Update app.py

app.py

@@ -790,6 +790,30 @@ avg_leadtime_nama = compute_executor_leadtime_by_nama(df_local)
 avg_leadtime_per_executor = compute_executor_leadtime_by_pic(df_local)
 
 
+# Helper: Dapatkan warna berdasarkan ranking global (bukan lokal subset)
+def add_color_by_global_rank(df, value_col, top_n=5, worst_n=5, high_is_good=True):
+    """
+    Menambahkan kolom 'color' berdasarkan ranking global.
+    - Jika high_is_good=True (e.g., ratio): top_n → hijau, worst_n → default
+    - Jika high_is_good=False (e.g., lead time): worst_n (tertinggi) → merah, top_n (terendah) → default
+    """
+    df = df.copy()
+    df = df.sort_values(value_col, ascending=not high_is_good).reset_index(drop=True)  # descending if high_is_good
+    df['color'] = '#1f77b4'  # default biru
+    
+    if high_is_good:
+        # Nilai tinggi = baik → top 5 hijau
+        top_idx = df.head(top_n).index
+        df.loc[top_idx, 'color'] = '#4CAF50'
+    else:
+        # Nilai tinggi = buruk → top 5 (tertinggi) = merah
+        # → Urut ascending, ambil tail 5 (tertinggi)
+        df_asc = df.sort_values(value_col, ascending=True)
+        worst_idx = df_asc.tail(worst_n).index
+        df.loc[worst_idx, 'color'] = '#D32F2F'
+    return df
+
+
 # ─── Layout: 2 Baris — 3a & 3c di baris pertama, 3b & 3d di baris kedua ─────
 # Baris 1: 3a & 3c
 col_3a, col_3c = st.columns(2)
@@ -801,123 +825,133 @@ with col_3a:
     else:
         sort_option_3a = st.selectbox("Show 3a:", ["Top 10", "Bottom 10"], key='sort_3a')
         
-        # Urutkan data dari tertinggi ke terendah
-        sorted_data_all = avg_ratio_per_nama.sort_values('avg_monthly_ratio', ascending=False)
+        # Full data sorted descending (high ratio = good)
+        sorted_all_3a = avg_ratio_per_nama.sort_values('avg_monthly_ratio', ascending=False).reset_index(drop=True)
         
-        # Ambil Top 10 atau Bottom 10
+        # Subset sesuai pilihan user
         if sort_option_3a == "Top 10":
-            sorted_data = sorted_data_all.head(10)
-        else:
-            sorted_data = sorted_data_all.tail(10)
+            subset_data = sorted_all_3a.head(10)
+        else:  # Bottom 10
+            subset_data = sorted_all_3a.tail(10).sort_values('avg_monthly_ratio', ascending=True)  # ascending dalam subset
         
-        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
-        sorted_data = sorted_data.sort_values('avg_monthly_ratio', ascending=False).reset_index(drop=True)
-        sorted_data = sorted_data.iloc[::-1]  # ← Balik posisi data
-
-        
-        # Tambahkan warna untuk top 5 dari data yang ditampilkan
-        sorted_data['color'] = '#1f77b4'
-        top_5_indices = sorted_data.head(5).index
-        sorted_data.loc[top_5_indices, 'color'] = '#4CAF50'
+        # Tambahkan warna berdasarkan rank global
+        avg_ratio_per_nama_colored = add_color_by_global_rank(
+            avg_ratio_per_nama, 'avg_monthly_ratio', top_n=5, high_is_good=True
+        )
+        subset_data = subset_data.merge(
+            avg_ratio_per_nama_colored[['nama', 'color']], on='nama', how='left'
+        ).fillna({'color': '#1f77b4'})
         
