for dirpath, dirnames, filenames in os.walk("./Test/"):
    # Remove regular files, ignore directories
    for filename in filenames:
        os.unlink(os.path.join(dirpath, filename))

json_list = [
    'a-1.json',
'a-2.json',
'a-3.json']

final_df = pd.DataFrame()

for i in json_list:
    try:
        df = pd.read_json(i)

        #df.to_csv(i+'.csv')
        
        df = pd.DataFrame(df)

        final_df = pd.concat([final_df,df])

    except:
        pass
        
final_df.to_csv('a1_a171_profiles.csv')

# pip
pip install camelot-py
# conda
conda install -c conda-forge camelot-py
import camelot
tables = camelot.read_pdf('foo.pdf', pages='1', flavor='lattice')
print(tables)
tables.export('foo.csv', f='csv', compress=True)
tables[0].to_csv('foo.csv')  # to a csv file
print(tables[0].df)  # to a df


# from website
import pandas as pd
simpsons = pd.read_html('https://en.wikipedia.org/wiki/List_of_The_Simpsons_episodes_(seasons_1%E2%80%9320)')
# getting the first 5 rows of the table "Season 1" (second table)
simpsons[1].head()

from pathlib import Path
import calendar

month_names = list(calendar.month_name[1:])
days = ['Day 1', 'Day 8', 'Day 15', 'Day 22', 'Day 28']

for i, month in enumerate(month_names):
    for day in days:
        Path(f'2022/{i+1}.{month}/{day}').mkdir(parents=True, exist_ok=True)

def create_dir(path, sub_dirs, label_dirs):
    for sub_dir in sub_dirs: 
        for label_dir in label_dirs:
            new_dir = os.path.join(path, sub_dir, label_dir)
            Path(new_dir).mkdir(parents=True, exist_ok=True)
            print(new_dir)
    
    print('All directories created successfully!')

FOLDER_PATH = 'dataset/'
SUB_DIRS = ['train/', 'test/']
LABEL_DIR = ['dogs/', 'cats/']

create_dir(FOLDER_PATH, SUB_DIRS, LABEL_DIR)

train_datagen = image.ImageDataGenerator(
  	rescale = 1./255,  # to normalize bigger values. Convert from 0-255 to 0-1 range.
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True
)


# only rescaling done on test dataset
test_datagen = image.ImageDataGenerator(
    rescale = 1./255
)

train_generator = train_datagen.flow_from_directory(
    directory=TRAIN_PATH,
    target_size=(224,224),
    batch_size=32,
    class_mode='binary',
    save_to_dir = SAVE_TRAIN_PATH,
    save_prefix='',
    save_format='png'
)

validation_generator = test_datagen.flow_from_directory(
    VAL_PATH,
    target_size = (224,224),
    batch_size = 32,
    class_mode = 'binary'
)

# 
train_generator.class_indices
validation_generator.class_indices

# generate augmented images and save into the directory
for i in range(5):
  train_generator.next()
  
  

import pathlib
from pathlib import Path

def create_folder(path):
  if Path(path).is_dir():
    print ("Folder already exists!")
  else:
    pathlib.Path(path).mkdir(parents=True, exist_ok=True) 
    print ("Folder created!")    


FOLDER_PATH = '/content/drive/MyDrive/detect-covid19-xray/data-preprocessed'
create_folder(FOLDER_PATH)