--- a
+++ b/app/backend/Utils.py
@@ -0,0 +1,73 @@
+#############################GRAD-Cam#############################
+
+#@title Grad-CAM code with full-credit to Jimin Tan (https://github.com/tanjimin/grad-cam-pytorch-light)
+import torch ,cv2
+from PIL import Image
+import numpy as np
+import os
+class InfoHolder():
+
+    def __init__(self, heatmap_layer):
+        self.gradient = None
+        self.activation = None
+        self.heatmap_layer = heatmap_layer
+
+    def get_gradient(self, grad):
+        self.gradient = grad
+
+    def hook(self, model, input, output):
+        if output.requires_grad:
+            output.register_hook(self.get_gradient)
+        self.activation = output.detach()
+
+def generate_heatmap(weighted_activation):
+    raw_heatmap = torch.mean(weighted_activation, 0)
+    heatmap = np.maximum(raw_heatmap.detach().cpu(), 0)
+    heatmap /= torch.max(heatmap) + 1e-10
+    return heatmap.numpy()
+
+def superimpose(input_img, heatmap):
+    img = to_RGB(input_img)  
+    heatmap = cv2.resize(heatmap, (img.shape[0], img.shape[1]))
+    heatmap = np.uint8(255 * heatmap)
+    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+    superimposed_img = np.uint8(heatmap * 0.6 + img * 0.4)
+    pil_img = cv2.cvtColor(superimposed_img,cv2.COLOR_BGR2RGB)
+    return pil_img
+
+def to_RGB(tensor):
+    tensor = (tensor - tensor.min())
+    tensor = tensor/(tensor.max() + 1e-10)
+    image_binary = np.transpose(tensor.numpy(), (1, 2, 0))
+    image = np.uint8(255 * image_binary)
+    return image
+
+def grad_cam(model, input_tensor, heatmap_layer, truelabel=None):
+    
+    info = InfoHolder(heatmap_layer)
+    heatmap_layer.register_forward_hook(info.hook)
+    
+    output = model(input_tensor.unsqueeze(0))[0]
+    truelabel = truelabel if truelabel else torch.argmax(output)
+
+    output[truelabel].backward()
+
+    weights = torch.mean(info.gradient, [0, 2, 3])
+    activation = info.activation.squeeze(0)
+
+    weighted_activation = torch.zeros(activation.shape)
+    for idx, (weight, activation) in enumerate(zip(weights, activation)):
+        weighted_activation[idx] = weight * activation
+    heatmap = generate_heatmap(weighted_activation)
+    return superimpose(input_tensor, heatmap)
+
+def use_gradcam(img_path,dest_path,model,transforms):
+    image=Image.open(img_path).convert('RGB')
+    layer4=model[0][-1]
+    heatmap_layer=layer4[2].conv2
+    input_tensor=transforms(image)
+
+    #get filename without extension
+    filename=os.path.splitext(os.path.basename(img_path))[0]
+    grad_cam_image= grad_cam(model, input_tensor, heatmap_layer)
+    return cv2.imwrite(os.path.join(dest_path,'(gradcam)'+filename+'.png'),grad_cam_image)
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