fix

app.py

@@ -6,14 +6,12 @@ import cas12
 import pandas as pd
 import streamlit as st
 import plotly.graph_objs as go
-import coolbox
-from coolbox.api import *
 import numpy as np
 from pathlib import Path
 import zipfile
 import io
-import pygenometracks.tracks as pygtk
-from pygenometracks import plotTracks as plot_tracks
+import gtracks
+
 
 
 # title and documentation
@@ -302,24 +300,20 @@ if selected_model == 'Cas9':
                     # Important: move the cursor to the beginning of the BytesIO buffer before reading it
                     zip_buffer.seek(0)
 
-                    # Specify the region you want to visualize
-                    region_to_visualize = "chr1:1000000-2000000"  # Example region, adjust as needed
-
-                    # Path for the temporary plot image
-                    plot_image_path = "coolbox_plot.png"
-
-                    # Generate the CoolBox plot
-                    generate_coolbox_plot(bigwig_file_path, region_to_visualize, plot_image_path)
+                    track = gtracks.Track(bigwig_file_path)
+                    plot = gtracks.Plot(tracks=[track])
 
-                    # Display the plot image in Streamlit
-                    st.image(plot_image_path)
+                    # Specify the region you want to visualize
+                    min_start = df['Start Pos'].min()
+                    max_end = df['End Pos'].max()
+                    chromosome = df['Chr'].mode()[0]  # Assumes most common chromosome is the target
+                    region = f"{chromosome}:{min_start}-{max_end}"
 
                     # Generate the pyGenomeTracks plot
-                    pygenometracks_plot_path = "pygenometracks_plot.png"
-                    generate_pygenometracks_plot(bigwig_file_path, region_to_visualize, pygenometracks_plot_path)
-
+                    plot_image_path = f"{gene_symbol}_gtracks_plot.png"
+                    plot.plot(region=region, output_file=plot_image_path)
                     # Display the pyGenomeTracks plot image in Streamlit
-                    st.image(pygenometracks_plot_path)
+                    st.image(plot_image_path)
 
                     # Display the download button for the ZIP file
                     st.download_button(

cas9on.py

@@ -204,9 +204,6 @@ def process_gene(gene_symbol, model_path):
 
 
 def create_bigwig(df, bigwig_path):
-    import pandas as pd
-    import pyBigWig
-
     if isinstance(df, list):
         df = pd.DataFrame(df, columns=["Chr", "Start Pos", "End Pos", "Strand", "Transcript", "Exon", "Target", "gRNA", "Prediction"])
 
@@ -214,12 +211,11 @@ def create_bigwig(df, bigwig_path):
     # Ensure the sizes are integers
     chrom_sizes = df.groupby('Chr')['End Pos'].max().astype(int).to_dict()
 
-    # Create a BigWig file
     with pyBigWig.open(bigwig_path, "w") as bw:
         # Add chromosome sizes to the header, ensuring sizes are integers
         bw.addHeader([(chr, size) for chr, size in chrom_sizes.items()])
 
-        # Add entries for each prediction
+        # Iterate over unique chromosomes and add entries for each
         for chrom in df['Chr'].unique():
             chrom_df = df[df['Chr'] == chrom]
             bw.addEntries(

requirements.txt

@@ -4,6 +4,6 @@ pandas==1.5.2
 tensorflow==2.11.0
 tensorflow-probability==0.19.0
 plotly==5.18.0
-coolbox
+gtracks
 pyBigWig
 pyGenomeTracks