Update app.py

app.py

@@ -51,7 +51,7 @@ from deepscreen.predict import predict
 sys.path.append(os.path.join(RDConfig.RDContribDir, 'SA_Score'))
 import sascorer
 
-DATASET_MAX_LEN = 10_000
+DATASET_MAX_LEN = 10_240
 SERVER_DATA_DIR = os.getenv('DATA')  # '/data'
 DB_EXPIRY = timedelta(hours=48).total_seconds()
 
@@ -212,7 +212,9 @@ TARGET_LIBRARY_MAP = {
 
 DRUG_LIBRARY_MAP = {
     'DrugBank (Human)': 'drugbank_compounds.csv',
-    'Drug Repurposing Hub': 'drug_repurposing_hub.csv'
+    'Drug Repurposing Hub': 'drug_repurposing_hub.csv',
+    'Enamine Discovery Diversity Set (DDS-10)': 'Enamine_Discovery_Diversity_Set_10_10240cmpds_20240130.csv',
+    'Enamine Phenotypic Screening Library (PSL-5760)': 'Enamine_Phenotypic_Screening_Library_plated_5760cmds_2020_07_20.csv'
 }
 
 COLUMN_ALIASES = {
@@ -730,11 +732,12 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, state):
 
     if 'Target Family' not in orig_df.columns:
         orig_df['Target Family'] = None
-    orig_df.loc[
-        orig_df['Target Family'].isna(), 'Target Family'
-    ] = orig_df.loc[
-        orig_df['Target Family'].isna(), 'X2'
-    ].parallel_apply(detect_family)
+    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'
+        ].parallel_apply(detect_family)
 
     detect_family.cache_clear()
 
@@ -835,7 +838,7 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, state):
                     max_tanimoto_similarity,
                     seen_smiles=tuple(get_seen_smiles(family=family, task=task_value))
                 )
-
+            max_tanimoto_similarity.cache_clear()
         if "Include Max. Sequence Identity" in opts:
             for family in prediction_df['Target Family'].unique():
                 prediction_df.loc[
@@ -844,7 +847,7 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, state):
                     max_sequence_identity,
                     seen_fastas=tuple(get_seen_fastas(family=family, task=task_value))
                 )
-
+            max_sequence_identity.cache_clear()
         prediction_df.drop(['N'], axis=1).to_csv(predictions_file, index=False, na_rep='')
         status = "COMPLETED"
 
@@ -2335,10 +2338,10 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
     )
 
     report_clr_btn.click(
-        lambda: [[]] * 3 + [None] * 5 +
+        lambda: [[]] * 3 + [None] * 3 +
                 [gr.Button(interactive=False)] * 3 +
                 [gr.File(visible=False, value=None)] * 2 +
-                [gr.Dropdown(visible=False, value=None), ''],
+                [gr.Dropdown(visible=False, value=None), gr.HTML(value='')],
         outputs=[
             scores, filters, html_opts,
             file_for_report, raw_df, report_df,