Update app.py

app.py

@@ -78,7 +78,7 @@ def get_most_frequent_phrase(results):
     phrase_counts = defaultdict(int)
     for words, book, chapter, verse, phrase_length, word_position in results:
         phrase_counts[words] += 1
-    most_frequent_phrase = max(phrase_counts, key=phrase_counts.get)
+    most_frequent_phrase = max(phrase_counts, key=phrase_counts.get) if phrase_counts else None  # Handle empty results
     return most_frequent_phrase
 
 # --- Functions from BOS app.py ---
@@ -198,23 +198,29 @@ with gr.Blocks() as app:
         els_results = perform_els_search(step, rounds_combination, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk, merge_results, include_torah, include_bible, include_quran)
 
         # --- Network Search Integration ---
-        network_search_results = []
+        df_data = []
         for result in els_results:
             gematria_sum = calculate_gematria(result['match'])
             max_words = len(result['match'].split())
             matching_phrases = search_gematria_in_db(gematria_sum, max_words)
-            network_search_results.extend(matching_phrases)
-
-        most_frequent_phrase = get_most_frequent_phrase(network_search_results)
-
-        # --- Prepare Dataframe ---
-        df = pd.DataFrame(els_results)
+            most_frequent_phrase = get_most_frequent_phrase(matching_phrases)
+
+            # Add data to the list for DataFrame creation
+            df_data.append({
+                'book': result['book'],
+                'chapter': result['chapter'],
+                'verse': result['verse'],
+                'match': result['match'],
+                'Most Frequent Phrase': most_frequent_phrase
+            })
+
+        # Create DataFrame
+        df = pd.DataFrame(df_data)
         df.index = range(1, len(df) + 1)
         df.reset_index(inplace=True)
         df.rename(columns={'index': 'Result Number'}, inplace=True)
-        df['Most Frequent Phrase'] = most_frequent_phrase # Add new column
 
-        return df, most_frequent_phrase
+        return df, df['Most Frequent Phrase'].iloc[0] if not df.empty else None
 
     # --- Event Triggers ---
     round_x.change(update_rounds_combination, inputs=[round_x, round_y], outputs=rounds_combination)