Update app.py

app.py

@@ -334,9 +334,8 @@ with st.container():
     with col2:
         st.markdown("""
             <div class="header-title">
-                <h1 style="margin-bottom:6px;">PLN Audit Insight & Intelligence Dashboard</h1>
+                <h1 style="margin-bottom:6px;">Proactive Safety Intelligence & Analytics Dashboard</h1>
                 <p style="color:#546e7a; font-size:1.05em; margin-top:-8px;">
-                    Operational Risk Intelligence for Audit & Compliance
                 </p>
             </div>
         """, unsafe_allow_html=True)
@@ -1264,7 +1263,6 @@ except Exception as e:
 # ✅ Panel 1: ONLY Coverage < 90% AND Slope < 0
 # ✅ Panel 3: ONLY Coverage = 100% AND Slope > 0 → Avg/Month
 # ✅ Estetik: Sortable, Hover, Zebra, PLN Blue, No Emoticons
-
 import streamlit as st
 import plotly.graph_objects as go
 import numpy as np
@@ -1408,7 +1406,7 @@ function makeSortable(tableId) {
     });
 }
 setTimeout(() => {
-    makeSortable('tbl-locations');
+    makeSortable('tbl-creators');
     makeSortable('tbl-issues');
 }, 800);
 </script>
@@ -1431,9 +1429,12 @@ def ascii_sparkline_pln(data):
     except:
         return "<span class='spark' style='color:#999;'>▁▁▁</span>"
 
-# ——————— 1. Locations: ONLY Coverage < 90% AND Slope < 0 ———————
-def predict_locations(df):
-    if 'nama_lokasi_full' not in df.columns or df.empty:
+# ——————— 1. Creators: ONLY Coverage < 90% AND Slope < 0 (Non-Positive Only) ———————
+def predict_creators(df):
+    # 🔥 Filter: Hanya yang bukan 'Positive'
+    df = df[df['temuan_kategori'] != 'Positive'].copy()  # ✅ Filter non-Positive
+    
+    if 'creator_name' not in df.columns or df.empty:
         return pd.DataFrame()
     
     start_month = df['created_at'].min().to_period('M')
@@ -1441,17 +1442,17 @@ def predict_locations(df):
     all_months = pd.period_range(start=start_month, end=end_month, freq='M')
     
     df_monthly = (
-        df.groupby(['nama_lokasi_full', df['created_at'].dt.to_period('M')])
+        df.groupby(['creator_name', 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 = ['Location', 'Month', 'Count']
+    df_monthly.columns = ['Creator', 'Month', 'Count']
     
     results = []
-    for lokasi, group in df_monthly.groupby('Location'):
+    for creator, group in df_monthly.groupby('Creator'):
         ts = group.set_index('Month')['Count']
         total = len(all_months)
         active = (ts > 0).sum()
@@ -1465,7 +1466,7 @@ def predict_locations(df):
                 if slope < 0 and coverage < 0.9:
                     reason = f"Slope = {slope:.3f}, Coverage = {coverage*100:.1f}%. Avg: {avg_rate:.2f}/mo."
                     results.append({
-                        'Location': lokasi,
+                        'Creator': creator,
                         'Avg Reports/Month': round(avg_rate, 2),
                         'Coverage (%)': round(coverage * 100, 1),
                         'Trend Slope': round(slope, 3),
@@ -1535,21 +1536,21 @@ def predict_issues(df):
     return df_res.reset_index(drop=True)
 
 # ——————— RUN ———————
-df_loc = predict_locations(df_filtered)
+df_creator = predict_creators(df_filtered)
 df_issue = predict_issues(df_filtered)
 
-# 🎯 PANEL 1: Locations (FILTERED: Coverage < 90% & Slope < 0)
+# 🎯 PANEL 1: Creators (FILTERED: Coverage < 90% & Slope < 0) — Non-Positive Only
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
-st.markdown("<div class='predictive-header'>1. Which areas are predicted to have no future inspections?</div>", unsafe_allow_html=True)
-if not df_loc.empty:
-    cols = ['Location', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend', 'Reason']
+st.markdown("<div class='predictive-header'>1. Which Reporters Are Predicted to Have No Future Inspections? (Non-Positive Only)</div>", unsafe_allow_html=True)
+if not df_creator.empty:
+    cols = ['Creator', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend', 'Reason']
 
