Add NuMarkdown-8B-Thinking OCR script with reasoning capabilities

numarkdown-ocr.py

@@ -0,0 +1,627 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets",
+#     "huggingface-hub[hf_transfer]",
+#     "pillow",
+#     "vllm",
+#     "tqdm",
+#     "toolz",
+#     "torch",  # Added for CUDA check
+# ]
+#
+# ///
+
+"""
+Convert document images to markdown using NuMarkdown-8B-Thinking with vLLM.
+
+This script processes images through the NuMarkdown model to extract
+text with advanced reasoning capabilities, ideal for complex document understanding.
+
+Features:
+- Reasoning-based document analysis with thinking tokens
+- Superior table extraction and formatting
+- Complex layout understanding
+- Mathematical formula recognition
+- Clean markdown output generation
+- Optional thinking trace inclusion
+"""
+
+import argparse
+import base64
+import io
+import json
+import logging
+import os
+import re
+import sys
+from typing import Any, Dict, List, Union, Optional, Tuple
+from datetime import datetime
+
+import torch
+from datasets import load_dataset
+from huggingface_hub import DatasetCard, login
+from PIL import Image
+from toolz import partition_all
+from tqdm.auto import tqdm
+from vllm import LLM, SamplingParams
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def check_cuda_availability():
+    """Check if CUDA is available and exit if not."""
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        logger.error("Please run on a machine with a CUDA-capable GPU.")
+        sys.exit(1)
+    else:
+        logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name(0)}")
+
+
+def validate_and_resize_image(
+    image: Image.Image,
+    min_pixels: int = 100 * 28 * 28,
+    max_pixels: int = 5000 * 28 * 28,
+) -> Image.Image:
+    """Validate and resize image to meet pixel constraints if necessary."""
+    width, height = image.size
+    total_pixels = width * height
+    
+    if total_pixels < min_pixels or total_pixels > max_pixels:
+        # Calculate scaling factor
+        if total_pixels < min_pixels:
+            scale = (min_pixels / total_pixels) ** 0.5
+        else:
+            scale = (max_pixels / total_pixels) ** 0.5
+        
+        new_width = int(width * scale)
+        new_height = int(height * scale)
+        
+        logger.debug(f"Resizing image from {width}x{height} to {new_width}x{new_height}")
+        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+    
+    return image
+
+
+def extract_answer_from_thinking(text: str, include_thinking: bool = False) -> str:
+    """
+    Extract the final answer from NuMarkdown's thinking output.
+    
+    The model generates output in format:
+    <think>reasoning process...</think>
+    <answer>final markdown output</answer>
+    """
+    if include_thinking:
+        # Return the full output including thinking traces
+        return text.strip()
+    
+    # Extract content between <answer> tags
+    answer_pattern = r'<answer>(.*?)</answer>'
+    answer_match = re.search(answer_pattern, text, re.DOTALL)
+    
+    if answer_match:
+        return answer_match.group(1).strip()
+    
+    # If no answer tags found, check if the entire text is markdown
+    # (sometimes the model might not use tags)
+    if not '<think>' in text and not '<answer>' in text:
+        return text.strip()
+    
+    # Fallback: return everything after </think> if present
+    think_end = text.find('</think>')
+    if think_end != -1:
+        remaining = text[think_end + 8:].strip()
+        # Remove <answer> tags if present
+        remaining = remaining.replace('<answer>', '').replace('</answer>', '').strip()
+        return remaining
+    
+    # Last resort: return the full text
+    logger.warning("Could not extract answer from thinking tokens, returning full text")
+    return text.strip()
+
+
+def make_numarkdown_message(
+    image: Union[Image.Image, Dict[str, Any], str],
+    prompt: str = "Convert this document to markdown. Focus on preserving structure, tables, formulas, and all textual content.",
+) -> List[Dict]:
+    """Create chat message for NuMarkdown processing."""
