Update monitoring_app.py

monitoring_app.py

@@ -47,12 +47,10 @@ def load_monitoring_summary() -> dict:
 def apply_filters(df: pd.DataFrame) -> pd.DataFrame:
     st.sidebar.header("Monitoring Filters")
 
-    status_options = sorted(df["overall_status"].dropna().unique().tolist()) if not df.empty else []
     label_judgment_options = sorted(df["label_judgment"].dropna().unique().tolist()) if not df.empty else []
     predicted_label_options = sorted(df["predicted_label"].dropna().unique().tolist()) if not df.empty else []
     source_options = sorted(df["source"].dropna().unique().tolist()) if not df.empty else []
 
-    selected_status = st.sidebar.multiselect("Overall status", status_options, default=status_options)
     selected_label_judgment = st.sidebar.multiselect("Label judgment", label_judgment_options, default=label_judgment_options)
     selected_predicted_labels = st.sidebar.multiselect("Predicted labels", predicted_label_options, default=[])
     selected_sources = st.sidebar.multiselect("Sources", source_options, default=[])
@@ -75,9 +73,6 @@ def apply_filters(df: pd.DataFrame) -> pd.DataFrame:
 
     filtered = df.copy()
 
-    if selected_status:
-        filtered = filtered[filtered["overall_status"].isin(selected_status)]
-
     if selected_label_judgment:
         filtered = filtered[filtered["label_judgment"].isin(selected_label_judgment)]
 
@@ -111,23 +106,22 @@ def render_summary(summary: dict, df: pd.DataFrame) -> None:
     st.subheader("Monitoring Overview")
 
     c1, c2, c3, c4 = st.columns(4)
+
     c1.metric("Total monitored", summary.get("total_monitored", 0))
     c2.metric("Needs review", summary.get("needs_review", 0))
     c3.metric("Shown after filters", len(df))
-    c4.metric("Problem rate", f"{(len(df[df['overall_status'] != 'ok']) / len(df) * 100):.1f}%" if len(df) else "0.0%")
+
+    c4.metric(
+        "Problem rate",
+        f"{(len(df[df['overall_status'] != 'ok']) / len(df) * 100):.1f}%"
+        if len(df)
+        else "0.0%"
+    )
 
     if df.empty:
         st.info("No monitoring results match the current filters.")
         return
 
-    st.markdown("#### Monitoring status distribution")
-    status_df = df["overall_status"].value_counts().rename_axis("overall_status").reset_index(name="count")
-    st.bar_chart(status_df.set_index("overall_status"))
-
-    st.markdown("#### Label judgment distribution")
-    label_df = df["label_judgment"].value_counts().rename_axis("label_judgment").reset_index(name="count")
-    st.bar_chart(label_df.set_index("label_judgment"))
-
 def render_problem_patterns(df: pd.DataFrame) -> None:
     st.subheader("Problem Patterns")
 
@@ -216,7 +210,6 @@ def render_full_table(df: pd.DataFrame) -> None:
             "predicted_label",
             "label_judgment",
             "label_confidence",
-            "overall_status",
             "requires_human_review",
             "title",
         ]