Update app.py

app.py

@@ -169,6 +169,9 @@ with st.expander("📆 시즈널리티 & 패턴 설명"):
         f"**연간 저점 월:** {int(month_season.idxmin())}월  \n"
         f"**연간 변동폭:** {month_season.max() - month_season.min():.1f}")
 
+# -------------------------------------------------
+# CORRELATION HEATMAP -----------------------------
+# -------------------------------------------------
 # -------------------------------------------------
 # CORRELATION HEATMAP -----------------------------
 # -------------------------------------------------
@@ -180,4 +183,29 @@ monthly_pivot = (raw_df.assign(month=lambda d: d.date.dt.to_period("M"))
 corr = monthly_pivot.corr()
 fig, ax = plt.subplots(figsize=(12, 10))
 mask = np.triu(np.ones_like(corr, dtype=bool))
-sns.heatmap(c
+sns.heatmap(corr, mask=mask, annot=False, cmap="coolwarm", center=0, 
+            square=True, linewidths=.5, cbar_kws={"shrink": .5})
+
+# Highlight correlations with selected item
+if selected_item in corr.columns:
+    item_corr = corr[selected_item].sort_values(ascending=False)
+    top_corr = item_corr.drop(selected_item).head(5)
+    bottom_corr = item_corr.drop(selected_item).tail(5)
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        st.markdown(f"**{selected_item}와 상관관계 높은 품목**")
+        for item, val in top_corr.items():
+            st.write(f"{item}: {val:.2f}")
+    with col2:
+        st.markdown(f"**{selected_item}와 상관관계 낮은 품목**")
+        for item, val in bottom_corr.items():
+            st.write(f"{item}: {val:.2f}")
+
+st.pyplot(fig)
+
+# -------------------------------------------------
+# FOOTER ------------------------------------------
+# -------------------------------------------------
+st.markdown("---")
+st.caption("© 2024 품목별 가격 예측 시스템 | 데이터 분석 자동화")