import os
import logging
from dotenv import load_dotenv
import litellm

# Disable LiteLLM callbacks to prevent missing dependency errors (APScheduler etc)
litellm.success_callback = []
litellm.failure_callback = []
litellm.callbacks = []

# Load environment variables
load_dotenv()

# Setup Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("AntigravityConfig")

class Config:
    """
    Central Configuration for LLM and Memory Providers.
    Supports Dual-Model Strategy (Fast vs Smart).
    """
    
    @staticmethod
    def _get_llm_dict(provider, model_name, base_url=None):
        """Helper to construct the LLM config dictionary."""
        if provider == "openai":
            return {"model": model_name, "temperature": 0.7}
        elif provider == "anthropic":
            return {"model": f"anthropic/{model_name}", "temperature": 0.7}
        elif provider == "gemini":
            # LiteLLM format for Gemini
            return {
                "model": f"gemini/{model_name}", 
                "temperature": 0.7,
                "api_key": os.getenv("GEMINI_API_KEY")
            }
        elif provider == "ollama":
            return {
                "model": f"ollama/{model_name}",
                "base_url": base_url,
                "temperature": 0.7
            }
        elif provider == "azure":
            return {"model": f"azure/{model_name}", "temperature": 0.7}
        else:
            return {"model": model_name, "temperature": 0.7}

    @staticmethod
    def get_llm_config(mode="smart"):
        """
        Returns the LLM configuration.
        :param mode: 'smart' (High Reasoning) or 'fast' (High Speed/Low Cost)
        """
        provider = os.getenv("LLM_PROVIDER", "openai").lower()
        base_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")

        # Select Model Name based on Mode
        if mode == "fast":
            model_name = os.getenv("LLM_MODEL_FAST", "gpt-3.5-turbo")
        else:
            model_name = os.getenv("LLM_MODEL_SMART", "gpt-4o")

        logger.info(f"Loading {mode.upper()} LLM: Provider={provider}, Model={model_name}")
        return Config._get_llm_dict(provider, model_name, base_url)

    @staticmethod
    def get_mem0_config():
        """
        Returns the Mem0 configuration with the correct LLM, Embedder, and Vector Store.
        Supports fully local mode with Ollama, or cloud with Gemini/OpenAI.
        """
        memory_provider = os.getenv("MEMORY_PROVIDER", "mem0").lower()
        embedding_provider = os.getenv("MEMORY_EMBEDDING_PROVIDER", "openai").lower()
        llm_provider = os.getenv("LLM_PROVIDER", "openai").lower()
        project_id = os.getenv("MEMORY_PROJECT_ID", "default_project")
        
        config = {
            "version": "v1.1",
        }

        # LLM Configuration (REQUIRED for Mem0 to process memories)
        if llm_provider == "gemini":
            config["llm"] = {
                "provider": "litellm",
                "config": {
                    "model": f"gemini/{os.getenv('LLM_MODEL_FAST', 'gemini-2.0-flash-exp')}",
                    "api_key": os.getenv("GEMINI_API_KEY"),
                    "temperature": 0.1
                }
            }
        elif llm_provider == "openai":
            config["llm"] = {
                "provider": "openai",
                "config": {
                    "model": os.getenv("LLM_MODEL_FAST", "gpt-3.5-turbo"),
                    "api_key": os.getenv("OPENAI_API_KEY"),
                    "temperature": 0.1
                }
            }
        elif llm_provider == "ollama":
            config["llm"] = {
                "provider": "ollama",
                "config": {
                    "model": os.getenv("LLM_MODEL_FAST", "llama3"),
                    "ollama_base_url": os.getenv("OLLAMA_BASE_URL", "http://localhost:11434"),
                    "temperature": 0.1
                }
            }

        # Embedder Configuration
        if embedding_provider == "openai":
            config["embedder"] = {
                "provider": "openai",
                "config": {
                    "model": "text-embedding-3-small",
                    "api_key": os.getenv("OPENAI_API_KEY")
                }
            }
        elif embedding_provider == "local":
            # Use sentence-transformers for fully local embeddings
            config["embedder"] = {
                "provider": "huggingface",
                "config": {
                    "model": "all-MiniLM-L6-v2"
                }
            }
        elif embedding_provider == "gemini":
            # Gemini embeddings
            config["embedder"] = {
                "provider": "gemini",
                "config": {
                    "model": "models/text-embedding-004",
                    "api_key": os.getenv("GEMINI_API_KEY")
                }
            }

        # Vector Store Configuration
        if memory_provider == "qdrant":
            config["vector_store"] = {
                "provider": "qdrant",
                "config": {
                    "host": os.getenv("QDRANT_HOST", "localhost"),
                    "port": int(os.getenv("QDRANT_PORT", 6333)),
                    "collection_name": project_id
                }
            }
        
        # Add user_id for memory scoping
        config["user_id"] = project_id
            
        return config

    @staticmethod
    def get_telegram_token():
        return os.getenv("TELEGRAM_BOT_TOKEN")

    @staticmethod
    def get_allowed_chats():
        try:
            chats = os.getenv("TELEGRAM_ALLOWED_CHAT_IDS", "").split(",")
            return [int(c.strip()) for c in chats if c.strip()]
        except ValueError:
            return []