import os # laod environment variables from .env file import dotenv dotenv.load_dotenv() pdf_path = r"F:\桌面文件\composes测试\Desktop\2005C:雨量预报方法优劣的评价模型.pdf" output_dir = r'F:\桌面文件\我的vue项目\文档翻译项目\后端重构-api项目\storage\translate' # 清空output_dir # import shutil # shutil.rmtree(output_dir, ignore_errors=True) def test_use_api_key(): from gptpdf import parse_pdf api_key = os.getenv('OPENAI_API_KEY') base_url = os.getenv('OPENAI_API_BASE') # Manually provide OPENAI_API_KEY and OPEN_API_BASE content, image_paths = parse_pdf(pdf_path, output_dir=output_dir, api_key=api_key, base_url=base_url, model='gpt-4o', gpt_worker=6) print(content) print(image_paths) # also output_dir/output.md is generated def test_use_env(): from gptpdf import parse_pdf # Use OPENAI_API_KEY and OPENAI_API_BASE from environment variables content, image_paths = parse_pdf(pdf_path, output_dir=output_dir, model='gpt-4o', verbose=True) print(content) print(image_paths) # also output_dir/output.md is generated def test_azure(): from pdf_parse import parse_pdf api_key = '8ef0b4df45e444079cd5a4xxxxx' # Azure API Key base_url = 'https://xxx.openai.azure.com/' # Azure API Base URL model = 'azure_xxxx' # azure_ with deploy ID name (not open ai model name), e.g. azure_cpgpt4 # Use OPENAI_API_KEY and OPENAI_API_BASE from environment variables content, image_paths = parse_pdf(pdf_path, output_dir=output_dir, api_key=api_key, base_url=base_url, model=model, verbose=True) print(content) print(image_paths) def test_qwen_vl_max(): from pdf_parse import parse_pdf api_key = '28032c969954994065d5520e1155418b.u8iXzIijE3qvkXsZ' base_url = "https://open.bigmodel.cn/api/paas/v4" model = 'glm-4v-flash' content, image_paths = parse_pdf(pdf_path, output_dir=output_dir, api_key=api_key, base_url=base_url, model=model, verbose=True, temperature=0.5, max_tokens=1000, top_p=0.9, frequency_penalty=1) print(content) print(image_paths) if __name__ == '__main__': # test_use_api_key() # test_use_env() # test_azure() test_qwen_vl_max()