auto-draft / auto_backgrounds.py
shaocongma
Remove some unnecessary codes.
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import os.path
from utils.references import References
from utils.file_operations import hash_name, make_archive, copy_templates
from utils.tex_processing import create_copies
from section_generator import keywords_generation, section_generation # figures_generation, section_generation_bg,
import logging
import time
TOTAL_TOKENS = 0
TOTAL_PROMPTS_TOKENS = 0
TOTAL_COMPLETION_TOKENS = 0
def log_usage(usage, generating_target, print_out=True):
global TOTAL_TOKENS
global TOTAL_PROMPTS_TOKENS
global TOTAL_COMPLETION_TOKENS
prompts_tokens = usage['prompt_tokens']
completion_tokens = usage['completion_tokens']
total_tokens = usage['total_tokens']
TOTAL_TOKENS += total_tokens
TOTAL_PROMPTS_TOKENS += prompts_tokens
TOTAL_COMPLETION_TOKENS += completion_tokens
message = f"For generating {generating_target}, {total_tokens} tokens have been used " \
f"({prompts_tokens} for prompts; {completion_tokens} for completion). " \
f"{TOTAL_TOKENS} tokens have been used in total.\n\n"
if print_out:
print(message)
logging.info(message)
def _generation_setup(title, description="", template="ICLR2022", tldr=False,
max_kw_refs=10, bib_refs=None, max_tokens=2048):
"""
This function handles the setup process for paper generation; it contains three folds
1. Copy the template to the outputs folder. Create the log file `generation.log`
2. Collect references based on the given `title` and `description`
3. Generate the basic `paper` object (a dictionary)
Parameters:
title (str): The title of the paper.
description (str, optional): A short description or abstract for the paper. Defaults to an empty string.
template (str, optional): The template to be used for paper generation. Defaults to "ICLR2022".
tldr (bool, optional): A flag indicating whether a TL;DR (Too Long; Didn't Read) summary should be used
for the collected papers. Defaults to False.
max_kw_refs (int, optional): The maximum number of references that can be associated with each keyword.
Defaults to 10.
max_num_refs (int, optional): The maximum number of references that can be included in the paper.
Defaults to 50.
bib_refs (list, optional): A list of pre-existing references in BibTeX format. Defaults to None.
Returns:
tuple: A tuple containing the following elements:
- paper (dict): A dictionary containing the generated paper information.
- destination_folder (str): The path to the destination folder where the generation log is saved.
- all_paper_ids (list): A list of all paper IDs collected for the references.
"""
# print("Generation setup...")
paper = {}
paper_body = {}
# Create a copy in the outputs folder.
bibtex_path, destination_folder = copy_templates(template, title)
logging.basicConfig(level=logging.INFO, filename=os.path.join(destination_folder, "generation.log") )
# Generate keywords and references
# print("Initialize the paper information ...")
input_dict = {"title": title, "description": description}
keywords, usage = keywords_generation(input_dict)
log_usage(usage, "keywords")
# generate keywords dictionary # todo: in some rare situations, collected papers will be an empty list.
keywords = {keyword:max_kw_refs for keyword in keywords}
ref = References(title, bib_refs)
ref.collect_papers(keywords, tldr=tldr)
all_paper_ids = ref.to_bibtex(bibtex_path)
print(f"The paper information has been initialized. References are saved to {bibtex_path}.")
paper["title"] = title
paper["description"] = description
paper["references"] = ref.to_prompts(max_tokens=max_tokens)
paper["body"] = paper_body
paper["bibtex"] = bibtex_path
return paper, destination_folder, all_paper_ids #todo: use `all_paper_ids` to check if all citations are in this list
def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-4"):
# todo: to match the current generation setup
paper, destination_folder, _ = _generation_setup(title, description, template, model)
for section in ["introduction", "related works", "backgrounds"]:
try:
usage = section_generation_bg(paper, section, destination_folder, model=model)
log_usage(usage, section)
except Exception as e:
message = f"Failed to generate {section}. {type(e).__name__} was raised: {e}"
print(message)
logging.info(message)
print(f"The paper '{title}' has been generated. Saved to {destination_folder}.")
input_dict = {"title": title, "description": description, "generator": "generate_backgrounds"}
filename = hash_name(input_dict) + ".zip"
return make_archive(destination_folder, filename)
def generate_draft(title, description="", template="ICLR2022",
tldr=True, max_kw_refs=10, sections=None, bib_refs=None, model="gpt-4"):
def _filter_sections(sections):
ordered_sections = ["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion",
"abstract"]
return [section for section in ordered_sections if section in sections]
# pre-processing `sections` parameter;
print("================START================")
print(f"Generating the paper '{title}'.")
print("\n") # todo: use a configuration file to define parameters
print("================PRE-PROCESSING================")
if sections is None:
sections = ["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", "abstract"]
else:
sections = _filter_sections(sections)
if model == "gpt-4":
max_tokens = 4096
else:
max_tokens = 2048
paper, destination_folder, _ = _generation_setup(title, description, template, tldr, max_kw_refs, bib_refs, max_tokens=max_tokens)
# main components
print(f"================PROCESSING================")
for section in sections:
print(f"Generate {section} part...")
max_attempts = 4
attempts_count = 0
while attempts_count < max_attempts:
try:
usage = section_generation(paper, section, destination_folder, model=model)
print(f"{section} part has been generated. ")
log_usage(usage, section)
break
except Exception as e:
message = f"Failed to generate {section}. {type(e).__name__} was raised: {e}\n"
print(message)
logging.info(message)
attempts_count += 1
time.sleep(15)
# post-processing
print("================POST-PROCESSING================")
create_copies(destination_folder)
input_dict = {"title": title, "description": description, "generator": "generate_draft"}
filename = hash_name(input_dict) + ".zip"
print("\nMission completed.\n")
return make_archive(destination_folder, filename)
if __name__ == "__main__":
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
target_title = "Using interpretable boosting algorithms for modeling environmental and agricultural data"
target_description = ""
output = generate_draft(target_title, target_description, tldr=True, max_kw_refs=10)
print(output)