Create app.py

app.py

@@ -0,0 +1,799 @@
+# -*- coding: utf-8 -*-
+"""
+app.py — Dashboard Kekurangan Sampel IPLM (TANPA HITUNG INDEKS)
+- Fokus: melihat kekurangan jumlah sampel IPLM per wilayah
+- Bandingkan "sampel masuk (DM)" vs "populasi target (meta)"
+- Pertahankan LLM untuk membuat laporan naratif kekurangan sampel
+
+Output:
+- Tabel verifikasi (coverage & gap)
+- Download Excel (rekap + detail subset)
+- Word report (opsional pie chart kalau kaleido tersedia)
+"""
+
+import os
+import re
+import math
+import tempfile
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+from huggingface_hub import InferenceClient
+
+# Word report
+from docx import Document
+from docx.shared import Inches
+
+# Pie chart opsional (kalau kaleido ada)
+import plotly.express as px
+try:
+    import kaleido  # noqa: F401
+    HAS_KALEIDO = True
+except Exception:
+    HAS_KALEIDO = False
+
+
+# ============================================================
+# 1) KONFIGURASI FILE
+# ============================================================
+DATA_FILE       = "DM_001.xlsx"                 # data sampel masuk (multi-sheet)
+META_KAB_FILE   = "jumlahdesa_fixed (1).xlsx"   # kecamatan & desa/kel per kab/kota
+META_SDSMP_FILE = "SD-SMP-kab.xlsx"             # jumlah SD & SMP per kab/kota
+META_SMA_FILE   = "SMA.xlsx"                    # jumlah SMA per provinsi
+
+# ============================================================
+# 1b) KONFIGURASI LLM (Hugging Face Inference)
+# ============================================================
+USE_LLM = True
+LLM_MODEL_NAME = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+HF_TOKEN = (
+    os.getenv("HF_TOKEN")
+    or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+    or os.getenv("HF_API_TOKEN")
+)
+
+_HF_CLIENT = None
+def get_llm_client():
+    global _HF_CLIENT
+    if _HF_CLIENT is not None:
+        return _HF_CLIENT
+    try:
+        if HF_TOKEN:
+            _HF_CLIENT = InferenceClient(model=LLM_MODEL_NAME, token=HF_TOKEN)
+        else:
+            _HF_CLIENT = InferenceClient(model=LLM_MODEL_NAME)
+        return _HF_CLIENT
+    except Exception:
+        _HF_CLIENT = None
+        return None
+
+
+# ============================================================
+# 2) UTIL
+# ============================================================
+def _canon(s: str) -> str:
+    return re.sub(r"[^a-z0-9]+", "", str(s).lower())
+
+def pick_col(df, candidates):
+    for c in candidates:
+        if c in df.columns:
+            return c
+    can_map = {_canon(c): c for c in df.columns}
+    for c in candidates:
+        k = _canon(c)
+        if k in can_map:
+            return can_map[k]
+    return None
+
+def coerce_num(val):
+    if pd.isna(val):
+        return np.nan
+    t = str(val).strip()
+    if t == "" or t in {"-", "–", "—"}:
+        return np.nan
+    t = t.replace("\u00a0", " ").replace("Rp", "").replace("%", "")
+    t = re.sub(r"[^0-9,.\-]", "", t)
+    if t.count(".") > 1 and t.count(",") == 1:
+        t = t.replace(".", "").replace(",", ".")
+    elif t.count(",") > 1 and t.count(".") == 1:
+        t = t.replace(",", "")
+    elif t.count(",") == 1 and t.count(".") == 0:
+        t = t.replace(",", ".")