+        # Reverse order for better visual (low at bottom)
+        if sort_option_3a == "Top 10":
+            subset_data = subset_data.iloc[::-1]  # descending (tertinggi di atas)
+        else:
+            subset_data = subset_data  # ascending (terendah di atas)
+
         fig_rep_nama = px.bar(
-            sorted_data,
+            subset_data,
             x='avg_monthly_ratio',
             y='nama',
             orientation='h',
-            title='Avg Monthly Finding by Division',
-            labels={'avg_monthly_ratio': 'Avg Monthly Finding/Person Ratio', 'nama': 'Division'},
+            title=f'Avg Monthly Finding/Person Ratio — {sort_option_3a}',
+            labels={'avg_monthly_ratio': 'Avg Monthly Ratio', 'nama': 'Division'},
             color='color',
-            color_discrete_map={c: c for c in sorted_data['color'].unique()},
-            text=sorted_data['avg_monthly_ratio'].apply(lambda x: f'{x:.2f}')
+            color_discrete_map={c: c for c in subset_data['color'].unique()},
+            text=subset_data['avg_monthly_ratio'].apply(lambda x: f'{x:.2f}')
         )
-        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
         fig_rep_nama.update_layout(
-            yaxis={
-                'categoryorder': 'array',
-                'categoryarray': sorted_data['nama'].tolist()
-            },
+            yaxis={'categoryorder': 'array', 'categoryarray': subset_data['nama'].tolist()},
             height=500,
             showlegend=False
         )
         fig_rep_nama.update_traces(textposition='auto')
         st.plotly_chart(fig_rep_nama, use_container_width=True)
         
-        # Insight
-        top = sorted_data.iloc[0] if sort_option_3a == "Top 10" else sorted_data_all.iloc[-1]
-        low = sorted_data.iloc[-1] if sort_option_3a == "Top 10" else sorted_data_all.iloc[0]
-        insight_text = (
-            f"<div class='ai-insight'>"
-            f"The division <strong>{top['nama']}</strong> has the lowest average finding-to-person ratio "
-            f"(<strong>{top['avg_monthly_ratio']:.2f}</strong>). "
-            f"<strong>{low['nama']}</strong> has the highest ratio "
-            f"(<strong>{low['avg_monthly_ratio']:.2f}</strong>). "
-            f"Monitor low-ratio divisions for potential systemic issues and verify reporting completeness."
-            f"</div>"
-        )
+        # 🔥 INSIGHT: SELALU dari FULL DATA, stabil
+        if len(sorted_all_3a) >= 2:
+            min_val = sorted_all_3a['avg_monthly_ratio'].min()
+            max_val = sorted_all_3a['avg_monthly_ratio'].max()
+            mean_val = sorted_all_3a['avg_monthly_ratio'].mean()
+            median_val = sorted_all_3a['avg_monthly_ratio'].median()
+            best_div = sorted_all_3a.iloc[0]['nama']
+            worst_div = sorted_all_3a.iloc[-1]['nama']
+            
+            insight_text = (
+                f"<div class='ai-insight'>"
+                f"<strong>Overall (all {len(sorted_all_3a)} divisions)</strong>: Ratio ranges from <strong>{min_val:.2f}</strong> "
+                f"to <strong>{max_val:.2f}</strong> (mean: <strong>{mean_val:.2f}</strong>, median: <strong>{median_val:.2f}</strong>). "
+                f"<strong>{best_div}</strong> is the most active division (highest ratio), while <strong>{worst_div}</strong> is the least. "
+                f"<strong>Recommendation:</strong> Investigate root causes in low-activity divisions (e.g., training, tool access, workload); replicate workflows from top performers like {best_div}."
+                f"</div>"
+            )
+        else:
+            insight_text = "<div class='ai-insight'>Insufficient data for insight.</div>"
         st.markdown(insight_text, unsafe_allow_html=True)
 
+
 with col_3c:
-    st.markdown("<h5>3b. Average Finding Rate per Reporter (Name)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5>3c. Average Finding Rate per Reporter (Name)</h5>", unsafe_allow_html=True)
     if avg_rate_per_creator.empty:
         st.warning("No data for reporter analysis by creator_name.")
     else:
-        sort_option_3c = st.selectbox("Show 3b:", ["Top 10", "Bottom 10"], key='sort_3c')
+        sort_option_3c = st.selectbox("Show 3c:", ["Top 10", "Bottom 10"], key='sort_3c')
         
-        # Urutkan data dari tertinggi ke terendah
-        sorted_data_all = avg_rate_per_creator.sort_values('avg_monthly_rate', ascending=False)
+        sorted_all_3c = avg_rate_per_creator.sort_values('avg_monthly_rate', ascending=False).reset_index(drop=True)
         
