from pydantic_settings import BaseSettings
from typing import List
import os
from pathlib import Path


class Settings(BaseSettings):
    # App Configuration
    APP_NAME: str = "aPersona"
    APP_VERSION: str = "1.0.0"
    DEBUG: bool = True
    API_V1_STR: str = "/api/v1"
    
    # Security
    SECRET_KEY: str = "your-secret-key-change-in-production"
    ACCESS_TOKEN_EXPIRE_MINUTES: int = 60 * 24 * 8  # 8 days
    ALGORITHM: str = "HS256"
    
    # Database
    DATABASE_URL: str = "sqlite:///./apersona.db"
    
    # File Storage
    UPLOAD_DIR: Path = Path("../data/uploads")
    PROCESSED_DIR: Path = Path("../data/processed")
    VECTOR_DB_DIR: Path = Path("../data/vectors")
    MAX_FILE_SIZE: int = 100 * 1024 * 1024  # 100MB
    
    # AI Configuration
    OLLAMA_BASE_URL: str = "http://localhost:11434"
    DEFAULT_LLM_MODEL: str = "mistral"
    EMBEDDING_MODEL: str = "all-MiniLM-L6-v2"
    VECTOR_COLLECTION_NAME: str = "apersona_documents"
    
    # CORS
    BACKEND_CORS_ORIGINS: List[str] = [
        "http://localhost:3000",
        "http://localhost:5173",
        "http://127.0.0.1:3000",
        "http://127.0.0.1:5173",
    ]
    
    # Auto-Learning Configuration
    LEARNING_UPDATE_INTERVAL: int = 3600  # 1 hour in seconds
    MIN_INTERACTIONS_FOR_LEARNING: int = 10
    FEEDBACK_WEIGHT: float = 0.1
    
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        # Create directories if they don't exist
        for directory in [self.UPLOAD_DIR, self.PROCESSED_DIR, self.VECTOR_DB_DIR]:
            directory.mkdir(parents=True, exist_ok=True)
    
    class Config:
        env_file = ".env"


settings = Settings()