GPT4o/mini translation sample that translate an input and calculates the costs based on Sweden Central

app.py

import os
import requests
import json

################################## Facts ######################################################
#GPT costs for GPT-4o and GPT-4o-Mini for Sweden Central per 1000 tokens
#https://azure.microsoft.com/en-us/pricing/calculator
GPT4O_GLOBAL_DEPLOYMENT_INPUT = 0.0025
GPT4O_GLOBAL_DEPLOYMENT_OUTPUT = 0.0100

GPT4O_MINI_GLOBAL_DEPLOYMENT_INPUT = 0.0002
GPT4O_MINI_GLOBAL_DEPLOYMENT_OUTPUT = 0.0006

################################## Functions ######################################################
def Call_GPT(Model: str, System_Message: str, Message: str):
    """
    Call the GPT model with the specified parameters.

    Args:
        model (str): The model type, e.g., "gpt-4o" or "gpt-4o-mini".
        system_message (str): The system message to set the context for the model.
        user_message (str): The user message to be processed by the model.

    Returns:
        dict: The JSON response from the GPT model.
    """
    ENDPOINT = f"https://<Your-Resource>.openai.azure.com/openai/deployments/{Model.lower()}/chat/completions?api-version=2024-02-15-preview"
    API_KEY = "<Your-API-Key>"
    headers = {
        "Content-Type": "application/json",
        "api-key": API_KEY,
    }

    # Payload for the request
    payload = {
    "messages": [
        {
        "role": "system",
        "content": [
            {
            "type": "text",
            "text": f"{System_Message}"
            }
        ]
        },
        {
        "role": "user",
        "content": [
            {
            "type": "text",
            "text": f"{Message}"
            }
        ]
        }
    ],
    "temperature": 0.0,
    "top_p": 0.95,
    "max_tokens": 800
    }

    # Send request
    try:
        response = requests.post(ENDPOINT, headers=headers, json=payload)
        response.raise_for_status()  # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
    except requests.RequestException as e:
        raise SystemExit(f"Failed to make the request. Error: {e}")

    # Handle the response as needed (e.g., print or process)
    response_json = response.json()

    message_content = response_json['choices'][0]['message']['content']
    prompt_tokens = response_json['usage']['prompt_tokens']
    completion_tokens = response_json['usage']['completion_tokens']
    total_tokens = response_json['usage']['total_tokens']

    return response_json

def priceCalculator(response_json, model):
    """
    Calculate the cost based on the response JSON and model type.

    Args:
        response_json (dict): The JSON response from the GPT model.
        model (str): The model type, e.g., "GPT-4o" or "GPT-4o-Mini".

    Returns:
        dict: A dictionary containing prompt tokens, completion tokens, and total cost.
    """
    prompt_tokens = response_json['usage']['prompt_tokens']
    completion_tokens = response_json['usage']['completion_tokens']

    inputprice = 0
    outputprice = 0

    if model.lower() == "gpt-4o":
        #USD per 1000 tokens Sweden
        inputprice = GPT4O_GLOBAL_DEPLOYMENT_INPUT
        outputprice = GPT4O_GLOBAL_DEPLOYMENT_OUTPUT
    if model.lower() == "gpt-4o-mini":
        inputprice = GPT4O_MINI_GLOBAL_DEPLOYMENT_INPUT
        outputprice = GPT4O_MINI_GLOBAL_DEPLOYMENT_OUTPUT


    prompt_tokens_cost = prompt_tokens /1000 * inputprice
    completion_tokens_cost = completion_tokens /1000 * outputprice

    total_cost = prompt_tokens_cost + completion_tokens_cost

    return {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, "total_cost": total_cost}

def OutputFormatter(response_json, response_cost):
    """
    Format and print the output based on the response JSON and calculated cost.

    Args:
        response_json (dict): The JSON response from the GPT model.
        response_cost (dict): The calculated cost including prompt tokens, completion tokens, and total cost.
    """
    message_content = response_json['choices'][0]['message']['content']
    prompt_tokens = response_cost['prompt_tokens']
    completion_tokens = response_cost['completion_tokens']
    total_cost = response_cost['total_cost']
    model = response_json['model']

    print (model ,": ", message_content, "\n\nPrompt Tokens:", prompt_tokens," | Completion Tokens:", completion_tokens, " | Total Cost:", total_cost, "USD")

################################## Configuration ######################################################

#Choose the model to use
model="gpt-4o" #"GPT-4o-mini"

#Get the translatetion from the model
response_json = Call_GPT(model, 
                         "Always translate received input into formal English.", 
                         """Der er ingen tvivl om, at det er følsomme data, der indgår i forskningsprojektet iPsych på Aarhus Universitet. Det understregede forskerne selv, da de for tolv år siden søgte myndighederne om at få projektet godkendt.
                            De forudså blandt andet, at forskningen i gener kunne ændre vores forestillinger om sundhed og sygdom og den måde, vi opfatter os selv og hinanden på. Og de vurderede, at forskningen ville få store følgevirkninger på samfundet.
                            Derfor lovede de, at de ville etablere en etisk rådgivningsgruppe, der skulle skabe den rigtige etiske ramme om projektet.
                            Projektet blev godkendt, og siden har 140.000 danskere i alderen 16 til 43 år uden at vide det fået kortlagt deres gener i projektet. Gendataene er koblet sammen med oplysninger om psykiatriske diagnoser og en lang række andre data.
                            """
                )

#Calculate the cost of the translation
response_cost = priceCalculator(response_json, model) # response_cost = priceCalculator(response_json, "GPT-4o-Mini")

#Print the message and the cost
OutputFormatter(response_json, response_cost)