Update app.py

app.py

@@ -299,9 +299,20 @@ st.markdown("""
     border: 1px solid #e0e0e0;
     border-radius: 12px;
     margin-bottom: 25px;
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
 }
 .header-title {
     text-align: center;
+    flex: 1;
+}
+.logo-container {
+    display: flex;
+    align-items: center;
+    justify-content: flex-end;
+    width: 100px;  /* Sesuaikan ukuran logo */
+    height: auto;
 }
 </style>
 """, unsafe_allow_html=True)
@@ -326,8 +337,22 @@ with st.container():
             </div>
         """, unsafe_allow_html=True)
 
+    # RIGHT — LOGO
+    with col3:
+        try:
+            st.image("pln.png", width=80)  # Sesuaikan width sesuai kebutuhan
+        except:
+            st.write("")  # Jika logo tidak ditemukan, kosongkan
+
+    st.markdown('</div>', unsafe_allow_html=True)
+
+
 
 
+import streamlit as st
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
 
 import streamlit as st
 import plotly.graph_objects as go
@@ -422,16 +447,19 @@ if 'temuan_kode_distrik' in df_local.columns:
     ]
     pln_color = "#FFD700"  # Kuning PLN
 
-    def assign_colors(df):
+    def assign_colors_and_create_legend(df):
         colors = []
+        names = []
         blue_idx = 0
         for company in df['nama_perusahaan']:
             if 'PLN' in str(company).upper():
                 colors.append(pln_color)
+                names.append(company)
             else:
                 colors.append(pastel_blues[blue_idx % len(pastel_blues)])
+                names.append(company)
                 blue_idx += 1
-        return colors
+        return colors, names
 
     # Fungsi untuk membuat polar bar chart
     def create_polar_bar_chart(df, area_name):
@@ -442,7 +470,7 @@ if 'temuan_kode_distrik' in df_local.columns:
         df = df.sort_values('avg_monthly_ratio', ascending=True)
         companies = df['nama_perusahaan'].tolist()
         ratios = df['avg_monthly_ratio'].tolist()
-        colors = assign_colors(df)
+        colors, legend_names = assign_colors_and_create_legend(df)
         
         # Hitung total temuan untuk menghitung sudut (proporsi)
         total_findings = df_local[df_local['Area_Type'] == area_name].groupby('nama_perusahaan').size()
@@ -461,17 +489,22 @@ if 'temuan_kode_distrik' in df_local.columns:
             current_angle += a
         
         fig = go.Figure()
-        fig.add_trace(go.Barpolar(
-            r=ratios,
-            theta=mid_angles,
-            width=angles,
-            marker_color=colors,
-            marker_line_color="white",
-            marker_line_width=1.2,
-            opacity=0.9,
-            hovertemplate="<b>%{text}</b><br>Avg Ratio: %{r:.2f}<extra></extra>",
-            text=companies,
-        ))
+        
+        # Tambahkan satu trace untuk setiap perusahaan agar muncul di legend
+        for i, (comp, ratio, color, angle) in enumerate(zip(companies, ratios, colors, angles)):
+            fig.add_trace(go.Barpolar(
+                r=[ratio],
+                theta=[mid_angles[i]],
+                width=[angle],
+                marker_color=[color],
+                marker_line_color="white",
+                marker_line_width=1.2,
+                opacity=0.9,
+                hovertemplate="<b>%{text}</b><br>Avg Ratio: %{r[0]:.2f}<extra></extra>",
+                text=[comp],
+                name=comp,  # 🔥 Nama perusahaan untuk legend
+                showlegend=True  # 🔥 Tampilkan di legend
+            ))
 
