import os
import time
from datetime import datetime, timedelta

import pandas as pd
from datasets import Dataset, DatasetDict, load_dataset
from huggingface_hub import login

from my_logger import setup_logger
from utilities.pushshift_data import scrape_submissions_by_day, submissions_to_dataframe

# Set dataset name, path to README.md, and existing dataset details
subreddit = os.environ["SUBREDDIT"]
username = os.environ["USERNAME"]
dataset_name = f"{username}/dataset-creator-{subreddit}"
dataset_readme_path = "README.md"

# Authenticate with Hugging Face using an auth token
auth_token = os.environ["HUGGINGFACE_AUTH_TOKEN"]
login(auth_token, add_to_git_credential=True)

logger = setup_logger(__name__)


def update_readme(dataset_name, subreddit, date_to_fetch):
    readme_text = f"""
    # {dataset_name}

    ## Dataset Overview
    The goal is to have an open dataset of `{subreddit}` submissions. This has been taken from the Pushshift API.

    ## Data Collection
    This has been collected with sequential calls that follow the pagination of the pushshift request. 


    ## Data Structure
    - `all_days`: All the data after `{os.environ["START_DATE"]}`

    ## Update Frequency
    The dataset is updated daily and covers the period from `{os.environ["START_DATE"]}` to two days ago.

    ## Attribution
    Data sourced from the Pushshift API.

    ## Change Log
    <details>
    <summary>Click to expand</summary>

    - **{datetime.now().strftime('%Y-%m-%d')}:** Added data for {date_to_fetch} to the 'all_days' split and saved as CSV

    </details>
    """

    return readme_text


def main(date_to_fetch):
    """
    Runs the main data processing function to fetch and process subreddit data for the specified date.

    Args:
        date_to_fetch (str): The date to fetch subreddit data for, in the format "YYYY-MM-DD".

    Returns:
        most_recent_date (str): Most recent date in dataset
    """

    # Load the existing dataset from the Hugging Face hub or create a new one
    try:
        dataset = load_dataset(dataset_name)
        logger.info("Loading existing dataset")
        if "__index_level_0__" in dataset["all_days"].column_names:
            dataset = dataset.remove_columns(["__index_level_0__"])
    except FileNotFoundError:
        logger.info("Creating new dataset")
        dataset = DatasetDict()

    # Call get_subreddit_day with the calculated date
    logger.info(f"Fetching data for {date_to_fetch}")
    submissions = scrape_submissions_by_day(subreddit, date_to_fetch)
    df = submissions_to_dataframe(submissions)
    logger.info(f"Data fetched for {date_to_fetch}")
    most_recent_date = datetime.strptime(date_to_fetch, '%Y-%m-%d').date()

    # Append DataFrame to split 'all_days' or create new split
    if "all_days" in dataset:
        logger.info("Appending data to split 'all_days'")
        # Merge the new submissions
        old_data = dataset['all_days'].to_pandas()
        new_data = pd.concat([old_data, df], ignore_index=True)
        if '__index_level_0__' in new_data.columns:
            new_data = new_data.drop('__index_level_0__', axis=1)

        # Drop duplicates just in case
        new_data = new_data.drop_duplicates(subset=['id'], keep="first")
        new_data_most_recent_date_raw = new_data['created_utc'].max()
        new_data_most_recent_date_dt = datetime.strptime(new_data_most_recent_date_raw.split(' ')[0], '%Y-%m-%d').date()
        # Adding timedelta in case there is rounding error
        most_recent_date = max(new_data_most_recent_date_dt - timedelta(days=1), most_recent_date)

        # Convert back to dataset
        dataset["all_days"] = Dataset.from_pandas(new_data)
    else:
        logger.info("Creating new split 'all_days'")
        dataset["all_days"] = Dataset.from_pandas(df)
    # Log appending or creating split 'all'
    logger.info("Appended or created split 'all_days'")

    # Push the augmented dataset to the Hugging Face hub
    logger.info(f"Pushing data for {date_to_fetch} to the Hugging Face hub")
    readme_text = update_readme(dataset_name, subreddit, date_to_fetch)
    dataset.description = readme_text
    dataset.push_to_hub(dataset_name, token=auth_token)
    logger.info(f"Processed and pushed data for {date_to_fetch} to the Hugging Face Hub")
    return most_recent_date


def run_main_continuously():
    """
    This function runs the given `main_function` continuously, starting from the date specified
    in the environment variable "START_DATE" until two days ago. Once it reaches two days ago,
    it will wait until tomorrow to start again at the same time as when it started today.
    """
    start_date_str = os.environ.get("START_DATE")
    start_date = datetime.strptime(start_date_str, "%Y-%m-%d").date()

    # Calculate the start time for running the main_function every day.
    start_time = datetime.now().time()

    while True:
        today = datetime.now().date()
        two_days_ago = today - timedelta(days=2)

        if start_date <= two_days_ago:
            logger.info(f"Running main function for date: {start_date}")
            most_recent_date = main(str(start_date))
            start_date = most_recent_date + timedelta(days=1)
        else:
            tomorrow = today + timedelta(days=1)
            now = datetime.now()
            start_of_tomorrow = datetime.combine(tomorrow, start_time)
            wait_until_tomorrow = (start_of_tomorrow - now).total_seconds()
            logger.info(f"Waiting until tomorrow: {wait_until_tomorrow} seconds")
            time.sleep(wait_until_tomorrow)


if __name__ == '__main__':
    run_main_continuously()