Update app.py

app.py

@@ -61,39 +61,38 @@ def predict_image(get_image):
    pred, idx, probs = learn.predict(get_image)
    return dict(zip(categories, map(float, probs)))
 
-    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():
+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, interpretation)
+        with gr.Column():
+            interpretation = gr.components.Interpretation(input_img)
+    interpret.click(interpretation_function(get_image,learn, learn.layer4[-1],target_category=None), input_img, interpretation)
 
 
 #interpretation="default"