example_with_processor.py

"""
Example usage of LogitsProcessors for document parsing.

This example shows how to use:
- TableInsertionLogitsProcessor: Force \begin{tabular} at the start of every object
- RepetitionStopProcessor: Detect hallucination/repetition and force coordinate tokens
"""

import torch
from PIL import Image, ImageDraw
from transformers import AutoModel, AutoProcessor, AutoTokenizer, GenerationConfig
from postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text
from hf_logits_processor import TableInsertionLogitsProcessor, RepetitionStopProcessor

# Load model and processor
model_path = "nvidia/NVIDIA-Nemotron-Parse-2.0"  # Or use a local path
device = "cuda:0"

model = AutoModel.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

# Load image
image = Image.open('example.png').convert("RGB")
task_prompt = "</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>"

# Process image
inputs = processor(images=[image], text=task_prompt, return_tensors="pt", add_special_tokens=False).to(device)

generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)

# Create the table processor - inserts \begin{tabular} after every <x_...><y_...> that starts a new object
table_processor = TableInsertionLogitsProcessor(
    tokenizer=tokenizer,
    table_prefix="\\begin{tabular}"
)

# Create the repetition stop processor - detects hallucination and forces <x_...> tokens
repetition_processor = RepetitionStopProcessor(
    tokenizer=tokenizer,
    max_repetitions=10,  # Force stop after any pattern repeats 10+ times
    ngram_sizes=[3, 4, 5, 6],  # Check these n-gram sizes for repetition
    window_size=500  # Only check the last 500 tokens
)

# Generate with both logits processors
outputs = model.generate(
    **inputs,
    generation_config=generation_config,
    logits_processor=[table_processor, repetition_processor]
)

# Reset processor states for next generation (important for batch processing)
table_processor.reset()
repetition_processor.reset()

# Decode and process the generated text
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(outputs)
print('--------------------------------')
print("Generated text:", generated_text)
print('--------------------------------')
classes, bboxes, texts = extract_classes_bboxes(generated_text)
bboxes = [transform_bbox_to_original(bbox, image.width, image.height) for bbox in bboxes]

# Specify output formats for postprocessing
table_format = 'HTML'  # latex | HTML | markdown | json | json_hierarchical | csv
text_format = 'markdown'  # markdown | plain
blank_text_in_figures = False  # remove text inside 'Picture' class

texts = [
    postprocess_text(
        text, 
        cls=cls, 
        table_format=table_format, 
        text_format=text_format, 
        blank_text_in_figures=blank_text_in_figures
    ) 
    for text, cls in zip(texts, classes)
]

for cl, bb, txt in zip(classes, bboxes, texts):
    print(cl, ': ', txt)

# OPTIONAL - Draw bounding boxes
draw = ImageDraw.Draw(image)
for bbox in bboxes:
    draw.rectangle((bbox[0], bbox[1], (max(bbox[0], bbox[2])), (max(bbox[1], bbox[3]))), outline="red", width=2)

# Save or display the image
image.save("output_with_boxes.jpg")
# image.show()