-        # Ambil Top 10 atau Bottom 10
         if sort_option_3c == "Top 10":
-            sorted_data = sorted_data_all.head(10)
+            subset_data = sorted_all_3c.head(10)
         else:
-            sorted_data = sorted_data_all.tail(10)
+            subset_data = sorted_all_3c.tail(10).sort_values('avg_monthly_rate', ascending=True)
         
-        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
-        sorted_data = sorted_data.sort_values('avg_monthly_rate', ascending=False).reset_index(drop=True)
-        sorted_data = sorted_data.iloc[::-1]  # ← Balik posisi data
-
-        
-        # Tambahkan warna untuk top 5 dari data yang ditampilkan
-        sorted_data['color'] = '#1f77b4'
-        top_5_indices = sorted_data.head(5).index
-        sorted_data.loc[top_5_indices, 'color'] = '#4CAF50'
+        # Warna global: top 5 tertinggi → hijau
+        avg_rate_per_creator_colored = add_color_by_global_rank(
+            avg_rate_per_creator, 'avg_monthly_rate', top_n=5, high_is_good=True
+        )
+        subset_data = subset_data.merge(
+            avg_rate_per_creator_colored[['creator_name', 'color']], on='creator_name', how='left'
+        ).fillna({'color': '#1f77b4'})
         
+        if sort_option_3c == "Top 10":
+            subset_data = subset_data.iloc[::-1]
+
         fig_rep_creator = px.bar(
-            sorted_data,
+            subset_data,
             x='avg_monthly_rate',
             y='creator_name',
             orientation='h',
-            title='Avg Monthly Finding by Creator Name',
-            labels={'avg_monthly_rate': 'Avg Monthly Finding Rate', 'creator_name': 'Creator Name'},
+            title=f'Avg Monthly Finding Rate — {sort_option_3c}',
+            labels={'avg_monthly_rate': 'Avg Monthly Findings', 'creator_name': 'Reporter'},
             color='color',
-            color_discrete_map={c: c for c in sorted_data['color'].unique()},
-            text=sorted_data['avg_monthly_rate'].apply(lambda x: f'{x:.2f}')
+            color_discrete_map={c: c for c in subset_data['color'].unique()},
+            text=subset_data['avg_monthly_rate'].apply(lambda x: f'{x:.2f}')
         )
-        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
         fig_rep_creator.update_layout(
-            yaxis={
-                'categoryorder': 'array',
-                'categoryarray': sorted_data['creator_name'].tolist()
-            },
+            yaxis={'categoryorder': 'array', 'categoryarray': subset_data['creator_name'].tolist()},
             height=500,
             showlegend=False
         )
         fig_rep_creator.update_traces(textposition='auto')
         st.plotly_chart(fig_rep_creator, use_container_width=True)
         
-        # Insight
-        top = sorted_data.iloc[0] if sort_option_3c == "Top 10" else sorted_data_all.iloc[-1]
-        low = sorted_data.iloc[-1] if sort_option_3c == "Top 10" else sorted_data_all.iloc[0]
-        insight_text = (
-            f"<div class='ai-insight'>"
-            f"The reporter <strong>{top['creator_name']}</strong> has the lowest average monthly finding rate "
-            f"(<strong>{top['avg_monthly_rate']:.2f}</strong>). "
-            f"<strong>{low['creator_name']}</strong> has the highest rate "
-            f"(<strong>{low['avg_monthly_rate']:.2f}</strong>). "
-            f"Recognize high performers and investigate low performers."
-            f"</div>"
-        )
+        # 🔥 INSIGHT: dari FULL DATA
+        if len(sorted_all_3c) >= 2:
+            min_val = sorted_all_3c['avg_monthly_rate'].min()
+            max_val = sorted_all_3c['avg_monthly_rate'].max()
+            mean_val = sorted_all_3c['avg_monthly_rate'].mean()
+            median_val = sorted_all_3c['avg_monthly_rate'].median()
+            best_reporter = sorted_all_3c.iloc[0]['creator_name']
+            worst_reporter = sorted_all_3c.iloc[-1]['creator_name']
+            
+            insight_text = (
+                f"<div class='ai-insight'>"
+                f"<strong>Overall (all {len(sorted_all_3c)} reporters)</strong>: Monthly rate ranges from <strong>{min_val:.2f}</strong> "
+                f"to <strong>{max_val:.2f}</strong> (mean: <strong>{mean_val:.2f}</strong>). "
+                f"<strong>{best_reporter}</strong> is the top reporter; <strong>{worst_reporter}</strong> reports least frequently. "
+                f"<strong>Recommendation:</strong> Conduct 1:1 coaching for reporters with <0.5 findings/month; recognize top contributors publicly to motivate peers."
+                f"</div>"
+            )
+        else:
+            insight_text = "<div class='ai-insight'>Insufficient data for insight.</div>"
         st.markdown(insight_text, unsafe_allow_html=True)
 
