Spaces:
Running
Running
| import openai | |
| import re | |
| import os | |
| import json | |
| import logging | |
| log = logging.getLogger(__name__) | |
| openai.api_key = os.environ['OPENAI_API_KEY'] | |
| 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): | |
| 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 | |
| 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") |