import os
import yaml
from dotenv import load_dotenv
from pydantic import BaseModel, Field

load_dotenv()

# --- 1. Define the Configuration Schema ---
class ApplicationSettings(BaseModel):
    project_name: str = "Cortex Hub"
    version: str = "1.0.0"
    log_level: str = "INFO"  # <-- New field

class DatabaseSettings(BaseModel):
    mode: str = "sqlite"
    url: str = "postgresql://user:password@localhost/ai_hub_db"

class LLMProviderSettings(BaseModel):
    deepseek_model_name: str = "deepseek-chat"
    gemini_model_name: str = "gemini-1.5-flash-latest"

class VectorStoreSettings(BaseModel):
    index_path: str = "data/faiss_index.bin"
    embedding_dimension: int = 768

class AppConfig(BaseModel):
    application: ApplicationSettings = Field(default_factory=ApplicationSettings)
    database: DatabaseSettings = Field(default_factory=DatabaseSettings)
    llm_providers: LLMProviderSettings = Field(default_factory=LLMProviderSettings)
    vector_store: VectorStoreSettings = Field(default_factory=VectorStoreSettings)

# --- 2. Create the Final Settings Object ---
class Settings:
    """
    Holds all application settings, validated and structured by Pydantic.
    Priority Order: Environment Variables > YAML File > Pydantic Defaults
    """
    def __init__(self):
        config_path = os.getenv("CONFIG_PATH", "config.yaml")
        yaml_data = {}
        if os.path.exists(config_path):
            print(f"✅ Loading configuration from {config_path}")
            with open(config_path, 'r') as f:
                yaml_data = yaml.safe_load(f) or {}
        else:
            print(f"⚠️  '{config_path}' not found. Using defaults and environment variables.")
        
        config_from_pydantic = AppConfig.parse_obj(yaml_data)

        def get_from_yaml(keys):
            d = yaml_data
            for key in keys:
                d = d.get(key) if isinstance(d, dict) else None
            return d

        self.PROJECT_NAME: str = os.getenv("PROJECT_NAME") or \
                                 get_from_yaml(["application", "project_name"]) or \
                                 config_from_pydantic.application.project_name
        self.VERSION: str = config_from_pydantic.application.version

        self.LOG_LEVEL: str = os.getenv("LOG_LEVEL") or \
                              get_from_yaml(["application", "log_level"]) or \
                              config_from_pydantic.application.log_level

        self.DB_MODE: str = os.getenv("DB_MODE") or \
                            get_from_yaml(["database", "mode"]) or \
                            config_from_pydantic.database.mode
        
        if self.DB_MODE == "sqlite":
            self.DATABASE_URL: str = "sqlite:///./data/ai_hub.db"
        else:
            self.DATABASE_URL: str = os.getenv("DATABASE_URL") or \
                                     get_from_yaml(["database", "url"]) or \
                                     config_from_pydantic.database.url
        
        self.DEEPSEEK_API_KEY: str = os.getenv("DEEPSEEK_API_KEY")
        self.GEMINI_API_KEY: str = os.getenv("GEMINI_API_KEY")
        if not self.DEEPSEEK_API_KEY or not self.GEMINI_API_KEY:
            raise ValueError("API keys must be set in the environment.")

        self.DEEPSEEK_MODEL_NAME: str = os.getenv("DEEPSEEK_MODEL_NAME") or \
                                        get_from_yaml(["llm_providers", "deepseek_model_name"]) or \
                                        config_from_pydantic.llm_providers.deepseek_model_name
                                        
        self.GEMINI_MODEL_NAME: str = os.getenv("GEMINI_MODEL_NAME") or \
                                      get_from_yaml(["llm_providers", "gemini_model_name"]) or \
                                      config_from_pydantic.llm_providers.gemini_model_name
        
        self.FAISS_INDEX_PATH: str = os.getenv("FAISS_INDEX_PATH") or \
                                     get_from_yaml(["vector_store", "index_path"]) or \
                                     config_from_pydantic.vector_store.index_path
                                     
        dimension_str = os.getenv("EMBEDDING_DIMENSION") or \
                        get_from_yaml(["vector_store", "embedding_dimension"]) or \
                        config_from_pydantic.vector_store.embedding_dimension
        self.EMBEDDING_DIMENSION: int = int(dimension_str)

# Instantiate the single settings object for the application
settings = Settings()