agents.py

import os
import json
import asyncio
import random


import os
import json
import asyncio
import random

# --- OpenAI ---
from openai import AsyncOpenAI, APIError # (Keep if needed for other parts)

# --- Google Gemini ---
from google import genai
from google.genai import types
# It's good practice to also import potential exceptions


# --- Mistral AI ---
from mistralai.async_client import MistralAsyncClient # (Keep if needed)

# --- Poke-Env ---
from poke_env.player import Player
from poke_env.environment.battle import Battle
from poke_env.environment.move import Move
from poke_env.environment.pokemon import Pokemon

# --- Helper Function & Base Class (Assuming they are defined above) ---
def normalize_name(name: str) -> str:
    """Lowercase and remove non-alphanumeric characters."""
    return "".join(filter(str.isalnum, name)).lower()

STANDARD_TOOL_SCHEMA = {
    "choose_move": {
        "name": "choose_move",
        "description": "Selects and executes an available attacking or status move.",
        "parameters": {
            "type": "object",
            "properties": {
                "move_name": {
                    "type": "string",
                    "description": "The exact name or ID (e.g., 'thunderbolt', 'swordsdance') of the move to use. Must be one of the available moves.",
                },
            },
            "required": ["move_name"],
        },
    },
    "choose_switch": {
        "name": "choose_switch",
        "description": "Selects an available Pokémon from the bench to switch into.",
        "parameters": {
            "type": "object",
            "properties": {
                "pokemon_name": {
                    "type": "string",
                    "description": "The exact name of the Pokémon species to switch to (e.g., 'Pikachu', 'Charizard'). Must be one of the available switches.",
                },
            },
            "required": ["pokemon_name"],
        },
    },
}


class LLMAgentBase(Player): # Make sure this base class exists
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.standard_tools = STANDARD_TOOL_SCHEMA
        self.battle_history = [] # Example attribute

    def _format_battle_state(self, battle: Battle) -> str:
        # (Implementation as provided in the question)
        active_pkmn = battle.active_pokemon
        active_pkmn_info = f"Your active Pokemon: {active_pkmn.species} " \
                           f"(Type: {'/'.join(map(str, active_pkmn.types))}) " \
                           f"HP: {active_pkmn.current_hp_fraction * 100:.1f}% " \
                           f"Status: {active_pkmn.status.name if active_pkmn.status else 'None'} " \
                           f"Boosts: {active_pkmn.boosts}"

        opponent_pkmn = battle.opponent_active_pokemon
        opp_info_str = "Unknown"
        if opponent_pkmn:
            opp_info_str = f"{opponent_pkmn.species} " \
                           f"(Type: {'/'.join(map(str, opponent_pkmn.types))}) " \
                           f"HP: {opponent_pkmn.current_hp_fraction * 100:.1f}% " \
                           f"Status: {opponent_pkmn.status.name if opponent_pkmn.status else 'None'} " \
                           f"Boosts: {opponent_pkmn.boosts}"
        opponent_pkmn_info = f"Opponent's active Pokemon: {opp_info_str}"

        available_moves_info = "Available moves:\n"
        if battle.available_moves:
            available_moves_info += "\n".join(
                [f"- {move.id} (Type: {move.type}, BP: {move.base_power}, Acc: {move.accuracy}, PP: {move.current_pp}/{move.max_pp}, Cat: {move.category.name})"
                 for move in battle.available_moves]
            )
        else:
             available_moves_info += "- None (Must switch or Struggle)"

        available_switches_info = "Available switches:\n"
        if battle.available_switches:
              available_switches_info += "\n".join(
                  [f"- {pkmn.species} (HP: {pkmn.current_hp_fraction * 100:.1f}%, Status: {pkmn.status.name if pkmn.status else 'None'})"
                   for pkmn in battle.available_switches]
              )
        else:
            available_switches_info += "- None"

        state_str = f"{active_pkmn_info}\n" \
                    f"{opponent_pkmn_info}\n\n" \
                    f"{available_moves_info}\n\n" \
                    f"{available_switches_info}\n\n" \
                    f"Weather: {battle.weather}\n" \
                    f"Terrains: {battle.fields}\n" \
                    f"Your Side Conditions: {battle.side_conditions}\n" \
                    f"Opponent Side Conditions: {battle.opponent_side_conditions}"
        return state_str.strip()


