import asyncio
import json
import logging
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple

import aiohttp
import openai

from .ai_responder import AIResponder, async_cache_to_file, exponential_backoff, pp
from .igdblib import IGDBQuery
from .leonardo_draw import LeonardoAIDrawMixIn


@async_cache_to_file("openai_chat.dat")
async def openai_chat(client, *args, **kwargs):
    return await client.chat.completions.create(*args, **kwargs)


@async_cache_to_file("openai_chat.dat")
async def openai_image(client, *args, **kwargs):
    response = await client.images.generate(*args, **kwargs)
    async with aiohttp.ClientSession() as session:
        async with session.get(response.data[0].url) as image:
            return BytesIO(await image.read())


class OpenAIResponder(AIResponder, LeonardoAIDrawMixIn):
    def __init__(self, config: Dict[str, Any], channel: Optional[str] = None) -> None:
        super().__init__(config, channel)
        self.client = openai.AsyncOpenAI(api_key=self.config.get("openai-token", self.config.get("openai-key", "")))
        
        # Initialize IGDB if enabled
        self.igdb = None
        if (self.config.get("enable-game-info", False) and 
            self.config.get("igdb-client-id") and 
            self.config.get("igdb-access-token")):
            try:
                self.igdb = IGDBQuery(
                    self.config["igdb-client-id"],
                    self.config["igdb-access-token"]
                )
                logging.info("IGDB integration enabled for game information")
            except Exception as e:
                logging.warning(f"Failed to initialize IGDB: {e}")
                self.igdb = None

    async def draw_openai(self, description: str) -> BytesIO:
        for _ in range(3):
            try:
                response = await openai_image(self.client, prompt=description, n=1, size="1024x1024", model="dall-e-3")
                logging.info(f"Drawed a picture with DALL-E on this description: {repr(description)}")
                return response
            except Exception as err:
                logging.warning(f"Failed to generate image {repr(description)}: {repr(err)}")
        raise RuntimeError(f"Failed to generate image {repr(description)} after multiple retries")

    async def chat(self, messages: List[Dict[str, Any]], limit: int) -> Tuple[Optional[Dict[str, Any]], int]:
        if isinstance(messages[-1]["content"], str):
            model = self.config["model"]
        elif "model-vision" in self.config:
            model = self.config["model-vision"]
        else:
            messages[-1]["content"] = messages[-1]["content"][0]["text"]
        try:
            # Prepare function calls if IGDB is enabled
            chat_kwargs = {
                "model": model,
                "messages": messages,
            }
            
            if self.igdb and self.config.get("enable-game-info", False):
                chat_kwargs["tools"] = [
                    {"type": "function", "function": func}
                    for func in self.igdb.get_openai_functions()
                ]
                chat_kwargs["tool_choice"] = "auto"
            
            result = await openai_chat(self.client, **chat_kwargs)
            
            # Handle function calls if present
            message = result.choices[0].message
            
            # Check if we have function/tool calls and IGDB is enabled
            has_tool_calls = (hasattr(message, 'tool_calls') and message.tool_calls and 
                             self.igdb and self.config.get("enable-game-info", False))
            
            if has_tool_calls:
                try:
                    # Process function calls
                    messages.append({
                        "role": "assistant", 
                        "content": message.content or "",
                        "tool_calls": [tc.dict() if hasattr(tc, 'dict') else tc for tc in message.tool_calls]
                    })
                    
                    # Execute function calls
                    for tool_call in message.tool_calls:
                        function_name = tool_call.function.name
                        function_args = json.loads(tool_call.function.arguments)
                        
                        # Execute IGDB function
                        function_result = await self._execute_igdb_function(function_name, function_args)
                        
                        messages.append({
                            "role": "tool",
                            "tool_call_id": tool_call.id,
                            "content": json.dumps(function_result) if function_result else "No results found"
                        })
                    
