Update app.py

app.py

@@ -15,18 +15,18 @@ import rdkit
 import streamlit as st
 
 st.title('predictproduct-t5')
-st.markdown('### At this space, you can predict the products of reactions from their inputs.')
-st.markdown('### The format of the string is like "REACTANT:{reactants of the reaction}CATALYST:{catalysts of the reaction}REAGENT:{reagents of the reaction}SOLVENT:{solvent of the reaction}".')
-st.markdown('### If there are no catalyst or reagent, fill the blank with a space. And if there are multiple reactants, concatenate them with "."')
+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}CATALYST:{catalysts of the reaction}REAGENT:{reagents of the reaction}SOLVENT:{solvent of the reaction}".')
+st.markdown('#### If there are no catalyst or reagent, fill the blank with a space. And if there are multiple reactants, concatenate them with "."')
 display_text = 'input the reaction smiles (e.g. REACTANT:CNc1nc(SC)ncc1CO.O.O=[Cr](=O)([O-])O[Cr](=O)(=O)[O-].[Na+]CATALYST: REAGENT: SOLVENT:CC(=O)O)'
 
 class CFG():
+    num_return_sequences = num_beams
     uploaded_file = st.file_uploader("Choose a CSV file")
     input_data = st.text_area(display_text)
     model_name_or_path = 'sagawa/ZINC-t5-productpredicition'
     model = 't5'
     num_beams = st.number_input(label='num beams', min_value=1, max_value=10, value=5, step=1)
-    num_return_sequences = st.number_input(label='num return sequences', min_value=1, max_value=num_beams, value=num_beams, step=1)
     seed = 42