import streamlit as st
from PIL import Image
import time
from dotenv import load_dotenv
import pickle
from huggingface_hub import Repository
from PyPDF2 import PdfReader
from streamlit_extras.add_vertical_space import add_vertical_space
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.callbacks import get_openai_callback
import os
import traceback

import pandas as pd
import pydeck as pdk
from urllib.error import URLError

# Initialize session state variables
if 'chat_history_page1' not in st.session_state:
    st.session_state['chat_history_page1'] = []



# Step 1: Clone the Dataset Repository
repo = Repository(
    local_dir="Private_Book",  # Local directory to clone the repository
    repo_type="dataset",  # Specify that this is a dataset repository
    clone_from="Anne31415/Private_Book",  # Replace with your repository URL
    token=os.environ["HUB_TOKEN"]  # Use the secret token to authenticate
)
repo.git_pull()  # Pull the latest changes (if any)

# Step 2: Load the PDF File
pdf_path = "Private_Book/grunddaten-krankenhaeuser-2016.pdf"  # Replace with your PDF file path


api_key = os.getenv("OPENAI_API_KEY")
# Retrieve the API key from st.secrets



# Updated caching mechanism using st.cache_data
#@st.cache_data(persist="disk")


def load_vector_store(file_path, store_name, force_reload=False):
    try:
        if force_reload or not os.path.exists(f"{store_name}.pkl"):
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=1000,
                chunk_overlap=200,
                length_function=len
            )
            
            text = load_pdf_text(file_path)
            chunks = text_splitter.split_text(text=text)
            
            embeddings = OpenAIEmbeddings()
            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)

            # Inspect the VectorStore object
            print("Inspecting VectorStore object...")
            print("Type of VectorStore:", type(VectorStore))
            print("Attributes of VectorStore:", dir(VectorStore))

            # Additional specific inspections if necessary
            # for example, if VectorStore has an attribute 'some_attribute':
            # print("VectorStore.some_attribute:", VectorStore.some_attribute)

            with open(f"{store_name}.pkl", "wb") as f:
                pickle.dump(VectorStore, f)
        else:
            with open(f"{store_name}.pkl", "rb") as f:
                VectorStore = pickle.load(f)

        return VectorStore

    except Exception as e:
        st.error(f"An error occurred: {e}")
        traceback.print_exc()
        return None

        
# Utility function to load text from a PDF
def load_pdf_text(file_path):
    pdf_reader = PdfReader(file_path)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text() or ""  # Add fallback for pages where text extraction fails
    return text

def load_chatbot():
    return load_qa_chain(llm=OpenAI(), chain_type="stuff")


def display_chat_history(chat_history):
    for chat in chat_history:
        background_color = "#ffeecf" if chat[2] == "new" else "#ffeecf" if chat[0] == "User" else "#ffeecf"
        st.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)



def page1():
    try:
        hide_streamlit_style = """
                <style>
                #MainMenu {visibility: hidden;}
                footer {visibility: hidden;}
                </style>
                """
        st.markdown(hide_streamlit_style, unsafe_allow_html=True)
    
        col1, col2 = st.columns([3, 1])

        with col1:
            st.title("Welcome to BinDocs ChatBot!")

        with col2:
            image = Image.open('BinDoc Logo (Quadratisch).png')
            st.image(image, use_column_width='always')

        if not os.path.exists(pdf_path):
            st.error("File not found. Please check the file path.")
            return

        VectorStore = load_vector_store(pdf_path, "vector_store_page1", force_reload=False)
        display_chat_history(st.session_state['chat_history_page1'])
    
        st.write("<!-- Start Spacer -->", unsafe_allow_html=True)
        st.write("<div style='flex: 1;'></div>", unsafe_allow_html=True)
        st.write("<!-- End Spacer -->", unsafe_allow_html=True)

        query = st.text_input("Ask questions about your PDF file (in any preferred language):")
        add_vertical_space(2)

        col1, col2 = st.columns(2)
        
        with col1:
            if st.button("Was kann ich mit dem Prognose-Analyse-Tool machen?"):
                query = "Was kann ich mit dem Prognose-Analyse-Tool machen?"
            if st.button("Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"):
                query = "Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"
            if st.button("Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"):
                query = "Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"

        with col2:
            if st.button("Dies ist eine reine Test Frage, welche aber eine ausreichende Länge hat."):
                query = "Dies ist eine reine Test Frage, welche aber eine ausreichende Länge hat."
            if st.button("Was sagt mir denn generell die wundervolle Bevölkerungsentwicklung?"):
                query = "Was sagt mir denn generell die wundervolle Bevölkerungsentwicklung?"
            if st.button("Ob ich hier wohl viel schreibe, dass die Fragen vom Layout her passen?"):
                query = "Ob ich hier wohl viel schreibe, dass die Fragen vom Layout her passen?"

        if query:
            st.session_state['chat_history_page1'].append(("User", query, "new"))
            start_time = time.time()
            with st.spinner('Bot is thinking...'):
                chain = load_chatbot()
                docs = VectorStore.similarity_search(query=query, k=3)
                with get_openai_callback() as cb:
                    response = chain.run(input_documents=docs, question=query)

            end_time = time.time()
            duration = end_time - start_time
            st.text(f"Response time: {duration:.2f} seconds")

            st.session_state['chat_history_page1'].append(("Bot", response, "new"))

            new_messages = st.session_state['chat_history_page1'][-2:]
            for chat in new_messages:
                background_color = "#ffeecf"
                st.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)

            query = ""

            st.session_state['chat_history_page1'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history_page1']]

    except Exception as e:
        st.error(f"Upsi, an unexpected error occurred: {e}")



def page2():
    st.title('BinDoc GmbH')
   

def main():
    # Sidebar content
    with st.sidebar:
        st.title('BinDoc GmbH')
        st.markdown("Experience revolutionary interaction with BinDocs Chat App, leveraging state-of-the-art AI technology.")
        add_vertical_space(1)
        page = st.sidebar.selectbox("Choose a page", ["Document Analysis Bot", "Coding Assistance Bot"])
        add_vertical_space(1)
        st.write('Made with ❤️ by BinDoc GmbH')

    # Main area content based on page selection
    if page == "Document Analysis Bot":
        page1()
    elif page == "Coding Assistance Bot":
        page2()


if __name__ == "__main__":
    main()