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Update rss_processor.py
Browse files- rss_processor.py +39 -38
rss_processor.py
CHANGED
@@ -1,3 +1,4 @@
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import os
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import feedparser
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from langchain.vectorstores import Chroma
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@@ -10,18 +11,17 @@ import rss_feeds
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from datetime import datetime
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import dateutil.parser
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import hashlib
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import re
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# Setup logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Constants
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MAX_ARTICLES_PER_FEED =
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LOCAL_DB_DIR = "chroma_db"
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RSS_FEEDS = rss_feeds.RSS_FEEDS
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COLLECTION_NAME = "news_articles"
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-
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HF_API_TOKEN = os.getenv("DEMO_HF_API_TOKEN", "YOUR_HF_API_TOKEN")
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REPO_ID = "broadfield-dev/news-rag-db"
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@@ -43,9 +43,7 @@ def clean_text(text):
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"""Clean text by removing HTML tags and extra whitespace."""
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if not text or not isinstance(text, str):
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return ""
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# Remove HTML tags
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text = re.sub(r'<.*?>', '', text)
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# Normalize whitespace (remove extra spaces, newlines, tabs)
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text = ' '.join(text.split())
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return text.strip().lower()
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@@ -67,14 +65,12 @@ def fetch_rss_feeds():
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link = entry.get("link", "")
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description = entry.get("summary", entry.get("description", ""))
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# Clean and normalize all text fields
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title = clean_text(title)
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link = clean_text(link)
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description = clean_text(description)
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# Try multiple date fields and parse flexibly
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published = "Unknown Date"
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for date_field in ["published", "updated", "created", "pubDate"]:
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if date_field in entry:
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try:
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parsed_date = dateutil.parser.parse(entry[date_field])
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@@ -84,13 +80,11 @@ def fetch_rss_feeds():
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logger.debug(f"Failed to parse {date_field} '{entry[date_field]}': {e}")
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continue
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description_hash = hashlib.sha256(description.encode('utf-8')).hexdigest() # Switched to SHA256 for better uniqueness
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key = f"{title}|{link}|{published}|{description_hash}"
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if key not in seen_keys:
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seen_keys.add(key)
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image = "svg" # Default fallback
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for img_source in [
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lambda e: clean_text(e.get("media_content", [{}])[0].get("url")) if e.get("media_content") else "",
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lambda e: clean_text(e.get("media_thumbnail", [{}])[0].get("url")) if e.get("media_thumbnail") else "",
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@@ -114,50 +108,58 @@ def fetch_rss_feeds():
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"image": image,
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})
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article_count += 1
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else:
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logger.debug(f"Duplicate article skipped in feed {feed_url}: {key}")
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except Exception as e:
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logger.error(f"Error fetching {feed_url}: {e}")
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logger.info(f"Total articles fetched: {len(articles)}")
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return articles
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def categorize_feed(url):
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return "Academic Papers"
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elif "reuters.com/business"
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return "Business"
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elif
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return "Stocks & Markets"
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elif
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return "Federal Government"
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elif
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return "Weather"
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elif "data.worldbank.org"
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return "Data & Statistics"
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elif
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return "Space"
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elif
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return "Science"
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elif
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return "Tech"
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elif
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return "Astrology"
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elif
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return "Politics"
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elif
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return "Earth Weather"
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elif "vogue" in url
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return "Lifestyle"
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elif
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return "Physics"
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def process_and_store_articles(articles):
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documents = []
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existing_ids = set(vector_db.get()["ids"]) #
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for article in articles:
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try:
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# Clean and normalize all fields
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title = clean_text(article["title"])
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link = clean_text(article["link"])
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description = clean_text(article["description"])
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@@ -177,29 +179,28 @@ def process_and_store_articles(articles):
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}
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doc = Document(page_content=description, metadata=metadata, id=doc_id)
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documents.append(doc)
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except Exception as e:
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logger.error(f"Error processing article {article['title']}: {e}")
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if documents:
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try:
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vector_db.add_documents(documents)
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vector_db.persist()
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logger.info(f"Added {len(documents)} new articles to DB")
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except Exception as e:
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logger.error(f"Error storing articles: {e}")
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def download_from_hf_hub():
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# Only download if the local DB doesn’t exist (initial setup)
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if not os.path.exists(LOCAL_DB_DIR):
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try:
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hf_api.create_repo(repo_id=REPO_ID, repo_type="dataset", exist_ok=True, token=HF_API_TOKEN)
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logger.info(f"Downloading Chroma DB from {REPO_ID}...")
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hf_api.
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except Exception as e:
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logger.error(f"Error downloading from Hugging Face Hub: {e}")
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raise
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else:
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logger.info("Local Chroma DB
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def upload_to_hf_hub():
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if os.path.exists(LOCAL_DB_DIR):
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@@ -219,9 +220,9 @@ def upload_to_hf_hub():
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logger.info(f"Database uploaded to: {REPO_ID}")
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except Exception as e:
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logger.error(f"Error uploading to Hugging Face Hub: {e}")
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raise
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if __name__ == "__main__":
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articles = fetch_rss_feeds()
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process_and_store_articles(articles)
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upload_to_hf_hub()
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+
# rss_processor.py
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import os
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import feedparser
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from langchain.vectorstores import Chroma
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from datetime import datetime
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import dateutil.parser
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import hashlib
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import re
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# Setup logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Constants
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MAX_ARTICLES_PER_FEED = 1000
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LOCAL_DB_DIR = "chroma_db"
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RSS_FEEDS = rss_feeds.RSS_FEEDS
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COLLECTION_NAME = "news_articles"
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HF_API_TOKEN = os.getenv("DEMO_HF_API_TOKEN", "YOUR_HF_API_TOKEN")
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REPO_ID = "broadfield-dev/news-rag-db"
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"""Clean text by removing HTML tags and extra whitespace."""
