import os
from pathlib import Path

from dotenv import load_dotenv

_HERE = Path(__file__).resolve().parent.parent
load_dotenv(_HERE / ".env")

DATA_DIR = Path(os.getenv("NEWS_MCP_DATA_DIR", Path(__file__).resolve().parent / "data"))
DATA_DIR.mkdir(parents=True, exist_ok=True)
DB_PATH = Path(os.getenv("NEWS_MCP_DB_PATH", str(DATA_DIR / "news.sqlite")))
PROMPTS_DIR = Path(os.getenv("NEWS_PROMPTS_DIR", str(_HERE / "prompts")))
ENTITY_ALIASES_FILE = Path(os.getenv("NEWS_ENTITY_ALIASES_FILE", str(_HERE / "config" / "entity_aliases.json")))

NEWS_FEED_URL = os.getenv("NEWS_FEED_URL", os.getenv("NEWS_RSS_FEED_URL", "https://breakingthenews.net/news-feed.xml"))
NEWS_FEED_URLS = os.getenv("NEWS_FEED_URLS", os.getenv("NEWS_RSS_FEED_URLS", "")).strip()
RSS_FEED_URL = NEWS_FEED_URL
RSS_FEED_URLS = NEWS_FEED_URLS

CLUSTERS_TTL_HOURS = float(os.getenv("NEWS_CLUSTERS_TTL_HOURS", "24"))
DEFAULT_TOPICS = ["crypto", "macro", "regulation", "ai", "other"]

# LLM extraction / summarization
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
NEWS_EXTRACT_PROVIDER = os.getenv("NEWS_EXTRACT_PROVIDER", "groq")
NEWS_EXTRACT_MODEL = os.getenv("NEWS_EXTRACT_MODEL", os.getenv("GROQ_MODEL", "llama4-16e"))
NEWS_SUMMARY_PROVIDER = os.getenv("NEWS_SUMMARY_PROVIDER", "groq")
NEWS_SUMMARY_MODEL = os.getenv("NEWS_SUMMARY_MODEL", os.getenv("GROQ_MODEL", "llama4-16e"))
GROQ_DEBUG = os.getenv("GROQ_DEBUG", "false").lower() == "true"

NEWS_ENTITY_BLACKLIST = [x.strip().lower() for x in os.getenv("ENTITY_BLACKLIST", "").split(",") if x.strip()]
GROQ_ENRICH_OTHER_ONLY = os.getenv("GROQ_ENRICH_OTHER_ONLY", "false").lower() == "true"
GROQ_MAX_CLUSTERS_PER_REFRESH = int(os.getenv("GROQ_MAX_CLUSTERS_PER_REFRESH", "20"))

# Optional embeddings path (Ollama first when enabled, fallback otherwise).
NEWS_EMBEDDINGS_ENABLED = os.getenv("NEWS_EMBEDDINGS_ENABLED", "false").lower() == "true"
OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", os.getenv("OLLAMA_URL", "http://127.0.0.1:11434"))
OLLAMA_EMBEDDING_MODEL = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
NEWS_EMBEDDING_SIMILARITY_THRESHOLD = float(os.getenv("NEWS_EMBEDDING_SIMILARITY_THRESHOLD", "0.885"))

NEWS_REFRESH_INTERVAL_SECONDS = int(os.getenv("NEWS_REFRESH_INTERVAL_SECONDS", "900"))
NEWS_BACKGROUND_REFRESH_ENABLED = os.getenv("NEWS_BACKGROUND_REFRESH_ENABLED", "true").lower() == "true"
NEWS_BACKGROUND_REFRESH_ON_START = os.getenv("NEWS_BACKGROUND_REFRESH_ON_START", "true").lower() == "true"