import os import feedparser from huggingface_hub import HfApi, InferenceClient, login from langchain.vectorstores import Chroma from langchain.embeddings import HuggingFaceEmbeddings from langchain.docstore.document import Document import shutil import logging # Setup logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Hugging Face setup HF_API_TOKEN = os.getenv("DEMO_HF_API_TOKEN", "YOUR_HF_API_TOKEN") HF_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct" REPO_ID = "broadfield-dev/news-rag-db" LOCAL_DB_DIR = "chroma_db" login(token=HF_API_TOKEN) client = InferenceClient(model=HF_MODEL, token=HF_API_TOKEN) RSS_FEEDS = [ "https://www.sciencedaily.com/rss/top/science.xml", "https://www.horoscope.com/us/horoscopes/general/rss/horoscope-rss.aspx", "http://rss.cnn.com/rss/cnn_allpolitics.rss", "https://phys.org/rss-feed/physics-news/", "https://www.spaceweatherlive.com/en/news/rss", "https://weather.com/feeds/rss", "https://www.wired.com/feed/rss", "https://www.nasa.gov/rss/dyn/breaking_news.rss", "https://www.nationalgeographic.com/feed/", "https://www.nature.com/nature.rss", "https://www.scientificamerican.com/rss/", "https://www.newscientist.com/feed/home/", "https://www.livescience.com/feeds/all", "https://astrostyle.com/feed/", "https://www.vogue.com/feed/rss", "https://feeds.bbci.co.uk/news/politics/rss.xml", "https://www.reuters.com/arc/outboundfeeds/newsletter-politics/?outputType=xml", "https://www.politico.com/rss/politics.xml", "https://thehill.com/feed/", "https://www.aps.org/publications/apsnews/updates/rss.cfm", "https://www.quantamagazine.org/feed/", "https://www.sciencedaily.com/rss/matter_energy/physics.xml", "https://physicsworld.com/feed/", "https://www.swpc.noaa.gov/rss.xml", "https://feeds.bbci.co.uk/weather/feeds/rss/5day/world/", "https://www.weather.gov/rss", "https://www.foxweather.com/rss", "https://techcrunch.com/feed/", "https://arstechnica.com/feed/", "https://gizmodo.com/rss", "https://www.theverge.com/rss/index.xml", "https://www.space.com/feeds/all", "https://www.universetoday.com/feed/", "https://skyandtelescope.org/feed/", "https://www.esa.int/rss", "https://www.smithsonianmag.com/rss/", "https://www.popsci.com/rss.xml", "https://www.discovermagazine.com/rss", "https://www.atlasobscura.com/feeds/latest" ] embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") vector_db = Chroma(persist_directory=LOCAL_DB_DIR, embedding_function=embedding_model) hf_api = HfApi() def fetch_rss_feeds(): articles = [] for feed_url in RSS_FEEDS: try: logger.info(f"Fetching feed: {feed_url}") feed = feedparser.parse(feed_url) if feed.bozo: logger.warning(f"Failed to parse {feed_url}: {feed.bozo_exception}") continue for entry in feed.entries[:5]: image = entry.get("media_content", [{}])[0].get("url") or entry.get("media_thumbnail", [{}])[0].get("url") or None articles.append({ "title": entry.get("title", "No Title"), "link": entry.get("link", ""), "description": entry.get("summary", entry.get("description", "No Description")), "published": entry.get("published", "Unknown Date"), "category": categorize_feed(feed_url), "image": image, }) logger.info(f"Processed {len(feed.entries[:5])} entries from {feed_url}") except Exception as e: logger.error(f"Error fetching {feed_url}: {e}") return articles def categorize_feed(url): if "sciencedaily" in url or "phys.org" in url: return "Science & Physics" elif "horoscope" in url: return "Astrology" elif "politics" in url: return "Politics" elif "spaceweather" in url or "nasa" in url: return "Solar & Space" elif "weather" in url: return "Earth Weather" else: return "Cool Stuff" def summarize_article(text): prompt = f"Summarize the following text concisely:\n\n{text}" try: response = client.text_generation(prompt, max_new_tokens=100, temperature=0.7) return response.strip() except Exception as e: logger.error(f"Error summarizing article: {e}") return "Summary unavailable" def categorize_article(text): prompt = f"Classify the sentiment as positive, negative, or neutral:\n\n{text}" try: response = client.text_generation(prompt, max_new_tokens=10, temperature=0.7) return response.strip() except Exception as e: logger.error(f"Error categorizing article: {e}") return "Neutral" def process_and_store_articles(articles, summarize=False): documents = [] for article in articles: try: summary = summarize_article(article["description"]) if summarize else article["description"] sentiment = categorize_article(article["description"]) doc = Document( page_content=summary, metadata={ "title": article["title"], "link": article["link"], "original_description": article["description"], "published": article["published"], "category": article["category"], "sentiment": sentiment, "image": article["image"], } ) documents.append(doc) except Exception as e: logger.error(f"Error processing article {article['title']}: {e}") vector_db.add_documents(documents) vector_db.persist() logger.info("Vector DB persisted") upload_to_hf_hub() def upload_to_hf_hub(): if 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"Repository {REPO_ID} created or exists.") except Exception as e: logger.error(f"Error creating repo: {e}") return 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) try: 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"Uploaded {file} to {REPO_ID}") except Exception as e: logger.error(f"Error uploading file {file}: {e}") logger.info(f"Database uploaded to: {REPO_ID}")