Update app.py

app.py

@@ -250,40 +250,42 @@ def create_detailed_analysis(image):
         # Create visualizations
         visualizations = create_heatmap_visualizations(heatmap, img_array.shape)
         
-        # Create comprehensive plot
-        fig = plt.figure(figsize=(20, 12))
+        # Create comprehensive plot with 4 rows to accommodate all visualizations
+        fig = plt.figure(figsize=(20, 16))
         color = 'green' if predicted_class == 0 else 'red'
         fig.suptitle(f'Comprehensive Grad-CAM++ Analysis\nPredicted: {predicted_label.upper()} ({confidence:.2%})',
                      fontsize=16, fontweight='bold', color=color)
 
+        # Row 1: Original image and heatmaps
         # Original image
-        plt.subplot(3, 5, 1)
+        plt.subplot(4, 5, 1)
         plt.imshow(processed_img)
         plt.title("Original Image", fontsize=12, fontweight='bold')
         plt.axis('off')
 
-        # Different heatmap visualizations
+        # Different heatmap visualizations (4 in first row)
         viz_names = ['jet', 'hot', 'plasma', 'viridis']
         for i, viz_name in enumerate(viz_names):
             viz = visualizations[viz_name]
-            plt.subplot(3, 5, i + 2)
+            plt.subplot(4, 5, i + 2)
             im = plt.imshow(viz['heatmap'], cmap=viz['colormap'])
             plt.title(viz['title'], fontsize=12)
             plt.axis('off')
             plt.colorbar(im, fraction=0.046, pad=0.04)
 
+        # Row 2: Remaining heatmaps, attention profile, and statistics
         # More heatmap styles
         viz_names2 = ['inferno', 'cool']
         for i, viz_name in enumerate(viz_names2):
             viz = visualizations[viz_name]
-            plt.subplot(3, 5, i + 6)
+            plt.subplot(4, 5, i + 6)
             im = plt.imshow(viz['heatmap'], cmap=viz['colormap'])
             plt.title(viz['title'], fontsize=12)
             plt.axis('off')
             plt.colorbar(im, fraction=0.046, pad=0.04)
 
         # Attention profile
-        plt.subplot(3, 5, 8)
+        plt.subplot(4, 5, 8)
         attention_profile = np.mean(heatmap, axis=1)
         plt.plot(attention_profile, range(len(attention_profile)), 'b-', linewidth=2)
         plt.title('Vertical Attention Profile', fontsize=12)
@@ -293,7 +295,7 @@ def create_detailed_analysis(image):
         plt.grid(True, alpha=0.3)
 
         # Statistics
-        plt.subplot(3, 5, 9)
+        plt.subplot(4, 5, 9)
         stats_text = f"""Heatmap Statistics:
 Mean: {np.mean(heatmap):.3f}
 Std: {np.std(heatmap):.3f}
@@ -309,14 +311,40 @@ Class: {predicted_label}"""
                  verticalalignment='center', bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue"))
         plt.axis('off')
 
-        # Superimposed views
-        superimposed_colormaps = ['jet', 'hot', 'plasma', 'viridis', 'inferno']
-        for i, cmap_name in enumerate(superimposed_colormaps):
+        # Empty space for symmetry
+        plt.subplot(4, 5, 10)
+        plt.axis('off')
+
+        # Rows 3-4: All 6 superimposed views in 2 rows of 3
+        superimposed_colormaps = ['jet', 'hot', 'plasma', 'viridis', 'inferno', 'cool']
+        
+        # Row 3: First 3 superimposed views (positions 11-13)
+        for i, cmap_name in enumerate(superimposed_colormaps[:3]):
             superimposed_img = superimpose_gradcam_enhanced(img_array, heatmap, colormap=cmap_name, alpha=0.4)
-            plt.subplot(3, 5, i + 11)
+            plt.subplot(4, 5, i + 11)
             plt.imshow(superimposed_img)
             plt.title(f'Superimposed ({cmap_name.title()})', fontsize=12)
             plt.axis('off')
+        
+        # Empty spaces in row 3
+        plt.subplot(4, 5, 14)
+        plt.axis('off')
+        plt.subplot(4, 5, 15)
+        plt.axis('off')
+        
+        # Row 4: Last 3 superimposed views (positions 16-18)
+        for i, cmap_name in enumerate(superimposed_colormaps[3:]):
+            superimposed_img = superimpose_gradcam_enhanced(img_array, heatmap, colormap=cmap_name, alpha=0.4)
+            plt.subplot(4, 5, i + 16)
+            plt.imshow(superimposed_img)
+            plt.title(f'Superimposed ({cmap_name.title()})', fontsize=12)
+            plt.axis('off')
+        
+        # Empty spaces in row 4
+        plt.subplot(4, 5, 19)
+        plt.axis('off')
+        plt.subplot(4, 5, 20)
+        plt.axis('off')
 
         plt.tight_layout()
         plt.subplots_adjust(top=0.92)
@@ -351,13 +379,13 @@ def create_quick_analysis(image):
         if heatmap is None:
             return "❌ Error generating heatmap."
         
