Upload example.py

example.py

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+#!/usr/bin/env python3
+
+from __future__ import annotations
+
+from datasets import load_dataset
+from PIL import ImageDraw
+
+
+REPO_ID = "Yannik019/llm_pack_detection"
+
+
+def draw_first_item_location(row: dict):
+    image = row["image"].copy()
+    first_label = row["labels"][0]
+    x = row["annotation_x"][0]
+    y = row["annotation_y"][0]
+
+    draw = ImageDraw.Draw(image)
+    radius = 18
+    draw.ellipse((x - radius, y - radius, x + radius, y + radius), outline="red", width=6)
+    draw.line((x - 28, y, x + 28, y), fill="red", width=4)
+    draw.line((x, y - 28, x, y + 28), fill="red", width=4)
+    draw.text((x + 24, y - 32), first_label, fill="red")
+
+    return image, first_label, x, y
+
+
+def summarize_row(row: dict, index: int) -> None:
+    image, first_label, first_x, first_y = draw_first_item_location(row)
+    image_size = getattr(image, "size", None)
+    labels = ", ".join(row["labels"])
+
+    print(f"Row {index}")
+    print(f"  caption: {row['caption']}")
+    print(f"  bucket: {row['bucket']}")
+    print(f"  sample_id: {row['sample_id']}")
+    print(f"  annotation_count: {row['annotation_count']}")
+    print(f"  labels: {labels}")
+    print(f"  first_item: {first_label} @ ({first_x}, {first_y})")
+    print(f"  image_type: {type(image).__name__}")
+    print(f"  image_size: {image_size}")
+    image.show()
+
+
+def main() -> int:
+    dataset = load_dataset(REPO_ID, split="train")
+
+    print(dataset)
+    print(f"Loaded {len(dataset)} rows from {REPO_ID}")
+
+    limit = min(3, len(dataset))
+    for index in range(limit):
+        summarize_row(dataset[index], index)
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())