Update model.py

model.py

@@ -35,21 +35,17 @@ def generate_download_signed_url_v4(blob_name):
         # Allow GET requests using this URL.
         method="GET",
     )
-
-    # print("Generated GET signed URL:")
-    # print(url)
-    print("You can use this URL with any user agent, for example:")
-    print(f"curl '{url}'")
     return url
 
 
 class Model(LabelStudioMLBase):
-
-    image_processor = AutoImageProcessor.from_pretrained("diegokauer/conditional-detr-coe-int")
-    model = AutoModelForObjectDetection.from_pretrained("diegokauer/conditional-detr-coe-int")
-    # pass
+    
     os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = get_credentials()
-    print(get_credentials())
+
+    def __init__(self):
+
+    self.image_processor = AutoImageProcessor.from_pretrained("diegokauer/conditional-detr-coe-int")
+    self.model = AutoModelForObjectDetection.from_pretrained("diegokauer/conditional-detr-coe-int")
 
     def predict(self, tasks, **kwargs):
         """ This is where inference happens: model returns 
@@ -68,10 +64,10 @@ class Model(LabelStudioMLBase):
             original_width, original_height = image.size
             with torch.no_grad():
                 
-                inputs = image_processor(images=image, return_tensors="pt")
-                outputs = model(**inputs)
+                inputs = self.image_processor(images=image, return_tensors="pt")
+                outputs = self.model(**inputs)
                 target_sizes = torch.tensor([image.size[::-1]])
-                results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
+                results = self.image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
 
             result_list = []
             for score, label, box in zip(results['scores'], results['labels'], results['boxes']):