import pandas as pd
import torch
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
from torchvision.utils import make_grid
import torch.nn as nn
import torchvision.models as models
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from tqdm.notebook import tqdm
from sklearn.metrics import f1_score

class config:
    input_dir = "../input/identify-the-dance-form"
    train_csv = input_dir+"/dataset/train.csv"
    test_csv = input_dir+"/dataset/test.csv"
    train_dir = input_dir+"/dataset/train/"
    test_dir = input_dir+"/dataset/test/"

df = pd.read_csv(config.train_csv).reset_index(drop=True)

df[df['Image']=='3.jpg']

from sklearn.preprocessing import LabelEncoder
classes = len(set(df['target']))
le = LabelEncoder()
df['label'] = le.fit_transform(df['target'])
df['targetfromlabel'] = le.inverse_transform(df['label'])
df = df.drop(['target','targetfromlabel'],axis=1)
df.head()