import os
import time
from datetime import datetime

import pandas as pd
import schedule
from datasets import Dataset

from utilities.user_defined_functions import (
    get_latest_data,
    merge_data,
    load_or_create_dataset,
    remove_filtered_rows,
    load_or_create_comment_dataset
)

from utilities.my_logger import setup_logger
from utilities.readme_update import update_dataset_readme

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

dataset_readme_path = "README.md"

frequency = os.environ.get("FREQUENCY", '').lower()
if frequency not in ["daily", "hourly"]:
    raise ValueError("FREQUENCY environment variable must be 'daily' or 'hourly'")

# Authenticate with Hugging Face using an auth token
auth_token = os.environ["HF_TOKEN"]

logger = setup_logger(__name__)

def upload(new_df, dataset, hf_dataset_name):
    date = datetime.now().strftime('%Y-%m-%d')
    
    # Using dataset from hub
    if 'train' in dataset.keys():
        old_df = dataset['train'].to_pandas() if 'train' in dataset.keys() else pd.DataFrame()
        df = merge_data(old_df=old_df, new_df=new_df)
        new_rows = len(df) - len(old_df)
    # New dataset
    else:
        df = new_df
        df['new'] = True
        df['updated'] = False
        new_rows = len(new_df)
    df = remove_filtered_rows(df)
    dataset['train'] = Dataset.from_pandas(df, preserve_index=False)

    # Update README
    logger.info(f"Adding {new_rows} rows for {date}.")

    # Push the augmented dataset to the Hugging Face hub
    logger.debug(f"Pushing data for {date} to {hf_dataset_name}")
    dataset.push_to_hub(hf_dataset_name, token=auth_token)
    logger.info(f"Processed and pushed data for {date} to {hf_dataset_name}")
    update_dataset_readme(dataset_name=hf_dataset_name, subreddit=subreddit, new_rows=new_rows)
    logger.info(f"Updated README.")


def main():
    date = datetime.now().strftime('%Y-%m-%d')
    
    logger.warning(f"Running main function for date: {date}")
    sub_dataset = load_or_create_dataset()
    new_df, new_df_comment = get_latest_data()
    
    upload(new_df, sub_dataset, dataset_name)
    
    comment_dataset = load_or_create_comment_dataset()
    upload(new_df_comment, comment_dataset, comment_dataset_name)


def schedule_periodic_task():
    """
    Schedule the main task to run at the user-defined frequency
    """
    if frequency == 'hourly':
        logger.info(f'Scheduling tasks to run every hour at the top of the hour')
        schedule.every().hour.at(":00").do(main)
    elif frequency == 'daily':
        start_time = '05:00'
        logger.info(f'Scheduling tasks to run every day at: {start_time} UTC+00')
        schedule.every().day.at(start_time).do(main)

    while True:
        schedule.run_pending()
        time.sleep(1)


if __name__ == "__main__":
    schedule_periodic_task()