Update app.py

app.py

@@ -20,11 +20,20 @@ model_dir = snapshot_download(
     repo_id="srijaydeshpande/spadesegresnet"
 )
 
+def superimpose_images(image1, image2, alpha=0.5):
+    if image1 is None or image2 is None:
+        return "Please upload both images."
+    try:
+        image1 = image1.resize(image2.size)
+        blended = Image.blend(image1, image2, alpha)
+        return blended
+    except Exception as e:
+        return f"Error superimposing images: {str(e)}"
+
 class SPADE(nn.Module):
     def __init__(self, norm_nc, label_nc, norm):
         super().__init__()
 
-
         if norm == 'instance':
             self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
         elif norm == 'batch':
@@ -279,12 +288,49 @@ examples = [
     ["sample2.png"]
 ]
 
-demo = gr.Interface(
-    segment_image,
-    inputs=gr.Image(),
-    examples=examples,
-    outputs=["image", "image", "image"],
-    title="Breast Cancer Semantic Segmentation"
-)
+input1 = gr.Image()
+output1 = gr.Image()
+output2 = gr.Image()
+output3 = gr.Image()
+
+with gr.Blocks() as demo:
+    
+    # demo = gr.Interface(
+        #     segment_image,
+        #     inputs=gr.Image(),
+        #     examples=examples,
+        #     outputs=["image", "image", "image"],
+        #     title="Breast Cancer Semantic Segmentation"
+        # )
+    
+    # Segmentation Section
+    segment_button = gr.Button("Segment Image")
+    segment_button.click(
+            segment_image,
+            inputs=input1,
+            examples=examples,
+            outputs=[output1, output2, output3],
+            title="Breast Cancer Semantic Segmentation"
+        )
+    
+    # Superimpose Section
+    gr.Markdown("### Superimpose Images")
+    
+    alpha_slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.1, label="Alpha (Transparency)")
+    superimpose_btn = gr.Button("Superimpose")
+    
+    superimpose_btn.click(
+        superimpose_images,
+        inputs=[input1, output1, alpha_slider],
+        outputs=output_image
+    )
+    
+# demo = gr.Interface(
+#     segment_image,
+#     inputs=gr.Image(),
+#     examples=examples,
+#     outputs=["image", "image", "image"],
+#     title="Breast Cancer Semantic Segmentation"
+# )
 
 demo.launch()