Update app.py

app.py

@@ -13,11 +13,13 @@ warnings.filterwarnings("ignore")
 
  
 st.title('ReactionT5_task_forward')
-st.markdown('##### At this space, you can predict the products of reactions from their inputs.')
-st.markdown('##### The code expects input_data as a string or CSV file that contains an "input" column. The format of the string or contents of the column are like "REACTANT:{reactants of the reaction}REAGENT:{reagents, catalysts, or solvents of the reaction}".')
-st.markdown('##### If there is no reagent, fill the blank with a space. And if there are multiple compounds, concatenate them with "."')
-st.markdown('##### The output contains smiles of predicted products and sum of log-likelihood for each prediction. Predictions are ordered by their log-likelihood.(0th is the most probable product.)')
-
+st.markdown('''
+##### At this space, you can predict the products of reactions from their inputs.
+##### The code expects input_data as a string or CSV file that contains an "input" column. 
+##### The format of the string or contents of the column should be "REACTANT:{reactants}REAGENT:{reagents}".
+##### If there is no reagent, fill the blank with a space. For multiple compounds, concatenate them with ".".
+##### The output contains SMILES of predicted products and the sum of log-likelihood for each prediction, ordered by their log-likelihood (0th is the most probable product).
+''')
 
 display_text = 'input the reaction smiles (e.g. REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1)'
 
@@ -177,18 +179,15 @@ if st.button('predict'):
         
             output_df = save_multiple_predictions(input_data, all_sequences, all_scores)
         
-            st.table(output_df)
-    
-            @st.cache
-            def convert_df(df):
-                # IMPORTANT: Cache the conversion to prevent computation on every rerun
-                return df.to_csv(index=False)
-    
-            csv = convert_df(output_df)
-            
-            st.download_button(
-                label="Download data as CSV",
-                data=csv,
-                file_name='output.csv',
-                mime='text/csv',
-            )
+        @st.cache
+        def convert_df(df):
+            return df.to_csv(index=False)
+        
+        csv = convert_df(output_df)
+        
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name='output.csv',
+            mime='text/csv',
+        )