 
@@ -925,66 +959,75 @@ with col_3c:
 col_3b, col_3d = st.columns(2)
 
 with col_3b:
-    st.markdown("<h5>3c. Average Lead Time by Division (Executor)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5>3b. Average Lead Time by Division (Executor)</h5>", unsafe_allow_html=True)
     if avg_leadtime_nama.empty:
         st.warning("No data for executor analysis by division.")
     else:
-        sort_option_3b = st.selectbox("Show 3c:", ["Top 10", "Bottom 10"], key='sort_3b')
+        sort_option_3b = st.selectbox("Show 3b:", ["Top 10", "Bottom 10"], key='sort_3b')
         
-        # Urutkan data dari tertinggi ke terendah
-            sorted_data_all = avg_leadtime_nama.sort_values('avg_monthly_leadtime', ascending=True)
+        # Full data: ascending (low = fast = good)
+        sorted_all_3b = avg_leadtime_nama.sort_values('avg_monthly_leadtime', ascending=True).reset_index(drop=True)
         
-        # Ambil Top 10 atau Bottom 10
         if sort_option_3b == "Top 10":
-            sorted_data = sorted_data_all.head(10)
+            # Top 10 tercepat (terendah)
+            subset_data = sorted_all_3b.head(10)
         else:
-            sorted_data = sorted_data_all.tail(10)
-        
-        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
-        sorted_data = sorted_data.sort_values('avg_monthly_leadtime', ascending=True).reset_index(drop=True)
+            # Bottom 10 = terlama (tertinggi)
+            subset_data = sorted_all_3b.tail(10).sort_values('avg_monthly_leadtime', ascending=False)  # descending dalam subset
         
-        # Tambahkan warna untuk top 5 dari data yang ditampilkan
-        sorted_data['color'] = '#1f77b4'
-        top_5_indices = sorted_data.head(5).index
-        sorted_data.loc[top_5_indices, 'color'] = '#D32F2F'
+        # Warna global: 5 division dengan lead time TERPANJANG → merah
+        avg_leadtime_nama_colored = add_color_by_global_rank(
+            avg_leadtime_nama, 'avg_monthly_leadtime', worst_n=5, high_is_good=False
+        )
+        subset_data = subset_data.merge(
+            avg_leadtime_nama_colored[['nama', 'color']], on='nama', how='left'
+        ).fillna({'color': '#1f77b4'})
         
+        # Reverse untuk visual yang intuitif (lama di atas)
+        if sort_option_3b == "Bottom 10":
+            subset_data = subset_data.iloc[::-1]  # biar tertinggi di atas
+
         fig_exec_nama = px.bar(
-            sorted_data,
+            subset_data,
             x='avg_monthly_leadtime',
             y='nama',
             orientation='h',
-            title='Avg Monthly Lead Time by Division',
+            title=f'Avg Monthly Lead Time (Days) — {sort_option_3b}',
             labels={'avg_monthly_leadtime': 'Avg Lead Time (Days)', 'nama': 'Division'},
             color='color',
-            color_discrete_map={c: c for c in sorted_data['color'].unique()},
-            text=sorted_data['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}')
+            color_discrete_map={c: c for c in subset_data['color'].unique()},
+            text=subset_data['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}')
         )
-        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
         fig_exec_nama.update_layout(
-            yaxis={
-                'categoryorder': 'array',
-                'categoryarray': sorted_data['nama'].tolist()
-            },
+            yaxis={'categoryorder': 'array', 'categoryarray': subset_data['nama'].tolist()},
             height=500,
             showlegend=False
         )
         fig_exec_nama.update_traces(textposition='auto')
         st.plotly_chart(fig_exec_nama, use_container_width=True)
         
