Update app.py

app.py

@@ -104,31 +104,8 @@ def load_shocks_from_zip(zip_file):
     except Exception as e:
         return None
 
-# def show_shock_value_only(df: pd.DataFrame, label: str):
-#     st.markdown(f"### Policy Shock Variables ({label})")
-#     show_option = st.radio(
-#         f"View Option for {label}",
-#         ["Only show changed (non-neutral) values", "Show all variables"],
-#         index=0,
-#         horizontal=True,
-#         key=f"shock_radio_{label}"
-#     )
-
-#     if show_option == "Only show changed (non-neutral) values":
-#         if "value" in df.columns:
-#             filtered_df = df[df["value"] != 1]
-#             if filtered_df.empty:
-#                 st.info(f"✅ No changed values found in {label} (all value = 1).")
-#             else:
-#                 st.dataframe(filtered_df, use_container_width=True)
-#         else:
-#             st.warning(f"⚠️ No 'value' column found in {label}.")
-#     else:
-#         st.dataframe(df, use_container_width=True)
-# ✅ 新版函数：不包含 st.expander，增加 region 和 commodity 筛选
 def show_shock_value_only(df: pd.DataFrame, label: str):
-    st.subheader(f"{label} Variables")
-
+    st.markdown(f"### Policy Shock Variables ({label})")
     show_option = st.radio(
         f"View Option for {label}",
         ["Only show changed (non-neutral) values", "Show all variables"],
@@ -137,46 +114,18 @@ def show_shock_value_only(df: pd.DataFrame, label: str):
         key=f"shock_radio_{label}"
     )
 
-    # 初步过滤
     if show_option == "Only show changed (non-neutral) values":
         if "value" in df.columns:
-            df = df[df["value"] != 1]
+            filtered_df = df[df["value"] != 1]
+            if filtered_df.empty:
+                st.info(f"✅ No changed values found in {label} (all value = 1).")
+            else:
+                st.dataframe(filtered_df, use_container_width=True)
         else:
             st.warning(f"⚠️ No 'value' column found in {label}.")
-
-    # 多条件筛选器
-    if "region" in df.columns:
-        regions = sorted(df["region"].dropna().unique())
-        selected_regions = st.multiselect(f"{label}: Filter by Region", regions, default=regions, key=f"region_{label}")
-        df = df[df["region"].isin(selected_regions)]
-
-    if "commodity" in df.columns:
-        commodities = sorted(df["commodity"].dropna().unique())
-        selected_commodities = st.multiselect(f"{label}: Filter by Commodity", commodities, default=commodities, key=f"commodity_{label}")
-        df = df[df["commodity"].isin(selected_commodities)]
-
-    if "attribute" in df.columns:
-        attrs = sorted(df["attribute"].dropna().unique())
-        selected_attrs = st.multiselect(f"{label}: Filter by Attribute", attrs, default=attrs, key=f"attribute_{label}")
-        df = df[df["attribute"].isin(selected_attrs)]
-
-    if "value" in df.columns and not df["value"].isnull().all():
-        val_min = float(df["value"].min())
-        val_max = float(df["value"].max())
-        value_range = st.slider(
-            f"{label}: Filter by Value Range",
-            min_value=val_min,
-            max_value=val_max,
-            value=(val_min, val_max),
-            key=f"value_range_{label}"
-        )
-        df = df[df["value"].between(value_range[0], value_range[1])]
-
-    # 展示表格
-    if df.empty:
-        st.info(f"No data available after filtering for {label}.")
     else:
         st.dataframe(df, use_container_width=True)
+
 shock_df_1 = load_shocks_from_zip(zip_file_1)
 shock_df_2 = load_shocks_from_zip(zip_file_2)
 
@@ -186,7 +135,6 @@ with st.expander("Policy Shock Variables", expanded=False):
     if zip_file_2 is not None and shock_df_2 is not None:
         show_shock_value_only(shock_df_2, "Shock 2")
 
-
 @st.cache_data
 def load_and_process_zip_pair(zip1, zip2):
     def load_zip(zip_obj):
@@ -233,7 +181,6 @@ def load_and_process_zip_pair(zip1, zip2):
 
         return df_baseline, df_scenario, shock_name
 
-
     df_base1, df_shock1, shock1_name = load_zip(zip1)
 
     df = pd.merge(df_base1, df_shock1, on=["region", "commodity", "year", "attribute"], how="left")
@@ -320,7 +267,6 @@ else:
             chart = build_topn_bar_chart(topn_df)
             st.altair_chart(chart, use_container_width=True)
 
-
     # ----------------- LINE CHART -----------------
     @st.cache_data
     def filter_data(df, region_code, commodity, attribute):