import os
import sys
import json
import asyncio
from typing import List, Dict

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

from components.indexers.news_indexer import get_or_build_index_from_docs
from components.fetchers.google_search import fetch_google_news
from components.fetchers.scraper import scrape_url
from llama_index.core.settings import Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.schema import Document

# ✅ Use local embedding model
Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/paraphrase-MiniLM-L3-v2")

# 🔐 Environment variables
API_KEY = os.environ.get("GOOGLE_API_KEY")
CSE_ID = os.environ.get("GOOGLE_CX_ID")

# 📰 Topics
QUERIES = ["India news", "World news", "Tech news", "Finance news", "Sports news"]

# 🗂️ Paths
DATA_DIR = "data/news"
RAW_JSON = os.path.join(DATA_DIR, "news.jsonl")
INDEX_DIR = "storage/index"

# 💾 Save articles to disk
def write_articles_jsonl(articles: List[Dict], file_path: str):
    os.makedirs(os.path.dirname(file_path), exist_ok=True)
    with open(file_path, "w", encoding="utf-8") as f:
        for article in articles:
            f.write(json.dumps(article, ensure_ascii=False) + "\n")

# 📄 Convert raw scraped data into Document objects
async def build_documents(data: List[Dict]) -> List[Document]:
    # --- IMPORTANT CHANGE HERE ---
    # The 'data' list from all_articles already contains the 'headline_id' (which was 'counter').
    # We will use that directly.
    return [
        Document(
            text=entry["content"],
            metadata={
                "headline_id": entry["headline_id"],  # Use the pre-assigned ID
                "title": entry["title"],
                "url": entry["url"],
                "topic": entry["topic"].lower().replace(" news", ""),  # normalized topic key
                "source": entry["source"]
            }
        )
        for entry in data
    ]

# 🚀 Main pipeline runner
async def main():
    if not API_KEY or not CSE_ID:
        raise EnvironmentError("Missing GOOGLE_API_KEY or GOOGLE_CX_ID in environment.")

    print("🌍 Fetching news URLs from Google...")

    all_articles = []
    # This counter will be used for your simple sequential ID
    global_headline_id_counter = 1

    for query in QUERIES:
        print(f"🔍 Searching for: {query}")
        try:
            results = fetch_google_news(query, API_KEY, CSE_ID, num_results=10)
            print(f"   → Found {len(results)} links for '{query}'.")

            for item in results:
                url = item.get("link", "").strip()
                title = item.get("title", "").strip()
                source = item.get("displayLink", "").strip()
                if not url or not title:
                    continue

                print(f"🌐 Scraping: {url}")
                article_text = scrape_url(url)

                if article_text:
                    # Assign the simple sequential ID here
                    all_articles.append({
                        "headline_id": global_headline_id_counter, # Assign the unique ID
                        "topic": query,
                        "title": title, # Keep title clean, the numbering can be for display later
                        "url": url,
                        "source": source,
                        "content": article_text
                    })
                    global_headline_id_counter += 1 # Increment for the next article
                else:
                    print(f"⚠️ Skipped: {url}")

        except Exception as e:
            print(f"❌ Error fetching '{query}': {e}")

    if not all_articles:
        print("⚠️ No content scraped. Exiting.")
        return

    print(f"📝 Writing {len(all_articles)} articles to {RAW_JSON}...")
    write_articles_jsonl(all_articles, RAW_JSON)

    print("🧠 Building index...")
    documents = await build_documents(all_articles)
    get_or_build_index_from_docs(documents)

    print("✅ Indexing complete.")

# 🏁 Entrypoint
if __name__ == "__main__":
    asyncio.run(main())