Update app.py

app.py

@@ -347,8 +347,8 @@ FILTER_MAP = {
 }
 
 TASK_MAP = {
-    'Compound-protein interaction': 'CPI',
-    'Compound-protein binding affinity': 'CPA',
+    'Compound-protein interaction': 'DTI',
+    'Compound-protein binding affinity': 'DTA',
 }
 
 PRESET_MAP = {
@@ -1194,10 +1194,10 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
 
     def screen_recommend_model(fasta, family, task):
         task = TASK_MAP[task]
-        if task == 'CPI':
+        if task == 'DTI':
             train = pd.read_csv('data/benchmarks/all_families_reduced_dti_train.csv')
             score = 'AUROC'
-        elif task == 'CPA':
+        elif task == 'DTA':
             train = pd.read_csv('data/benchmarks/all_families_reduced_dta_train.csv')
             score = 'CI'
 
@@ -1264,10 +1264,10 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
 
     def identify_recommend_model(smiles, task):
         task = TASK_MAP[task]
-        if task == 'CPI':
+        if task == 'DTI':
             train = pd.read_csv('data/benchmarks/all_families_reduced_dti_train.csv')
             score = 'AUROC'
-        elif task == 'CPA':
+        elif task == 'DTA':
             train = pd.read_csv('data/benchmarks/all_families_reduced_dta_train.csv')
             score = 'CI'
         if smiles not in train['X1']: