Create app.py

app.py

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+import torch
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image
+import gradio as gr
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import io
+
+# Load the processor and model
+processor = DetrImageProcessor.from_pretrained('facebook/detr-resnet-101')
+model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101')
+
+def object_detection(image):
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
+
+    # Perform object detection
+    outputs = model(**inputs)
+
+    # Extract 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]
+
+    # Plot the image with bounding boxes
+    plt.figure(figsize=(16, 10))
+    plt.imshow(image)
+    ax = plt.gca()
+
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        xmin, ymin, xmax, ymax = box
+        width, height = xmax - xmin, ymax - ymin
+
+        ax.add_patch(plt.Rectangle((xmin, ymin), width, height, fill=False, color='red', linewidth=3))
+        text = f'{model.config.id2label[label.item()]}: {round(score.item(), 3)}'
+        ax.text(xmin, ymin, text, fontsize=15, bbox=dict(facecolor='yellow', alpha=0.5))
+
+    plt.axis('off')
+
+    # Save the plot to an image buffer
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    plt.close()
+
+    return buf
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=object_detection,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Image(type="pil", label="Detected Objects"),
+    title="Object Detection with DETR (ResNet-101)",
+    description="Upload an image and get object detection results using the DETR model with a ResNet-101 backbone.",
+)
+
+# Launch the Gradio interface
+if __name__ == "__main__":
+    demo.launch()