Spaces:
Sleeping
Sleeping
File size: 6,882 Bytes
0e072c0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
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 section_generation_bg, keywords_generation, figures_generation, section_generation
from references_generator import generate_top_k_references
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 ({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, max_num_refs=50, 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 generated 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
keywords = {keyword:max_kw_refs for keyword in keywords}
print(f"keywords: {keywords}\n\n")
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, max_num_refs=30, sections=None, bib_refs=None, model="gpt-4"):
# pre-processing `sections` parameter;
print("================PRE-PROCESSING================")
if sections is None:
sections = ["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", "abstract"]
# todo: add more parameters; select which section to generate; select maximum refs.
paper, destination_folder, _ = _generation_setup(title, description, template, tldr, max_kw_refs, max_num_refs, bib_refs)
# main components
for section in sections:
print(f"================Generate {section}================")
max_attempts = 4
attempts_count = 0
while attempts_count < max_attempts:
try:
usage = section_generation(paper, section, destination_folder, model=model)
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")
title = "Using interpretable boosting algorithms for modeling environmental and agricultural data"
description = ""
output = generate_draft(title, description, tldr=True, max_kw_refs=10)
print(output) |