Update app.py

app.py

@@ -777,121 +777,186 @@ def create_simple_genome_diagram(gene_results: List[Dict[str, Any]], genome_leng
     
     return img
 
-def analyze_gene_features(sequence_file: str, 
-                        features_file: str, 
-                        fasta_text: str = "", 
-                        features_text: str = "") -> Tuple[str, Optional[str], Optional[Image.Image]]:
-    """Analyze SHAP values for each gene feature"""
-    # First analyze whole sequence
-    sequence_results = analyze_sequence(sequence_file, top_kmers=10, fasta_text=fasta_text)
-    if isinstance(sequence_results[0], str) and "Error" in sequence_results[0]:
-        return f"Error in sequence analysis: {sequence_results[0]}", None, None
-        
-    # Get SHAP values
-    shap_means = sequence_results[3]["shap_means"]
+def create_simple_genome_diagram(gene_results: List[Dict[str, Any]], genome_length: int) -> Image.Image:
+    """Create a simple genome diagram using PIL with proper coordinate handling"""
+    # Validate inputs and ensure genome_length is an integer
+    if not gene_results or not isinstance(genome_length, int) or genome_length <= 0:
+        img = Image.new('RGB', (800, 100), color='white')
+        draw = ImageDraw.Draw(img)
+        draw.text((10, 40), "Error: Invalid input data", fill='black')
+        return img
     
-    # Parse gene features
-    try:
-        if features_text.strip():
-            genes = parse_gene_features(features_text)
-        else:
-            with open(features_file, 'r') as f:
-                genes = parse_gene_features(f.read())
-    except Exception as e:
-        return f"Error reading features file: {str(e)}", None, None
-            
-    # Analyze each gene
-    gene_results = []
-    for gene in genes:
+    # Pre-process gene coordinates and handle type conversion
+    processed_genes = []
+    for gene in gene_results:
         try:
-            location = gene['metadata'].get('location', '')
-            if not location:
-                continue
-                
-            start, end = parse_location(location)
-            if start is None or end is None:
+            # Ensure start and end are integers
+            start = int(float(gene['start']))
+            end = int(float(gene['end']))
+            
+            # Validate coordinates
+            if start < 0 or end > genome_length or start >= end:
+                print(f"Warning: Skipping gene {gene.get('gene_name', 'unknown')} due to invalid coordinates: {start}-{end}")
                 continue
                 
-            # Get SHAP values for this region
-            gene_shap = shap_means[start:end]
-            stats = compute_gene_statistics(gene_shap)
+            processed_gene = gene.copy()
+            processed_gene['start'] = start
+            processed_gene['end'] = end
+            processed_genes.append(processed_gene)
+        except (ValueError, TypeError) as e:
+            print(f"Warning: Error processing gene coordinates: {str(e)}")
+            continue
+    
+    if not processed_genes:
+        img = Image.new('RGB', (800, 100), color='white')
+        draw = ImageDraw.Draw(img)
+        draw.text((10, 40), "Error: No valid genes to display", fill='black')
+        return img
+    
+    # Image dimensions
+    width = 1500
+    height = 600
+    margin = 50
+    track_height = 40
+    
+    # Create image with white background
+    img = Image.new('RGB', (width, height), 'white')
+    draw = ImageDraw.Draw(img)
+    
+    # Try to load font, fall back to default if unavailable
+    try:
+        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 12)
+        title_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 16)
+    except:
+        font = None
+        title_font = None
+    
+    # Draw title
+    draw.text((margin, margin//2), "Genome SHAP Analysis", 
+              fill='black', font=title_font or font)
+    
+    # Draw genome line
+    line_y = height // 2
+    line_coords = [(margin, line_y), (width - margin, line_y)]
+    draw.line(line_coords, fill='black', width=2)
+    
+    # Calculate scale factor
+    scale = float(width - 2 * margin) / float(genome_length)
+    
+    # Draw scale markers
+    for i in range(0, genome_length + 1, max(1, genome_length // 10)):
+        x = int(margin + i * scale)
+        marker_coords = [(x, line_y - 5), (x, line_y + 5)]
+        draw.line(marker_coords, fill='black', width=1)
+        draw.text((x - 20, line_y + 10), f"{i:,}", fill='black', font=font)
+    
+    # Sort genes by absolute SHAP value for drawing
+    sorted_genes = sorted(processed_genes, key=lambda x: abs(float(x.get('avg_shap', 0))))
+    
+    # Draw genes
+    for gene in sorted_genes:
+        try:
+            # Calculate position
+            start_x = int(margin + gene['start'] * scale)
+            end_x = int(margin + gene['end'] * scale)
+            
+            # Ensure minimum visible width
+            if end_x - start_x < 2:
+                end_x = start_x + 2
+            
+            # Calculate color based on SHAP value
+            avg_shap = float(gene.get('avg_shap', 0))
+            if avg_shap > 0:
+                intensity = min(255, int(abs(avg_shap * 500)))
+                color = (255, 255 - intensity, 255 - intensity)  # Red
+            else:
+                intensity = min(255, int(abs(avg_shap * 500)))
+                color = (255 - intensity, 255 - intensity, 255)  # Blue
             
