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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") | |