Update ASCARIS.py

ASCARIS.py

@@ -31,14 +31,18 @@ st.write('')
 st.write('')
 st.write('')
 st.write('')
-mode = 1
+
 with st.form('mform', clear_on_submit=False):
     #source = st.selectbox('Select the protein structure resource (1: PDB-SwissModel-Modbase, 2: AlphaFold)',[1,2])
-    
+    source = 1
     impute = st.selectbox('Imputation',[True, False])
     
     input_data = st.text_input('Enter SAV data points (Example: Q9Y4W6-N-432-T)')
+            
+
+
     parser = argparse.ArgumentParser(description='ASCARIS')
+    
     #parser.add_argument('-s', '--source_option',
     #                    help='Selection of input structure data.\n 1: PDB Structures (default), 2: AlphaFold Structures',
     #                    default=1)
@@ -49,9 +53,10 @@ with st.form('mform', clear_on_submit=False):
     #                    help='Whether resulting feature vector should be imputed or not. Default True.')
     
     #args = parser.parse_args()
+    
     input_set = input_data
+    mode = 1
     impute = impute
-    
     submitted = st.form_submit_button(label="Submit", help=None, on_click=None, args=None, kwargs=None, type="secondary", disabled=False, use_container_width=False)
     print('*****************************************')
     print('Feature vector generation is in progress. \nPlease check log file for updates..')
@@ -62,7 +67,6 @@ with st.form('mform', clear_on_submit=False):
 selected_df = pd.DataFrame()
 st.write('The online tool may be slow, especially while processing multiple SAVs, please consider using the local programmatic version at https://github.com/HUBioDataLab/ASCARIS/')
 if submitted:
-    st.write('submitted.')
     with st.spinner('In progress...This may take a while...'):
         try:
             if mode == 1:
@@ -76,10 +80,10 @@ if submitted:
         except:
             selected_df = pd.DataFrame()
             pass
-    st.write('selected_df')
-    st.write(selected_df)
+
     if selected_df is None:
         st.success('Feature vector failed. Check log file.')
+        st.write('Failed here1')
 
     else:
         if len(selected_df) != 0 :