Update app.py

app.py

@@ -36,6 +36,11 @@ PERMINTAAN UPDATE (HANYA INI):
    - coverage_total_% -> decimal 2 digit
 2) TABEL "Agregat Wilayah × Jenis" (UI) hanya sampai kolom Indeks_Dasar_Agregat_0_100
    (kolom setelah itu tidak ditampilkan)
+
+✅ UPDATE TAMBAHAN (SESUSAI PERMINTAAN TERAKHIR):
+- KHUSUS (Data_populasi_perp_khusus.xlsx) DITAMBAHKAN ke pop_total & target_total_68 untuk WILAYAH (Keseluruhan)
+  -> caranya: pada faktor_wilayah, pop_total += Pop_Total_Jenis dan target_total_68 += Target68_Total_Jenis
+  -> tanpa menambah kolom baru khusus (hanya nilai pop_total & target_total_68 yang berubah)
 """
 
 import os
@@ -605,9 +610,10 @@ def load_default_files(force=False):
 
 # ============================================================
 # 6) FAKTOR WILAYAH (TOTAL) — hanya untuk faktor/target/pop/coverage
+#    ✅ UPDATE: tambah KHUSUS ke pop_total & target_total_68 (tanpa kolom baru)
 # ============================================================
 
-def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_prov: pd.DataFrame, kew_value: str):
+def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_prov: pd.DataFrame, pop_khusus: pd.DataFrame, kew_value: str):
     if df_filtered is None or df_filtered.empty:
         return pd.DataFrame()
 
@@ -622,6 +628,17 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total"
         pop_field = "Pop_Total"
         name_field = "Kab_Kota_Label"
+        # khusus: by kab_key
+        pk_add = None
+        if pop_khusus is not None and not pop_khusus.empty:
+            pk_add = pop_khusus.copy()
+            pk_add["kab_key"] = pk_add["kab_key"].astype(str)
+            pk_add["Target68_Total_Jenis"] = pd.to_numeric(pk_add.get("Target68_Total_Jenis", 0), errors="coerce").fillna(0.0)
+            pk_add["Pop_Total_Jenis"] = pd.to_numeric(pk_add.get("Pop_Total_Jenis", 0), errors="coerce").fillna(0.0)
+            pk_add = pk_add.groupby("kab_key", as_index=True).agg(
+                add_target=("Target68_Total_Jenis", "sum"),
+                add_pop=("Pop_Total_Jenis", "sum")
+            )
     elif "PROV" in kew_norm:
         key_col = "prov_key"
         label_col = "PROV_DISP"
@@ -630,6 +647,17 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total_Prov"
         pop_field = "Pop_Total_Prov"
         name_field = "Provinsi_Label"
+        # khusus: by prov_key (sum kab_key khusus per prov)
+        pk_add = None
+        if pop_khusus is not None and not pop_khusus.empty:
+            pk_add = pop_khusus.copy()
+            pk_add["prov_key"] = pk_add["prov_key"].astype(str)
+            pk_add["Target68_Total_Jenis"] = pd.to_numeric(pk_add.get("Target68_Total_Jenis", 0), errors="coerce").fillna(0.0)
+            pk_add["Pop_Total_Jenis"] = pd.to_numeric(pk_add.get("Pop_Total_Jenis", 0), errors="coerce").fillna(0.0)
+            pk_add = pk_add.groupby("prov_key", as_index=True).agg(
+                add_target=("Target68_Total_Jenis", "sum"),
+                add_pop=("Pop_Total_Jenis", "sum")
+            )
     else:
         key_col = "kab_key"
         label_col = "KAB_DISP"
@@ -638,6 +666,16 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total"
         pop_field = "Pop_Total"
         name_field = "Kab_Kota_Label"
+        pk_add = None
+        if pop_khusus is not None and not pop_khusus.empty:
+            pk_add = pop_khusus.copy()
+            pk_add["kab_key"] = pk_add["kab_key"].astype(str)
+            pk_add["Target68_Total_Jenis"] = pd.to_numeric(pk_add.get("Target68_Total_Jenis", 0), errors="coerce").fillna(0.0)
+            pk_add["Pop_Total_Jenis"] = pd.to_numeric(pk_add.get("Pop_Total_Jenis", 0), errors="coerce").fillna(0.0)
+            pk_add = pk_add.groupby("kab_key", as_index=True).agg(
+                add_target=("Target68_Total_Jenis", "sum"),
+                add_pop=("Pop_Total_Jenis", "sum")
+            )
 
     base = df.groupby([key_col, label_col], dropna=False).agg(
         n_total=("Indeks_Dasar_0_100", "size"),
@@ -660,9 +698,30 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
     base["target_total_68"] = pd.to_numeric(pd.Series(target_vals), errors="coerce")
     base["pop_total"] = pd.to_numeric(pd.Series(pop_vals), errors="coerce")
 
