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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] return message='{response_msg}' for paper title='{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):
now_utc = datetime.datetime.now(datetime.timezone.utc)
start_utc = now_utc - datetime.timedelta(days=days)
base_url = "http://export.arxiv.org/api/query"
step = 100
start = 0
all_entries = []
while True:
params = {
"search_query": "cat:cs.* OR cat:stat.ML",
"sortBy": "submittedDate",
"sortOrder": "descending",
"start": start,
"max_results": step
}
print(f"[DEBUG] Fetching batch {start} to {start+step}")
try:
resp = requests.get(base_url, params=params, timeout=30)
if resp.status_code != 200:
print(f"[ERROR] HTTP Status Code: {resp.status_code}")
break
feed = feedparser.parse(resp.content)
batch = feed.entries
if not batch:
print("[DEBUG] No entries returned, stopping fetch.")
break
for e in batch:
published_dt = datetime.datetime.strptime(
e.published, "%Y-%m-%dT%H:%M:%SZ"
).replace(tzinfo=datetime.timezone.utc)
if published_dt >= start_utc:
all_entries.append(e)
else:
print("[DEBUG] Reached older entries beyond date range.")
break # 超出范围，停止继续获取
if published_dt < start_utc:
break # 日期已经超过，完全停止外层循环
start += step
if start >= 3000:
print("[DEBUG] Reached max limit (3000), stopping fetch.")
break
except Exception as e:
print(f"[ERROR] Exception during fetching: {e}")
break
print(f"[DEBUG] Total papers fetched: {len(all_entries)} after date filtering.")
local_candidates = [
{
"title": e.title,
"summary": e.summary,
"published": e.published,
"link": e.link,
"categories": [t.term for t in e.tags]
}
for e in all_entries
if any(cat in ALLOWED_CATEGORIES for cat in [t.term for t in e.tags]) and advanced_filter(e)
]
print(f"[DEBUG] Number of papers after local filtering: {len(local_candidates)}")
openai_api_key = os.getenv("OPENAI_API_KEY")
if not openai_api_key:
print("[WARNING] No OPENAI_API_KEY found. Skip second filter.")
return local_candidates
client = OpenAI(api_key=openai_api_key)
final_matched = []
for idx, p in enumerate(local_candidates, 1):
if is_relevant_by_api(p["title"], p["summary"], client):
final_matched.append(p)
else:
print(f"[DEBUG][API] Paper #{idx} excluded by API.")
print(f"[DEBUG] Number of papers after OpenAI API filtering: {len(final_matched)}")
return final_matched
def update_readme_in_repo(papers, token, repo_name):
if not papers:
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"
)
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.")
if not papers:
print("[DEBUG] No papers matched after both local and API filters.")
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 target_repo_name else 'Not Set'}")
if github_token and target_repo_name and papers:
print("[DEBUG] Proceeding to update README in repo...")
update_readme_in_repo(papers, github_token, target_repo_name)
print("[DEBUG] README update completed.")
else:
print("[INFO] Skipped README update due to missing credentials or no papers matched.")
if __name__ == "__main__":
main()
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