Update model.py

model.py

@@ -36,35 +36,32 @@ 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'], scores['boxes']):
+            for score, label, box in zip(results['scores'], results['labels'], results['boxes']):
                 label_id = str(uuid4())[:4]
                 x, y, x2, y2 = tuple(box)
-                result_list.append(
-                    {
-                        'id': id,
-                        'original_width': original_width,
-                        'original_height': original_height,
-                        'from_name': "label",
-                        'to_name': "image",
-                        'type': 'labels',
-                        'score': score,  # per-region score, visible in the editor 
-                        'value': {
-                            'x': x,
-                            'y': y,
-                            'width': x2-x,
-                            'height': y2-y,
-                            'rotation': 0
-                            'labels': [self.id2label[label]]
-                        } 
+                result_list.append({
+                    'id': label_id,
+                    'original_width': original_width,
+                    'original_height': original_height,
+                    'from_name': "label",
+                    'to_name': "image",
+                    'type': 'labels',
+                    'score': score,  # per-region score, visible in the editor 
+                    'value': {
+                        'x': x,
+                        'y': y,
+                        'width': x2-x,
+                        'height': y2-y,
+                        'rotation': 0,
+                        'labels': [self.id2label[label]]
                     }
-                )
-        
+                })
             
             predictions.append({
                 'score': results['scores'].mean(),  # prediction overall score, visible in the data manager columns