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if not text or not isinstance(text, str):
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return ""
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text = re.sub(r'<.*?>', '', text)
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text = ' '.join(text.split())
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return text.strip().lower()
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link = entry.get("link", "")
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description = entry.get("summary", entry.get("description", ""))
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title = clean_text(title)
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link = clean_text(link)
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description = clean_text(description)
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published = "Unknown Date"
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for date_field in ["published", "updated", "created", "pubDate"]:
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if date_field in entry:
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try:
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parsed_date = dateutil.parser.parse(entry[date_field])
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logger.debug(f"Failed to parse {date_field} '{entry[date_field]}': {e}")
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continue
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description_hash = hashlib.sha256(description.encode('utf-8')).hexdigest()
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key = f"{title}|{link}|{published}|{description_hash}"
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if key not in seen_keys:
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seen_keys.add(key)
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image = "svg"
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for img_source in [
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lambda e: clean_text(e.get("media_content", [{}])[0].get("url")) if e.get("media_content") else "",
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lambda e: clean_text(e.get("media_thumbnail", [{}])[0].get("url")) if e.get("media_thumbnail") else "",
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"image": image,
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})
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article_count += 1
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except Exception as e:
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logger.error(f"Error fetching {feed_url}: {e}")
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logger.info(f"Total articles fetched: {len(articles)}")
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return articles
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def categorize_feed(url):
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"""Categorize an RSS feed based on its URL."""
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if not url or not isinstance(url, str):
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logger.warning(f"Invalid URL provided for categorization: {url}")
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return "Uncategorized"
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url = url.lower().strip() # Normalize the URL
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logger.debug(f"Categorizing URL: {url}") # Add debugging for visibility
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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"]):
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return "Academic Papers"
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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"]):
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return "Business"
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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"]):
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return "Stocks & Markets"
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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"]):
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return "Federal Government"
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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"]):
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return "Weather"
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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"]):
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return "Data & Statistics"
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elif any(keyword in url for keyword in ["nasa", "spaceweatherlive", "space", "universetoday", "skyandtelescope", "esa"]):
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return "Space"
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elif any(keyword in url for keyword in ["sciencedaily", "quantamagazine", "smithsonianmag", "popsci", "discovermagazine", "scientificamerican", "newscientist", "livescience", "atlasobscura"]):
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return "Science"
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elif any(keyword in url for keyword in ["wired", "techcrunch", "arstechnica", "gizmodo", "theverge"]):
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return "Tech"
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elif any(keyword in url for keyword in ["horoscope", "astrostyle"]):
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return "Astrology"
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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"]):
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return "Politics"
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elif any(keyword in url for keyword in ["weather", "swpc.noaa.gov", "foxweather"]):
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return "Earth Weather"
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elif "vogue" in url:
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return "Lifestyle"
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elif any(keyword in url for keyword in ["phys.org", "aps.org", "physicsworld"]):
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return "Physics"
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else:
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logger.warning(f"No matching category found for URL: {url}")
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return "Uncategorized"
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def process_and_store_articles(articles):
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documents = []
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existing_ids = set(vector_db.get()["ids"]) # Load existing IDs once
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for article in articles:
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try:
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title = clean_text(article["title"])
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link = clean_text(article["link"])
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description = clean_text(article["description"])
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}
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doc = Document(page_content=description, metadata=metadata, id=doc_id)
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documents.append(doc)
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existing_ids.add(doc_id) # Update in-memory set to avoid duplicates within this batch
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except Exception as e:
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logger.error(f"Error processing article {article['title']}: {e}")
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if documents:
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try:
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vector_db.add_documents(documents)
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vector_db.persist()
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logger.info(f"Added {len(documents)} new articles to DB. Total documents: {len(vector_db.get()['ids'])}")
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except Exception as e:
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logger.error(f"Error storing articles: {e}")
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def download_from_hf_hub():
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if not os.path.exists(LOCAL_DB_DIR):
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try:
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hf_api.create_repo(repo_id=REPO_ID, repo_type="dataset", exist_ok=True, token=HF_API_TOKEN)
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logger.info(f"Downloading Chroma DB from {REPO_ID}...")
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hf_api.hf_hub_download(repo_id=REPO_ID, filename="chroma_db", local_dir=LOCAL_DB_DIR, repo_type="dataset", token=HF_API_TOKEN)
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except Exception as e:
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logger.error(f"Error downloading from Hugging Face Hub: {e}")
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else:
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logger.info("Local Chroma DB exists, loading existing data.")
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def upload_to_hf_hub():
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if os.path.exists(LOCAL_DB_DIR):
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logger.info(f"Database uploaded to: {REPO_ID}")
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except Exception as e:
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logger.error(f"Error uploading to Hugging Face Hub: {e}")
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if __name__ == "__main__":
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download_from_hf_hub() # Ensure DB is initialized
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articles = fetch_rss_feeds()
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process_and_store_articles(articles)
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upload_to_hf_hub()
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