+    # Convert to PIL Image if needed
+    if isinstance(image, Image.Image):
+        pil_img = image.convert("RGB")
+    elif isinstance(image, dict) and "bytes" in image:
+        pil_img = Image.open(io.BytesIO(image["bytes"])).convert("RGB")
+    elif isinstance(image, str):
+        pil_img = Image.open(image).convert("RGB")
+    else:
+        raise ValueError(f"Unsupported image type: {type(image)}")
+    
+    # Validate and resize if necessary
+    pil_img = validate_and_resize_image(pil_img)
+    
+    # Convert to base64 data URI
+    buf = io.BytesIO()
+    pil_img.save(buf, format="PNG")
+    data_uri = f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}"
+    
+    # Return message in vLLM chat format
+    return [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": data_uri}},
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+
+
+def create_dataset_card(
+    source_dataset: str,
+    model: str,
+    num_samples: int,
+    processing_time: str,
+    batch_size: int,
+    max_model_len: int,
+    max_tokens: int,
+    gpu_memory_utilization: float,
+    include_thinking: bool,
+    image_column: str = "image",
+    split: str = "train",
+) -> str:
+    """Create a dataset card documenting the OCR process."""
+    model_name = model.split("/")[-1]
+    
+    return f"""---
+tags:
+- ocr
+- document-processing
+- numarkdown
+- markdown
+- reasoning
+- thinking-tokens
+- uv-script
+- generated
+---
+
+# Document OCR using {model_name}
+
+This dataset contains markdown-formatted OCR results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using NuMarkdown-8B-Thinking.
+
+## Processing Details
+
+- **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
+- **Model**: [{model}](https://huggingface.co/{model})
+- **Number of Samples**: {num_samples:,}
+- **Processing Time**: {processing_time}
+- **Processing Date**: {datetime.now().strftime("%Y-%m-%d %H:%M UTC")}
+
+### Configuration
+
+- **Image Column**: `{image_column}`
+- **Output Column**: `markdown`
+- **Dataset Split**: `{split}`
+- **Batch Size**: {batch_size}
+- **Max Model Length**: {max_model_len:,} tokens
+- **Max Output Tokens**: {max_tokens:,}
+- **GPU Memory Utilization**: {gpu_memory_utilization:.1%}
+- **Thinking Traces**: {"Included" if include_thinking else "Excluded (only final answers)"}
+
+## Model Information
+
+NuMarkdown-8B-Thinking is a state-of-the-art reasoning-based document OCR model that excels at:
+- 🧠 **Reasoning Process** - Analyzes document layout before generation
+- 📊 **Complex Tables** - Superior table extraction and formatting
+- 📐 **Mathematical Formulas** - Accurate LaTeX/math notation preservation
+- 📝 **Document Structure** - Maintains hierarchical document organization
+- 🔍 **Layout Analysis** - Understands complex multi-column layouts
+- ✨ **Clean Output** - Generates well-formatted markdown
+
+### Thinking Tokens
+
+This model uses a unique "thinking" process where it:
+1. Analyzes the document structure internally (`<think>` phase)
+2. Generates the final markdown output (`<answer>` phase)
+
+{"The dataset includes both thinking traces and final answers." if include_thinking else "Only the final answers are included (thinking traces removed)."}
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The extracted text in markdown format
+- `inference_info`: JSON list tracking all OCR models applied to this dataset
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+# Load the dataset
+dataset = load_dataset("{{output_dataset_id}}", split="{split}")
+
+# Access the markdown text
+for example in dataset:
+    print(example["markdown"])
+    break
+
+# View all OCR models applied to this dataset
+inference_info = json.loads(dataset[0]["inference_info"])
+for info in inference_info:
+    print(f"Column: {{info['column_name']}} - Model: {{info['model_id']}}")
+```
+
+## Reproduction
+
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) NuMarkdown OCR script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/numarkdown-ocr.py \\
+    {source_dataset} \\
+    <output-dataset> \\
+    --image-column {image_column} \\
+    --batch-size {batch_size} \\
+    --max-model-len {max_model_len} \\
+    --max-tokens {max_tokens} \\
+    --gpu-memory-utilization {gpu_memory_utilization} \\
+    {"--include-thinking" if include_thinking else ""}
+```
+
+## Performance
+
+- **Processing Speed**: ~{num_samples / (float(processing_time.split()[0]) * 60):.1f} images/second
+- **GPU Configuration**: vLLM with {gpu_memory_utilization:.0%} GPU memory utilization
+- **Model Size**: 8.29B parameters
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+"""
+
+
+def main(
+    input_dataset: str,
+    output_dataset: str,
+    image_column: str = "image",
+    batch_size: int = 16,
+    model: str = "numind/NuMarkdown-8B-Thinking",
+    max_model_len: int = 16384,
+    max_tokens: int = 8192,
+    gpu_memory_utilization: float = 0.9,
+    hf_token: str = None,
+    split: str = "train",
+    max_samples: int = None,
+    private: bool = False,
+    shuffle: bool = False,
+    seed: int = 42,
+    include_thinking: bool = False,
+    temperature: float = 0.0,
+    custom_prompt: Optional[str] = None,
+):
+    """Process images from HF dataset through NuMarkdown model."""
