import streamlit as st
from streamlit_chat import message
import os
from utils import (
    parse_docx,
    parse_pdf,
    parse_txt,
    parse_csv,
    search_docs,
    embed_docs,
    text_to_docs,
    get_answer,
    get_sources,
    wrap_text_in_html,
)
from openai.error import OpenAIError

def clear_submit():
    st.session_state["submit"] = False

def set_openai_api_key(api_key: str):
    st.session_state["OPENAI_API_KEY"] = api_key

st.markdown('<h1>Chat with your Files </h1>', unsafe_allow_html=True)
st.markdown('<h3>Developed with LangChain and OpenAI Embeddings </h3>', unsafe_allow_html=True)

# Sidebar
index = None
doc = None
with st.sidebar:
    user_secret = st.text_input(
        "OpenAI API Key",
        type="password",
        placeholder="Paste your OpenAI API key here (sk-...)",
        help="You can get your API key from https://platform.openai.com/account/api-keys.",
        value=st.session_state.get("OPENAI_API_KEY", ""),
    )
    if user_secret:
        set_openai_api_key(user_secret)

    uploaded_file = st.file_uploader(
        "Upload a pdf, docx, or txt file",
        type=["pdf", "docx", "txt", "csv", "pptx", "js", "py", "json", "html", "css", "md"],
        help="Scanned documents are not supported yet!",
        on_change=clear_submit,
    )

    if uploaded_file is not None:
        if uploaded_file.name.endswith(".pdf"):
            doc = parse_pdf(uploaded_file)
        elif uploaded_file.name.endswith(".docx"):
            doc = parse_docx(uploaded_file)
        elif uploaded_file.name.endswith(".csv"):
            doc = parse_csv(uploaded_file)
        elif uploaded_file.name.endswith(".txt"):
            doc = parse_txt(uploaded_file)
        else:
            doc = parse_any(uploaded_file)
            # st.error("File type not supported")
            # doc = None
        text = text_to_docs(doc)
        st.write(text)
        try:
            with st.spinner("Indexing document..."):
                index = embed_docs(text)
                st.session_state["api_key_configured"] = True
        except OpenAIError as e:
            st.error(e._message)

tab1, tab2 = st.tabs(["Chat with the File", "About the Application"])
with tab1:
    # st.write('To obtain an API Key you must create an OpenAI account at the following link: https://openai.com/api/')
    if 'generated' not in st.session_state:
        st.session_state['generated'] = []

    if 'past' not in st.session_state:
        st.session_state['past'] = []

    def get_text():
        if user_secret:
            st.header("Ask me something about the document:")
            input_text = st.text_area("You:", on_change=clear_submit)
            return input_text
    user_input = get_text()

    button = st.button("Submit")
    if button or st.session_state.get("submit"):
        if not user_input:
            st.error("Please enter a question!")
        else:
            st.session_state["submit"] = True
            sources = search_docs(index, user_input)
            try:
                answer = get_answer(sources, user_input)
                st.session_state.past.append(user_input)
                st.session_state.generated.append(answer["output_text"].split("SOURCES: ")[0])
            except OpenAIError as e:
                st.error(e._message)
            if st.session_state['generated']:
                for i in range(len(st.session_state['generated'])-1, -1, -1):
                    message(st.session_state["generated"][i], key=str(i))
                    message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')

with tab2:
    st.write('About the Application')
    st.write('Chat with Files enables user to extract all the information from a file. User can obtain the transcription, the embedding of each segment and also ask questions to the file through a chat.')
    st.write('Features include- ')
    st.write('1. Reading any pdf, docx, txt or csv file')
    st.write('2. Embedding texts segments with Langchain and OpenAI')
    st.write('3. Chatting with the file using streamlit-chat and LangChain QA with source and GPT model')