fix

app.py

@@ -6,6 +6,7 @@ import cas12
 import pandas as pd
 import streamlit as st
 import plotly.graph_objs as go
+from pygenomeviz import Genbank, GenomeViz
 import numpy as np
 from pathlib import Path
 
@@ -95,9 +96,29 @@ if selected_model == 'Cas9':
         ('on-target', 'off-target'),
         key='target_selection'
     )
+    if 'current_gene_symbol' not in st.session_state:
+        st.session_state['current_gene_symbol'] = ""
+
+    # Define a function to clean up old files
+    def clean_up_old_files(gene_symbol):
+        genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
+        bed_file_path = f"{gene_symbol}_crispr_targets.bed"
+        csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
+        for path in [genbank_file_path, bed_file_path, csv_file_path]:
+            if os.path.exists(path):
+                os.remove(path)
+
     # Gene symbol entry
     gene_symbol = st.text_input('Enter a Gene Symbol:', placeholder="e.g., FOXA1", key='gene_symbol')
 
+    # Handle gene symbol change and file cleanup
+    if gene_symbol != st.session_state['current_gene_symbol'] and gene_symbol:
+        if st.session_state['current_gene_symbol']:
+            # Clean up files only if a different gene symbol is entered and a previous symbol exists
+            clean_up_old_files(st.session_state['current_gene_symbol'])
+        # Update the session state with the new gene symbol
+        st.session_state['current_gene_symbol'] = gene_symbol
+
     if target_selection == 'on-target':
         # Prediction button
         predict_button = st.button('Predict on-target')
@@ -114,21 +135,12 @@ if selected_model == 'Cas9':
             st.success('Prediction completed!')
             st.session_state['prediction_made'] = True
 
+
             if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
                 # 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"])
                 st.dataframe(df)
-                # Define file paths
-                genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
-                bed_file_path = f"{gene_symbol}_crispr_targets.bed"
-                csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
-
-                # Generate files
-                cas9on.generate_genbank_file_from_df(df, gene_sequence, gene_symbol, genbank_file_path)
-                cas9on.create_bed_file_from_df(df, bed_file_path)
-                cas9on.create_csv_from_df(df, csv_file_path)
-
                 # Now create a Plotly plot with the sorted_predictions
                 fig = go.Figure()
 
@@ -184,27 +196,43 @@ if selected_model == 'Cas9':
                     ),
                     showlegend=False  # Hide the legend if it's not necessary
                 )
+
                 # Display the plot
                 st.plotly_chart(fig)
-                # Download buttons
-                col1, col2, col3 = st.columns(3)
-                with col1:
-                    with open(genbank_file_path, "rb") as file:
-                        st.download_button(label="Download GenBank File", data=file, file_name=genbank_file_path,
-                                           mime="text/x-genbank")
-                with col2:
-                    with open(bed_file_path, "rb") as file:
-                        st.download_button(label="Download BED File", data=file, file_name=bed_file_path,
-                                           mime="text/plain")
-                with col3:
-                    with open(csv_file_path, "rb") as file:
-                        st.download_button(label="Download CSV File", data=file, file_name=csv_file_path,
-                                           mime="text/csv")
 
-                # Links for user guidance on using the downloaded files
-                st.markdown(
-                    "GenBank files can be visualized using [PyGenomeViz](https://pygenomeviz.streamlit.app/). "
-                    "BED files can be used with the [UCSC Genome Browser](https://genome.ucsc.edu/cgi-bin/hgCustom).")
+                if 'gene_sequence' in st.session_state and st.session_state['gene_sequence']:
+                    gene_symbol = st.session_state['current_gene_symbol']
+                    gene_sequence = st.session_state['gene_sequence']
+
+                    # Define file paths
+                    genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
+                    bed_file_path = f"{gene_symbol}_crispr_targets.bed"
+                    csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
+
+                    # Generate files
+                    cas9on.generate_genbank_file_from_df(df, gene_sequence, gene_symbol, genbank_file_path)
+                    cas9on.create_bed_file_from_df(df, bed_file_path)
+                    cas9on.create_csv_from_df(df, csv_file_path)
+
+                    # Layout for download buttons
+                    col1, col2, col3 = st.columns(3)
+                    with col1:
+                        with open(genbank_file_path, "rb") as file:
+                            st.download_button(label="Download GenBank File", data=file, file_name=genbank_file_path,
+                                               mime="text/x-genbank")
+                    with col2:
+                        with open(bed_file_path, "rb") as file:
+                            st.download_button(label="Download BED File", data=file, file_name=bed_file_path,
+                                               mime="text/plain")
+                    with col3:
+                        with open(csv_file_path, "rb") as file:
+                            st.download_button(label="Download CSV File", data=file, file_name=csv_file_path,
+                                               mime="text/csv")
+
+                    # Links for user guidance on using the downloaded files
+                    st.markdown(
+                        "GenBank files can be visualized using [PyGenomeViz](https://pygenomeviz.streamlit.app/). "
+                        "BED files can be used with the [UCSC Genome Browser](https://genome.ucsc.edu/cgi-bin/hgCustom).")
 
 
     elif target_selection == 'off-target':
@@ -297,11 +325,31 @@ elif selected_model == 'Cas12':
     # Gene symbol entry
     gene_symbol = st.text_input('Enter a Gene Symbol:', placeholder="e.g., FOXA1", key='gene_symbol')
 
+    # Initialize the current_gene_symbol in the session state if it doesn't exist
+    if 'current_gene_symbol' not in st.session_state:
+        st.session_state['current_gene_symbol'] = ""
+
     # Prediction button
     predict_button = st.button('Predict on-target')
 
+    # Function to clean up old files
+    def clean_up_old_files(gene_symbol):
+        genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
+        bed_file_path = f"{gene_symbol}_crispr_targets.bed"
+        csv_file_path = f"{gene_symbol}_crispr_predictions.csv"
+        for path in [genbank_file_path, bed_file_path, csv_file_path]:
+            if os.path.exists(path):
+                os.remove(path)
+
+    # Clean up files if a new gene symbol is entered
+    if st.session_state['current_gene_symbol'] and gene_symbol != st.session_state['current_gene_symbol']:
+        clean_up_old_files(st.session_state['current_gene_symbol'])
+
     # Process predictions
     if predict_button and gene_symbol:
+        # Update the current gene symbol
+        st.session_state['current_gene_symbol'] = gene_symbol
+
         # Run the prediction process
         with st.spinner('Predicting... Please wait'):
             predictions, gene_sequence = cas12.process_gene(gene_symbol,cas12_path)