import os
import feedparser
from huggingface_hub import HfApi, InferenceClient
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
import shutil

# Hugging Face setup
HF_API_TOKEN = os.getenv("HF_API_TOKEN", "YOUR_HF_API_TOKEN")
HF_MODEL = "Qwen/Qwen-72B-Instruct"
REPO_ID = "broadfield-dev/news-rag-db"
LOCAL_DB_DIR = "chroma_db"
client = InferenceClient(model=HF_MODEL, token=HF_API_TOKEN)

# RSS feeds
RSS_FEEDS = [
    "https://www.sciencedaily.com/rss/top/science.xml",
    "https://www.horoscope.com/us/horoscopes/general/rss/horoscope-rss.aspx",
    "http://rss.cnn.com/rss/cnn_allpolitics.rss",
    "https://phys.org/rss-feed/physics-news/",
    "https://www.spaceweatherlive.com/en/news/rss",
    "https://weather.com/feeds/rss",
    "https://www.wired.com/feed/rss",
    "https://www.nasa.gov/rss/dyn/breaking_news.rss",
    "https://www.nationalgeographic.com/feed/",
    "https://www.nature.com/nature.rss",
    "https://www.scientificamerican.com/rss/",
    "https://www.newscientist.com/feed/home/",
    "https://www.livescience.com/feeds/all",
    "https://astrostyle.com/feed/",
    "https://www.vogue.com/feed/rss",
    "https://feeds.bbci.co.uk/news/politics/rss.xml",
    "https://www.reuters.com/arc/outboundfeeds/newsletter-politics/?outputType=xml",
    "https://www.politico.com/rss/politics.xml",
    "https://thehill.com/feed/",
    "https://www.aps.org/publications/apsnews/updates/rss.cfm",
    "https://www.quantamagazine.org/feed/",
    "https://www.sciencedaily.com/rss/matter_energy/physics.xml",
    "https://physicsworld.com/feed/",
    "https://www.swpc.noaa.gov/rss.xml",
    "https://www.nasa.gov/rss/dyn/solar_system.rss",
    "https://weather.com/science/space/rss",
    "https://www.space.com/feeds/space-weather",
    "https://www.accuweather.com/en/rss",
    "https://feeds.bbci.co.uk/weather/feeds/rss/5day/world/",
    "https://www.weather.gov/rss",
    "https://www.foxweather.com/rss",
    "https://techcrunch.com/feed/",
    "https://arstechnica.com/feed/",
    "https://gizmodo.com/rss",
    "https://www.theverge.com/rss/index.xml",
    "https://www.space.com/feeds/all",
    "https://www.universetoday.com/feed/",
    "https://skyandtelescope.org/feed/",
    "https://www.esa.int/rss",
    "https://www.smithsonianmag.com/rss/",
    "https://www.popsci.com/rss.xml",
    "https://www.discovermagazine.com/rss",
    "https://www.atlasobscura.com/feeds/latest"
]

# Embedding model and vector DB
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vector_db = Chroma(persist_directory=LOCAL_DB_DIR, embedding_function=embedding_model)
hf_api = HfApi()

def fetch_rss_feeds():
    articles = []
    for feed_url in RSS_FEEDS:
        print('processing ', feed_url)
        feed = feedparser.parse(feed_url)
        for entry in feed.entries[:5]:  # Limit to 5 per feed
            articles.append({
                "title": entry.get("title", "No Title"),
                "link": entry.get("link", ""),
                "description": entry.get("summary", entry.get("description", "No Description")),
                "published": entry.get("published", "Unknown Date"),
                "category": categorize_feed(feed_url),
            })
            print(entry)
    return articles

def categorize_feed(url):
    if "sciencedaily" in url or "phys.org" in url:
        return "Science & Physics"
    elif "horoscope" in url:
        return "Astrology"
    elif "politics" in url:
        return "Politics"
    elif "spaceweather" in url or "nasa" in url:
        return "Solar & Space"
    elif "weather" in url:
        return "Earth Weather"
    else:
        return "Cool Stuff"

def summarize_article(text):
    prompt = f"Summarize the following text concisely:\n\n{text}"
    try:
        response = client.text_generation(prompt, max_new_tokens=100, temperature=0.7)
        return response.strip()
    except Exception as e:
        print(f"Error summarizing article: {e}")
        return "Summary unavailable"

def categorize_article(text):
    prompt = f"Classify the sentiment as positive, negative, or neutral:\n\n{text}"
    try:
        response = client.text_generation(prompt, max_new_tokens=10, temperature=0.7)
        return response.strip()
    except Exception as e:
        print(f"Error categorizing article: {e}")
        return "Neutral"

def process_and_store_articles(articles):
    documents = []
    for article in articles:
        summary = summarize_article(article["description"])
        sentiment = categorize_article(article["description"])
        doc = Document(
            page_content=summary,
            metadata={
                "title": article["title"],
                "link": article["link"],
                "original_description": article["description"],
                "published": article["published"],
                "category": article["category"],
                "sentiment": sentiment,
            }
        )
        documents.append(doc)
    vector_db.add_documents(documents)
    vector_db.persist()
    upload_to_hf_hub()

def upload_to_hf_hub():
    if os.path.exists(LOCAL_DB_DIR):
        try:
            hf_api.create_repo(repo_id=REPO_ID, repo_type="dataset", exist_ok=True)
        except Exception as e:
            print(f"Error creating repo: {e}")
        for root, _, files in os.walk(LOCAL_DB_DIR):
            for file in files:
                local_path = os.path.join(root, file)
                remote_path = os.path.relpath(local_path, LOCAL_DB_DIR)
                try:
                    hf_api.upload_file(
                        path_or_fileobj=local_path,
                        path_in_repo=remote_path,
                        repo_id=REPO_ID,
                        repo_type="dataset",
                        token=HF_API_TOKEN
                    )
                except Exception as e:
                    print(f"Error uploading file {file}: {e}")
        print(f"Database uploaded to: {REPO_ID}")