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Update app.py
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app.py
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# app.py
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# @title Beer Game Final Version (v4.
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# -----------------------------------------------------------------------------
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# 1. Import Libraries
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import uuid
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from pathlib import Path
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from datetime import datetime
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from huggingface_hub import HfApi
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from huggingface_hub.utils import RepositoryNotFoundError, EntryNotFoundError
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import json
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import numpy as np
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# -----------------------------------------------------------------------------
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# 0. Page Configuration (Must be the first Streamlit command)
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# -----------------------------------------------------------------------------
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st.set_page_config(page_title="Beer Game: Human-AI Collaboration", layout="wide")
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# -----------------------------------------------------------------------------
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# 2. Game Parameters & API Configuration
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# -----------------------------------------------------------------------------
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@@ -32,10 +30,10 @@ st.set_page_config(page_title="Beer Game: Human-AI Collaboration", layout="wide"
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WEEKS = 24
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INITIAL_INVENTORY = 12
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INITIAL_BACKLOG = 0
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ORDER_PASSING_DELAY = 1 #
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SHIPPING_DELAY = 2 #
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FACTORY_LEAD_TIME = 1
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FACTORY_SHIPPING_DELAY = 1 #
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HOLDING_COST = 0.5
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BACKLOG_COST = 1.0
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OPENAI_MODEL = "gpt-4o-mini"
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LOCAL_LOG_DIR = Path("logs")
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LOCAL_LOG_DIR.mkdir(exist_ok=True)
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IMAGE_PATH = "beer_game_diagram.png"
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LEADERBOARD_FILE = "leaderboard.json"
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# --- API & Secrets Configuration ---
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try:
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@@ -58,6 +55,7 @@ except Exception as e:
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else:
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st.session_state.initialization_error = None
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# -----------------------------------------------------------------------------
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# 3. Core Game Logic Functions
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# -----------------------------------------------------------------------------
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def get_customer_demand(week: int) -> int:
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return 4 if week <= 4 else 8
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# ===============
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def init_game_state(llm_personality: str, info_sharing: str
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roles = ["Retailer", "Wholesaler", "Distributor", "Factory"]
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human_role = "Distributor"
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st.session_state.game_state = {
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'game_running': True, 'participant_id': participant_id, 'week': 1,
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'human_role': human_role, 'llm_personality': llm_personality,
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'info_sharing': info_sharing, 'logs': [], 'echelons': {},
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# 管道现在必须模拟总延迟
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# Factory: 1 (订单) + 1 (生产) = 2 周延迟
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# 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)
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'factory_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
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'factory_production_pipeline': deque([0] * FACTORY_LEAD_TIME, maxlen=FACTORY_LEAD_TIME),
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# Distributor -> Factory: 1(订单) + 1(生产) + 1(运输) = 3 周
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# Wholesaler -> Distributor: 1(订单) + 2(运输) = 3 周
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# Retailer -> Wholesaler: 1(订单) + 2(运输) = 3 周
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'distributor_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
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'wholesaler_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
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'retailer_order_pipeline': deque([0] * ORDER_PASSING_DELAY, maxlen=ORDER_PASSING_DELAY),
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'decision_step': 'initial_order',
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'human_initial_order': None,
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'
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}
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for i, name in enumerate(roles):
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downstream = roles[i - 1] if i - 1 >= 0 else None
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if name == "Distributor": shipping_weeks = FACTORY_SHIPPING_DELAY
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elif name == "Factory": shipping_weeks = 0
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else: shipping_weeks = SHIPPING_DELAY
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st.session_state.game_state['echelons'][name] = {
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'name': name, 'inventory': INITIAL_INVENTORY, 'backlog': INITIAL_BACKLOG,
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'incoming_order': 0, 'order_placed': 0, 'shipment_sent': 0,
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'weekly_cost': 0, 'total_cost': 0, 'upstream_name': upstream, 'downstream_name': downstream,
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}
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st.info(f"New game started
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# ==============================================================================
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def get_llm_order_decision(prompt: str, echelon_name: str) -> (int, str):
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match = re.search(r'\d+', raw_text)
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if match: return int(match.group(0)), raw_text
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st.warning(f"LLM for {echelon_name} did not return a valid number. Defaulting to 4. Raw Response: '{raw_text}'")
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return 4, raw_text
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except Exception as e:
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st.error(f"API call failed for {echelon_name}: {e}. Defaulting to 4.")
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return 4, f"API_ERROR: {e}"
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# =============== MODIFIED FUNCTION (Prompt uses new pipeline view) ===============
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def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personality: str, info_sharing: str, all_echelons_state_decision_point: dict) -> str:
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e_state = echelon_state_decision_point
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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"
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# 查找正确的订单队列
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order_pipeline_to_show = deque()
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if e_state['name'] == 'Distributor':
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order_pipeline_to_show = st.session_state.game_state['distributor_order_pipeline']
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elif e_state['name'] == 'Wholesaler':
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order_pipeline_to_show = st.session_state.game_state['wholesaler_order_pipeline']
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elif e_state['name'] == 'Retailer':
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order_pipeline_to_show = st.session_state.game_state['retailer_order_pipeline']
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if e_state['name'] == 'Factory':
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task_word = "production quantity"
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base_info += f"- Your Production Pipeline (
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base_info += f"- Orders waiting for production (Just Arrived): {list(st.session_state.game_state['factory_order_pipeline'])}"
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else:
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task_word = "order quantity"
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base_info += f"- Shipments In Transit To You (
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base_info += f"- Orders You Placed (In transit to supplier): {list(order_pipeline_to_show)}"
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# --- Perfect Rational ---
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if llm_personality == 'perfect_rational' and info_sharing == 'full':
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stable_demand = 8
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if e_state['name'] == 'Factory': total_lead_time = FACTORY_LEAD_TIME
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elif e_state['name'] == 'Distributor': total_lead_time = ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY
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else: total_lead_time = ORDER_PASSING_DELAY + SHIPPING_DELAY
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safety_stock = 4
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target_inventory_level = (stable_demand * total_lead_time) + safety_stock
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if e_state['name'] == 'Factory':
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-
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-
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+ sum(st.session_state.game_state['factory_production_pipeline'])
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+ sum(st.session_state.game_state['factory_order_pipeline']))
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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'])})"
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else:
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inventory_position = (e_state['inventory'] - e_state['backlog']
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+ sum(order_pipeline_to_show))
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inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={sum(e_state['incoming_shipments'])} + InTransitOrder={sum(order_pipeline_to_show)})"
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optimal_order = max(0, int(target_inventory_level - inventory_position))
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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."