-            gene_results.append({
-                'gene_name': gene['metadata'].get('gene', 'Unknown'),
-                'location': location,
-                'start': start,
-                'end': end,
-                'locus_tag': gene['metadata'].get('locus_tag', ''),
-                'avg_shap': stats['avg_shap'],
-                'median_shap': stats['median_shap'],
-                'std_shap': stats['std_shap'],
-                'max_shap': stats['max_shap'],
-                'min_shap': stats['min_shap'],
-                'pos_fraction': stats['pos_fraction'],
-                'classification': 'Human' if stats['avg_shap'] > 0 else 'Non-human',
-                'confidence': abs(stats['avg_shap'])
-            })
+            # Draw gene box
+            box_coords = [
+                start_x,
+                int(line_y - track_height // 2),
+                end_x,
+                int(line_y + track_height // 2)
+            ]
+            draw.rectangle(box_coords, fill=color, outline='black')
             
+            # Draw gene name
+            label = str(gene.get('gene_name', 'Unknown'))
+            if font:
+                label_bbox = draw.textbbox((0, 0), label, font=font)
+                label_width = label_bbox[2] - label_bbox[0]
+            else:
+                label_width = len(label) * 6  # Rough estimate if no font
+            
+            # Try to place label, alternating above and below
+            if sorted_genes.index(gene) % 2 == 0:
+                text_y = line_y - track_height - 15
+            else:
+                text_y = line_y + track_height + 5
+            
+            # Draw label with rotation if space is tight
+            gene_width = end_x - start_x
+            if gene_width > label_width:
+                # Horizontal label
+                text_x = int(start_x + (gene_width - label_width) // 2)
+                draw.text((text_x, text_y), label, fill='black', font=font)
+            elif gene_width > 20:
+                # Create rotated text image
+                txt_img = Image.new('RGBA', (label_width, 20), (255, 255, 255, 0))
+                txt_draw = ImageDraw.Draw(txt_img)
+                txt_draw.text((0, 0), label, font=font, fill='black')
+                txt_img = txt_img.rotate(90, expand=True)
+                img.paste(txt_img, (int(start_x), text_y), txt_img)
+        
         except Exception as e:
-            print(f"Error processing gene {gene['metadata'].get('gene', 'Unknown')}: {str(e)}")
+            print(f"Warning: Error drawing gene {gene.get('gene_name', 'unknown')}: {str(e)}")
             continue
     
-    if not gene_results:
-        return "No valid genes could be processed", None, None
-            
-    # Sort genes by absolute SHAP value
-    sorted_genes = sorted(gene_results, key=lambda x: abs(x['avg_shap']), reverse=True)
-    
-    # Create results text
-    results_text = "Gene Analysis Results:\n\n"
-    results_text += f"Total genes analyzed: {len(gene_results)}\n"
-    results_text += f"Human-like genes: {sum(1 for g in gene_results if g['classification'] == 'Human')}\n"
-    results_text += f"Non-human-like genes: {sum(1 for g in gene_results if g['classification'] == 'Non-human')}\n\n"
+    # Draw legend
+    legend_x = margin
+    legend_y = height - margin
+    draw.text((legend_x, legend_y - 60), "SHAP Values:", fill='black', font=font)
     
