app.py

import streamlit as st 
from langchain_huggingface import HuggingFaceEndpoint 
from langchain.prompts import PromptTemplate 
from langchain.chains import LLMChain 
from langchain.memory import ConversationBufferMemory 
import os 
 
# Set up your Hugging Face API token 
sec_key = os.getenv('HUGGINGFACE_API_TOKEN') 
os.environ['HUGGINGFACE_API_TOKEN'] = sec_key 
 
# Define your Hugging Face model 
repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" 
llm = HuggingFaceEndpoint(repo_id=repo_id, temperature=0.7) 
 
# Define the prompt template 
template = """The following is a conversation between a user and an AI assistant. 
history:{history} 
Final Message by Human: {user_input} 
Final Message by AI: """ 
 
prompt = PromptTemplate( 
 template=template, 
 input_variables=["history", "user_input"], 
) 
 
# Initialize memory 
memory = ConversationBufferMemory() 
 
# Create the LLM chain 
llm_chain = LLMChain( 
 prompt=prompt, 
 llm=llm, 
 memory=memory 
) 
 
# Streamlit app 
st.title("AI Chatbot") 
st.write("Welcome to the AI Chatbot! Ask anything you like.") 
 
# User input 
user_input = st.text_input("You:", key="input") 
 
if st.button("Send"): 
 if user_input: 
 # Generate response 
 response = llm_chain.invoke({"history": memory.chat_memory.messages, 'user_input': user_input}) 
 response_text = response['text'] 
 # Display the response 
 st.text_area("ChatBot:", response_text, height=100) 
 # Save the interaction in memory 
 memory.save_context({"input": user_input}, {"output": response_text})