pages/llama_custom_demo.py

import random
import time
import streamlit as st
import os
import pathlib
from typing import List
from models.llms import (
    llm_llama_2_7b_chat,
    llm_mixtral_8x7b,
    llm_bloomz_560m,
    llm_gpt_3_5_turbo,
    llm_gpt_3_5_turbo_0125,
    llm_gpt_4_0125,
    llm_llama_13b_v2_replicate
)
from models.embeddings import hf_embed_model, openai_embed_model
from models.llamaCustom import LlamaCustom

# from models.llamaCustom import LlamaCustom
from utils.chatbox import show_previous_messages, show_chat_input
from llama_index.core import (
    SimpleDirectoryReader,
    Document,
    VectorStoreIndex,
    StorageContext,
    Settings,
    load_index_from_storage,
)
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.base.llms.types import ChatMessage

SAVE_DIR = "uploaded_files"
VECTOR_STORE_DIR = "vectorStores"

# global
Settings.embed_model = hf_embed_model

llama_llms = {
    "bigscience/bloomz-560m": llm_bloomz_560m,
    "mistral/mixtral": llm_mixtral_8x7b,
    "meta-llama/Llama-2-7b-chat-hf": llm_llama_2_7b_chat,
    # "openai/gpt-3.5-turbo": llm_gpt_3_5_turbo,
    "openai/gpt-3.5-turbo-0125": llm_gpt_3_5_turbo_0125,
    # "openai/gpt-4-0125-preview": llm_gpt_4_0125,
    # "meta/llama-2-13b-chat": llm_llama_13b_v2_replicate,
}

def init_session_state():
    if "llama_messages" not in st.session_state:
        st.session_state.llama_messages = [
            {"role": "assistant", "content": "How can I help you today?"}
        ]

    # TODO: create a chat history for each different document
    if "llama_chat_history" not in st.session_state:
        st.session_state.llama_chat_history = [
            ChatMessage.from_str(role="assistant", content="How can I help you today?")
        ]

    if "llama_custom" not in st.session_state:
        st.session_state.llama_custom = None

# @st.cache_resource
def index_docs(
    filename: str,
) -> VectorStoreIndex:
    try:
        index_path = pathlib.Path(f"{VECTOR_STORE_DIR}/{filename.replace('.', '_')}")
        if pathlib.Path.exists(index_path):
            print("Loading index from storage ...")
            storage_context = StorageContext.from_defaults(persist_dir=index_path)
            index = load_index_from_storage(storage_context=storage_context)

            # test the index
            index.as_query_engine().query("What is the capital of France?")
            
        else:
            reader = SimpleDirectoryReader(input_files=[f"{SAVE_DIR}/{filename}"])
            docs = reader.load_data(show_progress=True)
            index = VectorStoreIndex.from_documents(
                documents=docs,
                show_progress=True,
            )
            index.storage_context.persist(persist_dir=f"vectorStores/{filename.replace(".", '_')}")

    except Exception as e:
        print(f"Error: {e}")
        index = None
    return index


def load_llm(model_name: str):
    return llama_llms[model_name]

init_session_state()

st.set_page_config(page_title="Llama", page_icon="🦙")

st.header("Llama Index with Custom LLM Demo")

tab1, tab2 = st.tabs(["Config", "Chat"])

with tab1:
    with st.form(key="llama_form"):
        selected_llm_name = st.selectbox(label="Select a model:", options=llama_llms.keys())

        if selected_llm_name.startswith("openai"):
            # ask for the api key
            if st.secrets.get("OPENAI_API_KEY") is None:
                # st.stop()
                st.info("OpenAI API Key not found in secrets. Please enter it below.")
                st.secrets["OPENAI_API_KEY"] = st.text_input(
                    "OpenAI API Key",
                    type="password",
                    help="Get your API key from https://platform.openai.com/account/api-keys",
                )

        selected_file = st.selectbox(
            label="Choose a file to chat with: ", options=os.listdir(SAVE_DIR)
        )

        if st.form_submit_button(label="Submit"):
            with st.status("Loading ...", expanded=True) as status:
                st.write("Loading Model ...")
                llama_llm = load_llm(selected_llm_name)
                Settings.llm = llama_llm

                st.write("Processing Data ...")
                index = index_docs(selected_file)
                if index is None:
                    st.error("Failed to index the documents.")
                    st.stop()

                st.write("Finishing Up ...")
                llama_custom = LlamaCustom(model_name=selected_llm_name, index=index)
                st.session_state.llama_custom = llama_custom

                status.update(label="Ready to query!", state="complete", expanded=False)

with tab2:
    messages_container = st.container(height=300)
    show_previous_messages(framework="llama", messages_container=messages_container)
    show_chat_input(disabled=False, framework="llama", model=st.session_state.llama_custom, messages_container=messages_container)

    def clear_history():
        messages_container.empty()
        st.session_state.llama_messages = [
            {"role": "assistant", "content": "How can I help you today?"}
        ]

        st.session_state.llama_chat_history = [
            ChatMessage.from_str(role="assistant", content="How can I help you today?")
        ]

    if st.button("Clear Chat History"):
        clear_history()
        st.rerun()