from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
import os
from streamlit_chat import message
from langchain import HuggingFaceHub

def LLM_pdf(model_name = 'google/flan-t5-large'):
    # st.header("Ask your PDF 💬")
    
    # upload file
    pdf = st.file_uploader("Upload your PDF", type="pdf")
    # extract the text
    if pdf is not None:
      pdf_reader = PdfReader(pdf)
      text = ""
      for page in pdf_reader.pages:
        text += page.extract_text()
        
      # split into chunks
      text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
      )
      print(text_splitter)
      chunks = text_splitter.split_text(text)
      
      # create embeddings
      embeddings = HuggingFaceEmbeddings()
      knowledge_base = FAISS.from_texts(chunks, embeddings)
        
      if 'generated' not in st.session_state:
          st.session_state['generated'] = []
      if 'past' not in st.session_state:
          st.session_state['past'] = []
      # print(st.session_state['generated'],st.session_state['past'])

      chat_placeholder = st.empty()
      
      # show user input
      with st.container():
          input_placeholder = st.empty()
          user_question = input_placeholder.text_input("Ask a question about your PDF:")
          if user_question:
            docs = knowledge_base.similarity_search(user_question)
            
            llm = HuggingFaceHub(repo_id=model_name, model_kwargs={"temperature":5,
                                                          "max_length":64})
            chain = load_qa_chain(llm, chain_type="stuff")
            response = chain.run(input_documents=docs,question=user_question)
               
            #st.write(response)
          
            # append user_input and output to state
            st.session_state.past.append(user_question)
            st.session_state.generated.append(response)
              
      with chat_placeholder.container():    
          # If responses have been generated by the model
          if st.session_state['generated']:
            # Reverse iteration through the list
            for i in range(len(st.session_state['generated'])-1, -1, -1):
                # message from streamlit_chat
                message(st.session_state['past'][::-1][i], is_user=True, key=str(i) + '_user')
                message(st.session_state['generated'][::-1][i], key=str(i))