+    else:
+        t = t.replace(",", "")
+    try:
+        return float(t)
+    except Exception:
+        return np.nan
+
+def norm_kew(v):
+    if pd.isna(v):
+        return None
+    t = str(v).strip().upper()
+    if "KAB" in t or "KOTA" in t:
+        return "KAB/KOTA"
+    if "PROV" in t:
+        return "PROVINSI"
+    if "PUSAT" in t or "NASIONAL" in t:
+        return "PUSAT"
+    return t
+
+def _norm_text(x):
+    if pd.isna(x):
+        return None
+    t = str(x).strip().upper()
+    return " ".join(t.split())
+
+def norm_prov_label(s):
+    if pd.isna(s):
+        return None
+    t = str(s).upper()
+    for bad in ["PROVINSI", "PROPINSI"]:
+        t = t.replace(bad, "")
+    t = " ".join(t.split())
+    return re.sub(r"[^A-Z0-9]+", "", t)
+
+def norm_kab_label(s):
+    if pd.isna(s):
+        return None
+    t = str(s).upper()
+    t = t.replace("KABUPATEN", "KAB")
+    t = t.replace("KAB.", "KAB")
+    t = t.replace("KOTA ADMINISTRASI", "KOTA")
+    t = t.replace("KOTA ADM.", "KOTA")
+    t = t.replace("KOTA.", "KOTA")
+    t = " ".join(t.split())
+    return re.sub(r"[^A-Z0-9]+", "", t)
+
+def make_pie_plotly(num, den, title):
+    if not HAS_KALEIDO:
+        return None
+    if den is None or pd.isna(den) or den <= 0:
+        values = [0, 1]
+        labels = ["Terjangkau", "Belum Terjangkau"]
+    else:
+        num = 0 if pd.isna(num) else float(num)
+        den = float(den)
+        values = [max(num, 0), max(den - num, 0)]
+        labels = ["Terjangkau", "Belum Terjangkau"]
+    fig = px.pie(values=values, names=labels, title=title, hole=0.3)
+    tmp = tempfile.mktemp(suffix=".png")
+    try:
+        fig.write_image(tmp, scale=2)
+        return tmp
+    except Exception:
+        return None
+
+
+# ============================================================
+# 3) LOAD DATA (DM + META)
+# ============================================================
+DATA_INFO = ""
+df_all_raw = None
+meta_kab_df = None     # kab_key -> kec, desa/kel, SD, SMP (gabungan)
+meta_sma_df = None     # prov_key -> Jml_SMA
+
+prov_col_glob = kab_col_glob = kew_col_glob = jenis_col_glob = subjenis_col_glob = nama_col_glob = None
+
+try:
+    fp = Path(DATA_FILE)
+    if not fp.exists():
+        raise FileNotFoundError(f"File tidak ditemukan: {DATA_FILE}")
+
+    xls = pd.ExcelFile(fp)
+    frames = [pd.read_excel(fp, sheet_name=s) for s in xls.sheet_names]
+    df_all_raw = pd.concat(frames, ignore_index=True, sort=False)
+
+    prov_col_glob  = pick_col(df_all_raw, ["provinsi", "Provinsi", "PROVINSI"])
+    kab_col_glob   = pick_col(df_all_raw, ["kab_kota", "Kab_Kota", "Kab/Kota", "KAB/KOTA", "kabupaten_kota", "kota"])
+    kew_col_glob   = pick_col(df_all_raw, ["kewenangan", "jenis_kewenangan", "Kewenangan", "KEWENANGAN"])
+    jenis_col_glob = pick_col(df_all_raw, ["jenis_perpustakaan", "JENIS_PERPUSTAKAAN", "Jenis Perpustakaan", "jenis perpustakaan"])
+    subjenis_col_glob = pick_col(df_all_raw, ["sub_jenis_perpus", "Sub Jenis", "SubJenis", "subjenis", "jenjang"])
+    nama_col_glob  = pick_col(df_all_raw, ["nama_perpustakaan", "nm_perpustakaan", "nm_instansi_lembaga", "Nama Perpustakaan"])
+
+    # kewenangan norm
+    if kew_col_glob:
+        df_all_raw["KEW_NORM"] = df_all_raw[kew_col_glob].apply(norm_kew)
+    else:
+        df_all_raw["KEW_NORM"] = None
+
+    # jenis perpustakaan -> dataset {sekolah/umum/khusus}
+    val_map_jenis = {
+        "PERPUSTAKAAN SEKOLAH": "sekolah",
+        "SEKOLAH": "sekolah",
+        "PERPUSTAKAAN UMUM": "umum",
+        "UMUM": "umum",
+        "PERPUSTAKAAN DAERAH": "umum",
+        "PERPUSTAKAAN KHUSUS": "khusus",
+        "KHUSUS": "khusus",
+    }
+    if jenis_col_glob:
+        df_all_raw["_dataset"] = df_all_raw[jenis_col_glob].apply(_norm_text).map(val_map_jenis)
