From 9cfd7dee9668d2b77af793ab8fb2293d01792482 Mon Sep 17 00:00:00 2001
From: Chats-001 <chaitanya10sah@gmail.com>
Date: Fri, 18 Sep 2026 12:04:29 -0400
Subject: [PATCH] fix: honor selected dashboard review capacity

---
 dashboard/app.py              | 26 +++++++++++++++++++-------
 dashboard/queue_data.py       | 18 ++++++++++++++++++
 tests/test_dashboard_queue.py | 24 ++++++++++++++++++++++++
 3 files changed, 61 insertions(+), 7 deletions(-)
 create mode 100644 dashboard/queue_data.py
 create mode 100644 tests/test_dashboard_queue.py

diff --git a/dashboard/app.py b/dashboard/app.py
index 3320c2c..05d8554 100644
--- a/dashboard/app.py
+++ b/dashboard/app.py
@@ -7,12 +7,13 @@
 import plotly.express as px
 import streamlit as st
 
+from dashboard.queue_data import capacity_views
+
 ROOT = Path(__file__).resolve().parents[1]
 SUMMARY_PATH = ROOT / "artifacts/figures/summary.json"
 CAPACITY_PATH = ROOT / "artifacts/figures/capacity_results.csv"
 THRESHOLD_PATH = ROOT / "artifacts/figures/threshold_policies.csv"
 SCORED_PATH = ROOT / "artifacts/figures/scored_test.csv"
-QUEUE_PATH = ROOT / "artifacts/figures/review_queue.csv"
 ERROR_PATH = ROOT / "artifacts/figures/error_analysis.csv"
 
 st.set_page_config(page_title="RiskQueue", page_icon="▦", layout="wide")
@@ -37,7 +38,6 @@
 capacity = pd.read_csv(CAPACITY_PATH)
 thresholds = pd.read_csv(THRESHOLD_PATH)
 scored = pd.read_csv(SCORED_PATH)
-queue = pd.read_csv(QUEUE_PATH)
 errors = pd.read_csv(ERROR_PATH)
 best = summary["metrics"][summary["best_model"]]
 
@@ -139,12 +139,11 @@
         min(250, int(capacity.capacity.max())),
         25,
     )
-    nearest = capacity.iloc[
-        (capacity.capacity - selected_capacity).abs().argsort()[:3]
-    ].sort_values("fraud_value_capture", ascending=False)
+    scorecard, ranked_queue = capacity_views(scored, selected_capacity)
+    scorecard = scorecard.sort_values("fraud_value_capture", ascending=False)
     st.subheader("Policy scorecard")
     st.dataframe(
-        nearest[
+        scorecard[
             [
                 "strategy",
                 "capacity",
@@ -170,7 +169,20 @@
         options=["critical", "high", "guarded", "low"],
         default=["critical", "high", "guarded", "low"],
     )
-    shown_queue = queue[queue.risk_band.isin(selected_bands)].head(display_count)
+    shown_queue = ranked_queue[ranked_queue.risk_band.isin(selected_bands)].head(display_count)
+    shown_queue = shown_queue[
+        [
+            "transaction_id",
+            "step",
+            "type",
+            "amount",
+            "fraud_probability",
+            "expected_loss",
+            "risk_band",
+            "rank",
+            "isFraud",
+        ]
+    ]
     st.dataframe(
         shown_queue.style.format(
             {
diff --git a/dashboard/queue_data.py b/dashboard/queue_data.py
new file mode 100644
index 0000000..225cd65
--- /dev/null
+++ b/dashboard/queue_data.py
@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+import pandas as pd
+
+from riskqueue.decisions.review_queue import build_review_queue, queue_metrics
+
+
+def capacity_views(scored: pd.DataFrame, capacity: int) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """Calculate the scorecard and expected-loss queue for the selected capacity."""
+    probabilities = scored["fraud_probability"].to_numpy()
+    rows = []
+    selected_queue = None
+    for strategy in ("probability", "expected_loss", "expected_review_value"):
+        queue = build_review_queue(scored, probabilities, capacity, strategy)
+        rows.append({"strategy": strategy, "capacity": capacity, **queue_metrics(queue, scored)})
+        if strategy == "expected_loss":
+            selected_queue = queue
+    return pd.DataFrame(rows), selected_queue
diff --git a/tests/test_dashboard_queue.py b/tests/test_dashboard_queue.py
new file mode 100644
index 0000000..6fa90b0
--- /dev/null
+++ b/tests/test_dashboard_queue.py
@@ -0,0 +1,24 @@
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from dashboard.queue_data import capacity_views
+
+
+def test_capacity_views_use_selected_capacity():
+    scored_path = Path(__file__).resolve().parents[1] / "artifacts/figures/scored_test.csv"
+    scored = pd.read_csv(scored_path)
+
+    scorecard, queue = capacity_views(scored, 75)
+
+    assert set(scorecard.strategy) == {
+        "probability",
+        "expected_loss",
+        "expected_review_value",
+    }
+    assert scorecard.capacity.tolist() == [75, 75, 75]
+    assert scorecard.reviews.tolist() == [75, 75, 75]
+    assert len(queue) == 75
+    assert queue["rank"].tolist() == list(range(1, 76))
+    assert queue.iloc[0].expected_loss == pytest.approx(scored.expected_loss.max())