Create app.py
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app.py
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# 📷 Object Detection Demo | CPU-only HF Space
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import gradio as gr
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from transformers import pipeline
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from PIL import Image, ImageDraw
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# Load the DETR object-detection pipeline (CPU)
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detector = pipeline("object-detection", model="facebook/detr-resnet-50", device=-1)
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def detect_objects(image: Image.Image):
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# Run object detection
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outputs = detector(image)
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# Draw bounding boxes
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annotated = image.convert("RGB")
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draw = ImageDraw.Draw(annotated)
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table = []
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for obj in outputs:
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# DETR returns box as [xmin, ymin, xmax, ymax]
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xmin, ymin, xmax, ymax = obj["box"]
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label = obj["label"]
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score = round(obj["score"], 3)
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# draw box and label
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draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=2)
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draw.text((xmin, ymin - 10), f"{label} ({score})", fill="red")
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table.append([label, score])
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# Return the annotated image and a table of detections
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return annotated, table
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with gr.Blocks(title="📷✨ Object Detection Demo") as demo:
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gr.Markdown(
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"""
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# 📷✨ Object Detection
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Upload an image and let DETR (a Transformer-based model) identify objects in real time.
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"""
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)
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with gr.Row():
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img_in = gr.Image(type="pil", label="Upload Image")
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detect_btn = gr.Button("Detect Objects 🔍", variant="primary")
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img_out = gr.Image(label="Annotated Image")
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table_out = gr.Dataframe(
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headers=["Label", "Score"],
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datatype=["str", "number"],
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wrap=True,
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interactive=False,
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label="Detections"
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)
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detect_btn.click(detect_objects, inputs=img_in, outputs=[img_out, table_out])
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0")
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