# /services.py
""" Manages interactions with all external LLM and search APIs. """

import os
import logging
from typing import Dict, Any, Generator, List

from dotenv import load_dotenv
from huggingface_hub import InferenceClient
from tavily import TavilyClient
from groq import Groq
import fireworks.client as Fireworks
import openai
import google.generativeai as genai

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
load_dotenv()

# --- API Keys from .env ---
HF_TOKEN = os.getenv("HF_TOKEN")
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
FIREWORKS_API_KEY = os.getenv("FIREWORKS_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")

Messages = List[Dict[str, Any]]

class LLMService:
    """A multi-provider wrapper for LLM Inference APIs."""
    def __init__(self):
        self.hf_client = InferenceClient(token=HF_TOKEN) if HF_TOKEN else None
        self.groq_client = Groq(api_key=GROQ_API_KEY) if GROQ_API_KEY else None
        self.openai_client = openai.OpenAI(api_key=OPENAI_API_KEY) if OPENAI_API_KEY else None
        
        if DEEPSEEK_API_KEY:
            self.deepseek_client = openai.OpenAI(api_key=DEEPSEEK_API_KEY, base_url="https://api.deepseek.com/v1")
        else:
            self.deepseek_client = None

        if FIREWORKS_API_KEY:
            Fireworks.api_key = FIREWORKS_API_KEY
            self.fireworks_client = Fireworks
        else:
            self.fireworks_client = None

        if GEMINI_API_KEY:
            genai.configure(api_key=GEMINI_API_KEY)
            self.gemini_model = genai.GenerativeModel('gemini-1.5-pro-latest')
        else:
            self.gemini_model = None

    def _prepare_messages_for_gemini(self, messages: Messages) -> List[Dict[str, Any]]:
        gemini_messages = []
        for msg in messages:
            if msg['role'] == 'system': continue # Gemini doesn't use a system role in this way
            role = 'model' if msg['role'] == 'assistant' else 'user'
            gemini_messages.append({'role': role, 'parts': [msg['content']]})
        return gemini_messages

    def generate_code_stream(self, model_id: str, messages: Messages, max_tokens: int = 8192) -> Generator[str, None, None]:
        provider, model_name = model_id.split('/', 1)
        logging.info(f"Dispatching to provider: {provider} for model: {model_name}")

        try:
            if provider in ['openai', 'groq', 'deepseek', 'fireworks']:
                client_map = {'openai': self.openai_client, 'groq': self.groq_client, 'deepseek': self.deepseek_client, 'fireworks': self.fireworks_client.ChatCompletion if self.fireworks_client else None}
                client = client_map.get(provider)
                if not client: raise ValueError(f"{provider.capitalize()} API key not configured.")
                
                stream = client.create(model=model_name, messages=messages, stream=True, max_tokens=max_tokens) if provider == 'fireworks' else client.chat.completions.create(model=model_name, messages=messages, stream=True, max_tokens=max_tokens)
                for chunk in stream:
                    if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content: yield chunk.choices[0].delta.content
            
            elif provider == 'gemini':
                if not self.gemini_model: raise ValueError("Gemini API key not configured.")
                system_prompt = next((msg['content'] for msg in messages if msg['role'] == 'system'), "")
                gemini_messages = self._prepare_messages_for_gemini(messages)
                # Prepend system prompt to first user message for Gemini
                if system_prompt and gemini_messages and gemini_messages[0]['role'] == 'user':
                    gemini_messages[0]['parts'][0] = f"{system_prompt}\n\n{gemini_messages[0]['parts'][0]}"
                stream = self.gemini_model.generate_content(gemini_messages, stream=True)
                for chunk in stream: yield chunk.text

            elif provider == 'huggingface':
                if not self.hf_client: raise ValueError("Hugging Face API token not configured.")
                hf_model_id = model_id.split('/', 1)[1]
                stream = self.hf_client.chat_completion(model=hf_model_id, messages=messages, stream=True, max_tokens=max_tokens)
                for chunk in stream:
                    if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content: yield chunk.choices[0].delta.content
            else:
                raise ValueError(f"Unknown provider: {provider}")
        except Exception as e:
            logging.error(f"LLM API Error with provider {provider}: {e}")
            yield f"Error from {provider.capitalize()}: {str(e)}"

class SearchService:
    def __init__(self, api_key: str = TAVILY_API_KEY):
        self.client = TavilyClient(api_key=api_key) if api_key else None
        if not self.client: logging.warning("TAVILY_API_KEY not set. Web search will be disabled.")
    def is_available(self) -> bool: return self.client is not None
    def search(self, query: str, max_results: int = 5) -> str:
        if not self.is_available(): return "Web search is not available."
        try:
            response = self.client.search(query, search_depth="advanced", max_results=min(max(1, max_results), 10))
            return "Web Search Results:\n\n" + "\n---\n".join([f"Title: {res.get('title', 'N/A')}\nURL: {res.get('url', 'N/A')}\nContent: {res.get('content', 'N/A')}" for res in response.get('results', [])])
        except Exception as e: return f"Search error: {str(e)}"

llm_service = LLMService()
search_service = SearchService()