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elif llm_personality == 'perfect_rational' and info_sharing == 'local':
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safety_stock = 4; anchor_demand = e_state['incoming_order']
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inventory_correction = safety_stock - (e_state['inventory'] - e_state['backlog'])
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if e_state['name'] == 'Factory':
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supply_line = sum(st.session_state.game_state['factory_production_pipeline'])
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supply_line_desc = "In Production
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else:
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calculated_order = anchor_demand + inventory_correction - supply_line
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rational_local_order = max(0, int(calculated_order))
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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."
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# --- Human-like ---
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elif llm_personality == 'human_like' and info_sharing == 'full':
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full_info_str = f"\n**Full Supply Chain Information (State Before Shipping):**\n- End-Customer Demand this week: {get_customer_demand(week)} units.\n"
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for name, other_e_state in all_echelons_state_decision_point.items():
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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.
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**React emotionally.** What is your knee-jerk {task_word}? Respond with a single integer.
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"""
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# ==============================================================================
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# =============== CORRECTED step_game FUNCTION (Fixed Lead Time Logic
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def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: int):
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state = st.session_state.game_state
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week, echelons, human_role = state['week'], state['echelons'], state['human_role']
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# --- Game Simulation Steps ---
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# Step
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for name in ["Retailer", "Wholesaler", "Distributor"]:
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arrived_shipment = 0
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if echelons[name]['incoming_shipments']:
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arrived_shipment = echelons[name]['incoming_shipments'].popleft()
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arrived_this_week[name] = arrived_shipment
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inventory_after_arrival[name] = echelons[name]['inventory'] + arrived_shipment
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# Step 2: Orders Arrive
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total_backlog_before_shipping = {}
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for name in echelon_order:
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incoming_order_for_this_week = 0
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if name == "Retailer":
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incoming_order_for_this_week = get_customer_demand(week)
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else:
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# Check the correct order pipeline based on the downstream partner
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downstream_name = echelons[name]['downstream_name']
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if downstream_name
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elif downstream_name == 'Wholesaler':
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if state['wholesaler_order_pipeline']: incoming_order_for_this_week = state['wholesaler_order_pipeline'].popleft()
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elif downstream_name == 'Retailer':
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if state['retailer_order_pipeline']: incoming_order_for_this_week = state['retailer_order_pipeline'].popleft()
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echelons[name]['incoming_order'] = incoming_order_for_this_week
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# Factory's 'incoming_order' is now set (from distributor_order_pipeline)
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# Calculate intermediate state for Factory (production completion)
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if name == "Factory":
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produced_units = 0
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if state['factory_production_pipeline']:
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produced_units = state['factory_production_pipeline'].popleft()
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arrived_this_week["Factory"] = produced_units
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inventory_after_arrival["Factory"] = factory_state['inventory'] + produced_units
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total_backlog_before_shipping[name] = echelons[name]['backlog'] + incoming_order_for_this_week
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# --- Create State Snapshot for AI/Human Decision Point ---
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for name in echelon_order:
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decision_point_states[name] = {
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'name': name,
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'inventory': inventory_after_arrival[name],
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'backlog': total_backlog_before_shipping[name],
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'incoming_order': echelons[name]['incoming_order'],
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'incoming_shipments': echelons[name]['incoming_shipments'].copy() if name != "Factory" else deque(),
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}
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# --- Step 4: Agent Decisions (Place Orders / Schedule Production) ---
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for name in echelon_order:
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e = echelons[name]
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prompt_state = decision_point_states[name]
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llm_raw_responses[name] = raw_resp
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e['order_placed'] = max(0, order_amount)
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elif name == 'Wholesaler': state['wholesaler_order_pipeline'].append(e['order_placed'])
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elif name == 'Retailer': state['retailer_order_pipeline'].append(e['order_placed'])
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# Factory's 'order_placed' is its production decision
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# --- Step 3 (Logic): Fulfill orders (Ship Beer) ---
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units_shipped = {name: 0 for name in echelon_order}
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for name in echelon_order:
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e = echelons[name]
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available_inv = inventory_after_arrival[name]
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e['shipment_sent'] = min(available_inv, demand_to_meet)
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units_shipped[name] = e['shipment_sent']
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# Update the main state dict's inventory and backlog to reflect END OF WEEK state
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e['inventory'] = available_inv - e['shipment_sent']
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e['backlog'] = demand_to_meet - e['shipment_sent']
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# --- Step 5: Advance Pipelines (New Logic) ---
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#
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# and schedules it for production (adds to factory_production_pipeline)
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# This simulates FACTORY_LEAD_TIME
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# *** BUG FIX: Factory's order_placed IS its production decision ***
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# *** Factory's incoming_order is what drives its decision ***
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# Factory's decision ('order_placed') from Step 4 enters the production pipeline
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state['factory_production_pipeline'].append(echelons["Factory"]['order_placed'])
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#
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#
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if units_shipped["Factory"] > 0:
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echelons['Distributor']['incoming_shipments'].append(units_shipped["Factory"])
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# What the Distributor *shipped* in Step 3...
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if units_shipped['Distributor'] > 0:
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echelons['Wholesaler']['incoming_shipments'].append(units_shipped['Distributor'])
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# What the Wholesaler *shipped* in Step 3...
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if units_shipped['Wholesaler'] > 0:
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echelons['Retailer']['incoming_shipments'].append(units_shipped['Wholesaler'])
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-
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# --- Calculate Costs & Log (End of Week) ---
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log_entry = {'timestamp': datetime.utcnow().isoformat() + "Z", 'week': week, **state}
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for key in ['distributor_order_pipeline', 'wholesaler_order_pipeline', 'retailer_order_pipeline', 'factory_order_pipeline']:
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if key in log_entry: del log_entry[key]
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for name in echelon_order:
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e = echelons[name]
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state['logs'].append(log_entry)
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# --- Advance Week ---
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state['week'] += 1; state['decision_step'] = 'initial_order'
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# We rely on the order pipelines
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if state['week'] > WEEKS: state['game_running'] = False
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# ==============================================================================
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|
| 384 |
-
|
| 385 |
def plot_results(df: pd.DataFrame, title: str, human_role: str):
|
| 386 |
# This function remains correct.
|
| 387 |
fig, axes = plt.subplots(4, 1, figsize=(12, 22))
|
|
@@ -412,8 +348,10 @@ def plot_results(df: pd.DataFrame, title: str, human_role: str):
|
|
| 412 |
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')
|
| 413 |
plt.tight_layout(rect=[0, 0, 1, 0.96]); return fig
|
| 414 |
|
| 415 |
-
|
| 416 |
-
|
|
|
|
|
|
|
| 417 |
def load_leaderboard_data():
|
| 418 |
if not hf_api or not HF_REPO_ID: return {}
|
| 419 |
try:
|
|
@@ -494,8 +432,10 @@ def show_leaderboard_ui():
|
|
| 494 |
except Exception as e:
|
| 495 |
st.error(f"Error displaying leaderboard: {e}")
|
| 496 |
st.dataframe(leaderboard_data)
|
|
|
|
| 497 |
|
| 498 |
def save_logs_and_upload(state: dict):
|
|
|
|
| 499 |
if not state.get('logs'):
|
| 500 |
st.warning("No log data to save.")