-        # Create quick visualization
-        fig, axes = plt.subplots(2, 3, figsize=(15, 10))
+        # Create quick visualization with 3x3 layout to accommodate all colormaps
+        fig, axes = plt.subplots(3, 3, figsize=(15, 15))
         color = 'green' if predicted_class == 0 else 'red'
         fig.suptitle(f'Quick Grad-CAM++ Analysis | Predicted: {predicted_label.upper()} ({confidence:.2%})',
                      fontsize=14, fontweight='bold', color=color)
 
-        # Original image
+        # Row 1: Original image and two main heatmaps
         axes[0, 0].imshow(processed_img)
         axes[0, 0].set_title("Original Image")
         axes[0, 0].axis('off')
@@ -375,21 +403,22 @@ def create_quick_analysis(image):
         axes[0, 2].axis('off')
         plt.colorbar(im2, ax=axes[0, 2], fraction=0.046)
 
-        # Superimposed views
-        superimposed_jet = superimpose_gradcam_enhanced(img_array, heatmap, 'jet')
-        axes[1, 0].imshow(superimposed_jet)
-        axes[1, 0].set_title("Superimposed (Jet)")
-        axes[1, 0].axis('off')
-
-        superimposed_hot = superimpose_gradcam_enhanced(img_array, heatmap, 'hot')
-        axes[1, 1].imshow(superimposed_hot)
-        axes[1, 1].set_title("Superimposed (Hot)")
-        axes[1, 1].axis('off')
-
-        superimposed_viridis = superimpose_gradcam_enhanced(img_array, heatmap, 'viridis')
-        axes[1, 2].imshow(superimposed_viridis)
-        axes[1, 2].set_title("Superimposed (Viridis)")
-        axes[1, 2].axis('off')
+        # Rows 2-3: All 6 superimposed views
+        superimposed_colormaps = ['jet', 'hot', 'plasma', 'viridis', 'inferno', 'cool']
+        
+        # Row 2: First 3 superimposed views
+        for i, cmap_name in enumerate(superimposed_colormaps[:3]):
+            superimposed_img = superimpose_gradcam_enhanced(img_array, heatmap, cmap_name)
+            axes[1, i].imshow(superimposed_img)
+            axes[1, i].set_title(f"Superimposed ({cmap_name.title()})")
+            axes[1, i].axis('off')
+        
+        # Row 3: Last 3 superimposed views
+        for i, cmap_name in enumerate(superimposed_colormaps[3:]):
+            superimposed_img = superimpose_gradcam_enhanced(img_array, heatmap, cmap_name)
+            axes[2, i].imshow(superimposed_img)
+            axes[2, i].set_title(f"Superimposed ({cmap_name.title()})")
+            axes[2, i].axis('off')
 
         plt.tight_layout()
         
@@ -510,22 +539,22 @@ def create_gradio_app():
             with gr.Column():
                 gr.Markdown("""
                 ### ⚡ Quick Analysis Features:
-                - **2x3 Grid Layout** for rapid evaluation
+                - **3x3 Grid Layout** for comprehensive quick view
                 - **Original Image** with preprocessing
                 - **Jet & Plasma Heatmaps** with colorbars
-                - **3 Superimposed Views** (Jet, Hot, Viridis)
-                - **Fast Processing** (~2-3 seconds)
+                - **6 Superimposed Views** (All color schemes)
+                - **Fast Processing** (~3-4 seconds)
                 - **Perfect for screening** multiple images
                 """)
             
             with gr.Column():
                 gr.Markdown("""
                 ### 🔬 Detailed Analysis Features:
-                - **3x5 Grid Layout** for comprehensive analysis
+                - **4x5 Grid Layout** for comprehensive analysis
                 - **6 Heatmap Color Schemes** with individual colorbars
                 - **Attention Profile Plot** showing vertical focus
                 - **Statistical Analysis Panel** with quantitative metrics
-                - **5 Enhanced Superimposed Views** with gamma correction
+                - **6 Enhanced Superimposed Views** with gamma correction
                 - **Clinical-grade visualization** for detailed examination
                 """)