app.py

import os
from flask import Flask, render_template, request, jsonify
from rss_processor import fetch_rss_feeds, process_and_store_articles, vector_db
import logging

app = Flask(__name__)

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@app.route('/')
def index():
    try:
        # Fetch and store articles synchronously on first load
        articles = fetch_rss_feeds()
        logger.info(f"Fetched {len(articles)} articles")
        process_and_store_articles(articles)
        logger.info("Articles processed and stored")

        # Retrieve all articles from Chroma DB
        all_docs = vector_db.get(include=['documents', 'metadatas'])
        if not all_docs.get('metadatas'):
            logger.warning("No articles in DB yet")
            return render_template("index.html", categorized_articles={}, has_articles=False)

        # Process retrieved documents
        enriched_articles = []
        seen_keys = set()
        for doc, meta in zip(all_docs['documents'], all_docs['metadatas']):
            if not meta:
                continue
            title = meta.get("title", "No Title")
            link = meta.get("link", "")
            description = meta.get("original_description", "No Description")
            key = f"{title}|{link}"
            if key not in seen_keys:
                seen_keys.add(key)
                enriched_articles.append({
                    "title": title,
                    "link": link,
                    "description": description,
                    "category": meta.get("category", "Uncategorized"),
                    "published": meta.get("published", "Unknown Date"),
                    "image": meta.get("image", "svg"),
                })
        logger.info(f"Displaying {len(enriched_articles)} unique articles")

        # Categorize articles
        categorized_articles = {}
        for article in enriched_articles:
            cat = article["category"]
            categorized_articles.setdefault(cat, []).append(article)

        return render_template("index.html", categorized_articles=categorized_articles, has_articles=True)
    except Exception as e:
        logger.error(f"Error in index: {e}")
        return render_template("index.html", categorized_articles={}, has_articles=False)

@app.route('/search', methods=['POST'])
def search():
    query = request.form.get('search')
    if not query:
        return render_template("index.html", categorized_articles={}, has_articles=False)

    try:
        logger.info(f"Searching for: {query}")
        results = vector_db.similarity_search(query, k=10)
        enriched_articles = []
        seen_keys = set()
        for doc in results:
            meta = doc.metadata
            title = meta.get("title", "No Title")
            link = meta.get("link", "")
            description = meta.get("original_description", "No Description")
            key = f"{title}|{link}"
            if key not in seen_keys:
                seen_keys.add(key)
                enriched_articles.append({
                    "title": title,
                    "link": link,
                    "description": description,
                    "category": meta.get("category", "Uncategorized"),
                    "published": meta.get("published", "Unknown Date"),
                    "image": meta.get("image", "svg"),
                })

        categorized_articles = {}
        for article in enriched_articles:
            cat = article["category"]
            categorized_articles.setdefault(cat, []).append(article)

        return render_template("index.html", categorized_articles=categorized_articles, has_articles=bool(enriched_articles))
    except Exception as e:
        logger.error(f"Search error: {e}")
        return render_template("index.html", categorized_articles={}, has_articles=False)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)