+    
+    # Check CUDA availability first
+    check_cuda_availability()
+    
+    # Track processing start time
+    start_time = datetime.now()
+    
+    # Enable HF_TRANSFER for faster downloads
+    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+    
+    # Login to HF if token provided
+    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+    
+    # Load dataset
+    logger.info(f"Loading dataset: {input_dataset}")
+    dataset = load_dataset(input_dataset, split=split)
+    
+    # Validate image column
+    if image_column not in dataset.column_names:
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+    
+    # Shuffle if requested
+    if shuffle:
+        logger.info(f"Shuffling dataset with seed {seed}")
+        dataset = dataset.shuffle(seed=seed)
+    
+    # Limit samples if requested
+    if max_samples:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+        logger.info(f"Limited to {len(dataset)} samples")
+    
+    # Initialize vLLM with trust_remote_code for NuMarkdown
+    logger.info(f"Initializing vLLM with model: {model}")
+    llm = LLM(
+        model=model,
+        trust_remote_code=True,  # Required for NuMarkdown
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_memory_utilization,
+        limit_mm_per_prompt={"image": 1},
+    )
+    
+    # Set up sampling parameters
+    sampling_params = SamplingParams(
+        temperature=temperature,
+        max_tokens=max_tokens,
+    )
+    
+    # Use custom prompt if provided, otherwise use default
+    prompt = custom_prompt or "Convert this document to markdown. Focus on preserving structure, tables, formulas, and all textual content."
+    
+    # Process images in batches
+    all_markdown = []
+    
+    logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
+    logger.info(f"Including thinking traces: {include_thinking}")
+    
+    # Process in batches to avoid memory issues
+    for batch_indices in tqdm(
+        partition_all(batch_size, range(len(dataset))),
+        total=(len(dataset) + batch_size - 1) // batch_size,
+        desc="OCR processing",
+    ):
+        batch_indices = list(batch_indices)
+        batch_images = [dataset[i][image_column] for i in batch_indices]
+        
+        try:
+            # Create messages for batch
+            batch_messages = [
+                make_numarkdown_message(img, prompt) for img in batch_images
+            ]
+            
+            # Process with vLLM
+            outputs = llm.chat(batch_messages, sampling_params)
+            
+            # Extract markdown from outputs
+            for output in outputs:
+                raw_text = output.outputs[0].text.strip()
+                # Extract answer from thinking tokens
+                markdown_text = extract_answer_from_thinking(raw_text, include_thinking)
+                all_markdown.append(markdown_text)
+        
+        except Exception as e:
+            logger.error(f"Error processing batch: {e}")
+            # Add error placeholders for failed batch
+            all_markdown.extend(["[OCR FAILED]"] * len(batch_images))
+    
+    # Add markdown column to dataset
+    logger.info("Adding markdown column to dataset")
+    dataset = dataset.add_column("markdown", all_markdown)
+    
+    # Handle inference_info tracking
+    logger.info("Updating inference_info...")
