Update app.py

app.py

@@ -284,15 +284,26 @@ st.sidebar.markdown('</div>', unsafe_allow_html=True)
 
 
 # =================== HEADER ===================
-st.markdown("""
-<div class="main-header">
-    <h1>PLN Audit Insight & Intelligence Dashboard</h1>
-    <p style="text-align:center; color:#546e7a; font-size:1.1em; margin-top:8px;">
-        Operational Risk Intelligence for Audit & Compliance
-    </p>
-</div>
-""", unsafe_allow_html=True)
-
+# Gunakan kolom untuk menyusun logo dan teks secara horizontal
+header_cols = st.columns([1, 3]) # Rasio lebar kolom: Logo (kecil), Teks (lebih besar)
+
+with header_cols[0]:
+    # Cek apakah file logo ada, lalu tampilkan
+    logo_path = "pln.png" # Sesuaikan nama file jika berbeda
+    if os.path.exists(logo_path):
+        st.image(logo_path, caption="", width=100) # Atur width sesuai kebutuhan
+    else:
+        st.warning(f"Logo `{logo_path}` not found. Please place it in the same directory as this script.")
+
+with header_cols[1]:
+    st.markdown("""
+    <div class="main-header">
+        <h1>PLN Audit Insight & Intelligence Dashboard</h1>
+        <p style="text-align:center; color:#546e7a; font-size:1.1em; margin-top:8px;">
+            Operational Risk Intelligence for Audit & Compliance
+        </p>
+    </div>
+    """, unsafe_allow_html=True)
 
 # =================== 1. Pie Charts: Temuan/Person by Company (PG & UM) - PERBAIKAN ===================
 st.markdown("<h3 class='section-title'>OBJECTIVE 1 - Company Reporting Activity: Who Reports the Most?</h3>", unsafe_allow_html=True)
@@ -303,28 +314,21 @@ df_local = df_filtered.copy()
 # Tambah kolom bulan
 df_local['created_month'] = df_local['created_at'].dt.to_period('M')
 
-# --- Langsung buat Area_Type PG / UM tanpa filter ---
-
+# --- Langsung buat Area_Type PG / UM tanpa filter manual ---
 if 'temuan_kode_distrik' in df_local.columns:
-
     df_local['Area_Type'] = df_local['temuan_kode_distrik'].apply(
         lambda x: 'PG' if 'PG' in str(x).upper()
         else 'UM' if 'UM' in str(x).upper()
         else 'Other'
     )
 
-    # Otomatis bagi dataset
+    # Otomatis bagi dataset berdasarkan Area_Type
     df_pg = df_local[df_local['Area_Type'] == 'PG'].copy()
     df_um = df_local[df_local['Area_Type'] == 'UM'].copy()
 
-else:
-    df_pg = pd.DataFrame()
-    df_um = pd.DataFrame()
-
     # --- Fungsi untuk menghitung rasio perusahaan ---
     def calculate_avg_ratio_per_company(df_area):
         if df_area.empty:
-            # Jika area tidak dipilih atau data kosong setelah filter
             return pd.DataFrame()
         # Hitung temuan per bulan per perusahaan
         findings_by_company_month = df_area.groupby(['created_month', 'nama_perusahaan']).size().reset_index(name='findings_count')
@@ -335,10 +339,8 @@ else:
         # Isi NaN dengan 0 untuk kolom yang mungkin hilang dari merge
         merged = merged.fillna({'findings_count': 0, 'unique_creators': 0})
         # Filter untuk menghindari pembagian dengan nol
-        # Kita hanya ingin menghitung rasio jika jumlah pelapor > 0
         merged = merged[merged['unique_creators'] > 0]
         # Hitung rasio (ignore NaN)
-        # Pembagian oleh 0 akan menghasilkan inf, jadi kita ganti inf dengan NaN
         merged['ratio'] = merged['findings_count'] / merged['unique_creators']
         merged['ratio'] = merged['ratio'].replace([np.inf, -np.inf], np.nan)
 
@@ -347,11 +349,9 @@ else:
             return pd.DataFrame()
 
         # Rata-rata bulanan per perusahaan
-        # Group by nama_perusahaan dan ambil mean dari rasio
-        # mean() akan mengabaikan NaN secara default
         avg_ratio = merged.groupby('nama_perusahaan')['ratio'].mean().reset_index(name='avg_monthly_ratio')
 
-        # Jika hasil akhirnya hanya NaN (karena semua rasio perusahaan adalah NaN), kembalikan DataFrame kosong
+        # Jika hasil akhirnya hanya NaN, kembalikan DataFrame kosong
         if avg_ratio['avg_monthly_ratio'].isna().all():
             return pd.DataFrame()
 
