Update app.py

app.py

@@ -549,7 +549,7 @@ if 'temuan_kode_distrik' in df_local.columns:
                     f"(<strong>{top_company_pg['avg_monthly_ratio']:.2f}</strong>), indicating potentially high exposure or active reporting. "
                     f"Consider reviewing their operational procedures. "
                     f"Conversely, <strong>{low_company_pg['nama_perusahaan']}</strong> has the lowest ratio "
-                    f"(<strong>{low_company_pg['avg_monthly_ratio']:.2f}</strong>), suggesting effective risk management or lower activity levels."
+                    f"(<strong>{low_company_pg['avg_monthly_ratio']:.2f}</strong>), suggesting the need to actively improve reporting frequency."
                     f"</div>"
                 )
                 st.markdown(insight_text, unsafe_allow_html=True)
@@ -570,10 +570,10 @@ if 'temuan_kode_distrik' in df_local.columns:
                 st.markdown("### Insight")
                 insight_text = (
                     f"<div class='ai-insight'>"
-                    f"In UM Area, <strong>{top_company_um['nama_perusahaan']}</strong> exhibits the highest average finding-to-person ratio "
-                    f"(<strong>{top_company_um['avg_monthly_ratio']:.2f}</strong>), warranting a focused safety audit. "
-                    f"<strong>{low_company_um['nama_perusahaan']}</strong> shows the lowest ratio "
-                    f"(<strong>{low_company_um['avg_monthly_ratio']:.2f}</strong>), which could reflect strong safety practices or requires verification of reporting completeness."
+                    f"In UM Area, all companies show almost the same average finding per person ration"
+                    # f"(<strong>{top_company_um['avg_monthly_ratio']:.2f}</strong>), warranting a focused safety audit. "
+                    # f"<strong>{low_company_um['nama_perusahaan']}</strong> shows the lowest ratio "
+                    # f"(<strong>{low_company_um['avg_monthly_ratio']:.2f}</strong>), which could reflect strong safety practices or requires verification of reporting completeness."
                     f"</div>"
                 )
                 st.markdown(insight_text, unsafe_allow_html=True)
@@ -848,11 +848,11 @@ with col_3a:
         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 highest average finding-to-person ratio "
+            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 lowest ratio "
+            f"<strong>{low['nama']}</strong> has the highest ratio "
             f"(<strong>{low['avg_monthly_ratio']:.2f}</strong>). "
-            f"Monitor high-ratio divisions for potential systemic issues and verify reporting completeness in low-ratio ones."
+            f"Monitor low-ratio divisions for potential systemic issues and verify reporting completeness."
             f"</div>"
         )
         st.markdown(insight_text, unsafe_allow_html=True)
@@ -911,9 +911,9 @@ with col_3c:
         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 highest average monthly finding rate "
+            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 lowest rate "
+            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>"
@@ -1124,7 +1124,7 @@ else:
 
 
 # =================== 5. Matrix (Tetap Dipertahankan) ===================
-st.markdown("<h3 class='section-title'>OBJECTIVE 5 - Findings vs Lead Time: Which Companies Move Slow?</h3>", unsafe_allow_html=True)
+st.markdown("<h3 class='section-title'>OBJECTIVE 5 - Findings vs Lead Time: Which Divisions Move Slow?</h3>", unsafe_allow_html=True)
 
 import math
 import plotly.express as px
@@ -1185,7 +1185,7 @@ try:
         # 6. Merge Risk Matrix
         # ============================
         risk_matrix = operator_avg.merge(operator_lead, on='nama', how='left')
-        risk_matrix = risk_matrix.rename(columns={'nama': 'Operator Name'})
+        risk_matrix = risk_matrix.rename(columns={'nama': 'Division'})
         # Handle operator tanpa lead time (e.g., belum closed)
         risk_matrix['Average Lead Time'] = risk_matrix['Average Lead Time'].fillna(0)
         # ============================
@@ -1638,14 +1638,14 @@ df_category = predict_categories(df_filtered)
 
 # 🎯 PANEL 1: Creators (FILTERED: Coverage < 90% & Slope < 0)
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
-st.markdown("<div class='predictive-header'>1. Which Reporters Are Predicted to Have No Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
+st.markdown("<div class='predictive-header'>1. Which Reporters Are Predicted to Have Less Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
 if not df_creator.empty:
-    cols = ['Creator', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend', 'Reason']
+    cols = ['Creator', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
 
     # 🔥 Rename hanya untuk DISPLAY, bukan data asli
-    df_display = df_creator[cols].rename(columns={
-        "Reason": "Reason Forecast"
-    })
+    # df_display = df_creator[cols].rename(columns={
+    #     "Reason": "Reason Forecast"
+    # })
 
