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)