Files
ALwrity/lib/main_arxiv_to_blog.py
2024-01-13 16:27:28 +05:30

210 lines
9.0 KiB
Python

import sys
import os
import datetime
import tiktoken
from .arxiv_schlorly_research import fetch_arxiv_data, create_dataframe, get_arxiv_main_content
from .arxiv_schlorly_research import arxiv_bibtex, scrape_images_from_arxiv, download_image
from .arxiv_schlorly_research import read_written_ids, extract_arxiv_ids_from_line, append_id_to_file
from .write_research_review_blog import review_research_paper
from .combine_research_and_blog import blog_with_research
from .write_blog_scholar_paper import write_blog_from_paper
from .gpt_providers.gemini_pro_text import gemini_text_response
from .generate_image_from_prompt import generate_image
from .convert_content_to_markdown import convert_tomarkdown_format
from .get_blog_metadata import blog_metadata
from .get_code_examples import gemini_get_code_samples
from .save_blog_to_file import save_blog_to_file
from .take_url_screenshot import screenshot_api
from loguru import logger
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
def blog_arxiv_keyword(query):
""" Write blog on given arxiv paper."""
arxiv_id = None
arxiv_url = None
bibtex = None
research_review = None
column_names = ['Title', 'Date', 'Id', 'Summary', 'PDF URL']
papers = fetch_arxiv_data(query)
df = create_dataframe(papers, column_names)
for paper in papers:
# Extracting the arxiv_id
arxiv_id = paper[2].split('/')[-1]
arxiv_url = "https://browse.arxiv.org/html/" + arxiv_id
bibtex = arxiv_bibtex(arxiv_id)
logger.info(f"Get research paper text from the url: {arxiv_url}")
research_content = get_arxiv_main_content(arxiv_url)
num_tokens = num_tokens_from_string(research_content, "cl100k_base")
logger.info(f"Number of tokens sent: {num_tokens}")
# If the number of tokens is below the threshold, process and print the review
if 1000 < num_tokens < 30000:
logger.info(f"Writing research review on {paper[0]}")
research_review = review_research_paper(research_content)
research_review = f"\n{research_review}\n\n" + f"```{bibtex}```"
#research_review = research_review + "\n\n\n" + f"{df.to_markdown()}"
research_review = convert_tomarkdown_format(research_review, "gemini")
break
else:
# Skip to the next iteration if the condition is not met
continue
logger.info(f"Final scholar article: \n\n{research_review}\n")
# TBD: Scrape images from research reports and pass to vision to get conclusions out of it.
#image_urls = scrape_images_from_arxiv(arxiv_url)
#print("Downloading images found on the page:")
#for img_url in image_urls:
# download_image(img_url, arxiv_url)
try:
blog_postprocessing(arxiv_id, research_review)
except Exception as err:
logger.error(f"Failed in blog post processing: {err}")
sys.exit(1)
logger.info(f"\n\n ################ Finished writing Blog for : #################### \n")
def blog_arxiv_url_list(file_path):
""" Write blogs on all the arxiv links given in a file. """
extracted_ids = []
try:
with open(file_path, 'r') as file:
for line in file:
arxiv_id = extract_arxiv_ids_from_line(line)
if arxiv_id:
extracted_ids.append(arxiv_id)
except FileNotFoundError:
logger.error(f"File not found: {file_path}")
raise FileNotFoundError
except Exception as e:
logger.error(f"Error while reading the file: {e}")
raise e
# Read already written IDs
written_ids = read_written_ids('papers_already_written_on.txt')
# Write blogs on each of arxiv_id from the file.
for arxiv_id in extracted_ids:
