Create app.py

app.py

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