Update app.py
Browse files
app.py
CHANGED
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@@ -195,99 +195,104 @@ def init_game(weeks=DEFAULT_WEEKS):
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def step_game(state: dict, distributor_order: int):
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"""
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Apply one week's dynamics.
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1. Customer demand hits retailer this week.
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2. Deliveries that are at pipeline[front] arrive to each role this week.
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3. Roles fulfill incoming orders from downstream (if backlog arises).
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4. Human (distributor) order is recorded; LLMs decide orders for their roles.
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5. Place orders into upstream's pipeline so they will arrive after TRANSPORT_DELAY.
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6. Log everything.
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"""
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roles = state["roles"]
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# 1) Customer demand for this week to retailer
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demand = state
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state["incoming_orders"]["retailer"] = demand
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# 2) Shipments arrive (front of pipeline)
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arriving = {}
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for r in roles:
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# Pop front arrival if exists
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arr = 0
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state["inventory"][r] += arr
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arriving[r] = arr
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# 3) Fulfill incoming orders from downstream
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shipments_out = {}
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for r in roles:
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incoming = state
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inv = state
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shipped = min(inv, incoming)
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state["inventory"][r]
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# any unfilled becomes backlog
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unfilled = incoming - shipped
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if unfilled > 0:
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state["backlog"][r] += unfilled
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shipments_out[r] = shipped
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state["shipments_history"][
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# 4) Record human distributor order
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# Also set downstream->upstream linking: the upstream (wholesaler) will see distributor_order as incoming next period
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state["incoming_orders"]["wholesaler"] = int(distributor_order)
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# 5) LLM decisions for AI roles (retailer, wholesaler, factory)
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demand_history_visible = []
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if state
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start_idx = max(0, (week - 1) - state
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demand_history_visible = state
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llm_outputs = {}
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for role in ["retailer", "wholesaler", "factory"]:
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order_val, raw = call_llm_for_order(role, state_snapshot_for_prompt(state), state
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order_val = max(0, int(order_val))
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state["orders_history"][
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llm_outputs[role] = {"order": order_val, "raw": raw}
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# set incoming_orders for
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# e.g., if retailer orders X, upstream (distributor) incoming_orders will be X
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if role == "retailer":
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state["incoming_orders"]["distributor"] = order_val
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elif role == "wholesaler":
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state["incoming_orders"]["factory"] = order_val
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# factory's upstream
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# 6) Place orders into pipelines:
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for role in roles:
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# Determine the order placed by this role this week:
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if role == "distributor":
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placed_order = int(distributor_order)
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else:
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# For the downstream partner (the entity that will receive the shipment), we append to that partner's pipeline tail
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# Example: distributor placed order to wholesaler -> wholesaler will receive shipment after TRANSPORT_DELAY
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# Map role -> downstream partner (who receives shipments from role)
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# shipments flow downstream: factory -> wholesaler -> distributor -> retailer
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downstream_map = {
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"factory": "wholesaler",
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"wholesaler": "distributor",
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"distributor": "retailer",
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"retailer": None
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}
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downstream = downstream_map.get(role)
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if downstream:
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#
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state["pipeline"][downstream].append(placed_order)
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# 7) Log the week's summary
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@@ -299,35 +304,49 @@ def step_game(state: dict, distributor_order: int):
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"shipments_out": shipments_out,
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"orders_submitted": {
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"distributor": int(distributor_order),
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"retailer": state["orders_history"]
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"wholesaler": state["orders_history"]
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"factory": state["orders_history"]
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},
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"inventory": dict(state
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"backlog": dict(state
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"info_sharing": state
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"info_history_weeks": state
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"llm_raw": {k: v["raw"] for k, v in llm_outputs.items()}
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}
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state
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# 8) Advance week
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state["week"]
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return state
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def state_snapshot_for_prompt(state):
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"""
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Prepare a compact snapshot of state for LLM prompt (avoid sending huge objects).
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"""
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snap = {
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"week": state
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"inventory": state
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"backlog": state
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"incoming_orders": state
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# pipeline front (arriving next week)
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"incoming_shipments_next_week": {
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}
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return snap
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def step_game(state: dict, distributor_order: int):
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"""
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Apply one week's dynamics.
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Defensive: ensure 'pipeline' exists and lists are padded; safe pops and appends.
