Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image, ImageDraw
+
+# Load the pre-trained DETR model and processor
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+def detect_objects(image: Image.Image) -> Image.Image:
+    try:
+        # Preprocess the image
+        inputs = processor(images=image, return_tensors="pt")
+        outputs = model(**inputs)
+
+        # Convert outputs to bounding boxes and labels
+        target_sizes = torch.tensor([image.size[::-1]])
+        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+        # Draw bounding boxes on the image
+        draw = ImageDraw.Draw(image)
+        for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+            box = [round(i, 2) for i in box.tolist()]
+            label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 3)}"
+            draw.rectangle(box, outline="red", width=3)
+            draw.text((box[0], box[1]), label_text, fill="red")
+        return image
+    except Exception as e:
+        print("Error during detection:", e)
+        return image  # In a robust production system, consider returning a message or a default image
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Image(label="Detection Result"),
+    title="Robust Object Detection with DETR",
+    description="Upload an image to detect objects using a pre-trained DETR model from Hugging Face Hub."
+)
+
+if __name__ == "__main__":
+    iface.launch()