Update app.py

app.py

@@ -1066,111 +1066,83 @@ with col_3d:
 
 
 
+# =================== OBJECTIVE 4 - What Unsafe Issues Appear Most Often? ===================
+st.markdown("<h3 class='section-title'>OBJECTIVE 4 — What Unsafe Issues Appear Most Often?</h3>", unsafe_allow_html=True)
+
+# Function for consistent title
+def create_consistent_title(title_text):
+    return f"<div style='font-family: Arial; font-size: 16px; font-weight: bold; color: #2c3e50; text-align: center; margin-bottom: 8px;'>{title_text}</div>"
+
+# Try to import wordcloud
 try:
     from wordcloud import WordCloud
     import matplotlib.pyplot as plt
     import plotly.express as px
+    import plotly.graph_objects as go
     WORDCLOUD_AVAILABLE = True
 except ImportError:
     WORDCLOUD_AVAILABLE = False
 
-st.markdown("<h3 class='section-title'>OBJECTIVE 4 - What Unsafe Issues Appear Most Often?</h3>", 
-            unsafe_allow_html=True)
-
-# 🔹 Fungsi untuk membuat judul seragam
-def create_consistent_title(title_text):
-    return f"<div style='font-family: Arial; font-size: 16px; font-weight: bold; color: #2c3e50; text-align: center; margin-bottom: 8px;'>{title_text}</div>"
-
 if WORDCLOUD_AVAILABLE:
+    # Prepare data
+    df_all = df_local.copy()
+    
+    # 2-column layout
+    col_chart, col_cloud = st.columns([1.2, 1])
 
-    # 🔥 Pie Chart: Semua data (tidak difilter)
-    df_all_kategori = df_local.copy()
-
-    # 🔥 WordCloud: Hanya data Non-Positive
-    df_filtered_kategori = df_local[df_local['temuan_kategori'] != 'Positive'] \
-        if 'temuan_kategori' in df_local.columns else df_local
-
-    # 2 Kolom
-    col1, col2 = st.columns(2)
-
-    # === PIE CHART: Semua temuan_kategori (Warna Sesuai Kategori) ===
-    with col1:
-        # Tampilkan judul terpisah
+    # === PIE CHART (with custom legend logic) ===
+    with col_chart:
         st.markdown(create_consistent_title("Distribution of All Issue Categories"), unsafe_allow_html=True)
-
-        if 'temuan_kategori' in df_all_kategori.columns:
-            # Hitung jumlah per kategori
-            category_counts = df_all_kategori['temuan_kategori'].value_counts()
-            
-            if not category_counts.empty:
-                # Buat warna sesuai kategori
-                colors = []
-                for cat in category_counts.index:
-                    if cat == 'Positive':
-                        colors.append('#2E7D32')  # Hijau
-                    elif cat == 'Unsafe Condition':
-                        colors.append('#EF5350')  # Merah Muda
-                    elif cat == 'Unsafe Action':
-                        colors.append('#F48FB1')  # Pink
-                    elif cat == 'Near Miss':
-                        colors.append('#BDBDBD')  # Abu-abu
-                    else:
-                        colors.append('#9E9E9E')  # Default abu-abu
-
-                # Buat pie chart tanpa judul
-                fig_pie = px.pie(
-                    names=category_counts.index,
-                    values=category_counts.values,
-                    color_discrete_sequence=colors
-                )
-                fig_pie.update_traces(textposition='inside', textinfo='percent+label')
-                fig_pie.update_layout(height=450)
-                st.plotly_chart(fig_pie, use_container_width=True)
-            else:
-                st.warning("No data available for pie chart.")
+        
+        if 'temuan_kategori' not in df_all.columns:
+            st.warning("Column 'temuan_kategori' not found.")
         else:
-            st.warning("Column 'temuan_kategori' not available.")
-
-    # === WORDCLOUD: Hanya Non-Positive (dari keyword_kategori) ===
-    with col2:
-        # Tampilkan judul terpisah
-        st.markdown(create_consistent_title("Unsafe Issues"), unsafe_allow_html=True)
+            # Count categories
+            cat_counts = df_all['temuan_kategori'].value_counts()
+            total = cat_counts.sum()
+            percentages = (cat_counts / total * 100).round(1)
+
+            # Define colors (sesuai preferensi Anda)
+            color_map = {
+                'Positive': '#2E7D32',        # Hijau
+                'Unsafe Condition': '#EF5350',  # Merah tua (pastel red)
+                'Unsafe Action': '#F48FB1',    # Pink pastel
+                'Near Miss': '#BDBDBD',        # Abu-abu
+            }
+            # Default color for unknown
+            default_color = '#9E9E9E'
+
+            # Assign colors
+            colors = [color_map.get(cat, default_color) for cat in cat_counts.index]
+
+            # Create base pie chart — tanpa legend
+            fig = go.Figure(data=[go.Pie(
+                labels=cat_counts.index,
+                values=cat_counts.values,
+                marker=dict(colors=colors),
+                textinfo='percent+label',
+                textposition='inside',
+                hoverinfo='label+percent',
+                sort=False  # pertahankan urutan asli
+            )])
+            fig.update_layout(
+                height=450,
+                showlegend=False,  # ❌ Matikan legend default Plotly
+                margin=dict(t=20, b=20, l=20, r=20)
+            )
+            st.plotly_chart(fig, use_container_width=True)
 
-        if df_filtered_kategori.empty:
-            st.warning("No data available after filtering out 'Positive' category.")
-        else:
-            if 'keyword_kategori' in df_filtered_kategori.columns:
-                # Gabungkan text
-                import re
-                text = ' '.join(df_filtered_kategori['keyword_kategori'].dropna().astype(str))
-                text = re.sub(r'[^a-zA-Z\s]', ' ', text)
-
-                if text.strip():
-                    # Buat WordCloud
-                    wordcloud = WordCloud(
-                        width=1600,          # resolusi besar agar HD
-                        height=800,
-                        background_color='white',
-                        colormap='viridis',
-                        max_words=1000,
-                        random_state=42
-                    ).generate(text)
-
-                    # Output kecil tapi tajam
-                    fig, ax = plt.subplots(figsize=(3, 2), dpi=200)
-                    ax.imshow(wordcloud, interpolation='bilinear')
-                    ax.axis('off')
-                    plt.tight_layout()
-
-                    st.pyplot(fig, use_container_width=True)
-                else:
-                    st.warning("No valid text remaining after cleaning.")
-            else:
-                st.warning("Column 'keyword_kategori' not available.")
+            # === 🔥 CUSTOM LEGEND: hanya tampilkan "Unsafe Action" & "Near Miss" sebagai garis jika <10% ===
+            st.markdown("#### Legend")
+            
+            # Buat container HTML untuk legend custom
+            legend_items = []
 
-else:
-    st.info("WordCloud library not installed. Install `wordcloud` and `matplotlib` to enable this feature.")
+            # Selalu tampilkan kategori lain sebagai blok biasa (opsional — bisa dihapus jika hanya ingin 2 line)
+            # Tapi sesuai permintaan: "hanya 2 garis untuk 2 kategori tsb"
+            # → Jadi kita tampilkan **hanya** UA & NM jika <10%, dalam bentuk garis
 
+            target
 # =================== 5. Matrix (Tetap Dipertahankan) ===================
 st.markdown("<h3 class='section-title'>OBJECTIVE 5 - Findings vs Lead Time: Which Divisions Move Slow?</h3>", unsafe_allow_html=True)