fix

app.py

@@ -97,13 +97,13 @@ def parse_gene_annotations(file_path):
     gene_dict = {}
     with open(file_path, 'r') as file:
         headers = file.readline().strip().split('\t')  # Assuming tab-delimited file
+        symbol_idx = headers.index('Approved symbol')  # Find index of 'Approved symbol'
+        ensembl_idx = headers.index('Ensembl gene ID')  # Find index of 'Ensembl gene ID'
         for line in file:
             values = line.strip().split('\t')
-            # Use min to avoid IndexError if there are fewer values than headers
-            gene_info = {headers[i]: values[i] if i < len(values) else '' for i in range(len(headers))}
-            approved_symbol = gene_info.get('Approved symbol', 'Unknown')  # Use .get to avoid KeyError
-            if approved_symbol != 'Unknown':  # Only add if there is an approved symbol
-                gene_dict[approved_symbol] = gene_info
+            # Ensure we have enough values and add mapping from symbol to Ensembl ID
+            if len(values) > max(symbol_idx, ensembl_idx):
+                gene_dict[values[symbol_idx]] = values[ensembl_idx]
     return gene_dict
 
 # Replace 'your_annotation_file.txt' with the path to your actual gene annotation file
@@ -167,6 +167,17 @@ if selected_model == 'Cas9':
 
 
             if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
+                ensembl_id = gene_annotations.get(gene_symbol, 'Unknown')  # Get Ensembl ID or default to 'Unknown'
+                col1, col2, col3 = st.columns(3)
+                with col1:
+                    st.markdown("**Genome**")
+                    st.markdown("Homo sapiens")
+                with col2:
+                    st.markdown("**Gene**")
+                    st.markdown(f"{gene_symbol} : {ensembl_id} (primary)")
+                with col3:
+                    st.markdown("**Nuclease**")
+                    st.markdown("SpCas9")
                 # Include "Target" in the DataFrame's columns
                 df = pd.DataFrame(st.session_state['on_target_results'],
                                   columns=["Gene ID", "Start Pos", "End Pos", "Strand", "Target", "gRNA", "Prediction"])