-        # Insight
-        top = sorted_data.iloc[0] if sort_option_3b == "Top 10" else sorted_data_all.iloc[-1]
-        low = sorted_data.iloc[-1] if sort_option_3b == "Top 10" else sorted_data_all.iloc[0]
-        insight_text = (
-            f"<div class='ai-insight'>"
-            f"The division <strong>{top['nama']}</strong> has the highest average lead time "
-            f"(<strong>{top['avg_monthly_leadtime']:.2f} days</strong>). "
-            f"<strong>{low['nama']}</strong> has the fastest average resolution "
-            f"(<strong>{low['avg_monthly_leadtime']:.2f} days</strong>). "
-            f"Focus on improving SLA compliance in divisions with longer lead times."
-            f"</div>"
-        )
+        # 🔥 INSIGHT: dari FULL DATA
+        if len(sorted_all_3b) >= 2:
+            min_lt = sorted_all_3b['avg_monthly_leadtime'].min()
+            max_lt = sorted_all_3b['avg_monthly_leadtime'].max()
+            mean_lt = sorted_all_3b['avg_monthly_leadtime'].mean()
+            median_lt = sorted_all_3b['avg_monthly_leadtime'].median()
+            fastest_div = sorted_all_3b.iloc[0]['nama']
+            slowest_div = sorted_all_3b.iloc[-1]['nama']
+            
+            insight_text = (
+                f"<div class='ai-insight'>"
+                f"<strong>Overall (all {len(sorted_all_3b)} divisions)</strong>: Resolution time ranges from <strong>{min_lt:.1f}</strong> "
+                f"to <strong>{max_lt:.1f}</strong> days (mean: <strong>{mean_lt:.1f}</strong>, median: <strong>{median_lt:.1f}</strong>). "
+                f"<strong>{slowest_div}</strong> has the longest lead time; <strong>{fastest_div}</strong> resolves fastest. "
+                f"<strong>Recommendation:</strong> Escalate SLA breach risk for divisions >7 days; initiate root-cause analysis for {slowest_div} and replicate efficiency from {fastest_div}."
+                f"</div>"
+            )
+        else:
+            insight_text = "<div class='ai-insight'>Insufficient data for insight.</div>"
         st.markdown(insight_text, unsafe_allow_html=True)
 
+
 with col_3d:
     st.markdown("<h5>3d. Average Lead Time by Executor (Name)</h5>", unsafe_allow_html=True)
     if avg_leadtime_per_executor.empty:
@@ -992,60 +1035,66 @@ with col_3d:
     else:
         sort_option_3d = st.selectbox("Show 3d:", ["Top 10", "Bottom 10"], key='sort_3d')
         
-        # Urutkan data dari tertinggi ke terendah
-        sorted_data_all = avg_leadtime_per_executor.sort_values('avg_monthly_leadtime', ascending=True)
+        sorted_all_3d = avg_leadtime_per_executor.sort_values('avg_monthly_leadtime', ascending=True).reset_index(drop=True)
         
-        # Ambil Top 10 atau Bottom 10
         if sort_option_3d == "Top 10":
-            sorted_data = sorted_data_all.head(10)
+            subset_data = sorted_all_3d.head(10)
         else:
-            sorted_data = sorted_data_all.tail(10)
+            subset_data = sorted_all_3d.tail(10).sort_values('avg_monthly_leadtime', ascending=False)
         
-        # 🔥 Urutkan data yang ditampilkan dari besar ke kecil (jika Bottom 10, tetap besar ke kecil)
-        sorted_data = sorted_data.sort_values('avg_monthly_leadtime', ascending=True).reset_index(drop=True)
-        
-        # Tambahkan warna untuk top 5 dari data yang ditampilkan
-        sorted_data['color'] = '#1f77b4'
-        top_5_indices = sorted_data.head(5).index
-        sorted_data.loc[top_5_indices, 'color'] = '#D32F2F'
+        # Warna global: 5 eksekutor TERLAMBAT → merah
+        avg_leadtime_per_executor_colored = add_color_by_global_rank(
+            avg_leadtime_per_executor, 'avg_monthly_leadtime', worst_n=5, high_is_good=False
+        )
+        subset_data = subset_data.merge(
+            avg_leadtime_per_executor_colored[['nama_pic', 'color']], on='nama_pic', how='left'
+        ).fillna({'color': '#1f77b4'})
         
