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# from utils.prompts import generate_paper_prompts, generate_keywords_prompts, generate_experiments_prompts, generate_bg_summary_prompts
from utils.prompts import generate_paper_prompts, generate_bg_summary_prompts
# from utils.gpt_interaction import get_responses #, extract_responses, extract_keywords, extract_json
from utils.figures import generate_random_figures
import time
import os
from utils.prompts import KEYWORDS_SYSTEM, SECTION_GENERATION_SYSTEM
from utils.gpt_interaction import get_gpt_responses
import json
# three GPT-based content generator:
# 1. section_generation: used to generate main content of the paper
# 2. keywords_generation: used to generate a json output {key1: output1, key2: output2} for multiple purpose.
# 3. figure_generation: used to generate sample figures.
# all generator should return the token usage.
MAX_ATTEMPTS = 6
def section_generation_bg(paper, section, save_to_path, model):
"""
todo: this part should be revised
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_bg_summary_prompts(paper, section)
# gpt_response, usage = get_responses(prompts, model)
gpt_response, usage = get_gpt_responses(prompts, model)
output = gpt_response # extract_responses(gpt_response)
paper["body"][section] = output
tex_file = os.path.join(save_to_path, f"{section}.tex")
# 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.upper()}}}\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 section_generation(paper, section, save_to_path, model, research_field="machine learning"):
"""
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
"""
prompts = generate_paper_prompts(paper, section)
output, usage = get_gpt_responses(SECTION_GENERATION_SYSTEM.format(research_field=research_field), prompts,
model=model, temperature=0.4)
paper["body"][section] = output
tex_file = os.path.join(save_to_path, f"{section}.tex")
with open(tex_file, "w") as f:
f.write(output)
time.sleep(5)
return usage
def keywords_generation(input_dict, default_keywords=None):
"""
Input:
input_dict: a dictionary containing the title of a paper.
default_keywords: if anything went wrong, return this keywords.
Output:
a dictionary including all keywords and their importance score.
Input example: {"title": "The title of a Machine Learning Paper"}
Output Example: {"machine learning": 5, "reinforcement learning": 2}
"""
title = input_dict.get("title")
attempts_count = 0
while (attempts_count < MAX_ATTEMPTS) and (title is not None):
try:
keywords, usage = get_gpt_responses(KEYWORDS_SYSTEM.format(min_refs_num=1, max_refs_num=10), title,
model="gpt-3.5-turbo", temperature=0.4)
print(keywords)
output = json.loads(keywords)
return output.keys(), usage
except json.decoder.JSONDecodeError:
attempts_count += 1
time.sleep(10)
# Default references
print("Error: Keywords generation has failed. Return the default keywords.")
if default_keywords is None or isinstance(default_keywords, dict):
return {"machine learning": 10}
else:
return default_keywords
# def figures_generation(paper, save_to_path, model):
# # todo: this function is not complete.
# prompts = generate_experiments_prompts(paper)
# gpt_response, usage = get_responses(prompts, model)
# list_of_methods = list(extract_json(gpt_response))
# generate_random_figures(list_of_methods, os.path.join(save_to_path, "comparison.png"))
# return usage
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