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import torch
from PIL import Image
from struct_eqtable import build_model
from pdf_extract_kit.registry.registry import MODEL_REGISTRY
@MODEL_REGISTRY.register("table_parsing_struct_eqtable")
class TableParsingStructEqTable:
def __init__(self, config):
"""
Initialize the TableParsingStructEqTable class.
Args:
config (dict): Configuration dictionary containing model parameters.
"""
assert torch.cuda.is_available(), "CUDA must be available for StructEqTable model."
self.model_dir = config.get('model_path', 'U4R/StructTable-InternVL2-1B')
self.max_new_tokens = config.get('max_new_tokens', 1024)
self.max_time = config.get('max_time', 30)
self.lmdeploy = config.get('lmdeploy', False)
self.flash_attn = config.get('flash_attn', True)
self.batch_size = config.get('batch_size', 1)
self.default_format = config.get('output_format', 'latex')
# Load the StructEqTable model
self.model = build_model(
model_ckpt=self.model_dir,
max_new_tokens=self.max_new_tokens,
max_time=self.max_time,
lmdeploy=self.lmdeploy,
flash_attn=self.flash_attn,
batch_size=self.batch_size,
).cuda()
def predict(self, images, result_path, output_format=None, **kwargs):
load_images = [Image.open(image_path) for image_path in images]
if output_format is None:
output_format = self.default_format
else:
if output_format not in ['latex', 'markdown', 'html']:
raise ValueError(f"Output format {output_format} is not supported.")
results = self.model(
load_images, output_format=output_format
)
return results
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