Update app.py

app.py

@@ -8,8 +8,7 @@ import requests
 from transformers import AutoImageProcessor, AutoModelForObjectDetection
 from transformers import AutoImageProcessor, DetrForObjectDetection
     
-def model():
-   
+def model():  
     
     url = "http://images.cocodataset.org/val2017/000000039769.jpg"
     image = Image.open(requests.get(url, stream=True).raw)
@@ -22,17 +21,15 @@ def model():
     
     # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
     target_sizes = torch.tensor([image.size[::-1]])
-    results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
-        0
-    ]
+    results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
     
     for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
         box = [round(i, 2) for i in box.tolist()]
         st.write("Prediction:", result[0]['label'], "| Score:", result[0]['score'])
-        print(
-            f"Detected {model.config.id2label[label.item()]} with confidence "
-            f"{round(score.item(), 3)} at location {box}"
-        )
+        #print(
+         #   f"Detected {model.config.id2label[label.item()]} with confidence "
+         #   f"{round(score.item(), 3)} at location {box}"
+        # )