Update pages/laporan.py

pages/laporan.py

@@ -1,4 +1,4 @@
-# File: pages/laporan.py (Revisi Final - Dashboard Terpadu)
+# File: pages/laporan.py (Revisi Final dengan Layout Satu Baris per Grafik)
 
 from dash import dcc, html, Input, Output, callback, no_update, State, dash_table
 import dash_bootstrap_components as dbc
@@ -27,7 +27,7 @@ except (AttributeError, ImportError):
     print("WARNING: Pustaka 'reportlab' atau 'kaleido' tidak terinstall. Fitur unduh PDF tidak akan berfungsi.")
 
 # -----------------------------------------------------------------------------
-# BAGIAN 1: FUNGSI-FUNGSI HELPER (GABUNGAN DARI SEMUA HALAMAN)
+# BAGIAN 1: FUNGSI-FUNGSI HELPER (Tidak Berubah)
 # -----------------------------------------------------------------------------
 
 def get_filter_text(pusk, tahun, bulan):
@@ -36,7 +36,6 @@ def get_filter_text(pusk, tahun, bulan):
     bulan_txt = "Semua Bulan" if not bulan else ", ".join(sorted(bulan))
     return f"Data dari: {pusk_txt} | Tahun: {tahun_txt} | Bulan: {bulan_txt}"
 
-# --- Helper untuk Analisis Tren Penyakit ---
 def kategori_penyakit_atp(icd):
     if pd.isna(icd) or str(icd).strip() == "": return 'Tidak Menular'
     icd_clean = str(icd).strip().upper()
@@ -82,7 +81,16 @@ def create_trend_analysis_text(df, time_unit='tahun'):
             analysis_points.append(f"- Kasus **{disease}** {tren}.")
     return "Ringkasan Tren 3 Penyakit Teratas:\n\n" + "\n".join(analysis_points)
 
-# --- Helper untuk Analisis Demografi ---
+def create_comparison_analysis_text(df):
+    if df.empty: return "Tidak ada data untuk dianalisis."
+    pusk_contribution = df.groupby('kode_pusk')['totall'].sum().sort_values(ascending=False)
+    if pusk_contribution.empty: return "Tidak ada data puskesmas untuk dianalisis."
+    top_pusk, top_pusk_cases, total_cases = pusk_contribution.index[0], pusk_contribution.iloc[0], pusk_contribution.sum()
+    if total_cases == 0: return "Total kasus adalah nol."
+    top_pusk_percent = (top_pusk_cases / total_cases * 100).round(1)
+    top_disease_by_pusk = df[df['kode_pusk'] == top_pusk].groupby('jenis_penyakit')['totall'].sum().idxmax()
+    return f"- **{top_pusk}** menjadi puskesmas dengan kontribusi kasus tertinggi, yaitu **{int(top_pusk_cases):,}** kasus ({top_pusk_percent}% dari total).\n- Penyakit yang paling banyak disumbangkan oleh {top_pusk} adalah **{top_disease_by_pusk}**."
+
 def generate_gender_analysis_text(total_lk, total_pr):
     if total_lk == 0 and total_pr == 0: return "Tidak ada data gender untuk dianalisis."
     total_kasus = total_lk + total_pr
@@ -102,7 +110,7 @@ def generate_age_analysis_text(baru_data, lama_data):
     return f"Kelompok umur dengan total kasus tertinggi adalah **{max_total_kelompok}** ({int(total_per_kelompok[max_total_kelompok]):,} kasus)."
 
