"NoneType' object has no attribute 'cost"

[Brutal15229]

`import json
import requests
from requests.auth import HTTPBasicAuth
from requests_cache import CachedSession
from types import SimpleNamespace
from dotenv import dotenv_values

Load environment variables

env_vars = dotenv_values('.env')

Configure cached session

session = CachedSession(
cache_name='cache/BearerToken',
expire_after=3600 # Cache expiry time in seconds
)

class CustomModelClient:
def init(self, config, **kwargs):
self.config = config
print(f"CustomModelClient config: {config}")
# Authentication URL and other relevant parameters
self.auth_url = env_vars.get('AUTH_URL')
self.username = env_vars.get('USERNAME')
self.password = env_vars.get('PASSWORD')
self.api_url = env_vars.get('OPENAI_API_URL')
self.subscription_key = env_vars.get('OCP-APIM-SUBSCRIPTION-KEY')
self.model="custom_llm_model"

def create(self, params):
    response = session.get(self.auth_url, verify=False, auth=HTTPBasicAuth(self.username, self.password))
    auth_token = response.headers.get("Authorization")
    num_of_responses = params.get("n",2)
    headers = {
        'Content-Type': 'application/json',
        'Authorization': auth_token,
        'x-request-type': 'sync',
        'Ocp-Apim-Subscription-Key': self.subscription_key,
        'Cache-Control': 'no-cache',
        'Accept-Encoding': 'gzip, deflate, br',
        'Connection': 'keep-alive',
        'Accept': '*/*'
    }
    payload={
        "model":self.model,
        "messages":params["messages"],
        "max_tokens":2500,
        "temperature":0.1

    }
    # Create message input using params

    
    # Fetch auth token
    # response = session.get(self.auth_url, verify=False, auth=HTTPBasicAuth(self.username, self.password))
    # auth_token = response.headers.get("Authorization")

    # Prepare the data for the request
    # openai_data = {
    #     "max_tokens": 5000,
    #     "temperature": 0.7,    
    #     "messages": [
    #         {"role": "system", "content": "You are an AI assistant"},
    #         {"role": "user", "content": prompt}
    #     ]
    # }
    # json_openai_data = json.dumps(openai_data)



    # Make the API request
    
    response = requests.post(self.api_url, json=payload, headers=headers)

    # Handle the response
    if response.status_code == 200:
            data = response.json()
            # response = response_data['choices'][0]['message']['content']
            return SimpleNamespace(choices = [SimpleNamespace(message=SimpleNamespace(content=choice["message"]["content"])) for choice in data.get("choices",[])],model=data.get("model",self.model))
    else:
            response = f"Error: {response.status_code} - {response.text}"

    # Build a response in the required format
    # return self._create_response(num_of_responses, response)

# def _create_response(self, num_of_responses, text):
#     response = SimpleNamespace()
#     response.choices = []
#     response.model = "custom_llm"  # or some model identifier

    # for _ in range(num_of_responses):
    #     choice = SimpleNamespace()
    #     choice.message = SimpleNamespace()
    #     choice.message.content = text
    #     choice.message.function_call = None
    #     response.choices.append(choice)

    # return response

def message_retrieval(self, response):
    choices = response.choices
    return [choice.message.content for choice in choices]
@staticmethod
def cost(response):
    response.cost = 0  # Placeholder for actual cost logic
    return 0

@staticmethod
def get_usage(response):
    return {}

`groupchat_1 = autogen.GroupChat(agents=[user_proxy_new_1, demand_forecaster_agent_updated_1,inventory_optimizer_updated_1], messages=[], max_round=6,speaker_selection_method=state_transition,)

manager_1 = autogen.GroupChatManager(groupchat=groupchat_1, llm_config=llm_custom_config,is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),code_execution_config=False,)
this is my CustomModelClient Code
file_path = r'retail_store_inventory.csv'

Start the conversation

user_proxy_new_1.initiate_chat(manager_1, message=f"load the data from {file_path} and Store_id = S002 and Region is West also provide me the recommendations or suggestions for what i should do in case of inventory optimization")
it is able to call the First agent and is able to show the output however when it is going to second agent it is showing
...
def cost(response):
-->response.cost = 0 # Placeholder for actual cost logic
return 0

AttributeError: 'NoneType' object has no attribute 'cost'
Why it is happening and what should i do to remove this error.