Update app.py

app.py

@@ -2,6 +2,11 @@ import gradio as gr
 from fastai.vision.all import *
 from efficientnet_pytorch import EfficientNet 
 
+import torch, torchvision
+from torchvision import transforms
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from PIL import Image
 
 title = "COVID_19 Infection Detectation App!"
 head = (  
@@ -56,7 +61,42 @@ def predict_image(get_image):
    pred, idx, probs = learn.predict(get_image)
    return dict(zip(categories, map(float, probs)))
 
-interpretation="default"
+    def interpretation_function(get_image):
+        # Create or load your PyTorch model
+        target_layer = learn.layer4[-1]
+        # Create an instance of GradCAM
+        cam = GradCAM(model=learn, target_layer=target_layer)
+        # Load and preprocess your image
+        image_path = get_image #'your_image.jpg'
+        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')
+        # Compute the CAM
+        cam_image = cam(input_tensor=input_image, target_category=None)
+        # Compute the CAM
+        cam_image = cam(input_tensor=input_image, target_category=None)
+        # Show the CAM on the original image
+        visualization = show_cam_on_image(input_image, cam_image)
+        return visualization 
+
+        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, interpretation)
+
+
+#interpretation="default"
 enable_queue=True
 
 gr.Interface(fn=predict_image, inputs=gr.Image(shape=(224,224)),