fixed

app.py

@@ -92,43 +92,31 @@ if selected_model == 'Cas9':
 
     # Actions based on the selected enzyme
     if target_selection == 'on-target':
-        # app initialization for Cas9 on-target
-        if 'gene_symbol' not in st.session_state:
-            st.session_state.gene_symbol = None
-        if 'on_target_results' not in st.session_state:
-            st.session_state.on_target_results = None
-
         # Gene symbol entry
-        st.text_input(
-            label='Enter a Gene Symbol:',
-            key='gene_symbol_entry',
-            placeholder='e.g., BRCA1'
-        )
-
-        # prediction button
-        if st.button('Predict on-target'):
-            gene_symbol = st.session_state.gene_symbol_entry
-            if gene_symbol:  # Check if gene_symbol is not empty
-                predictions = cas9on.process_gene(gene_symbol, cas9on_path)
-                st.session_state.on_target_results = predictions[:10]  # Store only first 10 for display
-
-        # on-target results display
-        on_target_results = st.empty()
-        if st.session_state.on_target_results is not None:
-            with on_target_results.container():
-                if len(st.session_state.on_target_results) > 0:
-                    st.write('On-target predictions:', st.session_state.on_target_results)
-                    full_predictions = cas9on.process_gene(gene_symbol, cas9on_path)  # Get full predictions for download
-                    st.download_button(
-                        label='Download on-target predictions',
-                        data=cas9on.convert_df(full_predictions),
-                        file_name='on_target_results.csv',
-                        mime='text/csv'
-                    )
-                else:
-                    st.write('No significant on-target effects detected!')
-        else:
-            on_target_results.empty()
+        gene_symbol = st.text_input('Enter a Gene Symbol:', key='gene_symbol')
+
+        # Prediction button
+        predict_button = st.button('Predict on-target')
+
+        # Process predictions
+        if predict_button and gene_symbol:
+            predictions = cas9on.process_gene(gene_symbol, cas9on_path)
+            # Store only first 10 for display and save full predictions for download
+            st.session_state['on_target_results'] = predictions[:10]
+            st.session_state['full_on_target_results'] = predictions
+
+        # On-target results display
+        if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
+            st.write('On-target predictions:', st.session_state['on_target_results'])
+            if 'full_on_target_results' in st.session_state:
+                # Provide a download button for the full results
+                full_predictions_csv = cas9on.convert_df(st.session_state['full_on_target_results'])
+                st.download_button(
+                    label='Download on-target predictions',
+                    data=full_predictions_csv,
+                    file_name='on_target_results.csv',
+                    mime='text/csv'
+                )
 
     elif target_selection == 'off-target':
         ENTRY_METHODS = dict(