+    
+    # Check for existing inference_info
+    if "inference_info" in dataset.column_names:
+        # Parse existing info from first row (all rows have same info)
+        try:
+            existing_info = json.loads(dataset[0]["inference_info"])
+            if not isinstance(existing_info, list):
+                existing_info = [existing_info]  # Convert old format to list
+        except (json.JSONDecodeError, TypeError):
+            existing_info = []
+        # Remove old column to update it
+        dataset = dataset.remove_columns(["inference_info"])
+    else:
+        existing_info = []
+    
+    # Add new inference info
+    new_info = {
+        "column_name": "markdown",
+        "model_id": model,
+        "processing_date": datetime.now().isoformat(),
+        "batch_size": batch_size,
+        "max_tokens": max_tokens,
+        "gpu_memory_utilization": gpu_memory_utilization,
+        "max_model_len": max_model_len,
+        "include_thinking": include_thinking,
+        "temperature": temperature,
+        "prompt": prompt,
+        "script": "numarkdown-ocr.py",
+        "script_version": "1.0.0",
+        "script_url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/numarkdown-ocr.py"
+    }
+    existing_info.append(new_info)
+    
+    # Add updated inference_info column
+    info_json = json.dumps(existing_info, ensure_ascii=False)
+    dataset = dataset.add_column("inference_info", [info_json] * len(dataset))
+    
+    # Push to hub
+    logger.info(f"Pushing to {output_dataset}")
+    dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
+    
+    # Calculate processing time
+    end_time = datetime.now()
+    processing_duration = end_time - start_time
+    processing_time = f"{processing_duration.total_seconds() / 60:.1f} minutes"
+    
+    # Create and push dataset card
+    logger.info("Creating dataset card...")
+    card_content = create_dataset_card(
+        source_dataset=input_dataset,
+        model=model,
+        num_samples=len(dataset),
+        processing_time=processing_time,
+        batch_size=batch_size,
+        max_model_len=max_model_len,
+        max_tokens=max_tokens,
+        gpu_memory_utilization=gpu_memory_utilization,
+        include_thinking=include_thinking,
+        image_column=image_column,
+        split=split,
+    )
+    
+    card = DatasetCard(card_content)
+    card.push_to_hub(output_dataset, token=HF_TOKEN)
+    logger.info("✅ Dataset card created and pushed!")
+    
+    logger.info("✅ OCR conversion complete!")
+    logger.info(
+        f"Dataset available at: https://huggingface.co/datasets/{output_dataset}"
+    )
+
+
+if __name__ == "__main__":
+    # Show example usage if no arguments
+    if len(sys.argv) == 1:
+        print("=" * 80)
+        print("NuMarkdown-8B-Thinking OCR with Reasoning")
+        print("=" * 80)
+        print("\nThis script converts document images to markdown using")
+        print("the NuMarkdown-8B-Thinking model with advanced reasoning capabilities.")
