import click

from app.settings import types
from app.models.dataset import Dataset
from app.utils import click_utils
from app.settings import app_cfg as cfg
from app.utils.logger_utils import Logger

log = Logger.getLogger()

@click.command()
@click.option('-i', '--input', 'opt_fp_in', required=True)
@click.option('-o', '--output', 'opt_fp_out', required=True)
@click.pass_context
def cli(ctx, opt_fp_in, opt_fp_out):
  """_template_"""
  
  import sys
  from glob import glob
  from os.path import join
  from pathlib import Path
  import time
  from pprint import pprint

  import pandas as pd
  from tqdm import tqdm
  
  from app.utils import file_utils

  log = Logger.getLogger()

  dataset_names = ['helen', 'megaface', 'adience', 'pipa', 'lfpw', 'brainwash', 'msceleb', 'duke_mtmc', 'uccs']
  
  df = pd.DataFrame()
  fp_out = opt_fp_out.replace('.csv', '_citations.csv')
  for dataset_name in dataset_names:
    fp_csv = join(opt_fp_in, f'{dataset_name}.csv')
    _df = pd.read_csv(fp_csv)
    _df = _df[_df.lat != 0]
    _df.drop('id', axis=1, inplace=True)
    print(dataset_name, len(_df))
    df = df.append(_df, ignore_index=True)

  df.to_csv(fp_out, index=False)

  # Print outputs by year
  results_year = []
  year_groups = df.groupby('year')
  for year, group in year_groups:
    if int(year) > 0:
      results_year.append({'year': year, 'count': len(group)})
  
  fp_out = opt_fp_out.replace('.csv', '_years.csv')
  df_years = pd.DataFrame.from_dict(results_year)
  df_years.to_csv(fp_out, index=False)
  pprint(df_years)
  
  # create country summary
  fp_out = opt_fp_out.replace('.csv', '_countries.csv')
  country_groups = df.groupby('country')
  summary = []
  for group_name, group in country_groups:
    summary.append({'country': group_name, 'citations': len(group)})
  df_summary = pd.DataFrame.from_dict(summary)
  df_summary.sort_values(by='citations', ascending=False, inplace=True)
  df_summary.to_csv(fp_out, index=False)
  pprint(df_summary)

  # summary sector
  summary = []
  fp_out = opt_fp_out.replace('.csv', '_sector.csv')
  groups = df.groupby('loc_type')
  for group_name, group in groups:
    summary.append({'type': group_name, 'citations': len(group)})
  df_types = pd.DataFrame.from_dict(summary)
  df_types.sort_values(by='citations', ascending=False, inplace=True)
  df_types.to_csv(fp_out, index=False)
  pprint(df_types)