refactor

apps/cluster_map/main.py

@@ -0,0 +1,132 @@
+"""
+Main application logic for the Discord Chat Embeddings Visualizer.
+"""
+
+import streamlit as st
+import warnings
+warnings.filterwarnings('ignore')
+
+# Import custom modules
+from ui_components import (
+    setup_page_config, display_title_and_description, get_all_ui_parameters,
+    display_performance_warnings
+)
+from data_loader import (
+    load_all_chat_data, parse_embeddings, filter_data, get_filtered_embeddings
+)
+from dimensionality_reduction import (
+    reduce_dimensions, apply_density_based_jittering
+)
+from clustering import apply_clustering
+from visualization import (
+    create_visualization_plot, display_clustering_metrics, display_summary_stats,
+    display_clustering_results, display_data_table
+)
+
+
+def main():
+    """Main application function"""
+    # Set up page configuration
+    setup_page_config()
+    
+    # Display title and description
+    display_title_and_description()
+    
+    # Load data
+    with st.spinner("Loading chat data..."):
+        df = load_all_chat_data()
+    
+    if df.empty:
+        st.error("No data could be loaded. Please check the data directory.")
+        st.stop()
+    
+    # Parse embeddings
+    with st.spinner("Parsing embeddings..."):
+        embeddings, valid_df = parse_embeddings(df)
+    
+    if len(embeddings) == 0:
+        st.error("No valid embeddings found!")
+        st.stop()
+    
+    # Get UI parameters
+    params = get_all_ui_parameters(valid_df)
+    
+    # Filter data
+    filtered_df = filter_data(valid_df, params['selected_sources'], params['selected_authors'])
+    
+    if filtered_df.empty:
+        st.warning("No data matches the current filters!")
+        st.stop()
+    
+    # Display performance warnings
+    display_performance_warnings(filtered_df, params['method'], params['clustering_method'])
+    
+    # Get corresponding embeddings
+    filtered_embeddings = get_filtered_embeddings(embeddings, valid_df, filtered_df)
+    
+    st.info(f"📈 Visualizing {len(filtered_df)} messages")
+    
+    # Reduce dimensions
+    with st.spinner(f"Reducing dimensions using {params['method']}..."):
+        reduced_embeddings = reduce_dimensions(
+            filtered_embeddings, 
+            method=params['method'],
+            spread_factor=params['spread_factor'],
+            perplexity_factor=params['perplexity_factor'],
+            min_dist_factor=params['min_dist_factor']
+        )
+    
+    # Apply clustering
+    with st.spinner(f"Applying {params['clustering_method']}..."):
+        cluster_labels, silhouette_avg, calinski_harabasz = apply_clustering(
+            filtered_embeddings,
+            clustering_method=params['clustering_method'],
+            n_clusters=params['n_clusters']
+        )
+    
+    # Apply jittering if requested
+    if params['apply_jittering']:
+        with st.spinner("Applying smart jittering to separate overlapping points..."):
+            reduced_embeddings = apply_density_based_jittering(
+                reduced_embeddings, 
+                density_scaling=params['density_based_jitter'], 
+                jitter_strength=params['jitter_strength']
+            )
+    
+    # Display clustering metrics
+    display_clustering_metrics(
+        cluster_labels, silhouette_avg, calinski_harabasz, 
+        params['show_cluster_metrics']
+    )
+    
+    # Create and display the main plot
+    fig = create_visualization_plot(
+        reduced_embeddings=reduced_embeddings,
+        filtered_df=filtered_df,
+        cluster_labels=cluster_labels,
+        selected_sources=params['selected_sources'] if params['selected_sources'] else None,
+        method=params['method'],
+        clustering_method=params['clustering_method'],
+        point_size=params['point_size'],
+        point_opacity=params['point_opacity'],
+        density_based_sizing=params['density_based_sizing'],
+        size_variation=params['size_variation']
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Display summary statistics
+    display_summary_stats(filtered_df, params['selected_sources'] or filtered_df['source_file'].unique())
+    
+    # Display clustering results and export options
+    display_clustering_results(
+        filtered_df, cluster_labels, reduced_embeddings, 
+        params['method'], params['clustering_method']
+    )
+    
+    # Display data table
+    display_data_table(filtered_df, cluster_labels)
+
+
+if __name__ == "__main__":
+    main()