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)