import streamlit as st
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.chat_models import ChatOpenAI
from dotenv import load_dotenv
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from download_chart import construct_plot
from langchain_core.runnables import RunnablePassthrough
from langchain import hub
from langchain_core.prompts.prompt import PromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain_experimental.text_splitter import SemanticChunker
load_dotenv() 

def get_docs_from_pdf(file):
    loader = PyPDFLoader(file)
    docs = loader.load_and_split()
    return docs

def get_doc_chunks(docs):
    text_splitter = SemanticChunker(OpenAIEmbeddings(model="text-embedding-3-small"))
    chunks = text_splitter.split_documents(docs)
    return chunks 

def get_vectorstore_from_docs(doc_chunks):
    embedding = OpenAIEmbeddings(model="text-embedding-3-small")
    vectorstore = FAISS.from_documents(documents=doc_chunks, embedding=embedding)
    return vectorstore

def get_conversation_chain(vectorstore):
    llm = ChatOpenAI(model="gpt-4o",temperature=0.5, max_tokens=2048)
    retriever=vectorstore.as_retriever()

    prompt = hub.pull("rlm/rag-prompt")
    # Chain
    rag_chain = (
        {"context": retriever , "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )
    return rag_chain

def create_db(file):
    # docs = get_docs_from_pdf(file)
    # doc_chunks = get_doc_chunks(docs)
    # vectorstore = get_vectorstore_from_docs(doc_chunks)
    vectorstore = FAISS.load_local(file, OpenAIEmbeddings(model="text-embedding-3-small"),allow_dangerous_deserialization= True)
    return vectorstore

def get_response(chain,user_query, chat_history):

    template = """
    Chat history: {chat_history}
    User question: {user_question}
    """
    

    question = ChatPromptTemplate.from_template(template)
    question = question.format(chat_history=chat_history, user_question=user_query)

    return chain.stream(question)


@st.experimental_dialog("Cast your vote")
def vote(item):
    st.write(f"Why is {item} your favorite?")
    reason = st.text_input("Because...")
    if st.button("Submit"):
        st.rerun()

def display_chat_te():
    # app config
    st.title("Chatbot")

    # session state
    if "chat_history_te" not in st.session_state:
        st.session_state.chat_history_te = [
            AIMessage(content="Salut, posez-moi vos question sur la transistion ecologique."),
        ]
    if "chain" not in st.session_state:
        db=create_db("./DATA_bziiit/vectorstore_op")
        chain = get_conversation_chain(db)
        st.session_state.chain = chain
        
    # conversation
    for message in st.session_state.chat_history_te:
        if isinstance(message, AIMessage):
            with st.chat_message("AI"):
                st.write(message.content)
        elif isinstance(message, HumanMessage):
            with st.chat_message("Moi"):
                st.write(message.content)

    style = """
   <style>
    .css-ocqkz7 {
        position: fixed;
        bottom: 0;
        width: 50%;
        justify-content: center;
        align-items: end;
        margin-bottom: 0.5rem;
       }
   </style>
   """
    # Inject the styling code for both elements
    # st.markdown(style, unsafe_allow_html=True)

    # # user input
    # col1 , col2 = st.columns([1,8])
    # if col1.button("chatbot"):
    #     vote("chatbot")
    # with col2:
    user_query = st.chat_input(placeholder="c'est quoi la transition écologique ?")
    if user_query is not None and user_query != "":
        st.session_state.chat_history_te.append(HumanMessage(content=user_query))

        with st.chat_message("Moi"):
            st.markdown(user_query)

        with st.chat_message("AI"):
            response = st.write_stream(get_response(st.session_state.chain,user_query, st.session_state.chat_history_te))

        st.session_state.chat_history_te.append(AIMessage(content=response))