TypeError: text_generation() got an unexpected keyword argument 'token'

I’m trying to create a Streamlit chatbot using Mistral-7B-Instruct-v0.3, and after entering my input in the chat, I received such error.
Here’s the code:
#########################################################################
import os
from langchain_huggingface import HuggingFaceEndpoint
import streamlit as st
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser

model_id=“mistralai/Mistral-7B-Instruct-v0.3”

def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
“”"
Returns a language model for HuggingFace inference.
Parameters:
- model_id (str): The ID of the HuggingFace model repository.
- max_new_tokens (int): The maximum number of new tokens to generate.
- temperature (float): The temperature for sampling from the model.
Returns:
- llm (HuggingFaceEndpoint): The language model for HuggingFace inference.
“”"
llm = HuggingFaceEndpoint(
repo_id=model_id,
max_new_tokens=max_new_tokens,
temperature=temperature,
token = os.getenv(“HF_TOKEN”)
)
return llm

Configure the Streamlit app

st.set_page_config(page_title=“HuggingFace ChatBot”, page_icon=“:hugs:”)
st.title(“Personal HuggingFace ChatBot”)
st.markdown(f"This is a simple chatbot that uses the HuggingFace transformers library to generate responses to your text input. It uses the {model_id}.")

Initialize session state for avatars

if “avatars” not in st.session_state:
st.session_state.avatars = {‘user’: None, ‘assistant’: None}

Initialize session state for user text input

if ‘user_text’ not in st.session_state:
st.session_state.user_text = None

Initialize session state for model parameters

if “max_response_length” not in st.session_state:
st.session_state.max_response_length = 256

if “system_message” not in st.session_state:
st.session_state.system_message = “friendly AI conversing with a human user”

if “starter_message” not in st.session_state:
st.session_state.starter_message = “Hello, there! How can I help you today?”

Sidebar for settings

with st.sidebar:
st.header(“System Settings”)

# AI Settings
st.session_state.system_message = st.text_area(
    "System Message", value="You are a friendly AI conversing with a human user."
)
st.session_state.starter_message = st.text_area(
    'First AI Message', value="Hello, there! How can I help you today?"
)

# Model Settings
st.session_state.max_response_length = st.number_input(
    "Max Response Length", value=128
)

# Avatar Selection
st.markdown("*Select Avatars:*")
col1, col2 = st.columns(2)
with col1:
    st.session_state.avatars['assistant'] = st.selectbox(
        "AI Avatar", options=["🤗", "💬", "🤖"], index=0
    )
with col2:
    st.session_state.avatars['user'] = st.selectbox(
        "User Avatar", options=["👤", "👱‍♂️", "👨🏾", "👩", "👧🏾"], index=0
    )
# Reset Chat History
reset_history = st.button("Reset Chat History")

Initialize or reset chat history

if “chat_history” not in st.session_state or reset_history:
st.session_state.chat_history = [{“role”: “assistant”, “content”: st.session_state.starter_message}]

def get_response(system_message, chat_history, user_text,
eos_token_id=[‘User’], max_new_tokens=256, get_llm_hf_kws={}):
“”"
Generates a response from the chatbot model.
Args:
system_message (str): The system message for the conversation.
chat_history (list): The list of previous chat messages.
user_text (str): The user’s input text.
model_id (str, optional): The ID of the HuggingFace model to use.
eos_token_id (list, optional): The list of end-of-sentence token IDs.
max_new_tokens (int, optional): The maximum number of new tokens to generate.
get_llm_hf_kws (dict, optional): Additional keyword arguments for the get_llm_hf function.
Returns:
tuple: A tuple containing the generated response and the updated chat history.
“”"
# Set up the model
hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)

# Create the prompt template
prompt = PromptTemplate.from_template(
    (
        "[INST] {system_message}"
        "\nCurrent Conversation:\n{chat_history}\n\n"
        "\nUser: {user_text}.\n [/INST]"
        "\nAI:"
    )
)
# Make the chain and bind the prompt
chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')

# Generate the response
response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
response = response.split("AI:")[-1]

# Update the chat history
chat_history.append({'role': 'user', 'content': user_text})
chat_history.append({'role': 'assistant', 'content': response})
return response, chat_history

Chat interface

chat_interface = st.container(border=True)
with chat_interface:
output_container = st.container()
st.session_state.user_text = st.chat_input(placeholder=“Enter your text here.”)

Display chat messages

with output_container:
# For every message in the history
for message in st.session_state.chat_history:
# Skip the system message
if message[‘role’] == ‘system’:
continue

    # Display the chat message using the correct avatar
    with st.chat_message(message['role'], 
                         avatar=st.session_state['avatars'][message['role']]):
        st.markdown(message['content'])

When the user enter new text:

if st.session_state.user_text:
    
    # Display the user's new message immediately
    with st.chat_message("user", 
                         avatar=st.session_state.avatars['user']):
        st.markdown(st.session_state.user_text)
        
    # Display a spinner status bar while waiting for the response
    with st.chat_message("assistant", 
                         avatar=st.session_state.avatars['assistant']):

        with st.spinner("Thinking..."):
            # Call the Inference API with the system_prompt, user text, and history
            response, st.session_state.chat_history = get_response(
                system_message=st.session_state.system_message, 
                user_text=st.session_state.user_text,
                chat_history=st.session_state.chat_history,
                max_new_tokens=st.session_state.max_response_length,
            )
            st.markdown(response)

##############################################
Here is the error:
TypeError: text_generation() got an unexpected keyword argument ‘token’

Traceback:

File "/app/src/streamlit_app.py", line 162, in <module>
    response, st.session_state.chat_history = get_response(File "/app/src/streamlit_app.py", line 121, in get_response
    response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))File "/usr/local/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3046, in invoke
    input_ = context.run(step.invoke, input_, config)File "/usr/local/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 5434, in invoke
    return self.bound.invoke(File "/usr/local/lib/python3.9/site-packages/langchain_core/language_models/llms.py", line 389, in invoke
    self.generate_prompt(File "/usr/local/lib/python3.9/site-packages/langchain_core/language_models/llms.py", line 766, in generate_prompt
    return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)File "/usr/local/lib/python3.9/site-packages/langchain_core/language_models/llms.py", line 971, in generate
    return self._generate_helper(File "/usr/local/lib/python3.9/site-packages/langchain_core/language_models/llms.py", line 792, in _generate_helper
    self._generate(File "/usr/local/lib/python3.9/site-packages/langchain_core/language_models/llms.py", line 1544, in _generate
    self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)File "/usr/local/lib/python3.9/site-packages/langchain_huggingface/llms/huggingface_endpoint.py", line 318, in _call
    response_text = self.client.text_generation(

######################
Please help me!

1 Like

I recommend updating the huggingface_hub library to the latest version and using max_new_tokens instead of max_length and huggingfacehub_api_token instead of token.

pip install -U huggingface_hub[hf_xet]

If you really need to use the old version of the code, I think there is a way to downgrade the huggingface_hub library.

pip install huggingface_hub<0.25

It still doesn’t work, I get additional error, and it took me so long to build and rebuild app.py on Hugging Face so I’m trying something else now. Thank you!

1 Like