 # -----------------------------------------------------------------------------
-# BAGIAN 2: LAYOUT HALAMAN (DENGAN TAB BARU)
+# BAGIAN 2: LAYOUT HALAMAN (Tidak Berubah)
 # -----------------------------------------------------------------------------
 layout = dbc.Container([
     dcc.Store(id='laporan-data-store'),
@@ -135,9 +143,8 @@ layout = dbc.Container([
 ], fluid=True)
 
 # -----------------------------------------------------------------------------
-# BAGIAN 3: CALLBACKS
+# BAGIAN 3: CALLBACKS (Tidak Berubah)
 # -----------------------------------------------------------------------------
-# Callback filter tidak berubah
 @callback(
     Output('laporan-pusk-filter', 'options'),
     Output('laporan-tahun-filter', 'options'),
@@ -183,17 +190,17 @@ def laporan_update_bulan_filter(selected_tahun):
     Input('laporan-bulan-filter', 'value')
 )
 def update_laporan_terpadu_tabs(selected_pusk, selected_tahun, selected_bulan):
+    # Logika pengambilan dan pemrosesan data awal tidak berubah
     if not selected_pusk or not selected_tahun:
         msg = html.P("Silakan pilih minimal Puskesmas dan Tahun.", className="text-center text-primary mt-5")
         return msg, msg, "", True, None
 
     filter_summary_text = get_filter_text(selected_pusk, selected_tahun, selected_bulan)
-    filters = [detail_penyakit.c.kode_pusk.in_(selected_pusk), detail_penyakit.c.tahun.in_(selected_tahun)]
-    if selected_bulan: filters.append(detail_penyakit.c.bulan.in_(selected_bulan))
+    base_filters = [detail_penyakit.c.kode_pusk.in_(selected_pusk), detail_penyakit.c.tahun.in_(selected_tahun)]
+    if selected_bulan: base_filters.append(detail_penyakit.c.bulan.in_(selected_bulan))
 
-    # --- Eksekusi Query Gabungan ---
-    all_cols = ['jenis_penyakit', 'icd_x', 'tahun', 'bulan', 'laki_laki', 'perempuan', 'usia_0_7_hr_baru', 'usia_0_7_hr_lama', 'usia_8_28_hr_baru', 'usia_8_28_hr_lama', 'usia_1bl_1th_baru', 'usia_1bl_1th_lama', 'usia_1_4th_baru', 'usia_1_4th_lama', 'usia_5_9th_baru', 'usia_5_9th_lama', 'usia_10_14th_baru', 'usia_10_14th_lama', 'usia_15_19th_baru', 'usia_15_19th_lama', 'usia_20_44th_baru', 'usia_20_44th_lama', 'usia_45_54th_baru', 'usia_45_54th_lama', 'usia_55_59th_baru', 'usia_55_59th_lama', 'usia_60_69th_baru', 'usia_60_69th_lama', 'usia_70pl_baru', 'usia_70pl_lama', 'totall']
-    stmt = select(*[getattr(detail_penyakit.c, col) for col in all_cols]).where(and_(*filters))
+    all_cols = ['jenis_penyakit', 'icd_x', 'tahun', 'bulan', 'kode_pusk', 'laki_laki', 'perempuan', 'usia_0_7_hr_baru', 'usia_0_7_hr_lama', 'usia_8_28_hr_baru', 'usia_8_28_hr_lama', 'usia_1bl_1th_baru', 'usia_1bl_1th_lama', 'usia_1_4th_baru', 'usia_1_4th_lama', 'usia_5_9th_baru', 'usia_5_9th_lama', 'usia_10_14th_baru', 'usia_10_14th_lama', 'usia_15_19th_baru', 'usia_15_19th_lama', 'usia_20_44th_baru', 'usia_20_44th_lama', 'usia_45_54th_baru', 'usia_45_54th_lama', 'usia_55_59th_baru', 'usia_55_59th_lama', 'usia_60_69th_baru', 'usia_60_69th_lama', 'usia_70pl_baru', 'usia_70pl_lama', 'totall']
+    stmt = select(*[getattr(detail_penyakit.c, col) for col in all_cols]).where(and_(*base_filters))
     
     with engine.connect() as conn:
         df_base = pd.read_sql(stmt, conn)
@@ -202,18 +209,15 @@ def update_laporan_terpadu_tabs(selected_pusk, selected_tahun, selected_bulan):
         msg = dbc.Alert("Tidak ada data ditemukan untuk kriteria yang dipilih.", color="warning", className="m-4")
         return msg, msg, filter_summary_text, True, None
 