# Check if we have already written on this research_paper. For this, all arxiv ids are written in
# a file called 'papers_already_written_on.txt'. If arxiv ID is found in this file, skip writing again.
# YUP, use a DB. KISS for now.
written_ids = read_written_ids('papers_already_written_on.txt')
# Loop through extracted IDs
for arxiv_id in extracted_ids:
if arxiv_id not in written_ids:
# This ID has not been written on yet
arxiv_url = "https://browse.arxiv.org/html/" + arxiv_id
logger.info(f"Get research paper text from the url: {arxiv_url}")
research_content = get_arxiv_main_content(arxiv_url)
try:
num_tokens = num_tokens_from_string(research_content, "cl100k_base")
except Exception as err:
logger.error(f"Failed in counting tokens: {err}")
sys.exit(1)
logger.info(f"Number of tokens sent: {num_tokens}")
# If the number of tokens is below the threshold, process and print the review
# FIXME: Docs over 30k tokens, need to be chunked and summarized.
if 1000 < num_tokens < 30000:
try:
logger.info(f"Getting bibtex for arxiv ID: {arxiv_id}")
bibtex = arxiv_bibtex(arxiv_id)
except Exception as err:
logger.error(f"Failed to get Bibtex: {err}")
try:
logger.info(f"Writing a research review..")
research_review = review_research_paper(research_content, "gemini")
logger.info(f"Research Review: \n{research_review}\n\n")
except Exception as err:
logger.error(f"Failed to write review on research paper: {arxiv_id}{err}")
research_blog = write_blog_from_paper(research_content, "gemini")
logger.info(f"\n\nResearch Blog: {research_blog}\n\n")
research_blog = f"\n{research_review}\n\n" + f"```\n{bibtex}\n```"
#research_review = blog_with_research(research_review, research_blog, "gemini")
#logger.info(f"\n\n\nBLOG_WITH_RESEARCh: {research_review}\n\n\n")
research_review = convert_tomarkdown_format(research_review, "gemini")
research_review = f"\n{research_review}\n\n" + f"```{bibtex}```"
logger.info(f"Final blog from research paper: \n\n{research_review}\n\n\n")
try:
blog_postprocessing(arxiv_id, research_review)
except Exception as err:
logger.error(f"Failed in blog post processing: {err}")
sys.exit(1)
logger.info(f"\n\n ################ Finished writing Blog for : #################### \n")
else:
# Skip to the next iteration if the condition is not met
logger.error("FIXME: Docs over 30k tokens, need to be chunked and summarized.")
continue
else:
logger.warning(f"Already written, skip writing on Arxiv paper ID: {arxiv_id}")
def blog_postprocessing(arxiv_id, research_review):
""" Common function to do blog postprocessing. """
try:
append_id_to_file(arxiv_id, "papers_already_written_on.txt")
except Exception as err:
logger.error(f"Failed to write/append ID to papers_already_written_on.txt: {err}")
raise err
try:
blog_title, blog_meta_desc, blog_tags, blog_categories = blog_metadata(research_review, "gemini")
except Exception as err:
logger.error(f"Failed to get blog metadata: {err}")
raise err
try:
arxiv_url_scrnsht = f"https://arxiv.org/abs/{arxiv_id}"
generated_image_filepath = take_paper_screenshot(arxiv_url_scrnsht)
except Exception as err:
logger.error(f"Failed to tsk paper screenshot: {err}")
raise err
try:
save_blog_to_file(research_review, blog_title, blog_meta_desc, blog_tags,\
blog_categories, generated_image_filepath)
except Exception as err:
logger.__repr__ror(f"Failed to save blog to a file: {err}")
raise err
def take_paper_screenshot(arxiv_url):
""" Common function to take paper screenshot. """
# fixme: Remove the hardcoding, need add another option OR in config ?
image_dir = os.path.join(os.getcwd(), "blog_images")
generated_image_name = f"generated_image_{datetime.datetime.now():%Y-%m-%d-%H-%M-%S}.png"
generated_image_filepath = os.path.join(image_dir, generated_image_name)
if arxiv_url:
try:
generated_image_filepath = screenshot_api(arxiv_url, generated_image_filepath)
except Exception as err:
logger.error(f"Failed in taking url screenshot: {err}")
return generated_image_filepath
def num_tokens_from_string(string, encoding_name):
"""Returns the number of tokens in a text string."""
try:
encoding = tiktoken.get_encoding(encoding_name)
num_tokens = len(encoding.encode(string))
return num_tokens
except Exception as err:
logger.error(f"Failed to count tokens: {err}")
sys.exit(1)