python_to_tkinter_to_influx

import tkinter as tk
from tkinter import filedialog, ttk
import pandas as pd
from datetime import datetime, timedelta
import calendar
# from influxdb import InfluxDBClient
# from influxdb import DataFrameClient

def select_csv_file():
    global csv_file_path
    csv_file_path = filedialog.askopenfilename(filetypes=[("CSV files", "*.csv")])
    if csv_file_path:
        print(f"Selected CSV file: {csv_file_path}")
        ask_month_and_cloud()

def ask_month_and_cloud():
    top = tk.Toplevel(root)
    top.title("Select Month, Cloud, and Add to CSV")

    # Month Selection
    tk.Label(top, text="Select Month:").pack()
    month_combobox = ttk.Combobox(top, values=["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"])
    month_combobox.pack()

    # Cloud Selection
    tk.Label(top, text="Select Cloud:").pack()
    cloud_combobox = ttk.Combobox(top, values=["Az-Prod", "Az-Non-Prod", "GCP"])
    cloud_combobox.pack()

    azureorgcp = ttk.Combobox(top, values=["Azure","GCP"])
    azureorgcp.pack()

    def add_to_csv():
        month = month_combobox.get()
        cloud = cloud_combobox.get()
        typecloud  = azureorgcp.get()
        
        # Calculate the last day of the selected month
        year = datetime.now().year
        last_day = calendar.monthrange(year, month_combobox.current() + 1)[1]

        # Create a timestamp in Unix format for the last second of the last day of the month
        timestamp = int((datetime(year, month_combobox.current() + 1, last_day, 23, 59, 59) - datetime(1970, 1, 1)).total_seconds())

        # Read the CSV file
        df = pd.read_csv(csv_file_path)

        # Add the selected Month, Cloud, and Unix Timestamp to the DataFrame
        df['Month'] = month
        df['Cloud'] = cloud
        df['TimeStamp'] = timestamp
        df['sre_component'] = "finops"
        df['platform_capability'] = "finops"
        df['platform_sub_capability'] = ""
        df['sre_application_name']= "finops_automation"

        print(df.info())
        df.index = pd.to_datetime(df['TimeStamp'], unit='s')

        # ### Store the data into InfluxDB
        # ### Add tags for fields that need to be indexed, avoid indexing on every field for performance reasons. 
        # datatags = ["ResourceId","ResourceType","ResourceLocation","ResourceGroupName","ServiceName","Meter","Tags","CostUSD","Cost","Month","Cloud","TimeStamp", "sre_component","platform_capability","platform_sub_capability","sre_application_name" ]

        # # Set the name of the measurement
        # measurement_name = "finops-test"

        # # List the field columns
        # field_columns = ["ResourceId","ResourceType","ResourceLocation","ResourceGroupName","ServiceName","Meter","Tags","CostUSD","Cost","Month","Cloud","TimeStamp", "sre_component","platform_capability","platform_sub_capability","sre_application_name" ]

        # # Name the query in the second argument to write the data to InfluxDB
        # client = DataFrameClient(dbhost, dbport, dbuser, dbpasswd, dbname, ssl=True, verify_ssl=False)
        # client.write_points(
        #     df, 
        #     measurement_name, 
        #     tag_columns=datatags, 
        #     field_columns=field_columns,
        #     time_precision='s', 
        #     protocol='line')

        # print("Data Posted to Influx")


        # # Save the modified data back to the CSV file
        # df.to_csv(csv_file_path, index=False)

        # top.destroy()
        # print("Data added to the CSV file.")

    tk.Button(top, text="Add to CSV", command=add_to_csv).pack()

if __name__ == "__main__":
    root = tk.Tk()
    root.title("CSV File, Month, Cloud, and Add to CSV")

    tk.Label(root, text="Step 1: Select a CSV File").pack()
    tk.Button(root, text="Select CSV File", command=select_csv_file).pack()

    root.mainloop()