+        if sort_option_3d == "Bottom 10":
+            subset_data = subset_data.iloc[::-1]
+
         fig_exec_pic = px.bar(
-            sorted_data,
+            subset_data,
             x='avg_monthly_leadtime',
             y='nama_pic',
             orientation='h',
-            title='Avg Monthly Lead Time by Executor (Name)',
-            labels={'avg_monthly_leadtime': 'Avg Monthly Lead Time (Days)', 'nama_pic': 'Executor Name'},
+            title=f'Avg Monthly Lead Time (Days) — {sort_option_3d}',
+            labels={'avg_monthly_leadtime': 'Avg Lead Time (Days)', 'nama_pic': 'Executor'},
             color='color',
-            color_discrete_map={c: c for c in sorted_data['color'].unique()},
-            text=sorted_data['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}')
+            color_discrete_map={c: c for c in subset_data['color'].unique()},
+            text=subset_data['avg_monthly_leadtime'].apply(lambda x: f'{x:.2f}')
         )
-        # 🔥 Atur urutan Y-axis sesuai data yang ditampilkan
         fig_exec_pic.update_layout(
-            yaxis={
-                'categoryorder': 'array',
-                'categoryarray': sorted_data['nama_pic'].tolist()
-            },
+            yaxis={'categoryorder': 'array', 'categoryarray': subset_data['nama_pic'].tolist()},
             height=500,
             showlegend=False
         )
         fig_exec_pic.update_traces(textposition='auto')
         st.plotly_chart(fig_exec_pic, use_container_width=True)
         
-        # Insight
-        top = sorted_data.iloc[0] if sort_option_3d == "Top 10" else sorted_data_all.iloc[-1]
-        low = sorted_data.iloc[-1] if sort_option_3d == "Top 10" else sorted_data_all.iloc[0]
-        insight_text = (
-            f"<div class='ai-insight'>"
-            f"The executor <strong>{top['nama_pic']}</strong> has the highest average monthly lead time "
-            f"(<strong>{top['avg_monthly_leadtime']:.2f} days</strong>). "
-            f"<strong>{low['nama_pic']}</strong> resolves tasks fastest on average "
-            f"(<strong>{low['avg_monthly_leadtime']:.2f} days</strong>). "
-            f"Focus on improving SLA compliance for executors with longer lead times."
-            f"</div>"
-        )
+        # 🔥 INSIGHT: dari FULL DATA
+        if len(sorted_all_3d) >= 2:
+            min_lt = sorted_all_3d['avg_monthly_leadtime'].min()
+            max_lt = sorted_all_3d['avg_monthly_leadtime'].max()
+            mean_lt = sorted_all_3d['avg_monthly_leadtime'].mean()
+            median_lt = sorted_all_3d['avg_monthly_leadtime'].median()
+            fastest_exec = sorted_all_3d.iloc[0]['nama_pic']
+            slowest_exec = sorted_all_3d.iloc[-1]['nama_pic']
+            
+            insight_text = (
+                f"<div class='ai-insight'>"
+                f"<strong>Overall (all {len(sorted_all_3d)} executors)</strong>: Lead time ranges from <strong>{min_lt:.1f}</strong> "
+                f"to <strong>{max_lt:.1f}</strong> days (mean: <strong>{mean_lt:.1f}</strong>). "
+                f"<strong>{slowest_exec}</strong> has the longest resolution time; <strong>{fastest_exec}</strong> is most efficient. "
+                f"<strong>Recommendation:</strong> Assign mentor to executors with >7-day average; document and share best practices from {fastest_exec} across the team."
+                f"</div>"
+            )
+        else:
+            insight_text = "<div class='ai-insight'>Insufficient data for insight.</div>"
         st.markdown(insight_text, unsafe_allow_html=True)
 
+
+
 try:
     from wordcloud import WordCloud
     import matplotlib.pyplot as plt