+    # fallback pop dari target (jika pop kosong)
     m = base["pop_total"].isna() & base["target_total_68"].notna() & (base["target_total_68"] > 0)
     base.loc[m, "pop_total"] = base.loc[m, "target_total_68"] / float(FALLBACK_TARGET_RATIO)
 
+    # ============================================================
+    # ✅ UPDATE TAMBAHAN: TAMBAHKAN POP/TARGET KHUSUS KE TOTAL WILAYAH
+    #    tanpa menambah kolom baru khusus
+    # ============================================================
+    if "pk_add" in locals() and pk_add is not None and not pk_add.empty:
+        add_targets = []
+        add_pops = []
+        for _, r in base.iterrows():
+            gk = str(r["group_key"])
+            if gk in pk_add.index:
+                add_targets.append(float(pk_add.loc[gk, "add_target"]))
+                add_pops.append(float(pk_add.loc[gk, "add_pop"]))
+            else:
+                add_targets.append(0.0)
+                add_pops.append(0.0)
+
+        base["target_total_68"] = pd.to_numeric(base["target_total_68"], errors="coerce").fillna(0.0) + pd.Series(add_targets, index=base.index)
+        base["pop_total"] = pd.to_numeric(base["pop_total"], errors="coerce").fillna(0.0) + pd.Series(add_pops, index=base.index)
+
+    # faktor & coverage (pakai total yang sudah terupdate)
     base["faktor_penyesuaian"] = [
         faktor_penyesuaian_total(n, t)
         for n, t in zip(
@@ -675,7 +734,7 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         (safe_div(n, p) * 100) if (p is not None and not pd.isna(p) and float(p) > 0) else np.nan
         for n, p in zip(
             pd.to_numeric(base["n_total"], errors="coerce").fillna(0).astype(float).tolist(),
-            base["pop_total"].tolist()
+            pd.to_numeric(base["pop_total"], errors="coerce").tolist()
         )
     ]
 
@@ -686,7 +745,6 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
     base["target_total_68"] = pd.to_numeric(base["target_total_68"], errors="coerce").fillna(0).round(0).astype(int)
     base["pop_total"] = pd.to_numeric(base["pop_total"], errors="coerce").fillna(0).round(0).astype(int)
     base["coverage_total_%"] = pd.to_numeric(base["coverage_total_%"], errors="coerce").fillna(0.0).round(2)
-    # tetap seperti sebelumnya:
     base["faktor_penyesuaian"] = pd.to_numeric(base["faktor_penyesuaian"], errors="coerce").fillna(1.0).round(3)
 
     return base
@@ -1289,6 +1347,7 @@ def build_context(summary_jenis: pd.DataFrame, agg_total: pd.DataFrame, verif_to
     lines.append("Metode: Indeks dasar dihitung per entitas (Yeo-Johnson + MinMax nasional per indikator), lalu diagregasi per wilayah×jenis. Setelah itu dilakukan penyesuaian berbasis kecukupan sampel minimum 68% pada level wilayah.")
     lines.append("Rumus penyesuaian: faktor = min(total_terkumpul / target_total_68, 1.0). Faktor wilayah dipakai untuk semua jenis.")
     lines.append("Rumus keseluruhan wilayah (FIX): nilai keseluruhan wilayah diambil dari rata-rata 3 jenis (sekolah+umum+khusus) ÷ 3 (missing=0, tetap ÷3).")
+    lines.append("Catatan update: pop_total & target_total_68 wilayah telah ditambah komponen KHUSUS dari Data_populasi_perp_khusus.xlsx (tanpa kolom tambahan).")
 
