import openai import re import json import logging log = logging.getLogger(__name__) def extract_responses(assistant_message): # pattern = re.compile(r"f\.write\(r'{1,3}(.*?)'{0,3}\){0,1}$", re.DOTALL) pattern = re.compile(r"f\.write\(r['\"]{1,3}(.*?)['\"]{0,3}\){0,1}$", re.DOTALL) match = re.search(pattern, assistant_message) if match: return match.group(1) else: log.info("Responses are not put in Python codes. Directly return assistant_message.\n") log.info(f"assistant_message: {assistant_message}") return assistant_message def extract_keywords(assistant_message, default_keywords=None): if default_keywords is None: default_keywords = {"machine learning": 5} try: keywords = json.loads(assistant_message) except ValueError: log.info("Responses are not in json format. Return the default dictionary.\n ") log.info(f"assistant_message: {assistant_message}") return default_keywords return keywords def extract_section_name(assistant_message, default_section_name=""): try: keywords = json.loads(assistant_message) except ValueError: log.info("Responses are not in json format. Return None.\n ") log.info(f"assistant_message: {assistant_message}") return default_section_name return keywords def extract_json(assistant_message, default_output=None): if default_output is None: default_keys = ["Method 1", "Method 2"] else: default_keys = default_output try: dict = json.loads(assistant_message) except: log.info("Responses are not in json format. Return the default keys.\n ") log.info(f"assistant_message: {assistant_message}") return default_keys return dict.keys() def get_responses(user_message, model="gpt-4", temperature=0.4, openai_key=None): if openai.api_key is None and openai_key is None: raise ValueError("OpenAI API key must be provided.") if openai_key is not None: openai.api_key = openai_key conversation_history = [ {"role": "system", "content": "You are an assistant in writing machine learning papers."} ] conversation_history.append({"role": "user", "content": user_message}) response = openai.ChatCompletion.create( model=model, messages=conversation_history, n=1, # Number of responses you want to generate temperature=temperature, # Controls the creativity of the generated response ) assistant_message = response['choices'][0]["message"]["content"] usage = response['usage'] log.info(assistant_message) return assistant_message, usage def get_gpt_responses(systems, prompts, model="gpt-4", temperature=0.4): conversation_history = [ {"role": "system", "content": systems}, {"role": "user", "content": prompts} ] response = openai.ChatCompletion.create( model=model, messages=conversation_history, n=1, # Number of responses you want to generate temperature=temperature, # Controls the creativity of the generated response ) assistant_message = response['choices'][0]["message"]["content"] usage = response['usage'] log.info(assistant_message) return assistant_message, usage if __name__ == "__main__": test_strings = [r"f.write(r'hello world')", r"f.write(r'''hello world''')", r"f.write(r'''hello world", r"f.write(r'''hello world'", r'f.write(r"hello world")', r'f.write(r"""hello world""")', r'f.write(r"""hello world"', r'f.write(r"""hello world'] for input_string in test_strings: print("input_string: ", input_string) pattern = re.compile(r"f\.write\(r['\"]{1,3}(.*?)['\"]{0,3}\){0,1}$", re.DOTALL) match = re.search(pattern, input_string) if match: extracted_string = match.group(1) print("Extracted string:", extracted_string) else: print("No match found")