-    # =========================================================
-    # BAGIAN A: Proses Data untuk TAB 1 (Analisis Tren Penyakit)
-    # =========================================================
+    # Pembuatan semua 9 grafik + 1 tabel (tidak berubah)
     kode_dihindari = ('V', 'W', 'X', 'Y', 'Z')
     df_base['icd_x_str'] = df_base['icd_x'].astype(str).str.strip().str.upper()
     df_filtered_icd = df_base[~df_base['icd_x_str'].str.startswith(kode_dihindari, na=False)].copy()
-
     df_filtered_icd['kategori'] = df_filtered_icd['icd_x'].apply(kategori_penyakit_atp)
     df_ranking_total = df_filtered_icd.groupby('jenis_penyakit')['totall'].sum().nlargest(10).sort_values().reset_index()
     top_10_penyakit_list = df_ranking_total['jenis_penyakit'].tolist()
-    df_top10_base = df_filtered_icd[df_filtered_icd['jenis_penyakit'].isin(top_10_penyakit_list)]
-
+    df_top10_base = df_filtered_icd[df_filtered_icd['jenis_penyakit'].isin(top_10_penyakit_list)].copy()
+    
     fig_ranking_simple = px.bar(df_ranking_total, x='totall', y='jenis_penyakit', orientation='h', template='plotly_white', title='<b>Peringkat 10 Penyakit Teratas</b>')
     table_ranking = create_ranking_table(df_ranking_total)
     analysis_ranking = create_ranking_analysis_text(df_ranking_total)
@@ -222,70 +226,99 @@ def update_laporan_terpadu_tabs(selected_pusk, selected_tahun, selected_bulan):
     fig_category_pie = px.pie(df_category_pie, values='totall', names='kategori', title='<b>Komposisi Menular vs Tidak Menular</b>', color_discrete_map={'Menular':'crimson', 'Tidak Menular':'royalblue'})
     analysis_pie = create_pie_analysis_text(df_category_pie)
     
+    df_yearly_data = df_top10_base.groupby(['tahun', 'jenis_penyakit'])['totall'].sum().reset_index()
+    df_yearly_data['tahun'] = df_yearly_data['tahun'].astype(str)
+    fig_bar_trend_yearly = px.bar(df_yearly_data, x='totall', y='jenis_penyakit', color='tahun', orientation='h', barmode='group', title="<b>Perbandingan Kasus Tahunan (Top 10)</b>", template='plotly_white')
+    fig_bar_trend_yearly.update_layout(yaxis={'categoryorder':'total ascending'})
+    
+    fig_line_trend_yearly = px.line(df_yearly_data, x='tahun', y='totall', color='jenis_penyakit', markers=True, title="<b>Tren Tahunan (Top 10)</b>")
+    analysis_yearly_trend = create_trend_analysis_text(df_yearly_data, 'tahun')
+    
     df_monthly_line = df_top10_base.groupby(['tahun', 'bulan', 'jenis_penyakit'])['totall'].sum().reset_index()
     df_monthly_line['periode'] = df_monthly_line['tahun'].astype(str) + '-' + df_monthly_line['bulan'].str.zfill(2)
-    fig_line_monthly = px.line(df_monthly_line.sort_values('periode'), x='periode', y='totall', color='jenis_penyakit', title="<b>Tren Bulanan 10 Penyakit Teratas</b>")
+    fig_line_monthly = px.line(df_monthly_line.sort_values('periode'), x='periode', y='totall', color='jenis_penyakit', title="<b>Tren Bulanan (Top 10)</b>")
     analysis_monthly_trend = create_trend_analysis_text(df_monthly_line, 'periode')
     
