Update app.py

app.py

@@ -61,7 +61,7 @@ def show_misclassified_images_wrapper(num_images=10, use_gradcam=False, gradcam_
 
     return model.show_misclassified_images(num_images, use_gradcam, gradcam_layer, transparency)
 
-description1 = "Test the model's prediction. Currently the model only supports the following classes - plane, car, bird, cat, deer, dog, frog, horse, ship, truck."
+description1 = "Supported Only - plane, car, bird, cat, deer, dog, frog, horse, ship, truck."
 
 
 # Define the full path to the images folder
@@ -85,22 +85,22 @@ input_interface = 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?"),
-                        gr.Slider(1, 10, value=3, step=1, label="How many top confidence classes to be shown?")],
+                        gr.Slider(1, 10, value=3, step=1, label="How many classes")],
                 outputs=[gr.Label(),
-                         gr.Image(shape=(32, 32), label="Model Prediction").style(width=300, height=300)],
+                         gr.Image(shape=(32, 32), label="Predicted Output").style(width=300, height=300)],
                 description=description1,examples=examples)
 
 description2 = "Missclassfied Images"
 
 # Create a separate interface for the "Misclassified Images" tab
 misclassified_interface = gr.Interface(show_misclassified_images_wrapper,
-                    inputs=[gr.Number(value=10, label="Number of images to display"),
+                    inputs=[gr.Number(value=10, label="Number of Images for display"),
                             gr.Radio(["Yes", "No"], value="No"  , label="Show GradCAM outputs"),
                             gr.Slider(-2, -1, value=-1, step=1, label="Which layer for GradCAM?"),
                             gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM")],
                     outputs=gr.Plot(), description=description2)
 
 demo = gr.TabbedInterface([input_interface, misclassified_interface], tab_names=["Input an image", "Misclassified Images"], 
-                          title="Custom Resnet on CIFAR10 using GradCAM")
+                          title="Gradcam using Cifar10 with CustomResnet Model")
 
 demo.launch()