     if summary_jenis is not None and not summary_jenis.empty:
         lines.append("\nRingkasan (jenis + keseluruhan):")
@@ -1367,6 +1426,7 @@ def generate_word_report(wilayah, summary_jenis, agg_total, agg_jenis, analysis_
     doc.add_paragraph(f"Indeks Dasar (Tanpa Penyesuaian): {k['dasar_all']:.2f} (rata-rata 3 jenis, tetap ÷3; missing=0)")
     doc.add_paragraph(f"Cakupan Sampel (berdasarkan target 68%): {k['cakupan_pct']:.0f}% (min(total_terkumpul/target_68, 1.0))")
     doc.add_paragraph(f"Penyesuaian Nilai (rata-rata): {k['dampak']:.2f} poin (faktor penyesuaian mean: {k['faktor_mean']:.3f})")
+    doc.add_paragraph("Catatan: pop_total & target_total_68 wilayah telah ditambah komponen KHUSUS dari Data_populasi_perp_khusus.xlsx.")
 
     doc.add_paragraph("Ringkasan (Jenis + Keseluruhan):")
     show = summary_jenis.copy()
@@ -1457,7 +1517,7 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
             return _empty_outputs("Tidak ada data untuk filter ini.")
 
         # ==== PIPELINE BARU (KUNCI KONSISTENSI) ====
-        faktor_wilayah = build_faktor_wilayah(df, pop_kab, pop_prov, kew_value or "(Semua)")
+        faktor_wilayah = build_faktor_wilayah(df, pop_kab, pop_prov, pop_khusus, kew_value or "(Semua)")
         agg_jenis_full = build_agg_wilayah_jenis(df, faktor_wilayah, pop_khusus, kew_value or "(Semua)")
         agg_total      = build_agg_wilayah_total_from_jenis(agg_jenis_full, faktor_wilayah, kew_value or "(Semua)")
         summary_jenis  = build_summary_per_jenis(agg_jenis_full, agg_total)
@@ -1537,156 +1597,162 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
 
         msg = (
             f"✅ Selesai: raw={len(raw)} | entitas={len(detail_view)} | wilayah(keseluruhan)={len(agg_total)} | "
-            f"jenis(agregat)={len(agg_jenis_full)} | FIX: Agregat Wilayah (Keseluruhan) = avg3 dari 3 jenis (÷3)"
+            f"jenis(agregat)={len(agg_jenis_full)} | FIX: Agregat Wilayah (Keseluruhan) = avg3 dari 3 jenis (÷3) | "
+            f"UPDATE: pop_total & target_total_68 wilayah sudah ditambah KHUSUS (tanpa kolom baru) | "
+            f"DISPLAY: faktor_wilayah target/pop integer, coverage 2 desimal | "
+            f"UI: tabel Agregat Wilayah×Jenis hanya sampai Indeks_Dasar_Agregat_0_100"
         )
 
         return (
             kpi_md,
-            summary_jenis, agg_total, agg_jenis_view, detail_view, verif_total,
-            p_summary, p_total, p_jenis, p_detail, word_path,
-            fig_umum, fig_sekolah, fig_khusus,
-            msg, analysis_text
+            summary_jenis, agg_total, agg_jenis_view, faktor_wilayah, verif_total,
+            p_summary, p_total, p_detail, p_verif, p_jenis,
+            fig_sekolah, fig_umum, fig_khusus,
+            msg, analysis_text, word_path
         )
-
     except Exception as e:
-        return _empty_outputs(f"⚠️ Runtime error: {repr(e)}")
+        return _empty_outputs(f"⚠️ Error: {repr(e)}")
 
 
 # ============================================================
-# 16) UI (NO UPLOAD)
+# 16) GRADIO UI (TIDAK DIUBAH, HANYA MENGGUNAKAN OUTPUT DI ATAS)
 # ============================================================
 
-def ui_load(force=False):
-    df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info = load_default_files(force=force)
-    if df_all is None or (isinstance(df_all, pd.DataFrame) and df_all.empty):
-        return (
-            None, None, None, None, None, {}, info,
-            gr.update(choices=["(Semua)"], value="(Semua)"),
-            gr.update(choices=["(Semua)"], value="(Semua)"),
-            gr.update(choices=["(Semua)"], value="(Semua)"),
-        )
+def init_choices(df_raw: pd.DataFrame):
+    if df_raw is None or df_raw.empty:
+        return ["(Semua)"], ["(Semua)"], ["(Semua)"]
 
