (Automatically):** The system ships beer *immediately* based on your inventory *after* Step 1 and the total demand *after* Step 2.\n\n**B) Your Dashboard (What You See for Your Turn):**\nThe dashboard shows your status **at the start of the week, BEFORE Steps 1, 2, and 3 happen**:\n* `Inventory (Opening)`: Your stock **at the beginning of the week**.\n* `Backlog (Opening)`: Unfilled orders **carried over from the end of last week**.\n* `Incoming Order (This Week)`: The specific order quantity that **will arrive** from the Wholesaler *during* this week (Step 2). Use this for your planning.\n* `Arriving Next Week`: The quantity scheduled to arrive at the start of the **next week**. Use this for your planning.\n\n**C) Your Decision (Step 4 - Two Parts):**\nNow, looking at the dashboard (showing the start-of-week state) and considering the incoming order and future arrivals, you decide how much to order:\n* **(Step 4a - Initial Order):** Submit your first estimate.\n* **(Step 4b - Final Order):** See the AI's suggestion, then submit your final decision. This order will arrive in 3 weeks.\n\nSubmitting your final order ends the week. The system then calculates your `Weekly Cost` based on your inventory/backlog *after* Step 3 shipping, logs everything, and advances to the next week.")
 
+        # --- Game Configuration ---
         st.markdown("---")
         st.header("⚙️ Game Configuration")
         c1, c2 = st.columns(2)
@@ -572,7 +353,7 @@ else:
     # --- 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['echelons'], state['human_role'], state['info_sharing']
+        week, human_role, echelons, info_sharing = state['week'], state['human_role'], state['echelons'], state['info_sharing']
         echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"] # Define here for UI
 
 
@@ -580,25 +361,32 @@ else:
         st.subheader(f"Your Role: **{human_role}** | AI Mode: **{state['llm_personality'].replace('_', ' ')}** | Information: **{state['info_sharing']}**")
         st.markdown("---")
         st.subheader("Supply Chain Status (Start of Week State)") # Clarified Timing
+
         if info_sharing == 'full':
             cols = st.columns(4)
             for i, name in enumerate(echelon_order): # Use the defined echelon_order
                 with cols[i]:
-                    e, icon = echelons[name], "👤" if name == human_role else "🤖"
+                    e = echelons[name] # Get the echelon state
+                    icon = "👤" if name == human_role else "🤖"
+
+                    # =============== UI CHANGE: Highlight Player ===============
                     if name == human_role:
-                        # Highlight Distributor for full info mode
-                        st.markdown(f"##### **<span style='color:#FF4B4B;'>{icon} {name} (You)</span>**", unsafe_allow_html=True) # Highlight
+                        # Use markdown with HTML/CSS for highlighting
+                        st.markdown(f"##### **<span style='color:#FF4B4B; border: 1px solid #FF4B4B; padding: 2px 5px; border-radius: 3px;'>{icon} {name} (You)</span>**", unsafe_allow_html=True)
                     else:
                         st.markdown(f"##### {icon} {name}")
+                    # ========================================================
+
                     # Display the END OF LAST WEEK state (which is OPENING state for this week)
                     st.metric("Inventory (Opening)", e['inventory'])
                     st.metric("Backlog (Opening)", e['backlog'])
 
-                    # 移除成本显示
+                    # =============== UI CHANGE: Removed Costs ===============
                     # if name == human_role:
                     #     st.metric("Total Cost (Cumulative)", f"${e['total_cost']:,.2f}")
                     #     last_week_cost = state['logs'][-1][f"{human_role}.weekly_cost"] if week > 1 and state['logs'] else 0
                     #     st.metric("Cost Last Week", f"${last_week_cost:,.2f}")
+                    # =======================================================
 
                     # Display info about THIS week's events / NEXT week's arrivals
                     st.write(f"Incoming Order (This Week): **{e['incoming_order']}**") # Order arriving in Step 2
@@ -610,10 +398,13 @@ else:
                         # Shipment arriving NEXT week
                         arriving_next = list(e['incoming_shipments'])[0] if e['incoming_shipments'] else 0
                         st.write(f"Arriving Next Week: **{arriving_next}**")
-        else:
+        else: # Local Info Mode
             st.info("In Local Information mode, you can only see your own status dashboard.")
             e = echelons[human_role]
+            # =============== UI CHANGE: Highlight Player ===============
             st.markdown(f"### 👤 **<span style='color:#FF4B4B;'>{human_role} (Your Dashboard - Start of Week State)</span>**", unsafe_allow_html=True) # Highlight self
+            # ========================================================
+
             col1, col2, col3, col4 = st.columns(4)
             # Display OPENING state
             col1.metric("Inventory (Opening)", e['inventory'])
@@ -622,10 +413,11 @@ else:
             col3.write(f"**Incoming Order (This Week):**\n# {e['incoming_order']}")
             col4.write(f"**Shipment Arriving (Next Week):**\n# {list(e['incoming_shipments'])[0] if e['incoming_shipments'] else 0}")
 
-            # 移除成本显示
+            # =============== UI CHANGE: Removed Costs ===============
             # st.metric("Your Total Cumulative Cost", f"${e['total_cost']:,.2f}")
             # last_week_cost = state['logs'][-1][f"{human_role}.weekly_cost"] if week > 1 and state['logs'] else 0
             # st.metric("Cost Last Week", f"${last_week_cost:,.2f}")
+            # =======================================================
 
 
         st.markdown("---")
@@ -638,10 +430,8 @@ else:
             arrived = 0
             # Peek at what *will* arrive this week (Step 1) based on current queues
             if name == "Factory":
-                 # Peek at production pipeline
                  if state['factory_production_pipeline']: arrived = list(state['factory_production_pipeline'])[0]
             else:
-                 # Peek at incoming shipments
                  if e_curr['incoming_shipments']: arrived = list(e_curr['incoming_shipments'])[0]
 
             # Calculate the state AFTER arrivals and incoming orders for the prompt
@@ -653,7 +443,6 @@ else:
                 'inventory': inv_after_arrival, # State for decision making
                 'backlog': backlog_after_new_order, # State for decision making
                 'incoming_order': e_curr['incoming_order'], # Info for decision making
-                 # Pass queue state as it is at start of week for prompt context
                 'incoming_shipments': e_curr['incoming_shipments'].copy() if name != "Factory" else deque()
             }
         human_echelon_state_for_prompt = all_decision_point_states[human_role]