|
| 501 |
return
|
|
@@ -548,12 +488,12 @@ else:
|
|
| 548 |
# --- Game Setup & Instructions ---
|
| 549 |
if 'game_state' not in st.session_state or not st.session_state.game_state.get('game_running', False):
|
| 550 |
|
| 551 |
-
# Introduction is removed as requested
|
| 552 |
-
|
| 553 |
st.markdown("---")
|
| 554 |
st.header("⚙️ Game Configuration")
|
| 555 |
|
|
|
|
| 556 |
participant_id = st.text_input("Enter Your Name or Team ID:", key="participant_id_input", placeholder="e.g., Team A")
|
|
|
|
| 557 |
|
| 558 |
c1, c2 = st.columns(2)
|
| 559 |
with c1:
|
|
@@ -561,14 +501,17 @@ else:
|
|
| 561 |
with c2:
|
| 562 |
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.")
|
| 563 |
|
|
|
|
| 564 |
if st.button("🚀 Start Game", type="primary", disabled=(client is None)):
|
| 565 |
if not participant_id:
|
| 566 |
st.error("Please enter a Name or Team ID to start!")
|
| 567 |
else:
|
|
|
|
| 568 |
existing_data = load_leaderboard_data()
|
| 569 |
if participant_id in existing_data:
|
| 570 |
-
#
|
| 571 |
if st.session_state.get('last_id_warning') == participant_id:
|
|
|
|
| 572 |
st.session_state.pop('last_id_warning', None)
|
| 573 |
init_game_state(llm_personality, info_sharing, participant_id)
|
| 574 |
st.rerun()
|
|
@@ -576,26 +519,29 @@ else:
|
|
| 576 |
st.session_state['last_id_warning'] = participant_id
|
| 577 |
st.warning(f"ID '{participant_id}' already exists! Your score will be overwritten. Click 'Start Game' again to confirm.")
|
| 578 |
else:
|
|
|
|
| 579 |
if 'last_id_warning' in st.session_state:
|
| 580 |
del st.session_state['last_id_warning']
|
| 581 |
init_game_state(llm_personality, info_sharing, participant_id)
|
| 582 |
st.rerun()
|
|
|
|
| 583 |
|
|
|
|
| 584 |
show_leaderboard_ui()
|
|
|
|
| 585 |
|
| 586 |
# --- Main Game Interface ---
|
| 587 |
elif 'game_state' in st.session_state and st.session_state.game_state.get('game_running'):
|
| 588 |
state = st.session_state.game_state
|
| 589 |
week, human_role, echelons, info_sharing = state['week'], state['human_role'], state['echelons'], state['info_sharing']
|
| 590 |
-
echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"]
|
| 591 |
|
| 592 |
|
| 593 |
st.header(f"Week {week} / {WEEKS}")
|
| 594 |
st.subheader(f"Your Role: **{human_role}** ({state['participant_id']}) | AI Mode: **{state['llm_personality'].replace('_', ' ')}** | Information: **{state['info_sharing']}**")
|
| 595 |
st.markdown("---")
|
| 596 |
-
st.subheader("Supply Chain Status (Start of Week State)")
|
| 597 |
|
| 598 |
-
# =============== MODIFIED UI LOGIC (v4.21) ===============
|
| 599 |
if info_sharing == 'full':
|
| 600 |
cols = st.columns(4)
|
| 601 |
for i, name in enumerate(echelon_order):
|
|
@@ -611,20 +557,16 @@ else:
|
|
| 611 |
st.metric("Inventory (Opening)", e['inventory'])
|
| 612 |
st.metric("Backlog (Opening)", e['backlog'])
|
| 613 |
|
| 614 |
-
#
|
| 615 |
-
|
|
|
|
| 616 |
current_incoming_order = 0
|
| 617 |
if name == "Retailer":
|
| 618 |
current_incoming_order = get_customer_demand(week)
|
| 619 |
else:
|
| 620 |
-
# PEEK at the order pipeline
|
| 621 |
downstream_name = e['downstream_name']
|
| 622 |
-
if downstream_name
|
| 623 |
-
|
| 624 |
-
elif downstream_name == 'Retailer': pipeline_peek = state['retailer_order_pipeline']
|
| 625 |
-
else: pipeline_peek = deque()
|
| 626 |
-
current_incoming_order = list(pipeline_peek)[0] if pipeline_peek else 0
|
| 627 |
-
|
| 628 |
st.write(f"Incoming Order (This Week): **{current_incoming_order}**")
|
| 629 |
|
| 630 |
if name == "Factory":
|
|
@@ -639,25 +581,19 @@ else:
|
|
| 639 |
arriving_next = 0
|
| 640 |
if len(e['incoming_shipments']) > 1:
|
| 641 |
arriving_next = list(e['incoming_shipments'])[1]
|
| 642 |
-
#
|
| 643 |
-
elif name in ('Wholesaler', 'Retailer') and e['incoming_shipments'].maxlen == 2:
|
| 644 |
-
|
| 645 |
-
|
| 646 |
-
|
| 647 |
-
#
|
| 648 |
-
|
| 649 |
-
|
| 650 |
-
|
| 651 |
-
# Overwrite for clarity
|
| 652 |
-
st.write(f"Arriving This Week: **{arriving_this_week}**")
|
| 653 |
-
st.write(f"Arriving Next Week: **{arriving_next}**")
|
| 654 |
-
else: # Distributor case (maxlen 1)
|
| 655 |
-
arriving_next = 0 # Peek at index 1 is correct, it doesn't exist for maxlen=1
|
| 656 |
-
st.write(f"Arriving Next Week: **{arriving_next}**")
|
| 657 |
|
| 658 |
else: # Local Info Mode
|
| 659 |
st.info("In Local Information mode, you can only see your own status dashboard.")
