Update app.py

app.py

@@ -7,7 +7,7 @@
 
 import sys
 import os
-os.system(f'pip install grad-cam')
+
 os.system(f'pip install dlib')
 import dlib
 import argparse
@@ -184,8 +184,8 @@ class TargetCategory:
         return output[self.category_index]
 
 
-def preprocess_image_cam(pil_img, 
-                         mean=[0.5482207536697388, 0.42340534925460815, 0.3654651641845703], 
+def preprocess_image_cam(pil_img,
+                         mean=[0.5482207536697388, 0.42340534925460815, 0.3654651641845703],
                          std=[0.2789176106452942, 0.2438540756702423, 0.23493893444538116]):
     img_np = np.array(pil_img)
     img_np = img_np.astype(np.float32) / 255.0
@@ -222,9 +222,9 @@ def FSFM3C_image_detection(image):
         image_results = f"The largest face in this image may be {max_prob_class} with probability: \n [{', '.join(probabilities)}]"
 
         # Generate CAM heatmap for the detected class
-        use_cuda = True
-        input_tensor = preprocess_image(img, 
-                                        mean=[0.5482207536697388, 0.42340534925460815, 0.3654651641845703], 
+        use_cuda = torch.cuda.is_available() 
+        input_tensor = preprocess_image(img,
+                                        mean=[0.5482207536697388, 0.42340534925460815, 0.3654651641845703],
                                         std=[0.2789176106452942, 0.2438540756702423, 0.23493893444538116])
         if use_cuda:
             input_tensor = input_tensor.cuda()