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  # For flexible date parsing

# 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"

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().lower()  # Normalize case and whitespace
                link = entry.get("link", "").strip().lower()
                description = entry.get("summary", entry.get("description", "No Description")).strip()

                # Try multiple date fields and parse flexibly
                published = "Unknown Date"
                for date_field in ["published", "updated", "created"]:
                    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

                # Use a robust key for deduplication
                key = f"{title}|{link}|{published}"
                if key not in seen_keys:
                    seen_keys.add(key)
                    # Try multiple image sources
                    image = "svg"  # Default fallback
                    for img_source in [
                        lambda e: e.get("media_content", [{}])[0].get("url"),
                        lambda e: e.get("media_thumbnail", [{}])[0].get("url"),
                        lambda e: e.get("enclosure", {}).get("url"),
                        lambda e: next((lnk.get("href") for lnk in e.get("links", []) if lnk.get("type", "").startswith("image")), None),
                    ]:
                        try:
                            img = img_source(entry)
                            if img and isinstance(img, str) and img.strip():
                                image = img.strip()
                                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
                else:
                    logger.debug(f"Duplicate article skipped in feed {feed_url}: {key}")
        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):
    # (Unchanged, keeping your existing categorization logic)
    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 based on normalized fields
            doc_id = f"{article['title'].lower()}|{article['link'].lower()}|{article['published']}"
            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=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()