|
| 660 |
-
e = echelons[human_role]
|
| 661 |
st.markdown(f"### 👤 **<span style='color:#FF4B4B;'>{human_role} (Your Dashboard - Start of Week State)</span>**", unsafe_allow_html=True)
|
| 662 |
|
| 663 |
col1, col2, col3 = st.columns(3)
|
|
@@ -666,10 +602,10 @@ else:
|
|
| 666 |
st.metric("Backlog (Opening)", e['backlog'])
|
| 667 |
|
| 668 |
with col2:
|
| 669 |
-
# Calculate Incoming Order for this week
|
| 670 |
current_incoming_order = 0
|
| 671 |
-
|
| 672 |
-
|
|
|
|
| 673 |
st.write(f"**Incoming Order (This Week):**\n# {current_incoming_order}")
|
| 674 |
|
| 675 |
with col3:
|
|
@@ -678,18 +614,18 @@ else:
|
|
| 678 |
st.write(f"**Shipment Arriving (This Week):**\n# {arriving_this_week}")
|
| 679 |
|
| 680 |
# Arriving NEXT week (Peek at the next item in the 1-week delay queue)
|
| 681 |
-
arriving_next = 0
|
|
|
|
|
|
|
| 682 |
st.write(f"**Shipment Arriving (Next Week):**\n# {arriving_next}")
|
| 683 |
|
| 684 |
-
# =======================================================
|
| 685 |
-
|
| 686 |
st.markdown("---")
|
| 687 |
st.header("Your Decision (Step 4)")
|
| 688 |
|
| 689 |
# Prepare the state snapshot for the AI prompt (State AFTER arrivals/orders, BEFORE shipping)
|
| 690 |
all_decision_point_states = {}
|
| 691 |
for name in echelon_order:
|
| 692 |
-
e_curr = echelons[name]
|
| 693 |
arrived = 0
|
| 694 |
if name == "Factory":
|
| 695 |
if state['factory_production_pipeline']: arrived = list(state['factory_production_pipeline'])[0]
|
|
@@ -700,11 +636,7 @@ else:
|
|
| 700 |
if name == "Retailer": inc_order_this_week = get_customer_demand(week)
|
| 701 |
else:
|
| 702 |
ds_name = e_curr['downstream_name']
|
| 703 |
-
|
| 704 |
-
if ds_name == 'Distributor': pipeline_peek = state['distributor_order_pipeline']
|
| 705 |
-
elif ds_name == 'Wholesaler': pipeline_peek = state['wholesaler_order_pipeline']
|
| 706 |
-
elif ds_name == 'Retailer': pipeline_peek = state['retailer_order_pipeline']
|
| 707 |
-
inc_order_this_week = list(pipeline_peek)[0] if pipeline_peek else 0
|
| 708 |
|
| 709 |
inv_after_arrival = e_curr['inventory'] + arrived
|
| 710 |
backlog_after_new_order = e_curr['backlog'] + inc_order_this_week
|
|
@@ -725,16 +657,20 @@ else:
|
|
| 725 |
state['human_initial_order'] = int(initial_order) if initial_order is not None else 0
|
| 726 |
state['decision_step'] = 'final_order'
|
| 727 |
|
|
|
|
| 728 |
prompt_sugg = get_llm_prompt(human_echelon_state_for_prompt, week, state['llm_personality'], state['info_sharing'], all_decision_point_states)
|
| 729 |
ai_suggestion, _ = get_llm_order_decision(prompt_sugg, f"{human_role} (Suggestion)")
|
| 730 |
state['current_ai_suggestion'] = ai_suggestion # Store it
|
|
|
|
| 731 |
|
| 732 |
st.rerun()
|
| 733 |
|
| 734 |
elif state['decision_step'] == 'final_order':
|
| 735 |
st.success(f"Your initial order was: **{state['human_initial_order']}** units.")
|
| 736 |
|
|
|
|
| 737 |
ai_suggestion = state.get('current_ai_suggestion', 4) # Read stored value
|
|
|
|
| 738 |
|
| 739 |
with st.form(key="final_order_form"):
|
| 740 |
st.markdown(f"#### **Step 4b:** The AI suggests ordering **{ai_suggestion}** units.")
|
|
@@ -809,7 +745,9 @@ else:
|
|
| 809 |
except Exception as e:
|
| 810 |
st.error(f"Error generating final report: {e}")
|
| 811 |
|
|
|
|
| 812 |
show_leaderboard_ui()
|
|
|
|
| 813 |
|
| 814 |
if st.button("✨ Start a New Game"):
|
| 815 |
del st.session_state.game_state
|
|
|
|
| 1 |
# app.py
|
| 2 |
+
# @title Beer Game Final Version (v4.21 - Corrected 3-Week Lead Time Logic & UI)
|
| 3 |
|
| 4 |
# -----------------------------------------------------------------------------
|
| 5 |
# 1. Import Libraries
|
|
|
|
| 15 |
import uuid
|
| 16 |
from pathlib import Path
|
| 17 |
from datetime import datetime
|
| 18 |
+
from huggingface_hub import HfApi
|
|
|
|
|
|
|
|
|
|
| 19 |
|
| 20 |
# -----------------------------------------------------------------------------
|
| 21 |
# 0. Page Configuration (Must be the first Streamlit command)
|
| 22 |
# -----------------------------------------------------------------------------
|
| 23 |
st.set_page_config(page_title="Beer Game: Human-AI Collaboration", layout="wide")
|
| 24 |
|
| 25 |
+
|
| 26 |
# -----------------------------------------------------------------------------
|
| 27 |
# 2. Game Parameters & API Configuration
|
| 28 |
# -----------------------------------------------------------------------------
|
|
|
|
| 30 |
WEEKS = 24
|
| 31 |
INITIAL_INVENTORY = 12
|
| 32 |
INITIAL_BACKLOG = 0
|
| 33 |
+
ORDER_PASSING_DELAY = 1 # Handled by last_week_orders
|
| 34 |
+
SHIPPING_DELAY = 2 # General shipping delay (R->W, W->D)
|
| 35 |
+
FACTORY_LEAD_TIME = 1
|
| 36 |
+
FACTORY_SHIPPING_DELAY = 1 # Specific delay from Factory to Distributor
|
| 37 |
HOLDING_COST = 0.5
|
| 38 |
BACKLOG_COST = 1.0
|
| 39 |
|
|
|
|
| 41 |
OPENAI_MODEL = "gpt-4o-mini"
|
| 42 |
LOCAL_LOG_DIR = Path("logs")
|
| 43 |
LOCAL_LOG_DIR.mkdir(exist_ok=True)
|
| 44 |
+
IMAGE_PATH = "beer_game_diagram.png" # Path to your uploaded image
|
|
|
|
| 45 |
|
| 46 |
# --- API & Secrets Configuration ---
|
| 47 |
try:
|
|
|
|
| 55 |
else:
|
| 56 |
st.session_state.initialization_error = None
|
| 57 |
|
| 58 |
+
|
| 59 |
# -----------------------------------------------------------------------------
|
| 60 |
# 3. Core Game Logic Functions
|
| 61 |
# -----------------------------------------------------------------------------
|
|
|
|
| 63 |
def get_customer_demand(week: int) -> int:
|
| 64 |
return 4 if week <= 4 else 8
|
| 65 |
|
| 66 |
+
# =============== CORRECTED Initialization (v4.17 logic) ===============
|
| 67 |
