cyclop/gemini_telegram_inference_bot.py

import json
import os
import logging
from base_telegram_inference_bot import BaseTelegramInferenceBot
from telegram_helper import TelegramHelper # This import might be unused if main() is removed or TelegramHelper is not directly instantiated here.
from openai import OpenAI

# logging.basicConfig(level=logging.INFO) # Usually configured in main execution script

class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
    def __init__(self):
        super().__init__()
        self.client = OpenAI(api_key=os.environ.get("GEMINI_API_KEY"), base_url=os.environ.get("GEMINI_API_BASE_URL"))
        
        self._configure_model_and_tokens(
            os.environ.get("GEMINI_SMALL_MODEL"),
            os.environ.get("GEMINI_SMALL_MODEL_MAX_TOKENS")
        )

    def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=1000):
        self.model = model_name if model_name else "default-gemini-model" # Ensure model has a default
        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_system_prompt_description(self) -> str:
        system_prompt_path = os.getenv("SYSTEM_PROMPT_PATH")
        if system_prompt_path and os.path.isfile(system_prompt_path):
            return f"System Prompt File: {os.path.basename(system_prompt_path)}"
        elif system_prompt_path: # Path is set but file not found
            return f"System Prompt File: {os.path.basename(system_prompt_path)} (Not found at path: {system_prompt_path})"
        else: # Path not set
            return "System Prompt File: Not configured (SYSTEM_PROMPT_PATH not set)."

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

    def get_chat_response(self, messages):
        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"Gemini API call failed: {e}")
            # Return a more structured error or re-raise a custom exception
            # For now, re-raising to be handled by the caller
            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)
        
        # Ensure response.choices[0].message exists before appending
        if response.choices and response.choices[0].message:
            messages.append(response.choices[0].message) # Append the assistant's response message
        else:
            logging.error("No valid response choice message from LLM.")
            return "Error: Could not get a valid response from the LLM."

        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 = 5 # Define a max to prevent infinite loops more explicitly

        while tool_calls_from_response and tool_use_count < MAX_TOOL_ITERATIONS:
            tool_results_for_model = [] # Results to be sent back to the 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)
                    # Ensure tool_response_content is a string for the API
                    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) # Add tool responses to message history

            # Get new response from model based on tool execution results
            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) # Append new assistant message

            # Check for new tool calls
            tool_calls_from_response = [] # Reset for this iteration
            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.")
                # May need to return a message indicating this to user

        # Conversation history management
        if len(self.conversation_history[user_id]) > 2000: # Assuming this limit is for messages, not tokens
            self.conversation_history[user_id] = self.conversation_history[user_id][-2000:]

        # Return the latest assistant content
        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("Gemini Bot started")
        # super().start() if Base class start() has common logic

    async def clear(self, user_id):
        super().clear_conversation(user_id) # Calls base class method

    # status() method is inherited from BaseTelegramInferenceBot

    async def abort_processing(self, user_id):
        if user_id in self.processing_status:
            self.processing_status[user_id]["processing"] = False
            # It's good practice to also clear the conversation for an aborted state
            await self.clear(user_id) 
            return "Processing aborted and conversation cleared."
        else:
            # If no specific status, clearing conversation is a safe default
            await self.clear(user_id)
            return "No active processing found to abort. Conversation cleared."

    async def switch_model(self):
        current_small_model = os.environ.get("GEMINI_SMALL_MODEL")
        current_large_model = os.environ.get("GEMINI_LARGE_MODEL")

        # Default to small model if current model is not recognized or if it's the large one
        if self.model == current_large_model or self.model != current_small_model :
            target_model = current_small_model
            target_max_tokens = os.environ.get("GEMINI_SMALL_MODEL_MAX_TOKENS")
        else: # Current is small, switch to large
            target_model = current_large_model
            target_max_tokens = os.environ.get("GEMINI_LARGE_MODEL_MAX_TOKENS")
        
        self._configure_model_and_tokens(target_model, target_max_tokens)
        logging.info(f"Switched to model: {self.model}")
        return f"Switched to model: {self.model}"

# The main() function and if __name__ == '__main__': block are for standalone execution.
# If this bot is imported as a module, these might not be necessary or might be handled differently.
# For now, keeping them as they were.
def main():
    if not os.environ.get("GEMINI_API_KEY"):
        logging.error("FATAL: GEMINI_API_KEY environment variable not set.")
        return

    # Configure logging here if it's the main entry point
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    
    bot = GeminiTelegramInferenceBot()
    # The instantiation of TelegramHelper and running it implies this file can be an entry point.
    # If it's purely a module, this main() would be removed.
    telegram_helper = TelegramHelper(bot) 
    telegram_helper.run()

if __name__ == '__main__':
    main()