Update app.py

app.py

@@ -1,7 +1,6 @@
 import os
 import gradio as gr
-from transformers import pipeline
-from transformers import DetrForObjectDetection, DetrConfig
+from transformers import pipeline, DetrForObjectDetection, DetrConfig, DetrImageProcessor
 
 # Initialize the configuration for DetrForObjectDetection
 config = DetrConfig.from_pretrained("facebook/detr-resnet-50")
@@ -9,8 +8,11 @@ config = DetrConfig.from_pretrained("facebook/detr-resnet-50")
 # Create the model for object detection using the specified configuration
 model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", config=config)
 
-# Initialize the object detection pipeline
-od_pipe = pipeline(task='object-detection', model=model)
+# Initialize the image processor for DETR
+image_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+
+# Initialize the object detection pipeline with the model and image processor
+od_pipe = pipeline(task='object-detection', model=model, image_processor=image_processor)
 
 def get_pipeline_prediction(pil_image):
     # Run the object detection pipeline on the input image
@@ -18,7 +20,7 @@ def get_pipeline_prediction(pil_image):
     
     # You might need to implement or adjust the rendering function based on the `pipeline_output`
     # The `render_results_in_image` function is assumed here to draw bounding boxes and labels on the input image,
-    # but you'll need to define it according to your specific needs. 
+    # but you'll need to define it according to your specific needs.
     # For now, the output is directly returned since the question doesn't define `render_results_in_image`.
     return pipeline_output