    def _find_move_by_name(self, battle: Battle, move_name: str) -> Move | None:
        # (Implementation as provided in the question)
        normalized_name = normalize_name(move_name)
        # Prioritize exact ID match
        for move in battle.available_moves:
            if move.id == normalized_name:
                return move
        # Fallback: Check display name (less reliable)
        for move in battle.available_moves:
            if move.name.lower() == move_name.lower():
                print(f"Warning: Matched move by display name '{move.name}' instead of ID '{move.id}'. Input was '{move_name}'.")
                return move
        return None

    def _find_pokemon_by_name(self, battle: Battle, pokemon_name: str) -> Pokemon | None:
        # (Implementation as provided in the question)
        normalized_name = normalize_name(pokemon_name)
        for pkmn in battle.available_switches:
            # Normalize the species name for comparison
            if normalize_name(pkmn.species) == normalized_name:
                return pkmn
        return None

    async def choose_move(self, battle: Battle) -> str:
        # (Implementation as provided in the question - relies on _get_llm_decision)
        battle_state_str = self._format_battle_state(battle)
        decision_result = await self._get_llm_decision(battle_state_str)
        decision = decision_result.get("decision")
        error_message = decision_result.get("error")
        action_taken = False
        fallback_reason = ""

        if decision:
            function_name = decision.get("name")
            args = decision.get("arguments", {})
            if function_name == "choose_move":
                move_name = args.get("move_name")
                if move_name:
                    chosen_move = self._find_move_by_name(battle, move_name)
                    if chosen_move and chosen_move in battle.available_moves:
                        action_taken = True
                        chat_msg = f"AI Decision: Using move '{chosen_move.id}'."
                        print(chat_msg) # Print to console for debugging
                        # await self.send_message(chat_msg, battle=battle) # Uncomment if send_message exists
                        return self.create_order(chosen_move)
                    else:
                        fallback_reason = f"LLM chose unavailable/invalid move '{move_name}'."
                else:
                     fallback_reason = "LLM 'choose_move' called without 'move_name'."
            elif function_name == "choose_switch":
                pokemon_name = args.get("pokemon_name")
                if pokemon_name:
                    chosen_switch = self._find_pokemon_by_name(battle, pokemon_name)
                    if chosen_switch and chosen_switch in battle.available_switches:
                        action_taken = True
                        chat_msg = f"AI Decision: Switching to '{chosen_switch.species}'."
                        print(chat_msg) # Print to console for debugging
                        # await self.send_message(chat_msg, battle=battle) # Uncomment if send_message exists
                        return self.create_order(chosen_switch)
                    else:
                        fallback_reason = f"LLM chose unavailable/invalid switch '{pokemon_name}'."
                else:
                    fallback_reason = "LLM 'choose_switch' called without 'pokemon_name'."
            else:
                fallback_reason = f"LLM called unknown function '{function_name}'."

        if not action_taken:
            if not fallback_reason: # If no specific reason yet, check for API errors
                 if error_message:
                     fallback_reason = f"API Error: {error_message}"
                 elif decision is None: # Model didn't call a function or response was bad
                      fallback_reason = "LLM did not provide a valid function call."
                 else: # Should not happen if logic above is correct
                      fallback_reason = "Unknown error processing LLM decision."

            print(f"Warning: {fallback_reason} Choosing random action.")
            # await self.send_message(f"AI Fallback: {fallback_reason} Choosing random action.", battle=battle) # Uncomment

            # Use poke-env's built-in random choice
            if battle.available_moves or battle.available_switches:
                 return self.choose_random_move(battle)
            else:
                 print("AI Fallback: No moves or switches available. Using Struggle/Default.")
                 # await self.send_message("AI Fallback: No moves or switches available. Using Struggle.", battle=battle) # Uncomment
                 return self.choose_default_move(battle) # Handles struggle

    async def _get_llm_decision(self, battle_state: str) -> dict:
        raise NotImplementedError("Subclasses must implement _get_llm_decision")

