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#loop over all docs in the bucket
s3 = boto3.resource('s3')
bucket = s3.Bucket(bucket_name)
for obj in bucket.objects.all():
    if obj.key.split('/')[0].split('.')[-1] == 'txt':
    
      obj_from_s3 = s3.Object(bucket, key)
      file_content = obj_from_s3.get()['Body'].read().decode('utf-8')
      json_content = json.loads(file_content)

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import boto3
from boto3.session import Session


def assume_role(arn, session_name):
    """aws sts assume-role --role-arn arn:aws:iam::00000000000000:role/example-role --role-session-name example-role"""

    client = boto3.client('sts')
    account_id = client.get_caller_identity()["Account"]
    print(account_id)

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import argparse

def parse_args():

    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script
    parser.add_argument('--epochs', type=int, default=1)
    parser.add_argument('--batch_size', type=int, default=64)

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import pandas as pd

# df now has two columns: name and country
df = pd.DataFrame({
        'name': ['josef','michael','john','bawool','klaus'],
        'country': ['russia', 'germany', 'australia','korea','germany']
    })

# use pd.concat to join the new columns with your original dataframe
df = pd.concat([df,pd.get_dummies(df['country'], prefix='country')],axis=1)

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import seaborn as sns
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels

def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None, 
                          cmap=None):
    """
    This function prints and plots the confusion matrix.

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import cv2
fname = 'eye_detection.avi'
vidcap = cv2.VideoCapture(fname)
success,image = vidcap.read()
count = 0
while success:
  cv2.imwrite("frame%d.jpg" % count, image)     # save frame as JPEG file      
  success,image = vidcap.read()
  print('Read a new frame: ', success)
  count += 1

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import json
with open('./data/generated/1127_22.json') as f:
  data = json.load(f)

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# scale data between 0 and 1
scaler = MinMaxScaler(feature_range=(0, 1))
scaler.fit(train_df.values)

train_scaled = scaler.transform(train_df.values)
val_scaled = scaler.transform(val_df.values)
test_scaled = scaler.transform(test_df.values)

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trainpct = 0.7
trainidx = int(np.round(len(df)*trainpct))
train_df = df.iloc[0:trainidx,:]

valpct = 0.2
validx = int(np.round(len(df)*(trainpct+valpct)))
val_df = df.iloc[trainidx:validx,:]
             
test_df = df.iloc[validx::,:]

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import boto3
import json

bucket = 'blah'
f = 'fileblah'

# get file from s3, read it, convert to json
s3 = boto3.resource('s3')
content_object = s3.Object(bucket, f)
file_content = content_object.get()['Body'].read().decode('utf-8')