Fix: No module named utils.google_utils.

app.py

@@ -10,6 +10,7 @@ from pytorch_grad_cam import EigenCAM
 from pytorch_grad_cam.utils.image import show_cam_on_image, scale_cam_image
 from PIL import Image
 import gradio as gr
+
 # Global Color Palette
 COLORS = np.random.uniform(0, 255, size=(80, 3))
 
@@ -39,15 +40,14 @@ def draw_detections(boxes, colors, names, img):
                     lineType=cv2.LINE_AA)
     return img
 
-
 # Load the appropriate YOLO model based on the version
 def load_yolo_model(version="yolov5"):
     if version == "yolov3":
         model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True)
     elif version == "yolov5":
-        model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)  # Load yolov5 small model
     elif version == "yolov8":
-        model = torch.hub.load('ultralytics/yolov5:v7.0', 'yolov5', pretrained=True)  # YOLOv8 is part of the yolov5 repo starting from v7.0
+        model = torch.hub.load('ultralytics/yolov5:v7.0', 'yolov5', pretrained=True)  # YOLOv8 is part of yolov5 repo starting v7.0
     else:
         raise ValueError(f"Unsupported YOLO version: {version}")
     
@@ -55,7 +55,6 @@ def load_yolo_model(version="yolov5"):
     model.cpu()
     return model
 
-# Main function for Grad-CAM visualization
 # Main function for Grad-CAM visualization
 def process_image(image, yolo_versions=["yolov5"]):
     image = np.array(image)
@@ -74,10 +73,10 @@ def process_image(image, yolo_versions=["yolov5"]):
     for yolo_version in yolo_versions:
         # Load the model based on YOLO version
         model = load_yolo_model(yolo_version)
-        target_layers = [model.model.model.model[-2]]  # Assumes last layer is used for Grad-CAM
+        target_layers = [model.model.model[-2]]  # Assumes last layer is used for Grad-CAM
 
         # Run YOLO detection
-        results = model([rgb_img])
+        results = model(rgb_img)
         boxes, colors, names = parse_detections(results)
         detections_img = draw_detections(boxes, colors, names, rgb_img.copy())