Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py
-# @title Beer Game Final Version (v4.13 - Classic UI Timing & Backlog Explanation)
+# @title Beer Game Final Version (v4.14 - Fixed NameError)
 
 # -----------------------------------------------------------------------------
 # 1. Import Libraries
@@ -126,11 +126,8 @@ def get_llm_order_decision(prompt: str, echelon_name: str) -> (int, str):
 def get_llm_prompt(echelon_state_after_arrivals: dict, week: int, llm_personality: str, info_sharing: str, all_echelons_state_after_arrivals: dict) -> str:
     """Generates the prompt for the LLM based on the game scenario.
        Uses the state AFTER arrivals and new orders are processed, as this is the decision point."""
-
     e_state = echelon_state_after_arrivals # Use the passed-in state for prompts
-
     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"
         # Factory needs access to the global pipeline state
@@ -148,6 +145,7 @@ def get_llm_prompt(echelon_state_after_arrivals: dict, week: int, llm_personalit
         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 state AFTER arrivals/orders
         if e_state['name'] == 'Factory':
             inv_pos_components = f"(Current Inv: {e_state['inventory']} - Current Backlog: {e_state['backlog']} + In_Production: {sum(st.session_state.game_state['factory_production_pipeline'])})"
@@ -155,21 +153,27 @@ def get_llm_prompt(echelon_state_after_arrivals: dict, week: int, llm_personalit
         else:
             inv_pos_components = f"(Current Inv: {e_state['inventory']} - Current Backlog: {e_state['backlog']} + In_Transit: {sum(e_state['incoming_shipments'])})"
             inventory_position = (e_state['inventory'] - e_state['backlog'] + sum(e_state['incoming_shipments']))
+
         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']
+
         # Use state AFTER arrivals/orders for inventory correction calculation
         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 = sum(e_state['incoming_shipments'])
             supply_line_desc = "In Transit Shipments"
+
         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 order.\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 ---
@@ -178,6 +182,7 @@ def get_llm_prompt(echelon_state_after_arrivals: dict, week: int, llm_personalit
         # Display other players' state AFTER arrivals/orders
         for name, other_e_state in all_echelons_state_after_arrivals.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.
@@ -198,22 +203,22 @@ def get_llm_prompt(echelon_state_after_arrivals: 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 ===============
 def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: int):
     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"]
+    echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"] # Defined here
     llm_raw_responses = {}
 
     # Store state at the very beginning of the week for logging opening balances
     # These are the inventory/backlog values from the END of the previous 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 roles} # Track arrivals for logging
+    arrived_this_week = {name: 0 for name in echelon_order} # Track arrivals for logging
 
     # --- Game Simulation Steps ---
 
@@ -228,7 +233,6 @@ def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: i
     else:
         inventory_after_production = factory_state['inventory']
 
-
     # Step 1b: Shipments arrive at downstream echelons
     inventory_after_arrival = {} # Store intermediate state
     for name in ["Retailer", "Wholesaler", "Distributor"]:
@@ -239,6 +243,7 @@ def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: i
         inventory_after_arrival[name] = echelons[name]['inventory'] + arrived_shipment
     inventory_after_arrival["Factory"] = inventory_after_production # Add factory's state
 
+
     # Step 2: Orders arrive from downstream partners (using LAST week's placed order)
     total_backlog_before_shipping = {} # Store intermediate state
     for name in echelon_order:
@@ -267,7 +272,6 @@ def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: i
              '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 = {}
@@ -280,6 +284,7 @@ def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: i
         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']
@@ -287,7 +292,6 @@ def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: i
     # Factory schedules production based on its 'order_placed' decision
     state['factory_production_pipeline'].append(echelons["Factory"]['order_placed'])
 
-
     # --- Step 3: Fulfill orders (Ship Beer) ---
     # Now perform the shipping based on the inventory_after_arrival and total_backlog_before_shipping
     for name in echelon_order:
@@ -310,6 +314,7 @@ def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: i
 
