Update app.py

app.py

@@ -15,7 +15,7 @@ def draw_detections(image, detections):
     for detection in detections:
         # Extract scores, labels, and bounding boxes properly
         score = detection['score']
-        label = detection['labels']
+        label = detection['label']
         box = detection['boxes']  # Make sure 'boxes' data structure matches expected in terms of naming and indexing
         x_min, y_min, x_max, y_max = map(int, [box[0], box[1], box[2], box[3]])
         
@@ -35,40 +35,41 @@ model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", config
 image_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
 od_pipe = pipeline(task='object-detection', model=model, image_processor=image_processor)
 
-##def get_pipeline_prediction(pil_image):
-##    try:
+def get_pipeline_prediction(pil_image):
+    try:
         # Run the object detection pipeline
-##        pipeline_output = od_pipe(pil_image)
+        pipeline_output = od_pipe(pil_image)
         
         # Draw the detection results on the image
-##        processed_image = draw_detections(pil_image, pipeline_output)
+        processed_image = draw_detections(pil_image, pipeline_output)
         
         # Provide both the image and the JSON detection results
-##        return processed_image, pipeline_output
-##    except Exception as e:
+        return processed_image, pipeline_output
+    except Exception as e:
         # Log the error
-##        print(f"An error occurred: {str(e)}")
+        print(f"An error occurred: {str(e)}")
         # Return a message and an empty JSON
-##        return pil_image, {"error": str(e)}
-def get_pipeline_prediction(pil_image):
-    try:
+        return pil_image, {"error": str(e)}
+
+##def get_pipeline_prediction(pil_image):
+##    try:
         # Run the object detection pipeline
-        pipeline_output = od_pipe(pil_image)
+##        pipeline_output = od_pipe(pil_image)
 
         # Debugging: print the keys in the output dictionary
-        if pipeline_output:
-            print("Keys available in the detection output:", pipeline_output[0].keys())
+##        if pipeline_output:
+##            print("Keys available in the detection output:", pipeline_output[0].keys())
 
         # Draw the detection results on the image
-        processed_image = draw_detections(pil_image, pipeline_output)
+##        processed_image = draw_detections(pil_image, pipeline_output)
         
         # Provide both the image and the JSON detection results
-        return processed_image, pipeline_output
-    except Exception as e:
+##        return processed_image, pipeline_output
+##    except Exception as e:
         # Log the error
-        print(f"An error occurred: {str(e)}")
+#3        print(f"An error occurred: {str(e)}")
         # Return a message and an empty JSON
-        return pil_image, {"error": str(e)}
+##        return pil_image, {"error": str(e)}
         
 demo = gr.Interface(
     fn=get_pipeline_prediction,