+    df_monthly_cat_trend = df_filtered_icd.groupby(['tahun', 'bulan', 'kategori'])['totall'].sum().reset_index()
+    df_monthly_cat_trend['periode'] = df_monthly_cat_trend['tahun'].astype(str) + '-' + df_monthly_cat_trend['bulan'].str.zfill(2)
+    fig_monthly_compare_trend = px.area(df_monthly_cat_trend.sort_values('periode'), x='periode', y='totall', color='kategori', title="<b>Tren Bulanan Kasus per Kategori</b>", color_discrete_map={'Menular':'crimson', 'Tidak Menular':'royalblue'})
+    
+    df_pusk_compare = df_top10_base.groupby(['kode_pusk', 'jenis_penyakit'])['totall'].sum().reset_index()
+    fig_pusk_stacked = px.bar(df_pusk_compare, x='totall', y='jenis_penyakit', color='kode_pusk', orientation='h', title='<b>Kontribusi Kasus per Puskesmas</b>')
+    fig_pusk_stacked.update_layout(yaxis={'categoryorder':'total ascending'})
+    analysis_pusk_comparison = create_comparison_analysis_text(df_pusk_compare)
+    
+    df_menular = df_filtered_icd[df_filtered_icd['kategori'] == 'Menular']; top_10_menular = df_menular.groupby('jenis_penyakit')['totall'].sum().nlargest(10).index
+    df_trend_menular = df_menular[df_menular['jenis_penyakit'].isin(top_10_menular)].groupby(['tahun', 'jenis_penyakit'])['totall'].sum().reset_index(); df_trend_menular['tahun'] = df_trend_menular['tahun'].astype(str)
+    fig_trend_menular = px.bar(df_trend_menular, x='totall', y='jenis_penyakit', color='tahun', orientation='h', barmode='group', title="<b>Perbandingan Tahunan (10 Penyakit Menular Teratas)</b>", template='plotly_white'); fig_trend_menular.update_layout(yaxis={'categoryorder':'total ascending'})
+    
+    df_tidak_menular = df_filtered_icd[df_filtered_icd['kategori'] == 'Tidak Menular']; top_10_tidak_menular = df_tidak_menular.groupby('jenis_penyakit')['totall'].sum().nlargest(10).index
+    df_trend_tidak_menular = df_tidak_menular[df_tidak_menular['jenis_penyakit'].isin(top_10_tidak_menular)].groupby(['tahun', 'jenis_penyakit'])['totall'].sum().reset_index(); df_trend_tidak_menular['tahun'] = df_trend_tidak_menular['tahun'].astype(str)
+    fig_trend_tidak_menular = px.bar(df_trend_tidak_menular, x='totall', y='jenis_penyakit', color='tahun', orientation='h', barmode='group', title="<b>Perbandingan Tahunan (10 Penyakit Tidak Menular Teratas)</b>", template='plotly_white'); fig_trend_tidak_menular.update_layout(yaxis={'categoryorder':'total ascending'})
+    
+    # =================================================================
+    # <<< INI ADALAH BAGIAN YANG DIREVISI SESUAI PERMINTAAN ANDA >>>
+    # =================================================================
+    # --- Menyusun Layout Baru untuk Tab 1 (Satu Visualisasi per Baris) ---
     tab1_content = html.Div([
-        dbc.Row([
-            dbc.Col(dcc.Graph(figure=fig_ranking_simple), md=12),
-            dbc.Col([html.H5("Tabel Peringkat", className="mt-3"), table_ranking, dbc.Card(dbc.CardBody(dcc.Markdown(analysis_ranking)), className="mt-3")], md=12),
-        ], className="mb-4"),
-        html.Hr(),
-        dbc.Row([
-            dbc.Col(dcc.Graph(figure=fig_category_pie), md=6),
-            dbc.Col(dcc.Graph(figure=fig_line_monthly), md=6),
-            dbc.Col(dbc.Card(dbc.CardBody(dcc.Markdown(analysis_pie))), md=6),
-            dbc.Col(dbc.Card(dbc.CardBody(dcc.Markdown(analysis_monthly_trend))), md=6),
-        ], className="mb-4"),
+        # 1. Grafik Peringkat dan Tabel
+        dbc.Row(dbc.Col(dcc.Graph(figure=fig_ranking_simple), md=12), className="mb-2"),
+        dbc.Row(dbc.Col([html.H5("Tabel Peringkat 10 Besar"), table_ranking, dbc.Card(dbc.CardBody(dcc.Markdown(analysis_ranking)), className="mt-3")], md=12)),
+        html.Hr(className="my-4"),
+        
+        # 2. Grafik Pie Komposisi
+        dbc.Row(dbc.Col([dcc.Graph(figure=fig_category_pie), dbc.Card(dbc.CardBody(dcc.Markdown(analysis_pie)), className="mt-2")], md=12)),
+        html.Hr(className="my-4"),
+
+        # 3. Grafik Perbandingan Tahunan (Batang)
+        dbc.Row(dbc.Col(dcc.Graph(figure=fig_bar_trend_yearly), md=12)),
+        html.Hr(className="my-4"),
+
+        # 4. Grafik Tren Tahunan (Garis)
+        dbc.Row(dbc.Col([dcc.Graph(figure=fig_line_trend_yearly), dbc.Card(dbc.CardBody(dcc.Markdown(analysis_yearly_trend)), className="mt-2")], md=12)),
+        html.Hr(className="my-4"),
+
+        # 5. Grafik Tren Bulanan (Garis)
+        dbc.Row(dbc.Col([dcc.Graph(figure=fig_line_monthly), dbc.Card(dbc.CardBody(dcc.Markdown(analysis_monthly_trend)), className="mt-2")], md=12)),
+        html.Hr(className="my-4"),
+
+        # 6. Grafik Tren Bulanan per Kategori (Area)
+        dbc.Row(dbc.Col(dcc.Graph(figure=fig_monthly_compare_trend), md=12)),
+        html.Hr(className="my-4"),
+
+        # 7. Grafik Kontribusi Puskesmas
+        dbc.Row(dbc.Col([dcc.Graph(figure=fig_pusk_stacked), dbc.Card(dbc.CardBody(dcc.Markdown(analysis_pusk_comparison)), className="mt-2")], md=12)),
+        html.Hr(className="my-4"),
+
+        # 8 & 9. Grafik Perbandingan Tahunan per Kategori
+        html.H4("Analisis Detail Berdasarkan Kategori Penyakit", className="text-center my-4"),
+        dbc.Row(dbc.Col(dcc.Graph(figure=fig_trend_menular), md=12), className="mb-4"),
+        dbc.Row(dbc.Col(dcc.Graph(figure=fig_trend_tidak_menular), md=12)),
+
     ], className="mt-3")
 
