Update model.py

model.py

@@ -14,15 +14,6 @@ class Model(LabelStudioMLBase):
     image_processor = AutoImageProcessor.from_pretrained("diegokauer/conditional-detr-coe-int")
     model = AutoModelForObjectDetection.from_pretrained("diegokauer/conditional-detr-coe-int")
 
-    def __init__(self, **kwargs):
-        # don't forget to call base class constructor
-        super(Model, self).__init__(**kwargs)
-    
-        # you can preinitialize variables with keys needed to extract info from tasks and annotations and form predictions
-        self.model = model
-        self.tokenizer = image_processor
-        self.id2label = model.config.id2label
-
     def predict(self, tasks, **kwargs):
         """ This is where inference happens: model returns 
             the list of predictions based on input list of tasks 
@@ -36,10 +27,10 @@ class Model(LabelStudioMLBase):
             original_width, original_height = image.size
             with torch.no_grad():
                 
-                inputs = self.image_processor(images=image, return_tensors="pt")
-                outputs = self.model(**inputs)
+                inputs = image_processor(images=image, return_tensors="pt")
+                outputs = model(**inputs)
                 target_sizes = torch.tensor([image.size[::-1]])
-                results = self.image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
+                results = 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']):