update

visualization/bow_visualizer.py

@@ -68,30 +68,6 @@ def create_bow_visualization(analysis_results):
                         
                         output_components.append(gr.Plot(value=fig))
                 
-                # Show comparison metrics
-                comparisons = bow_results.get("comparisons", {})
-                if comparisons:
-                    for comparison_key, metrics in comparisons.items():
-                        output_components.append(gr.Markdown(f"### Similarity Metrics for {comparison_key}"))
-                        
-                        # Format metrics for better display
-                        if "jaccard_similarity" in metrics:
-                            output_components.append(gr.Markdown(
-                                f"- **Jaccard Similarity**: {metrics['jaccard_similarity']:.2f} "
-                                f"(measures word overlap between responses)"
-                            ))
-                        
-                        if "cosine_similarity" in metrics:
-                            output_components.append(gr.Markdown(
-                                f"- **Cosine Similarity**: {metrics['cosine_similarity']:.2f} "
-                                f"(measures how similar the word frequency distributions are)"
-                            ))
-                        
-                        if "common_word_count" in metrics:
-                            output_components.append(gr.Markdown(
-                                f"- **Common Words**: {metrics['common_word_count']} words appear in both responses"
-                            ))
-                
                 # Visualize differential words (words with biggest frequency difference)
                 diff_words = bow_results.get("differential_words", [])
                 word_matrix = bow_results.get("word_count_matrix", {})
@@ -190,19 +166,6 @@ def process_and_visualize_analysis(analysis_results):
                         word_list = [f"{item['word']} ({item['count']})" for item in words[:10]]
                         components.append(gr.Markdown(f"**{model}**: {', '.join(word_list)}"))
                 
-                # Display comparison metrics
-                if "comparisons" in bow_results:
-                    components.append(gr.Markdown("#### Similarity Metrics"))
-                    
-                    for comparison, metrics in bow_results["comparisons"].items():
-                        cosine = metrics.get("cosine_similarity", 0)
-                        jaccard = metrics.get("jaccard_similarity", 0)
-                        components.append(gr.Markdown(
-                            f"**{comparison}**:\n"
-                            f"- Cosine similarity: {cosine:.2f}\n"
-                            f"- Jaccard similarity: {jaccard:.2f}"
-                        ))
-                
                 # Add visualizations for word frequency differences
                 if "differential_words" in bow_results and "word_count_matrix" in bow_results and len(bow_results["models"]) >= 2:
                     diff_words = bow_results["differential_words"]