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 = """
Please Answer the Question: 
previous chat: {previous_history}
Human:{question}
chatbot: 
"""

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

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

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


st.title('MaxMini')
st.info("MaxMini-Instruct-248M is a T5 (Text-To-Text Transfer Transformer) model fine-tuned on a variety of tasks. This model is designed to perform a range of instructional tasks, enabling users to generate instructions for various inputs.")

st.session_state['history'] = []

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

st.session_state['past'] = []


# 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['past'].append(user_input)
        st.session_state['message'].append(output)
        
        
# Display chat history
if st.session_state['message']:
    with response_container:
        for i in range(len(st.session_state['message'])):
            if i != 0:
                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="adventurer")
            message(st.session_state["message"][i], key=str(i), avatar_style="bottts")