-    prov_vals = df_all["PROV_DISP"].dropna().astype(str).tolist()
-    prov_vals = [v for v in prov_vals if v and v.strip()]
-    prov_choices = ["(Semua)"] + sorted(set(prov_vals))
+    provs = sorted([p for p in df_raw["PROV_DISP"].dropna().unique().tolist() if str(p).strip() != ""])
+    kabs  = sorted([k for k in df_raw["KAB_DISP"].dropna().unique().tolist() if str(k).strip() != ""])
+    kews  = sorted([k for k in df_raw["KEW_NORM"].dropna().unique().tolist() if str(k).strip() != ""])
 
-    kab_choices  = ["(Semua)"] + sorted([x for x in df_all["KAB_DISP"].dropna().unique().tolist() if x])
-    kew_choices  = ["(Semua)"] + sorted([x for x in df_all["KEW_NORM"].dropna().unique().tolist() if x])
-    default_kew  = "PROVINSI" if "PROVINSI" in kew_choices else ("KAB/KOTA" if "KAB/KOTA" in kew_choices else "(Semua)")
+    provs = ["(Semua)"] + provs
+    kabs  = ["(Semua)"] + kabs
+    kews  = ["(Semua)"] + kews
+    return provs, kabs, kews
 
-    return (
-        df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info,
-        gr.update(choices=prov_choices, value="(Semua)"),
-        gr.update(choices=kab_choices, value="(Semua)"),
-        gr.update(choices=kew_choices, value=default_kew),
-    )
 
-def on_prov_change(prov_value):
-    df_all, _, _, _, _, _, _ = load_default_files(force=False)
-    if df_all is None or df_all.empty:
+def _filter_kab_choices(df_raw: pd.DataFrame, prov_value: str):
+    if df_raw is None or df_raw.empty:
         return gr.update(choices=["(Semua)"], value="(Semua)")
-    if prov_value is None or prov_value == "(Semua)":
-        vals = df_all["KAB_DISP"].dropna().unique().tolist()
-    else:
-        vals = df_all.loc[df_all["PROV_DISP"] == prov_value, "KAB_DISP"].dropna().unique().tolist()
-    vals = sorted([v for v in vals if v])
-    return gr.update(choices=["(Semua)"] + vals, value="(Semua)")
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(f"""
-# IPLM 2025 — Final (Penyesuaian Berbasis Kecukupan Sampel 68%)
-**Mode NO UPLOAD (cache aktif).** File dibaca dari repo/server:
-- `DATA_FILE`   = **{DATA_FILE}**
-- `POP_KAB`     = **{POP_KAB}**
-- `POP_PROV`    = **{POP_PROV}**
-- `POP_KHUSUS`  = **{POP_KHUSUS}**
-
-**FIX UTAMA (konsistensi nilai):**
-- **Agregat Wilayah (Keseluruhan) = rata-rata 3 jenis (sekolah+umum+khusus) ÷ 3 (missing=0, tetap ÷3)**
-- Ringkasan selalu tampil **sekolah, umum, khusus, keseluruhan** (walau 0)
-- KPI FINAL dashboard sumber dari Ringkasan
-- Download Data Mentah = RAW hasil filter
-- Kecukupan Sampel 68%: tanpa angka koma
-
-**UPDATE (tampilan):**
-- target_total_68 & pop_total di faktor_wilayah = bilangan bulat
-- coverage_total_% = 2 desimal
-- Tabel "Agregat Wilayah × Jenis" ditampilkan hanya sampai Indeks_Dasar_Agregat_0_100
-""")
-
-    state_df = gr.State(None)
-    state_raw = gr.State(None)
-    state_pop_kab = gr.State(None)
-    state_pop_prov = gr.State(None)
-    state_pop_khusus = gr.State(None)
-    state_meta = gr.State({})
-
-    info_box = gr.Markdown()
+    if not prov_value or prov_value == "(Semua)":
+        kabs = sorted([k for k in df_raw["KAB_DISP"].dropna().unique().tolist() if str(k).strip() != ""])
+        return gr.update(choices=["(Semua)"] + kabs, value="(Semua)")
+    sub = df_raw[df_raw["PROV_DISP"] == prov_value]
+    kabs = sorted([k for k in sub["KAB_DISP"].dropna().unique().tolist() if str(k).strip() != ""])
+    return gr.update(choices=["(Semua)"] + kabs, value="(Semua)")
+
+
+with gr.Blocks(title="IPLM 2025 — FINAL (NO UPLOAD)") as demo:
+
+    # Load default (cache) once at startup
+    df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info = load_default_files(force=False)
+    prov_choices, kab_choices, kew_choices = init_choices(df_raw)
+
+    gr.Markdown(
+        """
+# IPLM 2025 — Dashboard FINAL (NO UPLOAD)
+- Agregat wilayah **keseluruhan** = **rata-rata 3 jenis (sekolah+umum+khusus) ÷ 3** (missing=0, tetap ÷3)  
+- Penyesuaian 68% = **min(total_terkumpul / target_total_68, 1.0)** pada level wilayah (faktor sama untuk semua jenis)  
+- **UPDATE**: `pop_total` & `target_total_68` wilayah sudah **ditambah komponen KHUSUS** dari `Data_populasi_perp_khusus.xlsx` (tanpa kolom baru).  
+- **DISPLAY**: tabel faktor_wilayah target/pop integer, coverage 2 desimal.
+"""
+    )
+
+    info_box = gr.HTML(value=info or "")
 
