import json
import os
import logging
from abc import abstractmethod
from base_telegram_inference_bot import BaseTelegramInferenceBot
from openai import OpenAI

class OpenAICompatibleInferenceBot(BaseTelegramInferenceBot):
    def __init__(self):
        super().__init__()
        # Client and model configuration will be handled by subclasses
        self.client = None
        self.model = None
        self.max_tokens = None

    def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=1000):
        self.model = model_name if model_name else "default-model" 
        try:
            self.max_tokens = int(max_tokens_str) if max_tokens_str is not None else default_max_tokens
        except ValueError:
            logging.error(f"Invalid value for max_tokens: {max_tokens_str}. Using default {default_max_tokens}.")
            self.max_tokens = default_max_tokens
        logging.info(f"Configured to use model: {self.model} with max_tokens: {self.max_tokens}")

    def get_llm_description(self) -> str:
        return f"LLM: {self.model}, Max Tokens: {self.max_tokens}"

    def get_chat_response(self, messages):
        if not self.client:
            raise ValueError("OpenAI client not initialized. Subclasses must initialize it.")
        try:
            response = self.client.chat.completions.create(
                model=self.model,
                messages=messages,
                tools=self.functions if hasattr(self, 'functions') and self.functions else None,
                tool_choice="auto" if hasattr(self, 'functions') and self.functions else None,
                max_tokens=self.max_tokens
            )
            return response
        except Exception as e:
            logging.error(f"API call failed: {e}")
            raise

    async def handle_message(self, user_id, user_message):
        if user_id not in self.conversation_history:
            self.conversation_history[user_id] = []
            if hasattr(self, 'system_prompt') and self.system_prompt:
                 self.conversation_history[user_id].append({"role": "system", "content": self.system_prompt})

        self.conversation_history[user_id].append({"role": "user", "content": user_message})
        messages = self.conversation_history[user_id]

        response = self.get_chat_response(messages)
        
        if not (response.choices and response.choices[0].message):
            logging.error("No valid response choice message from LLM.")
            return "Error: Could not get a valid response from the LLM."
        
        messages.append(response.choices[0].message) # Append the assistant's response message

        tool_calls_from_response = []
        if response.choices[0].message.tool_calls:
            tool_calls_from_response.extend(response.choices[0].message.tool_calls)
        
        tool_use_count = 0
        MAX_TOOL_ITERATIONS = 200 

        while tool_calls_from_response and tool_use_count < MAX_TOOL_ITERATIONS:
            tool_results_for_model = [] 

            for tool_call in tool_calls_from_response:
                tool_call_id = tool_call.id
                function_to_call = tool_call.function
                
                logging.info(f"Attempting to call tool: {function_to_call.name} with args: {function_to_call.arguments}")
                try:
                    tool_response_content = self.call_tool(function_to_call.name, function_to_call.arguments)
                    if not isinstance(tool_response_content, str):
                        tool_response_content = json.dumps(tool_response_content) 
                except Exception as e:
                    logging.error(f"Error calling tool {function_to_call.name}: {e}")
                    tool_response_content = f"Error executing tool {function_to_call.name}: {str(e)}"
                
                tool_results_for_model.append({
                    "role": "tool",
                    "tool_call_id": tool_call_id,
                    "name": function_to_call.name,
                    "content": tool_response_content
                })

            messages.extend(tool_results_for_model)

            response = self.get_chat_response(messages)
            if not (response.choices and response.choices[0].message):
                logging.error("No valid response choice message from LLM after tool call.")
                return "Error: Could not get a valid response from the LLM after tool call."

            messages.append(response.choices[0].message)

            tool_calls_from_response = [] 
            if response.choices[0].message.tool_calls:
                tool_calls_from_response.extend(response.choices[0].message.tool_calls)
            
            tool_use_count += 1
            if tool_use_count >= MAX_TOOL_ITERATIONS and tool_calls_from_response:
                logging.warning(f"Max tool iterations ({MAX_TOOL_ITERATIONS}) reached. Returning last assistant message.")

        # Conversation history management
        # This limit should be reviewed and potentially made configurable
        if len(self.conversation_history[user_id]) > 20: # Example limit, adjust as needed
            self.conversation_history[user_id] = self.conversation_history[user_id][-20:]

        final_assistant_message = messages[-1]
        return final_assistant_message.content if final_assistant_message.role == "assistant" and final_assistant_message.content else "No content in final message."

    async def start(self):
        logging.info(f"{self.__class__.__name__} started.")

    def clear(self, user_id):
        super().clear_conversation_history(user_id)

    async def abort_processing(self, user_id):
        if user_id in self.processing_status:
            self.processing_status[user_id]["processing"] = False
            self.clear(user_id)
            return "Processing aborted and conversation cleared."
        else:
            self.clear(user_id)
            return "No active processing found to abort. Conversation cleared."

    @abstractmethod
    async def switch_model(self):
        pass