"""LLM client abstraction for pluggable LLM providers."""

import logging
from typing import Optional

from app.config import settings

logger = logging.getLogger(__name__)


class LLMClient:
    """
    Abstraction layer for LLM providers.
    
    Supports: OpenAI, Ollama, LM Studio, or offline/mock mode.
    Can be extended to support other providers.
    """

    def __init__(
        self,
        provider: Optional[str] = None,
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        model: Optional[str] = None,
    ):
        """
        Initialize LLM client.
        
        Args:
            provider: "openai", "ollama", "lm_studio", or None for mock
            api_key: API key for provider
            base_url: Base URL for API (for ollama/lm_studio)
            model: Model identifier
        """
        self.provider = provider or settings.LLM_PROVIDER
        self.api_key = api_key or settings.LLM_API_KEY
        self.base_url = base_url or settings.LLM_BASE_URL
        self.model = model or settings.LLM_MODEL

        logger.info(f"LLMClient initialized with provider: {self.provider}")

    async def generate(self, prompt: str, max_tokens: int = 200) -> str:
        """
        Generate text from a prompt.
        
        Args:
            prompt: Input prompt
            max_tokens: Maximum tokens to generate
            
        Returns:
            Generated text
            
        TODO: Implement OpenAI API integration
        TODO: Implement Ollama API integration
        TODO: Implement LM Studio API integration
        """
        if self.provider == "openai":
            return await self._generate_openai(prompt, max_tokens)
        elif self.provider == "ollama":
            return await self._generate_ollama(prompt, max_tokens)
        elif self.provider == "lm_studio":
            return await self._generate_lm_studio(prompt, max_tokens)
        else:
            return self._generate_mock(prompt)

    async def _generate_openai(self, prompt: str, max_tokens: int) -> str:
        """
        Generate using OpenAI API.
        
        TODO: Implement using openai library
        - Create client with api_key
        - Call ChatCompletion
        - Handle errors and retries
        """
        logger.warning("OpenAI provider not yet implemented (stub)")
        return self._generate_mock(prompt)

    async def _generate_ollama(self, prompt: str, max_tokens: int) -> str:
        """
        Generate using Ollama local API.
        
        TODO: Implement using httpx or requests
        - POST to base_url/api/generate
        - Stream response and accumulate
        - Handle model pulling if needed
        """
        logger.warning("Ollama provider not yet implemented (stub)")
        return self._generate_mock(prompt)

    async def _generate_lm_studio(self, prompt: str, max_tokens: int) -> str:
        """
        Generate using LM Studio local API.
        
        TODO: Implement OpenAI-compatible API calls
        - POST to base_url/v1/chat/completions
        - Use same logic as OpenAI but with local endpoint
        """
        logger.warning("LM Studio provider not yet implemented (stub)")
        return self._generate_mock(prompt)

    def _generate_mock(self, prompt: str) -> str:
        """
        Generate deterministic mock response (no API needed).
        
        Used when no provider is configured or for testing.
        """
        logger.debug(f"Mock generation for prompt: {prompt[:50]}...")

        if "content angle:" in prompt.lower():
            for line in prompt.splitlines():
                if line.lower().startswith("content angle:"):
                    angle = line.split(":", 1)[1].strip()
                    return f"The quiet here keeps circling back to {angle}."
        
        # Simple deterministic responses for testing
        if "hello" in prompt.lower():
            return "Greetings, traveler! Welcome to The Sanctum."
        elif "help" in prompt.lower():
            return "I am here to guide your discourse through the streams."
        elif "topic" in prompt.lower():
            return "The archives speak of many topics worthy of discussion."
        else:
            return "An interesting observation. Tell me more."