import streamlit as st
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.memory import ConversationBufferMemory
from langchain.llms import HuggingFaceHub
from langchain.chains import RetrievalQA
from transformers import AutoModelForCausalLM, AutoTokenizer

from pdfminer.high_level import extract_text
def get_pdf_text(files):
    full_text = ""
    for file in files:
            text = extract_text(file)
            text = text.replace("\n", " ")
            full_text = text + full_text
    return full_text

st.title("Embedding Creation for Langchain")
st.header("File Upload")
files = st.file_uploader("Upload your files", accept_multiple_files=True, type="pdf")
    
if files:
    st.header("Start Conversion")
    if st.button("Ready!"):
       with st.spinner("Creating chain..."):
        full_text = get_pdf_text(files)
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
        chunks = text_splitter.split_text(full_text)
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
        db = FAISS.from_texts(chunks, embeddings)
        memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True,)

def retrieve_info(query):
    similar_response = db.similarity_search(query, k=3)
    page_contents_array = [doc.page_contents for doc in similar_response]
    page_contents = " ".join(page_contents_array)
    return page_contents

st.header("Chatbot")
st.subheader("Ask a question")
question = st.text_input("Question")
if question:
    st.subheader("Answer")
    answer = retrieve_info(question)
    st.write(answer)