+
def init_game_state(llm_personality: str, info_sharing: str):
|
| 68 |
roles = ["Retailer", "Wholesaler", "Distributor", "Factory"]
|
| 69 |
+
human_role = "Distributor" # Role is fixed
|
| 70 |
+
participant_id = str(uuid.uuid4())[:8]
|
| 71 |
+
|
| 72 |
st.session_state.game_state = {
|
| 73 |
'game_running': True, 'participant_id': participant_id, 'week': 1,
|
| 74 |
'human_role': human_role, 'llm_personality': llm_personality,
|
| 75 |
'info_sharing': info_sharing, 'logs': [], 'echelons': {},
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 76 |
'factory_production_pipeline': deque([0] * FACTORY_LEAD_TIME, maxlen=FACTORY_LEAD_TIME),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 77 |
'decision_step': 'initial_order',
|
| 78 |
'human_initial_order': None,
|
| 79 |
+
# Initialize last week's orders to 0
|
| 80 |
+
'last_week_orders': {name: 0 for name in roles}
|
| 81 |
}
|
| 82 |
|
| 83 |
for i, name in enumerate(roles):
|
|
|
|
| 85 |
downstream = roles[i - 1] if i - 1 >= 0 else None
|
| 86 |
|
| 87 |
if name == "Distributor": shipping_weeks = FACTORY_SHIPPING_DELAY
|
| 88 |
+
elif name == "Factory": shipping_weeks = 0
|
| 89 |
+
else: shipping_weeks = SHIPPING_DELAY
|
| 90 |
|
| 91 |
st.session_state.game_state['echelons'][name] = {
|
| 92 |
'name': name, 'inventory': INITIAL_INVENTORY, 'backlog': INITIAL_BACKLOG,
|
|
|
|
| 94 |
'incoming_order': 0, 'order_placed': 0, 'shipment_sent': 0,
|
| 95 |
'weekly_cost': 0, 'total_cost': 0, 'upstream_name': upstream, 'downstream_name': downstream,
|
| 96 |
}
|
| 97 |
+
st.info(f"New game started! AI Mode: **{llm_personality} / {info_sharing}**. You are playing as the: **{human_role}**.")
|
| 98 |
# ==============================================================================
|
| 99 |
|
| 100 |
def get_llm_order_decision(prompt: str, echelon_name: str) -> (int, str):
|
|
|
|
| 115 |
match = re.search(r'\d+', raw_text)
|
| 116 |
if match: return int(match.group(0)), raw_text
|
| 117 |
st.warning(f"LLM for {echelon_name} did not return a valid number. Defaulting to 4. Raw Response: '{raw_text}'")
|
| 118 |
+
return 4, raw_text # Default to 4
|
| 119 |
except Exception as e:
|
| 120 |
st.error(f"API call failed for {echelon_name}: {e}. Defaulting to 4.")
|
| 121 |
return 4, f"API_ERROR: {e}"
|
| 122 |
|
|
|
|
| 123 |
def get_llm_prompt(echelon_state_decision_point: dict, week: int, llm_personality: str, info_sharing: str, all_echelons_state_decision_point: dict) -> str:
|
| 124 |
+
# This function's logic remains correct (from v4.17).
|
| 125 |
e_state = echelon_state_decision_point
|
| 126 |
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"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 127 |
if e_state['name'] == 'Factory':
|
| 128 |
task_word = "production quantity"
|
| 129 |
+
base_info += f"- Your Production Pipeline (completing next week onwards): {list(st.session_state.game_state['factory_production_pipeline'])}"
|
|
|
|
| 130 |
else:
|
| 131 |
task_word = "order quantity"
|
| 132 |
+
base_info += f"- Shipments In Transit To You (arriving next week onwards): {list(e_state['incoming_shipments'])}"
|
|
|
|
|
|
|
|
|
|
| 133 |
if llm_personality == 'perfect_rational' and info_sharing == 'full':
|
| 134 |
stable_demand = 8
|
| 135 |
+
if e_state['name'] == 'Factory': total_lead_time = FACTORY_LEAD_TIME
|
| 136 |
elif e_state['name'] == 'Distributor': total_lead_time = ORDER_PASSING_DELAY + FACTORY_LEAD_TIME + FACTORY_SHIPPING_DELAY
|
| 137 |
else: total_lead_time = ORDER_PASSING_DELAY + SHIPPING_DELAY
|
| 138 |
safety_stock = 4
|
| 139 |
target_inventory_level = (stable_demand * total_lead_time) + safety_stock
|
|
|
|
| 140 |
if e_state['name'] == 'Factory':
|
| 141 |
+
inventory_position = (e_state['inventory'] - e_state['backlog'] + sum(st.session_state.game_state['factory_production_pipeline']))
|
| 142 |
+
inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InProd={sum(st.session_state.game_state['factory_production_pipeline'])})"
|
|
|
|
|
|
|
|
|
|
| 143 |
else:
|
| 144 |
+
order_in_transit_to_supplier = st.session_state.game_state['last_week_orders'].get(e_state['name'], 0)
|
| 145 |
+
inventory_position = (e_state['inventory'] - e_state['backlog'] + sum(e_state['incoming_shipments']) + order_in_transit_to_supplier)
|
| 146 |
+
inv_pos_components = f"(Inv={e_state['inventory']} - Backlog={e_state['backlog']} + InTransitShip={sum(e_state['incoming_shipments'])} + OrderToSupplier={order_in_transit_to_supplier})"
|
|
|
|
|
|
|
|
|
|
| 147 |
optimal_order = max(0, int(target_inventory_level - inventory_position))
|
| 148 |
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."
|
|
|
|
| 149 |
elif llm_personality == 'perfect_rational' and info_sharing == 'local':
|
| 150 |
safety_stock = 4; anchor_demand = e_state['incoming_order']
|
| 151 |
inventory_correction = safety_stock - (e_state['inventory'] - e_state['backlog'])
|
|
|
|
| 152 |
if e_state['name'] == 'Factory':
|
| 153 |
+
supply_line = sum(st.session_state.game_state['factory_production_pipeline'])
|
| 154 |
+
supply_line_desc = "In Production"
|
| 155 |
else:
|
| 156 |
+
order_in_transit_to_supplier = st.session_state.game_state['last_week_orders'].get(e_state['name'], 0)
|
| 157 |
+
supply_line = sum(e_state['incoming_shipments']) + order_in_transit_to_supplier
|
| 158 |
+
supply_line_desc = "Supply Line (In Transit Shipments + Order To Supplier)"
|
| 159 |
calculated_order = anchor_demand + inventory_correction - supply_line
|
| 160 |
rational_local_order = max(0, int(calculated_order))
|
| 161 |
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."