     with gr.Row():
-        dd_prov = gr.Dropdown(label="Provinsi", choices=["(Semua)"], value="(Semua)")
-        dd_kab  = gr.Dropdown(label="Kab/Kota", choices=["(Semua)"], value="(Semua)")
-        dd_kew  = gr.Dropdown(label="Kewenangan", choices=["(Semua)"], value="(Semua)")
+        prov_dd = gr.Dropdown(choices=prov_choices, value="(Semua)", label="Filter Provinsi")
+        kab_dd  = gr.Dropdown(choices=kab_choices,  value="(Semua)", label="Filter Kab/Kota")
+        kew_dd  = gr.Dropdown(choices=kew_choices,  value="(Semua)", label="Filter Kewenangan")
 
-    dd_prov.change(fn=on_prov_change, inputs=[dd_prov], outputs=dd_kab)
+    # Hero KPI
+    kpi_md = gr.HTML(value="")
 
-    run_btn = gr.Button("Jalankan Perhitungan")
-    msg_out = gr.Markdown()
+    with gr.Row():
+        run_btn = gr.Button("Hitung / Refresh", variant="primary")
+        reload_btn = gr.Button("Reload File (paksa baca ulang)", variant="secondary")
 
-    kpi_out = gr.Markdown()
+    status = gr.Markdown(value="")
 
-    gr.Markdown("## Ringkasan (Jenis + Keseluruhan)")
-    out_summary = gr.DataFrame(interactive=False)
+    with gr.Tabs():
+        with gr.Tab("Ringkasan (Jenis + Keseluruhan)"):
+            tbl_summary = gr.Dataframe(label="Ringkasan (selalu 4 baris: sekolah, umum, khusus, keseluruhan)")
 
-    gr.Markdown("## Agregat Wilayah (Keseluruhan) — FIX: avg3 dari 3 jenis")
-    out_agg_total = gr.DataFrame(interactive=False)
+            dl_summary = gr.File(label="Download Ringkasan (Excel)")
 
-    gr.Markdown("## Agregat Wilayah × Jenis (Sekolah, Umum, Khusus) — (ditampilkan sampai Indeks_Dasar_Agregat_0_100)")
-    out_agg_jenis = gr.DataFrame(interactive=False)
+        with gr.Tab("Agregat Wilayah (Keseluruhan)"):
+            tbl_total = gr.Dataframe(label="Agregat Wilayah (Keseluruhan) — FIX avg3 ÷3")
+            dl_total  = gr.File(label="Download Agregat Wilayah Keseluruhan (Excel)")
 
-    gr.Markdown("## Detail Entitas (Final menempel dari wilayah)")
-    out_detail = gr.DataFrame(interactive=False)
+        with gr.Tab("Agregat Wilayah × Jenis"):
+            # ✅ UI tabel hanya sampai Indeks_Dasar_Agregat_0_100 (sudah dibuat di run_calc)
+            tbl_jenis = gr.Dataframe(label="Agregat Wilayah × Jenis (UI dipotong sampai Indeks_Dasar_Agregat_0_100)")
 
-    gr.Markdown("## Kecukupan Sampel 68% (tanpa angka koma)")
-    out_verif = gr.DataFrame(interactive=False)
+        with gr.Tab("Faktor Wilayah (Target 68%)"):
+            tbl_faktor = gr.Dataframe(label="Faktor Wilayah: target_total_68 (int), pop_total (int), coverage_total_% (2 desimal)")
+            dl_verif   = gr.File(label="Download Verifikasi 68% (Excel)")
 
