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import streamlit as st
from langchain.llms import LlamaCpp
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
# Streamlit page configuration
st.set_page_config(page_title="Simple AI Chatbot")
st.header("Simple AI Chatbot")
# Initialize the Language Model Chain
@st.experimental_singleton
def initialize_chain():
n_gpu_layers = 20
n_batch = 1024
llm = LlamaCpp(
model_path="models/mistral-7b-instruct-v0.1.Q5_0.gguf",
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
n_ctx=2048,
temperature=0,
verbose=False,
streaming=True,
)
# Setup memory for contextual conversation
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
# Initialize the conversational chain
chat_chain = ConversationalChain(llm=llm, memory=memory, verbose=False)
return chat_chain
llm_chain = initialize_chain()
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "assistant", "content": "Hello! How can I assist you today?"}]
# Display conversation messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Handling user input
user_input = st.chat_input("Type your message...", key="user_input")
if user_input:
# Append user message to the conversation
st.session_state.messages.append({"role": "user", "content": user_input})
# Get response from the LLM
response = llm_chain.run(user_input)
# Append LLM response to the conversation
st.session_state.messages.append({"role": "assistant", "content": response})
# Update chat window with the assistant's response
with st.chat_message("assistant"):
st.markdown(response)
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