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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")
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