app.py

import streamlit as st
import os
from streamlit_chat import message
from langchain.prompts import PromptTemplate
from langchain import LLMChain
from langchain_community.llms.huggingface_hub import HuggingFaceHub

llm = HuggingFaceHub(repo_id="suriya7/MaxMini-Instruct-248M",
                     task ='text2text-generation',
                    huggingfacehub_api_token=os.getenv('HF_TOKEN'),
                    model_kwargs={
                    "do_sample":True,
                    "max_new_tokens":250
                    })


template = """You are a friendly chatbot called "MAXMINI" who give clear an well having a conversation with a human and you are created by suriya an AI Enthusiastic.
previous chat: 
{chat_history}
User:
{question}
Chatbot:"""

prompt = PromptTemplate(template=template,input_variables=['question','chat_history'])

llm_chain = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True,
)

previous_response = ""
def conversational_chat(user_query):
    global previous_response
    previous_response = "".join([f"User: {i[0]}\nChatbot: {i[1]}" for i in st.session_state['history'] if i is not None])
    print(f"this is my previous {previous_response}")
    result = llm_chain.predict(
        question=user_query, 
        chat_history = previous_response
    )
    st.session_state['history'].append((user_query, result))
    return result


st.title("Chat Bot MaxMini:")
st.text("I am MaxMini Your Friendly Assitant")
st.markdown("Built by [Suriya❤️](https://github.com/theSuriya)")

if 'history' not in st.session_state:
    st.session_state['history'] = []

if 'human' not in st.session_state:
    st.session_state['human'] = ["Hello MaxMini"]

if 'assistant' not in st.session_state:
    st.session_state['assistant'] = ['Hey There! How Can I Assist You']
   


# Create containers for chat history and user input
response_container = st.container()
container = st.container()

# User input form
user_input = st.chat_input("Ask Your Questions 👉..")
with container:
    if user_input:
        output = conversational_chat(user_input)
        # answer = response_generator(output)
        st.session_state['human'].append(user_input)
        st.session_state['assistant'].append(output)
        
        
# Display chat history
if st.session_state['assistant']:
    with response_container:
        for i in range(len(st.session_state['assistant'])):
            message(st.session_state["human"][i], is_user=True, key=str(i) + '_user', avatar_style="adventurer")
            message(st.session_state["assistant"][i], key=str(i), avatar_style="bottts")