import yaml
from typing import Optional, List
from pydantic_settings import BaseSettings
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
from llama_index.llms.ollama import Ollama
from llama_index.llms.groq import Groq
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from pydantic import Field, ValidationError


class ProjectSettings(BaseSettings):

    OPENAI_API_KEY: Optional[str] = Field(None, env='OPENAI_API_KEY')
    GROQ_KEY: Optional[str] = Field(None, env='GROQ_KEY')
    config: Optional[dict] = None
    moduleList: List[str] = Field(default_factory=list)

    class Config:
        env_file = '.env'

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.config = self._read_yaml_config()

        if not self.config:
            raise ValidationError("Config file could not be loaded")

        self._instantiate_services()

        self.moduleList = self.config.get('userPreference', {})['ModuleList']

    def _instantiate_services(self):
        llm_service = self.config.get('userPreference', {}).get('llmService', '').lower()

        if llm_service == 'ollama':
            self._initialize_ollama()
        elif llm_service == 'openai':
            self._initialize_openai()
        elif llm_service == 'groq':
            self._initialize_groq()
        else:
            raise ValueError(f"Invalid LLM service: {llm_service}")

    def _initialize_ollama(self):
        ollama_config = self.config.get('OllamaConfig', {})
        Settings.llm = Ollama(base_url=ollama_config['baseURL'], model=ollama_config['llm'])
        Settings.embed_model = OllamaEmbedding(base_url=ollama_config['baseURL'], model_name=ollama_config['llm'])

    def _initialize_openai(self):
        openai_config = self.config.get('OpenAIConfig', {})
        Settings.llm = OpenAI(model=openai_config['llm'], api_key=self.OPENAI_API_KEY)
        Settings.embed_model = OpenAIEmbedding(model=openai_config['embeddingModel'], api_key=self.OPENAI_API_KEY)

    def _initialize_groq(self):
        groq_config = self.config.get('GroqConfig', {})
        Settings.llm = Groq(model=groq_config['llm'], api_key=self.GROQ_KEY)
        Settings.embed_model = HuggingFaceEmbedding(model_name=groq_config['hfEmbedding'])
        
    @staticmethod
    def _read_yaml_config():
        with open("config.yaml", "r") as file:
            config = yaml.safe_load(file)

        return config


settings = ProjectSettings()