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import os
import requests
import feedparser
import datetime
from github import Github
from openai import OpenAI
ALLOWED_CATEGORIES = [
"cs.AI", "cs.CL", "cs.CV", "cs.LG", "cs.NE", "cs.RO",
"cs.IR", "stat.ML"
]
SYSTEM_PROMPT = (
"You are a helpful assistant. The user will give you a paper title and abstract. "
"Your task: Decide if this paper is about large language models (or generative text models) AND about bias/fairness. "
"If yes, respond with just a single character: 1. Otherwise, respond with a single character: 0. "
"No extra explanation, no punctuation—only the number."
)
def advanced_filter(entry):
title = getattr(entry, 'title', '').lower()
summary = getattr(entry, 'summary', '').lower()
full_text = title + " " + summary
general_terms = ["bias", "fairness"]
model_terms = ["llm", "language model", "transformer", "gpt", "nlp",
"pretrained", "embedding", "generation", "alignment", "ai"]
negative_terms = ["estimation", "variance", "quantum", "physics",
"sensor", "circuit", "electronics", "hardware"]
has_general = any(term in full_text for term in general_terms)
has_model = any(term in full_text for term in model_terms)
has_negative = any(term in full_text for term in negative_terms)
return (has_general and has_model) and (not has_negative)
def is_relevant_by_api(title, summary, client, model="gpt-4-turbo"):
prompt = f"Title: {title}\nAbstract: {summary}"
try:
dialogue = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": prompt}
],
temperature=0.0,
max_tokens=1
)
response_msg = dialogue.choices[0].message.content.strip()
print(f"[DEBUG][API] OpenAI response='{response_msg}' for paper '{title[:60]}...'")
return response_msg == "1"
except Exception as e:
print("[ERROR][API] calling OpenAI API:", e)
return False
def fetch_papers_combined(days=1):
import datetime, requests, feedparser, os
from openai import OpenAI
# 1) Compute & log the window
now_utc = datetime.datetime.now(datetime.timezone.utc)
cutoff_utc = now_utc - datetime.timedelta(days=days)
print(f"[DEBUG] now_utc = {now_utc.isoformat()}")
print(f"[DEBUG] cutoff_utc = {cutoff_utc.isoformat()}")
# 2) Build (or disable) category filtering
cat_query = " OR ".join(f"cat:{c}" for c in ALLOWED_CATEGORIES)
# To disable completely, you could instead do:
# cat_query = "all:*"
base_url = "http://export.arxiv.org/api/query"
step, start = 100, 0
all_entries = []
while True:
params = {
"search_query": cat_query,
"sortBy": "submittedDate",
"sortOrder": "descending",
"start": start,
"max_results": step
}
resp = requests.get(base_url, params=params, timeout=30)
resp.raise_for_status()
print(f"[DEBUG] arXiv query URL: {resp.url}")
feed = feedparser.parse(resp.content)
batch = feed.entries
print(f"[DEBUG] fetched batch size: {len(batch)}")
if not batch:
break
# 3) Use the *updated* time (announcement) for your 24h filter
kept = []
for e in batch:
updated = datetime.datetime(
*e.updated_parsed[:6],
tzinfo=datetime.timezone.utc
)
print(f"[DEBUG] entry.updated → {updated.isoformat()}")
if updated >= cutoff_utc:
kept.append(e)
print(f"[DEBUG] kept {len(kept)} of {len(batch)} in this batch")
if not kept:
print("[DEBUG] no recent entries → stopping fetch loop")
break
all_entries.extend(kept)
if len(batch) < step:
break
start += step
print(f"[DEBUG] total fetched papers in last {days} day(s): {len(all_entries)}")
# 4) Now run your OpenAI filter and category check
openai_api_key = os.getenv("OPENAI_API_KEY")
if not openai_api_key:
print("[ERROR] OPENAI_API_KEY missing, aborting.")
return []
client = OpenAI(api_key=openai_api_key)
final_matched = []
for idx, entry in enumerate(all_entries, start=1):
title = entry.title
summary = entry.summary
cats = [t.term for t in getattr(entry, 'tags', [])]
# (optional) re‑enable or disable category filtering here
if not any(cat in ALLOWED_CATEGORIES for cat in cats):
continue
if is_relevant_by_api(title, summary, client):
final_matched.append({
"title": title,
"summary": summary,
"published": entry.published,
"link": entry.link,
"categories": cats
})
print(f"[DEBUG][API] Included #{idx}: {title[:60]}...")
else:
print(f"[DEBUG][API] Excluded #{idx}: {title[:60]}...")
print(f"[DEBUG] final matched papers: {len(final_matched)}")
return final_matched
def update_readme_in_repo(papers, token, repo_name):
if not papers:
print("[INFO] No matched papers, skip README update.")
return
g = Github(token)
repo = g.get_repo(repo_name)
readme_file = repo.get_contents("README.md", ref="main")
old_content = readme_file.decoded_content.decode("utf-8")
now_utc_str = datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
new_section = f"\n\n### Auto-captured papers on {now_utc_str}\n"
for p in papers:
cat_str = ", ".join(p["categories"])
new_section += f"- **{p['title']}** (Published={p['published']}) \n"
new_section += f" - Categories: {cat_str} \n"
new_section += f" - Link: {p['link']}\n\n"
updated_content = old_content + new_section
commit_msg = f"Auto update README with {len(papers)} new papers"
repo.update_file(
path="README.md",
message=commit_msg,
content=updated_content,
sha=readme_file.sha,
branch="main"
)
print(f"[INFO] README updated with {len(papers)} papers.")
def main():
days = 1
print(f"[DEBUG] Starting fetch_papers_combined with days={days}")
papers = fetch_papers_combined(days=days)
print(f"[DEBUG] After fetch_papers_combined: {len(papers)} papers matched.")
github_token = os.getenv("TARGET_REPO_TOKEN")
target_repo_name = os.getenv("TARGET_REPO_NAME")
print(f"[DEBUG] Github Token Set: {'Yes' if github_token else 'No'}")
print(f"[DEBUG] Target Repo Name: {target_repo_name}")
if github_token and target_repo_name and papers:
update_readme_in_repo(papers, github_token, target_repo_name)
else:
print("[INFO] Skipped README update due to missing credentials or no papers matched.")
if __name__ == "__main__":
main()
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