Update app.py

app.py

@@ -48,19 +48,19 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # Use GPU
 model = attempt_load(model_path, device=device)  # Placeholder for model loading
 model.eval()  # Set the model to evaluation mode
 
-#def preprocess_image(image_path):
-def preprocess_image(image):
-    #img0 = cv2.imread(image_path)
-    img0 = letterbox(image, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
+def preprocess_image(image_path):
+#def preprocess_image(image):
+    img0 = cv2.imread(image_path)
+    #img0 = letterbox(image, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
     img = letterbox(img0, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
-    #print("in preprocess:",img.shape)
-    #img = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, to 3x416x416
+    print("in preprocess:",img.shape)
+    img = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, to 3x416x416
     img = np.ascontiguousarray(img)
     img = torch.from_numpy(img).to(device)
     img = img.float()  # uint8 to fp16/32
     img /= 255.0  # 0 - 255 to 0.0 - 1.0
     if img.ndimension() == 3:
-        img = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, 
+        #img = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, 
         img = img.unsqueeze(0)
     return img, img0
 
@@ -113,7 +113,7 @@ def show_preds_image(filepath):
     img0 = cv2.imread(filepath)
     img_with_boxes = draw_bounding_boxes(img0, results)
     return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB)
-'''
+
 # Define Gradio components
 input_component = gr.components.Image(type="filepath", label="Input Image")
 output_component = gr.components.Image(type="numpy", label="Output Image")
@@ -219,4 +219,5 @@ def build_interface():
 
 if __name__ == '__main__':
     demo = build_interface()
-    demo.launch()
+    demo.launch
+'''