import pandas as pd
import datetime
import json
 
# Creating the Dates used to rename the files at the end of the process
today = datetime.date.today()
first = today.replace(day=1)
last_month = first - datetime.timedelta(days=1)
 
year = last_month.strftime("%Y")
month = last_month.strftime("%m")
 
# Dictionary to map month numbers to month names
months_dict = {
    '01': 'jan',
    '02': 'feb',
    '03': 'mar',
    '04': 'apr',
    '05': 'may',
    '06': 'jun',
    '07': 'jul',
    '08': 'aug',
    '09': 'sep',
    '10': 'oct',
    '11': 'nov',
    '12': 'dec'
}
 
# Update month name using the dictionary
month_name = months_dict.get(month)
 
# Retrieving DataFrame
df = pd.read_csv('license_usage_report.csv')
 
# Dropping last 5 rows
df.drop(df.tail(5).index, inplace=True)
 
# Convert "Time" column to datetime format with specified format
df['Time'] = pd.to_datetime(df['Time'], format='%Y-%m-%d %H:%M:%S (%Z)')
 
# Filter DataFrame based on year and month
filtered_df = df[(df['Time'].dt.year == int(year)) & (df['Time'].dt.month == int(month))]
 
# Initialize an empty list to store the formatted rows
formatted_rows = []
 
# filtered_df.to_csv("license_usage_report_08_2024.csv", index=False)
 
# Iterate over each row in the filtered DataFrame
for index, row in filtered_df.iterrows():
    # Extract the values from the columns
    time = row['Time']
    user = row['Number of Users']
    tenant = row['Tenant Name']
 
    # Format the row as a JSON object with a comma at the end
    formatted_row = '\n{"time":"' + str(time) + '", "users":' + str(user) + ', "tenant_name":"' + str(tenant) + '"},'
    formatted_rows.append(formatted_row)
 
# Remove the comma and newline from the last row
if formatted_rows:
    formatted_rows[-1] = formatted_rows[-1][:-2]  # Remove the last two characters, which are the comma and newline
 
# Construct the JSON array
json_data = '[' + ''.join(formatted_rows) + '}\n]'
 
# Request user whether it's FK or SYD
region = input('Is the region FK or SYD? ')
 
list_region = ['FK', 'fk', 'SYD', 'syd']
 
if region not in list_region:
    print('Error ecountered, check script and fix the issue...')
    exit()
else:
    match region:
        case 'FK':
            region = 'fk'
        case 'SYD':
            region = 'syd'
        case 'fk':
            region = 'fk'
        case 'syd':
            region = 'syd'
 
# Save the .csv file
filtered_df.to_csv('usage_' + month_name + '_' + year + '_' + region + '.csv', index=False)
 
# Write the JSON data to a file
with open('usage_' + month_name + '_' + year + '_' + region + '.json', 'w') as json_file:
    json_file.write(json_data)
 
# Re-loads the file
array = json.loads(json_data)
 
# Setting variables
t = ''
sum = 0
max = []
 
# Saves a list of UNIQUE tenant names
ten = [{"tenant": array[0]["tenant_name"], "check": False}]
b = False
 
for i in range(len(array)):
    for j in range(len(ten)):
        if ten[j]["tenant"] == array[i]["tenant_name"]:
            b = True
    if not b:
        tmp = {"tenant": array[i]["tenant_name"], "check": False}
        ten.append(tmp)
    b = False
 
 
# Sums of the concurrent tenants per each timeframe, used for the next step to find the maximum licence usage
for i in range(len(array)):
    if t == array[i]["time"]:
        for j in range(len(ten)):
            if ten[j]["tenant"] == array[i]["tenant_name"]:
                if not ten[j]["check"]:
                    sum += array[i]["users"]
                    ten[j]["check"] = True
    else:
        t = array[i]["time"]
        temp = {"users": sum, "time": array[i]["time"]}
        max.append(temp)
        for j in range(len(ten)):
            ten[j]["check"] = False
        for j in range(len(ten)):
            if ten[j]["tenant"] == array[i]["tenant_name"]:
                if not ten[j]["check"]:
                    sum = array[i]["users"]
                    ten[j]["check"] = True
 
 
# This line will just find the maximum licence usage
res = None
tmp = 0
for i in range(len(max)):
    if tmp < max[i]["users"]:
        res = max[i]
        tmp = max[i]["users"]
 
# Prints the maximum of connected licence on the platform
print("This is the max --> ", res['users'])
 
 
filtered_df.to_csv("usage.csv", index=False)