updated 10 classes examples

app.py

@@ -6,8 +6,6 @@ from PIL import Image
 from pytorch_grad_cam import GradCAM
 from pytorch_grad_cam.utils.image import show_cam_on_image
 from custom_resnet import Net
-from PIL import Image
-import io
 
 model = Net('batch')
 model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
@@ -15,37 +13,26 @@ model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')),
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
-def inference(input_img_files, transparency = 0.5, target_layer_number = -1):
-    confidences_list = []
-    visualizations_list = []
-    
-    for input_img_file in input_img_files:
-        # Convert the temporary file wrapper to a PIL Image
-        with open(input_img_file.name, "rb") as f:
-            input_img = Image.open(io.BytesIO(f.read())).convert("RGB")
-        transform = transforms.ToTensor()
-        org_img = input_img
-        input_img = transform(input_img)
-        # input_img = input_img
-        input_img = input_img.unsqueeze(0)
-        outputs = model(input_img)
-        softmax = torch.nn.Softmax(dim=0)
-        o = softmax(outputs.flatten())
-        confidences = {classes[i]: float(o[i]) for i in range(10)}
-        confidences_list.append(confidences)
-
-        _, prediction = torch.max(outputs, 1)
-        target_layers = [model.layer2[target_layer_number]]
-        cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False)
-        grayscale_cam = cam(input_tensor=input_img, targets=None)
-        grayscale_cam = grayscale_cam[0, :]
-        img = input_img.squeeze(0)
-        rgb_img = np.transpose(img, (1, 2, 0))
-        rgb_img = rgb_img.numpy()
-        visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
-        visualizations_list.append(visualization)
-
-    return confidences_list, visualizations_list
+def inference(input_img, transparency = 0.5, target_layer_number = -1):
+    transform = transforms.ToTensor()
+    org_img = input_img
+    input_img = transform(input_img)
+    # input_img = input_img
+    input_img = input_img.unsqueeze(0)
+    outputs = model(input_img)
+    softmax = torch.nn.Softmax(dim=0)
+    o = softmax(outputs.flatten())
+    confidences = {classes[i]: float(o[i]) for i in range(10)}
+    _, prediction = torch.max(outputs, 1)
+    target_layers = [model.layer2[target_layer_number]]
+    cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False)
+    grayscale_cam = cam(input_tensor=input_img, targets=None)
+    grayscale_cam = grayscale_cam[0, :]
+    img = input_img.squeeze(0)
+    rgb_img = np.transpose(img, (1, 2, 0))
+    rgb_img = rgb_img.numpy()
+    visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
+    return confidences, visualization
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
@@ -55,15 +42,11 @@ examples = [["airplane.png", 0.5, -1],["bird.jpeg", 0.5, -1], ["car.jpeg", 0.5,
 
 demo = gr.Interface(
     inference, 
-    inputs=[
-        gr.inputs.File(file_count="multiple"),
-        gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
-        gr.Slider(-2, -1, value=-2, step=1, label="Which Layer?")
-    ], 
-outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output", style={"width": "128px", "height": "128px"})],
-    title=title,
-    description=description,
-    examples=examples,
+    inputs = [gr.Image(shape=(32, 32), label="Input Image"), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")], 
+    outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output", style={"width": "128px", "height": "128px"})],
+    title = title,
+    description = description,
+    examples = examples,
 )
 
 demo.launch()