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| 162 |
elif llm_personality == 'human_like' and info_sharing == 'full':
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| 163 |
full_info_str = f"\n**Full Supply Chain Information (State Before Shipping):**\n- End-Customer Demand this week: {get_customer_demand(week)} units.\n"
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| 164 |
for name, other_e_state in all_echelons_state_decision_point.items():
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| 182 |
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.
|
| 183 |
**React emotionally.** What is your knee-jerk {task_word}? Respond with a single integer.
|
| 184 |
"""
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|
| 185 |
|
| 186 |
+
# =============== CORRECTED step_game FUNCTION (Fixed Lead Time Logic) ===============
|
| 187 |
def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: int):
|
| 188 |
state = st.session_state.game_state
|
| 189 |
week, echelons, human_role = state['week'], state['echelons'], state['human_role']
|
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|
| 198 |
|
| 199 |
# --- Game Simulation Steps ---
|
| 200 |
|
| 201 |
+
# Step 1a: Factory Production completes
|
| 202 |
+
factory_state = echelons["Factory"]
|
| 203 |
+
produced_units = 0
|
| 204 |
+
if state['factory_production_pipeline']:
|
| 205 |
+
produced_units = state['factory_production_pipeline'].popleft() # Pop completed production
|
| 206 |
+
arrived_this_week["Factory"] = produced_units
|
| 207 |
+
inventory_after_arrival = {} # Store intermediate inventory state
|
| 208 |
+
inventory_after_arrival["Factory"] = factory_state['inventory'] + produced_units
|
| 209 |
+
|
| 210 |
+
# Step 1b: Shipments arrive at downstream echelons
|
| 211 |
for name in ["Retailer", "Wholesaler", "Distributor"]:
|
| 212 |
arrived_shipment = 0
|
| 213 |
if echelons[name]['incoming_shipments']:
|
| 214 |
+
arrived_shipment = echelons[name]['incoming_shipments'].popleft() # Pop arrived shipment
|
| 215 |
arrived_this_week[name] = arrived_shipment
|
| 216 |
inventory_after_arrival[name] = echelons[name]['inventory'] + arrived_shipment
|
| 217 |
|
| 218 |
+
# Step 2: Orders Arrive from Downstream
|
| 219 |
+
total_backlog_before_shipping = {} # Store intermediate backlog state
|
| 220 |
for name in echelon_order:
|
| 221 |
incoming_order_for_this_week = 0
|
| 222 |
if name == "Retailer":
|
| 223 |
incoming_order_for_this_week = get_customer_demand(week)
|
| 224 |
else:
|
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|
| 225 |
downstream_name = echelons[name]['downstream_name']
|
| 226 |
+
if downstream_name:
|
| 227 |
+
incoming_order_for_this_week = state['last_week_orders'].get(downstream_name, 0)
|
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|
| 228 |
|
| 229 |
+
echelons[name]['incoming_order'] = incoming_order_for_this_week # Store for logging/UI this week
|
| 230 |
total_backlog_before_shipping[name] = echelons[name]['backlog'] + incoming_order_for_this_week
|
| 231 |
|
| 232 |
# --- Create State Snapshot for AI/Human Decision Point ---
|
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|
| 234 |
for name in echelon_order:
|
| 235 |
decision_point_states[name] = {
|
| 236 |
'name': name,
|
| 237 |
+
'inventory': inventory_after_arrival[name], # Inventory available
|
| 238 |
+
'backlog': total_backlog_before_shipping[name], # Total demand to meet
|
| 239 |
+
'incoming_order': echelons[name]['incoming_order'], # Order received this week
|
| 240 |
'incoming_shipments': echelons[name]['incoming_shipments'].copy() if name != "Factory" else deque(),
|
| 241 |
}
|
| 242 |
|
| 243 |
# --- Step 4: Agent Decisions (Place Orders / Schedule Production) ---
|
| 244 |
+
current_week_orders = {} # Store THIS week's decisions
|
| 245 |
for name in echelon_order:
|
| 246 |
e = echelons[name]
|
| 247 |
prompt_state = decision_point_states[name]
|
|
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|
| 254 |
|
| 255 |
llm_raw_responses[name] = raw_resp
|
| 256 |
e['order_placed'] = max(0, order_amount)
|
| 257 |
+
current_week_orders[name] = e['order_placed'] # Store for NEXT week's Step 2
|
| 258 |
|
| 259 |
+
# --- Step 3 (Logic Moved): Fulfill orders (Ship Beer) ---
|
| 260 |
+
# This MUST happen BEFORE Step 5 (Pipelines Advance)
|
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|
| 261 |
units_shipped = {name: 0 for name in echelon_order}
|
| 262 |
for name in echelon_order:
|
| 263 |
e = echelons[name]
|
|
|
|
| 265 |
available_inv = inventory_after_arrival[name]
|
| 266 |
|
| 267 |
e['shipment_sent'] = min(available_inv, demand_to_meet)
|
| 268 |
+
units_shipped[name] = e['shipment_sent'] # Store temporarily
|
| 269 |
|
| 270 |
# Update the main state dict's inventory and backlog to reflect END OF WEEK state
|
| 271 |
e['inventory'] = available_inv - e['shipment_sent']
|
| 272 |
e['backlog'] = demand_to_meet - e['shipment_sent']
|
| 273 |
|
| 274 |
# --- Step 5: Advance Pipelines (New Logic) ---
|
| 275 |
+
# Factory's decision ('order_placed') from this week enters the production pipeline
|
| 276 |
+
# This simulates the FACTORY_LEAD_TIME
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 277 |
state['factory_production_pipeline'].append(echelons["Factory"]['order_placed'])
|
| 278 |
+
|
| 279 |
+
# Items shipped in Step 3 now enter their respective shipping pipelines
|
| 280 |
+
# Factory -> Distributor (uses FACTORY_SHIPPING_DELAY)
|
| 281 |
if units_shipped["Factory"] > 0:
|
| 282 |
echelons['Distributor']['incoming_shipments'].append(units_shipped["Factory"])
|
| 283 |
+
# Distributor -> Wholesaler (uses SHIPPING_DELAY)
|
|
|
|
| 284 |
if units_shipped['Distributor'] > 0:
|
| 285 |
echelons['Wholesaler']['incoming_shipments'].append(units_shipped['Distributor'])
|
| 286 |
+
# Wholesaler -> Retailer (uses SHIPPING_DELAY)
|
|
|
|
| 287 |
if units_shipped['Wholesaler'] > 0:
|
| 288 |
echelons['Retailer']['incoming_shipments'].append(units_shipped['Wholesaler'])
|
| 289 |
|
|
|
|
| 290 |
# --- Calculate Costs & Log (End of Week) ---
|
| 291 |
log_entry = {'timestamp': datetime.utcnow().isoformat() + "Z", 'week': week, **state}
|
| 292 |
+
del log_entry['echelons'], log_entry['factory_production_pipeline'], log_entry['logs'], log_entry['last_week_orders']
|
| 293 |
+
if 'current_ai_suggestion' in log_entry: del log_entry['current_ai_suggestion']
|
|
|
|
|
|
|
|
|
|
| 294 |
|
| 295 |
for name in echelon_order:
|
| 296 |
e = echelons[name]
|
|
|
|
| 313 |
state['logs'].append(log_entry)
|
| 314 |
|
| 315 |
# --- Advance Week ---
|
| 316 |
+
state['week'] += 1; state['decision_step'] = 'initial_order'; state['last_week_orders'] = current_week_orders
|
| 317 |
+
state['current_ai_suggestion'] = None # Clean up
|
|
|
|
| 318 |
if state['week'] > WEEKS: state['game_running'] = False
|
| 319 |
# ==============================================================================
|
| 320 |
|
|
|
|
| 321 |
def plot_results(df: pd.DataFrame, title: str, human_role: str):
