auto_draft.py

# from utils.references import References
# from utils.prompts import generate_paper_prompts, generate_keywords_prompts, generate_experiments_prompts
# from utils.gpt_interaction import get_responses, extract_responses, extract_keywords, extract_json
# from utils.tex_processing import replace_title
# from utils.figures import generate_random_figures
# import datetime
# import shutil
# import time
# import logging
# import os
#
# TOTAL_TOKENS = 0
# TOTAL_PROMPTS_TOKENS = 0
# TOTAL_COMPLETION_TOKENS = 0
#
# def make_archive(source, destination):
#     base = os.path.basename(destination)
#     name = base.split('.')[0]
#     format = base.split('.')[1]
#     archive_from = os.path.dirname(source)
#     archive_to = os.path.basename(source.strip(os.sep))
#     shutil.make_archive(name, format, archive_from, archive_to)
#     shutil.move('%s.%s'%(name,format), destination)
#     return destination
#
#
# 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."
#     if print_out:
#         print(message)
#     logging.info(message)
#
# def pipeline(paper, section, save_to_path, model):
#     """
#     The main pipeline of generating a section.
#         1. Generate prompts.
#         2. Get responses from AI assistant.
#         3. Extract the section text.
#         4. Save the text to .tex file.
#     :return usage
#     """
#     print(f"Generating {section}...")
#     prompts = generate_paper_prompts(paper, section)
#     gpt_response, usage = get_responses(prompts, model)
#     output = extract_responses(gpt_response)
#     paper["body"][section] = output
#     tex_file = save_to_path + f"{section}.tex"
#     if section == "abstract":
#         with open(tex_file, "w") as f:
#             f.write(r"\begin{abstract}")
#         with open(tex_file, "a") as f:
#             f.write(output)
#         with open(tex_file, "a") as f:
#             f.write(r"\end{abstract}")
#     else:
#         with open(tex_file, "w") as f:
#             f.write(f"\section{{{section}}}\n")
#         with open(tex_file, "a") as f:
#             f.write(output)
#     time.sleep(5)
#     print(f"{section} has been generated. Saved to {tex_file}.")
#     return usage
#
#
#
# def generate_draft(title, description="", template="ICLR2022", model="gpt-4"):
#     """
#     The main pipeline of generating a paper.
#         1. Copy everything to the output folder.
#         2. Create references.
#         3. Generate each section using `pipeline`.
#         4. Post-processing: check common errors, fill the title, ...
#     """
#     paper = {}
#     paper_body = {}
#
#     # Create a copy in the outputs folder.
#     # todo: use copy_templates function instead.
#     now = datetime.datetime.now()
#     target_name = now.strftime("outputs_%Y%m%d_%H%M%S")
#     source_folder = f"latex_templates/{template}"
#     destination_folder = f"outputs/{target_name}"
#     shutil.copytree(source_folder, destination_folder)
#
#     bibtex_path = destination_folder + "/ref.bib"
#     save_to_path = destination_folder +"/"
#     replace_title(save_to_path, title)
#     logging.basicConfig( level=logging.INFO, filename=save_to_path+"generation.log")
#
#     # Generate keywords and references
#     print("Initialize the paper information ...")
#     prompts = generate_keywords_prompts(title, description)
#     gpt_response, usage = get_responses(prompts, model)
#     keywords = extract_keywords(gpt_response)
#     log_usage(usage, "keywords")
#     ref = References(load_papers = "") #todo: allow users to upload bibfile.
#     ref.collect_papers(keywords, method="arxiv") #todo: add more methods to find related papers
#     all_paper_ids = ref.to_bibtex(bibtex_path) #todo: this will used to check if all citations are in this list
#
#     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() #todo: see if this prompts can be compressed.
#     paper["body"] = paper_body
#     paper["bibtex"] = bibtex_path
#
#     print("Generating figures ...")
#     prompts = generate_experiments_prompts(paper)
#     gpt_response, usage = get_responses(prompts, model)
#     list_of_methods = list(extract_json(gpt_response))
#     log_usage(usage, "figures")
#     generate_random_figures(list_of_methods, save_to_path + "comparison.png")
#
#     for section in ["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", "abstract"]:
#         try:
#             usage = pipeline(paper, section, save_to_path, model=model)
#             log_usage(usage, section)
#         except Exception as e:
#             print(f"Failed to generate {section} due to the error: {e}")
#     print(f"The paper {title} has been generated. Saved to {save_to_path}.")
#     return make_archive(destination_folder, "output.zip")
#
# if __name__ == "__main__":
#     # title = "Training Adversarial Generative Neural Network with Adaptive Dropout Rate"
#     title = "Playing Atari Game with Deep Reinforcement Learning"
#     description = ""
#     template = "ICLR2022"
#     model = "gpt-4"
#     # model = "gpt-3.5-turbo"
#
#     generate_draft(title, description, template, model)