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

app = Flask(__name__)

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

def load_feeds_in_background():
    logger.info("Starting to fetch and process RSS feeds in background")
    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")

@app.route('/')
def index():
    # Show all existing articles immediately, even if empty
    try:
        # Get all documents from Chroma DB using get()
        all_docs = vector_db.get(include=['documents', 'metadatas'])
        if 'metadatas' in all_docs and all_docs['metadatas']:
            stored_docs = [
                Document(page_content=doc['documents'][0] if doc['documents'] else "", metadata=meta)
                for doc, meta in zip(all_docs['documents'], all_docs['metadatas'])
            ]
            logger.info(f"Found {len(stored_docs)} documents in vector DB")
        else:
            stored_docs = []
            logger.warning("No metadata or documents found in vector DB")
        # Use a set to ensure unique articles by title, link, and full description hash
        unique_articles = {}
        for doc in stored_docs:
            if not doc.metadata:  # Handle potential None metadata
                continue
            title = doc.metadata.get("title", "No Title")
            link = doc.metadata.get("link", "")
            description = doc.metadata.get("original_description", "No Description")
            desc_hash = hashlib.md5(description.encode()).hexdigest()
            key = f"{title}|{link}|{desc_hash}"
            if key not in unique_articles:
                unique_articles[key] = {
                    "title": title,
                    "link": link,
                    "description": description,
                    "category": doc.metadata.get("category", "Uncategorized"),
                    "published": doc.metadata.get("published", "Unknown Date"),
                    "image": doc.metadata.get("image", "svg"),
                }
        enriched_articles = list(unique_articles.values())
        logger.info(f"Enriched {len(enriched_articles)} unique articles for display")
    except Exception as e:
        logger.error(f"Error retrieving documents from vector DB: {e}")
        enriched_articles = []  # Fallback if DB is empty or inaccessible

    # Start loading new feeds in the background
    subprocess.Popen(["python", "rss_processor.py", "load_feeds"])

    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, loading_new_feeds=True, has_articles=bool(enriched_articles))

@app.route('/search', methods=['POST'])
def search():
    query = request.form.get('search')
    if query:
        logger.info(f"Processing search query: {query}")
        try:
            results = vector_db.similarity_search(query, k=10)
            unique_search_articles = {}
            for doc in results:
                title = doc.metadata.get("title", "No Title")
                link = doc.metadata.get("link", "")
                description = doc.metadata.get("original_description", "No Description")
                desc_hash = hashlib.md5(description.encode()).hexdigest()
                key = f"{title}|{link}|{desc_hash}"
                if key not in unique_search_articles:
                    unique_search_articles[key] = {
                        "title": title,
                        "link": link,
                        "description": description,
                        "category": doc.metadata.get("category", "Uncategorized"),
                        "published": doc.metadata.get("published", "Unknown Date"),
                        "image": doc.metadata.get("image", "svg"),
                    }
            enriched_articles = list(unique_search_articles.values())
            logger.info(f"Search returned {len(enriched_articles)} unique results")
        except Exception as e:
            logger.error(f"Error performing search: {e}")
            enriched_articles = []

        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, loading_new_feeds=True, has_articles=bool(enriched_articles))
    return render_template("index.html", categorized_articles={}, loading_new_feeds=True, has_articles=False)

@app.route('/check_feeds', methods=['GET'])
def check_feeds():
    try:
        # Check if vector DB has any documents
        all_docs = vector_db.get(include=['documents', 'metadatas'])
        if 'metadatas' in all_docs and all_docs['metadatas']:
            logger.info("Feeds loaded successfully in vector DB")
            return jsonify({"status": "loaded"})
        return jsonify({"status": "loading"}), 202
    except Exception as e:
        logger.error(f"Error checking feeds: {e}")
        return jsonify({"status": "error", "message": str(e)}), 500

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