+    else:
+        df_all_raw["_dataset"] = None
+
+    DATA_INFO = f"Data terbaca dari: **{DATA_FILE}** | Jumlah baris: **{len(df_all_raw)}**"
+except Exception as e:
+    df_all_raw = None
+    DATA_INFO = f"⚠️ Gagal memuat `{DATA_FILE}` | Error: `{e}`"
+
+extra_info = []
+
+# --- META kab: kec + desa/kel ---
+try:
+    meta_kab_raw = pd.read_excel(META_KAB_FILE)
+    col_kab = pick_col(meta_kab_raw, ["Kab/Kota", "Kab_Kota", "kab/kota", "kabupaten_kota"])
+    col_kec = pick_col(meta_kab_raw, ["Kecamatan", "jml_kecamatan", "jumlah_kecamatan"])
+    col_des = pick_col(meta_kab_raw, ["Desa/Kel", "Desa Kelurahan", "Desa", "Desa_kel"])
+
+    if col_kab and col_kec and col_des:
+        meta_kab_df = pd.DataFrame({
+            "Kab_Kota_Label": meta_kab_raw[col_kab].astype(str).str.strip(),
+            "Jml_Kecamatan":  meta_kab_raw[col_kec].apply(coerce_num),
+            "Jml_DesaKel":    meta_kab_raw[col_des].apply(coerce_num),
+        })
+        meta_kab_df["kab_key"] = meta_kab_df["Kab_Kota_Label"].apply(norm_kab_label)
+        extra_info.append(f"Meta Kab/Kota (Kec/Desa) terbaca: **{META_KAB_FILE}** (n={len(meta_kab_df)})")
+    else:
+        meta_kab_df = None
+        extra_info.append(f"⚠️ Kolom kunci meta kab tidak lengkap di `{META_KAB_FILE}`")
+except Exception as e:
+    meta_kab_df = None
+    extra_info.append(f"⚠️ Gagal memuat `{META_KAB_FILE}` ({e})")
+
+# --- META SD/SMP per kab/kota ---
+try:
+    sd_smp_raw = pd.read_excel(META_SDSMP_FILE)
+    col_kab2 = pick_col(sd_smp_raw, [
+        "Kabupaten/Kota_Kabupaten/Kota", "Kabupaten/Kota",
+        "Kab/Kota", "Kab_Kota", "kab/kota", "kabupaten_kota"
+    ])
+    col_sd   = pick_col(sd_smp_raw, ["SD", "Jumlah SD", "Total SD", "SD_Total", "jml_sd", "Jml_SD"])
+    col_smp  = pick_col(sd_smp_raw, ["SMP", "Jumlah SMP", "Total SMP", "SMP_Total", "jml_smp", "Jml_SMP"])
+
+    if col_kab2 and (col_sd or col_smp):
+        df_sd_smp = pd.DataFrame({
+            "Kab_Kota_Label_SD": sd_smp_raw[col_kab2].astype(str).str.strip(),
+        })
+        df_sd_smp["Jml_SD"]  = sd_smp_raw[col_sd].apply(coerce_num) if col_sd else 0.0
+        df_sd_smp["Jml_SMP"] = sd_smp_raw[col_smp].apply(coerce_num) if col_smp else 0.0
+        df_sd_smp["kab_key"] = df_sd_smp["Kab_Kota_Label_SD"].apply(norm_kab_label)
+
+        df_sd_smp_grp = df_sd_smp.groupby("kab_key", as_index=False).agg({
+            "Jml_SD": "sum",
+            "Jml_SMP": "sum",
+        })
+
+        if meta_kab_df is not None:
+            meta_kab_df = meta_kab_df.merge(df_sd_smp_grp, on="kab_key", how="left")
+        else:
+            meta_kab_df = df_sd_smp_grp.copy()
+            meta_kab_df["Kab_Kota_Label"] = df_sd_smp.groupby("kab_key")["Kab_Kota_Label_SD"].first().values
+
+        extra_info.append(f"Meta SD/SMP terbaca: **{META_SDSMP_FILE}** (n={len(df_sd_smp_grp)})")
+    else:
+        extra_info.append(f"⚠️ Kolom kunci SD/SMP tidak lengkap di `{META_SDSMP_FILE}`")
+except Exception as e:
+    extra_info.append(f"⚠️ Gagal memuat `{META_SDSMP_FILE}` ({e})")
+
+# --- META SMA per provinsi ---
+try:
+    meta_sma_raw = pd.read_excel(META_SMA_FILE)
+
+    col_prov_sma = pick_col(meta_sma_raw, [
+        "Provinsi", "provinsi", "PROVINSI", "NAMA_PROVINSI", "Nama Provinsi",
+        "nm_prov", "nm_provinsi", "prov"
+    ])
+    col_sma = pick_col(meta_sma_raw, [
+        "Total SMA", "TOTAL_SMA", "TOTAL", "total",
+        "Jml_SMA", "Jumlah SMA", "SMA", "SMA_Total",
+        "jumlah_sma", "total_sma", "jml_sma"
+    ])
+    if col_prov_sma is None:
+        raise ValueError("Kolom provinsi tidak ditemukan di file SMA.")
+    if col_sma is None:
+        raise ValueError("Kolom jumlah SMA tidak ditemukan di file SMA.")