# --- Google Gemini Agent ---
class GeminiAgent(LLMAgentBase):
    """Uses Google Gemini API for decisions."""
    def __init__(self, api_key: str | None = None, model: str = "gemini-1.5-flash", *args, **kwargs): # Default to flash for speed/cost
        super().__init__(*args, **kwargs)
        self.model_name = model
        used_api_key = api_key or os.environ.get("GOOGLE_API_KEY")
        self.model_name=model
        if not used_api_key:
            raise ValueError("Google API key not provided or found in GOOGLE_API_KEY env var.")
        self.client = genai.Client(
            api_key='GEMINI_API_KEY',
            http_options=types.HttpOptions(api_version='v1alpha')
        )
        # --- Correct Tool Definition ---
        # Create a list of function declaration dictionaries from the values in STANDARD_TOOL_SCHEMA
        function_declarations = list(self.standard_tools.values())
        # Create the Tool object expected by the API
        self.gemini_tool_config = types.Tool(function_declarations=function_declarations)
        # --- End Tool Definition ---

        # --- Correct Model Initialization ---
        # Pass the Tool object directly to the model's 'tools' parameter

        # --- End Model Initialization ---

    async def _get_llm_decision(self, battle_state: str) -> dict:
        """Sends state to the Gemini API and gets back the function call decision."""
        prompt = (
            "You are a skilled Pokemon battle AI. Your goal is to win the battle. "
            "Based on the current battle state, decide the best action: either use an available move or switch to an available Pokémon. "
            "Consider type matchups, HP, status conditions, field effects, entry hazards, and potential opponent actions. "
            "Only choose actions listed as available using their exact ID (for moves) or species name (for switches). "
            "Use the provided functions to indicate your choice.\n\n"
            f"Current Battle State:\n{battle_state}\n\n"
            "Choose the best action by calling the appropriate function ('choose_move' or 'choose_switch')."
        )

        try:
            # --- Correct API Call ---
            # Call generate_content_async directly on the model object.
            # Tools are already configured in the model, no need to pass config here.
            response = await client.aio.models.generate_content(
                model=self.model_name, 
                contents=prompt
            )
            # --- End API Call ---

            # --- Response Parsing (Your logic was already good here) ---
            # Check candidates and parts safely
            if not response.candidates:
                 finish_reason_str = "No candidates found"
                 try: finish_reason_str = response.prompt_feedback.block_reason.name
                 except AttributeError: pass
                 return {"error": f"Gemini response issue. Reason: {finish_reason_str}"}

            candidate = response.candidates[0]
            if not candidate.content or not candidate.content.parts:
                finish_reason_str = "Unknown"
                try: finish_reason_str = candidate.finish_reason.name
                except AttributeError: pass
                return {"error": f"Gemini response issue. Finish Reason: {finish_reason_str}"}

            part = candidate.content.parts[0]

            # Check for function_call attribute
            if hasattr(part, 'function_call') and part.function_call:
                fc = part.function_call
                function_name = fc.name
                # fc.args is a proto_plus.MapComposite, convert to dict
                arguments = dict(fc.args) if fc.args else {}

                if function_name in self.standard_tools:
                    # Valid function call found
                    return {"decision": {"name": function_name, "arguments": arguments}}
                else:
                    # Model hallucinated a function name
                    return {"error": f"Model called unknown function '{function_name}'. Args: {arguments}"}
            elif hasattr(part, 'text'):
                 # Handle case where the model returns text instead of a function call
                 text_response = part.text
                 return {"error": f"Gemini did not return a function call. Response: {text_response}"}
            else:
                  # Unexpected part type
                  return {"error": f"Gemini response part type unknown. Part: {part}"}
            # --- End Response Parsing ---
        except Exception as e:
            # Catch any other unexpected errors during the API call or processing
            print(f"Unexpected error during Gemini processing: {e}")
            import traceback
            traceback.print_exc() # Print stack trace for debugging
            return {"error": f"Unexpected error: {str(e)}"}
# --- OpenAI Agent ---
class OpenAIAgent(LLMAgentBase):
    """Uses OpenAI API for decisions."""
    def __init__(self, api_key: str | None = None, model: str = "gpt-4o", *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model = model
        used_api_key = api_key or os.environ.get("OPENAI_API_KEY")
        if not used_api_key:
            raise ValueError("OpenAI API key not provided or found in OPENAI_API_KEY env var.")
        self.openai_client = AsyncOpenAI(api_key=used_api_key)

