Update app.py

reverted the code

app.py

@@ -61,40 +61,7 @@ def predict_image(get_image):
    pred, idx, probs = learn.predict(get_image)
    return dict(zip(categories, map(float, probs)))
 
-def interpretation_function(image_path, model, target_layer, target_category=None):
-    # Load and preprocess the image
-    image = Image.open(image_path)
-    preprocess = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-    ])
-    input_image = preprocess(image).unsqueeze(0)  # Add a batch dimension
-    input_image = input_image.to('cuda' if torch.cuda.is_available() else 'cpu')
-
-    # Create an instance of GradCAM
-    cam = GradCAM(model=model, target_layer=target_layer)
-
-    # Compute the CAM
-    cam_image = cam(input_tensor=input_image, target_category=target_category)
-
-    # Show the CAM on the original image
-    visualization = show_cam_on_image(input_image, cam_image)
-    #visualization.show()
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            input_img = gr.Image(label="Input Image", shape=(224, 224))
-            with gr.Row():
-                interpret = gr.Button("Interpret")
-        with gr.Column():
-            label = gr.Label(label="Predicted Class")
-        with gr.Column():
-            interpretation = gr.components.Interpretation(input_img)
-    interpret.click(interpretation_function(input_img,learn, "_conv_head",target_category=None), input_img, interpretation)
-
-#interpretation="default"
+interpretation= 'shap'
 enable_queue=True
 
 gr.Interface(fn=predict_image, inputs=gr.Image(shape=(224,224)),