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import os | |
from flask import Flask, render_template, request, Response | |
from rss_processor import fetch_rss_feeds, process_and_store_articles, vector_db | |
import logging | |
import time | |
app = Flask(__name__) | |
# Setup logging | |
logging.basicConfig(level=logging.INFO) | |
logger = logging.getLogger(__name__) | |
def loading(): | |
return render_template("loading.html") | |
def load_feeds(): | |
logger.info("Starting to fetch and process RSS feeds") | |
start_time = time.time() | |
articles = fetch_rss_feeds() | |
logger.info(f"Fetched {len(articles)} articles") | |
process_and_store_articles(articles) | |
logger.info("Articles processed and stored") | |
end_time = time.time() | |
logger.info(f"RSS feed loading took {end_time - start_time:.2f} seconds") | |
return Response("Feeds loaded", status=200) | |
def index(): | |
stored_docs = vector_db.similarity_search("news", k=1000) # Increased k to ensure all unique articles | |
# Use a set to ensure unique articles by title and link | |
unique_articles = {} | |
for doc in stored_docs: | |
key = f"{doc.metadata['title']}|{doc.metadata['link']}" | |
if key not in unique_articles: | |
unique_articles[key] = { | |
"title": doc.metadata["title"], | |
"link": doc.metadata["link"], | |
"description": doc.metadata["original_description"], | |
"category": doc.metadata["category"], | |
"published": doc.metadata["published"], | |
"image": doc.metadata.get("image", "svg"), | |
} | |
enriched_articles = list(unique_articles.values()) | |
logger.info(f"Enriched {len(enriched_articles)} unique articles for display") | |
if request.method == 'POST' and 'search' in request.form: | |
query = request.form.get('search') | |
if query: | |
logger.info(f"Processing search query: {query}") | |
results = vector_db.similarity_search(query, k=10) | |
unique_search_articles = {} | |
for doc in results: | |
key = f"{doc.metadata['title']}|{doc.metadata['link']}" | |
if key not in unique_search_articles: | |
unique_search_articles[key] = { | |
"title": doc.metadata["title"], | |
"link": doc.metadata["link"], | |
"description": doc.metadata["original_description"], | |
"category": doc.metadata["category"], | |
"published": doc.metadata["published"], | |
"image": doc.metadata.get("image", "svg"), | |
} | |
enriched_articles = list(unique_search_articles.values()) | |
logger.info(f"Search returned {len(enriched_articles)} unique results") | |
categorized_articles = {} | |
for article in enriched_articles: | |
cat = article["category"] | |
if cat not in categorized_articles: | |
categorized_articles[cat] = [] | |
categorized_articles[cat].append(article) | |
return render_template("index.html", categorized_articles=categorized_articles) | |
if __name__ == "__main__": | |
app.run(host="0.0.0.0", port=7860) |