+        print("\nFeatures:")
+        print("- 🧠 Reasoning-based document analysis")
+        print("- 📊 Superior table extraction and formatting")
+        print("- 📐 Mathematical formula recognition")
+        print("- 📝 Complex layout understanding")
+        print("- ✨ Clean markdown generation")
+        print("- 🔍 Optional thinking trace inclusion")
+        print("\nExample usage:")
+        print("\n1. Basic OCR conversion:")
+        print("   uv run numarkdown-ocr.py document-images markdown-docs")
+        print("\n2. Include thinking traces:")
+        print("   uv run numarkdown-ocr.py complex-docs analyzed-docs --include-thinking")
+        print("\n3. With custom settings:")
+        print("   uv run numarkdown-ocr.py scientific-papers extracted-text \\")
+        print("       --batch-size 8 \\")
+        print("       --max-tokens 8192 \\")
+        print("       --gpu-memory-utilization 0.9")
+        print("\n4. Process a subset for testing:")
+        print("   uv run numarkdown-ocr.py large-dataset test-output --max-samples 10")
+        print("\n5. Custom prompt for specific needs:")
+        print("   uv run numarkdown-ocr.py invoices invoice-data \\")
+        print('       --custom-prompt "Extract all invoice details including line items"')
+        print("\n6. Running on HF Jobs:")
+        print("   hf jobs uv run --flavor l4x1 \\")
+        print('     -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \\')
+        print("     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/numarkdown-ocr.py \\")
+        print("       your-document-dataset \\")
+        print("       your-markdown-output")
+        print("\n" + "=" * 80)
+        print("\nFor full help, run: uv run numarkdown-ocr.py --help")
+        sys.exit(0)
+    
+    parser = argparse.ArgumentParser(
+        description="OCR images to markdown using NuMarkdown-8B-Thinking with reasoning",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Basic usage
+  uv run numarkdown-ocr.py my-images-dataset ocr-results
+  
+  # Include thinking traces in output
+  uv run numarkdown-ocr.py documents analyzed-docs --include-thinking
+  
+  # Process subset for testing
+  uv run numarkdown-ocr.py large-dataset test-output --max-samples 100
+  
+  # Custom prompt for specific extraction
+  uv run numarkdown-ocr.py forms form-data --custom-prompt "Extract all form fields and values"
+  
+  # Random sample from dataset
+  uv run numarkdown-ocr.py ordered-dataset random-sample --max-samples 50 --shuffle
+        """,
+    )
+    
+    parser.add_argument("input_dataset", help="Input dataset ID from Hugging Face Hub")
+    parser.add_argument("output_dataset", help="Output dataset ID for Hugging Face Hub")
+    parser.add_argument(
+        "--image-column",
+        default="image",
+        help="Column containing images (default: image)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=16,
+        help="Batch size for processing (default: 16, lower than others due to model size)",
+    )
+    parser.add_argument(
+        "--model",
+        default="numind/NuMarkdown-8B-Thinking",
+        help="Model to use (default: numind/NuMarkdown-8B-Thinking)",
+    )
+    parser.add_argument(
+        "--max-model-len",
+        type=int,
+        default=16384,
+        help="Maximum model context length (default: 16384)",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=8192,
+        help="Maximum tokens to generate (default: 8192)",
+    )
+    parser.add_argument(
+        "--gpu-memory-utilization",
+        type=float,
+        default=0.9,
+        help="GPU memory utilization (default: 0.9)",
+    )
+    parser.add_argument("--hf-token", help="Hugging Face API token")
+    parser.add_argument(
+        "--split", default="train", help="Dataset split to use (default: train)"
+    )
+    parser.add_argument(
+        "--max-samples",
+        type=int,
+        help="Maximum number of samples to process (for testing)",
+    )
+    parser.add_argument(
+        "--private", action="store_true", help="Make output dataset private"
+    )
+    parser.add_argument(
+        "--shuffle",
+        action="store_true",
+        help="Shuffle the dataset before processing (useful for random sampling)",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for shuffling (default: 42)",
+    )
+    parser.add_argument(
+        "--include-thinking",
+        action="store_true",
+        help="Include thinking traces in output (default: only final answers)",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.0,
+        help="Temperature for generation (default: 0.0 for deterministic)",
+    )
+    parser.add_argument(
+        "--custom-prompt",
+        type=str,
+        help="Custom prompt for the model (overrides default)",
+    )
+    
+    args = parser.parse_args()
+    
+    main(
+        input_dataset=args.input_dataset,
+        output_dataset=args.output_dataset,
+        image_column=args.image_column,
+        batch_size=args.batch_size,
+        model=args.model,
+        max_model_len=args.max_model_len,
+        max_tokens=args.max_tokens,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+        hf_token=args.hf_token,
+        split=args.split,
+        max_samples=args.max_samples,
+        private=args.private,
+        shuffle=args.shuffle,
+        seed=args.seed,
+        include_thinking=args.include_thinking,
+        temperature=args.temperature,
+        custom_prompt=args.custom_prompt,
+    )