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| import time | |
| import importlib | |
| from toolbox import trimmed_format_exc, gen_time_str, get_log_folder | |
| from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder | |
| from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg | |
| import multiprocessing | |
| def get_class_name(class_string): | |
| import re | |
| # Use regex to extract the class name | |
| class_name = re.search(r'class (\w+)\(', class_string).group(1) | |
| return class_name | |
| def try_make_module(code, chatbot): | |
| module_file = 'gpt_fn_' + gen_time_str().replace('-','_') | |
| fn_path = f'{get_log_folder(plugin_name="gen_plugin_verify")}/{module_file}.py' | |
| with open(fn_path, 'w', encoding='utf8') as f: f.write(code) | |
| promote_file_to_downloadzone(fn_path, chatbot=chatbot) | |
| class_name = get_class_name(code) | |
| manager = multiprocessing.Manager() | |
| return_dict = manager.dict() | |
| p = multiprocessing.Process(target=is_function_successfully_generated, args=(fn_path, class_name, return_dict)) | |
| # only has 10 seconds to run | |
| p.start(); p.join(timeout=10) | |
| if p.is_alive(): p.terminate(); p.join() | |
| p.close() | |
| return return_dict["success"], return_dict['traceback'] | |
| # check is_function_successfully_generated | |
| def is_function_successfully_generated(fn_path, class_name, return_dict): | |
| return_dict['success'] = False | |
| return_dict['traceback'] = "" | |
| try: | |
| # Create a spec for the module | |
| module_spec = importlib.util.spec_from_file_location('example_module', fn_path) | |
| # Load the module | |
| example_module = importlib.util.module_from_spec(module_spec) | |
| module_spec.loader.exec_module(example_module) | |
| # Now you can use the module | |
| some_class = getattr(example_module, class_name) | |
| # Now you can create an instance of the class | |
| instance = some_class() | |
| return_dict['success'] = True | |
| return | |
| except: | |
| return_dict['traceback'] = trimmed_format_exc() | |
| return | |
| def subprocess_worker(code, file_path, return_dict): | |
| return_dict['result'] = None | |
| return_dict['success'] = False | |
| return_dict['traceback'] = "" | |
| try: | |
| module_file = 'gpt_fn_' + gen_time_str().replace('-','_') | |
| fn_path = f'{get_log_folder(plugin_name="gen_plugin_run")}/{module_file}.py' | |
| with open(fn_path, 'w', encoding='utf8') as f: f.write(code) | |
| class_name = get_class_name(code) | |
| # Create a spec for the module | |
| module_spec = importlib.util.spec_from_file_location('example_module', fn_path) | |
| # Load the module | |
| example_module = importlib.util.module_from_spec(module_spec) | |
| module_spec.loader.exec_module(example_module) | |
| # Now you can use the module | |
| some_class = getattr(example_module, class_name) | |
| # Now you can create an instance of the class | |
| instance = some_class() | |
| return_dict['result'] = instance.run(file_path) | |
| return_dict['success'] = True | |
| except: | |
| return_dict['traceback'] = trimmed_format_exc() | |