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from transformers import AutoModel, AutoTokenizer
import os
import torch
class OCRModel:
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super(OCRModel, cls).__new__(cls)
cls._instance.initialize()
return cls._instance
def initialize(self):
self.tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
self.model = AutoModel.from_pretrained(
'ucaslcl/GOT-OCR2_0',
trust_remote_code=True,
low_cpu_mem_usage=True,
device_map='cuda' if torch.cuda.is_available() else 'cpu',
use_safetensors=True,
pad_token_id=self.tokenizer.eos_token_id
)
self.model = self.model.eval()
if torch.cuda.is_available():
self.model = self.model.cuda()
def process_image(self, image_path):
try:
result = self.model.chat(self.tokenizer, image_path, ocr_type='format')
return result
except Exception as e:
return str(e) |