Upload deepseek-ocr2.py with huggingface_hub

deepseek-ocr2.py

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+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets>=4.0.0",
+#     "huggingface-hub",
+#     "pillow",
+#     "torch",
+#     "transformers>=5.0.0",
+#     "tqdm",
+#     "accelerate",
+# ]
+# ///
+
+"""Convert document images to markdown using DeepSeek-OCR-2 via transformers."""
+
+import argparse
+import json
+import os
+import sys
+import tempfile
+from datetime import datetime
+
+import torch
+from datasets import load_dataset, Dataset
+from huggingface_hub import login
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import AutoModel, AutoTokenizer
+
+PROMPT = "<image>\n<|grounding|>Convert the document to markdown. "
+
+
+def main(input_dataset: str, output_dataset: str, split: str = "train",
+         max_samples: int | None = None, image_column: str = "image"):
+
+    if not torch.cuda.is_available():
+        print("ERROR: CUDA not available. GPU required.")
+        sys.exit(1)
+    print(f"GPU: {torch.cuda.get_device_name(0)}")
+
+    token = os.environ.get("HF_TOKEN")
+    if token:
+        login(token=token)
+
+    print("Loading model deepseek-ai/DeepSeek-OCR-2...")
+    tokenizer = AutoTokenizer.from_pretrained(
+        "deepseek-ai/DeepSeek-OCR-2", trust_remote_code=True
+    )
+    model = AutoModel.from_pretrained(
+        "deepseek-ai/DeepSeek-OCR-2",
+        trust_remote_code=True,
+        use_safetensors=True,
+        torch_dtype=torch.bfloat16,
+    ).cuda()
+
+    print(f"Loading dataset {input_dataset}...")
+    ds = load_dataset(input_dataset, split=split)
+    if max_samples:
+        ds = ds.select(range(min(max_samples, len(ds))))
+    print(f"Processing {len(ds)} samples...")
+
+    results = []
+    with tempfile.TemporaryDirectory() as tmpdir:
+        for i, row in enumerate(tqdm(ds)):
+            img_path = os.path.join(tmpdir, f"img_{i}.jpg")
+            img = row[image_column]
+            if isinstance(img, dict):
+                img = Image.open(__import__("io").BytesIO(img["bytes"]))
+            img.save(img_path, format="JPEG", quality=95)
+
+            try:
+                out = model.infer(
+                    tokenizer,
+                    prompt=PROMPT,
+                    image_file=img_path,
+                    output_path=tmpdir,
+                    base_size=1024,
+                    image_size=768,
+                    crop_mode=True,
+                    save_results=False,
+                )
+                if i == 0:
+                    print(f"[DEBUG] out type={type(out)}, value={repr(out)[:200]}")
+                markdown = out if isinstance(out, str) else str(out)
+            except Exception as e:
+                print(f"Error on sample {i}: {e}")
+                markdown = ""
+
+            results.append({
+                "image": row[image_column],
+                "gt_json": row.get("gt_json", ""),
+                "markdown": markdown,
+                "inference_info": json.dumps([{
+                    "column_name": "markdown",
+                    "model_id": "deepseek-ai/DeepSeek-OCR-2",
+                    "processing_date": datetime.now().strftime("%Y-%m-%d"),
+                    "backend": "transformers",
+                }]),
+            })
+
+    print(f"Pushing to {output_dataset}...")
+    Dataset.from_list(results).push_to_hub(output_dataset, private=False)
+    print(f"Done → https://huggingface.co/datasets/{output_dataset}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input_dataset")
+    parser.add_argument("output_dataset")
+    parser.add_argument("--split", default="train")
+    parser.add_argument("--max-samples", type=int, default=None)
+    parser.add_argument("--image-column", default="image")
+    args = parser.parse_args()
+    main(args.input_dataset, args.output_dataset, args.split,
+         args.max_samples, args.image_column)