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from utils.prompts import generate_paper_prompts, generate_keywords_prompts, generate_experiments_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
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.
def section_generation_bg(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_bg_summary_prompts(paper, section)
gpt_response, usage = get_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):
"""
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 = 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(output)
else:
with open(tex_file, "w") as f:
f.write(output)
time.sleep(5)
print(f"{section} has been generated. Saved to {tex_file}.")
return usage
# def keywords_generation(input_dict, model, max_kw_refs = 10):
# title = input_dict.get("title")
# description = input_dict.get("description", "")
# if title is not None:
# prompts = generate_keywords_prompts(title, description, max_kw_refs)
# gpt_response, usage = get_responses(prompts, model)
# keywords = extract_keywords(gpt_response)
# return keywords, usage
# else:
# raise ValueError("`input_dict` must include the key 'title'.")
def keywords_generation(input_dict):
title = input_dict.get("title")
max_attempts = 10
attempts_count = 0
while attempts_count < max_attempts:
try:
keywords, usage= get_gpt_responses(KEYWORDS_SYSTEM.format(min_refs_num=3, max_refs_num=5), title,
model="gpt-3.5-turbo", temperature=0.4)
print(keywords)
output = json.loads(keywords)
return output, usage
except json.decoder.JSONDecodeError:
attempts_count += 1
time.sleep(20)
raise RuntimeError("Fail to generate keywords.")
def figures_generation(paper, save_to_path, model):
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 |