import openai 
import os

def get_api_key(local = False):
    if local:
        from dotenv import load_dotenv
        load_dotenv()
    return os.getenv('OPEN_API_KEY')

def get_user_input(prompt):
    return input(prompt)

def choose_cooperation_type():
    choice = get_user_input("Please choose the cooperation type: \n" 
                            +"1. Sequential: human provide an answer first and then AI provide the answer based on it.\n" 
                            +"2. Parallel: human and AI give answers seperately and then AI does the merge.\n")
    if choice == '1':
        return 'sequential'
    elif choice == '2':
        return 'parallel'
    else:
        print("Invalid choice. Please try again.")
        return choose_cooperation_type()

def describe_task():
    task_description = get_user_input("Please describe your task: ")
    if task_description.strip() == "":
        # print("Task description cannot be empty. Please try again.")
        # return describe_task()
        task_description = "Write a poem about the moon in 3 lines."
    return task_description

def generate_text_with_gpt(prompts, api_key = None):
    """Generate text using the GPT-3 model."""
    if api_key:
        openai.api_key = api_key

    try:
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "Please assist."},
                {"role": "user", "content": prompts}
            ]
        )
        return response['choices'][0]['message']['content']
    except Exception as e:
        print(f"Error occurred when generating texts: {e}")
        return ""


def generate_ai_initial_answer(task_description, api_key=None):
    prompt = f"Given the task: {task_description}, provide an answer: "
    return generate_text_with_gpt(prompt, api_key)

def merge_texts_parallel(task_description, human_text, ai_text, api_key = None):
    prompt = f"Given the task: {task_description}, there are two answers provided:\n" + \
             f"The first answer:  {human_text}\nThe second answer: {ai_text}\n" + \
             f"Merge the two answers into one in a coherent way: "
    return generate_text_with_gpt(prompt, api_key)

def merge_texts_sequential(task_description, human_text, api_key = None):
    prompt = f"Given the task:{task_description}, here is the answer provided by the human: {human_text}\n" + \
             f"Refine this response and ensure the final answer aligns with the human's intent:"
    return generate_text_with_gpt(prompt, api_key)

def modify_with_suggestion(task_description, text, suggestions, api_key = None):
    prompt = f"Given the task:{task_description}, the answer provided is: {text}\n" + \
             f"Modify the answer based on the following suggestions: {suggestions}"
    return generate_text_with_gpt(prompt, api_key)

def get_evaluation_with_gpt(task_description, text, api_key=None):
    prompt = (
        f"Given the task: {task_description}, the provided answer is: {text}\n"
        f"Please evaluate the answer based on the following criteria, using a scale from 0 to 10, where:\n"
        f"0-2 reflects very poor quality, with minimal value or relevance.\n"
        f"3-4 indicates below-average quality with significant shortcomings.\n"
        f"5 represents acceptable quality.\n"
        f"6-8 signifies good to very good quality, showing substantial thought and accuracy.\n"
        f"9-10 represents exceptional quality, with outstanding insight or depth.\n\n"
        f"Provide both a score and a brief comment (1 sentence) for each criterion.\n"
        f"Please format the output exactly as follows:\n"
        f"Novelty: [Score]\nComment: [Short comment on Novelty]\n"
        f"Implementability: [Score]\nComment: [Short comment on Feasibility]\n"
        f"Defensibility: [Score]\nComment: [Short comment on Defensibility]\n"
        f"Overall Score: [Score]\nOverall Comment: [Overall feedback on the answer]\n"
    )
    return generate_text_with_gpt(prompt, api_key)