+
+    meta_sma_df = pd.DataFrame({
+        "Provinsi_Label": meta_sma_raw[col_prov_sma].astype(str).str.strip(),
+        "Jml_SMA":        meta_sma_raw[col_sma].apply(coerce_num),
+    })
+    meta_sma_df["prov_key"] = meta_sma_df["Provinsi_Label"].apply(norm_prov_label)
+    meta_sma_df = meta_sma_df.groupby(["prov_key"], as_index=False).agg({
+        "Provinsi_Label": "first",
+        "Jml_SMA": "sum"
+    })
+
+    extra_info.append(f"Meta SMA terbaca: **{META_SMA_FILE}** ({len(meta_sma_df)} provinsi)")
+except Exception as e:
+    meta_sma_df = None
+    extra_info.append(f"⚠️ Gagal memuat file SMA: {e}")
+
+if extra_info:
+    DATA_INFO = DATA_INFO + "<br>" + "<br>".join(extra_info)
+
+
+# ============================================================
+# 4) PILIHAN DROPDOWN
+# ============================================================
+def all_prov_choices():
+    if df_all_raw is None or prov_col_glob is None:
+        return ["(Semua)"]
+    s = df_all_raw[prov_col_glob].dropna().astype(str).str.strip()
+    vals = sorted([o for o in s.unique() if o != ""])
+    return ["(Semua)"] + vals
+
+def get_kab_choices_for_prov(prov_value):
+    if df_all_raw is None or kab_col_glob is None:
+        return ["(Semua)"]
+    if prov_value is None or prov_value == "(Semua)" or prov_col_glob is None:
+        s = df_all_raw[kab_col_glob].dropna().astype(str).str.strip()
+    else:
+        m = df_all_raw[prov_col_glob].astype(str).str.strip() == prov_value
+        s = df_all_raw.loc[m, kab_col_glob].dropna().astype(str).str.strip()
+    vals = sorted([x for x in s.unique() if x != ""])
+    return ["(Semua)"] + vals
+
+def all_kew_choices():
+    if df_all_raw is None:
+        return ["(Semua)"]
+    s = df_all_raw.get("KEW_NORM", pd.Series(dtype=object)).dropna().astype(str).str.strip()
+    vals = sorted([o for o in s.unique() if o != ""])
+    return ["(Semua)"] + vals if vals else ["(Semua)"]
+
+prov_choices = all_prov_choices()
+kab_choices  = get_kab_choices_for_prov(prov_choices[0] if prov_choices else "(Semua)")
+kew_choices  = all_kew_choices()
+default_kew  = "KAB/KOTA" if "KAB/KOTA" in kew_choices else (kew_choices[0] if kew_choices else "(Semua)")
+
+
+# ============================================================
+# 5) INTI: HITUNG COVERAGE & GAP
+# ============================================================
+def _infer_jenjang_sd_smp(x):
+    if pd.isna(x):
+        return "OTHER"
+    t = str(x).upper()
+    # heuristik sederhana
+    if " SD " in f" {t} " or " SD/" in t or " MI " in f" {t} ":
+        return "SD"
+    if " SMP " in f" {t} " or " SMP/" in t or " MTS " in f" {t} ":
+        return "SMP"
+    return "OTHER"
+
+def safe_pct(num, den):
+    if den is None or pd.isna(den) or den <= 0:
+        return np.nan
+    if num is None or pd.isna(num):
+        num = 0
+    return 100.0 * float(num) / float(den)
+
+def compute_gap_verification(df_filtered: pd.DataFrame, kew_value: str) -> pd.DataFrame:
+    """
+    Keluaran: tabel coverage & GAP (kekurangan sampel) sesuai kewenangan.
+    - KAB/KOTA: bandingkan sampel sekolah vs (SD+SMP), umum vs (kec+desa/kel)
+    - PROVINSI: bandingkan sampel SMA vs (jumlah SMA)
+    """
+    if df_filtered is None or len(df_filtered) == 0:
+        return pd.DataFrame()
+
+    kew_norm = str(kew_value or "").upper()
+
+    # ================= KAB/KOTA =================
+    if ("KAB" in kew_norm or "KOTA" in kew_norm):
+        if kab_col_glob is None or meta_kab_df is None:
+            return pd.DataFrame({"Info": ["Kolom kab/kota atau meta kab tidak tersedia."]})
+
+        tmp = df_filtered.copy()
+        tmp = tmp[pd.notna(tmp[kab_col_glob])]
+        if tmp.empty:
+            return pd.DataFrame()
+
+        tmp["kab_key"] = tmp[kab_col_glob].apply(norm_kab_label)
+
+        # total sampel per kab
+        g_total = tmp.groupby("kab_key").size().rename("Sampel_Total").reset_index()
+
+        # sekolah & jenjang
+        if subjenis_col_glob and subjenis_col_glob in tmp.columns:
+            tmp["jenjang"] = tmp[subjenis_col_glob].apply(_infer_jenjang_sd_smp)
+        else:
+            tmp["jenjang"] = "OTHER"
+
+        tmp_sek = tmp[tmp["_dataset"] == "sekolah"].copy() if "_dataset" in tmp.columns else tmp.copy()