     # --- Calculate Costs & Log (End of Week) ---
     log_entry = {'timestamp': datetime.utcnow().isoformat() + "Z", 'week': week, **state}
+    # These fields are nested in echelons, no need to log them top-level
     del log_entry['echelons'], log_entry['factory_production_pipeline'], log_entry['logs'], log_entry['last_week_orders']
 
     for name in echelon_order:
@@ -349,14 +354,16 @@ def step_game(human_final_order: int, human_initial_order: int, ai_suggestion: i
     state['week'] += 1
     state['decision_step'] = 'initial_order'
     state['last_week_orders'] = current_week_orders # Store current decisions for next week's Step 2
+
     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():
@@ -366,24 +373,49 @@ def plot_results(df: pd.DataFrame, title: str, human_role: str):
                 '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)')
+    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 ($)')
+    total_costs.plot(kind='bar', ax=axes[2], rot=0)
+    axes[2].set_title('Total Cumulative Cost')
+    axes[2].set_ylabel('Cost ($)')
+
     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]
     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'
+        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')
+        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:
-        axes[3].set_title(f'Analysis of Your ({human_role}) Decisions - No Data'); 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 - No Data')
+        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.
@@ -391,16 +423,25 @@ def save_logs_and_upload(state: dict):
     participant_id = state['participant_id']
     df = pd.json_normalize(state['logs'])
     fname = LOCAL_LOG_DIR / f"log_{participant_id}_{int(time.time())}.csv"
+
     for col in df.columns:
         if df[col].dtype == 'object': 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")
+
     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:
                 st.error(f"Upload to Hugging Face failed: {e}")
@@ -458,7 +499,6 @@ else:
             * **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.
             """)
 
@@ -478,13 +518,13 @@ else:
         """)
         # ====================================================================
 
-        #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.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.
+        """)
 
         # =============== UPDATED: How Each Week Works & Dashboard Explanation ===============
-        st.subheader("5. How Each Week Works & Understanding Your Dashboard")
+        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.
 
@@ -495,10 +535,10 @@ else:
 
         **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`: Your stock **at the beginning of the week**. This is the inventory carried over from the end of last week.
-        * `Backlog`: Unfilled orders **carried over from the end of last week**.
-        * `Incoming Order`: The specific order quantity that **will arrive** from the Wholesaler *during* this week (Step 2).
-        * `Shipment Arriving (Next Week)`: The quantity scheduled to arrive at the start of the **next week**.
+        * `Inventory (Opening)`: Your stock **at the beginning of the week**. This is the inventory carried over from the end of last 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**.
 
@@ -526,7 +566,10 @@ 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['human_role'], state['echelons'], state['info_sharing']
+        week, human_role, echelons, info_sharing = state['week'], state['echelons'], state['info_sharing']
+        # Define echelon order for display in the UI
+        echelon_order = ["Retailer", "Wholesaler", "Distributor", "Factory"]
+
 
         st.header(f"Week {week} / {WEEKS}")
         st.subheader(f"Your Role: **{human_role}** | AI Mode: **{state['llm_personality'].replace('_', ' ')}** | Information: **{state['info_sharing']}**")
@@ -534,15 +577,18 @@ else:
         st.subheader("Supply Chain Status (Start of Week State)") # Clarified Timing
         if info_sharing == 'full':
             cols = st.columns(4)
-            for i, name in enumerate(["Retailer", "Wholesaler", "Distributor", "Factory"]):
+            for i, name in enumerate(echelon_order): # Use the defined echelon_order
                 with cols[i]:
                     e, icon = echelons[name], "👤" if name == human_role else "🤖"
                     st.markdown(f"##### {icon} {name} {'(You)' if name == human_role else ''}")
                     # 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'])
+                    st.metric("Inventory (Opening)", e['inventory'])
+                    st.metric("Backlog (Opening)", e['backlog'])
 
+                    # Display cumulative cost and last week's cost for the human player
                     if name == human_role:
                         st.metric("Total Cost (Cumulative)", f"${e['total_cost']:,.2f}")
+                        # Display last week's cost if available (week > 1)
                         last_week_cost = state['logs'][-1][f"{human_role}.weekly_cost"] if week > 1 and state['logs'] else 0
                         st.metric("Cost Last Week", f"${last_week_cost:,.2f}")
 
