clockify_api_invoice.py

from dotenv import load_dotenv
import os
from clockify_sdk import Clockify
from datetime import datetime, timedelta
import pandas as pd

# Load environment variables from .env file
load_dotenv()
rate_per_hour = 70  # euros per hour
# Initialize the client
client = Clockify(
    api_key=os.getenv("CLOCKIFY_API_KEY"),
    # workspace_id=os.getenv("CLOCKIFY_WORKSPACE_ID")  # Optional
)

# Get time entries for a specific date range
time_entries = client.time_entries.get_by_user_id(
    user_id=client.user_id,
    # 7 days ago 
    start=datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) - timedelta(days=7), 
    end=datetime.now()
)

# Store invoice data
invoice_data = []

# Print the time entries
for entry in time_entries:
    print(f"Time Entry: {entry['timeInterval']['start']} - {entry['timeInterval']['end']}")

    x = entry['timeInterval']['start']
    y = entry['timeInterval']['end']

    # making start time TO  datetime object !!!!
    x_time_object = datetime.fromisoformat(x)
    y_time_object = datetime.fromisoformat(y)

    billable_duration = y_time_object - x_time_object
    total_billable_hours = billable_duration.total_seconds() / 3600
    final_invoice_amount = total_billable_hours * rate_per_hour
    
    print(f"Bill this amount {final_invoice_amount:.2f} euros")
    
    # Add to invoice data
    invoice_data.append({
        'date': x_time_object.date(),
        'invoice_amount': round(final_invoice_amount, 2)
    })

# Save to CSV
df = pd.DataFrame(invoice_data)
df.to_csv("invoice.csv", index=False)