Update app.py

app.py

@@ -34,9 +34,15 @@ task_names = {
 task_names_to_tasks = {v: k for k, v in task_names.items()}
 tasks = list(candidate_models.keys())
 task_descriptions = {
-    'mit_synthesis': 'Predict the reaction products given the reactants and reagents (reactants and reagents are mixed; different compounds are separated by ".").' + \
-                     'C1CCOC1.N#Cc1ccsc1N.O=[N+]([O-])c1cc(F)c(F)cc1F.[H-].[Na+]',
-    'full_retro': 'Predict the reaction precursors given the reaction products (different compounds are separated by ".").'
+    'mit_synthesis': 'Predict the reaction products given the reactants and reagents. \n' + \
+                     '1. This model is trained on the USPTO MIT dataset. \n' + \
+                     '2. The reactants and reagents are mixed in the input SMILES string. \n' + \
+                     '3. Different compounds are separated by ".". \n' + \
+                     '4. Input SMILES string example: C1CCOC1.N#Cc1ccsc1N.O=[N+]([O-])c1cc(F)c(F)cc1F.[H-].[Na+]',
+    'full_retro': 'Predict the reaction precursors given the reaction products. \n' + \
+                    '1. This model is trained on the USPTO Full dataset. \n' + \
+                    '2. In this dataset, we consider only a single product in the input SMILES string. \n' + \
+                    '3. Input SMILES string example: CC(=O)OCC(=O)[C@@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)C3=CC[C@@]21C'
 }
 
 #property_names = list(candidate_models.keys())