Create app.py

app.py

@@ -0,0 +1,34 @@
+import os
+import gradio as gr
+from transformers import pipeline, DetrForObjectDetection, DetrConfig, DetrImageProcessor
+
+# Initialize the configuration for DetrForObjectDetection
+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 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
+    pipeline_output = od_pipe(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.
+    # For now, the output is directly returned since the question doesn't define `render_results_in_image`.
+    return pipeline_output
+
+demo = gr.Interface(
+  fn=get_pipeline_prediction,
+  inputs=gr.Image(label="Input image", 
+                  type="pil"),
+  outputs=gr.JSON(label="Detected objects")  # Adjusted to show JSON output if rendering function is not defined
+)
+
+demo.launch()