+        g_sek_total = tmp_sek.groupby("kab_key").size().rename("Sampel_Sekolah_Total").reset_index()
+        g_sd  = tmp_sek[tmp_sek["jenjang"] == "SD"].groupby("kab_key").size().rename("Sampel_SD").reset_index()
+        g_smp = tmp_sek[tmp_sek["jenjang"] == "SMP"].groupby("kab_key").size().rename("Sampel_SMP").reset_index()
+
+        # umum
+        tmp_umum = tmp[tmp["_dataset"] == "umum"].copy() if "_dataset" in tmp.columns else tmp.copy()
+        g_umum = tmp_umum.groupby("kab_key").size().rename("Sampel_Umum").reset_index()
+
+        use_cols = ["kab_key", "Kab_Kota_Label", "Jml_Kecamatan", "Jml_DesaKel", "Jml_SD", "Jml_SMP"]
+        use_cols = [c for c in use_cols if c in meta_kab_df.columns]
+
+        merged = (
+            g_total
+            .merge(g_sek_total, on="kab_key", how="left")
+            .merge(g_sd, on="kab_key", how="left")
+            .merge(g_smp, on="kab_key", how="left")
+            .merge(g_umum, on="kab_key", how="left")
+            .merge(meta_kab_df[use_cols], on="kab_key", how="left")
+        )
+
+        for c in ["Sampel_Total", "Sampel_Sekolah_Total", "Sampel_SD", "Sampel_SMP", "Sampel_Umum"]:
+            if c in merged.columns:
+                merged[c] = merged[c].fillna(0).astype(int)
+
+        merged["Pop_SD_SMP"] = merged[["Jml_SD", "Jml_SMP"]].sum(axis=1, skipna=True)
+        merged["Pop_Kec_DesaKel"] = merged.get("Jml_Kecamatan", np.nan) + merged.get("Jml_DesaKel", np.nan)
+
+        merged["Coverage_Sekolah_%"] = merged.apply(
+            lambda r: safe_pct(r["Sampel_Sekolah_Total"], r.get("Pop_SD_SMP", np.nan)), axis=1
+        )
+        merged["Coverage_Umum_%"] = merged.apply(
+            lambda r: safe_pct(r["Sampel_Umum"], r.get("Pop_Kec_DesaKel", np.nan)), axis=1
+        )
+
+        # GAP (kekurangan sampel)
+        merged["Gap_Sekolah"] = merged.apply(
+            lambda r: max(int(math.ceil(r["Pop_SD_SMP"] - r["Sampel_Sekolah_Total"])) if pd.notna(r["Pop_SD_SMP"]) else 0, 0),
+            axis=1
+        )
+        merged["Gap_Umum"] = merged.apply(
+            lambda r: max(int(math.ceil(r["Pop_Kec_DesaKel"] - r["Sampel_Umum"])) if pd.notna(r["Pop_Kec_DesaKel"]) else 0, 0),
+            axis=1
+        )
+
+        out = pd.DataFrame({
+            "Kab/Kota": merged.get("Kab_Kota_Label", merged["kab_key"]),
+            "Sampel Total": merged["Sampel_Total"],
+            "Sampel Sekolah (Total)": merged["Sampel_Sekolah_Total"],
+            "Populasi Sekolah (SD+SMP)": merged["Pop_SD_SMP"],
+            "Coverage Sekolah (%)": merged["Coverage_Sekolah_%"],
+            "Kekurangan Sampel Sekolah": merged["Gap_Sekolah"],
+            "Sampel Umum": merged["Sampel_Umum"],
+            "Populasi Admin (Kec+Desa/Kel)": merged["Pop_Kec_DesaKel"],
+            "Coverage Umum (%)": merged["Coverage_Umum_%"],
+            "Kekurangan Sampel Umum": merged["Gap_Umum"],
+        })
+
+        return out.sort_values("Kab/Kota").reset_index(drop=True).round(3)
+
+    # ================= PROVINSI =================
+    if ("PROV" in kew_norm):
+        if meta_sma_df is None:
+            return pd.DataFrame({"Info": ["Meta SMA tidak tersedia."]})
+
+        if prov_col_glob is None:
+            return pd.DataFrame({"Info": ["Kolom provinsi tidak ditemukan di DM."]})
+
+        tmp = df_filtered.copy()
+        tmp = tmp[pd.notna(tmp[prov_col_glob])]
+        if tmp.empty:
+            return pd.DataFrame({"Info": ["Tidak ada data sampel kewenangan provinsi."]})
+
+        tmp["prov_key"] = tmp[prov_col_glob].apply(norm_prov_label)
+
+        g_total = tmp.groupby("prov_key").size().rename("Sampel_Total").reset_index()
+        tmp_sek = tmp[tmp["_dataset"] == "sekolah"].copy() if "_dataset" in tmp.columns else tmp.copy()
+        g_sma = tmp_sek.groupby("prov_key").size().rename("Sampel_SMA").reset_index()
+
+        merged = (
+            meta_sma_df.merge(g_total, on="prov_key", how="left")
+                      .merge(g_sma, on="prov_key", how="left")
+        )
+
+        merged["Sampel_Total"] = merged["Sampel_Total"].fillna(0).astype(int)
+        merged["Sampel_SMA"]   = merged["Sampel_SMA"].fillna(0).astype(int)
+
+        merged["Coverage_SMA_%"] = merged.apply(
