# Business Data Challenge - Team 1

import pandas as pd
import numpy as np
import os
import s3fs
import re
import warnings

# Import cleaning and merge functions
exec(open('BDC-team-1/0_Cleaning_and_merge_functions.py').read())
exec(open('BDC-team-1/0_KPI_functions.py').read())

# Create filesystem object
S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})

# Ignore warning
warnings.filterwarnings('ignore')

# Data loading
BUCKET = "bdc2324-data/1"
liste_database = fs.ls(BUCKET)

# loop to create dataframes from liste
client_number = liste_database[0].split("/")[1]
df_prefix = "df" + str(client_number) + "_"

for i in range(len(liste_database)) :
    current_path = liste_database[i]
    with fs.open(current_path, mode="rb") as file_in:
        df = pd.read_csv(file_in)
        # the pattern of the name is df1xxx
        nom_dataframe = df_prefix + re.search(r'\/(\d+)\/(\d+)([a-zA-Z_]+)\.csv$', current_path).group(3)
        globals()[nom_dataframe] = df

# Cleaning customerplus
df1_customerplus_clean = preprocessing_customerplus(df1_customersplus)

# Cleaning ticket area
df1_ticket_information = preprocessing_tickets_area(tickets = df1_tickets, purchases = df1_purchases, suppliers = df1_suppliers, type_ofs = df1_type_ofs)

# Cleaning target area
df1_target_information = preprocessing_target_area(targets = df1_targets, target_types = df1_target_types, customer_target_mappings = df1_customer_target_mappings)

# Cleaning campaign area
df1_campaigns_information = preprocessing_campaigns_area(campaign_stats = df1_campaign_stats, campaigns = df1_campaigns)

# Cleaning product area
BUCKET = "bdc2324-data"
directory_path = '1'

products_theme = create_products_table()
events_theme= create_events_table()
representation_theme = create_representations_table()
products_global = uniform_product_df()

# Fusion liée au product
df1_products_purchased = pd.merge(df1_ticket_information, products_global, left_on = 'product_id', right_on = 'id_products', how = 'inner')

# Selection des variables d'intérêts
df1_products_purchased_reduced = df1_products_purchased[['ticket_id', 'customer_id', 'purchase_id' ,'event_type_id', 'supplier_name', 'purchase_date', 'type_of_ticket_name', 'amount', 'children', 'is_full_price', 'name_event_types', 'name_facilities', 'name_categories', 'name_events', 'name_seasons']]

# Fusion de l'ensemble et creation des KPI
df1_campaigns_kpi = campaigns_kpi_function(campaigns_information = df1_campaigns_information) 

df1_tickets_kpi = tickets_kpi_function(tickets_information = df1_products_purchased_reduced)

# Fusion avec KPI liés au customer
df1_customer = pd.merge(df1_customerplus_clean, df1_campaigns_kpi, on = 'customer_id', how = 'left')

# Fill NaN values
df1_customer[['nb_campaigns', 'nb_campaigns_opened']] = df1_customer[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)

# Fusion avec KPI liés au comportement d'achat
df1_customer_product = pd.merge(df1_tickets_kpi, df1_customer, on = 'customer_id', how = 'outer')

# Fill NaN values
df1_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']] = df1_customer_product[['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'nb_tickets_internet']].fillna(0)

## Exportation

# Exportation vers 'projet-bdc2324-team1'
BUCKET_OUT = "projet-bdc2324-team1"
FILE_KEY_OUT_S3 = "1_Output/Company 1 - Segmentation base.csv"
FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3

with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
    df1_customer_product.to_csv(file_out, index = False)