beter clusters and qol

apps/cluster_map/visualization.py

@@ -47,7 +47,8 @@ def calculate_point_sizes(reduced_embeddings, density_based_sizing=False,
 
 
 def create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels, hover_text, 
-                         point_sizes, point_opacity=DEFAULT_POINT_OPACITY, method="PCA", enable_3d=False):
+                         point_sizes, point_opacity=DEFAULT_POINT_OPACITY, method="PCA", enable_3d=False,
+                         cluster_names=None):
     """Create a plot colored by clusters"""
     fig = go.Figure()
     
@@ -61,7 +62,11 @@ def create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels, hover
             cluster_hover = [hover_text[j] for j, mask in enumerate(cluster_mask) if mask]
             cluster_sizes = [point_sizes[j] for j, mask in enumerate(cluster_mask) if mask]
             
-            cluster_name = f"Cluster {cluster_id}" if cluster_id != -1 else "Noise"
+            # Use generated name if available, otherwise fall back to default
+            if cluster_names and cluster_id in cluster_names:
+                cluster_name = cluster_names[cluster_id]
+            else:
+                cluster_name = f"Cluster {cluster_id}" if cluster_id != -1 else "Noise"
             
             if enable_3d:
                 fig.add_trace(go.Scatter3d(
@@ -149,7 +154,8 @@ def create_source_colored_plot(reduced_embeddings, filtered_df, selected_sources
 def create_visualization_plot(reduced_embeddings, filtered_df, cluster_labels=None, 
                              selected_sources=None, method="PCA", clustering_method="None",
                              point_size=DEFAULT_POINT_SIZE, point_opacity=DEFAULT_POINT_OPACITY,
-                             density_based_sizing=False, size_variation=2.0, enable_3d=False):
+                             density_based_sizing=False, size_variation=2.0, enable_3d=False,
+                             cluster_names=None):
     """Create the main visualization plot"""
     
     # Create hover text
@@ -162,7 +168,8 @@ def create_visualization_plot(reduced_embeddings, filtered_df, cluster_labels=No
     # Create plot based on coloring strategy
     if cluster_labels is not None:
         fig = create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels, 
-                                   hover_text, point_sizes, point_opacity, method, enable_3d)
+                                   hover_text, point_sizes, point_opacity, method, enable_3d,
+                                   cluster_names)
     else:
         if selected_sources is None:
             selected_sources = filtered_df['source_file'].unique()
@@ -276,3 +283,29 @@ def display_data_table(filtered_df, cluster_labels=None):
         
     display_df['content'] = display_df['content'].str[:100] + '...'  # Truncate for display
     st.dataframe(display_df, use_container_width=True)
+
+
+def display_cluster_summary(cluster_names, cluster_labels):
+    """Display a summary of cluster names and their sizes"""
+    if not cluster_names or cluster_labels is None:
+        return
+        
+    st.subheader("🏷️ Cluster Summary")
+    
+    # Create summary data
+    cluster_summary = []
+    for cluster_id, name in cluster_names.items():
+        count = np.sum(cluster_labels == cluster_id)
+        cluster_summary.append({
+            'Cluster ID': cluster_id,
+            'Cluster Name': name,
+            'Message Count': count,
+            'Percentage': f"{100 * count / len(cluster_labels):.1f}%"
+        })
+    
+    # Sort by message count
+    cluster_summary.sort(key=lambda x: x['Message Count'], reverse=True)
+    
+    # Display as table
+    summary_df = pd.DataFrame(cluster_summary)
+    st.dataframe(summary_df, use_container_width=True, hide_index=True)