Update app.py

app.py

@@ -1484,62 +1484,62 @@ def predict_creators(df):
     # ✅ Ambil 10 creator dengan slope paling negatif (paling turun)
     return df_res.sort_values('Trend Slope', ascending=True).head(10) 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()
+# # ——————— 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
+#     # 🔥 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')
-    n_months = len(all_months)
+#     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')
+#     n_months = len(all_months)
     
-    results = []
-    for cat, group in df.groupby('kategori'):
-        ts_data = (
-            group.groupby(group['created_at'].dt.to_period('M'))
-            .size()
-            .reindex(all_months, fill_value=0)
-        )
-        total_reports = ts_data.sum()
-        avg_per_month = total_reports / n_months if n_months > 0 else 0
-        active_months = (ts_data > 0).sum()
-        coverage = active_months / n_months if n_months > 0 else 0
+#     results = []
+#     for cat, group in df.groupby('kategori'):
+#         ts_data = (
+#             group.groupby(group['created_at'].dt.to_period('M'))
+#             .size()
+#             .reindex(all_months, fill_value=0)
+#         )
+#         total_reports = ts_data.sum()
+#         avg_per_month = total_reports / n_months if n_months > 0 else 0
+#         active_months = (ts_data > 0).sum()
+#         coverage = active_months / n_months if n_months > 0 else 0
         
-        slope = 0.0
-        if len(ts_data) >= 2:
-            try:
-                slope = np.polyfit(np.arange(len(ts_data)), ts_data.values, 1)[0]
-            except:
-                pass
+#         slope = 0.0
+#         if len(ts_data) >= 2:
+#             try:
+#                 slope = np.polyfit(np.arange(len(ts_data)), ts_data.values, 1)[0]
+#             except:
+#                 pass
         
-        results.append({
-            'Category': cat,
-            'Avg/Month': round(avg_per_month, 2),
-            'Coverage (%)': round(coverage * 100, 1),
-            'Trend Slope': round(slope, 3),
-            'Trend': ascii_sparkline_pln(ts_data.values.tolist())
-        })
+#         results.append({
+#             'Category': cat,
+#             'Avg/Month': round(avg_per_month, 2),
+#             'Coverage (%)': round(coverage * 100, 1),
+#             'Trend Slope': round(slope, 3),
+#             'Trend': ascii_sparkline_pln(ts_data.values.tolist())
+#         })
     
-    df_res = pd.DataFrame(results)
+#     df_res = pd.DataFrame(results)
     
-    # ✅ FILTER: Coverage = 100% AND Trend Slope > 0
-    if not df_res.empty:
-        df_res = df_res[
-            (df_res['Coverage (%)'] == 100.0) &
-            (df_res['Trend Slope'] > 0)
-        ].copy()
+#     # ✅ FILTER: Coverage = 100% AND Trend Slope > 0
+#     if not df_res.empty:
+#         df_res = df_res[
+#             (df_res['Coverage (%)'] == 100.0) &
+#             (df_res['Trend Slope'] > 0)
+#         ].copy()
         
-        df_res['Status'] = df_res['Trend Slope'].apply(
-            lambda s: "<span class='trend-rising'>High-Risk Rising</span>" if s > 0.2 else
-                      "<span class='trend-stable'>Emerging Rising</span>"
-        )
-        df_res = df_res.sort_values('Trend Slope', ascending=False)
+#         df_res['Status'] = df_res['Trend Slope'].apply(
+#             lambda s: "<span class='trend-rising'>High-Risk Rising</span>" if s > 0.2 else
+#                       "<span class='trend-stable'>Emerging Rising</span>"
+#         )
+#         df_res = df_res.sort_values('Trend Slope', ascending=False)
     
-    return df_res.reset_index(drop=True)
+#     return df_res.reset_index(drop=True)
 
 # ——————— RUN ———————
 df_creator = predict_creators(df_filtered)
@@ -1585,7 +1585,7 @@ st.markdown("</div>", unsafe_allow_html=True)
 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)"
+    "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>"
@@ -1593,45 +1593,45 @@ st.markdown(
     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)
-
-# Buat chart scatter dengan gaya whiteboard
+# 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)
+
+# # Buat chart scatter dengan gaya whiteboard
 if not df_issue.empty:
     # Ambil data untuk scatter
     df_plot = df_issue.copy()