|
| 322 |
# This function remains correct.
|
| 323 |
fig, axes = plt.subplots(4, 1, figsize=(12, 22))
|
|
|
|
| 348 |
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')
|
| 349 |
plt.tight_layout(rect=[0, 0, 1, 0.96]); return fig
|
| 350 |
|
| 351 |
+
|
| 352 |
+
# =============== NEW: Leaderboard Functions ===============
|
| 353 |
+
|
| 354 |
+
@st.cache_data(ttl=60) # 缓存1分钟
|
| 355 |
def load_leaderboard_data():
|
| 356 |
if not hf_api or not HF_REPO_ID: return {}
|
| 357 |
try:
|
|
|
|
| 432 |
except Exception as e:
|
| 433 |
st.error(f"Error displaying leaderboard: {e}")
|
| 434 |
st.dataframe(leaderboard_data)
|
| 435 |
+
# ==============================================================================
|
| 436 |
|
| 437 |
def save_logs_and_upload(state: dict):
|
| 438 |
+
# This function is now responsible for CSV *and* leaderboard updates.
|
| 439 |
if not state.get('logs'):
|
| 440 |
st.warning("No log data to save.")
|
| 441 |
return
|
|
|
|
| 488 |
# --- Game Setup & Instructions ---
|
| 489 |
if 'game_state' not in st.session_state or not st.session_state.game_state.get('game_running', False):
|
| 490 |
|
|
|
|
|
|
|
| 491 |
st.markdown("---")
|
| 492 |
st.header("⚙️ Game Configuration")
|
| 493 |
|
| 494 |
+
# =============== NEW: Participant ID Input ===============
|
| 495 |
participant_id = st.text_input("Enter Your Name or Team ID:", key="participant_id_input", placeholder="e.g., Team A")
|
| 496 |
+
# =======================================================
|
| 497 |
|
| 498 |
c1, c2 = st.columns(2)
|
| 499 |
with c1:
|
|
|
|
| 501 |
with c2:
|
| 502 |
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.")
|
| 503 |
|
| 504 |
+
# =============== MODIFIED: Start Game Button ===============
|
| 505 |
if st.button("🚀 Start Game", type="primary", disabled=(client is None)):
|
| 506 |
if not participant_id:
|
| 507 |
st.error("Please enter a Name or Team ID to start!")
|
| 508 |
else:
|
| 509 |
+
# 检查ID是否已存在
|
| 510 |
existing_data = load_leaderboard_data()
|
| 511 |
if participant_id in existing_data:
|
| 512 |
+
# 如果ID已存在,添加一个session_state标志,要求再次点击
|
| 513 |
if st.session_state.get('last_id_warning') == participant_id:
|
| 514 |
+
# 这是第二次点击,确认覆盖
|
| 515 |
st.session_state.pop('last_id_warning', None)
|
| 516 |
init_game_state(llm_personality, info_sharing, participant_id)
|
| 517 |
st.rerun()
|
|
|
|
| 519 |
st.session_state['last_id_warning'] = participant_id
|
| 520 |
st.warning(f"ID '{participant_id}' already exists! Your score will be overwritten. Click 'Start Game' again to confirm.")
