Update app.py

app.py

@@ -18,8 +18,33 @@ inv_normalize = transforms.Normalize(
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
+def resize_image_pil(image, new_width, new_height):
+
+    # Convert to PIL image
+    img = Image.fromarray(np.array(image))
+    
+    # Get original size
+    width, height = img.size
+
+    # Calculate scale
+    width_scale = new_width / width
+    height_scale = new_height / height 
+    scale = min(width_scale, height_scale)
+
+    # Resize
+    resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST)
+    
+    # Crop to exact size
+    resized = resized.crop((0, 0, new_width, new_height))
+
+    return resized
+
 def inference(input_img, transparency = 0.5, target_layer_number = -1):
+    input_img = resize_image_pil(input_img, 32, 32)
+    
+    input_img = np.array(input_img)
     org_img = input_img
+    input_img = input_img.reshape((32, 32, 3))
     transform = transforms.ToTensor()
     input_img = transform(input_img)
     input_img = input_img
@@ -39,20 +64,23 @@ def inference(input_img, transparency = 0.5, target_layer_number = -1):
     visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
     return classes[prediction[0].item()], visualization, confidences
 
+title = "CIFAR10 trained on ResNet18 Model with GradCAM"
+description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
+examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
 demo = gr.Interface(
     inference, 
     inputs = [
-        gr.Image(height=32, width=32, label="Input Image"), gr.Slider
+        gr.Image(width=256, height=256, label="Input Image"), gr.Slider
         (0, 1, value = 0.5, label="Overall Opacity of Image"), 
         gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")
         ], 
     outputs = [
         "text", 
-        gr.Image(height=32, width=32, label="Output").style(width=128, height=128),
+        gr.Image(width=256, height=256, label="Output"),
         gr.Label(num_top_classes=3)
         ],
-    title = "CIFAR10 trained on ResNet18 Model with GradCAM",
-    description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results",
-    examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.7, -2]],
+    title = title,
+    description = description,
+    examples = examples,
 )
 demo.launch()