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

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

# Constants
MAX_ARTICLES_PER_FEED = 5
LOCAL_DB_DIR = "chroma_db"
RSS_FEEDS = rss_feeds.RSS_FEEDS
COLLECTION_NAME = "news_articles"  # Explicitly name the collection

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 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").strip()
                link = entry.get("link", "").strip()
                description = entry.get("summary", entry.get("description", "No Description"))
                published = entry.get("published", "Unknown Date").strip()
                key = f"{title}|{link}|{published}"
                if key not in seen_keys:
                    seen_keys.add(key)
                    image = (entry.get("media_content", [{}])[0].get("url") or
                             entry.get("media_thumbnail", [{}])[0].get("url") or "svg")
                    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):
    if "nature" in url or "science.org" in url or "arxiv.org" in url or "plos.org" in url or "annualreviews.org" in url or "journals.uchicago.edu" in url or "jneurosci.org" in url or "cell.com" in url or "nejm.org" in url or "lancet.com" in url:
        return "Academic Papers"
    elif "reuters.com/business" in url or "bloomberg.com" in url or "ft.com" in url or "marketwatch.com" in url or "cnbc.com" in url or "foxbusiness.com" in url or "wsj.com" in url or "bworldonline.com" in url or "economist.com" in url or "forbes.com" in url:
        return "Business"
    elif "investing.com" in url or "cnbc.com/market" in url or "marketwatch.com/market" in url or "fool.co.uk" in url or "zacks.com" in url or "seekingalpha.com" in url or "barrons.com" in url or "yahoofinance.com" in url:
        return "Stocks & Markets"
    elif "whitehouse.gov" in url or "state.gov" in url or "commerce.gov" in url or "transportation.gov" in url or "ed.gov" in url or "dol.gov" in url or "justice.gov" in url or "federalreserve.gov" in url or "occ.gov" in url or "sec.gov" in url or "bls.gov" in url or "usda.gov" in url or "gao.gov" in url or "cbo.gov" in url or "fema.gov" in url or "defense.gov" in url or "hhs.gov" in url or "energy.gov" in url or "interior.gov" in url:
        return "Federal Government"
    elif "weather.gov" in url or "metoffice.gov.uk" in url or "accuweather.com" in url or "weatherunderground.com" in url or "noaa.gov" in url or "wunderground.com" in url or "climate.gov" in url or "ecmwf.int" in url or "bom.gov.au" in url:
        return "Weather"
    elif "data.worldbank.org" in url or "imf.org" in url or "un.org" in url or "oecd.org" in url or "statista.com" in url or "kff.org" in url or "who.int" in url or "cdc.gov" in url or "bea.gov" in url or "census.gov" in url or "fdic.gov" in url:
        return "Data & Statistics"
    elif "nasa" in url or "spaceweatherlive" in url or "space" in url or "universetoday" in url or "skyandtelescope" in url or "esa" in url:
        return "Space"
    elif "sciencedaily" in url or "quantamagazine" in url or "smithsonianmag" in url or "popsci" in url or "discovermagazine" in url or "scientificamerican" in url or "newscientist" in url or "livescience" in url or "atlasobscura" in url:
        return "Science"
    elif "wired" in url or "techcrunch" in url or "arstechnica" in url or "gizmodo" in url or "theverge" in url:
        return "Tech"
    elif "horoscope" in url or "astrostyle" in url:
        return "Astrology"
    elif "cnn_allpolitics" in url or "bbci.co.uk/news/politics" in url or "reuters.com/arc/outboundfeeds/newsletter-politics" in url or "politico.com/rss/politics" in url or "thehill" in url:
        return "Politics"
    elif "weather" in url or "swpc.noaa.gov" in url or "foxweather" in url:
        return "Earth Weather"
    elif "vogue" in url:
        return "Lifestyle"
    elif "phys.org" in url or "aps.org" in url or "physicsworld" in url:
        return "Physics"
    return "Uncategorized"
    
def process_and_store_articles(articles):
    documents = []
    existing_ids = set(vector_db.get()["ids"])  # Get existing document IDs to avoid duplicates
    for article in articles:
        try:
            # Create a unique ID for deduplication
            doc_id = f"{article['title']}|{article['link']}|{article['published']}"
            if doc_id in existing_ids:
                continue  # Skip if already in DB
            metadata = {
                "title": article["title"],
                "link": article["link"],
                "original_description": article["description"],
                "published": article["published"],
                "category": article["category"],
                "image": article["image"],
            }
            doc = Document(page_content=article["description"], metadata=metadata, id=doc_id)
            documents.append(doc)
        except Exception as e:
            logger.error(f"Error processing article {article['title']}: {e}")
    
    if documents:
        try:
            vector_db.add_documents(documents)
            vector_db.persist()  # Explicitly persist changes
            logger.info(f"Added {len(documents)} new articles to DB")
        except Exception as e:
            logger.error(f"Error storing articles: {e}")

def download_from_hf_hub():
    # Only download if the local DB doesn’t exist (initial setup)
    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.download_repo(repo_id=REPO_ID, repo_type="dataset", local_dir=LOCAL_DB_DIR, token=HF_API_TOKEN)
        except Exception as e:
            logger.error(f"Error downloading from Hugging Face Hub: {e}")
            raise
    else:
        logger.info("Local Chroma DB already exists, skipping download.")

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}")
            raise

if __name__ == "__main__":
    articles = fetch_rss_feeds()
    process_and_store_articles(articles)
    upload_to_hf_hub()