Update app.py

app.py

@@ -13,12 +13,12 @@ import requests
   #      location = [round(value, 2) for value in item['box'].values()]
    #     print(f'Detected {label} with confidence {score} at location {location}')
 
-def prettify_results(results):
-    for item in results:
-        score = round(item['score'].item(), 3)
-        label = model.config.id2label[item['label']]  # Get label from id2label mapping in model config
-        box = [round(coord, 2) for coord in item['box']]
-        st.write(f'Detected {label} with confidence {score} at location {box}')
+#def prettify_results(results):
+ #   for item in results:
+  #      score = round(item['score'].item(), 3)
+   #     label = model.config.id2label[item['label']]  # Get label from id2label mapping in model config
+    #    box = [round(coord, 2) for coord in item['box']]
+     #   st.write(f'Detected {label} with confidence {score} at location {box}')
 # Function to process uploaded image and prepare input for model
 
 def input_image_setup(uploaded_file):
@@ -58,10 +58,10 @@ if submit:
     # print results
     target_sizes = torch.tensor([image.size[::-1]])
     results = processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
-    prettify_results(results)
-#for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
- #       box = [round(i, 2) for i in box.tolist()]
-  #      print(
-   #         f"Detected {model.config.id2label[label.item()]} with confidence "
-    #        f"{round(score.item(), 3)} at location {box}"
-     #   )
+  #  prettify_results(results)
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        st.write(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+        )