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"""
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# Defensive defaults
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if "roles" not in state:
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state["roles"] = ["retailer", "wholesaler", "distributor", "factory"]
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roles = state["roles"]
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# Ensure pipeline structure exists and each role has a list of length >= TRANSPORT_DELAY
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if "pipeline" not in state or not isinstance(state["pipeline"], dict):
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state["pipeline"] = {r: [0] * TRANSPORT_DELAY for r in roles}
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else:
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for r in roles:
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state["pipeline"].setdefault(r, [0] * TRANSPORT_DELAY)
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if len(state["pipeline"][r]) < TRANSPORT_DELAY:
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state["pipeline"][r].extend([0] * (TRANSPORT_DELAY - len(state["pipeline"][r])))
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week = state.get("week", 1)
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# 1) Customer demand for this week to retailer
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demand = state.get("customer_demand", [])[week - 1] if (state.get("customer_demand") and week - 1 < len(state["customer_demand"])) else 0
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state.setdefault("incoming_orders", {})
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state["incoming_orders"]["retailer"] = demand
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# 2) Shipments arrive (front of pipeline) -- safe pop
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arriving = {}
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for r in roles:
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arr = 0
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pl = state["pipeline"].get(r, [])
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if len(pl) > 0:
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try:
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arr = pl.pop(0)
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except Exception:
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arr = 0
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# ensure inventory keys
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state.setdefault("inventory", {})
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state["inventory"].setdefault(r, 0)
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state["inventory"][r] += arr
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arriving[r] = arr
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# 3) Fulfill incoming orders from downstream
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state.setdefault("backlog", {})
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state.setdefault("shipments_history", {r: [] for r in roles})
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shipments_out = {}
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for r in roles:
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incoming = state.get("incoming_orders", {}).get(r, 0) or 0
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inv = state.get("inventory", {}).get(r, 0) or 0
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shipped = min(inv, incoming)
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state["inventory"][r] = inv - shipped
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unfilled = incoming - shipped
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if unfilled > 0:
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state["backlog"].setdefault(r, 0)
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state["backlog"][r] += unfilled
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shipments_out[r] = shipped
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state["shipments_history"].setdefault(r, []).append(shipped)
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# 4) Record human distributor order
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state.setdefault("orders_history", {r: [] for r in roles})
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state["orders_history"].setdefault("distributor", []).append(int(distributor_order))
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# set incoming_orders for upstream relation
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state["incoming_orders"]["wholesaler"] = int(distributor_order)
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# 5) LLM decisions for AI roles (retailer, wholesaler, factory)
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demand_history_visible = []
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if state.get("info_sharing") and state.get("info_history_weeks", 0) > 0:
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start_idx = max(0, (week - 1) - state.get("info_history_weeks", 0))
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demand_history_visible = state.get("customer_demand", [])[start_idx: (week - 1)]
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llm_outputs = {}
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for role in ["retailer", "wholesaler", "factory"]:
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order_val, raw = call_llm_for_order(role, state_snapshot_for_prompt(state), state.get("info_sharing", False), demand_history_visible)
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order_val = max(0, int(order_val))
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state["orders_history"].setdefault(role, []).append(order_val)
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llm_outputs[role] = {"order": order_val, "raw": raw}
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# set incoming_orders mapping for next period
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if role == "retailer":
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state["incoming_orders"]["distributor"] = order_val
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elif role == "wholesaler":
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state["incoming_orders"]["factory"] = order_val
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# factory's upstream beyond model
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# 6) Place orders into pipelines: append to downstream partner's pipeline tail
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downstream_map = {
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"factory": "wholesaler",
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"wholesaler": "distributor",
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"distributor": "retailer",
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"retailer": None
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}
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for role in roles:
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if role == "distributor":
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placed_order = int(distributor_order)
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else:
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placed_order = int(state["orders_history"].get(role, [0])[-1] if state["orders_history"].get(role) else 0)
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downstream = downstream_map.get(role)
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if downstream:
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# ensure downstream pipeline exists
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state["pipeline"].setdefault(downstream, [0] * TRANSPORT_DELAY)
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# append placed_order at tail (arrival after TRANSPORT_DELAY)
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state["pipeline"][downstream].append(placed_order)
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# 7) Log the week's summary
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"shipments_out": shipments_out,
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"orders_submitted": {
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"distributor": int(distributor_order),
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"retailer": state["orders_history"].get("retailer", [None])[-1],
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"wholesaler": state["orders_history"].get("wholesaler", [None])[-1],
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"factory": state["orders_history"].get("factory", [None])[-1],
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},
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"inventory": dict(state.get("inventory", {})),
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"backlog": dict(state.get("backlog", {})),
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"info_sharing": state.get("info_sharing", False),
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"info_history_weeks": state.get("info_history_weeks", 0),
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"llm_raw": {k: v["raw"] for k, v in llm_outputs.items()}
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}
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state.setdefault("logs", []).append(log_entry)
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# 8) Advance week
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state["week"] = state.get("week", 1) + 1
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return state
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def state_snapshot_for_prompt(state):
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"""
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Prepare a compact snapshot of state for LLM prompt (avoid sending huge objects).
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Robust to missing 'pipeline' key or missing role entries.
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"""
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roles = state.get("roles", ["retailer", "wholesaler", "distributor", "factory"])
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# Ensure pipeline exists and has at least TRANSPORT_DELAY entries per role
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pipeline = state.get("pipeline", {})
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for r in roles:
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if r not in pipeline:
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pipeline[r] = [0] * TRANSPORT_DELAY
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else:
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# if pipeline exists but is shorter than transport delay, pad with zeros
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if len(pipeline[r]) < TRANSPORT_DELAY:
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pipeline[r] = pipeline[r] + [0] * (TRANSPORT_DELAY - len(pipeline[r]))
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snap = {
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"week": state.get("week", 1),
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"inventory": state.get("inventory", {}).copy(),
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"backlog": state.get("backlog", {}).copy(),
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"incoming_orders": state.get("incoming_orders", {}).copy(),
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# pipeline front (arriving next week) -- safe access
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"incoming_shipments_next_week": {
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r: (pipeline.get(r, [0])[0] if pipeline.get(r) and len(pipeline.get(r)) > 0 else 0)
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for r in roles
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}
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}
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return snap
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