+            lambda r: safe_pct(r["Sampel_SMA"], r.get("Jml_SMA", np.nan)), axis=1
+        )
+        merged["Kekurangan Sampel SMA"] = merged.apply(
+            lambda r: max(int(math.ceil(r["Jml_SMA"] - r["Sampel_SMA"])) if pd.notna(r["Jml_SMA"]) else 0, 0),
+            axis=1
+        )
+
+        out = pd.DataFrame({
+            "Provinsi": merged["Provinsi_Label"],
+            "Sampel Total (Prov)": merged["Sampel_Total"],
+            "Sampel SMA (di DM)": merged["Sampel_SMA"],
+            "Populasi SMA (Meta)": merged["Jml_SMA"],
+            "Coverage SMA (%)": merged["Coverage_SMA_%"],
+            "Kekurangan Sampel SMA": merged["Kekurangan Sampel SMA"],
+        })
+
+        return out.sort_values("Provinsi").reset_index(drop=True).round(3)
+
+    return pd.DataFrame({"Info": ["Kewenangan tidak dikenali / tidak didukung."]})
+
+
+# ============================================================
+# 6) BUILD CONTEXT UNTUK LLM + FALLBACK
+# ============================================================
+def build_context_gap(verif_df: pd.DataFrame, prov: str, kab: str, kew: str) -> str:
+    wilayah = kab if kab and kab != "(Semua)" else (prov if prov and prov != "(Semua)" else "NASIONAL")
+    lines = []
+    lines.append(f"Wilayah filter: {wilayah}")
+    lines.append(f"Kewenangan: {kew}")
+    lines.append(f"Jumlah baris verifikasi: {len(verif_df)}")
+
+    # ringkas total gap
+    gap_cols = [c for c in verif_df.columns if "Kekurangan" in c]
+    for gc in gap_cols:
+        try:
+            total_gap = float(pd.to_numeric(verif_df[gc], errors="coerce").fillna(0).sum())
+            lines.append(f"Total {gc}: {int(total_gap)}")
+        except Exception:
+            pass
+
+    # top 10 terbesar
+    if gap_cols:
+        gc = gap_cols[0]
+        try:
+            t = verif_df.copy()
+            t[gc] = pd.to_numeric(t[gc], errors="coerce").fillna(0)
+            top = t.sort_values(gc, ascending=False).head(10)
+            keycol = "Kab/Kota" if "Kab/Kota" in top.columns else ("Provinsi" if "Provinsi" in top.columns else top.columns[0])
+            lines.append("\nTop prioritas (gap terbesar):")
+            for _, r in top.iterrows():
+                lines.append(f"- {r[keycol]}: {gc}={int(r[gc])}")
+        except Exception:
+            pass
+
+    return "\n".join(lines)
+
+def rule_based_gap_report(verif_df: pd.DataFrame, prov: str, kab: str, kew: str) -> str:
+    if verif_df is None or verif_df.empty:
+        return "Tidak ada data verifikasi yang dapat dilaporkan."
+    wilayah = kab if kab and kab != "(Semua)" else (prov if prov and prov != "(Semua)" else "NASIONAL")
+
+    lines = []
+    lines.append("## Ringkasan Kekurangan Sampel IPLM (Rule-based)\n")
+    lines.append(f"Wilayah: {wilayah}")
+    lines.append(f"Kewenangan: {kew}")
+    lines.append(f"Jumlah unit analisis: {len(verif_df)}\n")
+
+    gap_cols = [c for c in verif_df.columns if "Kekurangan" in c]
+    if not gap_cols:
+        lines.append("Kolom kekurangan sampel tidak ditemukan pada tabel verifikasi.")
+        return "\n".join(lines)
+
+    for gc in gap_cols:
+        total_gap = int(pd.to_numeric(verif_df[gc], errors="coerce").fillna(0).sum())
+        lines.append(f"- Total {gc}: **{total_gap}** unit yang perlu dilengkapi.")
+
+    lines.append(
+        "\nRekomendasi operasional: fokuskan pengumpulan data pada unit/wilayah dengan gap terbesar, "
+        "mulai dari area yang memiliki populasi target besar namun sampel masuk masih terbatas. "
+        "Pastikan konsistensi penamaan provinsi/kab-kota agar matching dengan meta tidak gagal."
+    )
+    return "\n".join(lines)
+
+def generate_llm_gap_report(verif_df: pd.DataFrame, prov: str, kab: str, kew: str) -> str:
+    ctx = build_context_gap(verif_df, prov, kab, kew)
+
+    client = get_llm_client()
+    if client is None or not USE_LLM:
+        return "⚠️ LLM tidak tersedia, memakai laporan rule-based.\n\n" + rule_based_gap_report(verif_df, prov, kab, kew)
+
+    system_prompt = (
+        "Anda adalah analis kebijakan dan manajer program IPLM. "
+        "Tugas Anda menyusun narasi singkat dan tegas tentang kekurangan sampel data IPLM "
+        "serta strategi pengumpulan data untuk menutup gap."
