Update app.py

app.py

@@ -1058,73 +1058,78 @@ with col_3d:
 
 
 ####OBJECTIVE 4
+# ================= WORDCLOUD (HIGH QUALITY) ==================
 try:
     from wordcloud import WordCloud
     import matplotlib.pyplot as plt
     WORDCLOUD_AVAILABLE = True
 except ImportError:
     WORDCLOUD_AVAILABLE = False
-    # Tidak perlu warning di sini, karena akan ditampilkan di bawah jika digunakan
 
-st.markdown("<h3 class='section-title'>4. Global Text Insights (Word Clouds)</h3>", unsafe_allow_html=True)
+st.markdown("<h3 class='section-title'>4. Global Text Insights (Word Clouds)</h3>", 
+            unsafe_allow_html=True)
 
 if WORDCLOUD_AVAILABLE:
-    # Filter data: hanya yang temuan_kategori != 'Positive'
-    df_filtered_kategori = df_local[df_local['temuan_kategori'] != 'Positive'] if 'temuan_kategori' in df_local.columns else df_local
+
+    df_filtered_kategori = df_local[df_local['temuan_kategori'] != 'Positive'] \
+        if 'temuan_kategori' in df_local.columns else df_local
 
     if df_filtered_kategori.empty:
         st.warning("No data available after filtering out 'Positive' category.")
     else:
+
         col_wc1 = st.columns(1)
 
-        # Fungsi untuk membuat dan menampilkan wordcloud
+        # === FUNCTION: GENERATE HIGH QUALITY WORDCLOUD ===
         def generate_wordcloud(text_data, title, col):
-            # Periksa apakah text_data adalah Series kosong atau None
+
             if text_data is None or text_data.empty:
-                col.warning(f"No data available in series for {title}.")
+                col.warning(f"No data available for {title}.")
                 return
-            # Periksa apakah semua nilai adalah NaN
+
             if text_data.isna().all():
                 col.warning(f"All data is NaN for {title}.")
                 return
-            # Gabung semua teks menjadi satu string
-            text = ' '.join(text_data.dropna().astype(str))
-            # Bersihkan teks dari karakter non-alfanumerik (opsional)
+
+            # Gabungkan text
             import re
+            text = ' '.join(text_data.dropna().astype(str))
             text = re.sub(r'[^a-zA-Z\s]', ' ', text)
-            if text.strip(): # Pastikan teks tidak kosong setelah pembersihan
-                # Buat WordCloud
-                wordcloud = WordCloud(
-                    width=100, 
-                    height=50,
-                    background_color='white',
-                    colormap='viridis',
-                    max_words=1000,
-                    relative_scaling=0.5,
-                    random_state=42
-                ).generate(text)
-
-                # Plot menggunakan matplotlib
-                fig, ax = plt.subplots(figsize=(3, 2))
-                ax.imshow(wordcloud, interpolation='bilinear')
-                ax.axis('off')
-                ax.set_title(title, fontsize=12)
-                plt.tight_layout()
-
-                # Tampilkan di Streamlit
-                col.pyplot(fig, use_container_width=True)
-            else:
-                col.warning(f"No valid text data for {title} after cleaning.")
 
-        # Kolom Temuan Nama (setelah filter)
-        with col_wc1[0]:  # 🔥 Perbaikan: akses kolom dengan indeks [0]
+            if not text.strip():
+                col.warning(f"No valid text remaining for {title}.")
+                return
+
+            # === HIGH RESOLUTION WORDCLOUD ===
+            wordcloud = WordCloud(
+                width=1600,          # resolusi besar
+                height=800,
+                background_color='white',
+                colormap='viridis',
+                max_words=1000,
+                random_state=42
+            ).generate(text)
+
+            fig, ax = plt.subplots(figsize=(5, 3), dpi=200)   # tajam
+            ax.imshow(wordcloud, interpolation='bilinear')
+            ax.axis('off')
+            ax.set_title(title, fontsize=16)
+            plt.tight_layout()
+
+            col.pyplot(fig, use_container_width=True)
+
+        # === CALL WORDCLOUD ===
+        with col_wc1[0]:
             if 'keyword_kategori' in df_filtered_kategori.columns:
-                generate_wordcloud(df_filtered_kategori['keyword_kategori'], col_wc1[0])
+                generate_wordcloud(df_filtered_kategori['keyword_kategori'], 
+                                   "WordCloud Temuan", 
+                                   col_wc1[0])
             else:
-                col_wc1[0].warning("Column 'temuan_nama' not available.")
+                col_wc1[0].warning("Column 'keyword_kategori' not available.")
 
 else:
     st.info("WordCloud library not installed. Install `wordcloud` and `matplotlib` to enable this feature.")
+
 # =================== 5. Matrix (Tetap Dipertahankan) ===================
 st.markdown("<h3 class='section-title'>OBJECTIVE 5 - Findings vs Lead Time: Which Companies Move Slow?</h3>", unsafe_allow_html=True)