Update app.py

app.py

@@ -380,10 +380,10 @@ PRESET_MAP = {
 TARGET_FAMILY_MAP = {
     'General': 'general',
     'Kinase': 'kinase',
-    'Non kinase enzyme': 'non_kinase_enzyme',
-    'Membrane receptor': 'membrane_receptor',
-    'Nuclear receptor': 'nuclear_receptor',
-    'Ion channel': 'ion_channel',
+    'Non-Kinase Enzyme': 'non_kinase_enzyme',
+    'Membrane Receptor': 'membrane_receptor',
+    'Nuclear Receptor': 'nuclear_receptor',
+    'Ion Channel': 'ion_channel',
     'Others': 'others',
 }
 
@@ -1596,7 +1596,11 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         task = TASK_MAP[task]
         score = TASK_METRIC_MAP[task]
         benchmark_df = pd.read_csv(f'data/benchmarks/{task}_test_metrics.csv')
-
+        
+        if not fasta:
+            gr.Warning('Please enter a valid FASTA for model recommendation.')
+            return None
+            
         if family == 'General':
             seen_targets = pd.read_csv(
                 f'data/benchmarks/seen_targets/all_families_full_{task.lower()}_random_split.csv')
@@ -1611,14 +1615,14 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         else:
             seen_targets_general = pd.read_csv(
                 f'data/benchmarks/seen_targets/all_families_full_{task.lower()}_random_split.csv')
-            if fasta in seen_targets_general['X2'].values:
+            if process_target_fasta(fasta) in seen_targets_general['X2'].values:
                 scenario_general = "Seen Target"
             else:
                 scenario_general = "Unseen Target"
 
             seen_targets_family = pd.read_csv(
-                f'data/benchmarks/seen_targets/{family.lower()}_{task.lower()}_random_split.csv')
-            if fasta in seen_targets_family['X2'].values:
+                f'data/benchmarks/seen_targets/{TARGET_FAMILY_MAP[family]}_{task.lower()}_random_split.csv')
+            if process_target_fasta(fasta) in seen_targets_family['X2'].values:
                 scenario_family = "Seen Target"
             else:
                 scenario_family = "Unseen Target"
@@ -1685,7 +1689,11 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         task = TASK_MAP[task]
         score = TASK_METRIC_MAP[task]
         benchmark_df = pd.read_csv(f'data/benchmarks/{task}_test_metrics.csv')
-
+        
+        if not smiles:
+            gr.Warning('Please enter a valid SMILES for model recommendation.')
+            return None
+        
         seen_drugs = pd.read_csv(
             f'data/benchmarks/seen_drugs/all_families_full_{task.lower()}_random_split.csv')
         if rdkit_canonicalize(smiles) in seen_drugs['X1'].values: