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app.py

@@ -19,10 +19,10 @@ from charts import (
     chart_outcome_breakdown,
     chart_queue_efficiency,
     chart_resource_sizing,
+    chart_spend_by_outcome,
     chart_usage_by_system,
-    chart_wasted_spend,
 )
-from config import ALLOCATIONS, AVOIDABLE_WASTE_CATEGORIES, INTERACTIVE_METHODS, PROJECT_CODES
+from config import ALLOCATIONS, INTERACTIVE_METHODS, PROJECT_CODES
 from data_loader import filter_jobs, load_data
 
 st.set_page_config(page_title="OSC Usage Dashboard", layout="wide")
@@ -110,17 +110,7 @@ interactive_dollars = filtered[filtered["launch_method"].isin(INTERACTIVE_METHOD
 ].sum()
 interactive_pct = interactive_dollars / total_spend * 100 if total_spend > 0 else 0
 
-# Avoidable waste
-if "outcome_category" in filtered.columns:
-    avoidable_waste = filtered.loc[
-        filtered["outcome_category"].isin(AVOIDABLE_WASTE_CATEGORIES), "dollars_used"
-    ].sum()
-else:
-    avoidable_waste = filtered.loc[
-        filtered["last_state"].isin({"FAILED", "TIMEOUT", "OUT_OF_MEMORY"}), "dollars_used"
-    ].sum()
-
-m1, m2, m3, m4, m5 = st.columns(5)
+m1, m2, m3, m4 = st.columns(4)
 m1.metric("Total Spend", f"${total_spend:,.2f}", help="Sum of all job costs in the filtered period")
 m2.metric("Total Jobs", f"{total_jobs:,}", help="Number of jobs in the filtered period")
 m3.metric(
@@ -133,11 +123,6 @@ m4.metric(
     f"{interactive_pct:.1f}%",
     help="% of total dollars spent on interactive sessions (Jupyter, Desktop, Code Server, etc.)",
 )
-m5.metric(
-    "Avoidable Waste",
-    f"${avoidable_waste:,.0f}",
-    help="Dollars from Quick Exit + Failed + Batch Timed Out + Out of Memory — actionable items only",
-)
 
 st.divider()
 
@@ -217,7 +202,7 @@ with tab_health:
         if fig:
             st.plotly_chart(fig, use_container_width=True)
     with right:
-        fig = chart_wasted_spend(filtered)
+        fig = chart_spend_by_outcome(filtered)
         if fig:
             st.plotly_chart(fig, use_container_width=True)
 
@@ -262,20 +247,11 @@ with tab_user:
         u_batch_total = len(u_batch)
         u_batch_completed = (u_batch["last_state"] == "COMPLETED").sum()
         u_batch_pct = u_batch_completed / u_batch_total * 100 if u_batch_total > 0 else 0
-        if "outcome_category" in user_df.columns:
-            u_waste = user_df.loc[
-                user_df["outcome_category"].isin(AVOIDABLE_WASTE_CATEGORIES), "dollars_used"
-            ].sum()
-        else:
-            u_waste = user_df.loc[
-                user_df["last_state"].isin({"FAILED", "TIMEOUT", "OUT_OF_MEMORY"}), "dollars_used"
-            ].sum()
 
-        um1, um2, um3, um4 = st.columns(4)
+        um1, um2, um3 = st.columns(3)
         um1.metric("User Spend", f"${u_spend:,.2f}")
         um2.metric("User Jobs", f"{u_jobs:,}")
         um3.metric("Batch Completion %", f"{u_batch_pct:.1f}%")
-        um4.metric("Avoidable Waste", f"${u_waste:,.0f}")
 
         # User charts
         left, right = st.columns(2)