Update app.py

app.py

@@ -91,44 +91,6 @@ def superimpose_images(base_image, overlay_image, alpha):
 
     return Image.fromarray(blended_array)
 
-def compute_gradcam(image_tensor, model):
-    target_layer = model.layer4[-1]
-    gradients, activations = [], []
-
-    def backward_hook(module, grad_input, grad_output):
-        gradients.append(grad_output[0])
-
-    def forward_hook(module, input, output):
-        activations.append(output)
-
-    target_layer.register_forward_hook(forward_hook)
-    target_layer.register_backward_hook(backward_hook)
-    
-    output = model(image_tensor)
-    class_idx = output.argmax(dim=1).item()
-    model.zero_grad()
-    output[:, class_idx].backward()
-
-    grads = gradients[0].cpu().data.numpy()[0]
-    acts = activations[0].cpu().data.numpy()[0]
-    weights = np.mean(grads, axis=(1, 2))
-    cam = np.zeros(acts.shape[1:], dtype=np.float32)
-
-    for i, w in enumerate(weights):
-        cam += w * acts[i]
-    
-    cam = np.maximum(cam, 0)
-    cam = cv2.resize(cam, (224, 224))
-    cam = (cam - cam.min()) / (cam.max() - cam.min())
-    return cam
-
-def overlay_heatmap(image, heatmap):
-    heatmap = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)
-    heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
-    image = np.array(image.resize((224, 224)))
-    superimposed_img = cv2.addWeighted(image, 0.6, heatmap, 0.4, 0)
-    return Image.fromarray(superimposed_img)
-
 # Prediction function
 def predict(image, brightness, contrast, hue, overlay_image, alpha):
     """Apply filters, superimpose, classify image, and visualize results."""
@@ -148,9 +110,6 @@ def predict(image, brightness, contrast, hue, overlay_image, alpha):
         output = model(image_tensor)
         probabilities = F.softmax(output, dim=1).cpu().numpy()[0]
 
-    heatmap = compute_gradcam(image_tensor, model)
-    heatmap_image = overlay_heatmap(final_image, heatmap)
-
     # Generate Bar Chart
     with plt.xkcd():
         fig, ax = plt.subplots(figsize=(5, 3))
@@ -161,7 +120,7 @@ def predict(image, brightness, contrast, hue, overlay_image, alpha):
         for i, v in enumerate(probabilities):
             ax.text(i, v + 0.02, f"{v:.2f}", ha='center', fontsize=10)
 
-    return final_image, heatmap_image, fig
+    return final_image, fig
 
 # Gradio Interface
 with gr.Blocks() as interface:
@@ -178,7 +137,6 @@ with gr.Blocks() as interface:
 
         with gr.Column():
             processed_image = gr.Image(label="Final Processed Image")
-            heatmap_image = gr.Image(label="Heatmap Visualization")
             bar_chart = gr.Plot(label="Class Probabilities")
 
     inputs = [image_input, brightness, contrast, hue, overlay_input, alpha]