app.py

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

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

def select_llm() -> LlamaCPP:
    return LlamaCPP(
        model_path = "/home/serafm/Documents/GitHub/hugging-face-chatbot/llama-2-7b-chat.Q2_K.gguf",
        temperature = 0.1,
        max_new_token = 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()