     # 🔥 Rename hanya untuk DISPLAY, bukan data asli
-    df_display = df_loc[cols].rename(columns={
+    df_display = df_creator[cols].rename(columns={
         "Reason": "Reason Forecast"
     })
 
-    html = df_display.to_html(escape=False, index=False, table_id="tbl-locations")
+    html = df_display.to_html(escape=False, index=False, table_id="tbl-creators")
     st.markdown(f"<div class='predictive-table-wrapper'>{html}</div>", unsafe_allow_html=True)
 
     # st.markdown(
@@ -1564,65 +1565,20 @@ if not df_loc.empty:
 #     st.markdown(
 #         "<div class='predictive-table-wrapper'>"
 #         "<p style='text-align:center; color:#666; padding:24px; font-style:italic;'>"
-#         "No locations meet criteria: Coverage < 90% and negative trend."
+#         "No creators meet criteria: Coverage < 90% and negative trend."
 #         "</p>"
 #         "<div class='warning-box'>"
-#         "💡 Note: Locations with Coverage ≥ 90% are excluded — they are considered stable reporting zones."
+#         "💡 Note: Creators with Coverage ≥ 90% are excluded — they are considered stable reporters."
 #         "</div>"
 #         "</div>",
 #         unsafe_allow_html=True
     # )
 st.markdown("</div>", unsafe_allow_html=True)
 
-# 🎯 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 (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>"
-    "</div>",
-    unsafe_allow_html=True
-)
-
-if not df_issue.empty:
-    cols = ['Category', 'Avg/Month', 'Coverage (%)', 'Trend Slope', 'Status', 'Trend']
-
-    # 🔵 Rename ONLY for display
-    df_display = df_issue[cols].rename(columns={
-        "Status": "Status Issue for Next Month"
-    })
-
-    html = df_display.to_html(escape=False, index=False, table_id="tbl-issues")
-    st.markdown(f"<div class='predictive-table-wrapper'>{html}</div>", unsafe_allow_html=True)
-
-#     st.markdown(
-#         "<div class='predictive-note'>"
-#         "<strong>Filtered:</strong> Reported every month (100% coverage) with increasing trend. "
-#         "<strong>Avg/Month</strong> = total ÷ months. "
-#         "<span class='trend-rising'>High-Risk Rising</span> = slope > 0.2."
-#         "</div>",
-#         unsafe_allow_html=True
-#     )
-
-# else:
-#     st.markdown(
-#         "<div class='predictive-table-wrapper'>"
-#         "<p style='text-align:center; color:#c62828; padding:24px; font-weight:500;'>"
-#         "⚠️ No rising categories with 100% monthly coverage."
-#         "</p>"
-#         "<p style='text-align:center; color:#666; font-size:0.9em;'>"
-#         "Consider relaxing coverage filter if data is sparse."
-#         "</p></div>",
-#         unsafe_allow_html=True
-#     )
-
-st.markdown("</div>", unsafe_allow_html=True)
 
 
 # =================== WHITEBOARD STYLE CHART FOR PANEL 3 ===================
-st.markdown("<h4 style='text-align: center; color: #2c3e50;'>Whiteboard Insight: Trend vs Frequency</h4>", unsafe_allow_html=True)
+st.markdown("<h4 style='text-align: center; color: #2c3e50;'>2. Which Issue Categories Are Likely to Appear in the Next 3 Months</h4>", unsafe_allow_html=True)
 
 # Buat chart scatter dengan gaya whiteboard
 if not df_issue.empty:
@@ -1726,6 +1682,7 @@ if not df_issue.empty:
     st.markdown(insight_text, unsafe_allow_html=True)
 else:
     st.info("No data available for non-positive issues with 100% coverage and positive trend.")
+
 # =================== 6. ✅ AI INSIGHT ENGINE (BARU - BERDASARKAN DATA & RATIO) ===================
 
 st.markdown("<h3 class='section-title'>OBJECTIVE 7 - Insight and Recommendation</h3>", unsafe_allow_html=True)