Update app.py

app.py

@@ -16,7 +16,7 @@ Secret_token = os.getenv('HF_token')
 dataset = load_dataset("FDSRashid/embed_matn", token = Secret_token)
 books = load_dataset('FDSRashid/Hadith_info', data_files='Books.csv', token=Secret_token)['train'].to_pandas()
 df = dataset["train"].to_pandas()
-taraf_max = np.max(df['taraf_ID'].unique())
+
 
 dataset = load_dataset("FDSRashid/hadith_info", data_files = 'All_Matns.csv',token = Secret_token, features = features)
 matn_info = dataset['train'].to_pandas()
@@ -27,10 +27,11 @@ matn_info['taraf_ID'] = matn_info['taraf_ID'].replace('KeyAbsent', -1)
 matn_info['Book ID'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[0]))
 matn_info['Hadith Number'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[1]))
 matn_info = matn_info.join(books, on='Book ID')
+cols_to_use = df.columns.difference(matn_info.columns)
 
-joined_df = matn_info.merge(df, left_index=True, right_on='__index_level_0__')
+joined_df = matn_info.merge(df[cols_to_use, left_index=True, right_on='__index_level_0__')
 df = joined_df.copy()
-
+taraf_max = np.max(df['taraf_ID'].unique())
 
 def plot_similarity_score(taraf_num):
     taraf_df = df[df['taraf_ID']== taraf_num]