        # Convert standard schema to OpenAI's format
        self.openai_functions = [v for k, v in self.standard_tools.items()]

    async def _get_llm_decision(self, battle_state: str) -> dict:
        system_prompt = (
            "You are a skilled Pokemon battle AI. Your goal is to win the battle. "
            "Based on the current battle state, decide the best action: either use an available move or switch to an available Pokémon. "
            "Consider type matchups, HP, status conditions, field effects, entry hazards, and potential opponent actions. "
            "Only choose actions listed as available using their exact ID (for moves) or species name (for switches). "
            "Use the provided functions to indicate your choice."
        )
        user_prompt = f"Current Battle State:\n{battle_state}\n\nChoose the best action by calling the appropriate function ('choose_move' or 'choose_switch')."

        try:
            response = await self.openai_client.chat.completions.create(
                model=self.model,
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_prompt},
                ],
                functions=STANDARD_TOOL_SCHEMA,
                function_call="auto",
                temperature=0.5,
            )
            message = response.choices[0].message
            print("MESSAGE BACK : ", message)
            if message.function_call:
                function_name = message.function_call.name
                try:
                    # Ensure arguments is always a dict, even if empty/null
                    arguments = json.loads(message.function_call.arguments or '{}')
                    if function_name in self.standard_tools: # Validate function name
                         return {"decision": {"name": function_name, "arguments": arguments}}
                    else:
                         return {"error": f"Model called unknown function '{function_name}'."}
                except json.JSONDecodeError:
                    return {"error": f"Error decoding function call arguments: {message.function_call.arguments}"}
            else:
                # Model decided not to call a function (or generated text instead)
                return {"error": f"OpenAI did not return a function call. Response: {message.content}"}

        except APIError as e:
            print(f"Error during OpenAI API call: {e}")
            return {"error": f"OpenAI API Error: {e.status_code} - {e.message}"}
        except Exception as e:
            print(f"Unexpected error during OpenAI API call: {e}")
            return {"error": f"Unexpected error: {e}"}


# --- Mistral Agent ---
class MistralAgent(LLMAgentBase):
    """Uses Mistral AI API for decisions."""
    def __init__(self, api_key: str | None = None, model: str = "mistral-large-latest", *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model = model
        used_api_key = api_key or os.environ.get("MISTRAL_API_KEY")
        if not used_api_key:
            raise ValueError("Mistral API key not provided or found in MISTRAL_API_KEY env var.")
        self.mistral_client = MistralAsyncClient(api_key=used_api_key)

        # Convert standard schema to Mistral's tool format (very similar to OpenAI's)
        self.mistral_tools = STANDARD_TOOL_SCHEMA

    async def _get_llm_decision(self, battle_state: str) -> dict:
        system_prompt = (
            "You are a skilled Pokemon battle AI. Your goal is to win the battle. "
            "Based on the current battle state, decide the best action: either use an available move or switch to an available Pokémon. "
            "Consider type matchups, HP, status conditions, field effects, entry hazards, and potential opponent actions. "
            "Only choose actions listed as available using their exact ID (for moves) or species name (for switches). "
            "Use the provided tools/functions to indicate your choice."
        )
        user_prompt = f"Current Battle State:\n{battle_state}\n\nChoose the best action by calling the appropriate function ('choose_move' or 'choose_switch')."

        try:
            response = await self.mistral_client.chat.complete(
                model=self.model,
                messages=[
                    {"role": "system", "content": f"{system}"},
                    {"role": "user", "content": f"{user_prompt}"}
                ],
                tools=self.mistral_tools,
                tool_choice="auto", # Let the model choose
                temperature=0.5,
            )
            
            message = response.choices[0].message
            # Mistral returns tool_calls as a list
            if message.tool_calls:
                tool_call = response.choices[0].message.tool_calls[0]
                function_name = tool_call.function.name
                function_params = json.loads(tool_call.function.arguments)
                print("\nfunction_name: ", function_name, "\nfunction_params: ", function_params)
                if function_name and function_params: # Validate function name
                    return {"decision": {"name": function_name, "arguments": arguments}}

            else:
                # Model decided not to call a tool (or generated text instead)
                 return {"error": f"Mistral did not return a tool call. Response: {message.content}"}

        # Mistral client might raise specific exceptions, add them here if known
        # from mistralai.exceptions import MistralAPIException # Example
        except Exception as e: # Catch general exceptions for now
            print(f"Error during Mistral API call: {e}")
            # Try to get specific details if it's a known exception type
            error_details = str(e)
            # if isinstance(e, MistralAPIException): # Example
            #     error_details = f"{e.status_code} - {e.message}"
            return {"error": f"Mistral API Error: {error_details}"}