# Imports & Config
import streamlit as st
from openai import OpenAI
from llama_index.core import VectorStoreIndex, download_loader, SimpleDirectoryReader
from llama_index.readers.web import BeautifulSoupWebReader
from bs4 import BeautifulSoup
import requests
import openai

# Title
st.title("✈️ Airline Compensation Bot 💬")

# Introduction
st.markdown("**Flight delayed or cancelled yet again?**")
st.markdown("Don’t waste time digging through complicated airline policies! **Just tell us your airline, delay/cancellation reason, and any other relevant details and our chatbot will instantly tell you what compensation you're entitled to - by law and by airline policy.")
st.divider()

#OpenAI API Key
with st.sidebar:
    openai_api_key = st.text_input("Enter your OpenAI API Key", type="password")
    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/Chatbot.py)"

# Check if an API key is provided
if not openai_api_key:
    st.warning("Please enter your OpenAI API to use this app 🔑")
    st.stop()  # Stop the app until an API key is provided

# Initialize OpenAI API Client with user-provided API key
openai.api_key = openai_api_key

# Load data
loader = BeautifulSoupWebReader()
documents = loader.load_data(urls=[
"https://content.spirit.com/Shared/en-us/Documents/Contract_of_Carriage.pdf",
"https://secure.dot.gov/air-travel-complaint",
"https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-259",
"https://www.ecfr.gov/current/title-14/chapter-II/subchapter-A/part-259#259.5",
"https://www.federalregister.gov/documents/2024/04/26/2024-07177/refunds-and-other-consumer-protections",
"https://www.federalregister.gov/documents/2024/08/12/2024-17602/refunds-and-other-consumer-protections-2024-faa-reauthorization",
"https://www.flyfrontier.com/legal/customer-service-plan?mobile=true",
"https://www.hawaiianairlines.com/about-us/customer-service-plan",
"https://www.jetblue.com/customer-assurance/customer-service-plan",
"https://www.jetblue.com/legal/customer-service-plan",
"https://www.reginfo.gov/public/do/eAgendaViewRule?pubId=201910&RIN=2105-AE57",
"https://www.reginfo.gov/public/do/eAgendaViewRule?pubId=202110&RIN=2105-AF04",
"https://www.southwest.com/assets/pdfs/corporate-commitments/customer-service-plan.pdf?clk=7396032",
"https://www.southwest.com/swa-resources/pdfs/corporate-commitments/contract-of-carriage.pdf?clk=CSP_Form",
"https://www.transportation.gov/airconsumer",
"https://www.transportation.gov/airconsumer/air-travel-tips#FAQ",
"https://www.transportation.gov/airconsumer/air-travelers-tell-it-judge",
"https://www.transportation.gov/airconsumer/airline-consumer-contacts",
"https://www.transportation.gov/airconsumer/fly-rights",
"https://www.transportation.gov/individuals/aviation-consumer-protection/bumping-oversales",
"https://www.transportation.gov/individuals/aviation-consumer-protection/refunds",
"https://www.transportation.gov/individuals/aviation-consumer-protection/tarmac-delays",
"https://www.transportation.gov/lost-delayed-or-damaged-baggage",
"https://www.transportation.gov/resources/individuals/aviation-consumer-protection/airline-cancellation-delay-dashboard-html",
    ])

# RAG
index = VectorStoreIndex.from_documents(documents)

def response_generator(query):
    try:
        # Create an index from the documents
        query_engine = index.as_query_engine()
        response = query_engine.query(query)

    except Exception as e:
        # Log or handle the exception
        response = f"An error occurred: {e}"

    return response


# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Accept user input
if prompt := st.chat_input("How can I help you?"):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    
    # Generate and display assistant response
    # st.write('Before Response generator')
    response = response_generator(prompt)
  
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        st.markdown(response)
    
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})