Update app.py

app.py

@@ -63,7 +63,7 @@ scaled_anchors = (
     * torch.tensor(config.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
 )
 
-def process_image_and_plot(image,iou_threshold=0.5, threshold=0.4,show_cam="No",target_layer=-2):
+def process_image_and_plot(image,iou_threshold=0.5, threshold=0.4,target_layer=-2):
 
     transformed_image = transforms(image=image)["image"].unsqueeze(0)
     output = model(transformed_image)
@@ -87,15 +87,13 @@ def process_image_and_plot(image,iou_threshold=0.5, threshold=0.4,show_cam="No",
       layer = [model.model.layers[-1]]
         
     cam = YoloCAM(model=model, target_layers=layer, use_cuda=False)
-    if  show_cam == "No":
-        return fig
-    else:
-        grayscale_cam = cam(transformed_image, scaled_anchors)[0, :, :]
-        img = cv2.resize(image, (416, 416))
-        img = np.float32(img) / 255
-        cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True)
-    
-        return fig,cam_image
+
+    grayscale_cam = cam(transformed_image, scaled_anchors)[0, :, :]
+    img = cv2.resize(image, (416, 416))
+    img = np.float32(img) / 255
+    cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True)
+
+    return fig,cam_image
 
 
 examples = [
@@ -132,7 +130,6 @@ demo = gr.Interface(process_image_and_plot,
     inputs=[gr.Image(label="Input Image"),
            gr.Slider(0, 1, value=0.5, label="Intersection over Union (IOU) Threshold",info="Determines how much overlap between two boxes, Set it low to filter out weaker predicts"),
            gr.Slider(0, 1, value=0.4, label="Threshold"),
-           gr.Radio(["Yes", "No"], value="No"  , label="Show GradCAM outputs"),
            gr.Slider(-2, -1, value=-1, step=1, label="Which Layer?"),],
     outputs=[
         gr.Plot(label="Output with Classes",),