                    # Get final response after function execution
                    final_result = await openai_chat(self.client, **chat_kwargs)
                    answer_obj = final_result.choices[0].message
                except Exception as e:
                    # If function calling fails, fall back to regular response
                    logging.warning(f"Function calling failed, using regular response: {e}")
                    answer_obj = message
            else:
                answer_obj = message
                
            answer = {"content": answer_obj.content, "role": answer_obj.role}
            self.rate_limit_backoff = exponential_backoff()
            logging.info(f"generated response {result.usage}: {repr(answer)}")
            return answer, limit
        except openai.BadRequestError as err:
            if "maximum context length is" in str(err) and limit > 4:
                logging.warning(f"context length exceeded, reduce the limit {limit}: {str(err)}")
                limit -= 1
                return None, limit
            raise err
        except openai.RateLimitError as err:
            rate_limit_sleep = next(self.rate_limit_backoff)
            if "retry-model" in self.config:
                model = self.config["retry-model"]
            logging.warning(f"got an rate limit error, sleep for {rate_limit_sleep} seconds: {str(err)}")
            await asyncio.sleep(rate_limit_sleep)
        except Exception as err:
            logging.warning(f"failed to generate response: {repr(err)}")
        return None, limit

    async def fix(self, answer: str) -> str:
        if "fix-model" not in self.config:
            return answer
        messages = [{"role": "system", "content": self.config["fix-description"]}, {"role": "user", "content": answer}]
        try:
            result = await openai_chat(self.client, model=self.config["fix-model"], messages=messages)
            logging.info(f"got this message as fix:\n{pp(result.choices[0].message.content)}")
            response = result.choices[0].message.content
            start, end = response.find("{"), response.rfind("}")
            if start == -1 or end == -1 or (start + 3) >= end:
                return answer
            response = response[start : end + 1]
            logging.info(f"fixed answer:\n{pp(response)}")
            return response
        except Exception as err:
            logging.warning(f"failed to execute a fix for the answer: {repr(err)}")
        return answer

    async def translate(self, text: str, language: str = "english") -> str:
        if "fix-model" not in self.config:
            return text
        message = [
            {
                "role": "system",
                "content": f"You are an professional translator to {language} language,"
                f" you translate everything you get directly to {language}"
                f" if it is not already in {language}, otherwise you just copy it.",
            },
            {"role": "user", "content": text},
        ]
        try:
            result = await openai_chat(self.client, model=self.config["fix-model"], messages=message)
            response = result.choices[0].message.content
            logging.info(f"got this translated message:\n{pp(response)}")
            return response
        except Exception as err:
            logging.warning(f"failed to translate the text: {repr(err)}")
        return text

    async def memory_rewrite(self, memory: str, message_user: str, answer_user: str, question: str, answer: str) -> str:
        if "memory-model" not in self.config:
            return memory
        messages = [
            {"role": "system", "content": self.config.get("memory-system", "You are an memory assistant.")},
            {
                "role": "user",
                "content": f"Here is my previous memory:\n```\n{memory}\n```\n\n"
                f"Here is my conversanion:\n```\n{message_user}: {question}\n\n{answer_user}: {answer}\n```\n\n"
                f"Please rewrite the memory in a way, that it contain the content mentioned in conversation. "
                f"Summarize the memory if required, try to keep important information. "
                f"Write just new memory data without any comments.",
            },
        ]
        logging.info(f"Rewrite memory:\n{pp(messages)}")
        try:
            # logging.info(f'send this memory request:\n{pp(messages)}')
            result = await openai_chat(self.client, model=self.config["memory-model"], messages=messages)
            new_memory = result.choices[0].message.content
            logging.info(f"new memory:\n{new_memory}")
            return new_memory
        except Exception as err:
            logging.warning(f"failed to create new memory: {repr(err)}")
        return memory

    async def _execute_igdb_function(self, function_name: str, function_args: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Execute IGDB function calls from OpenAI.
        """
        if not self.igdb:
            return None
            
        try:
            if function_name == "search_games":
                query = function_args.get("query", "")
                limit = function_args.get("limit", 5)
                
                if not query:
                    return {"error": "No search query provided"}
                
                results = self.igdb.search_games(query, limit)
                if results:
                    return {"games": results}
                else:
                    return {"games": [], "message": f"No games found matching '{query}'"}
                    
            elif function_name == "get_game_details":
                game_id = function_args.get("game_id")
                
                if not game_id:
                    return {"error": "No game ID provided"}
                
                result = self.igdb.get_game_details(game_id)
                if result:
                    return {"game": result}
                else:
                    return {"error": f"Game with ID {game_id} not found"}
            else:
                return {"error": f"Unknown function: {function_name}"}
                
        except Exception as e:
            logging.error(f"Error executing IGDB function {function_name}: {e}")
            return {"error": f"Failed to execute {function_name}: {str(e)}"}