import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

!pip install jovian --upgrade
import jovian
from PIL import Image
import os
import torch
import torchvision
import tarfile
from torchvision.datasets.utils import download_url
from torch.utils.data import random_split
from tqdm.notebook import tqdm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import model_selection
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
from torchvision.datasets import ImageFolder
from torchvision.transforms import ToTensor
import torchvision.transforms as T
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models

from torch.utils.data import Dataset, random_split, DataLoader




# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

project_name='Melanoma-CNN'

print(os.listdir("../input/siim-isic-melanoma-classification"))

image_path = "../input/siim-isic-melanoma-classification"
train_df = pd.read_csv(image_path + "/train.csv")
test_df = pd.read_csv(image_path + "/test.csv")
print(train_df.shape)
print(test_df.shape)
train_df.groupby(['benign_malignant']).count()['sex'].to_frame()
train_df.head()