Update app.py

app.py

@@ -1265,8 +1265,13 @@ except Exception as e:
 # ✅ Panel 3: ONLY Coverage = 100% AND Slope > 0 → Avg/Month
 # ✅ Estetik: Sortable, Hover, Zebra, PLN Blue, No Emoticons
 
-st.markdown("<h3 class='section-title'>OBJECTIVE 6 - Predictive Dashboard & Early Warning Signals</h3>", unsafe_allow_html=True)
+import streamlit as st
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
 
+# =================== OBJECTIVE 6 - Predictive Dashboard & Early Warning Signals ===================
+st.markdown("<h3 class='section-title'>OBJECTIVE 6 — Predictive Dashboard & Early Warning Signals</h3>", unsafe_allow_html=True)
 
 # ✅ Enhanced CSS + Minimal Sortable JS
 st.markdown("""
@@ -1404,7 +1409,6 @@ function makeSortable(tableId) {
 }
 setTimeout(() => {
     makeSortable('tbl-locations');
-    makeSortable('tbl-divisions');
     makeSortable('tbl-issues');
 }, 800);
 </script>
@@ -1473,58 +1477,14 @@ 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()
-    
-#     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']
-    
-#     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>"
-        
-#         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):
     if 'kategori' not in df.columns or df.empty:
         return pd.DataFrame()
     
+    # 🔥 Filter: Hanya yang bukan 'Positive'
+    df = df[df['kategori'] != 'Positive'].copy()  # ✅ Filter non-Positive
+    
     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')
@@ -1576,7 +1536,6 @@ def predict_issues(df):
 
 # ——————— RUN ———————
 df_loc = predict_locations(df_filtered)
-df_div = predict_divisions(df_filtered)
 df_issue = predict_issues(df_filtered)
 
 # 🎯 PANEL 1: Locations (FILTERED: Coverage < 90% & Slope < 0)
@@ -1615,46 +1574,11 @@ 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 3: Issues (FILTERED: Coverage=100% & Rising)
+# 🎯 PANEL 3: Issues (FILTERED: Coverage=100% & Rising) — Hanya Non-Positive
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
 st.markdown(
     "<div class='predictive-header'>"
-    "3. Which Issue Categories Are Likely to Appear in the Next 3 Months"
+    "3. Which Issue Categories Are Likely to Appear in the Next 3 Months (Non-Positive Only)"
     "<span style='font-size:0.75em; font-weight:400; color:#003DA5;'>"
     " &nbsp;&nbsp;(* Categorization uses NLP — Natural Language Processing from random text)"
     "</span>"
@@ -1696,8 +1620,6 @@ if not df_issue.empty:
 
 st.markdown("</div>", unsafe_allow_html=True)
 
-
-
 # =================== 6. ✅ AI INSIGHT ENGINE (BARU - BERDASARKAN DATA & RATIO) ===================
 
 st.markdown("<h3 class='section-title'>OBJECTIVE 7 - Insight and Recommendation</h3>", unsafe_allow_html=True)