import logging
import os
from abc import ABC, abstractmethod
from gradio_client import Client
from openai import OpenAI
import tiktoken
from transformers import T5Tokenizer

# Authentication tokens from environment variables
HUB_TOKEN = os.getenv("HUB_TOKEN")
FLAN_HF_SPACE = "jerpint/i-like-flan"

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class TextCompletion(ABC):
    @abstractmethod
    def get_token_count(self, prompt: str) -> int:
        ...

    @abstractmethod
    def complete(self, prompt: str):
        ...

    def get_score_multiplier(self) -> float:
        return 1.0

class ChatGPTCompletor(TextCompletion):
    def __init__(self, openai_api_key, model):
        self.openai_api_key = openai_api_key
        self.model = model
        # Initialize the client inside the class using the provided key
        self.client = OpenAI(api_key=self.openai_api_key)

    def get_token_count(self, prompt: str) -> int:
        encoding = tiktoken.encoding_for_model(self.model)
        return len(encoding.encode(prompt))

    def complete(self, prompt: str):
        messages = [
            {"role": "user", "content": prompt},
        ]

        # Call the API using the instance's client
        response = self.client.chat.completions.create(
            messages=messages,
            model=self.model,
            temperature=0
        )

        # Get the response text
        response_text = response.choices[0].message.content
        return response_text

    def get_score_multiplier(self) -> float:
        return 2.0

# Kept for compatibility if other parts of the app reference the dictionary
completers = {
    "gpt-3.5-turbo": ChatGPTCompletor,
}

def get_completer(model: str, openai_api_key: str = ""):
    """
    Factory function to get the appropriate completer.
    Modified to prioritize gpt-3.5-turbo as requested.
    """
    logger.info(f"Loading completer for {model=}")
    
    # Check if a key was provided; if not, try to pull from environment
    api_key = openai_api_key or os.getenv("OPENAI_API_KEY")
    
    if model == "gpt-3.5-turbo":
        return ChatGPTCompletor(openai_api_key=api_key, model=model)
    else:
        # Fallback for the specific competition setup
        raise NotImplementedError(f"Model {model} is not supported. Please use gpt-3.5-turbo.")