# rss_processor.py
import os
import feedparser
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
import logging
from huggingface_hub import HfApi, login
import shutil
import rss_feeds
from datetime import datetime
import dateutil.parser
import hashlib
import re

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Constants
MAX_ARTICLES_PER_FEED = 10
LOCAL_DB_DIR = "chroma_db"
RSS_FEEDS = rss_feeds.RSS_FEEDS
COLLECTION_NAME = "news_articles"
HF_API_TOKEN = os.getenv("DEMO_HF_API_TOKEN", "YOUR_HF_API_TOKEN")
REPO_ID = "broadfield-dev/news-rag-db"

# Initialize Hugging Face API
login(token=HF_API_TOKEN)
hf_api = HfApi()

# Initialize embedding model (global, reusable)
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# Initialize vector DB with a specific collection name
vector_db = Chroma(
    persist_directory=LOCAL_DB_DIR,
    embedding_function=embedding_model,
    collection_name=COLLECTION_NAME
)

def clean_text(text):
    """Clean text by removing HTML tags and extra whitespace."""
    if not text or not isinstance(text, str):
        return ""
    text = re.sub(r'<.*?>', '', text)
    text = ' '.join(text.split())
    return text.strip().lower()

def fetch_rss_feeds():
    articles = []
    seen_keys = set()
    for feed_url in RSS_FEEDS:
        try:
            logger.info(f"Fetching {feed_url}")
            feed = feedparser.parse(feed_url)
            if feed.bozo:
                logger.warning(f"Parse error for {feed_url}: {feed.bozo_exception}")
                continue
            article_count = 0
            for entry in feed.entries:
                if article_count >= MAX_ARTICLES_PER_FEED:
                    break
                title = entry.get("title", "No Title")
                link = entry.get("link", "")
                description = entry.get("summary", entry.get("description", ""))
                
                title = clean_text(title)
                link = clean_text(link)
                description = clean_text(description)

                published = "Unknown Date"
                for date_field in ["published", "updated", "created", "pubDate"]:
                    if date_field in entry:
                        try:
                            parsed_date = dateutil.parser.parse(entry[date_field])
                            published = parsed_date.strftime("%Y-%m-%d %H:%M:%S")
                            break
                        except (ValueError, TypeError) as e:
                            logger.debug(f"Failed to parse {date_field} '{entry[date_field]}': {e}")
                            continue

                description_hash = hashlib.sha256(description.encode('utf-8')).hexdigest()
                key = f"{title}|{link}|{published}|{description_hash}"
                if key not in seen_keys:
                    seen_keys.add(key)
                    image = "svg"
                    for img_source in [
                        lambda e: clean_text(e.get("media_content", [{}])[0].get("url")) if e.get("media_content") else "",
                        lambda e: clean_text(e.get("media_thumbnail", [{}])[0].get("url")) if e.get("media_thumbnail") else "",
                        lambda e: clean_text(e.get("enclosure", {}).get("url")) if e.get("enclosure") else "",
                        lambda e: clean_text(next((lnk.get("href") for lnk in e.get("links", []) if lnk.get("type", "").startswith("image")), "")),
                    ]:
                        try:
                            img = img_source(entry)
                            if img and img.strip():
                                image = img
                                break
                        except (IndexError, AttributeError, TypeError):
                            continue

                    articles.append({
                        "title": title,
                        "link": link,
                        "description": description,
                        "published": published,
                        "category": categorize_feed(feed_url),
                        "image": image,
                    })
                    article_count += 1
        except Exception as e:
            logger.error(f"Error fetching {feed_url}: {e}")
    logger.info(f"Total articles fetched: {len(articles)}")
    return articles

def categorize_feed(url):
    """Categorize an RSS feed based on its URL."""
    if not url or not isinstance(url, str):
        logger.warning(f"Invalid URL provided for categorization: {url}")
        return "Uncategorized"