|
| 521 |
else:
|
| 522 |
+
# 新ID,直接开始
|
| 523 |
if 'last_id_warning' in st.session_state:
|
| 524 |
del st.session_state['last_id_warning']
|
| 525 |
init_game_state(llm_personality, info_sharing, participant_id)
|
| 526 |
st.rerun()
|
| 527 |
+
# ===========================================================
|
| 528 |
|
| 529 |
+
# =============== NEW: Show Leaderboard on Start Page ===============
|
| 530 |
show_leaderboard_ui()
|
| 531 |
+
# =================================================================
|
| 532 |
|
| 533 |
# --- Main Game Interface ---
|
| 534 |
elif 'game_state' in st.session_state and st.session_state.game_state.get('game_running'):
|
| 535 |
state = st.session_state.game_state
|
| 536 |
week, human_role, echelons, info_sharing = state['week'], state['human_role'], state['echelons'], state['info_sharing']
|
| 537 |
+
echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"] # Define here for UI
|
| 538 |
|
| 539 |
|
| 540 |
st.header(f"Week {week} / {WEEKS}")
|
| 541 |
st.subheader(f"Your Role: **{human_role}** ({state['participant_id']}) | AI Mode: **{state['llm_personality'].replace('_', ' ')}** | Information: **{state['info_sharing']}**")
|
| 542 |
st.markdown("---")
|
| 543 |
+
st.subheader("Supply Chain Status (Start of Week State)") # Clarified Timing
|
| 544 |
|
|
|
|
| 545 |
if info_sharing == 'full':
|
| 546 |
cols = st.columns(4)
|
| 547 |
for i, name in enumerate(echelon_order):
|
|
|
|
| 557 |
st.metric("Inventory (Opening)", e['inventory'])
|
| 558 |
st.metric("Backlog (Opening)", e['backlog'])
|
| 559 |
|
| 560 |
+
# 移除成本显示
|
| 561 |
+
|
| 562 |
+
# --- UI FIX: Display "Arrived This Week" ---
|
| 563 |
current_incoming_order = 0
|
| 564 |
if name == "Retailer":
|
| 565 |
current_incoming_order = get_customer_demand(week)
|
| 566 |
else:
|
|
|
|
| 567 |
downstream_name = e['downstream_name']
|
| 568 |
+
if downstream_name:
|
| 569 |
+
current_incoming_order = state['last_week_orders'].get(downstream_name, 0)
|
|
|
|
|
|
|
|
|
|
|
|
|
| 570 |
st.write(f"Incoming Order (This Week): **{current_incoming_order}**")
|
| 571 |
|
| 572 |
if name == "Factory":
|
|
|
|
| 581 |
arriving_next = 0
|
| 582 |
if len(e['incoming_shipments']) > 1:
|
| 583 |
arriving_next = list(e['incoming_shipments'])[1]
|
| 584 |
+
# 修正 2-week delay (R/W) 的显示
|
| 585 |
+
elif name in ('Wholesaler', 'Retailer') and len(e['incoming_shipments']) > 0 and e['incoming_shipments'].maxlen == 2:
|
| 586 |
+
# 如果队列长度为1但最大长度为2,说明下一个是0
|
| 587 |
+
if len(e['incoming_shipments']) == 1:
|
| 588 |
+
arriving_next = 0
|
| 589 |
+
else: # 这种情况不应该发生,但作为保险
|
| 590 |
+
arriving_next = list(e['incoming_shipments'])[0]
|
| 591 |
+
|
| 592 |
+
st.write(f"Arriving Next Week: **{arriving_next}**")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 593 |
|
| 594 |
else: # Local Info Mode
|
| 595 |
st.info("In Local Information mode, you can only see your own status dashboard.")
|
| 596 |
+
e = echelons[human_role]
|
| 597 |
st.markdown(f"### 👤 **<span style='color:#FF4B4B;'>{human_role} (Your Dashboard - Start of Week State)</span>**", unsafe_allow_html=True)
|
| 598 |
|
| 599 |
col1, col2, col3 = st.columns(3)
|
|
|
|
| 602 |
st.metric("Backlog (Opening)", e['backlog'])
|
| 603 |
|
| 604 |
with col2:
|
|
|
|
| 605 |
current_incoming_order = 0
|
| 606 |
+
downstream_name = e['downstream_name'] # Wholesaler
|
| 607 |
+
if downstream_name:
|
| 608 |
+
current_incoming_order = state['last_week_orders'].get(downstream_name, 0)
|
| 609 |
st.write(f"**Incoming Order (This Week):**\n# {current_incoming_order}")
|
| 610 |
|
| 611 |
with col3:
|
|
|
|
| 614 |
st.write(f"**Shipment Arriving (This Week):**\n# {arriving_this_week}")
|
| 615 |
|
| 616 |
# Arriving NEXT week (Peek at the next item in the 1-week delay queue)
|
| 617 |
+
arriving_next = 0
|
| 618 |
+
if len(e['incoming_shipments']) > 1: # 仅当队列中有多于1个元素时,才显示 [1]
|
| 619 |
+
arriving_next = list(e['incoming_shipments'])[1]
|
| 620 |
st.write(f"**Shipment Arriving (Next Week):**\n# {arriving_next}")
|
| 621 |
|
|
|
|
|
|
|
| 622 |
st.markdown("---")
|
| 623 |
st.header("Your Decision (Step 4)")
|
| 624 |
|
| 625 |
# Prepare the state snapshot for the AI prompt (State AFTER arrivals/orders, BEFORE shipping)
|
| 626 |
all_decision_point_states = {}
|
| 627 |
for name in echelon_order:
|
| 628 |
+
e_curr = echelons[name] # This is END OF LAST WEEK state
|
| 629 |
arrived = 0
|
| 630 |
if name == "Factory":
|
| 631 |
if state['factory_production_pipeline']: arrived = list(state['factory_production_pipeline'])[0]
|
|
|
|
| 636 |
if name == "Retailer": inc_order_this_week = get_customer_demand(week)
|
| 637 |
else:
|
| 638 |
ds_name = e_curr['downstream_name']
|
| 639 |
+
if ds_name: inc_order_this_week = state['last_week_orders'].get(ds_name, 0)
|
|
|
|
|
|
|
|
|
|
|
|
|
| 640 |
|
| 641 |
inv_after_arrival = e_curr['inventory'] + arrived
|
| 642 |
backlog_after_new_order = e_curr['backlog'] + inc_order_this_week
|
|
|
|
| 657 |
state['human_initial_order'] = int(initial_order) if initial_order is not None else 0
|
| 658 |
state['decision_step'] = 'final_order'
|
| 659 |
|
| 660 |
+
# --- NEW: Calculate and store suggestion ONCE ---
|
| 661 |
prompt_sugg = get_llm_prompt(human_echelon_state_for_prompt, week, state['llm_personality'], state['info_sharing'], all_decision_point_states)
|
| 662 |
ai_suggestion, _ = get_llm_order_decision(prompt_sugg, f"{human_role} (Suggestion)")
|
| 663 |
state['current_ai_suggestion'] = ai_suggestion # Store it
|
| 664 |
+
# ------------------------------------------------
|
| 665 |
|
| 666 |
st.rerun()
|
| 667 |
|
| 668 |
elif state['decision_step'] == 'final_order':
|
| 669 |
st.success(f"Your initial order was: **{state['human_initial_order']}** units.")
|
| 670 |
|
| 671 |
+
# --- NEW: Read stored suggestion ---
|
| 672 |
ai_suggestion = state.get('current_ai_suggestion', 4) # Read stored value
|
| 673 |
+
# -----------------------------------
|
| 674 |
|
| 675 |
with st.form(key="final_order_form"):
|
| 676 |
st.markdown(f"#### **Step 4b:** The AI suggests ordering **{ai_suggestion}** units.")
|
|
|
|
| 745 |
except Exception as e:
|
| 746 |
st.error(f"Error generating final report: {e}")
|
| 747 |
|
| 748 |
+
# =============== NEW: Show Leaderboard on End Page ===============
|
| 749 |
show_leaderboard_ui()
|
| 750 |
+
# ===============================================================
|
| 751 |
|
| 752 |
if st.button("✨ Start a New Game"):
|
| 753 |
del st.session_state.game_state
|