import os
import streamlit as st

from langchain.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings
from langchain.vectorstores.faiss import FAISS
from huggingface_hub import snapshot_download

from langchain.callbacks import StreamlitCallbackHandler
from langchain.agents import OpenAIFunctionsAgent, AgentExecutor
from langchain.agents.agent_toolkits import create_retriever_tool
from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
    AgentTokenBufferMemory,
)
from langchain.chat_models import ChatOpenAI
from langchain.schema import SystemMessage, AIMessage, HumanMessage
from langchain.prompts import MessagesPlaceholder
from langsmith import Client

client = Client()

st.set_page_config(
    page_title="Investor Education ChatChain",
    page_icon="📖",
    layout="wide",
    initial_sidebar_state="collapsed",
)

#Load API Key
api_key = os.environ["OPENAI_API_KEY"]   

#### sidebar section 1 ####

site_options = {'US': 'vanguard_embeddings_US',
                'AUS': 'vanguard-embeddings'}

site_options_list = list(site_options.keys())

site_radio = st.radio(
    "Which Vanguard website location would you want to chat to?",
    ('US', 'AUS'))

@st.cache_data
def load_vectorstore(site):
    '''load embeddings and vectorstore'''
        
    emb = HuggingFaceEmbeddings(model_name="all-mpnet-base-v2")

    vectorstore = FAISS.load_local(site_options[site], emb,allow_dangerous_deserialization=True)
    
    return vectorstore.as_retriever(search_kwargs={"k": 4})


tool = create_retriever_tool(
    load_vectorstore(site_radio),
    "search_vaguard_website",
    "Searches and returns documents regarding the Vanguard website across US and AUS locations. The websites provide investment related information to the user")

tools = [tool]
llm = ChatOpenAI(temperature=0, streaming=True, model="gpt-4o")
message = SystemMessage(
    content=(
        "You are a helpful chatbot who is tasked with answering questions about investments using informationn that has been scraped from a website to answer the users question accurately."
    "Do not use any information not provided in the website context."
        "Unless otherwise explicitly stated, it is probably fair to assume that questions are about the CFA program and materials. "
        "If there is any ambiguity, you probably assume they are about that."
    )
)

prompt = OpenAIFunctionsAgent.create_prompt(
    system_message=message,
    extra_prompt_messages=[MessagesPlaceholder(variable_name="history")],
)
agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=True,
    return_intermediate_steps=True,
)
memory = AgentTokenBufferMemory(llm=llm)
starter_message = "Ask me anything about information on the Vanguard US/AUS websites!"
if "messages" not in st.session_state or st.sidebar.button("Clear message history"):
    st.session_state["messages"] = [AIMessage(content=starter_message)]


def send_feedback(run_id, score):
    client.create_feedback(run_id, "user_score", score=score)


for msg in st.session_state.messages:
    if isinstance(msg, AIMessage):
        st.chat_message("assistant").write(msg.content)
    elif isinstance(msg, HumanMessage):
        st.chat_message("user").write(msg.content)
    memory.chat_memory.add_message(msg)


if prompt := st.chat_input(placeholder=starter_message):
    st.chat_message("user").write(prompt)
    with st.chat_message("assistant"):
        st_callback = StreamlitCallbackHandler(st.container())
        response = agent_executor(
            {"input": prompt, "history": st.session_state.messages},
            callbacks=[st_callback],
            include_run_info=True,
        )
        st.session_state.messages.append(AIMessage(content=response["output"]))
        st.write(response["output"])
        memory.save_context({"input": prompt}, response)
        st.session_state["messages"] = memory.buffer
        run_id = response["__run"].run_id

        col_blank, col_text, col1, col2 = st.columns([10, 2, 1, 1])
        with col_text:
            st.text("Feedback:")

        # with col1:
        #     st.button("👍", on_click=send_feedback, args=(run_id, 1))

        # with col2:
        #     st.button("👎", on_click=send_feedback, args=(run_id, 0)