    url = url.lower().strip()  # Normalize the URL

    logger.debug(f"Categorizing URL: {url}")  # Add debugging for visibility

    if any(keyword in url for keyword in ["nature", "science.org", "arxiv.org", "plos.org", "annualreviews.org", "journals.uchicago.edu", "jneurosci.org", "cell.com", "nejm.org", "lancet.com"]):
        return "Academic Papers"
    elif any(keyword in url for keyword in ["reuters.com/business", "bloomberg.com", "ft.com", "marketwatch.com", "cnbc.com", "foxbusiness.com", "wsj.com", "bworldonline.com", "economist.com", "forbes.com"]):
        return "Business"
    elif any(keyword in url for keyword in ["investing.com", "cnbc.com/market", "marketwatch.com/market", "fool.co.uk", "zacks.com", "seekingalpha.com", "barrons.com", "yahoofinance.com"]):
        return "Stocks & Markets"
    elif any(keyword in url for keyword in ["whitehouse.gov", "state.gov", "commerce.gov", "transportation.gov", "ed.gov", "dol.gov", "justice.gov", "federalreserve.gov", "occ.gov", "sec.gov", "bls.gov", "usda.gov", "gao.gov", "cbo.gov", "fema.gov", "defense.gov", "hhs.gov", "energy.gov", "interior.gov"]):
        return "Federal Government"
    elif any(keyword in url for keyword in ["weather.gov", "metoffice.gov.uk", "accuweather.com", "weatherunderground.com", "noaa.gov", "wunderground.com", "climate.gov", "ecmwf.int", "bom.gov.au"]):
        return "Weather"
    elif any(keyword in url for keyword in ["data.worldbank.org", "imf.org", "un.org", "oecd.org", "statista.com", "kff.org", "who.int", "cdc.gov", "bea.gov", "census.gov", "fdic.gov"]):
        return "Data & Statistics"
    elif any(keyword in url for keyword in ["nasa", "spaceweatherlive", "space", "universetoday", "skyandtelescope", "esa"]):
        return "Space"
    elif any(keyword in url for keyword in ["sciencedaily", "quantamagazine", "smithsonianmag", "popsci", "discovermagazine", "scientificamerican", "newscientist", "livescience", "atlasobscura"]):
        return "Science"
    elif any(keyword in url for keyword in ["wired", "techcrunch", "arstechnica", "gizmodo", "theverge"]):
        return "Tech"
    elif any(keyword in url for keyword in ["horoscope", "astrostyle"]):
        return "Astrology"
    elif any(keyword in url for keyword in ["cnn_allpolitics", "bbci.co.uk/news/politics", "reuters.com/arc/outboundfeeds/newsletter-politics", "politico.com/rss/politics", "thehill"]):
        return "Politics"
    elif any(keyword in url for keyword in ["weather", "swpc.noaa.gov", "foxweather"]):
        return "Earth Weather"
    elif "vogue" in url:
        return "Lifestyle"
    elif any(keyword in url for keyword in ["phys.org", "aps.org", "physicsworld"]):
        return "Physics"
    else:
        logger.warning(f"No matching category found for URL: {url}")
        return "Uncategorized"

def process_and_store_articles(articles):
    documents = []
    existing_ids = set(vector_db.get()["ids"])  # Load existing IDs once
    for article in articles:
        try:
            title = clean_text(article["title"])
            link = clean_text(article["link"])
            description = clean_text(article["description"])
            published = article["published"]
            description_hash = hashlib.sha256(description.encode('utf-8')).hexdigest()
            doc_id = f"{title}|{link}|{published}|{description_hash}"
            if doc_id in existing_ids:
                logger.debug(f"Skipping duplicate in DB: {doc_id}")
                continue
            metadata = {
                "title": article["title"],
                "link": article["link"],
                "original_description": article["description"],
                "published": article["published"],
                "category": article["category"],
                "image": article["image"],
            }
            doc = Document(page_content=description, metadata=metadata, id=doc_id)
            documents.append(doc)
            existing_ids.add(doc_id)  # Update in-memory set to avoid duplicates within this batch
        except Exception as e:
            logger.error(f"Error processing article {article['title']}: {e}")
    
    if documents:
        try:
            vector_db.add_documents(documents)
            vector_db.persist()
            logger.info(f"Added {len(documents)} new articles to DB. Total documents: {len(vector_db.get()['ids'])}")
        except Exception as e:
            logger.error(f"Error storing articles: {e}")

def download_from_hf_hub():
    if not os.path.exists(LOCAL_DB_DIR):
        try:
            hf_api.create_repo(repo_id=REPO_ID, repo_type="dataset", exist_ok=True, token=HF_API_TOKEN)
            logger.info(f"Downloading Chroma DB from {REPO_ID}...")
            hf_api.hf_hub_download(repo_id=REPO_ID, filename="chroma_db", local_dir=LOCAL_DB_DIR, repo_type="dataset", token=HF_API_TOKEN)
        except Exception as e:
            logger.error(f"Error downloading from Hugging Face Hub: {e}")
    else:
        logger.info("Local Chroma DB exists, loading existing data.")

def upload_to_hf_hub():
    if os.path.exists(LOCAL_DB_DIR):
        try:
            logger.info(f"Uploading updated Chroma DB to {REPO_ID}...")
            for root, _, files in os.walk(LOCAL_DB_DIR):
                for file in files:
                    local_path = os.path.join(root, file)
                    remote_path = os.path.relpath(local_path, LOCAL_DB_DIR)
                    hf_api.upload_file(
                        path_or_fileobj=local_path,
                        path_in_repo=remote_path,
                        repo_id=REPO_ID,
                        repo_type="dataset",
                        token=HF_API_TOKEN
                    )
            logger.info(f"Database uploaded to: {REPO_ID}")
        except Exception as e:
            logger.error(f"Error uploading to Hugging Face Hub: {e}")

if __name__ == "__main__":
    download_from_hf_hub()  # Ensure DB is initialized
    articles = fetch_rss_feeds()
    process_and_store_articles(articles)
    upload_to_hf_hub()