add columns to show

app.py

@@ -66,6 +66,13 @@ def get_semantic_resources():
 country_opts = sorted(df["country"].dropna().unique())
 year_opts = sorted(df["year"].dropna().unique())
 
+ALL_COLUMNS = ["country","year","section","question_code","question_text","answer_code","answer_text","Score"]
+w_columns = pn.widgets.MultiChoice(
+    name="Columns to show",
+    options=ALL_COLUMNS,
+    value=["country","year","question_text","answer_text"]
+)
+
 w_countries = pn.widgets.MultiSelect(name="Countries", options=country_opts)
 w_years = pn.widgets.MultiSelect(name="Years", options=year_opts)
 w_keyword = pn.widgets.TextInput(name="Keyword Search", placeholder="Search questions or answers with exact string matching")
@@ -107,6 +114,18 @@ def _group_by_question(df_in: pd.DataFrame) -> pd.DataFrame:
         .rename(columns={"country": "Countries", "year": "Years", "answer_text": "Sample Answers"})
     )
     return grouped
+
+def _selected_cols(has_score: bool = False):
+    allowed = set(df.columns.tolist())
+    cols = [c for c in w_columns.value if c in allowed]
+    if has_score and "Score" in w_columns.value:
+        cols = ["Score"] + cols
+    if not cols:
+        cols = ["country","year","question_text","answer_text"]
+        if has_score and "Score" in w_columns.options:
+            cols = ["Score"] + cols
+    return cols
+
     
 def search(event=None):
     query = w_semquery.value.strip()
@@ -123,7 +142,7 @@ def search(event=None):
         ]
 
     if not query:
-        result_table.value = _group_by_question(filt) if w_group.value else filt[["country", "year", "question_text", "answer_text"]]
+        result_table.value = _group_by_question(filt) if w_group.value else filt[_selected_cols(False)]
         return
 
     model, ids_list, emb_tensor = get_semantic_resources()
@@ -131,7 +150,7 @@ def search(event=None):
     id_to_index = {id_: i for i, id_ in enumerate(ids_list)}
     filtered_indices = [id_to_index[id_] for id_ in filtered_ids if id_ in id_to_index]
     if not filtered_indices:
-        result_table.value = _group_by_question(filt.iloc[0:0]) if w_group.value else pd.DataFrame(columns=["Score", "country", "year", "question_text", "answer_text"])
+        result_table.value = _group_by_question(filt.iloc[0:0]) if w_group.value else pd.DataFrame(columns=_selected_cols(True))
         return
 
     top_k = min(int(w_topk.value), len(filtered_indices))
@@ -146,8 +165,7 @@ def search(event=None):
     sem_rows["Score"] = sem_rows["id"].map(score_map)
     sem_rows = sem_rows.sort_values("Score", ascending=False)
 
-    result_table.value = _group_by_question(sem_rows.drop(columns=["Score"])) if w_group.value else sem_rows[["Score", "country", "year", "question_text", "answer_text"]]
-
+    result_table.value = _group_by_question(sem_rows.drop(columns=["Score"])) if w_group.value else sem_rows[_selected_cols(True)]
 
 
 def clear_filters(event=None):
@@ -184,7 +202,7 @@ result_table.value = df[["country", "year", "question_text", "answer_text"]].cop
 # ──────────────────────────────────────────────────────────────────────
 sidebar = pn.Column(
     "## 🔎 Filters",
-    w_countries, w_years, w_keyword, w_group,
+    w_countries, w_years, w_keyword, w_group, w_columns,
     pn.Spacer(height=20),
     "## 🧠 Semantic Search",
     w_semquery,