Create app.py

app.py

@@ -0,0 +1,33 @@
+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+from PIL import Image
+import requests
+import gradio as gr
+
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+def predict_bounding_boxes(imageurl:str):
+    try:
+        response = requests.get(imageurl, stream=True)
+        response.raise_for_status()  # Raise an exception for HTTP errors
+
+        image_data = Image.open(response.raw)
+        inputs = processor(images=image_data, return_tensors="pt")
+        outputs = model(**inputs)
+
+        target_sizes = torch.tensor([image_data.size[::-1]])
+        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+        detections = [{"score": score.item(), "label": model.config.id2label[label.item()], "box": box.tolist()} for score, label, box in zip(results["scores"], results["labels"], results["boxes"])]
+
+        # Wrap detections list inside a dictionary
+        return {"detections": detections}
+    
+    except Exception as e:
+        # Handle errors gracefully
+        return {"error": str(e)}
+
+app = gr.Interface(fn=predict_bounding_boxes, inputs="text", outputs="json")
+app.api = True
+app.launch(share=True)