Update app.py

app.py

@@ -75,9 +75,11 @@ def inference_visualization(input_img, transparency = 0.5, target_layer_number =
     return visualization
 
 # Callback function for the Gradio interface
-def gradio_callback(view_gradcam, num_gradcam_images, layer_name, opacity,
-                    view_misclassified, num_misclassified_images,
-                        input_img,submit):
+# def gradio_callback(view_gradcam, num_gradcam_images, layer_name, opacity,
+#                     view_misclassified, num_misclassified_images,
+#                         input_img,submit):
+def gradio_callback(input_img, transparency = 0.5, target_layer_number = -1):
+    
     confidence = inference_confidences(input_img, transparency = 0.5, target_layer_number = -1)
     visualization = inference_visualization(input_img, transparency = 0.5, target_layer_number = -1)
     return confidence, visualization
@@ -99,14 +101,17 @@ demo = gr.Interface(
     # examples = examples,
     fn=gradio_callback,  # We'll add the function later after defining all functions,  # We'll add the function later after defining all functions
     inputs=[
-        gr.Radio(["Yes", "No"], label="View GradCAM images?"),
-        gr.Number(label="Number of GradCAM images to view", default=5, max=10),
-        gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
-        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.5, label="Opacity"),
-        gr.Radio(["Yes", "No"], label="View misclassified images?"),
-        gr.Number(label="Number of misclassified images to view", default=5, min=1, max=10),
-        gr.Image(shape=(32, 32), label="Input Image")
-        # gr.Button(label="Submit", type="boolean")      
+        # gr.Radio(["Yes", "No"], label="View GradCAM images?"),
+        # gr.Number(label="Number of GradCAM images to view", default=5, max=10),
+        # gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
+        # gr.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.5, label="Opacity"),
+        # gr.Radio(["Yes", "No"], label="View misclassified images?"),
+        # gr.Number(label="Number of misclassified images to view", default=5, min=1, max=10),
+        # gr.Image(shape=(32, 32), label="Input Image")
+        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=128, height=128)],
     examples = examples,