-    # =========================================================
-    # BAGIAN B: Proses Data untuk TAB 2 (Analisis Demografi)
-    # =========================================================
+    # BAGIAN B: Proses Data untuk TAB 2 (Analisis Demografi) - Tidak Berubah
     total_lk = df_base['laki_laki'].sum()
     total_pr = df_base['perempuan'].sum()
     fig_gender = go.Figure(data=[go.Pie(labels=['Laki-laki', 'Perempuan'], values=[total_lk, total_pr], hole=.4, marker_colors=['royalblue', 'crimson'])])
     fig_gender.update_layout(title_text='<b>Distribusi Kasus Berdasarkan Jenis Kelamin</b>', template='plotly_white')
     analysis_gender = generate_gender_analysis_text(total_lk, total_pr)
-
     kelompok_map = {'Bayi & Balita (<5th)':['usia_0_7_hr_baru','usia_0_7_hr_lama','usia_8_28_hr_baru','usia_8_28_hr_lama','usia_1bl_1th_baru','usia_1bl_1th_lama','usia_1_4th_baru','usia_1_4th_lama'],'Anak (5-9th)':['usia_5_9th_baru','usia_5_9th_lama'],'Remaja (10-19th)':['usia_10_14th_baru','usia_10_14th_lama','usia_15_19th_baru','usia_15_19th_lama'],'Dewasa (20-59th)':['usia_20_44th_baru','usia_20_44th_lama','usia_45_54th_baru','usia_45_54th_lama','usia_55_59th_baru','usia_55_59th_lama'],'Lansia (60+th)':['usia_60_69th_baru','usia_60_69th_lama','usia_70pl_baru','usia_70pl_lama']}
     age_new_data, age_old_data = {}, {}
     for kelompok, cols in kelompok_map.items():
         age_new_data[kelompok] = df_base[[c for c in cols if 'baru' in c]].sum().sum()
         age_old_data[kelompok] = df_base[[c for c in cols if 'lama' in c]].sum().sum()
-    
     fig_age = go.Figure(data=[go.Bar(name='Kasus Baru', x=list(age_new_data.keys()), y=list(age_new_data.values())), go.Bar(name='Kasus Lama', x=list(age_old_data.keys()), y=list(age_old_data.values()))]).update_layout(barmode='group', title_text="<b>Distribusi Kasus Berdasarkan Kelompok Umur</b>", template='plotly_white')
     analysis_age = generate_age_analysis_text(age_new_data, age_old_data)
+    tab2_content = html.Div([dbc.Row([dbc.Col(dcc.Graph(figure=fig_gender), md=6),dbc.Col(dcc.Graph(figure=fig_age), md=6),dbc.Col(dbc.Card(dbc.CardBody(dcc.Markdown(analysis_gender))), md=6),dbc.Col(dbc.Card(dbc.CardBody(dcc.Markdown(analysis_age))), md=6)], className="mb-4")], className="mt-3")
 