-    results_text += "Top 10 most distinctive genes:\n"
-    for gene in sorted_genes[:10]:
-        results_text += (
-            f"Gene: {gene['gene_name']}\n"
-            f"Location: {gene['location']}\n"
-            f"Classification: {gene['classification']} "
-            f"(confidence: {gene['confidence']:.4f})\n"
-            f"Average SHAP: {gene['avg_shap']:.4f}\n\n"
-        )
+    # Draw legend boxes
+    box_width = 20
+    box_height = 20
+    spacing = 15
     
-    # Create CSV content
-    csv_content = "gene_name,location,avg_shap,median_shap,std_shap,max_shap,min_shap,"
-    csv_content += "pos_fraction,classification,confidence,locus_tag\n"
+    # Strong human-like
+    draw.rectangle([
+        (legend_x, legend_y - 45),
+        (legend_x + box_width, legend_y - 45 + box_height)
+    ], fill=(255, 0, 0), outline='black')
+    draw.text((legend_x + box_width + spacing, legend_y - 45), 
+              "Strong human-like signal", fill='black', font=font)
     
-    for gene in gene_results:
-        csv_content += (
-            f"{gene['gene_name']},{gene['location']},{gene['avg_shap']:.4f},"
-            f"{gene['median_shap']:.4f},{gene['std_shap']:.4f},{gene['max_shap']:.4f},"
-            f"{gene['min_shap']:.4f},{gene['pos_fraction']:.4f},{gene['classification']},"
-            f"{gene['confidence']:.4f},{gene['locus_tag']}\n"
-        )
+    # Weak human-like
+    draw.rectangle([
+        (legend_x, legend_y - 20),
+        (legend_x + box_width, legend_y - 20 + box_height)
+    ], fill=(255, 200, 200), outline='black')
+    draw.text((legend_x + box_width + spacing, legend_y - 20), 
+              "Weak human-like signal", fill='black', font=font)
     
-    # Save CSV to temp file
-    try:
-        temp_dir = tempfile.gettempdir()
-        temp_path = os.path.join(temp_dir, f"gene_analysis_{os.urandom(4).hex()}.csv")
-        
-        with open(temp_path, 'w') as f:
-            f.write(csv_content)
-    except Exception as e:
-        print(f"Error saving CSV: {str(e)}")
-        temp_path = None
+    # Weak non-human-like
+    draw.rectangle([
+        (legend_x + 250, legend_y - 45),
+        (legend_x + 250 + box_width, legend_y - 45 + box_height)
+    ], fill=(200, 200, 255), outline='black')
+    draw.text((legend_x + 250 + box_width + spacing, legend_y - 45), 
+              "Weak non-human-like signal", fill='black', font=font)
     
-    # Create visualization
-    try:
-        diagram_img = create_simple_genome_diagram(gene_results, len(shap_means))
-    except Exception as e:
-        print(f"Error creating visualization: {str(e)}")
-        # Create error image
-        diagram_img = Image.new('RGB', (800, 100), color='white')
-        draw = ImageDraw.Draw(diagram_img)
-        draw.text((10, 40), f"Error creating visualization: {str(e)}", fill='black')
+    # Strong non-human-like
+    draw.rectangle([
+        (legend_x + 250, legend_y - 20),
+        (legend_x + 250 + box_width, legend_y - 20 + box_height)
+    ], fill=(0, 0, 255), outline='black')
+    draw.text((legend_x + 250 + box_width + spacing, legend_y - 20), 
+              "Strong non-human-like signal", fill='black', font=font)
     
-    return results_text, temp_path, diagram_img
+    return img
     
 ###############################################################################
 # 12. DOWNLOAD FUNCTIONS