Update app.py

app.py

@@ -1,26 +1,32 @@
-from transformers import DetrImageProcessor, DetrForObjectDetection
-import torch
-from PIL import Image
-import requests
+import os
+import gradio as gr
+from transformers import pipeline
+from transformers import DetrForObjectDetection, DetrConfig
 
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
+# Initialize the configuration for DetrForObjectDetection
+config = DetrConfig.from_pretrained("facebook/detr-resnet-50")
 
-# you can specify the revision tag if you don't want the timm dependency
-processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
-model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+# Create the model for object detection using the specified configuration
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", config=config)
 
-inputs = processor(images=image, return_tensors="pt")
-outputs = model(**inputs)
+# Initialize the object detection pipeline
+od_pipe = pipeline(task='object-detection', model=model)
 
-# convert outputs (bounding boxes and class logits) to COCO API
-# let's only keep detections with score > 0.9
-target_sizes = torch.tensor([image.size[::-1]])
-results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+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
 
-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}"
-    )
+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()