         fig.update_layout(
             title=f'{area_name} Area',
@@ -490,7 +523,15 @@ if 'temuan_kode_distrik' in df_local.columns:
                     showline=False
                 ),
             ),
-            showlegend=False,
+            showlegend=True,  # 🔥 Aktifkan legend
+            legend=dict(
+                orientation="v",
+                yanchor="top",
+                y=1,
+                xanchor="left",
+                x=1.02,
+                font=dict(size=10)
+            ),
             margin=dict(t=40, b=20, l=20, r=20),
             height=400,
             paper_bgcolor="rgba(0,0,0,0)",
@@ -653,111 +694,6 @@ if not avg_ratio_per_location.empty:
         st.markdown(insight_text, unsafe_allow_html=True)
 else:
     st.warning("No data available for location ratio calculation or all ratios are NaN.")
-# =================== OBJECTIVE 2 — Active vs Inactive Locations (Treemap with Color Gradient) ===================
-st.markdown(
-    """
-    <style>
-    .section-title {
-        text-align: center;
-        font-size: 1.5rem;
-        font-weight: 600;
-        color: #2c3e50;
-        margin-bottom: 1.2rem;
-    }
-    .ai-insight {
-        background-color: #f8f9fa;
-        padding: 12px;
-        border-left: 4px solid #27ae60;
-        border-radius: 0 4px 4px 0;
-        font-size: 0.95rem;
-        line-height: 1.5;
-        margin-top: 1rem;
-    }
-    </style>
-    <h3 class='section-title'>OBJECTIVE 3 — Active vs Inactive Division: Who Leads?</h3>
-    """,
-    unsafe_allow_html=True
-)
-
-df_local = df_filtered.copy()
-if df_local.empty:
-    st.warning("No data available after filtering.")
-    st.stop()
-
-df_local['created_month'] = df_local['created_at'].dt.to_period('M')
-
-# Hitung temuan per bulan per lokasi
-findings_by_location_month = df_local.groupby(['created_month', 'nama']).size().reset_index(name='findings_count')
-# Hitung jumlah orang unik per bulan per lokasi
-creators_by_location_month = df_local.groupby(['created_month', 'nama'])['creator_nid'].nunique().reset_index(name='unique_creators')
-# Gabung
-merged_loc = findings_by_location_month.merge(creators_by_location_month, on=['created_month', 'nama'], how='outer')
-# Isi NaN dengan 0 untuk kolom yang mungkin hilang dari merge
-merged_loc = merged_loc.fillna({'findings_count': 0, 'unique_creators': 0})
-# Filter untuk menghindari pembagian dengan nol
-merged_loc = merged_loc[merged_loc['unique_creators'] > 0]
-# Hitung rasio (ignore NaN)
-merged_loc['ratio'] = merged_loc['findings_count'] / merged_loc['unique_creators']
-merged_loc['ratio'] = merged_loc['ratio'].replace([np.inf, -np.inf], np.nan)
-
-# Rata-rata bulanan per lokasi
-avg_ratio_per_location = merged_loc.groupby('nama')['ratio'].mean().reset_index(name='avg_monthly_ratio')
-
-# Filter hasil akhir untuk menghindari NaN
-avg_ratio_per_location = avg_ratio_per_location.dropna(subset=['avg_monthly_ratio'])
-
-# Plot Treemap dengan gradasi warna
-if not avg_ratio_per_location.empty:
-    # Gunakan color_continuous_scale untuk gradasi warna: merah → kuning → hijau
-    fig_treemap = px.treemap(
-        avg_ratio_per_location,
-        path=['nama'],  # Path untuk hierarki (hanya satu level di sini)
-        values='avg_monthly_ratio', # Nilai yang menentukan ukuran area
-        title='Avg Monthly Finding by Division',
-        labels={'avg_monthly_ratio': 'Avg Monthly Finding/Person Ratio', 'nama': 'Location'},
-        color='avg_monthly_ratio', # Warna berdasarkan nilai rasio (bukan kategori)
-        color_continuous_scale=[
-            [0.0, '#D32F2F'],  # Merah untuk rendah
-            [0.5, '#FFB300'],  # Kuning untuk sedang
-            [1.0, '#4CAF50']   # Hijau untuk tinggi
-        ]
-    )
-    # Format hover
-    fig_treemap.update_traces(
-        hovertemplate="<b>%{label}</b><br>Avg Ratio: %{value:.2f}<extra></extra>"
-    )
-    fig_treemap.update_layout(height=600)
-    st.plotly_chart(fig_treemap, use_container_width=True)
-
-    # AI Insight untuk Treemap Lokasi (Business-focused)
-    if not avg_ratio_per_location.empty:
-        # Temukan lokasi dengan rasio tertinggi dan terendah
-        top_location = avg_ratio_per_location.loc[avg_ratio_per_location['avg_monthly_ratio'].idxmax()]
-        low_location = avg_ratio_per_location.loc[avg_ratio_per_location['avg_monthly_ratio'].idxmin()]
-
-        st.markdown("### Insight")
-        insight_text = (
-            f"<div class='ai-insight'>"
-            f"The treemap visualizes the average finding-to-person ratio per location using a <strong>color gradient</strong>, indicating reporting activity levels. "
-            f"Locations with <span style='color:#4CAF50; font-weight:bold;'>green</span> color have a high ratio, indicating high reporting activity or exposure. "
-            f"Those with <span style='color:#FFB300; font-weight:bold;'>yellow</span> color have a medium ratio, indicating moderate reporting. "
-            f"Locations with <span style='color:#D32F2F; font-weight:bold;'>red</span> color have a low ratio, indicating lower activity levels or potentially under-reporting. "
-            f"<strong>{top_location['nama']}</strong> shows the highest activity level "
-            f"(<strong>{top_location['avg_monthly_ratio']:.2f}</strong>). "
-            f"<strong>{low_location['nama']}</strong> shows the lowest activity level "
-            f"(<strong>{low_location['avg_monthly_ratio']:.2f}</strong>). "
-            f"Areas with high activity (green) warrant investigation into the underlying causes of frequent findings. "
-            f"Areas with low activity (red) should be reviewed to ensure reporting completeness and identify any hidden risks."
-            f"</div>"
-        )
-        st.markdown(insight_text, unsafe_allow_html=True)
-else:
-    st.warning("No data available for location ratio calculation or all ratios are NaN.")
-
-import streamlit as st
-import plotly.express as px
-import numpy as np
-import pandas as pd
 
