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

    # 1. Compute & log your 24 h 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 your category query (or replace with "all:*" to disable)
    cat_query = " OR ".join(f"cat:{c}" for c in ALLOWED_CATEGORIES)
    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. Parse & filter every entry in this batch
        kept = []
        for e in batch:
            # parse the Z‑timestamp
            pub = datetime.datetime.strptime(
                e.published, "%Y-%m-%dT%H:%M:%SZ"
            ).replace(tzinfo=datetime.timezone.utc)
            print(f"[DEBUG]  entry.published → {pub.isoformat()}")
            if pub >= cutoff_utc:
                kept.append(e)

        # 4. Collect those in window
        all_entries.extend(kept)
        print(f"[DEBUG]  kept {len(kept)} of {len(batch)} in this batch")

        # 5. Stop if *none* in this batch were new enough
        if not kept:
            print("[DEBUG]  no entries in window → stopping fetch loop")
            break

        # 6. Otherwise page on (or stop if fewer than a full page)
        if len(batch) < step:
            break
        start += step

    print(f"[DEBUG] total fetched papers from arXiv in last {days} day(s): {len(all_entries)}")

    # …then proceed with your OpenAI filtering as before…
    # (unchanged code for OpenAI calls, category checks, README updates)

    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()