+    )
+
+    user_prompt = f"""
+DATA RINGKAS GAP SAMPEL IPLM:
+
+{ctx}
+
+TULIS LAPORAN (BAHASA INDONESIA FORMAL) DENGAN STRUKTUR:
+1) Ringkasan kondisi pengumpulan data (1 paragraf).
+2) Angka total kekurangan sampel yang masih perlu dikumpulkan (1 paragraf).
+3) Prioritas wilayah (top gap) dan alasan operasionalnya (1 paragraf).
+4) Rencana aksi 30–60 hari (paragraf naratif, bukan bullet).
+
+BATASAN:
+- Jangan bahas indeks / skor IPLM sama sekali.
+- Fokus murni pada coverage, kekurangan sampel, dan strategi pelengkapannya.
+"""
+
+    try:
+        resp = client.chat_completion(
+            model=LLM_MODEL_NAME,
+            messages=[{"role": "system", "content": system_prompt},
+                      {"role": "user", "content": user_prompt}],
+            max_tokens=900,
+            temperature=0.2,
+            top_p=0.9,
+        )
+        text = resp.choices[0].message.content.strip()
+        if not text:
+            raise ValueError("Respon LLM kosong.")
+        return text
+    except Exception as e:
+        return (
+            "⚠️ Error saat memanggil LLM, memakai laporan rule-based.\n\n"
+            f"(Detail teknis: {repr(e)})\n\n"
+            + rule_based_gap_report(verif_df, prov, kab, kew)
+        )
+
+
+# ============================================================
+# 7) WORD REPORT
+# ============================================================
+def generate_word_report_gap(verif_df: pd.DataFrame, prov: str, kab: str, kew: str, analysis_text: str):
+    wilayah = kab if kab and kab != "(Semua)" else (prov if prov and prov != "(Semua)" else "NASIONAL")
+    doc = Document()
+    doc.add_heading(f"Laporan Kekurangan Sampel IPLM – {wilayah}", level=1)
+
+    doc.add_paragraph(f"Kewenangan: {kew}")
+    doc.add_paragraph(f"Jumlah unit analisis: {len(verif_df)}")
+
+    # tabel verifikasi (batasi 200 baris biar gak jebol)
+    doc.add_heading("Tabel Verifikasi Coverage & Kekurangan Sampel", level=2)
+    view = verif_df.copy()
+    if len(view) > 200:
+        doc.add_paragraph("Catatan: tabel dipotong (200 baris pertama) untuk menjaga ukuran dokumen.")
+        view = view.head(200)
+
+    table = doc.add_table(rows=1, cols=len(view.columns))
+    hdr = table.rows[0].cells
+    for i, c in enumerate(view.columns):
+        hdr[i].text = str(c)
+
+    for _, row in view.iterrows():
+        r = table.add_row().cells
+        for i, c in enumerate(view.columns):
+            r[i].text = str(row[c])
+
+    # pie chart opsional: hanya 1 ringkasan total (bukan per kab/prov biar gak kebanyakan)
+    doc.add_heading("Ringkasan Visual (Opsional)", level=2)
+    if not HAS_KALEIDO:
+        doc.add_paragraph("Grafik pie tidak dibuat karena 'kaleido' tidak tersedia di server.")
+    else:
+        # cari kolom pop & sampel yang paling relevan (ambil pertama yang cocok)
+        pie_made = False
+        if "Sampel Sekolah (Total)" in verif_df.columns and "Populasi Sekolah (SD+SMP)" in verif_df.columns:
+            samp = pd.to_numeric(verif_df["Sampel Sekolah (Total)"], errors="coerce").fillna(0).sum()
+            pop  = pd.to_numeric(verif_df["Populasi Sekolah (SD+SMP)"], errors="coerce").fillna(0).sum()
+            img = make_pie_plotly(samp, pop, "Coverage Perpustakaan Sekolah (Total)")
+            if img:
+                doc.add_picture(img, width=Inches(5))
+                pie_made = True
+
+        if (not pie_made) and ("Sampel SMA (di DM)" in verif_df.columns and "Populasi SMA (Meta)" in verif_df.columns):
+            samp = pd.to_numeric(verif_df["Sampel SMA (di DM)"], errors="coerce").fillna(0).sum()
+            pop  = pd.to_numeric(verif_df["Populasi SMA (Meta)"], errors="coerce").fillna(0).sum()
+            img = make_pie_plotly(samp, pop, "Coverage Perpustakaan SMA (Total)")
+            if img:
+                doc.add_picture(img, width=Inches(5))
+                pie_made = True
+
+        if not pie_made:
+            doc.add_paragraph("Tidak ada pasangan kolom sampel-populasi yang valid untuk dibuat pie chart.")