 # =================== OBJECTIVE 3 - Frequency & Response Time ===================
 st.markdown(
@@ -780,7 +716,7 @@ st.markdown(
         margin-top: 1rem;
     }
     </style>
-    <h3 class='section-title'>OBJECTIVE 4 — Frequency & Response Time: Who Reports Well? Who Executes Well?</h3>
+    <h3 class='section-title'>OBJECTIVE 3 — Frequency & Response Time: Who Reports Well? Who Executes Well?</h3>
     """,
     unsafe_allow_html=True
 )
@@ -869,11 +805,11 @@ avg_leadtime_per_executor = compute_executor_leadtime_by_pic(df_local)
 col_3a, col_3c = st.columns(2)
 
 with col_3a:
-    st.markdown("<h5>4a. Average Finding by Division (Reporter)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5>3a. Average Finding by Division (Reporter)</h5>", unsafe_allow_html=True)
     if avg_ratio_per_nama.empty:
         st.warning("No data for reporter analysis by division.")
     else:
-        sort_option_3a = st.selectbox("Show 4a:", ["Top 10", "Bottom 10"], key='sort_3a')
+        sort_option_3a = st.selectbox("Show 3a:", ["Top 10", "Bottom 10"], key='sort_3a')
         
         # Urutkan data dari tertinggi ke terendah
         sorted_data_all = avg_ratio_per_nama.sort_values('avg_monthly_ratio', ascending=False)
@@ -932,11 +868,11 @@ with col_3a:
         st.markdown(insight_text, unsafe_allow_html=True)
 
 with col_3c:
-    st.markdown("<h5>4b. Average Finding Rate per Reporter (Name)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5>3b. Average Finding Rate per Reporter (Name)</h5>", unsafe_allow_html=True)
     if avg_rate_per_creator.empty:
         st.warning("No data for reporter analysis by creator_name.")
     else:
-        sort_option_3c = st.selectbox("Show 4b:", ["Top 10", "Bottom 10"], key='sort_3c')
+        sort_option_3c = st.selectbox("Show 3b:", ["Top 10", "Bottom 10"], key='sort_3c')
         
         # Urutkan data dari tertinggi ke terendah
         sorted_data_all = avg_rate_per_creator.sort_values('avg_monthly_rate', ascending=False)
@@ -999,11 +935,11 @@ with col_3c:
 col_3b, col_3d = st.columns(2)
 
 with col_3b:
-    st.markdown("<h5>4c. Average Lead Time by Division (Executor)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5>3c. Average Lead Time by Division (Executor)</h5>", unsafe_allow_html=True)
     if avg_leadtime_nama.empty:
         st.warning("No data for executor analysis by division.")
     else:
-        sort_option_3b = st.selectbox("Show 4c:", ["Top 10", "Bottom 10"], key='sort_3b')
+        sort_option_3b = st.selectbox("Show 3c:", ["Top 10", "Bottom 10"], key='sort_3b')
         
         # Urutkan data dari tertinggi ke terendah
         sorted_data_all = avg_leadtime_nama.sort_values('avg_monthly_leadtime', ascending=False)
@@ -1060,11 +996,11 @@ with col_3b:
         st.markdown(insight_text, unsafe_allow_html=True)
 
 with col_3d:
-    st.markdown("<h5>4d. Average Lead Time by Executor (Name)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5>3d. Average Lead Time by Executor (Name)</h5>", unsafe_allow_html=True)
     if avg_leadtime_per_executor.empty:
         st.warning("No data for executor analysis by nama_pic.")
     else:
-        sort_option_3d = st.selectbox("Show 4d:", ["Top 10", "Bottom 10"], key='sort_3d')
+        sort_option_3d = st.selectbox("Show 3d:", ["Top 10", "Bottom 10"], key='sort_3d')
         
         # Urutkan data dari tertinggi ke terendah
         sorted_data_all = avg_leadtime_per_executor.sort_values('avg_monthly_leadtime', ascending=False)