<jupyter_start><jupyter_text><jupyter_code>''' install 3dparty sheit '''
from IPython.display import clear_output as cle
from pprint import pprint as print
from PIL import Image
import os
import sys
import json
import IPython

''' default sample data delete '''
os.system('rm -r sample_data')

''' set root paths '''
root = '/content'
gdrive_root = '/content/drive/My Drive'
helpers_root = root + '/installed_repos/Python_Helpers'

''' setup install the Helpers module '''
os.system('git clone https://github.com/bxck75/Python_Helpers.git ' + helpers_root) 
os.system('python ' + helpers_root + 'setup.py install')

''' import helpers '''
os.chdir(helpers_root)
import main as main_core
MainCore = main_core.main()
HelpCore = MainCore.Helpers_Core
Helpers = main_core.Helpers
FScrape = HelpCore.flickr_scrape
Old_H = MainCore.Helpers_Core.H
ICrawl = Helpers.GoImgScrape.GoogleImageCrawler()
Old_Horse = Old_H.Me

# Scrape flickr for images
search_list, qty, img_dir = ['rachell+luttrel'], 50, 'test_images'
FScrape(search_list, qty, img_dir)


cle()
from google.colab import drive
drive.mount('/content/drive')
# Scrape for images method 1
search_list, img_dir, qty = ['portait','face'], 'images', 21
FScrape(search_list, qty, img_dir)
cle()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12352478363493312, 'fraction_numerical': 0.006294256490952006, 'mean_word_length': 5.950819672131147, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '658972', 'license': 'Apache-2.0', 'n_tokens_mistral': 459, 'n_tokens_neox': 444, 'n_words': 114, 'path': 'research.ipynb', 'repo_name': 'bxck75/face-recognition', 'size': '1271'}