Update app.py

app.py

@@ -73,7 +73,7 @@ file_path = hf_hub_download(
 
 with open(file_path, 'r') as f:
     hadith_lookup_dict = json.load(f)
-hadith_lookup = defaultdict(list, hadith_lookup_dict)
+HADITH_LOOKUP = defaultdict(list, hadith_lookup_dict)
 
     
 def value_to_hex(value):
@@ -90,6 +90,27 @@ def get_node_info(node):
     node_name = info.get('Famous Name', 'فلان')
     return info, student_narrations, student_gen, student_rank, node_name
 
+def lookup_hadith(taraf_hadith, hadith_lookup):
+    """
+    Returns a list of unique elements from the hadith_lookup for the given taraf_hadith.
+
+    Parameters:
+        taraf_hadith (str or list of str): A string or list of strings to look up.
+        hadith_lookup (defaultdict): A defaultdict containing the hadith data.
+
+    Returns:
+        list: A list of unique elements from the lookup results.
+    """
+    # Ensure taraf_hadith is always a list
+    if isinstance(taraf_hadith, str):
+        taraf_hadith = [taraf_hadith]
+    
+    # Create a set to accumulate unique elements
+    unique_elements = {elem for key in taraf_hadith for elem in hadith_lookup[key]}
+
+    # Convert the set to a list for consistency
+    return list(unique_elements)
+
 
 def visualize_isnad(taraf_num, yaxis):
     # Precompute filtered dataframes
@@ -105,7 +126,7 @@ def visualize_isnad(taraf_num, yaxis):
     
     for i, hadith_parts in enumerate(taraf_hadith):
         # look up hadith for each bookid_hadithid
-        isnad_hadith1 = isnad_info.iloc[hadith_lookup[taraf_hadith[i]]][['Source', 'Destination']]
+        isnad_hadith1 = isnad_info.iloc[lookup_hadith(taraf_hadith[i], HADITH_LOOKUP)][['Source', 'Destination']]
         
         # Create graph and find end nodes
         G = nx.from_pandas_edgelist(isnad_hadith1, source='Source', target='Destination', create_using=nx.DiGraph())
@@ -220,11 +241,7 @@ def visualize_subTaraf(taraf_num, hadith_str, yaxis):
     taraf['Index'] = np.arange(num_hadith)
     sub_taraf = taraf[taraf['Index'].isin(hadiths)]
 
-    taraf_hadith = sub_taraf['bookid_hadithid'].to_list()
-    list_of_lists = [hadith_lookup[i] for i in taraf_hadith]
-    flattened = list(set([elem for sublist in list_of_lists for elem in sublist]))
-
-    isnad_hadith = isnad_info.iloc[flattened]
+    isnad_hadith = isnad_info.iloc[lookup_hadith(taraf_hadith, HADITH_LOOKUP)]
     isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(int)
     
     # Merge isnad_hadith with narrator_bios for Teacher and Student
@@ -269,10 +286,7 @@ def visualize_hadith_isnad(df, yaxis):
     df['bookid_hadithid'] = df['Book_ID'].astype(str) + '_' + df['Hadith Number'].astype(str)
     hadith = matn_info[matn_info['bookid_hadithid'].isin(df['bookid_hadithid'])]
     taraf_hadith = df['bookid_hadithid'].to_list()
-    list_of_lists = [hadith_lookup[i] for i in taraf_hadith]
-    flattened = list(set([elem for sublist in list_of_lists for elem in sublist]))
-
-    isnad_hadith = isnad_info.iloc[flattened]
+    isnad_hadith = isnad_info.iloc[lookup_hadith(taraf_hadith, HADITH_LOOKUP)]
     isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(int)
     
     # Merge isnad_hadith with narrator_bios for Teacher and Student
@@ -330,7 +344,7 @@ def visualize_narrator_taraf(taraf_num, narrator, yaxis):
 
     # Process each hadith in taraf_hadith_split
     for idx, split_hadith in enumerate(taraf_hadith):
-        isnad_hadith1 = isnad_info.iloc[hadith_lookup[taraf_hadith[i]]]
+        isnad_hadith1 = isnad_info.iloc[lookup_hadith(taraf_hadith[i], HADITH_LOOKUP)]
         G1 = nx.from_pandas_edgelist(isnad_hadith1, source='Source', target='Destination', create_using=nx.DiGraph())
         if narrator in G1.nodes:
             matns_with_narrator.append(taraf_hadith[idx])