Update app.py

app.py

@@ -90,28 +90,19 @@ with st.sidebar:
 
 
     with st.form("model_selection_from"):
-
-        # model_name = st.radio(
-        # "Select a model to make inference (Prot and CrossLoss models are pretrained on AKT1 inhibitors)",
-        # ('Prot', 'CrossLoss', 'NoTarget'))
-        model_names = (
-            "**DrugGEN-Prot**: composed of two GANs, incorporates protein features to the transformer decoder module of GAN2 (together with the de novo molecules generated by GAN1) to direct the target centric molecule design.",
-            "**DrugGEN-CrossLoss**: composed of one GAN, the input of the GAN1 generator is the real molecules dataset and the GAN1 discriminator compares the generated molecules with the real inhibitors of the given target.",
-            "**DrugGEN-NoTarget**: composed of one GAN, focuses on learning the chemical properties from the ChEMBL training dataset, no target-specific generation."
-        )
-
-        model_names_index_to_name = {
-            0: "Prot",
-            1: "CrossLoss",
-            2: "NoTarget",
-        }
-        
         model_name = st.radio(
-            "Select a model to make inference (DrugGEN-Prot and -CrossLoss models design molecules to target the AKT1 protein)",
-            model_names
+            '''
+Select a model to make inference (DrugGEN-Prot and DrugGEN-CrossLoss models design molecules to target the AKT1 protein)
+
+- **DrugGEN-Prot**: composed of two GANs, incorporates protein features to the transformer decoder module of GAN2 (together with the de novo molecules generated by GAN1) to direct the target centric molecule design.
+- **DrugGEN-CrossLoss**: composed of one GAN, the input of the GAN1 generator is the real molecules dataset and the GAN1 discriminator compares the generated molecules with the real inhibitors of the given target.
+- **DrugGEN-NoTarget**: composed of one GAN, focuses on learning the chemical properties from the ChEMBL training dataset, no target-specific generation.
+            '''
+            ",
+            ('DrugGEN-Prot', 'DrugGEN-CrossLoss', 'DrugGEN-NoTarget')
         )
 
-        model_name = model_names_index_to_name[model_names.index(model_name)]
+        model_name = model_name.replace("DrugGEN-", "")
 
         molecule_num_input = st.number_input('Number of molecules to generate', min_value=1, max_value=100_000, value=1000, step=1)