@@ -364,14 +364,12 @@ else:
     # Fungsi untuk menentukan warna
     def get_color_map(company_series):
         pln_color = "#FFD700"  # Kuning untuk PLN
-        # Daftar warna biru (dari gelap ke terang)
         blue_colors = ["#1E90FF", "#87CEEB", "#B0E0E6", "#ADD8E6", "#E0F6FF"] 
         color_map = {}
         for company in company_series:
             if 'PLN' in str(company).upper():
                 color_map[company] = pln_color
             else:
-                # Pilih warna biru berdasarkan indeks, ulangi jika perlu
                 idx = len([c for c in color_map.values() if c != pln_color]) % len(blue_colors)
                 color_map[company] = blue_colors[idx]
         return color_map
@@ -380,14 +378,14 @@ else:
     col1, col2 = st.columns(2)
 
     with col1:
-        st.markdown("<h5>Avg Monthly Finding by Company</h5>", unsafe_allow_html=True)
+        st.markdown("<h5>Unit Pembangkit: Avg Monthly Finding/Person by Company</h5>", unsafe_allow_html=True)
         if not avg_ratio_pg.empty:
             color_discrete_map_pg = get_color_map(avg_ratio_pg['nama_perusahaan'])
             fig_pg = px.pie(
                 avg_ratio_pg, 
                 values='avg_monthly_ratio', 
                 names='nama_perusahaan', 
-                title='Unit Pembangkit Company',
+                title='PG Area',
                 color='nama_perusahaan',
                 color_discrete_map=color_discrete_map_pg
             )
@@ -395,11 +393,10 @@ else:
             
             # AI Insight untuk PG
             if not avg_ratio_pg.empty:
-                # Temukan perusahaan dengan rasio tertinggi dan terendah di PG
                 top_company_pg = avg_ratio_pg.loc[avg_ratio_pg['avg_monthly_ratio'].idxmax()]
                 low_company_pg = avg_ratio_pg.loc[avg_ratio_pg['avg_monthly_ratio'].idxmin()]
                 
-                st.markdown("### Insight")
+                st.markdown("### Insight (PG)")
                 insight_text = (
                     f"<div class='ai-insight'>"
                     f"In PG Area, <strong>{top_company_pg['nama_perusahaan']}</strong> has the highest average finding-to-person ratio "
@@ -414,14 +411,14 @@ else:
             st.warning("No data for PG area or all ratios are NaN.")
 
     with col2:
-        st.markdown("<h5>Avg Monthly Finding by Company</h5>", unsafe_allow_html=True)
+        st.markdown("<h5>Unit Maintenance: Avg Monthly Finding/Person by Company</h5>", unsafe_allow_html=True)
         if not avg_ratio_um.empty:
             color_discrete_map_um = get_color_map(avg_ratio_um['nama_perusahaan'])
             fig_um = px.pie(
                 avg_ratio_um, 
                 values='avg_monthly_ratio', 
                 names='nama_perusahaan', 
-                title='Unit Maintenance',
+                title='UM Area',
                 color='nama_perusahaan',
                 color_discrete_map=color_discrete_map_um
             )
@@ -429,11 +426,10 @@ else:
             
             # AI Insight untuk UM
             if not avg_ratio_um.empty:
-                # Temukan perusahaan dengan rasio tertinggi dan terendah di UM
                 top_company_um = avg_ratio_um.loc[avg_ratio_um['avg_monthly_ratio'].idxmax()]
                 low_company_um = avg_ratio_um.loc[avg_ratio_um['avg_monthly_ratio'].idxmin()]
                 
-                st.markdown("### Insight")
+                st.markdown("### Insight (UM)")
                 insight_text = (
                     f"<div class='ai-insight'>"
                     f"In UM Area, <strong>{top_company_um['nama_perusahaan']}</strong> exhibits the highest average finding-to-person ratio "
@@ -445,7 +441,9 @@ else:
                 st.markdown(insight_text, unsafe_allow_html=True)
         else:
             st.warning("No data for UM area or all ratios are NaN.")
-
+else:
+    st.error("Column 'temuan_kode_distrik' not found in the data. Cannot determine PG/UM areas.")
+    st.stop()
 
 # =================== 2. Treemap: Distribusi Temuan per Area (nama_lokasi_full) - PERBAIKAN ===================
 st.markdown("<h3 class='section-title'>OBJECTIVE 2 - Active vs Inactive Locations: Who Leads?</h3>", unsafe_allow_html=True)