Update app.py

app.py

@@ -1473,52 +1473,52 @@ def predict_locations(df):
     df_res = pd.DataFrame(results)
     return df_res.sort_values('Trend Slope', ascending=True) if not df_res.empty else df_res  # most negative first
 
-# ——————— 2. Divisions ———————
-def predict_divisions(df):
-    if 'nama' not in df.columns:
-        return pd.DataFrame()
+# # ——————— 2. Divisions ———————
+# def predict_divisions(df):
+#     if 'nama' not in df.columns:
+#         return pd.DataFrame()
     
-    start_month = df['created_at'].min().to_period('M')
-    end_month = df['created_at'].max().to_period('M')
-    all_months = pd.period_range(start=start_month, end=end_month, freq='M')
+#     start_month = df['created_at'].min().to_period('M')
+#     end_month = df['created_at'].max().to_period('M')
+#     all_months = pd.period_range(start=start_month, end=end_month, freq='M')
     
-    df_monthly = (
-        df.groupby(['nama', df['created_at'].dt.to_period('M')])
-        .size()
-        .unstack(fill_value=0)
-        .reindex(columns=all_months, fill_value=0)
-        .stack()
-        .reset_index(name='count')
-    )
-    df_monthly.columns = ['Division', 'Month', 'Count']
+#     df_monthly = (
+#         df.groupby(['nama', df['created_at'].dt.to_period('M')])
+#         .size()
+#         .unstack(fill_value=0)
+#         .reindex(columns=all_months, fill_value=0)
+#         .stack()
+#         .reset_index(name='count')
+#     )
+#     df_monthly.columns = ['Division', 'Month', 'Count']
     
-    results = []
-    for div, group in df_monthly.groupby('Division'):
-        ts = group.set_index('Month')['Count']
-        total = len(all_months)
-        active = (ts > 0).sum()
-        gaps = total - active
-        coverage = active / total if total > 0 else 0
+#     results = []
+#     for div, group in df_monthly.groupby('Division'):
+#         ts = group.set_index('Month')['Count']
+#         total = len(all_months)
+#         active = (ts > 0).sum()
+#         gaps = total - active
+#         coverage = active / total if total > 0 else 0
         
-        if gaps > 2:
-            status = "<span class='status-inactive'>Inactive</span>"
-        elif gaps == 0:
-            status = "<span class='status-active'>Active</span>"
-        else:
-            status = "<span class='status-neutral'>Neutral</span>"
+#         if gaps > 2:
+#             status = "<span class='status-inactive'>Inactive</span>"
+#         elif gaps == 0:
+#             status = "<span class='status-active'>Active</span>"
+#         else:
+#             status = "<span class='status-neutral'>Neutral</span>"
         
-        bar = ''.join(['●' if c > 0 else '○' for c in ts.values])
-        trend_line = f"<span class='spark' style='color:#003DA5;'>{bar}</span>"
-        results.append({
-            'Division': div,
-            'Active Months': int(active),
-            'Total Months': int(total),
-            'Coverage (%)': round(coverage * 100, 1),
-            'Status': status,
-            'Trend': trend_line
-        })
-    df_res = pd.DataFrame(results)
-    return df_res.sort_values('Coverage (%)', ascending=True) if not df_res.empty else df_res
+#         bar = ''.join(['●' if c > 0 else '○' for c in ts.values])
+#         trend_line = f"<span class='spark' style='color:#003DA5;'>{bar}</span>"
+#         results.append({
+#             'Division': div,
+#             'Active Months': int(active),
+#             'Total Months': int(total),
+#             'Coverage (%)': round(coverage * 100, 1),
+#             'Status': status,
+#             'Trend': trend_line
+#         })
+#     df_res = pd.DataFrame(results)
+#     return df_res.sort_values('Coverage (%)', ascending=True) if not df_res.empty else df_res
 
 # ——————— 3. Issues: ONLY Coverage=100% & Trend Slope > 0 → Avg/Month ———————
 def predict_issues(df):
@@ -1615,39 +1615,39 @@ if not df_loc.empty:
     # )
 st.markdown("</div>", unsafe_allow_html=True)
 
-# 🎯 PANEL 2: Divisions
-st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
-st.markdown("<div class='predictive-header'>2. Which Areas Are Likely to Be Frequently or Rarely Inspected?</div>", unsafe_allow_html=True)
-if not df_div.empty:
-    cols = ['Division', 'Active Months', 'Total Months', 'Coverage (%)', 'Status', 'Trend']
-
-    # 🔵 Rename ONLY for display (Status → Forecast Inspection)
-    df_display = df_div[cols].rename(columns={
-        "Status": "Forecast Inspection"
-    })
-
-    html = df_display.to_html(escape=False, index=False, table_id="tbl-divisions")
-    st.markdown(f"<div class='predictive-table-wrapper'>{html}</div>", unsafe_allow_html=True)
-
-#     st.markdown(
-#         "<div class='predictive-note'>"
-#         "<strong>Forecast Inspection:</strong> "
-#         "<span class='status-active'>Active</span> (0 gaps), "
-#         "<span class='status-neutral'>Neutral</span> (1–2 gaps), "
-#         "<span class='status-inactive'>Inactive</span> (>2 gaps)."
-#         "</div>",
-#         unsafe_allow_html=True
-#     )
-# else:
-#     st.markdown(
-#         "<div class='predictive-table-wrapper'>"
-#         "<p style='text-align:center; color:#666; padding:24px; font-style:italic;'>"
-#         "Insufficient division data (≥2 months required)."
-#         "</p></div>",
-#         unsafe_allow_html=True
-#     )
-
-st.markdown("</div>", unsafe_allow_html=True)
+# # 🎯 PANEL 2: Divisions
+# st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
+# st.markdown("<div class='predictive-header'>2. Which Areas Are Likely to Be Frequently or Rarely Inspected?</div>", unsafe_allow_html=True)
+# if not df_div.empty:
+#     cols = ['Division', 'Active Months', 'Total Months', 'Coverage (%)', 'Status', 'Trend']
+
+#     # 🔵 Rename ONLY for display (Status → Forecast Inspection)
+#     df_display = df_div[cols].rename(columns={
+#         "Status": "Forecast Inspection"
+#     })
+
+#     html = df_display.to_html(escape=False, index=False, table_id="tbl-divisions")
+#     st.markdown(f"<div class='predictive-table-wrapper'>{html}</div>", unsafe_allow_html=True)
+
+# #     st.markdown(
+# #         "<div class='predictive-note'>"
+# #         "<strong>Forecast Inspection:</strong> "
+# #         "<span class='status-active'>Active</span> (0 gaps), "
+# #         "<span class='status-neutral'>Neutral</span> (1–2 gaps), "
+# #         "<span class='status-inactive'>Inactive</span> (>2 gaps)."
+# #         "</div>",
+# #         unsafe_allow_html=True
+# #     )
+# # else:
+# #     st.markdown(
+# #         "<div class='predictive-table-wrapper'>"
+# #         "<p style='text-align:center; color:#666; padding:24px; font-style:italic;'>"
+# #         "Insufficient division data (≥2 months required)."
+# #         "</p></div>",
+# #         unsafe_allow_html=True
+# #     )
+
+# st.markdown("</div>", unsafe_allow_html=True)
 
 
 # 🎯 PANEL 3: Issues (FILTERED: Coverage=100% & Rising)