from functools import lru_cache
import logging
from pathlib import Path

from pydantic_settings import BaseSettings

logger = logging.getLogger(__name__)


class Settings(BaseSettings):
    # LLM access (OpenRouter / vLLM)
    llm_base_url: str = "https://openrouter.ai/api/v1"
    llm_api_key: str = ""
    llm_model_name: str = "qwen/qwen3.5-35b-a3b"
    llm_enable_thinking: bool = False
    vllm_engine: bool = False

    # Deepseek API (decompose step only, Package 6)
    dp_base_url: str = "https://api.deepseek.com"
    dp_api_key: str = ""
    dp_model_name: str = "deepseek-v4-flash"

    # Embeddings
    embedding_model: str = "qwen/qwen3-embedding-4b"
    embedding_base_url: str = "https://openrouter.ai/api/v1"
    embedding_api_key: str = ""

    # ChromaDB
    chroma_db_path: str = "./chroma_db"

    # Chunk PDF storage (extracted PDF pages)
    document_chunk_path: str = "./document_chunk"

    # SQLite databases (Package 3)
    prompts_db_path: str = "./data/prompts.db"
    history_db_path: str = "./data/history.db"

    # App configuration moved to settings for easier testing/configuration
    # Cross-origin settings and chunking parameters (Phase 1 plan)
    cors_origins: list[str] = ["http://localhost:5173", "http://localhost:3000"]
    chunk_size: int = 1000
    chunk_overlap: int = 200
    retrieval_n_results: int = 10
    relevance_threshold: float = 7.0
    llm_timeout: float = 60.0

    # Alibaba Cloud DashScope ASR (Phase 2)
    dashscope_api_key: str = ""
    asr_model_name: str = "qwen3-asr-flash"
    asr_realtime_model_name: str = "qwen3-asr-flash-realtime"

    # Video upload (Phase 2)
    video_upload_dir: str = "./uploads"
    max_video_size_mb: int = 300
    supported_video_formats: list[str] = [".mp4", ".webm", ".mov", ".avi", ".mkv"]

    # Phase 4 — Live audio capture toggles
    system_audio_enabled: bool = True
    mic_enabled: bool = True

    # Development helpers
    model_config = {"env_file": ".env", "env_file_encoding": "utf-8"}


@lru_cache
def get_settings() -> Settings:
    s = Settings()
    logger.info("Settings loaded: llm_model=%s  embedding_model=%s", s.llm_model_name, s.embedding_model)
    return s