diff --git a/dashboard.py b/dashboard.py
index 378b77f..82200c6 100644
--- a/dashboard.py
+++ b/dashboard.py
@@ -26,6 +26,16 @@ st.set_page_config(
     initial_sidebar_state="expanded",
 )
 
+import streamlit.components.v1 as components
+components.html("""
+<script>
+// Ping cada 30s para mantener el WebSocket activo y evitar el error 401 por inactividad
+setInterval(function() {
+    fetch('/_stcore/health').catch(function() {});
+}, 30000);
+</script>
+""", height=0)
+
 _STRATEGY_LABELS = {
     "LOWEST_COST_WITHOUT_CAP": "Menor coste",
     "LOWEST_COST_WITH_BID_CAP": "Cap. puja",
@@ -497,23 +507,35 @@ with tab5:
     df_all["score"]      = pd.to_numeric(df_all.get("score", 0), errors="coerce").fillna(0)
     df_all["created_at"] = df_all.get("created_at", pd.Series(dtype=str))
 
+    # Map campaign_id → campaign_name using already-loaded Meta data
+    camp_id_to_name = {cid: m["name"] for cid, m in campaign_metrics.items()}
+    df_all["campaign_name"] = df_all["campaign_id"].map(
+        lambda cid: camp_id_to_name.get(str(cid), str(cid))
+    )
+    df_all["vertical"] = df_all["campaign_name"].map(_extract_vertical)
+
     # ── Filters ───────────────────────────────────────────────────────────────
-    f1, f2, f3 = st.columns([2, 2, 2])
+    f1, f2, f3, f4 = st.columns([2, 2, 2, 2])
 
     dates_available = sorted(df_all["created_at"].dropna().unique(), reverse=True)
     sel_date = f1.selectbox("Fecha análisis", dates_available)
 
-    camp_ids = sorted(df_all["campaign_id"].dropna().unique().tolist()) if "campaign_id" in df_all.columns else []
-    sel_camp = f2.selectbox("Campaña", ["Todas"] + camp_ids)
+    verts_available = sorted(df_all["vertical"].dropna().unique().tolist())
+    sel_vert_cr = f2.selectbox("Vertical", ["Todas"] + verts_available, key="cr_vert")
 
-    score_min = f3.slider("Score mínimo", 0.0, 10.0, 0.0, step=0.5)
+    camp_names = sorted(df_all["campaign_name"].dropna().unique().tolist())
+    sel_camp = f3.selectbox("Campaña", ["Todas"] + camp_names, key="cr_camp")
+
+    score_min = f4.slider("Score mínimo", 0.0, 10.0, 0.0, step=0.5)
 
     # Apply filters
     df = df_all.copy()
     if sel_date:
         df = df[df["created_at"] == sel_date]
-    if sel_camp != "Todas" and "campaign_id" in df.columns:
-        df = df[df["campaign_id"] == sel_camp]
+    if sel_vert_cr != "Todas":
+        df = df[df["vertical"] == sel_vert_cr]
+    if sel_camp != "Todas":
+        df = df[df["campaign_name"] == sel_camp]
     if score_min > 0:
         df = df[df["score"] >= score_min]
 
@@ -540,12 +562,11 @@ with tab5:
 
     # ── Table + Detail panel ──────────────────────────────────────────────────
     rename_map = {
-        "campaign_id":     "Campaña ID",
+        "campaign_name":   "Campaña",
+        "vertical":        "Vertical",
         "ad_name":         "Anuncio",
         "score":           "Score",
         "created_at":      "Fecha",
-        "analysis":        "Análisis",
-        "recommendations": "Recomendaciones",
     }
     display_cols = [c for c in rename_map if c in df.columns]
     df_display   = df[display_cols].rename(columns=rename_map).reset_index(drop=True)