add igv

app.py

@@ -5,6 +5,7 @@ import cas9off
 import pandas as pd
 import streamlit as st
 from pathlib import Path
+from igv_component import igv_component
 
 # title and documentation
 st.markdown(Path('crisprTool.md').read_text(), unsafe_allow_html=True)
@@ -91,38 +92,36 @@ if selected_model == 'Cas9':
     )
 
     if target_selection == 'on-target':
-        # Gene symbol entry
         gene_symbol = st.text_input('Enter a Gene Symbol:', key='gene_symbol')
 
-        # Prediction button
+        # Add genome reference selection
+        genome_reference = st.selectbox('Select a Genome Reference:', ['hg19', 'hg38'], key='genome_reference')
+
         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']:
-            # Convert the results to a pandas DataFrame for better display
             df = pd.DataFrame(st.session_state['on_target_results'],
                               columns=["Gene ID", "Start Pos", "End Pos", "Strand", "gRNA", "Prediction"])
             st.write('On-target predictions:')
             st.dataframe(df)
 
+            # Pass the gene symbol, genome reference, and predictions to IGV Component
+            igv_component(crispr_results=st.session_state['on_target_results'],
+                          genome_reference=genome_reference,
+                          gene_symbol=gene_symbol,
+                          key="igv_viewer")
+
             if 'full_on_target_results' in st.session_state:
-                # Convert full results to a CSV for download
                 full_df = pd.DataFrame(st.session_state['full_on_target_results'],
                                        columns=["Gene ID", "Start Pos", "End Pos", "Strand", "gRNA", "Prediction"])
                 full_predictions_csv = full_df.to_csv(index=False).encode('utf-8')
-                st.download_button(
-                    label='Download on-target predictions',
-                    data=full_predictions_csv,
-                    file_name='on_target_results.csv',
-                    mime='text/csv'
-                )
+                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(