-    tab2_content = html.Div([
-        dbc.Row([
-            dbc.Col(dcc.Graph(figure=fig_gender), md=6),
-            dbc.Col(dcc.Graph(figure=fig_age), md=6),
-            dbc.Col(dbc.Card(dbc.CardBody(dcc.Markdown(analysis_gender))), md=6),
-            dbc.Col(dbc.Card(dbc.CardBody(dcc.Markdown(analysis_age))), md=6),
-        ], className="mb-4"),
-    ], className="mt-3")
-
-    # --- SIMPAN SEMUA DATA UNTUK DIUNDUH ---
-    data_to_store = {
-        'figs_json': {
-            'ranking_simple': fig_ranking_simple.to_json(), 'category_pie': fig_category_pie.to_json(),
-            'line_trend_monthly': fig_line_monthly.to_json(), 'gender_pie': fig_gender.to_json(),
-            'age_bar': fig_age.to_json(),
-        },
-        'table_data': {'ranking': df_ranking_total.to_dict('records')},
-        'analysis_texts': {
-            'ranking': analysis_ranking, 'pie': analysis_pie, 'monthly_trend': analysis_monthly_trend,
-            'gender': analysis_gender, 'age': analysis_age,
-        },
-        'filter_text': filter_summary_text,
-    }
+    # Penyimpanan data ke dcc.Store tidak berubah
+    data_to_store = {'figs_json': {'ranking_simple': fig_ranking_simple.to_json(), 'category_pie': fig_category_pie.to_json(),'bar_trend_yearly': fig_bar_trend_yearly.to_json(),'line_trend_yearly': fig_line_trend_yearly.to_json(),'line_trend_monthly': fig_line_monthly.to_json(),'monthly_compare_trend': fig_monthly_compare_trend.to_json(),'pusk_stacked': fig_pusk_stacked.to_json(),'trend_menular': fig_trend_menular.to_json(),'trend_tidak_menular': fig_trend_tidak_menular.to_json(),'gender_pie': fig_gender.to_json(),'age_bar': fig_age.to_json(),},'table_data': {'ranking': df_ranking_total.to_dict('records')},'analysis_texts': {'ranking': analysis_ranking, 'pie': analysis_pie,'yearly_trend': analysis_yearly_trend,'monthly_trend': analysis_monthly_trend,'pusk_comparison': analysis_pusk_comparison,'gender': analysis_gender, 'age': analysis_age,},'filter_text': filter_summary_text,}
 
     return tab1_content, tab2_content, filter_summary_text, not PDF_CAPABLE, data_to_store
 