+
+    doc.add_heading("Analisis Naratif (LLM)", level=2)
+    for p in analysis_text.split("\n"):
+        if p.strip():
+            doc.add_paragraph(p)
+
+    outpath = tempfile.mktemp(suffix=".docx")
+    doc.save(outpath)
+    return outpath
+
+
+# ============================================================
+# 8) CORE RUN (FILTER + EXPORT)
+# ============================================================
+def run_core(prov_value, kab_value, kew_value):
+    if df_all_raw is None or df_all_raw.empty:
+        empty = pd.DataFrame()
+        return empty, empty, None, None, None, "Data DM tidak terbaca.", "Tidak ada analisis."
+
+    df = df_all_raw.copy()
+
+    # filter prov
+    if prov_col_glob and prov_value and prov_value != "(Semua)":
+        df = df[df[prov_col_glob].astype(str).str.strip() == prov_value]
+
+    # filter kab
+    if kab_col_glob and kab_value and kab_value != "(Semua)":
+        df = df[df[kab_col_glob].astype(str).str.strip() == kab_value]
+
+    # filter kew
+    if kew_value and kew_value != "(Semua)":
+        df = df[df["KEW_NORM"] == kew_value]
+
+    if len(df) == 0:
+        empty = pd.DataFrame()
+        return empty, empty, None, None, None, "Tidak ada data untuk filter tersebut.", "Tidak ada analisis."
+
+    # hitung verifikasi gap
+    verif_df = compute_gap_verification(df, kew_value)
+
+    # buat detail subset untuk download (ringkas)
+    cols = []
+    for c in [prov_col_glob, kab_col_glob, nama_col_glob, kew_col_glob, jenis_col_glob, subjenis_col_glob, "_dataset", "KEW_NORM"]:
+        if c and c in df.columns and c not in cols:
+            cols.append(c)
+    detail_df = df[cols].copy() if cols else df.copy()
+
+    # simpan excel
+    tmpdir = tempfile.mkdtemp()
+    out_excel = os.path.join(tmpdir, "Kekurangan_Sampel_IPLM.xlsx")
+
+    with pd.ExcelWriter(out_excel, engine="openpyxl") as w:
+        verif_df.to_excel(w, sheet_name="Verifikasi_Gap", index=False)
+        detail_df.to_excel(w, sheet_name="Detail_Subset_DM", index=False)
+
+    # analisis LLM
+    analysis_text = generate_llm_gap_report(verif_df, prov_value, kab_value, kew_value)
+
+    # word report
+    out_word = generate_word_report_gap(verif_df, prov_value, kab_value, kew_value, analysis_text)
+
+    msg = f"OK. Subset DM: {len(df)} baris | Verifikasi: {len(verif_df)} baris."
+    return verif_df, detail_df, out_excel, out_word, None, msg, analysis_text
+
+
+def on_prov_change(prov_value):
+    return gr.update(choices=get_kab_choices_for_prov(prov_value), value="(Semua)")
+
+
+# ============================================================
+# 9) UI GRADIO
+# ============================================================
+with gr.Blocks() as demo:
+    gr.Markdown(
+        f"""
+# Dashboard Kekurangan Sampel IPLM (Tanpa Hitung Indeks)
+
+Aplikasi ini hanya mengecek **kekurangan sampel** berdasarkan:
+- **DM (sampel masuk)** vs **Meta populasi (SD/SMP, SMA, Kec/DesaKel)**
+
+**File:**
+- `{DATA_FILE}` (DM)
+- `{META_KAB_FILE}` (Kecamatan + Desa/Kel)
+- `{META_SDSMP_FILE}` (SD + SMP)
+- `{META_SMA_FILE}` (SMA)
+
+{DATA_INFO}
+"""
+    )
+
+    with gr.Row():
+        dd_prov = gr.Dropdown(label="Provinsi", choices=prov_choices, value=prov_choices[0])
+        dd_kab  = gr.Dropdown(label="Kab/Kota", choices=kab_choices, value=kab_choices[0])
+        dd_kew  = gr.Dropdown(label="Kewenangan", choices=kew_choices, value=default_kew)
+
+    dd_prov.change(fn=on_prov_change, inputs=dd_prov, outputs=dd_kab)
+
+    run_btn = gr.Button("Hitung Kekurangan Sampel")
+    msg_out = gr.Markdown()
+
+    gr.Markdown("### Verifikasi Coverage & Kekurangan Sampel")
+    verif_out = gr.DataFrame(interactive=False)
+
+    gr.Markdown("### Detail Subset DM (yang terfilter)")
+    detail_out = gr.DataFrame(interactive=False)
+
+    gr.Markdown("### Analisis Naratif (LLM)")
+    analysis_out = gr.Markdown()
+
+    with gr.Row():
+        excel_out = gr.File(label="Download Rekap Excel (.xlsx)")
+        word_out  = gr.File(label="Download Laporan Word (.docx)")
+
+    run_btn.click(
+        fn=run_core,
+        inputs=[dd_prov, dd_kab, dd_kew],
+        outputs=[verif_out, detail_out, excel_out, word_out, gr.State(), msg_out, analysis_out],
+    )
+
+demo.launch()