Add robust error handling and data validation for KPI health check panel with automatic fallback selection for sites and KPIs

panel_app/kpi_health_check_panel.py

@@ -222,13 +222,6 @@ def _update_kpi_options() -> None:
 
 
 def _update_site_view(event=None) -> None:
-    if current_status_df is None or current_status_df.empty:
-        site_kpi_table.value = pd.DataFrame()
-        trend_plot_pane.object = None
-        heatmap_plot_pane.object = None
-        hist_plot_pane.object = None
-        return
-
     code = site_select.value
     rat = rat_select.value
     kpi = kpi_select.value
@@ -240,20 +233,62 @@ def _update_site_view(event=None) -> None:
         hist_plot_pane.object = None
         return
 
-    site_df = current_status_df[
-        (current_status_df["site_code"] == int(code))
-        & (current_status_df["RAT"] == rat)
-    ].copy()
+    status_df = (
+        current_status_df
+        if isinstance(current_status_df, pd.DataFrame)
+        else pd.DataFrame()
+    )
+    if status_df is None or status_df.empty:
+        site_df = pd.DataFrame()
+    else:
+        site_df = status_df[
+            (status_df["site_code"] == int(code)) & (status_df["RAT"] == rat)
+        ].copy()
     site_kpi_table.value = site_df
 
     daily = current_daily_by_rat.get(rat)
-    if daily is None or daily.empty or not kpi or kpi not in daily.columns:
+    if daily is None or daily.empty:
         trend_plot_pane.object = None
         heatmap_plot_pane.object = None
         hist_plot_pane.object = None
         return
 
     d = _filtered_daily(daily)
+    if (
+        d is None
+        or d.empty
+        or "site_code" not in d.columns
+        or "date_only" not in d.columns
+    ):
+        trend_plot_pane.object = None
+        heatmap_plot_pane.object = None
+        hist_plot_pane.object = None
+        return
+
+    try:
+        available_sites = set(
+            pd.to_numeric(d["site_code"], errors="coerce").dropna().astype(int).tolist()
+        )
+    except Exception:  # noqa: BLE001
+        available_sites = set()
+    if available_sites and int(code) not in available_sites:
+        site_select.value = next(iter(sorted(available_sites)))
+        return
+
+    if not kpi or kpi not in d.columns:
+        candidate_kpis = [
+            c
+            for c in d.columns
+            if c
+            not in {"site_code", "date_only", "Longitude", "Latitude", "City", "RAT"}
+        ]
+        candidate_kpis = sorted([str(c) for c in candidate_kpis])
+        if candidate_kpis:
+            kpi_select.value = candidate_kpis[0]
+        trend_plot_pane.object = None
+        heatmap_plot_pane.object = None
+        hist_plot_pane.object = None
+        return
     s = d[d["site_code"] == int(code)].copy().sort_values("date_only")
     if s.empty:
         trend_plot_pane.object = None
@@ -290,6 +325,15 @@ def _update_site_view(event=None) -> None:
             tmp = tmp.sort_values(by=["_sev", "KPI"], ascending=[False, True])
         kpis_for_heatmap = tmp["KPI"].astype(str).tolist()[:30]
 
+    if not kpis_for_heatmap:
+        try:
+            if isinstance(rules_df, pd.DataFrame) and not rules_df.empty:
+                kpis_for_heatmap = (
+                    rules_df[rules_df["RAT"] == rat]["KPI"].astype(str).tolist()[:30]
+                )
+        except Exception:  # noqa: BLE001
+            kpis_for_heatmap = []
+
     heatmap_plot_pane.object = _build_site_heatmap(
         d, rules_df, int(code), rat, kpis_for_heatmap
     )
@@ -781,6 +825,12 @@ def load_datasets(event=None) -> None:
         if not loaded_any:
             raise ValueError("Please upload at least one KPI report")
 
+        rats_loaded = [r for r in ["2G", "3G", "LTE"] if r in current_daily_by_rat]
+        if rats_loaded:
+            rat_select.options = rats_loaded
+            if rat_select.value not in rats_loaded:
+                rat_select.value = rats_loaded[-1]
+
         datasets_table.value = pd.DataFrame(rows)
 
         rules_df = (