     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)
@@ -1674,14 +1674,14 @@ st.markdown("</div>", unsafe_allow_html=True)
 
 # 🎯 PANEL 2: Locations (FILTERED: Coverage < 90% & Slope < 0)
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
-st.markdown("<div class='predictive-header'>2. Which Locations Are Predicted to Have No Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
+st.markdown("<div class='predictive-header'>2. Which Locations Are Predicted to Have Less Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
 if not df_location.empty:
-    cols = ['Location', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend', 'Reason']
+    cols = ['Location', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
 
-    # 🔥 Rename hanya untuk DISPLAY, bukan data asli
-    df_display = df_location[cols].rename(columns={
-        "Reason": "Reason Forecast"
-    })
+    # # 🔥 Rename hanya untuk DISPLAY, bukan data asli
+    # df_display = df_location[cols].rename(columns={
+    #     "Reason": "Reason Forecast"
+    # })
 
     html = df_display.to_html(escape=False, index=False, table_id="tbl-locations")
     st.markdown(f"<div class='predictive-table-wrapper'>{html}</div>", unsafe_allow_html=True)
@@ -1710,14 +1710,14 @@ st.markdown("</div>", unsafe_allow_html=True)
 
 # 🎯 PANEL 3: Divisions (FILTERED: Coverage < 90% & Slope < 0)
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
-st.markdown("<div class='predictive-header'>3. Which Divisions Are Predicted to Have No Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
+st.markdown("<div class='predictive-header'>3. Which Divisions Are Predicted to Have Less Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
 if not df_division.empty:
-    cols = ['Division', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend', 'Reason']
+    cols = ['Division', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
 
-    # 🔥 Rename hanya untuk DISPLAY, bukan data asli
-    df_display = df_division[cols].rename(columns={
-        "Reason": "Reason Forecast"
-    })
+    # # 🔥 Rename hanya untuk DISPLAY, bukan data asli
+    # df_display = df_division[cols].rename(columns={
+    #     "Reason": "Reason Forecast"
+    # })
 
     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)
@@ -1748,7 +1748,7 @@ st.markdown("</div>", unsafe_allow_html=True)
 # st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
 st.markdown(
     "<div class='predictive-header'>"
-    "4. Which Issue Categories Are Likely to Appear in the Next 3 Months (Non-Positive Only)"
+    "4. Which Issue Categories Are Likely to Appear in the Next 3 Months (Increasing Trend Only)"
     "<span style='font-size:0.75em; font-weight:400; color:#003DA5;'>"
     " &nbsp;&nbsp;(* Categorization uses NLP — Natural Language Processing from random text)"
     "</span>"
@@ -1808,9 +1808,9 @@ if not df_category.empty:
     fig.add_trace(go.Scatter(
         x=df_plot['Category'],
         y=df_plot['Y'],
-        mode='markers+text',
+        mode='markers',
         marker=dict(
-            size=df_plot['Size'] * 0.5,  # Skala ukuran agar tidak terlalu besar
+            size=df_plot['Size'] * 1.5,  # Skala ukuran agar tidak terlalu besar
             color='#003DA5',
             line=dict(width=2, color='white'),
             opacity=0.8
@@ -1885,15 +1885,15 @@ if not df_category.empty:
     st.plotly_chart(fig, use_container_width=True)
 
     # Insight
-    st.markdown("### 💡 Insight")
-    insight_text = (
-        f"<div class='ai-insight'>"
-        f"The scatter plot visualizes issue categories with 100% coverage and positive trend. "
-        f"Categories higher on the Y-axis indicate stronger upward trends, while larger circles indicate higher average monthly frequency. "
-        f"This helps identify which non-positive issues are both increasing in trend and frequently reported — these should be prioritized for intervention."
-        f"</div>"
-    )
-    st.markdown(insight_text, unsafe_allow_html=True)
+    # st.markdown("### 💡 Insight")
+    # insight_text = (
+    #     f"<div class='ai-insight'>"
+    #     f"The scatter plot visualizes issue categories with 100% coverage and positive trend. "
+    #     f"Categories higher on the Y-axis indicate stronger upward trends, while larger circles indicate higher average monthly frequency. "
+    #     f"This helps identify which non-positive issues are both increasing in trend and frequently reported — these should be prioritized for intervention."
+    #     f"</div>"
+    # )
+    # st.markdown(insight_text, unsafe_allow_html=True)
 else:
     st.info("No data available for non-positive issue categories with 100% coverage and positive trend.")