added book and author information to Taraf

app.py

@@ -40,20 +40,27 @@ matn_info['taraf_ID'] = matn_info['taraf_ID'].replace('KeyAbsent', -1)
 
 
 matn_info['taraf_ID'] = matn_info['taraf_ID'].astype(int)
-matn_info = matn_info.sort_values('taraf_ID')
-tarafs = matn_info['taraf_ID'].unique()
-for i, taraf in enumerate(tarafs):
-    matn_info.loc[matn_info['taraf_ID'] == taraf, 'taraf_ID_New'] = i + 1 # Replace 'a' with 'e' in column 'C' where the condition is met
-matn_info['taraf_ID_New'] = matn_info['taraf_ID_New'].astype(int)
+# matn_info = matn_info.sort_values('taraf_ID')
+# tarafs = matn_info['taraf_ID'].unique()
+# for i, taraf in enumerate(tarafs):
+#     matn_info.loc[matn_info['taraf_ID'] == taraf, 'taraf_ID_New'] = i + 1 # Replace 'a' with 'e' in column 'C' where the condition is met
+# matn_info['taraf_ID_New'] = matn_info['taraf_ID_New'].astype(int)
 
 
 
 isnad_info = load_dataset('FDSRashid/hadith_info',token = Secret_token, data_files = 'isnad_info.csv', split = 'train').to_pandas()
 isnad_info['Hadiths Cleaned'] = isnad_info['Hadiths'].apply(lambda x: [re.findall(pattern, string)[0].split("_") for string in x[1:-1].split(',')])
 
-taraf_max = np.max(matn_info['taraf_ID_New'].unique())
+taraf_max = np.max(matn_info['taraf_ID'].unique())
 
 cmap = plt.colormaps['cool']
+
+books = load_dataset('FDSRashid/Hadith_info', data_files='Books.csv', token = Secret_token)['train'].to_pandas()
+
+matn_info['Book'] = matn_info['bookid_hadithid'].apply(lambda x: books[books['Book_ID'] == int(x.split('_')[0])]['Book_Name'].to_list()[0])
+matn_info['Author'] = matn_info['bookid_hadithid'].apply(lambda x: books[books['Book_ID'] == int(x.split('_')[0])]['Author'].to_list()[0])
+matn_info['Hadith Number'] = matn_info['bookid_hadithid'].apply(lambda x: x.split('_')[1])
+
 def value_to_hex(value):
     rgba_color = cmap(value)
     return "#{:02X}{:02X}{:02X}".format(int(rgba_color[0] * 255), int(rgba_color[1] * 255), int(rgba_color[2] * 255))
@@ -61,9 +68,12 @@ def value_to_hex(value):
 #edge_info, matn_info, narrator_bios, isnad_info
 
 def visualize_isnad(taraf_num, yaxis):
-    taraf_hadith = matn_info[matn_info['taraf_ID_New'] == taraf_num]['bookid_hadithid'].to_list()
-    taraf_matns = matn_info[matn_info['taraf_ID_New'] == taraf_num]['matn'].to_list()
+    taraf_hadith = matn_info[matn_info['taraf_ID'] == taraf_num]['bookid_hadithid'].to_list()
+    taraf_matns = matn_info[matn_info['taraf_ID'] == taraf_num]['matn'].to_list()
     taraf_hadith_split = [i.split('_') for i in taraf_hadith]
+    taraf_book = matn_info[matn_info['taraf_ID'] == taraf_num]['Book'].to_list()
+    taraf_author = matn_info[matn_info['taraf_ID'] == taraf_num]['Author'].to_list()
+    taraf_hadith_number = taraf_book = matn_info[matn_info['taraf_ID'] == taraf_num]['Hadith Number'].to_list()
     lst_hadith = []
     for i in range(len(taraf_hadith_split)):
         isnad_in_hadith1 = isnad_info['Hadiths Cleaned'].apply(lambda x: taraf_hadith_split[i] in x )
@@ -72,7 +82,7 @@ def visualize_isnad(taraf_num, yaxis):
         node = [int(n) for n, d in G.out_degree() if d == 0][0]
         gen_node = narrator_bios[narrator_bios['Rawi ID']==node]['Generation'].iloc[0]
         name_node = narrator_bios[narrator_bios['Rawi ID']==node]['Famous Name'].iloc[0]
-        lst_hadith.append([taraf_matns[i], gen_node, name_node])
+        lst_hadith.append([taraf_matns[i], gen_node, name_node, taraf_book[i], taraf_author[i], taraf_hadith_number[i]])
     df = pd.DataFrame(lst_hadith, columns = ['Matn', 'Generation', 'Name'])
     
     hadith_cleaned = isnad_info['Hadiths Cleaned'].apply(lambda x: any(i in x for i in taraf_hadith_split) )