@@ -575,19 +621,35 @@ else:
 
         st.markdown("---")
         st.header("Your Decision (Step 4)")
-        human_echelon_state_for_prompt = { # Construct the state needed for the prompt (AFTER arrivals/orders)
-             'name': human_role,
-             'inventory': echelons[human_role]['inventory'] + (list(echelons[human_role]['incoming_shipments'])[0] if echelons[human_role]['incoming_shipments'] else 0), # Opening + Arriving This Week (approx)
-             'backlog': echelons[human_role]['backlog'] + echelons[human_role]['incoming_order'], # Opening + Incoming Order This Week
-             'incoming_order': echelons[human_role]['incoming_order'],
-             'incoming_shipments': echelons[human_role]['incoming_shipments'] # Pass the queue
-        }
+
+        # Prepare the state snapshot for the AI prompt (State AFTER arrivals/orders, BEFORE shipping)
+        all_decision_point_states = {}
+        for name in echelon_order:
+            e_curr = echelons[name]
+            arrived = 0
+            # Peek at what *will* arrive this week (Step 1)
+            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]
+
+            all_decision_point_states[name] = {
+                'name': name,
+                'inventory': e_curr['inventory'] + arrived, # Opening Inv + Arriving This Week
+                'backlog': e_curr['backlog'] + e_curr['incoming_order'], # Opening Backlog + Incoming Order This Week
+                'incoming_order': e_curr['incoming_order'],
+                'incoming_shipments': e_curr['incoming_shipments'].copy() if name != "Factory" else deque()
+            }
+        human_echelon_state_for_prompt = all_decision_point_states[human_role]
 
 
         if state['decision_step'] == 'initial_order':
             with st.form(key="initial_order_form"):
                 st.markdown("#### **Step 4a:** Based on the dashboard, submit your **initial** order to the Factory.")
-                default_initial = echelons[human_role]['incoming_order'] if echelons[human_role]['incoming_order'] > 0 else 4
+                # Default initial order based on incoming order, minimum 4
+                default_initial = max(4, echelons[human_role]['incoming_order'])
                 initial_order = st.number_input("Your Initial Order Quantity:", min_value=0, step=1, value=default_initial)
                 if st.form_submit_button("Submit Initial Order & See AI Suggestion", type="primary"):
                     state['human_initial_order'] = int(initial_order)
@@ -596,25 +658,8 @@ else:
 
         elif state['decision_step'] == 'final_order':
             st.success(f"Your initial order was: **{state['human_initial_order']}** units.")
-            # Prepare all states AFTER arrivals/orders for the full info prompt context
-            all_decision_point_states = {}
-            for name in echelon_order:
-                e_curr = echelons[name]
-                arrived = 0
-                if name == "Factory":
-                     if state['factory_production_pipeline']: arrived = list(state['factory_production_pipeline'])[0] # Peek, don't pop here
-                else:
-                     if e_curr['incoming_shipments']: arrived = list(e_curr['incoming_shipments'])[0] # Peek
-
-                all_decision_point_states[name] = {
-                    'name': name,
-                    'inventory': e_curr['inventory'] + arrived,
-                    'backlog': e_curr['backlog'] + e_curr['incoming_order'],
-                    'incoming_order': e_curr['incoming_order'],
-                    'incoming_shipments': e_curr['incoming_shipments'].copy() if name != "Factory" else deque()
-                }
-
-            prompt_sugg = get_llm_prompt(all_decision_point_states[human_role], week, state['llm_personality'], state['info_sharing'], all_decision_point_states)
+            # Use the correctly timed state for the prompt
+            prompt_sugg = get_llm_prompt(human_echelon_state_for_prompt, week, state['llm_personality'], state['info_sharing'], all_decision_point_states)
             ai_suggestion, _ = get_llm_order_decision(prompt_sugg, f"{human_role} (Suggestion)")
 
             if 'final_order_input' not in st.session_state: