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Update app.py
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app.py
CHANGED
@@ -1,13 +1,21 @@
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import os
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from flask import Flask, render_template, request
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from rss_processor import fetch_rss_feeds, process_and_store_articles, vector_db
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app = Flask(__name__)
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@app.route('/', methods=['GET', 'POST'])
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def index():
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articles = fetch_rss_feeds()
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process_and_store_articles(articles)
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stored_docs = vector_db.similarity_search("news", k=len(articles))
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enriched_articles = [
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{
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@@ -17,14 +25,16 @@ def index():
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"category": doc.metadata["category"],
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"sentiment": doc.metadata["sentiment"],
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"published": doc.metadata["published"],
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"image": doc.metadata.get("image", "
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}
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for doc in stored_docs
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]
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if request.method == 'POST':
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query = request.form.get('search')
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if query:
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results = vector_db.similarity_search(query, k=10)
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enriched_articles = [
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{
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@@ -34,10 +44,11 @@ def index():
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"category": doc.metadata["category"],
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"sentiment": doc.metadata["sentiment"],
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"published": doc.metadata["published"],
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"image": doc.metadata.get("image", "
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}
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for doc in results
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]
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categorized_articles = {}
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for article in enriched_articles:
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import os
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from flask import Flask, render_template, request
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from rss_processor import fetch_rss_feeds, process_and_store_articles, vector_db
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import logging
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app = Flask(__name__)
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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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@app.route('/', methods=['GET', 'POST'])
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def index():
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logger.info("Starting to fetch RSS feeds")
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articles = fetch_rss_feeds()
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logger.info(f"Fetched {len(articles)} articles")
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process_and_store_articles(articles)
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logger.info("Articles processed and stored")
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stored_docs = vector_db.similarity_search("news", k=len(articles))
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enriched_articles = [
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{
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"category": doc.metadata["category"],
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"sentiment": doc.metadata["sentiment"],
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"published": doc.metadata["published"],
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"image": doc.metadata.get("image", "svg"), # Use "svg" as a flag for default
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}
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for doc in stored_docs
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]
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logger.info(f"Enriched {len(enriched_articles)} articles for display")
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if request.method == 'POST':
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query = request.form.get('search')
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if query:
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logger.info(f"Processing search query: {query}")
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results = vector_db.similarity_search(query, k=10)
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enriched_articles = [
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{
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"category": doc.metadata["category"],
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"sentiment": doc.metadata["sentiment"],
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"published": doc.metadata["published"],
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"image": doc.metadata.get("image", "svg"),
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}
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for doc in results
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]
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logger.info(f"Search returned {len(enriched_articles)} results")
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categorized_articles = {}
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for article in enriched_articles:
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