import openai
import re
import os
from dotenv import load_dotenv
from transformers import pipeline

# Load environment variables from .env file
load_dotenv()

# Get OpenAI API key from environment variables
openai.api_key = os.getenv("OPENAI_API_KEY")



def generate(prompt, use_openai=True):
    """
    Generates a text completion for a given prompt using either the OpenAI GPT-3 API or the Hugging Face GPT-3 model.
    
    Args:
    - prompt (str): The text prompt to generate a completion for.
    - use_openai (bool): A boolean flag indicating whether to use the OpenAI API (True) or the Hugging Face GPT-3 model (False).
    
    Returns:
    - str: The generated text completion.
    """
    if use_openai:
        model_engine = "text-davinci-002"
        response = openai.Completion.create(
            engine=model_engine,
            prompt=prompt,
            max_tokens=1024,
            n=1,
            stop=None,
            temperature=0.5,
        )

        message = response.choices[0].text
        return message.strip()

    else:
        hf_generator = pipeline('text-generation', model='EleutherAI/gpt-neo-1.3B', device=0)
        output = hf_generator(prompt, max_length=len(prompt)+128, do_sample=True)
        out = output[0]['generated_text']
        if '### Response:' in out:
            out = out.split('### Response:')[1]
        if '### Instruction:' in out:
            out = out.split('### Instruction:')[0]
        return out.strip()


def get_rating(x):
    """
    Extracts a rating from a string.
    
    Args:
    - x (str): The string to extract the rating from.
    
    Returns:
    - int: The rating extracted from the string, or None if no rating is found.
    """
    nums = [int(i) for i in re.findall(r'\d+', x)]
    if len(nums)>0:
        return min(nums)
    else:
        return None

# Summarize simulation loop with OpenAI GPT-4
def summarize_simulation(log_output):
    prompt = f"Summarize the simulation loop:\n\n{log_output}"
    response = generate(prompt)
    return response