import random

from main import extract_content_from_result, write_config_yml
from src import gpt, jina_cloud
from src.prompt_tasks import general_guidelines, executor_file_task, chain_of_thought_creation, test_executor_file_task, \
    chain_of_thought_optimization, requirements_file_task, docker_file_task
from src.utils.io import recreate_folder, persist_file
from src.utils.string_tools import print_colored


def wrap_content_in_code_block(executor_content, file_name, tag):
    return f'**{file_name}**\n```{tag}\n{executor_content}\n```\n\n'


def main(
        executor_description,
        input_modality,
        # input_doc_field,
        output_modality,
        # output_doc_field,
        test_scenario,
        do_validation=True
):
    input_doc_field = 'text' if input_modality == 'text' else 'blob'
    output_doc_field = 'text' if output_modality == 'text' else 'blob'
    # random integer at the end of the executor name to avoid name clashes
    executor_name = f'MicroChainExecutor{random.randint(0, 1000_000)}'
    recreate_folder('executor')
    recreate_folder('flow')

    print_colored('', '############# Executor #############', 'red')
    user_query = (
            general_guidelines()
            + executor_file_task(executor_name, executor_description, input_modality, input_doc_field,
                                 output_modality, output_doc_field)
            + chain_of_thought_creation()
    )
    conversation = gpt.Conversation()
    conversation.query(user_query)
    executor_content_raw = conversation.query(chain_of_thought_optimization('python', 'executor.py'))
    executor_content = extract_content_from_result(executor_content_raw, 'executor.py')
    persist_file(executor_content, 'executor.py')

    print_colored('', '############# Test Executor #############', 'red')
    user_query = (
            general_guidelines()
            + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
            + test_executor_file_task(executor_name, test_scenario)
    )
    conversation = gpt.Conversation()
    conversation.query(user_query)
    test_executor_content_raw = conversation.query(
        chain_of_thought_optimization('python', 'test_executor.py')
        + "Don't add any additional tests. "
    )
    test_executor_content = extract_content_from_result(test_executor_content_raw, 'test_executor.py')
    persist_file(test_executor_content, 'test_executor.py')

    print_colored('', '############# Requirements #############', 'red')
    user_query = (
            general_guidelines()
            + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
            + wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
            + requirements_file_task()
    )
    conversation = gpt.Conversation()
    conversation.query(user_query)
    requirements_content_raw = conversation.query(chain_of_thought_optimization('', 'requirements.txt'))

    requirements_content = extract_content_from_result(requirements_content_raw, 'requirements.txt')
    persist_file(requirements_content, 'requirements.txt')

    print_colored('', '############# Dockerfile #############', 'red')
    user_query = (
            general_guidelines()
            + wrap_content_in_code_block(executor_content, 'executor.py', 'python')
            + wrap_content_in_code_block(test_executor_content, 'test_executor.py', 'python')
            + wrap_content_in_code_block(requirements_content, 'requirements.txt', '')
            + docker_file_task()
    )
    conversation = gpt.Conversation()
    conversation.query(user_query)
    dockerfile_content_raw = conversation.query(chain_of_thought_optimization('dockerfile', 'Dockerfile'))
    dockerfile_content = extract_content_from_result(dockerfile_content_raw, 'Dockerfile')
    persist_file(dockerfile_content, 'Dockerfile')

    write_config_yml(executor_name, 'executor')

    jina_cloud.push_executor('executor')

    host = jina_cloud.deploy_flow(executor_name, do_validation, 'flow')

    # create playgorund and client.py


if __name__ == '__main__':
    ######## Level 1 task #########
    main(
        executor_description="OCR detector",
        input_modality='image',
        # input_doc_field='blob',
        output_modality='text',
        # output_doc_field='text',
        test_scenario='Takes https://miro.medium.com/v2/resize:fit:1024/0*4ty0Adbdg4dsVBo3.png as input and returns a string that contains "Hello, world"',
        do_validation=False
    )