Update app.py

app.py

@@ -45,7 +45,7 @@ import panel as pn
 from apscheduler.schedulers.background import BackgroundScheduler
 from tinydb import TinyDB, Query
 
-import swifter
+#import swifter
 from tqdm.auto import tqdm
 
 from deepscreen.data.dti import validate_seq_str, rdkit_canonicalize, FASTA_PAT, SMILES_PAT
@@ -837,7 +837,7 @@ def apply_advanced_opts(prediction_df, opts, df_training):
     if "Calculate Max. Tanimoto Similarity between the Hit Compound and Known Ligands of the Input Target" in opts:
         x2 = prediction_df['X2'].iloc[0]
         pos_compounds_df = df_training.loc[(df_training['X2'] == x2) & (df_training['Y'] == 1)].copy()
-        pos_compounds_df['FP'] = pos_compounds_df['X1'].swifter.apply(smiles_to_ecfp)
+        pos_compounds_df['FP'] = pos_compounds_df['X1'].parallel_apply(smiles_to_ecfp)
 
         @cache
         def max_sim(smiles):
@@ -846,13 +846,13 @@ def apply_advanced_opts(prediction_df, opts, df_training):
         prediction_df[[
             'Max. Tanimoto Similarity to Known Ligands',
             'Max. Sim. Ligand'
-        ]] = prediction_df['X1'].swifter.apply(max_sim).apply(pd.Series)
+        ]] = prediction_df['X1'].parallel_apply(max_sim).apply(pd.Series)
 
         max_sim.cache_clear()
 
     if "Calculate Max. Sequence Identity between the Input Target and Known Targets of Hit Compound" in opts:
         x2 = prediction_df['X2'].iloc[0]
-        prediction_df['X1^'] = prediction_df['X1'].swifter.apply(rdkit_canonicalize)
+        prediction_df['X1^'] = prediction_df['X1'].parallel_apply(rdkit_canonicalize)
 
         @cache
         def max_id(compound):
@@ -861,7 +861,7 @@ def apply_advanced_opts(prediction_df, opts, df_training):
 
         prediction_df[['Max. Sequence Identity to Known Targets of Hit Compound',
                        'Max. Id. Target']] = (
-            prediction_df['X1^'].swifter.apply(max_id).apply(pd.Series)
+            prediction_df['X1^'].parallel_apply(max_id).apply(pd.Series)
         )
         prediction_df.drop(['X1^'], axis=1, inplace=True)
 
@@ -870,7 +870,7 @@ def apply_advanced_opts(prediction_df, opts, df_training):
     # Advanced options for Target Protein Identification
     if "Calculate Max. Tanimoto Similarity between the Input Compound and Compounds in the Training Set" in opts:
         x1 = rdkit_canonicalize(prediction_df['X1'].iloc[0])
-        df_training['FP'] = df_training['X1'].swifter.apply(smiles_to_ecfp)
+        df_training['FP'] = df_training['X1'].parallel_apply(smiles_to_ecfp)
 
         prediction_df[[
             'Max. Tanimoto Similarity to Training Compounds',
@@ -888,7 +888,7 @@ def apply_advanced_opts(prediction_df, opts, df_training):
         prediction_df[[
             'Max. Sequence Identity to Known Targets of Input Compound',
             'Max. Id. Target'
-        ]] = prediction_df['X2'].swifter.apply(max_id).apply(pd.Series)
+        ]] = prediction_df['X2'].parallel_apply(max_id).apply(pd.Series)
 
         max_id.cache_clear()
 
@@ -904,7 +904,7 @@ def apply_advanced_opts(prediction_df, opts, df_training):
         prediction_df[[
             'Max. Tanimoto Similarity to Known Ligands of Identified Target',
             'Max. Sim. Ligand'
-        ]] = prediction_df['X2'].swifter.apply(max_sim).apply(pd.Series)
+        ]] = prediction_df['X2'].parallel_apply(max_sim).apply(pd.Series)
 
         max_sim.cache_clear()
 
@@ -949,12 +949,12 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, job_info
         orig_df['Target Family'] = None
     if orig_df['Target Family'].isna().any():
         orig_df.loc[orig_df['Target Family'].isna(), 'Target Family'] = (
-            orig_df.loc[orig_df['Target Family'].isna(), 'X2'].swifter.apply(detect_family)
+            orig_df.loc[orig_df['Target Family'].isna(), 'X2'].parallel_apply(detect_family)
         )
     orig_df['Target Family'] = orig_df['Target Family'].str.capitalize()
     detect_family.cache_clear()
 
