app.py

import streamlit as st 
from llama_index import (
  SimpleDirectoryReader,
  VectorStoreIndex,
  ServiceContext,
)
from llama_index.llms import LlamaCPP
from llama_index.llms.llama_utils import (
  messages_to_prompt,
  completion_to_prompt,
)
from langchain.schema import(SystemMessage, HumanMessage, AIMessage)

def init_page() -> None:
  st.set_page_config(
    page_title="Personal Chatbot"
  )
  st.header("Persoanl Chatbot")
  st.sidebar.title("Options")

def select_llm() -> LlamaCPP:
  return LlamaCPP(
    model_path="/content/llama-2-7b-chat.Q2_K.gguf",
    temperature=0.1,
    max_new_tokens=500,
    context_window=3900,
    generate_kwargs={},
    model_kwargs={"n_gpu_layers":1},
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
    verbose=True,
  )

def init_messages() -> None:
  clear_button = st.sidebar.button("Clear Conversation", key="clear")
  if clear_button or "messages" not in st.session_state:
    st.session_state.messages = [
      SystemMessage(
        content="you are a helpful AI assistant. Reply your answer in markdown format."
      )
    ]

def get_answer(llm, messages) -> str:
  response = llm.complete(messages)
  return response.text

def main() -> None:
  init_page()
  llm = select_llm()
  init_messages()

  if user_input := st.chat_input("Input your question!"):
    st.session_state.messages.append(HumanMessage(content=user_input))
    with st.spinner("Bot is typing ..."):
      answer = get_answer(llm, user_input)
      print(answer)
    st.session_state.messages.append(AIMessage(content=answer))
    

  messages = st.session_state.get("messages", [])
  for message in messages:
    if isinstance(message, AIMessage):
      with st.chat_message("assistant"):
        st.markdown(message.content)
    elif isinstance(message, HumanMessage):
      with st.chat_message("user"):
        st.markdown(message.content)

if __name__ == "__main__":
  main()