import openai
import os
import streamlit as st
from streamlit import session_state
import base64
import tempfile
from pathlib import Path
from langchain.document_loaders import WebBaseLoader, PyPDFLoader, TextLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
openai.api_key = os.getenv("OPENAI_API_KEY")
from langchain.document_loaders import PyPDFLoader
from langchain.chat_models import ChatOpenAI


st.title("Chat with data")
model = ChatOpenAI(model = 'gpt-4', max_tokens = 100,temperature=0)
uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
        # Make temp file path from uploaded file
    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
        fp = Path(tmp_file.name)
        fp.write_bytes(uploaded_file.getvalue())
        print(tmp_file.name,"path")
def extract(uploaded_file):
    res = []
    loader = PyPDFLoader(uploaded_file)
    pages = loader.load()
    for i in pages:
        res.append(i.page_content.replace('\n',''))
    a = " ".join(res)
    return a
def lang(ques):
    context = extract(tmp_file.name)    
    docs =  Document(page_content=context)
    index2 = VectorstoreIndexCreator().from_documents([docs])
    answer = index2.query(llm = model, question = ques)
    index2.vectorstore.delete_collection()
    return answer
def qna(ques):
    session_state['answer']= lang(ques)
    
if 'answer' not in session_state:
    session_state['answer']= ""
    
ques= st.text_area(label= "Please enter the Question that you wanna ask.", 
              placeholder="Question")

st.text_area("result", value=session_state['answer'])

st.button("Submit", on_click=qna, args=[ques])