Update app.py

app.py

@@ -314,7 +314,7 @@ if 'xq' not in st.session_state:
 
         st.session_state.db_name_ref = 'default.esm_protein'
         if option == function_list[0]:
-            sequence = st.text_input('protein sequence', '')
+            sequence = st.text_input('protein sequence(Capital letters only)', '')
             if st.button('Cas9 Enzyme'):
                 sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
             elif st.button('PETase'):
@@ -330,7 +330,7 @@ if 'xq' not in st.session_state:
                 (Rao et al. 2020) The MSA Transformer (ESM-MSA-1) takes a multiple sequence alignment (MSA) as input, and uses the tied row self-attention maps in the same way.""")
             st.session_state['xq'] = st.session_state.model
         elif option == function_list[1]:
-            sequence = st.text_input('protein sequence', '')
+            sequence = st.text_input('protein sequence(Capital letters only)', '')
             st.write('Try an example:')
             if st.button('Cas9 Enzyme'):
                 sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
@@ -391,7 +391,7 @@ else:
 
         st.session_state.db_name_ref = 'default.esm_protein'
         if option == 'self-contact prediction':
-            sequence = st.text_input('protein sequence', '')
+            sequence = st.text_input('protein sequence(Capital letters only)', '')
             if st.button('Cas9 Enzyme'):
                 sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'
             elif st.button('PETase'):
@@ -406,7 +406,7 @@ else:
                 """<span style="word-wrap:break-word;">Contact prediction is based on a logistic regression over the model's attention maps. This methodology is based on ICLR 2021 paper, Transformer protein language models are unsupervised structure learners. (Rao et al. 2020)The MSA Transformer (ESM-MSA-1) takes a multiple sequence alignment (MSA) as input, and uses the tied row self-attention maps in the same way.</span>
                 """, unsafe_allow_html=True)
         elif option == 'search the database for similar proteins':
-            sequence = st.text_input('protein sequence', '')
+            sequence = st.text_input('protein sequence(Capital letters only)', '')
             st.write('Try an example:')
             if st.button('Cas9 Enzyme'):
                 sequence = 'GSGHMDKKYSIGLAIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRILYLQEIFSNEMAKV'