Update app.py

app.py

@@ -25,6 +25,9 @@ def inference(input_img, transparency = 0.5, target_layer_number = -1):
     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)
@@ -35,7 +38,7 @@ def inference(input_img, transparency = 0.5, target_layer_number = -1):
     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 classes[prediction[0].item()], visualization
+    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"
@@ -43,7 +46,7 @@ examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
 demo = gr.Interface(
     inference, 
     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 = ["text", gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
+    outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
     title = title,
     description = description,
     examples = examples,