Update app.py

app.py

@@ -149,24 +149,28 @@ def get_grad(img):
     text = "Raw Score: " + str(y_pred[0]) + "\nClassification: " + infer
     return grad_img_c, grad_img_d, text
 
-# demo = gr.Interface(
-#     fn = get_grad,
-#     with gr.Row():
-#         inputs = gr.Image(type = "pil", shape = (224,224), width = 320, height = 320),
-#         outputs = [gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t cat"), gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t dog"), gr.Textbox(label = 'Prediction', info = '[P of cat, P of dog]')], 
-#     description = "Visual Explanations from Deep Networks",
-#     title = "Gradient-Weighted Class Activation Mapping (Grad-CAM)"
-# )
-
-with gr.Blocks() as demo:
-    with gr.Row().style(equal_height=True):
-        img_input = gr.Image(type = "pil", shape = (224,224), width = 320, height = 320)
-        img_output1 = gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t cat")
-        img_output2 = gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t dog")
-
+demo = gr.Interface(
+    fn = get_grad,
+    with gr.Row():
+        inputs = gr.Image(type = "pil", shape = (224,224), width = 320, height = 320),
+        outputs = [gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t cat"), gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t dog"), gr.Textbox(label = 'Prediction', info = '[P of cat, P of dog]')], 
     description = "Visual Explanations from Deep Networks",
     title = "Gradient-Weighted Class Activation Mapping (Grad-CAM)"
-    img_input.upload(get_grad, inputs = img_input, outputs = [img_output1, img_output2])
+)
+
+# with gr.Blocks() as demo:
+#     gr.Markdown(
+#     "# Gradient-Weighted Class Activation Mapping (Grad-CAM)\nVisual Explanations from Deep Networks"
+#     )
+    
+#     with gr.Row().style(equal_height=True):
+#         img_input = gr.Image(type = "pil", shape = (224,224), width = 320, height = 320)
+#         img_output1 = gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t cat")
+#         img_output2 = gr.Image(type = "numpy", width = 320, height = 320, label = "Grad_CAM w.r.t dog")
+
+#     description = "Visual Explanations from Deep Networks",
+#     title = "Gradient-Weighted Class Activation Mapping (Grad-CAM)"
+#     img_input.upload(get_grad, inputs = img_input, outputs = [img_output1, img_output2])
 
 demo.launch()