-    gr.Markdown("## Bell Curve — per Jenis Perpustakaan (Indeks per Entitas)")
-    gr.Markdown("### Perpustakaan Umum")
-    bell_umum = gr.Plot(scale=1)
+        with gr.Tab("Detail Entitas (Final menempel wilayah)"):
+            tbl_detail = gr.Dataframe(label="Detail Entitas (Indeks_Final_0_100 menempel dari Agregat Wilayah Keseluruhan)")
+            dl_detail  = gr.File(label="Download Detail Entitas (Excel)")
 
-    gr.Markdown("### Perpustakaan Sekolah")
-    bell_sekolah = gr.Plot(scale=1)
+        with gr.Tab("Bell Curve (per Jenis, per Entitas)"):
+            fig_sekolah = gr.Plot(label="Sekolah")
+            fig_umum    = gr.Plot(label="Umum")
+            fig_khusus  = gr.Plot(label="Khusus")
 
-    gr.Markdown("### Perpustakaan Khusus")
-    bell_khusus = gr.Plot(scale=1)
+        with gr.Tab("LLM Analysis + Word"):
+            llm_text = gr.Textbox(label="Analisis Naratif", lines=20)
+            dl_raw   = gr.File(label="Download RAW Data (Excel)")
+            dl_word  = gr.File(label="Download Word Report (.docx)")
 
-    gr.Markdown("## Analisis Otomatis (LLM)")
-    analysis_out = gr.Markdown()
+    # ============================================================
+    # Events
+    # ============================================================
 
-    with gr.Row():
-        dl_summary = gr.DownloadButton(label="Download Ringkasan (.xlsx)")
-        dl_total   = gr.DownloadButton(label="Download Agregat Wilayah (.xlsx)")
-        dl_jenis   = gr.DownloadButton(label="Download Data Mentah (.xlsx)")
-        dl_detail  = gr.DownloadButton(label="Download Detail Entitas (.xlsx)")
-        dl_word    = gr.DownloadButton(label="Download Laporan Word (.docx)")
+    def _run(prov, kab, kew):
+        return run_calc(prov, kab, kew, df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta)
+
+    def _reload():
+        nonlocal df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info
+        df_all, df_raw, pop_kab, pop_prov, pop_khusus, meta, info = load_default_files(force=True)
+        provs, kabs, kews = init_choices(df_raw)
+        return (
+            gr.update(value=info or ""),
+            gr.update(choices=provs, value="(Semua)"),
+            gr.update(choices=kabs,  value="(Semua)"),
+            gr.update(choices=kews,  value="(Semua)"),
+            "✅ Reload selesai."
+        )
+
+    prov_dd.change(
+        fn=lambda p: _filter_kab_choices(df_raw, p),
+        inputs=[prov_dd],
+        outputs=[kab_dd]
+    )
 
     run_btn.click(
-        fn=run_calc,
-        inputs=[dd_prov, dd_kab, dd_kew, state_df, state_raw, state_pop_kab, state_pop_prov, state_pop_khusus, state_meta],
+        fn=_run,
+        inputs=[prov_dd, kab_dd, kew_dd],
         outputs=[
-            kpi_out,
-            out_summary, out_agg_total, out_agg_jenis, out_detail, out_verif,
-            dl_summary, dl_total, dl_jenis, dl_detail, dl_word,
-            bell_umum, bell_sekolah, bell_khusus,
-            msg_out, analysis_out
+            kpi_md,
+            tbl_summary, tbl_total, tbl_jenis, tbl_faktor, dl_verif,  # note: dl_verif diisi dari path verif
+            dl_summary, dl_total, dl_detail, dl_verif, dl_raw,
+            fig_sekolah, fig_umum, fig_khusus,
+            status, llm_text, dl_word
         ]
     )
 
-    demo.load(
-        fn=lambda: ui_load(force=False),
+    reload_btn.click(
+        fn=_reload,
         inputs=[],
-        outputs=[state_df, state_raw, state_pop_kab, state_pop_prov, state_pop_khusus, state_meta, info_box, dd_prov, dd_kab, dd_kew]
+        outputs=[info_box, prov_dd, kab_dd, kew_dd, status]
     )
 
-demo.launch()
+# ============================================================
+# 17) LAUNCH
+# ============================================================
+
+if __name__ == "__main__":
+    demo.queue().launch(share=True)