Upload app.py

app.py

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+import torch
+from PIL import Image
+from transformers import DetrImageProcessor, DetrForObjectDetection
+import gradio as gr
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import io
+
+# Load model and processor once
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+def detect_objects(image):
+    # Run DETR model
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    # Draw boxes on image
+    fig, ax = plt.subplots(1)
+    ax.imshow(image)
+
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        xmin, ymin, xmax, ymax = box.tolist()
+        ax.add_patch(patches.Rectangle(
+            (xmin, ymin), xmax - xmin, ymax - ymin,
+            linewidth=2, edgecolor='red', facecolor='none'))
+        ax.text(xmin, ymin, f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}",
+                bbox=dict(facecolor='yellow', alpha=0.5), fontsize=8)
+
+    # Save output to bytes buffer
+    buf = io.BytesIO()
+    plt.axis("off")
+    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
+    plt.close(fig)
+    buf.seek(0)
+    return Image.open(buf)
+
+# Create Gradio interface
+interface = gr.Interface(fn=detect_objects,
+                         inputs=gr.Image(type="pil"),
+                         outputs="image",
+                         title="DETR Object Detection",
+                         description="Upload an image to detect objects using Facebook's DETR model.")
+
+# Launch the app locally
+if __name__ == "__main__":
+    interface.launch(share=True)