#from src.extract_lambda import extract_bucket
import json
import boto3
import re
import io
from io import StringIO
import pandas as pd

##add trigger window from extract bucket (on console?)
##suffix: must .csv --> reads only this file type that is uploaded to extract
##In-order to use PANDAS module in lambda function, a Lambda Layer needs to be attached to the AWS Lambda Function.
##need a function that normalises the data

#s3_resource = boto3.resource('s3') ##need this for a way of reuploading data after transformation

def lambda_handler(event, context):
    s3_client = boto3.client('s3')  
    try:
        s3_bucket_name = event["Records"][0]["s3"]["bucket"]["name"]
        s3_file_name = event["Records"][0]["s3"]["object"]["key"]

        ## concatanating the file per table - most recent
        ## iterate through the subfolders
        ## table name prefix to iterate through the files written to that table

        object = s3_client.get_object(Bucket=s3_bucket_name, Key=s3_file_name)
        body = object['Body']
        csv_string = body.read().decode('utf-8')
        dataframe = pd.read_csv(StringIO(csv_string)) ##this is the streaming body
        
        print(dataframe.head(3))

    except Exception as err:
        print(err)
        
    # TODO implement
    return {
        'statusCode': 200,
        'body': json.dumps('')
    }

## Started from fresh on Wed 21st Aug:

tables = ['sales_order', 
        'transaction', 
        'payment', 
        'counterparty', 
        'address', 
        'staff', 
        'purchase_order', 
        'department', 
        'currency', 
        'design', 
        'payment_type']

def read_from_s3_subfolder_to_df(tables, bucket, client=boto3.client('s3')):
    table_dfs = {}
    for table in tables:
        response = client.list_objects_v2(Bucket=bucket, Prefix=table)
        list_of_keys = ['s3://'+bucket+'/'+object['Key'] for object in response['Contents']] 
        list_of_df = [pd.read_csv(key) for key in list_of_keys]
        table_dfs[table] = pd.concat(list_of_df)
    return table_dfs