fix

app.py

@@ -132,7 +132,7 @@ if selected_model == 'Cas9':
 
     # Gene symbol entry with autocomplete-like feature
     gene_symbol = st.selectbox('Enter a Gene Symbol:', [''] + gene_symbol_list, key='gene_symbol',
-                               format_func=lambda x: x if x else "e.g., FOXA1")
+                               format_func=lambda x: x if x else "")
 
     # Handle gene symbol change and file cleanup
     if gene_symbol != st.session_state['current_gene_symbol'] and gene_symbol:
@@ -176,30 +176,25 @@ if selected_model == 'Cas9':
                 fig = go.Figure()
 
                 # Plot Exons as horizontal lines or rectangles
-                exon_y = 0.2  # Adjust this as needed
                 for exon in st.session_state['exons']:
                     exon_start, exon_end = int(exon['start']), int(exon['end'])
-                    fig.add_trace(go.Scatter(
-                        x=[exon_start, exon_end],
-                        y=[exon_y, exon_y],
-                        mode='lines',
-                        line=dict(color='purple', width=10),  # Adjust styling as needed
-                        name='Exon'
-                    ))
-
-                # Plot CDS as horizontal lines or rectangles
-                cds_y = 0.3  # Adjust this as needed
+                    # Create a rectangle for each exon
+                    fig.add_shape(type="rect",
+                                  x0=exon_start, y0=-1,  # Start slightly below the axis for visibility
+                                  x1=exon_end, y1=1,  # End slightly above the axis
+                                  line=dict(color="purple", width=2),
+                                  fillcolor="rgba(128, 0, 128, 0.3)")  # Semi-transparent purple
+
+                # Plot CDS areas with similar approach but different color
                 for cds in st.session_state['cds']:
                     cds_start, cds_end = int(cds['start']), int(cds['end'])
-                    fig.add_trace(go.Scatter(
-                        x=[cds_start, cds_end],
-                        y=[cds_y, cds_y],
-                        mode='lines',
-                        line=dict(color='blue', width=10),  # Adjust styling as needed
-                        name='CDS'
-                    ))
+                    fig.add_shape(type="rect",
+                                  x0=cds_start, y0=-1,
+                                  x1=cds_end, y1=1,
+                                  line=dict(color="blue", width=2),
+                                  fillcolor="rgba(0, 0, 255, 0.3)")
 
-                # Plot gRNAs using triangles to indicate direction
+                    # Plot gRNAs using triangles to indicate direction
                 # Initialize the y position for the positive and negative strands
                 positive_strand_y = 0.1
                 negative_strand_y = -0.1
@@ -208,41 +203,27 @@ if selected_model == 'Cas9':
                 # Iterate over the sorted predictions to create the plot
                 for i, prediction in enumerate(st.session_state['on_target_results'], start=1):
                     chrom, start, end, strand, target, gRNA, pred_score = prediction
-                    start, end = int(start), int(end)
                     midpoint = (start + end) / 2
-
-                    if strand == '1':  # Positive strand
-                        y_value = positive_strand_y
-                        arrow_symbol = 'triangle-right'
-                        positive_strand_y += offset
-                    else:  # Negative strand
-                        y_value = negative_strand_y
-                        arrow_symbol = 'triangle-left'
-                        negative_strand_y -= offset
+                    y_value = i * 0.1 if strand == '1' else -i * 0.1  # Adjust multiplier for spacing
 
                     fig.add_trace(go.Scatter(
                         x=[midpoint],
                         y=[y_value],
                         mode='markers+text',
-                        marker=dict(symbol=arrow_symbol, size=10),
-                        name=f"gRNA: {gRNA}",
-                        text=f"Rank: {i}",
+                        marker=dict(symbol='triangle-up' if strand == '1' else 'triangle-down', size=12),
+                        text=f"Rank: {i}",  # Adjust based on your data
                         hoverinfo='text',
                         hovertext=f"Rank: {i}<br>Chromosome: {chrom}<br>Target Sequence: {target}<br>gRNA: {gRNA}<br>Start: {start}<br>End: {end}<br>Strand: {'+' if strand == '1' else '-'}<br>Prediction Score: {pred_score:.4f}",
                     ))
 
-                # Update the layout of the plot
+                # Update the layout of the plot for better clarity and interactivity
                 fig.update_layout(
                     title='Top 10 gRNA Sequences by Prediction Score',
                     xaxis_title='Genomic Position',
-                    yaxis=dict(
-                        title='Strand',
-                        showgrid=True,
-                        zeroline=False,
-                        tickvals=[positive_strand_y, negative_strand_y, exon_y, cds_y],
-                        ticktext=['+ Strand gRNAs', '- Strand gRNAs', 'Exons', 'CDS']
-                    ),
-                    showlegend=True
+                    yaxis_title='Strand',
+                    showlegend=False,  # Toggle based on preference
+                    xaxis=dict(showspikes=True, spikecolor="grey", spikesnap="cursor", spikemode="across"),
+                    hovermode='closest'  # Adjust for best hover interaction
                 )
 
                 # Display the plot