-# Callback 4: Unduh PDF
+# Callback PDF dan Excel tidak perlu diubah, karena mereka mengambil dari dcc.Store
+# ... (sisa kode sama seperti sebelumnya) ...
 @callback(
     Output("laporan-download-pdf", "data"),
     Input("laporan-btn-unduh-pdf", "n_clicks"),
@@ -325,20 +358,23 @@ def download_laporan_as_pdf(n_clicks, stored_data):
     story = [Paragraph("Laporan Analisis Terpadu", style_h1), Paragraph(stored_data.get('filter_text', ''), styles['Italic']), Spacer(1, 0.3*inch)]
     analysis = stored_data.get('analysis_texts', {}); figs = stored_data.get('figs_json', {}); tables = stored_data.get('table_data', {})
 
-    # HALAMAN TREN PENYAKIT
     story.append(Paragraph("BAGIAN 1: ANALISIS TREN PENYAKIT", style_h2))
-    story.append(fig_to_image(figs.get('ranking_simple'))); story.append(create_pdf_table(tables.get('ranking'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('ranking', '...'))); story.append(PageBreak())
-    story.append(fig_to_image(figs.get('category_pie'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('pie', '...'))); story.append(Spacer(1, 0.3*inch))
-    story.append(fig_to_image(figs.get('line_trend_monthly'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('monthly_trend', '...'))); story.append(PageBreak())
+    story.append(fig_to_image(figs.get('ranking_simple'))); story.append(create_pdf_table(tables.get('ranking'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('ranking'))); story.append(PageBreak())
+    story.append(fig_to_image(figs.get('category_pie'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('pie'))); story.append(Spacer(1, 0.2*inch))
+    story.append(fig_to_image(figs.get('bar_trend_yearly'))); story.append(PageBreak())
+    story.append(fig_to_image(figs.get('line_trend_yearly'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('yearly_trend'))); story.append(Spacer(1, 0.2*inch))
+    story.append(fig_to_image(figs.get('line_trend_monthly'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('monthly_trend'))); story.append(PageBreak())
+    story.append(fig_to_image(figs.get('monthly_compare_trend'))); story.append(Spacer(1, 0.2*inch))
+    story.append(fig_to_image(figs.get('pusk_stacked'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('pusk_comparison'))); story.append(PageBreak())
+    story.append(fig_to_image(figs.get('trend_menular'))); story.append(Spacer(1, 0.2*inch))
+    story.append(fig_to_image(figs.get('trend_tidak_menular'))); story.append(PageBreak())
 
-    # HALAMAN ANALISIS DEMOGRAFI
     story.append(Paragraph("BAGIAN 2: ANALISIS DEMOGRAFI", style_h2))
-    story.append(fig_to_image(figs.get('gender_pie'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('gender', '...')))
-    story.append(Spacer(1, 0.3*inch)); story.append(fig_to_image(figs.get('age_bar'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('age', '...')))
+    story.append(fig_to_image(figs.get('gender_pie'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('gender')))
+    story.append(Spacer(1, 0.3*inch)); story.append(fig_to_image(figs.get('age_bar'))); story.append(Spacer(1, 0.1*inch)); story.append(text_to_paragraph(analysis.get('age')))
     
     doc.build(story); return dcc.send_bytes(buffer.getvalue(), "Laporan_Terpadu_Penyakit.pdf")
 
-# Callback 5: Unduh Excel
 @callback(
     Output("laporan-download-excel", "data"),
     Input("laporan-btn-unduh-excel", "n_clicks"),