-    orig_df['X1^'] = orig_df['X1'].swifter.apply(rdkit_canonicalize)
+    orig_df['X1^'] = orig_df['X1'].parallel_apply(rdkit_canonicalize)
 
     orig_df = orig_df.merge(df_training[['X1^', 'X2', 'Y']], on=['X1^', 'X2'], how='left', indicator=False)
     annotated_df = orig_df[~orig_df['Y'].isna()].copy()
@@ -1109,10 +1109,10 @@ def update_df(file, progress=gr.Progress(track_tqdm=True)):
 
         if 'X1' in df.columns:
             if 'Compound' not in df.columns or df['Compound'].dtype != 'object':
-                df['Compound'] = df['X1'].swifter.apply(
+                df['Compound'] = df['X1'].parallel_apply(
                     lambda smiles: PandasTools._MolPlusFingerprint(Chem.MolFromSmiles(smiles)))
-            df['Scaffold'] = df['Compound'].swifter.apply(MurckoScaffold.GetScaffoldForMol)
-            df['Scaffold SMILES'] = df['Scaffold'].swifter.apply(lambda x: Chem.MolToSmiles(x))
+            df['Scaffold'] = df['Compound'].parallel_apply(MurckoScaffold.GetScaffoldForMol)
+            df['Scaffold SMILES'] = df['Scaffold'].parallel_apply(lambda x: Chem.MolToSmiles(x))
             df['Pharmacophore'] = None
         if task == 'Compound-Protein Binding Affinity':
             # Convert Y^ from pIC50 (nM) to IC50 (nM)
@@ -1182,17 +1182,17 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
     columns_unique = None
 
     if 'Exclude Pharmacophore 3D' not in opts:
-        df_html['Pharmacophore'] = df_html['Compound'].swifter.apply(
+        df_html['Pharmacophore'] = df_html['Compound'].parallel_apply(
             lambda x: mol_to_pharm3d(x) if not pd.isna(x) else x)
 
     if 'Compound' in df_html.columns and 'Exclude Molecular Graph' not in opts:
-        df_html['Compound'] = df_html['Compound'].swifter.apply(
+        df_html['Compound'] = df_html['Compound'].parallel_apply(
             lambda x: PandasTools.PrintAsImageString(x) if not pd.isna(x) else x)
     else:
         df_html.drop(['Compound'], axis=1, inplace=True)
 
     if 'Scaffold' in df_html.columns and 'Exclude Scaffold Graph' not in opts:
-        df_html['Scaffold'] = df_html['Scaffold'].swifter.apply(
+        df_html['Scaffold'] = df_html['Scaffold'].parallel_apply(
             lambda x: PandasTools.PrintAsImageString(x) if not pd.isna(x) else x)
     else:
         df_html.drop(['Scaffold'], axis=1, inplace=True)
@@ -1227,7 +1227,7 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
     df_html.rename(columns=column_aliases, inplace=True)
     df_html.index.name = 'Index'
     if 'Target FASTA' in df_html.columns:
-        df_html['Target FASTA'] = df_html['Target FASTA'].swifter.apply(
+        df_html['Target FASTA'] = df_html['Target FASTA'].parallel_apply(
             lambda x: wrap_text(x) if not pd.isna(x) else x)
 
     num_cols = df_html.select_dtypes('number').columns
@@ -1247,7 +1247,7 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
         if 'Target ID' in df_html.columns:
             df_html.drop(['Target FASTA'], axis=1, inplace=True)
         if 'Target FASTA' in df_html.columns:
-            df_html['Target FASTA'] = df_html['Target FASTA'].swifter.apply(
+            df_html['Target FASTA'] = df_html['Target FASTA'].parallel_apply(
                 lambda x: wrap_text(x) if not pd.isna(x) else x)
         if 'Scaffold SMILES' in df_html.columns:
             df_html.drop(['Scaffold SMILES'], axis=1, inplace=True)
@@ -1555,11 +1555,11 @@ def submit_report(df, score_list, filter_list, opt_list, task, progress=gr.Progr
     df_report = df.copy()
     try:
         for filter_name in filter_list:
-            df_report[filter_name] = df_report['Compound'].swifter.apply(
+            df_report[filter_name] = df_report['Compound'].parallel_apply(
                 lambda x: FILTER_MAP[filter_name](x) if not pd.isna(x) else x)
 
         for score_name in score_list:
-            df_report[score_name] = df_report['Compound'].swifter.apply(
+            df_report[score_name] = df_report['Compound'].parallel_apply(
                 lambda x: SCORE_MAP[score_name](x) if not pd.isna(x) else x)
 
         if opt_list:
@@ -2263,7 +2263,7 @@ higher similarities usually correspond to greater prediction confidence.<br>
                 alignment = aligner.align(processed_fasta, query)
                 return alignment.score / max(len(processed_fasta), len(query))
 
-            alignment_df['score'] = alignment_df['X2'].swifter.apply(align_score)
+            alignment_df['score'] = alignment_df['X2'].parallel_apply(align_score)
             row = alignment_df.loc[alignment_df['score'].idxmax()]
             family = str(row['Target Family']).title()
             return gr.Dropdown(value=family,
@@ -2595,13 +2595,13 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
             infer_df = pd.read_csv(drug_target_pair_upload)
             validate_columns(infer_df, ['X1', 'X2'])
 
-            infer_df['X1_ERR'] = infer_df['X1'].swifter.apply(
+            infer_df['X1_ERR'] = infer_df['X1'].parallel_apply(
                 validate_seq_str, regex=SMILES_PAT)
             if not infer_df['X1_ERR'].isna().all():
                 raise ValueError(
                     f"Encountered invalid SMILES:\n{infer_df[~infer_df['X1_ERR'].isna()][['X1', 'X1_ERR']]}")
 
-            infer_df['X2_ERR'] = infer_df['X2'].swifter.apply(
+            infer_df['X2_ERR'] = infer_df['X2'].parallel_apply(
                 validate_seq_str, regex=FASTA_PAT)
             if not infer_df['X2_ERR'].isna().all():
                 raise ValueError(