Update monitoring_app.py

monitoring_app.py

@@ -206,6 +206,10 @@ def render_review_queue(df: pd.DataFrame) -> None:
 def render_correct_cases(df: pd.DataFrame) -> None:
     st.subheader("Correct Classification Examples")
 
+    if df.empty:
+        st.info("No monitoring results available.")
+        return
+
     correct_df = df[df["label_judgment"] == "correct"].copy()
 
     if correct_df.empty:
@@ -215,38 +219,52 @@ def render_correct_cases(df: pd.DataFrame) -> None:
     max_rows = st.slider(
         "Number of correct examples to display",
         5,
-        50,
-        10,
+        100,
+        20,
         key="correct_slider",
     )
 
-    correct_df = correct_df.sort_values(
-        "evaluated_at",
-        ascending=False,
-    ).head(max_rows)
+    correct_df = correct_df.sort_values("evaluated_at", ascending=False).head(max_rows)
 
     for _, row in correct_df.iterrows():
-
         published_str = (
             row["published_at"].strftime("%Y-%m-%d %H:%M UTC")
             if pd.notnull(row["published_at"])
             else "Unknown"
         )
 
-        with st.expander(f"{row['title']}"):
+        evaluated_str = (
+            row["evaluated_at"].strftime("%Y-%m-%d %H:%M UTC")
+            if pd.notnull(row["evaluated_at"])
+            else "Unknown"
+        )
 
-            c1, c2, c3 = st.columns(3)
+        with st.expander(f"{row['title']}"):
+            m1, m2, m3, m4 = st.columns(4)
 
-            c1.markdown(f"**Predicted label:** {row['predicted_label']}")
-            c2.markdown(f"**Confidence:** {row['label_confidence']}")
-            c3.markdown(f"**Published:** {published_str}")
+            m1.markdown(f"**Predicted label:** {row['predicted_label']}")
+            m2.markdown(f"**Overall status:** {row['overall_status']}")
+            m3.markdown(f"**Source:** {row['source']}")
+            m4.markdown(f"**Published:** {published_str}")
 
-            if pd.notnull(row["description"]):
-                st.write(row["description"])
+            st.markdown("**Description**")
+            st.write(
+                row["description"]
+                if pd.notnull(row["description"])
+                else "No description"
+            )
 
-            st.markdown("**Judge explanation**")
+            st.markdown("**Judge output**")
+            st.markdown(
+                f"**Label quality:** {row['label_judgment']} "
+                f"({row['label_confidence']})"
+            )
             st.write(row["label_explanation"])
 
+            st.markdown("**Metadata**")
+            st.caption(f"Article ID: {row['article_id']}")
+            st.caption(f"Evaluated at: {evaluated_str}")
+
             if pd.notnull(row["url"]) and str(row["url"]).strip():
                 st.markdown(f"[Open article]({row['url']})")